research on health behaviour change

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Understanding and Predicting Health Behaviour Change: A Contemporary View Through the Lenses of Meta-Reviews

Karina w. davidson.

Feinstein Institutes for Medical Research, Northwell Health

Urte Scholz

University of Zurich

Research on health behaviour change examines how to help people engage in healthy behaviours to prevent the development or worsening of chronic disease and early mortality and to improve mental health and well-being. While some of that research has been successful, it is often unclear why or how certain behaviour change interventions have worked ( Michie & Abraham, 2004 ; Nielsen et al., 2018 ; Sumner et al., 2018 ). Understanding why successful behaviour change occurs is the key to creating healthy behaviour, reducing the burden of chronic disease worldwide, and promoting health. Without understanding why a behaviour change intervention succeeds, researchers will remain with an evidence base that is fragmented and uninformed. As a result, a great deal of research wastes opportunities to build forward momentum and thereby limits opportunities to harness and synthesise findings to systematically improve behaviour change interventions. Conversely, with an understanding of the causal mechanisms, researchers can build more efficient behaviour change interventions and so create an evidence base that reveals what works for which populations in what contexts and for which behaviours. Researchers have completed thousands of health behaviour change interventions on topics ranging from improving medication adherence behaviour, to decreasing risky sexual activity, to promoting physical activity. In turn, numerous meta-analyses have attempted to examine the effectiveness and to understand the results of such interventions. These meta-analyses have tended to focus on specific behaviours, types of behaviour change interventions, ways of delivering the behaviour change intervention, health outcomes, or populations. At this point, there are often so many meta-analyses focused on a given phenomenon that it is challenging for any individual to summarise the conclusions of these analyses accurately. We present here a special issue devoted to advancing the science of behaviour change in two main ways. First, this special issue presents information across several articles to aid researchers in locating information on both effectiveness and possible explanations for the (in-)effectiveness of behaviour change interventions combined across existing meta-analyses. Second, this special issue provides information on the most important implications for future research on advancing the science of health behaviour change interventions. The first goal will be achieved by a series of meta-reviews of meta-analyses on behaviour change interventions, and the second by three narrative reviews and a series of commentaries.

So, what is a meta-review ? It is essentially a systematic review of systematic reviews ( Blackwood, 2016 ). The intent is to synthesise meta-analyses and thus examine at the highest level only the summary of current evidence. These types of reviews provide evidence to make better decisions about what exists in the research landscape, and what is missing after a comprehensive and thorough search. Similar to published guidelines on quality and reporting standard put forward for meta-analyses, best practice guidelines for meta-reviews have also been proposed, which include pre-registration and standardised quality ratings for their constituent meta-analyses ( Shea et al., 2017 ). By presenting a series of meta-reviews on differing aspects of health behaviour change, this special issue provides a clear overall picture of the current state of the research on health behaviour change research and its quality. It also delivers a clear message about what should be done now to advance the science of behaviour change to improve health.

The meta-reviews presented here were undertaken by personnel supported by The Science of Behavior Change (SOBC) Research Network. To move the health behaviour change field forward, the SOBC Research Network (funded by the U.S. National Institutes of Health) seeks to improve the understanding of underlying mechanisms of human behaviour change by promoting and a basic mechanism of action research by use of an experimental medicine method ( Nielsen et al., 2018 ; Suls et al., 2020 ; Aklin et al., 2020 ). SOBC aims to bring together basic and applied scientists to support this mechanistic research across health-related behaviours to ultimately develop more effective behavioural interventions. Work during SOBC Stage 1 (2009–2014) identified three broad classes of intervention targets that are highly relevant to the mechanisms relating to behaviour change: self-regulation, stress reactivity/stress resilience, and interpersonal and social processes. Stage 1 work also determined the need for reliable and valid ways to measure whether these hypothesised mechanisms of actions were engaged or influenced through experimental manipulation or interventions, which became the focus of SOBC Stage 2 (2015-present). In this work, when a change in the mechanism results in an observed change in behaviour, the inference is that the identified mechanism is indeed a valid mechanism of action. SOBC’s goal is to use the results of this method to optimise behaviour change interventions across disciplines.

Thus, the central goal of SOBC is to identify key mechanisms underlying successful behaviour change interventions aimed to change health behaviour, such as by improving positive health behaviours (e.g., diet and exercise) or by reducing unhealthy behaviours (e.g., smoking). SOBC also seeks to answer the critical question: What works, for whom, and under what circumstances? The SOBC network reviewed, provided feedback, and endorsed a plan for SOBC-supported personnel to undertake a systematic review of the current literature using extant meta-analyses, with the goal of a meta-review being created to understand what meta-analyses have been published thus far examining self-regulation as a means to influence health behaviour. By compiling meta-analyses across a wide range of interventions, behaviour change targets, and distal health outcomes, the results of the parent comprehensive meta-review ( Hennessy, Johnson, Acabchuk, McCloskey, & Stewart-James, 2020 ), and the accompanying targeted meta-reviews ( Protogerou, McHugh, & Johnson, 2020 ; Suls et al., 2020 ; Wilson et al., 2020 ) presented in this special issue promise to inform future studies by identifying gaps in current knowledge and advancing our knowledge where science has already established findings on the mechanisms of self-regulation.

Three salient facts make the current evidence base ripe for meta-reviewing the effectiveness and the explanatory mechanisms of behaviour change interventions: First, new strategies for characterising the content of interventions have led to a more standardised approach to descriptions in a taxonomic form, which has done much to resolve the fragmented and inconsistent way in which interventions have been previously described (e.g., Abraham & Michie, 2008 ; Knittle et al., 2020 ; Kok et al., 2016 ; Michie et al., 2013 ), with ongoing advances in nomenclature, definition, and structure promising even more precision. Thus, synthesising the evidence in meta-reviews by using existing taxonomies for identifying mechanisms most prominently and most effectively applied in behaviour change interventions is now possible. This approach also allows a more comprehensive and precise means for identifying shortcomings, gaps, and open questions in this field. The latter then allows for stimulating further improvements in planning, implementing, and describing intervention content. A long-term benefit of such an approach may be increasingly precise replication efforts together with substantial improvements in the effectiveness of the interventions tested across health behaviour change intervention research ( Byrne, 2020 ).

Second, theories to understand health behaviour itself have also grown more complex, relative to the health behaviour theories proposed in the 1970s and 1980s. Contemporary models for example consider not only reflective, but also automatic processes involved in behaviour change ( Deutsch & Strack, in press ), or place behaviour change within several contexts, such as the romantic relationship ( Lewis et al., 2006 ; Pietromonaco & Collins, 2017 ; Scholz, Berli, Lüscher, & Knoll, in press ) or broader social networks ( Berkman, Glass, Brissette, & Seeman, 2000 ) with individuals connected to others through reciprocal exchanges that vary depending both on the needs (or goals) of the individual and the needs (and goals) of the network partners. Furthermore, recent models also take into account that all this occurs within an overarching environment that facilitates or hinders behaviour change (e.g., via the presence of health-promoting policies and settings, such as bans on smoking in restaurants or streets with designated walking or bike paths; (e.g., Ruiter, Crutzen, de Leeuw, & Kok, in press ; Schuz, 2017 ). As a consequence, contemporary theories do not only more precisely specify potential mechanisms for explaining health behaviour change, but also address the crucial question about what factors are likely to moderate the intervention’s effectiveness. Using these models as theoretical frameworks for synthesising evidence in a meta-review allows a more purposive approach to this task.

Third, standards for conducting meta-analyses and meta-reviews have become increasingly rigorous, transparent, and, with this, more useful (e.g., Shea et al., 2017 ). The level of sophistication now available while exploring multiple meta-analyses creates the ability to address study-level nuances and a growing understanding of the assumptions involved in pooling the results of independent studies on a subject across summaries. Thus, the synthesis of available research results of behaviour change interventions pooled in meta-analyses can be evaluated while considering the quality of the meta-analyses. This allows a more sophisticated view on the existing research. It is also important for considering how to improve future meta-analyses and how to understand the validity of the results.

To concentrate on the most comprehensive and methodologically sophisticated meta-analyses, the parent meta-review focuses on relatively recent published meta-analyses of interventions seeking to change participants’ health behaviours, with the intent of engaging self-regulation. Results of this meta-review indicate that self-regulation is usually addressed in the form of intervention components that administer specific behaviour change techniques. Effectiveness is inconclusive and seems to be dependent on the target population and the behaviour. The following articles of this special issue address critical questions that could best be answered by targeted meta-reviews. Wilson and others examine self-regulation-related changes focused on improving medication adherence ( Wilson et al., 2020 ), while Suls et al. (2020) address the role of self-regulation for improving cardiovascular disease prevention behaviours. Taking a slightly different approach, Protogerou and colleagues examine health-behaviour related self-regulation interventions to reduce risky health behaviour ( Protogerou et al., 2020 ).

Aside from this series of meta-reviews, this special issue also includes narrative reviews complementing the topics covered by the meta-reviews. Alcántara et al. (2020) examine health behaviour self-regulation-related interventions through the lens of the social disparities of health, and so they test the way these factors potentially moderate the effectiveness of behaviour change interventions. Next, Miller et al. (2020) investigate how a developmental perspective is, or is not, considered in the science of behaviour change for self-regulation interventions and provide a strong case for the importance of doing so. As meta-review methodology has advanced so rapidly recently, this special issue also includes one article on how artificial intelligence can be combined with manual systematic searching to support reviewing the existing evidence more efficiently and to enhance the breadth and precision of the meta-analyses found to be eligible when reviewing literature ( Marshall, Johnson, Wang, Rajasekaran, & Wallace, 2020 ).

We conclude this special issue with a series of commentaries on the state of the behaviour change science, and the perspective of funders ( Aklin et al., 2020 ) that further complement the comprehensive overview provided by this special issue as a whole. The commentaries go beyond the implications for future research outlined in the meta-reviews and narrative reviews ( O’Carroll, 2020 ) by e.g. addressing highly topical themes, such as the strong need for improving methods and quality in the area of health behaviour change research ( Byrne, 2020 ), the role of interpersonal differences and environmental factors ( O’Connor, 2020 ), and the interplay between intrapersonal and interpersonal processes ( Rothman, Simpson, Huelsnitz, Jones, & Scholz, 2020 ) as well as the call for taking implementation science into account ( Luszczynska, 2020 ). Finally, the special issue concludes with the perspective of a longstanding editor in chief of Health Psychology Review as the landmark journal for systematic reviews and meta-analyses of the science of behaviour change ( Hagger, 2020 ).

We are convinced that this selection of outstanding articles serves the dual functions of (a) providing a comprehensive overview of the state of the science of behaviour change in terms of knowledge of the role of self-regulatory processes for successful behaviour change interventions and (b) serving as a catalyst for promoting further highest-quality behaviour change interventions addressing the most pressing questions in the science of behaviour change.

Acknowledgments

Role of Funding Sources and Disclosures: This study was supported by the National Institutes of Health (NIH) Science of Behavior Change Common Fund Program through an award administered by the National Institute on Aging (U24AG052175). Karina W. Davidson is a member of the United States Preventive Services Task Force (USPSTF). This article does not represent the views and policies of the USPSTF.

Contributor Information

Karina W. Davidson, Feinstein Institutes for Medical Research, Northwell Health.

Urte Scholz, University of Zurich.

Abraham C, & Michie S (2008). A taxonomy of behavior change techniques used in interventions . Health Psychology , 27 ( 3 ), 379–387. doi: 10.1037/0278-6133.27.3.379 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Aklin WM, Stoeckel LE, Green PA, Keller C, King JW, Nielsen L, & Hunter C (2020). Commentary: National Institutes of Health (NIH) Science of Behavior Change (SOBC) . Heatlh Psychology Review . doi: 10.1080/17437199.2020.1716383. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Alcántara C, Diaz SV, Giorgio Cosenzo L, Loucks EB, Penedo FJ, & Williams NJ (2020). Social Determinants as Moderators of Health Behaviour Change Interventions: Scientific Gaps and Opportunities . Health Psychology Review . [ PubMed ] [ Google Scholar ]
Berkman LF, Glass T, Brissette I, & Seeman TE (2000). From social integration to health: Durkheim in the new millennium . Social Science & Medicine , 51 ( 6 ), 843–857. doi: 10.1016/s0277-9536(00)00065-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Blackwood D (2016). Taking it to the next level…reviews of systematic reviews . HLA News ( Winter ), 13–15. [ Google Scholar ]
Byrne M (2020). Gaps and priorities in advancing methods for health behaviour change research . Health Psychology Review . 10.1080/17437199.2019.1707106 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Deutsch R, & Strack F (in press). Changing behavior using the reflective-impulsive model In Hagger MS, Cameron LD, Hamilton K, Hankonen N, & Lintunen T (Eds.), The Handbook of Behavior Change . Cambridge, U.K.: Cambridge University Press. [ Google Scholar ]
Hagger MS, Moyers S, McAnally K, & McKinley LE (2020). Known knowns and known unknowns on behaviour change interventions and mechanisms of action . Health Psychology Review . doi: 10.1080/17437199.2020.1719184. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Hennessy EA, Johnson BT, Acabchuk RL, McCloskey K, & Stewart-James J (2020). Self-Regulation Mechanisms in Health Behaviour Change: A Systematic Meta-Review of Meta-Analyses, 2006–2017 . Health Psychology Review , 1–142. doi: 10.1080/17437199.2019.1679654 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Knittle K, Heino MTJ, Marques MM, Stenius M, Beattie M, Ehbrecht F, … Hankonen N (2020). The compendium of self-enactable techniques to change and self-manage motivation and behaviour v1. 0 . Nature Human Behaviour . doi: 10.1038/s41562-019-0798-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kok G, Gottlieb NH, Peters GJ, Mullen PD, Parcel GS, Ruiter RA, … Bartholomew LK (2016). A taxonomy of behaviour change methods: an Intervention Mapping approach . Health Psychology Review , 10 ( 3 ), 297–312. doi: 10.1080/17437199.2015.1077155 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Lewis MA, McBride CM, Pollak KI, Puleo E, Butterfield RM, & Emmons KM (2006). Understanding health behavior change among couples: an interdependence and communal coping approach . Social Science & Medicine , 62 ( 6 ), 1369–1380. doi: 10.1016/j.socscimed.2005.08.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Luszczynska A (2020). It’s time for effectiveness-implementation hybrid research on behaviour change . Health Psychology Review , 1–5. doi: 10.1080/17437199.2019.1707105 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Marshall IJ, Johnson BT, Wang Z, Rajasekaran S, & Wallace BC (2020). Semi-automated evidence synthesis in health Psychology: Current methods and future potential . Health Psychology Review . doi: 10.1080/17437199.2020. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Michie S, & Abraham C (2004). Interventions to change health behaviours: evidence-based or evidence-inspired? Psychology & Health , 19 ( 1 ), 29–49. doi: 10.1080/0887044031000141199 [ CrossRef ] [ Google Scholar ]
Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, … Wood CE (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions . Annals of Behavioral Medicine , 46 ( 1 ), 81–95. doi: 10.1007/s12160-013-9486-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Miller AL, Lo S, Bauer KW, & Fredericks EM (2020). Considering developmental factors in health-focused self-regulation interventions . Health Psychology Review . doi: 10.1080/17437199.2020.1718530. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Nielsen L, Riddle M, King JW, Aklin WM, Chen W, Clark D, … Weber W (2018). The NIH Science of Behavior Change Program: Transforming the science through a focus on mechanisms of change . Behaviour Research and Therapy , 101 , 3–11. doi: 10.1016/j.brat.2017.07.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
O’Carroll RE (2020). Self-regulation interventions - what do we know and where should we go? Health Psychology Review , 1–6. doi: 10.1080/17437199.2019.1709529 [ PubMed ] [ CrossRef ] [ Google Scholar ]
O’Connor DB (2020). The future of health behaviour change interventions: Opportunities for open science and personality research . Health Psychology Review , 1–10. doi: 10.1080/17437199.2019.1707107 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Pietromonaco PR, & Collins NL (2017). Interpersonal mechanisms linking close relationships to health . The American Psychologist , 72 ( 6 ), 531–542. doi: 10.1037/amp0000129 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Protogerou C, McHugh RK, & Johnson BT (2020). How best to reduce unhealthy risk-taking behaviours? A meta-review of evidence syntheses of interventions using self-regulation principles . Health Psychology Review , 1–30. doi: 10.1080/17437199.2019.1707104 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Rothman AJ, Simpson JA, Huelsnitz CO, Jones RE, & Scholz U (2020). Integrating intrapersonal and interpersonal processes: A key step in advancing the science of behavior change . Health Psychology Review . doi: 10.1080/17437199.2020.1719183. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ruiter R, Crutzen R, de Leeuw E, & Kok G (in press). Changing behavior using theories at the interpersonal, organizational, community and societal levels In Hagger MS, Cameron LD, Hamilton K, Hankonen N, & Lintunen T (Eds.), The Handbook of Behavior Change . Cambridge, U.K.: Cambridge University Press. [ Google Scholar ]
Scholz U, Berli C, Lüscher J, & Knoll N (in press). Changing behavior using theories at the interpersonal, organizational, community and societal levels In Hagger MS, Cameron LD, Hamilton K, Hankonen N, & Lintunen T (Eds.), The Handbook of Behavior Change . Cambridge, U.K.: Cambridge University Press. [ Google Scholar ]
Schuz B (2017). Socio-economic status and theories of health behaviour: Time to upgrade a control variable . British Journal of Health Psychology , 22 ( 1 ), 1–7. doi: 10.1111/bjhp.12205 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, … Henry DA (2017). AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both . BMJ , 358 , j4008. doi: 10.1136/bmj.j4008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Suls J, Mogavero JN, Falzon L, Pescatello LS, Hennessy EA, & Davidson KW (2020). Health Behaviour Change in Cardiovascular Disease Prevention and Management: Meta-Review of Behaviour Change Techniques to Affect Self-Regulation . Health Psychology Review , 1–23. doi: 10.1080/17437199.2019.1691622 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sumner JA, Carey RN, Michie S, Johnston M, Edmondson D, & Davidson KW (2018). Using rigorous methods to advance behaviour change science . Nature Human Behaviour , 2 ( 11 ), 797–799. doi: 10.1038/s41562-018-0471-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wilson TE, Hennessy EA, Falzon L, Boyd R, Kronish IM, & Birk JL (2020). Effectiveness of interventions targeting self-regulation to improve adherence to chronic disease medications: A meta-review of meta-analyses . Health Psychology Review , 1–41. doi: 10.1080/17437199.2019.1706615 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Using these brief interventions, you can help your patients make healthy behavior changes.

STEPHANIE A. HOOKER, PHD, MPH, ANJOLI PUNJABI, PHARMD, MPH, KACEY JUSTESEN, MD, LUCAS BOYLE, MD, AND MICHELLE D. SHERMAN, PHD, ABPP

Fam Pract Manag. 2018;25(2):31-36

Author disclosures: no relevant financial affiliations disclosed.

Effectively encouraging patients to change their health behavior is a critical skill for primary care physicians. Modifiable health behaviors contribute to an estimated 40 percent of deaths in the United States. 1 Tobacco use, poor diet, physical inactivity, poor sleep, poor adherence to medication, and similar behaviors are prevalent and can diminish the quality and length of patients' lives. Research has found an inverse relationship between the risk of all-cause mortality and the number of healthy lifestyle behaviors a patient follows. 2

Family physicians regularly encounter patients who engage in unhealthy behaviors; evidence-based interventions may help patients succeed in making lasting changes. This article will describe brief, evidence-based techniques that family physicians can use to help patients make selected health behavior changes. (See “ Brief evidence-based interventions for health behavior change .”)

Modifiable health behaviors, such as poor diet or smoking, are significant contributors to poor outcomes.

Family physicians can use brief, evidence-based techniques to encourage patients to change their unhealthy behaviors.

Working with patients to develop health goals, eliminate barriers, and track their own behavior can be beneficial.

Interventions that target specific behaviors, such as prescribing physical activity for patients who don't get enough exercise or providing patient education for better medication adherence, can help patients to improve their health.

CROSS-BEHAVIOR TECHNIQUES

Although many interventions target specific behaviors, three techniques can be useful across a variety of behavioral change endeavors.

“SMART” goal setting . Goal setting is a key intervention for patients looking to make behavioral changes. 3 Helping patients visualize what they need to do to reach their goals may make it more likely that they will succeed. The acronym SMART can be used to guide patients through the goal-setting process:

Specific. Encourage patients to get as specific as possible about their goals. If patients want to be more active or lose weight, how active do they want to be and how much weight do they want to lose?

Measurable. Ensure that the goal is measurable. For how many minutes will they exercise and how many times a week?

Attainable. Make sure patients can reasonably reach their goals. If patients commit to going to the gym daily, how realistic is this goal given their schedule? What would be a more attainable goal?

Relevant. Ensure that the goal is relevant to the patient. Why does the person want to make this change? How will this change improve his or her life?

Timely. Help patients define a specific timeline for the goal. When do they want to reach their goal? When will you follow-up with them? Proximal, rather than distal, goals are preferred. Helping patients set a goal to lose five pounds in the next month may feel less overwhelming than a goal of losing 50 pounds in the next year.

Problem-solving barriers . Physicians may eagerly talk with patients about making changes — only to become disillusioned when patients do not follow through. Both physicians and patients may grow frustrated and less motivated to work on the problem. One way to prevent this common phenomenon and set patients up for success is to brainstorm possible obstacles to behavior change during visits.

After offering a suggestion or co-creating a plan, physicians can ask simple, respectful questions such as, “What might get in the way of your [insert behavior change]?” or “What might make it hard to [insert specific step]?” Physicians may anticipate some common barriers raised by patients but be surprised by others. Once the barriers are defined, the physician and patient can develop potential solutions, or if a particular barrier cannot be overcome, reevaluate or change the goal. This approach can improve clinical outcomes for numerous medical conditions and for patients of various income levels. 4

For example, a patient wanting to lose weight may commit to regular short walks around the block. Upon further discussion, the patient shares that the cold Minnesota winters and the violence in her neighborhood make walking in her area difficult. The physician and patient may consider other options such as walking around a local mall or walking with a family member instead. Anticipating every barrier may be impossible, and the problem-solving process may unfold over several sessions; however, exploring potential challenges during the initial goal setting can be helpful.

Self-monitoring . Another effective strategy for facilitating a variety of behavioral changes involves self-monitoring, defined as regularly tracking some specific element of behavior (e.g., minutes of exercise, number of cigarettes smoked) or a more distal outcome (e.g., weight). Having patients keep diaries of their behavior over a short period rather than asking them to remember it at a visit can provide more accurate and valuable data, as well as provide a baseline from which to track change.

When patients agree to self-monitor their behavior, physicians can increase the chance of success by discussing the specifics of the plan. For example, at what time of day will the patient log his or her behavior? How will the patient remember to observe and record the behavior? What will the patient write on the log? Logging the behavior soon after it occurs will provide the most accurate data. Although patients may be tempted to omit unhealthy behaviors or exaggerate healthy ones, physicians should encourage patients to be completely honest to maximize their records' usefulness. For self-monitoring to be most effective, physicians should ask patients to bring their tracking forms to follow-up visits, review them together, celebrate successes, discuss challenges, and co-create plans for next steps. (Several diary forms are available in the Patient Handouts section of the FPM Toolbox .)

A variety of digital tracking tools exist, including online programs, smart-phone apps, and smart-watch functions. Physicians can help patients select which method is most convenient for daily use. Most online programs can present data in charts or graphs, allowing patients and physicians to easily track change over time. SuperTracker , a free online program created by the U.S. Department of Agriculture, helps patients track nutrition and physical activity plans, set goals, and work with a group leader or coach. Apps like Lose It! or MyFitnessPal can also help.

The process of consistently tracking one's behavior is sometimes an intervention itself, with patients often sharing that it created self-reflection and resulted in some changes. Research shows self-monitoring is effective across several health behaviors, especially using food intake monitoring to produce weight loss. 5

BEHAVIOR-SPECIFIC TECHNIQUES

The following evidence-based approaches can be useful in encouraging patients to adopt specific health behaviors.

Physical activity prescriptions . Many Americans do not engage in the recommended amounts of physical activity, which can affect their physical and psychological health. Physicians, however, rarely discuss physical activity with their patients. 6 Clinicians ought to act as guides and work with patients to develop personalized physical activity prescriptions, which have the potential to increase patients' activity levels. 7 These prescriptions should list creative options for exercise based on the patient's experiences, strengths, values, and goals and be adapted to a patient's condition and treatment goals over time. For example, a physician working with a patient who has asthma could prescribe tai chi to help the patient with breathing control as well as balance and anxiety.

In creating these prescriptions, physicians should help the patient recognize the personal benefits of physical activity; identify barriers to physical activity and how to overcome them; set small, achievable goals; and give patients the confidence to attempt their chosen activity. Physicians should also put the prescriptions in writing, give patients logs to track their activity, and ask them to bring those logs to follow-up appointments for further discussion and coaching. 8 More information about exercise prescriptions and sample forms are available online.

Healthy eating goals . Persuading patients to change their diets is daunting enough without unrealistic expectations and the constant bombardment of fad diets, cleanses, fasts, and other food trends that often leave both patients and physicians uncertain about which food options are actually healthy. Moreover, physicians in training receive little instruction on what constitutes sound eating advice and ideal nutrition. 9 This confusion can prevent physicians from broaching the topic with patients. Even if they identify healthy options, common setbacks can leave both patients and physicians less motivated to readdress the issue. However, physicians can help patients set realistic healthy eating goals using two simple methods:

Small steps. Studies have shown that one way to combat the inertia of unhealthy eating is to help patients commit to small, actionable, and measurable steps. 10 First, ask the patient what small change he or she would like to make — for example, decrease the number of desserts per week by one, eat one more fruit or vegetable serving per day, or swap one fast food meal per week with a homemade sandwich or salad. 11 Agree on these small changes to empower patients to take control of their diets.

The Plate Method. This model of meal design encourages patients to visualize their plates split into the following components: 50 percent fruits and non-starchy vegetables, 25 percent protein, and 25 percent grains or starchy foods. 12 Discuss healthy options that would fit in each of the categories, or combine this method with the small steps described above. By providing a standard approach that patients can adapt to many forms of cuisine, the model helps physicians empower their patients to assess their food options and adopt healthy eating behaviors.

Brief behavioral therapy for insomnia . Many adults struggle with insufficient or unrestful sleep, and approximately 18.8 percent of adults in the United States meet the criteria for an insomnia disorder. 13 The first-line treatment for insomnia is Cognitive Behavioral Therapy for Insomnia (CBT-I), which involves changing patients' behaviors and thoughts related to their sleep and is delivered by a trained mental health professional. A physician in a clinic visit can easily administer shorter versions of CBT-I, such as Brief Behavioral Therapy for Insomnia (BBT-I). 14 BBT-I is a structured therapy that includes restricting the amount of time spent in bed but not asleep and maintaining a regular sleep schedule from night to night. Here's how it works:

Sleep diary. Have patients maintain a sleep diary for two weeks before starting the treatment. Patients should track when they got in bed, how long it took to fall asleep, how frequently they woke up and for how long, what time they woke up for the day, and what time they got out of bed. Many different sleep diaries exist, but the American Academy of Sleep Medicine's version is especially user-friendly.

Education. In the next clinic appointment, briefly explain how the body regulates sleep. This includes the sleep drive (how the pressure to sleep is based on how long the person has been awake) and circadian rhythms (the 24-hour biological clock that regulates the sleep-wake cycle).

Set a wake-up time. Have patients pick a wake-up time that will work for them every day. Encourage them to set an alarm for that time and get up at that time every day, no matter how the previous night went.

Limit “total time in bed.” Review the patient's sleep diary and calculate the average number of hours per night the patient slept in the past two weeks. Add 30 minutes to that average and explain that the patient should be in bed only for that amount of time per night until your next appointment.

Set a target bedtime. Subtract the total time in bed from the chosen wake-up time, and encourage patients to go to bed at that “target” time only if they are sleepy and definitely not any earlier.

For example, if a patient brings in a sleep diary with an average of six hours of sleep per night for the past two weeks, her recommended total time in bed will be 6.5 hours. If she picks a wake-up time of 7 a.m., her target bedtime would be 12:30 a.m. It usually takes up to three weeks of regular sleep scheduling and sleep restriction for patients to start seeing improvements in their sleep. As patients' sleep routines become more solid (i.e., they are falling asleep quickly and sleeping more than 90 percent of the time they are in bed), slowly increase the total time in bed to possibly increase time asleep. Physicians should encourage patients to increase time in bed in increments of 15 to 30 minutes per week until the ideal amount of sleep is reached. This amount is different for each patient, but patients generally have reached their ideal amount of sleep when they are sleeping more than 85 percent of the time in bed and feel rested during the day.

Patient education to prevent medication nonadherence . Medication adherence can be challenging for many patients. In fact, approximately 20 percent to 30 percent of prescriptions are never picked up from the pharmacy, and 50 percent of medications for chronic diseases are not taken as prescribed. 15 Nonadherence is associated with poor therapeutic outcomes, further progression of disease, and decreased quality of life. To help patients improve medication adherence, physicians must determine the reason for nonadherence. The most common reasons are forgetfulness, fear of side effects, high drug costs, and a perceived lack of efficacy. To help patients change these beliefs, physicians can take several steps:

Educate patients on four key aspects of drug therapy — the reason for taking it (indication), what they should expect (efficacy), side effects and interactions (safety), and how it structurally and financially fits into their lifestyle (convenience). 16

Help patients make taking their medication a routine of their daily life. For example, if a patient needs to use a controller inhaler twice daily, recommend using the inhaler before brushing his or her teeth each morning and night. Ask patients to describe their day, including morning routines, work hours, and other responsibilities to find optimal opportunities to integrate this new behavior.

Ask patients, “Who can help you manage your medications?” Social networks, including family members or close friends, can help patients set up pillboxes or provide medication reminders.

The five Rs to quitting smoking . Despite the well-known consequences of smoking and nationwide efforts to reduce smoking rates, approximately 15 percent of U.S. adults still smoke cigarettes. 17 As with all kinds of behavioral change, patients present in different stages of readiness to quit smoking. Motivational interviewing techniques can be useful to explore a patient's ambivalence in a way that respects his or her autonomy and bolsters self-efficacy. Discussing the five Rs is a helpful approach for exploring ambivalence with patients: 18

Relevance. Explore why quitting smoking is personally relevant to the patient.

Risks. Advise the patient on negative consequences of continuing to smoke.

Rewards. Ask the patient to identify the benefits of quitting smoking.

Roadblocks. Help the patient determine obstacles he or she may face when quitting. Common barriers include weight gain, stress, fear of withdrawal, fear of failure, and having other smokers such as coworkers or family in close proximity.

Repeat. Incorporate these aspects into each clinical contact with the patient.

Many patients opt to cut back on the amount of tobacco they use before their quit date. However, research shows that cutting back on the number of cigarettes is no more effective than quitting abruptly, and setting a quit date is associated with greater long-term success. 19

Once the patient sets a quit date, repeated physician contact to reinforce smoking cessation messages is key. Physicians, care coordinators, or clinical staff should consider calling or seeing the patient within one to three days of the quit date to encourage continued efforts to quit, as this time period has the highest risk for relapse. Evidence shows that contacting the patient four or more times increases the success rate in staying abstinent. 18 Quitting for good may take multiple a empts, but continued encouragement and efforts such as setting new quit dates or offering other pharmacologic and behavioral therapies can be helpful.

GETTING STARTED

Family physicians are uniquely positioned to provide encouragement and evidence-based advice to patients to change unhealthy behaviors. The proven techniques described in this article are brief enough to attempt during clinic visits. They can be used to encourage physical activity, healthy eating, better sleep, medication adherence, and smoking cessation, and they can help patients adjust their lifestyle, improve their quality of life, and, ultimately, lower their risk of early mortality.

Loef M, Walach H. The combined effects of healthy lifestyle behaviors on all-cause mortality: a systematic review and meta-analysis. Prev Med . 2012;55(3):163-170.

Bodenheimer T, Handley MA. Goal-setting for behavior change in primary care: an exploration and status report. Patient Educ Couns . 2009;76(2):174-180.

Lilly CL, Bryant LL, Leary JM, et al.; Evaluation of the effectiveness of a problem-solving intervention addressing barriers to cardiovascular disease prevention behaviors in three underserved populations: Colorado, North Carolina, West Virginia, 2009. Prev Chronic Dis . 2014;11:E32.

U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines for Americans (7th Ed). Washington, D.C: U.S. Government Printing Office; 2010.

Sreedhara M, Silfee VJ, Rosal MC, Waring ME, Lemon SC. Does provider advice to increase physical activity differ by activity level among U.S. adults with cardiovascular disease risk factors? Fam Pract . 2018;35(4):420-425.

Pinto BM, Lynn H, Marcus BH, DePue J, Goldstein MG. Physician-based activity counseling: intervention effects on mediators of motivational readiness for physical activity. Ann Behav Med . 2001;23(1):2-10.

Hechanova RL, Wegler JL, Forest CP. Exercise: a vitally important prescription. JAAPA . 2017;30(4):17-22.

Guo H, Pavek M, Loth K. Management of childhood obesity and overweight in primary care visits: gaps between recommended care and typical practice. Curr Nutr Rep . 2017;6(4):307-314.

Perkins-Porras L, Cappuccio FP, Rink E, Hilton S, McKay C, Steptoe A. Does the effect of behavioral counseling on fruit and vegetable intake vary with stage of readiness to change?. Prev Med . 2005;40(3):314-320.

Kahan S, Manson JE. Nutrition counseling in clinical practice: how clinicians can do better. JAMA . 2017;318(12):1101-1102.

Choose My Plate. U.S. Department of Agriculture website. https://www.choosemyplate.gov/ . Updated January 31, 2018. Accessed February 1, 2018.

Ford ES, Cunningham TJ, Giles WH, Croff JB. Trends in insomnia and excessive daytime sleepiness among U.S. adults from 2002 to 2012. Sleep Med . 2015;16(3):372-378.

Edinger JD, Sampson WS. A primary care “friendly” cognitive behavioral insomnia therapy. Sleep . 2003;26(2):177-182.

Viswanathan M, Golin CE, Jones CD, et al.; Interventions to improve adherence to self-administered medications for chronic diseases in the United States: a systematic review. Ann Intern Med . 2012;157(11):785-795.

Cipolle RJ, Strand LM, Morley PC. Pharmaceutical care practice: the patient-centered approach to medication management services . 3rd ed. New York: McGraw-Hill; 2012.

Jamal A, King BA, Neff LJ, Whitmill J, Babb SD, Graffunder CM. Current cigarette smoking among adults — United States, 2005–2015. MMWR Morb Mortal Wkly Rep . 2016;65(44):1205-1211.

Patients not ready to make a quit attempt now (the “5 Rs”). Agency for Healthcare Research and Quality website. http://bit.ly/2jVvpoY . Updated December 2012. Accessed February 2, 2018.

Larzelere MM, Williams DE. Promoting smoking cessation. Am Fam Physician . 2012;85(6):591-598.

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Habit formation and behavior change.

Benjamin Gardner Benjamin Gardner Department of Psychology, King's College London
and Amanda L. Rebar Amanda L. Rebar Department of Human, Health, and Social Sciences, Central Queensland University
https://doi.org/10.1093/acrefore/9780190236557.013.129
Published online: 26 April 2019

Within psychology, the term habit refers to a process whereby contexts prompt action automatically, through activation of mental context–action associations learned through prior performances. Habitual behavior is regulated by an impulsive process, and so can be elicited with minimal cognitive effort, awareness, control, or intention. When an initially goal-directed behavior becomes habitual, action initiation transfers from conscious motivational processes to context-cued impulse-driven mechanisms. Regulation of action becomes detached from motivational or volitional control. Upon encountering the associated context, the urge to enact the habitual behavior is spontaneously triggered and alternative behavioral responses become less cognitively accessible.

By virtue of its cue-dependent automatic nature, theory proposes that habit strength will predict the likelihood of enactment of habitual behavior, and that strong habitual tendencies will tend to dominate over motivational tendencies. Support for these effects has been found for many health-related behaviors, such as healthy eating, physical activity, and medication adherence. This has stimulated interest in habit formation as a behavior change mechanism: It has been argued that adding habit formation components into behavior change interventions should shield new behaviors against motivational lapses, making them more sustainable in the long-term. Interventions based on the habit-formation model differ from non-habit-based interventions in that they include elements that promote reliable context-dependent repetition of the target behavior, with the aim of establishing learned context–action associations that manifest in automatically cued behavioral responses. Interventions may also seek to harness these processes to displace an existing “bad” habit with a “good” habit.

Research around the application of habit formation to health behavior change interventions is reviewed, drawn from two sources: extant theory and evidence regarding how habit forms, and previous interventions that have used habit formation principles and techniques to change behavior. Behavior change techniques that may facilitate movement through discrete phases in the habit formation trajectory are highlighted, and techniques that have been used in previous interventions are explored based on a habit formation framework. Although these interventions have mostly shown promising effects on behavior, the unique impact on behavior of habit-focused components and the longevity of such effects are not yet known. As an intervention strategy, habit formation has been shown to be acceptable to intervention recipients, who report that through repetition, behaviors gradually become routinized. Whether habit formation interventions truly offer a route to long-lasting behavior change, however, remains unclear.

automaticity
behavior change
dual process

What Are Habits and Habitual Behaviors ?

Everyday behaviors shape human health. Many of the dominant causes of death, including heart disease, diabetes, cancer, chronic lower respiratory diseases, and stroke, are preventable (World Health Organization, 2017 ). Adopting health-promoting behaviors such as eating more healthily or increasing physical activity may improve quality of life, physical and mental health, and extend lives (Aune et al., 2017 ; Centers for Disease Control and Prevention, 2014 ; Rebar et al., 2015 ; World Health Organization, 2015 ). For some behaviors, one performance is sufficient to attain desired health outcomes; a single vaccination, for example, can yield immunity to disease (e.g., Harper et al., 2004 ). For many behaviors, however, achieving meaningful health outcomes depends on repeated performance: Going for a run once, for example, will not achieve the same health benefits as regular activity over a prolonged period (Erikssen et al., 1998 ). In such instances, behavior change must be viewed as a long-term process, which can be conceptually separated into stages of initiation and maintenance (Prochaska & DiClemente, 1986 ; Rothman, 2000 ). This distinction is important from a practical perspective because while people may possess the capability, opportunity, and motivation to initiate behavior change (Michie, van Stralen, & West, 2011 ), they often fail to maintain it over time, lapsing back into old patterns of behavior (Dombrowski, Knittle, Avenell, Araujo-Soares, & Sniehotta, 2014 ). Some have attributed this to changes in motivation after initial experiences of action (Armitage, 2005 ; Rothman, 2000 ). People may overestimate the likelihood of positive outcomes or the valence of such outcomes, or they may fail to anticipate negative outcomes (Rothman, 2000 ). Alternatively, a newly adopted behavior may lose value and so become deprioritized over time. Motivation losses threaten to derail initially successful behavior change attempts.

Habit formation has attracted special attention as a potential mechanism for behavior change maintenance (Rothman, Sheeran, & Wood, 2009 ; Verplanken & Wood, 2006 ) because habitual behaviors are thought to be protected against any dips in conscious motivation. Viewing habit as a means to maintenance may seem truistic; in everyday discourse, a habit is an action done repetitively and frequently, and so making action habitual will necessarily entail maintenance. Within psychology, however, the term habit denotes a process whereby exposure to a cue automatically triggers a non-conscious impulse to act due to the activation of a learned association between the cue and the action (Gardner, 2015 ). Habit is learned through “context-dependent repetition” (Lally, van Jaarsveld, Potts, & Wardle, 2010 ): Repeated performance following exposure to a reliably co-occurring cue reinforces mental cue-action associations. As these associations develop, the habitual response gradually becomes the default, with alternative actions becoming less cognitively accessible (Danner, Aarts, & de Vries, 2008 ). Habit is formed when exposure to the cue is sufficient to arouse the impulse to enact the associated behavior without conscious oversight (Gardner, 2015 ; Neal, Wood, Labrecque, & Lally, 2012 ; Wood, Labrecque, Lin, & Rünger, 2014 ). In the absence of stronger influences favoring alternative actions, the habit impulse will translate smoothly and non-consciously into action, and the actor will experience behavior as directly cued by the context (Wood & Neal, 2007 ).

Defining habit as a process that generates behavior breaks with earlier definitions, which depicted habit as a form of behavior (see Gardner, 2015 ). This definition of habit as a process resolves a logical inconsistency that arises from portraying habit as a determinant of behavior (e.g., Hall & Fong, 2007 ; Triandis, 1980 ); as Maddux ( 1997 , pp. 335–336) noted, “a habit cannot be both the behavior and the cause of the behavior.” It also allows for the habit process to manifest in multiple ways for any behavior. A distinction has been drawn between habitually instigated and habitually executed behavior (Gardner, Phillips, & Judah, 2016 ; Phillips & Gardner, 2016 ). Habitual instigation refers to habitual triggering of the selection of an action and a non-conscious commitment to performing it upon encountering a cue that has consistently been paired with the action in the past. Habitual execution refers to habit facilitating completion of the sub-actions that comprise any given action such that the cessation of one action in a sequence automatically triggers the next. Take, for example, “eating a bag of chips.” While people typically mentally represent this activity as a single unit of action (Wegner, Connally, Shearer, & Vallacher, 1983 , cited in Vallacher & Wegner, 1987 ), it can be deconstructed into a series of discrete sub-actions (e.g., “opening bag,” “putting hand in bag,” “putting food in mouth,” “chewing,” “swallowing”; Cooper & Shallice, 2000 ). “Eating a bag of chips” is habitually instigated to the extent that the actor is automatically cued to select “eating chips” from available behavioral options. This may also activate the first sub-action in the sequence (“opening bag”). “Eating a bag of chips” is habitually executed to the extent that the cessation of, for example, “putting my hand in the bag” habitually cues “putting food in mouth,” the cessation of which habitually cues “chewing,” and so on, until the perceptually unitary action (“eating a bag of chips”) is complete. 1 The term habitual behavior describes any action that is either instigated or executed habitually. This includes actions that are habitually instigated but non-habitually executed (e.g., habitually triggered to begin eating a bag of chips, but deliberates about how many chips to put in mouth), non-habitually instigated but habitually executed (e.g., consciously decides to eat a bag of chips, but habitually puts the chips in mouth, chews, and swallows), or both habitually instigated and habitually executed (e.g., habitually starts eating chips, and habitually puts them in mouth, chews, and swallows; Gardner, 2015 ). This description allows for a behavior to be habitual, yet not fully automated (see Aarts, Paulussen, & Schaalma, 1997 ; Marien, Custers, & Aarts, 2019 ) and better resonates with everyday experiences of complex health behaviors such as physical activity, which may be partly habit-driven, yet also require conscious oversight to be successfully completed (Rhodes & Rebar, 2019 ).

Habit has been implicated in behaviors across a range of domains, including media consumption (LaRose, 2010 ), purchasing patterns (Ji & Wood, 2007 ), environmentally relevant actions (Kurz, Gardner, Verplanken, & Abraham, 2014 ), and health behaviors. Studies have pointed to a multitude of health-related actions that may potentially be performed habitually, including dietary consumption (Adriaanse, Kroese, Gillebaart, & De Ridder, 2014 ), physical activity (Rebar, Elavsky, Maher, Doerksen, & Conroy, 2014 ), medication adherence (Hoo, Boote, Wildman, Campbell, & Gardner, 2017 ), handwashing (Aunger et al., 2010 ), and dental hygiene (Wind, Kremers, Thijs, & Brug, 2005 ). Habit strength is consistently found to correlate positively with behavioral frequency (Gardner, de Bruijn, & Lally, 2011 ; Rebar et al., 2016 ) and may bridge the “gap” between intention and behavior, though there are varying accounts regarding interplay between habits and intentions in regulating behavior. Some have argued that people are more likely to act on intentions when they have habits for doing so (Rhodes & de Bruijn, 2013 ). When motivation is momentarily low upon encountering associated contexts, habit may translate into performance despite motivational lapses. In this way, habit has been proposed to represent a form of self-control, protecting regularly performed behaviors that are desired in the longer-term against shorter-term motivation losses (Galla & Duckworth, 2015 ). Other studies have suggested that habit can direct action despite intentions not to act (Neal, Wood, Wu, & Kurlander, 2011 ; Orbell & Verplanken, 2010 ; but see Rebar et al., 2014 ). For example, one study showed that United Kingdom smokers with habits for smoking while drinking alcohol reported “action slips” after the introduction of a smoking ban in public houses; despite intending to adhere to the ban, several reporting “finding themselves” beginning to light up cigarettes while consuming alcohol (Orbell & Verplanken, 2010 ). These two perspectives concur in highlighting the potential for habit to override conscious motivational tendencies. Such effects may be attributable to habitual instigation rather than execution (Gardner et al., 2016 ); someone who is habitually prompted to act is more likely to frequently perform those actions and to do so without relying on intention.

The effects of habit—or more specifically, instigation habit (Gardner et al., 2016 )—have important implications for behavior maintenance. By virtue of their cue-dependent, automatic nature (Orbell & Verplanken, 2010 ), habitually instigated behaviors should, in theory, persist even when they no longer serve the goal that initially motivated performance, or where motivation has eroded (Wood & Neal, 2007 ). For example, a person starting a new job out of town may consistently decide to commute by bicycle, which will likely create a habit for bicycle commuting whereby the workday morning context automatically prompts bicycle use without any deliberation over available alternatives (Verplanken, Aarts, Knippenberg, & Moonen, 1998 ). This may, however, lead to instances whereby the commuter “accidentally” uses the bicycle out of habit, despite, for example, knowing of road closures that will slow the journey and which would render alternative transport modes preferable (see Verplanken, Aarts, & Van Knippenberg, 1997 ). This example demonstrates several key features of habitual responses: learning via consistent pairing of cues (e.g., 8 a.m. on a workday) and action (selecting the bicycle); cue-dependent automaticity (using the bicycle at 8 a.m. on a workday without deliberation); and goal-independence, persisting even where an actor no longer has the motivation to act or is motivated to act in another way (e.g., when roads are closed). It also demonstrates how habit formation can maintain behavior by “locking in” new behaviors, protecting them against losses in conscious motivation. Habit development may also play a useful role in cessation of unwanted behaviors. Many ingrained behaviors—for example, eating high-calorie snacks—persist because they have become habitual and so are difficult to change. The lack of reliance on conscious intentions that is characteristic of habitual behavior, and which is thought to protect new behaviors against motivation losses, makes it difficult to break unwanted habits despite strong intentions to do so (Webb & Sheeran, 2006 ). While habit formation per se is not a sufficient strategy for “giving up” an unwanted behavior, behavior change can be made easier by seeking to form a new (“good”) habit in place of the old (“bad”) habit, rather than attempting only to inhibit the unwanted action (Adriaanse, van Oosten, de Ridder, de Wit, & Evers, 2011 ). Indeed, in the real world, habit development often involves displacing existing actions with more desirable alternatives such as eating healthy snacks in place of higher-calorie foods (Lally, Wardle, & Gardner, 2011 ; McGowan et al., 2013 ). Such “habit substitution” can take one of two basic forms, involving either avoidance of cues to the unwanted action or the development of new responses that compete with the unwanted habitual response. The “habit discontinuity hypothesis” speaks to the former of these, arguing that naturally occurring disruption of contexts—such as a residential relocation, for example—discontinues exposure to old habit cues (Walker, Thomas, & Verplanken, 2015 ). This represents an opportunity for people to act on their conscious motivation in response to newly encountered cues, and so to develop new, potentially more desirable habitual responses such as using active travel modes in place of more sedentary travel options like driving (Verplanken & Roy, 2016 ). Bad habits offer established cue-response structures that can hasten learning of new, good habits. Thus, where discontinued cue exposure is not feasible, people may seek to develop new cue-behavior associations to compete with and ultimately override old associations (Bouton, 2000 ; Walker et al., 2015 ). For example, people wishing to reduce habitual unhealthy snacking may form plans that dictate that when they are watching television and wish to snack (cue), they will eat fruit (new, desired behavior) instead of high-calorie foods (undesired, habitual behavior; e.g., Adriaanse, Gollwitzer, De Ridder, De Wit, & Kroese, 2011 ). In both instances of discontinued cue exposure and the adoption of competing responses to existing cues, the development of new habit associations and the decaying (or deprioritizing) of old habit associations are thought to occur concurrently (Adriaanse et al., 2011 ; Walker et al., 2015 ; Wood & Neal, 2007 ).

How Does Habit Form?

There have been calls for habit formation, whether focused solely on establishing new actions or displacing unwanted actions, to be adopted as an explicit goal for behavior change interventions (Rothman et al., 2009 ; Verplanken & Wood, 2006 ). Developing effective habit formation interventions requires an understanding of how habit forms.

The concept of behavior as an automatic response to covarying contextual cues, directed by learned cue-action associations, is rooted in behaviorist principles and studies of animal learning (e.g., Hull, 1943 ; Skinner, 1938 ; Thorndike, 1911 ). For example, in his maze-learning studies, Tolman ( 1932 ) noted that his rats, having repeatedly run down the route at the end of which was a food reward, continued to pursue that route even when the reward was removed. Adams ( 1982 ) trained rats to press a lever in a cage so as to receive intermittently delivered sucrose pellets. After receiving a lithium chloride injection that caused ingestion of the sucrose to induce nausea, those rats that were more highly trained (i.e., had pressed and received the sucrose reward a greater number of times in the training phase) were likely to persist longer in pressing the lever. Of course, unlike rats, humans possess the cognitive capacity to anticipate and reflect on their actions, and health-related behaviors among humans are inherently more complex than selecting maze routes or pressing levers. Yet, homologous neural processes are implicated in the acquisition and practice of habitual responses in rats and humans (Balleine & O’Doherty, 2010 ), and, like rats, people can acquire habitual behavioral responses despite a lack of insight into those behaviors or the associations that govern their performance (Bayley, Frascino, & Squire, 2005 ).

The route to human habit formation is conceptually simple: A behavior must be repeatedly performed in the presence of a cue or set of cues (i.e., context) so that cue-behavior associations may develop. For behaviors that are initially purposeful and goal-directed, the habit-formation process represents a period of transition whereby behavioral regulation transfers from a reflective and deliberative processing system to an impulsive system, which generates action rapidly and automatically based solely on activation of associative stores of knowledge (Strack & Deutsch, 2004 ). While there has been much lab-based research into the learning of relatively simple habitual responses in humans (e.g., button pressing; Webb, Sheeran, & Luszczynska, 2009 ), only relatively recently have studies focused on formation of real-world health-related habits (Fournier et al., 2017 ; Judah, Gardner, & Aunger, 2013 ; Lally et al., 2010 ). This work has largely been facilitated by the development of the Self-Report Habit Index (SRHI; Verplanken & Orbell, 2003 ), which affords reflections on the “symptoms” of habit, such as repetitive performance, mental efficiency, and lack of awareness.

Lally et al.’s ( 2010 ) seminal habit formation study used an SRHI sub-scale to assess the trajectory of the relationship between repetition and habit development among 96 participants for a 12-week period. They were instructed to perform a self-chosen physical activity or diet-related behavior (e.g., “going for a walk”) in response to a naturally occurring once-daily cue (e.g., “after breakfast”). Each day, they reported whether they had performed the action on the previous day, and if so, rated the experienced automaticity of its performance. Habit development within individuals was found to be most accurately depicted by an asymptotic curve, with early repetitions achieving sharpest habit gains, which later slowed to a plateau. The level at which habit peaked differed across participants, with some reportedly attaining scores at the high end of the automaticity index and others peaking below the scale mean. This plateau was reached at a median of 66 days post-baseline, though there was considerable between-person variation in the time taken to reach the plateau (18–254 days, the latter a statistical forecast assuming continued performance beyond the study period). These findings were echoed in a study of adoption of a novel stretching behavior (Fournier et al., 2017 ). Once-daily performance was found to yield asymptotic increases in self-reported habit strength. Habit plateaued at a median of 106 days for a group that performed the stretch every morning upon waking, and 154 days for those who stretched in the evening before bed, which the authors interpreted as evidence of the role of cortisol (which naturally peaks in the morning) in habit learning.

These studies reveal that habit development is not linear; if this were so, the fourth repetition of a behavior would have the same reinforcing impact on habit as would, say, the 444th. Rather, the asymptotic growth curve demonstrates that initial repetitions have the greatest impact on habit development. This in turn demands that the habit formation process be broken down into discrete phases and that the early phase, characterized by the sharpest gains in automaticity, may be a critical period during which people require most support to sustain motivation before the action becomes automatic (Gardner, Lally, & Wardle, 2012 ). Lally and Gardner ( 2013 ) have proposed a framework that organizes habit formation (and substitution) into four interlinked phases (see also Gardner & Lally, 2019 ). It argues that, for new behaviors initially driven by conscious motivation, habit forms when a person (1) makes a decision to act and (2) acts on his or her decision (3) repeatedly, (4) in a manner conducive to the development of cue-behavior associations. Phases 1 and 2 may be taken together to represent pre-initiation, occurring before the first enactment of the new behavior, whereas phases 3 and 4 are post-initiation phases, addressing the motivational and volitional elements needed to sustain behavior after initial performance (phase 3) and the effect of repetition on habit associations (phase 4) (see also Kuhl, 1984 ; Rhodes & de Bruijn, 2013 ; Rothman, 2000 ). Phase 3 captures the critical period after initiation but before habit strength has peaked (Fournier et al., 2017 ; Lally et al., 2010 ).

The framework is not intended as a theory or model of the habit formation process, but rather as a means to conceptually organize the processes and mechanisms that underpin habit development. According to the framework, any variable can promote habit formation in one or more of four ways: It may enhance motivation (phase 1) or action control (i.e., the enactment of intentions into behavior; Kuhl, 1984 ; Rhodes & de Bruijn, 2013 ) (phase 2) so as to initiate the behavior; it may modify motivation and other action control processes to continue to perform the behavior (phase 3); or it may strengthen cue-behavior associations (phase 4). One variable may operate through multiple processes: For example, anticipating pleasure from action can motivate people to perform it for the first time (phase 1) and to continue to perform it (phase 3) (Radel, Pelletier, Pjevac, & Cheval, 2017 ; Rothman et al., 2009 ). The experience of pleasure can also quicken learning of cue-behavior associations (phase 4) (de Wit & Dickinson, 2009 ). By extension, Lally and Gardner’s ( 2013 ) framework categorizes techniques that promote habit formation according to their likely mechanism (or mechanisms) of action; techniques may enhance motivation (phase 1) or action control (phase 2) to initiate change, sustain motivation and action control over time (phase 3), or reinforce cue-behavior associations (phase 4).

Which Behavior Change Techniques Should Be Used to Form Habit?

The most comprehensive taxonomy of behavior change techniques currently available defines habit formation as a discrete technique, which it defines as any effort to “prompt rehearsal and repetition of the behavior in the same context repeatedly so that the context elicits the behaviour” (Michie et al., 2013 , Suppl. Table 3 , p. 10). Yet, this definition incorporates only context-dependent repetition and not any other technique that may promote habit by increasing the likelihood of context-dependent repetition (i.e., promoting motivation or action control; phases 1–3 of Lally and Gardner’s framework) or enhancing the contribution of each repetition to the learning of habit associations (phase 4). Although context-dependent repetition is necessary for habit to form, it realistically requires supplementation with techniques targeting pre- and post-initiation phases en route to habit formation (Gardner Lally, & Wardle, 2012 ). While Michie et al. ( 2013 ) treat habit formation as a unitary technique, habit formation may perhaps be more realistically seen as an intervention approach that comprises a broader suite of techniques, which marry context-dependent repetition with strategies that: reinforce motivation; boost action control capacity, opportunity, or skills; facilitate post-initiation repetition; or quicken the learning of associations arising from repetition.

Theory points to techniques that may facilitate progression through these phases. Intention formation (phase 1 of Lally & Gardner’s [ 2013 ] framework) is likely when people anticipate that the action or its likely consequences will be positive and believe that they have a realistic opportunity and capability to perform the behavior (Ajzen, 1991 ; Bandura, 2001 ; Michie et al., 2011 ; Rogers, 1983 ; Schwarzer, Lippke, & Luszczynska, 2011 ). Providing information on the likely positive consequences of action, or choosing to pursue actions that are already most highly valued, may therefore aid habit development by enhancing motivation. Action control skills are required to initiate intention enactment (phase 2) and to maintain the behavior by consistently prioritizing the intention over competing alternatives (phase 3). This will likely be facilitated by self-regulatory techniques such as planning, setting reminders, self-monitoring, and reviewing goals to ensure they remain realistic and attractive, and receiving (intrinsic) rewards contingent on successful performance (Gardner et al., 2012 ; Lally & Gardner, 2013 ). People are most likely to engage in context-dependent repetition in response to highly salient cues (e.g., event- rather than time-based cues, which likely require conscious monitoring; McDaniel & Einstein, 1993 ). Pairing the action with more frequently and consistently encountered cues may quicken habit learning at phase 4 (Gardner & Lally, 2019 ). Highly specific action plans detailing exactly what will be done and in exactly which situation (i.e., implementation intentions; Gollwitzer, 1999 ) should therefore be conducive to the acquisition of associations (but see Webb et al., 2009 ). Implementation intentions can also facilitate habit substitution: By consistently enacting new, pre-specified cue responses that directly compete with existing habitual responses, such as feeding children water instead of sugary drinks (McGowan et al., 2013 ), new responses may acquire the potential to override and erode old habitual responses (Adriaanse et al., 2011 ). The reinforcing value of repetition may also be strengthened where intrinsic reward is delivered or attention is drawn to an undervalued intrinsic reward arising from action (Radel et al., 2017 ).

Which Behavior Change Techniques Have Been Used to Form Habit, and with What Effect?

While theory can recommend techniques that should be used to promote habit formation, evaluations of habit-based interventions are needed to show which techniques have been used, and with what effect, in real-world behavior change contexts. To this end, a systematic literature search was run to identify habit-based health-promotion interventions and to document the behavior change methods used.

Four psychology and health databases (Embase, Medline, PsycInfo, Web of Science) were searched in March 2018 to identify sources that had cited one of nine key papers about habit and health. These sources were selected to capture topics of habit measurement (Gardner, Abraham, Lally, & de Bruijn, 2012 ; Ouellette & Wood, 1998 ; Verplanken & Orbell, 2003 ), principles and processes of habit formation (Gardner, Lally, & Wardle, 2012 ; Lally & Gardner, 2013 ; Lally et al., 2010 ; Lally et al., 2011 ), and conceptual commentaries (Gardner, 2015 ; Wood & Rünger, 2016 ). Papers were eligible for review if they (a) were published in English, (b) were peer-reviewed, (c) reported primary quantitative or qualitative data, (d) had tested efficacy or effectiveness for changing behavior or habit, (e) used interventions designed to promote habit formation for health behaviors, (f) targeted context-dependent repetition, and (g) were informed by theory or evidence around habit, operationalized as a learned automatic response to contextual cues or a process that generates such responses. Interventions adopted primarily to elucidate the habit formation process (rather than to develop or assess intervention effectiveness; e.g., Judah et al., 2013 ; Lally et al., 2010 ) and any that focused exclusively on breaking existing habits (e.g., Armitage, 2016 ) were excluded. For each eligible intervention, all available material was coded, including linked publications (e.g., protocols), to identify component techniques using the Behavior Change Technique Taxonomy v1 (Michie et al, 2013 ).

Twenty papers, reporting evaluations of 19 interventions, were identified. Four of the 19 interventions represented variants of interventions used elsewhere in the 20 papers. For example, one trial evaluated the same habit-based intervention component in two conditions, which varied only in the frequency of supplementary motivational interviews and booster phone calls (Simpson et al., 2015 ). Thus, the 19 could be reduced to 15 unique habit-based interventions, of which four focused on both dietary and physical activity habits, six on physical activity (or sedentary behavior) only, two on dietary consumption only, two on dental hygiene, and one on food safety. In all of the studies, habit measures were self-reported.

Diet and Physical Activity Interventions

One randomized controlled trial (RCT) compared, in overweight and obese adults, an intervention that included advice on forming and substituting healthy for unhealthy habits, with a non-habit-based intervention that emphasized relationships with food, body image, and weight biases (Carels et al., 2014 ; see also Carels et al., 2011 ). Those in the habit-based intervention received training on changing old routines and developing new ones, including advice on using cues and forming implementation intentions. Both intervention groups received weekly weight assessments and monitored their physical activity, calorie intake, and output. At a 6-month follow-up, both the habit-based ( n = 30) and non-habit intervention groups ( n = 29) were eating a healthier diet, exercising more regularly, and had lost weight. Physical activity habit strengthened and sitting habit weakened in both groups, though no between-group differences were found in weight loss or habit strength.

Lally et al.’s ( 2008 ) “Ten Top Tips” weight loss intervention centered on a leaflet outlining recommendations for forming healthy eating and physical activity habits, as supplemented by a daily adherence monitoring diary. The leaflet included advice on routinization, identifying effective cues, and habit substitution. A small non-randomized trial compared the intervention, augmented with monthly ( n = 35) or weekly weighing ( n = 34), against a no-treatment control. The intervention group lost more weight than the control group at 8 weeks and maintained weight loss at 32 weeks. Scores at 32 weeks suggested the tips had become habitual, and habit change correlated positively with weight loss (Lally et al., 2008 ; see also Lally et al., 2011 ). In a subsequent RCT (Beeken et al., 2012 , 2017 ), intervention recipients ( n = 267) lost more weight at 3 months than did a usual-care group ( n = 270). At 24 months, the intervention group had maintained weight loss, though the usual care group had lost a similar amount of weight. Habit strength, measured only at baseline and 3 months, increased more in the intervention than in the control group (Beeken et al., 2017 ). Weight loss at 3 months was attributable to gains in both habit and self-regulatory skill (Kliemann et al., 2017 ).

Simpson et al.’s ( 2015 ) weight-loss intervention provided participants with motivational advice designed to prompt intention formation, with information about how to form dietary and activity habits, and social support. Two intervention variants, differing according to the frequency of sessions, were evaluated against a minimal-treatment control, which did not feature habit-based advice, in a feasibility RCT among obese patients. Recipients of the more intensive intervention variant ( n = 55) showed greater BMI reduction at a 12-month follow-up than did the less intensive intervention ( n = 55) or control groups ( n = 60). There were no between-group differences at 12 months in physical activity or overall healthy eating, nor were there differences in activity or diet habit scores.

One RCT compared an 8-week computer-tailored intervention designed to reduce cardiovascular risk against a no-treatment control among cardiac and diabetes rehabilitation patients who already intended to increase their activity and fruit and vegetable consumption (Storm et al., 2016 ). The intervention provided information about health risks of inactivity and unhealthy diet and enhancing self-regulatory skills. Immediately following intervention cessation, fruit and vegetable consumption and physical activity habit and behavior scores were greater among the intervention ( n = 403) than control group ( n = 387), but no differences were observed 3 months post-baseline.

Physical Activity and Sedentary Behavior Interventions

An intervention for new gym members promoted habits for both physical activity and preparatory actions for gym attendance (e.g., packing a gym bag; Kaushal, Rhodes, Meldrum, & Spence, 2017 ). Members received advice on how to form habits, including selecting time cues, setting action plans, and using accessories to increase enjoyment and so support cue-consistent performance and foster intrinsic motivation, which theory suggests can strengthen the impact of repetition on habit development (Lally & Gardner, 2013 ). Moderate-to-vigorous physical activity gains, objectively observed at an 8-week follow-up, were greater among intervention recipients ( n = 47) than the no-treatment control group ( n = 47). Habit strength was not assessed.

All 49 participants in Fournier et al.’s ( 2017 ) RCT were given access to twice-weekly, 1-hour tailored physical activity sessions for 28 weeks, with one group ( n = 23) also sent SMS reminders targeting intrinsic motivation and consistent performance to the intervention group to foster habitual attendance. Although physical activity habit strength (assessed using a subscale of the SRHI) increased for both groups immediately post-intervention, the SMS group experienced quicker habit gains. Marginally greater activity was observed in the SMS group at 12 months.

One 4-month intervention for middle- to older-aged adults comprised seven 2-hour group sessions and sought to create new balance and strength exercise habits by recommending small modifications to everyday routines (e.g., placing frequently used items on high shelves to promote stretching to reach them) (Fleig et al., 2016 ; see also Clemson et al., 2012 ). An uncontrolled trial among 13 participants showed that, while there were no apparent changes in objectively measured physical performance, there were considerable habit strength gains for the recommended actions over 6 months. Notably, participants reported in interviews that the exercises had become automatically triggered, yet they performed them consciously, suggesting that the intervention promoted habitual instigation rather than execution.

Another intervention promoting small activity changes in older adulthood was evaluated in two papers (Matei et al., 2015 ; White et al., 2017 ). Drawing on Lally et al.’s ( 2008 ) “Ten Top Tips,” it comprised a leaflet offering recommendations for integrating and substituting light-intensity physical activities into everyday routines, with supplementary self-monitoring record sheets (Gardner, Thune-Boyle, et al., 2014 ). An 8-week uncontrolled trial was undertaken among two discrete samples (Matei et al., 2015 ). No changes were found in sitting time, physical activity, or sitting or physical activity habit among one sample ( n = 16), but a second sample ( n = 27) reported decreased sitting time and increased walking. Qualitative data suggested both groups experienced automaticity gains and some health benefits. A subsequent pilot RCT showed that intervention recipients ( n = 45) experienced no greater change than did a control group ( n = 46) who received a pre-existing fact sheet promoting activity and reducing sitting, but with no habit-based advice (White et al., 2017 ). Both groups reduced sitting time and sitting habit and increased activity and activity habit.

Using an experience sampling design, Luo et al. ( 2018 ) tracked change in standing or moving breaks from sedentary behavior in office workers given 3 weeks of access to automated computer-based reminders to break up sitting, timed to occur based on daily self-selected work and break durations. Although sitting behavior was not monitored, habit strength and self-regulation for taking “moving breaks” during work hours both increased significantly across the study.

Similarly, Pedersen et al. ( 2014 ) evaluated a software package that automatically deactivated desk-based employees’ computer screens every 45 minutes to substitute new physical activity habits for existing prolonged sitting habits. Although all participants received information on the detrimental health impact of sitting and benefits of activity, self-report activity data suggested that those who used the software for 13 weeks ( n = 17) expended greater energy per day than did those not given the software ( n = 17).

Dietary Interventions

One intervention promoted habitual healthy child-feeding practices among parents of children aged 2–6 years (McGowan et al., 2013 ). On each of four occasions over 8 weeks, parents chose to pursue one of four families of habit formation targets (increased feeding of fruit, vegetables, water, and healthy snacks). They received advice on the importance of child dietary consumption and on self-regulatory strategies, including action planning, goal setting, and context-dependent repetition. An RCT showed that intervention parents ( n = 58) reported greater child intake of vegetables, water, and healthy snacks but a waiting-list control group ( n = 68) did not. Habit strength increased for all three behaviors, and a habit score averaged across behaviors correlated with behavior change (McGowan et al., 2013 ; see also Gardner, Sheals, Wardle, & McGowan, 2014 ).

In one RCT, fruit and vegetable consumption changes were compared between participants who received habit-based messages, and those receiving general, non-habit-based tips for increasing consumption or messages about healthy eating more broadly (Rompotis et al., 2014 ). Notably, habit-based messages focused on anticipating stimulus control and environmental modification and on eating the same fruits and vegetables at the same time each day, so targeting both habitual instigation and execution (see Phillips & Gardner, 2016 ). The intervention was delivered via SMS in one set of conditions and email in the other. At 8-weeks post-intervention, both intervention groups (SMS n = 26, email n = 30) had increased fruit consumption and fruit habit strength, but those in all other conditions had not (SMS fruit and vegetable tips, n = 24, SMS healthy eating tips, n = 23; email fruit and vegetable tips, n = 29, email healthy eating n = 29). No effects were found on vegetable consumption or habit.

Oral Hygiene

Two school-based interventions aimed to increase tooth brushing in primary school children. One involved weekly dental hygiene lessons and daily tooth brushing practice time (Gaeta, Cavazos, Cabrera, & Rosário, 2018 ). School visits were also made by health promoters, and a seminar was held for teachers. One control group ( n = 52) received the visits and seminar only, and a second control group ( n = 52) received the seminar only. A quasi-experiment showed that children in the habit-based intervention ( n = 106) and visits-and-seminar control group had less dental plaque, and a stronger tooth brushing habit at 12-week follow-up than did the seminar-only control group. The habit-based intervention group had the lowest plaque.

Wind et al.’s ( 2005 ) intervention also involved allocation of a designated tooth brushing time during the school day and encouragement from teachers. Tooth brushing rates increased in the intervention group ( n = 141) during treatment but not in the control group (the nature of which could not be identified from the published report; n = 155). There were no differences in behavior at 12-months post-intervention nor in habit at any follow-up.

Food Safety

An intervention promoted the microwaving of dishcloths or sponges, for hygiene reasons (Mullan, Allom, Fayn, & Johnston, 2014 ). Recipients received emails and a poster providing instructions on how and why to microwave the dishcloths and sponges, designed to be placed in kitchens to act as a cue to the action. In an RCT, one intervention group was instructed to self-monitor their action, for intervention purposes, every 3 days ( n = 15) and another every 5 days ( n = 17). Relative to those who received an unrelated control treatment ( n = 13), frequency and habit strength increased in the two intervention groups at 3 weeks and was sustained to the final 6-week follow-up.

Behavior Change Techniques Used in Previous Interventions

A total of 32 discrete behavior change techniques were each identified in at least one of the 15 interventions (see Table 1 and Table 2 ). Aside from context-dependent repetition itself—which, as an inclusion criterion, was necessarily present in all interventions—the most commonly used were “use prompts and cues” (present in 11 interventions; 73%), “action planning” (8 interventions; 53%), “provide instruction on how to perform the behavior” (8 interventions; 53%), “set behavioral goals” (8 interventions; 53%), and “self-monitor behavior” (7 interventions; 47%). Also common were “behavioral practice or rehearsal” (6 interventions; 40%), “provide information on health consequences” (6 interventions; 40%), and “problem solving” (5 interventions; 33%). “Behavioral substitution” and habit substitution (labeled “habit reversal” in the taxonomy) were each used in 4 interventions (27%).

Table 1. Behavior Change Techniques Identified in 15 Habit Formation Interventions

Note . With the exception of “context-dependent repetition,” all technique labels are taken from the BCT Taxonomy v1 (Michie et al., 2013 ).

* This technique is labeled “habit formation” in the BCT Taxonomy v1 (Michie et al., 2013 ). Rephrasing this as “context-dependent repetition” more clearly delineates the underlying technique (i.e., to consistently repeat behavior in an unvarying context) from the outcome that it is designed to serve (i.e., to form habit). It also better acknowledges the possibility that such repetition may not lead to the formation of habit. For example, Lally et al. ( 2010 ) observed some participants who failed to attain peak habit strength in an 84-day study period, and some who experienced gains that peaked at low levels, suggesting that while repetition had rendered the behavior more habitual, the action remained predominantly regulated by conscious motivation rather than habit.

Table 2. Behavior Change Techniques Documented in 15 Habit Formation Interventions

Note . All technique labels are taken from the BCT Taxonomy v1 (Michie et al., 2013 ).

While all 15 interventions were based on the principle of habit formation, none used context-dependent repetition as a standalone technique. 2 The use of techniques additional to repetition echoes the view that in the real world, habit is best promoted by embedding context-dependent repetition into a broader package of techniques that also target motivation and action control, which are prerequisites for repetition (Lally & Gardner, 2013 ). Techniques most commonly adopted in past interventions have focused predominantly on action control (e.g., planning, goal-setting, identifying cues, rehearsing action, problem solving). The relative paucity of techniques targeting motivation may reflect an assumption that, for most of the behaviors targeted, intervention recipients generally recognize the value of behavior change, but lack the volitional skills, opportunities, or resources to change. Whether motivation should be targeted as part of a habit-formation intervention will depend on whether target populations understand the need for change and prioritize the target behavior above alternatives.

Fewer than half of the 15 interventions appear to have addressed factors that may moderate the relationship between repetition and habit development. Theory and evidence suggest that the mental associations that underlie habit will develop most strongly or quickly where actions are more simple or intrinsically rewarding and in response to cues that are salient and consistently encountered (Lally & Gardner, 2013 ; McDaniel & Einstein, 1993 ; Radel et al., 2017 ). Several of the reviewed interventions purposively promoted habit formation for simple behaviors (Beeken et al., 2017 ; Fleig et al., 2016 ; Lally et al., 2010 , 2011 ; Matei et al., 2015 ; Mullan et al., 2014 ; White et al., 2017 ). Kaushal et al. ( 2017 ) emphasized the importance of intrinsic reward in their physical activity promotion intervention, and Fournier et al. ( 2017 ) targeted intrinsic motivation. These studies highlight how interventions may move beyond simply promoting repetition toward targeting factors that may reduce the number of repetitions required for a target behavior to become habitual.

How Should Habit-Based Interventions Be Evaluated?

Previous interventions attest to the potential for habit-based approaches to change behavior. Although many intervention studies were not designed to test effectiveness, 13 of the 15 interventions were associated with positive change on at least one index of behavior or behavior-contingent outcomes (e.g., weight loss) at one or more follow-ups. Process evaluations pointed to the strengthening of habit as a key mechanism underpinning behavioral change based on increases in self-reported automaticity scores or qualitative reflections on the subjective experience of automaticity (Fleig et al., 2016 ; Gardner, Sheals, et al., 2014 ; Kliemann et al., 2017 ; Lally et al., 2011 ; Matei et al., 2015 ). Additionally, acceptability studies have suggested that recipients find the concept of context-dependent repetition—which distinguishes habit-based and non-habit-based interventions—easy to understand and follow (Fleig et al., 2016 ; Gardner, Sheals, et al., 2014 ; Lally et al., 2011 ; Matei et al., 2015 ).

Limitations of evaluation methods preclude understanding of how best to support habit formation. It is not yet clear whether promotion of context-dependent repetition is necessary for habit to develop or, indeed, whether it represents the most “active” ingredient of a habit formation intervention. One study found that a control group that did not receive habit-based advice reported similar physical activity habit gains to those among a group that received habit guidance (White et al., 2017 ). Conversely, another study showed that intervention recipients deviated from habit-based advice (e.g., by setting goals that were not specific, measurable, or achievable), yet habit strengthened (Gardner, Sheals, et al., 2014 ). Habit formation may therefore arise as a byproduct of interventions that do not explicitly target habit development. The unique contribution of context-dependent repetition to behavior change remains unclear because none of the reviewed studies compared a habit-based intervention with an otherwise identical non-habit-based equivalent. Indeed, most studies have evaluated habit formation interventions against minimal-treatment control groups or used uncontrolled designs. Future research should seek to compare matched habit- and non-habit-based interventions or otherwise use factorial designs, which allow testing for isolated effects within a multicomponent intervention, or mediation analyses, which can assess whether habit change underpins intervention effects.

Intervention evaluations have also been limited by short follow-up periods, which is ironic given that the key purported benefit of incorporating habit formation into interventions is the potential to increase longevity of behavior change. Few studies evaluated outcomes over 12 months or longer, with the longest observed follow-up being 24 months (Beeken et al., 2017 ). Beeken et al.’s ( 2017 ) “Ten Top Tips” intervention showed greater impact than did a non-habit-based usual-care treatment on dietary and physical activity habits, and weight loss, at the 3-month follow-up, which the authors found to be due in part to habit development (Kliemann et al., 2017 ). Yet, while weight loss was maintained at 24 months, the advantage conferred by the habit-based intervention over usual care was lost, suggesting that any habit gains may have dissipated, or alternatively, that for those who were successful in maintaining the behaviors over the 2-year period, habit formation had occurred regardless of condition. These possibilities cannot be investigated because habit strength was not evaluated at 24 months. Elsewhere, however, a small exploratory (non-intervention) study suggested that habit gains may erode over time: Among a group of participants forming dental flossing habits over 8 weeks, habit strength had considerably eroded in the subgroup of participants who provided data at a 6-month follow-up (Judah et al., 2013 ). Until more is done to assess the longevity of habit-based intervention effects, the hypothesis that habit persists over time, and so supports behavior maintenance, remains insufficiently tested.

Theory proposes that, through consistent performance, behaviors become habitual such that they are initiated automatically upon encountering cues via the activation of learned context-behavior associations. Habitual behaviors are thought to be self-sustaining, and so forming a habit has been proposed as a means to promote long-term maintenance of behavior. Interventions that seek to promote habit formation should include not only advice on context-dependent repetition, but also techniques that support the motivation and action control needed to repeat the action and that may enhance the reinforcing value of repetition on habit development. Fifteen interventions were found to have used habit formation principles to encourage engagement in health-promoting behaviors, and these have tended to supplement advice on repetition with action control techniques. Previous research suggests a habit-based approach has much to offer to behavior change initiatives; habit formation offers an acceptable, easily understood intervention strategy, with the potential to change behavior and yield favorable health outcomes. Yet, the unique effects of habit-specific techniques, and the longevity of effects, have not been adequately explored. The central assumption of the habit-based approach—that habit gains translate into long-term behavior maintenance—remains largely untested.

Aarts, H. , Paulussen, T. , & Schaalma, H. (1997). Physical exercise habit: On the conceptualization and formation of habitual health behaviors. Health Education Research , 12 (3), 363–374.
Adriaanse, M. A. , Gollwitzer, P. M. , De Ridder, D. T. D. , De Wit, J. B. F. , & Kroese, F. M. (2011). Breaking habits with implementation intentions: A test of underlying processes . Personality and Social Psychology Bulletin , 37 (4), 502–513.
Adams, C. D. (1982). Variations in the sensitivity of instrumental responding to reinforcer devaluation . Quarterly Journal of Experimental Psychology B: Comparative and Physiological Psychology , 34B (2), 77–98.
Adriaanse, M. A. , Kroese, F. M. , Gillebaart, M. , & De Ridder, D. T. (2014). Effortless inhibition: Habit mediates the relation between self-control and unhealthy snack consumption. Frontiers in Psychology , 5 , 444.
Adriaanse, M. A. , van Oosten, J. M. F. , de Ridder, D. T. D. , de Wit, J. B. F. , & Evers, C. (2011). Planning what not to eat: Ironic effects of implementation intentions negating unhealthy habits. Personality and Social Psychology Bulletin , 37 , 69–81.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes , 50 (2), 179–211.
Armitage, C. J. (2005). Can the theory of planned behavior predict the maintenance of physical activity? Health Psychology , 24 (3), 235–245.
Armitage, C. J. (2016). Evidence that implementation intentions can overcome the effects of smoking habits . Health Psychology , 35 (9), 935–943.
Aune, D. , Giovannucci, E. , Boffetta, P. , Fadnes, L. T. , Keum, N. , Norat, T. , . . . Tonstad, S. (2017). Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality: A systematic review and dose-response meta-analysis of prospective studies . International Journal of Epidemiology , 46 (3), 1029–1056.
Aunger, R. , Schmidt, W.-P. , Ranpura, A. , Coombes, Y. , Maina, P. M. , Matiko, C. N. , & Curtis, V. (2010). Three kinds of psychological determinants for hand-washing behavior in Kenya . Social Science & Medicine , 70 (3), 383–391.
Balleine, B. W. , & O’Doherty, J. (2010). Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action . Neuropsychopharmacology Reviews , 35 , 48–69.
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology , 52 (1), 1–26.
Bayley, P. J. , Frascino, J. C. , & Squire, L. R. (2005). Robust habit learning in the absence of awareness and independent of the medial temporal lobe . Nature , 436 (7050), 550–553.
Beeken, R. J. , Croker, H. , Morris, S. , Leurent, B. , Omar, R. , Nazareth, I. , & Wardle, J. (2012). Study protocol for the 10 Top Tips (10TT) trial: Randomised controlled trial of habit-based advice for weight control in general practice . BMC Public Health , 12 (1), 667.
Beeken, R. J. , Leurent, B. , Vickerstaff, V. , Wilson, R. , Croker, H. , Morris, S. , . . . Wardle, J. (2017). A brief intervention for weight control based on habit-formation theory delivered through primary care: Results from a randomised controlled trial . International Journal of Obesity , 41 (2), 246–254.
Bouton, M. E. (2000). A learning theory perspective on lapse, relapse, and the maintenance of behavior change. Health Psychology , 19 (1S), 57–63.
Carels, R. A. , Burmeister, J. M. , Koball, A. M. , Oehlhof, M. W. , Hinman, N. , LeRoy, M. , . . . Darby, L. A. (2014). A randomized trial comparing two approaches to weight loss: Differences in weight loss maintenance. Journal of Health Psychology , 19 (2), 296–311.
Carels, R. A. , Young, K. M. , Koball, A. M. , Gumble, A. , Darby, L.A. , . . . Hinman, N. (2011). Transforming your life: An environmental modification approach to weight loss . Journal of Health Psychology , 16 , 430–438.
Centers for Disease Control and Prevention . (2014). Physical activity . Atlanta, GA: U.S. Department of Health & Human Services.
Clemson, L. , Singh, M. A. F. , Bundy, A. , Cumming, R. G. , Manollaras, K. , O’Loughlin, P. , & Black, D. (2012). Integration of balance and strength training into daily life activity to reduce rate of falls in older people (the LiFE study): Randomised parallel trial . BMJ , 345 , e4547.
Cooper, R. , & Shallice, T. (2000). Contention scheduling and the control of routine activities . Cognitive Neuropsychology , 17 (4), 297–338.
Danner, U. N. , Aarts, H. , & de Vries, N. K. (2008). Habit vs. intention in the prediction of future behavior: The role of frequency, context stability and mental accessibility of past behavior . British Journal of Social Psychology , 47 (2), 245–265.
de Wit, S. , & Dickinson, A. (2009). Associative theories of goal-directed behavior: A case for animal–human translational models. Psychological Research , 73 (4), 463–476.
Dombrowski, S. U. , Knittle, K. , Avenell, A. , Araujo-Soares, V. , & Sniehotta, F. F. (2014). Long-term maintenance of weight loss with non-surgical interventions in obese adults: Systematic review and meta-analyses of randomised controlled trials . BMJ , 348 , g2646.
Erikssen, G. , Liestøl, K. , Bjørnholt, J. , Thaulow, E. , Sandvik, L. , & Erikssen, J. (1998). Changes in physical fitness and changes in mortality . The Lancet , 352 (9130), 759–762.
Fleig, L. , McAllister, M. M. , Chen, P. , Iverson, J. , Milne, K. , McKay, H. A. , . . . Ashe, M. C. (2016). Health behavior change theory meets falls prevention: Feasibility of a habit-based balance and strength exercise intervention for older adults . Psychology of Sport and Exercise , 22 , 114–122.
Fournier, M. , d’Arripe-Longueville, F. , Rovere, C. , Easthope, C. S. , Schwabe, L. , El Methni, J. , & Radel, R. (2017). Effects of circadian cortisol on the development of a health habit. Health Psychology , 36 (11), 1059–1064.
Gaeta, M. L. , Cavazos, J. , Cabrera, M. del R. , & Rosário, P. (2018). Fostering oral hygiene habits and self-regulation skills: An intervention with preschool children . Family & Community Health , 41 (1), 47–54.
Galla, B. M. , & Duckworth, A. L. (2015). More than resisting temptation: Beneficial habits mediate the relationship between self-control and positive life outcomes . Journal of Personality and Social Psychology , 109 (3), 508–525.
Gardner, B. (2015). A review and analysis of the use of “habit” in understanding, predicting and influencing health-related behavior . Health Psychology Review , 9 (3), 277–295.
Gardner, B. , Abraham, C. , Lally, P. , & de Bruijn, G.-J. (2012). Towards parsimony in habit measurement: Testing the convergent and predictive validity of an automaticity subscale of the self-report habit index . International Journal of Behavioral Nutrition and Physical Activity , 9 (1), 102–113.
Gardner, B. , de Bruijn, G.-J. , & Lally, P. (2011). A systematic review and meta-analysis of applications of the self-report habit index to nutrition and physical activity behaviors. Annals of Behavioral Medicine , 42 (2), 174–187.
Gardner, B. , & Lally, P. (2019). Modeling habit formation and its determinants. In B. Verplanken (Ed.), The psychology of habit (pp. 207–229). New York, NY: Springer.
Gardner, B. , Lally, P. , & Wardle, J. (2012). Making health habitual: The psychology of “habit-formation” and general practice . British Journal of General Practice , 62 (605), 664–666.
Gardner, B. , Phillips, L. A. , & Judah, G. (2016). Habitual instigation and habitual execution: Definition, measurement, and effects on behavior frequency . British Journal of Health Psychology , 21 (3), 613–630.
Gardner, B. , Sheals, K. , Wardle, J. , & McGowan, L. (2014). Putting habit into practice, and practice into habit: A process evaluation and exploration of the acceptability of a habit-based dietary behavior change intervention . International Journal of Behavioral Nutrition and Physical Activity , 11 (1), 135.
Gardner, B. , Thune-Boyle, I. , Iliffe, S. , Fox, K. R. , Jefferis, B. J. , Hamer, M. , . . . Wardle, J. (2014). “On Your Feet to Earn Your Seat,” a habit-based intervention to reduce sedentary behavior in older adults: Study protocol for a randomized controlled trial . Trials , 15 , 368.
Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist , 54 (7), 493.
Hall, P. A. , & Fong, G. T. (2007). Temporal self-regulation theory: A model for individual health behavior . Health Psychology Review , 1 (1), 6–52.
Harper, D. M. , Franco, E. L. , Wheeler, C. , Ferris, D. G. , Jenkins, D. , Schuind, A. , . . . De Carvalho, N. S. (2004). Efficacy of a bivalent L1 virus-like particle vaccine in prevention of infection with human papillomavirus types 16 and 18 in young women: A randomised controlled trial . The Lancet , 364 (9447), 1757–1765.
Hoo, Z. H. , Boote, J. , Wildman, M. J. , Campbell, M. J. , & Gardner, B. (2017). Determinants of objective adherence to nebulised medications among adults with cystic fibrosis: An exploratory mixed methods study comparing low and high adherers . Health Psychology and Behavioral Medicine , 5 (1), 299–316.
Hull, C. L. (1943). Principles of behavior: An introduction to behavior theory . New York, NY: Appleton-Century-Crofts.
Ji, M. F. , & Wood, W. (2007). Purchase and consumption habits: Not necessarily what you intend . Journal of Consumer Psychology , 17 (4), 261–276.
Judah, G. , Gardner, B. , & Aunger, R. (2013). Forming a flossing habit: An exploratory study of the psychological determinants of habit formation. British Journal of Health Psychology , 18 (2), 338–353.
Kaushal, N. , Rhodes, R. E. , Meldrum, J. T. , & Spence, J. C. (2017). The role of habit in different phases of exercise . British Journal of Health Psychology , 22 (3), 429–448.
Kliemann, N. , Vickerstaff, V. , Croker, H. , Johnson, F. , Nazareth, I. , & Beeken, R. J. (2017). The role of self-regulatory skills and automaticity on the effectiveness of a brief weight loss habit-based intervention: Secondary analysis of the 10 top tips randomised trial . International Journal of Behavioral Nutrition and Physical Activity , 14 , 119.
Kuhl, J. (1984). Volitional aspects of achievement motivation and learned helplessness: Toward a comprehensive theory of action control. In B. A. Maher & W. A. Maher (Eds.), Progress in experimental personality research (Vol. 13, pp. 99–171). New York, NY: Academic Press.
Kurz, T. , Gardner, B. , Verplanken, B. , & Abraham, C. (2014). Habitual behaviors or patterns of practice? Explaining and changing repetitive climate-relevant actions . WIRES Climate Change , 6 , 113–128.
Lally, P. , Chipperfield, A. , & Wardle, J. (2008). Healthy habits: Efficacy of simple advice on weight control based on a habit-formation model . International Journal of Obesity , 32 (4), 700–707.
Lally, P. , & Gardner, B. (2013). Promoting habit formation. Health Psychology Review , 7 (Suppl. 1), S137–S158.
Lally, P. , Van Jaarsveld, C. H. , Potts, H. W. , & Wardle, J. (2010). How are habits formed: Modelling habit formation in the real world . European Journal of Social Psychology , 40 (6), 998–1009.
Lally, P. , Wardle, J. , & Gardner, B. (2011). Experiences of habit formation: A qualitative study. Psychology, Health & Medicine , 16 (4), 484–489.
LaRose, R. (2010). The problem of media habits . Communication Theory , 20 , 194–222.
Luo, Y. , Lee, B. , Wohn, D. Y. , Rebar, A. L. , Conroy, D. E. , & Choe, E. K. (2018, April). Time for break: Understanding information workers’ behavior through a break prompting system . Paper presented at the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, Canada.
Maddux, J. E. (1997). Habit, health and happiness. Journal of Sport & Exercise Psychology , 19 (4), 331–346.
Marien, H. , Custers, R. , & Aarts, H. (2019). Understanding the formation of human habits: An analysis of mechanisms of habitual behavior. In B. Verplanken (Ed.), The psychology of habit (pp. 51–69), New York, NY: Springer.
Matei, R. , Thuné-Boyle, I. , Hamer, M. , Iliffe, S. , Fox, K. R. , Jefferis, B. J. , & Gardner, B. (2015). Acceptability of a theory-based sedentary behavior reduction intervention for older adults (“On Your Feet to Earn Your Seat”) . BMC Public Health , 15 (1), 606.
McDaniel, M. A. , & Einstein, G. O. (1993). The importance of cue familiarity and cue distinctiveness in prospective memory . Memory , 1 (1), 23–41.
McGowan, L. , Cooke, L. J. , Gardner, B. , Beeken, R. J. , Croker, H. , & Wardle, J. (2013). Healthy feeding habits: Efficacy results from a cluster-randomized, controlled exploratory trial of a novel, habit-based intervention with parents . The American Journal of Clinical Nutrition , 98 (3), 769–777.
Michie, S. , Richardson, M. , Johnston, M. , Abraham, C. , Francis, J. , Hardeman, W. , . . . Wood, C. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine , 46 (1), 81–95.
Michie, S. , Van Stralen, M. M. , & West, R. (2011). The behavior change wheel: A new method for characterising and designing behavior change interventions . Implementation Science , 6 (1), 42–53.
Mullan, B. , Allom, V. , Fayn, K. , & Johnston, I. (2014). Building habit strength: A pilot intervention designed to improve food-safety behavior . Food Research International , 66 , 274–278.
Neal, D. T. , Wood, W. , Labrecque, J. S. , & Lally, P. (2012). How do habits guide behavior? Perceived and actual triggers of habits in daily life . Journal of Experimental Social Psychology , 48 (2), 492–498.
Neal, D. T. , Wood, W. , Wu, M. , & Kurlander, D. (2011). The pull of the past: When do habits persist despite conflict with motives? Personality and Social Psychology Bulletin , 37 (11), 1428–1437.
Orbell, S. , & Verplanken, B. (2010). The automatic component of habit in health behavior: Habit as cue-contingent automaticity. Health Psychology , 29 (4), 374–383.
Ouellette, J. A. , & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin , 124 (1), 54–74.
Pedersen, S. J. , Cooley, P. D. , & Mainsbridge, C. (2014). An e-health intervention designed to increase workday energy expenditure by reducing prolonged occupational sitting habits . Work , 49 (2), 289–295.
Phillips, L. A. , & Gardner, B. (2016). Habitual exercise instigation (vs. execution) predicts healthy adults’ exercise frequency . Health Psychology , 35 (1), 69–77.
Prochaska, J. O. , & DiClemente, C. C. (1986). Toward a comprehensive model of change. In W. Miller & N. Heather (Eds.), Treating addictive behavior (pp. 3–27). New York, NY: Plenum.
Quinn, J. M. , Pascoe, A. , Wood, W. , & Neal, D. T. (2010). Can’t control yourself? Monitor those bad habits. Personality and Social Psychology Bulletin , 36 (4), 499–511.
Radel, R. , Pelletier, L. , Pjevac, D. , & Cheval, B. (2017). The links between self-determined motivations and behavioral automaticity in a variety of real-life behaviors. Motivation and Emotion , 41 (4), 443–454.
Rebar, A. L. , Dimmock, J. A. , Jackson, B. , Rhodes, R. E. , Kates, A. , Starling, J. , & Vandelanotte, C. (2016). A systematic review of the effects of non-conscious regulatory processes in physical activity . Health Psychology Review , 10 (4), 395–407.
Rebar, A. L. , Elavsky, S. , Maher, J. P. , Doerksen, S. E. , & Conroy, D. E. (2014). Habits predict physical activity on days when intentions are weak. Journal of Sport and Exercise Psychology , 36 (2), 157–165.
Rebar, A. L. , Stanton, R. , Geard, D. , Short, C. , Duncan, M. J. , & Vandelanotte, C. (2015). A meta-meta-analysis of the effect of physical activity on depression and anxiety in non-clinical adult populations . Health Psychology Review , 9 (3), 366–378.
Rhodes, R. E. , & de Bruijn, G.-J. (2013). What predicts intention-behavior discordance? A review of the action control framework. Exercise and Sport Sciences Reviews , 41 (4), 201–207.
Rhodes, R. E. , & Rebar, A. L. (2019). Physical activity habit: Complexities and controversies. In B. Verplanken (Ed.), The psychology of habit (pp. 91–109). New York, NY: Springer.
Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In B. L. Cacioppo & L. L. Petty (Eds.), Social psychophysiology: A sourcebook . London, U.K.: Guilford Press.
Rompotis, C. J. , Grove, J. R. , & Byrne, S. M. (2014). Benefits of habit-based informational interventions: A randomised controlled trial of fruit and vegetable consumption . Australian and New Zealand Journal of Public Health , 38 (3), 247–252.
Rothman, A. J. (2000). Toward a theory-based analysis of behavioral maintenance . Health Psychology , 19 (1S), S64–S69.
Rothman, A. J. , Sheeran, P. , & Wood, W. (2009). Reflective and automatic processes in the initiation and maintenance of dietary change. Annals of Behavioral Medicine , 38 (Suppl. 1), s4–s17.
Schwarzer, R. , Lippke, S. , & Luszczynska, A. (2011). Mechanisms of health behavior change in persons with chronic illness or disability: The Health Action Process Approach (HAPA) . Rehabilitation Psychology , 56 (3), 161–170.
Simpson, S. A. , McNamara, R. , Shaw, C. , Kelson, M. , Moriarty, Y. , Randell, E. , . . . Duncan, D. (2015). A feasibility randomised controlled trial of a motivational interviewing-based intervention for weight loss maintenance in adults . Health Technology Assessment , 19 (50).
Skinner, B. F. (1938). The behavior of organisms: An experimental analysis . New York, NY: Appleton-Century-Crofts.
Storm, V. , Dörenkämper, J. , Reinwand, D. A. , Wienert, J. , De Vries, H. , & Lippke, S. (2016). Effectiveness of a web-based computer-tailored multiple-lifestyle intervention for people interested in reducing their cardiovascular risk: A randomized controlled trial . Journal of Medical Internet Research , 18 (4), e78.
Strack, F. , & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior . Personality and Social Psychology Review , 8 (3), 220–247.
Thorndike, E. L. (1911). Animal intelligence (2nd ed.). New York, NY: Hafner.
Tolman, E. C. (1932). Purposive behavior in animals and men . New York, NY: Appleton-Century-Crofts.
Triandis, H. C. (1980). Values, attitudes, and interpersonal behavior. In Nebraska symposium on motivation (Vol. 27, pp. 195–259). Lincoln: University of Nebraska Press.
Vallacher, R. R. , & Wegner, D. M. (1987). What do people think they’re doing? Action identification and human behavior. Psychological Review , 94 (1), 3–15.
Verplanken, B. , Aarts, H. , Knippenberg, A. , & Moonen, A. (1998). Habit versus planned behavior: A field experiment. British Journal of Social Psychology , 37 (1), 111–128.
Verplanken, B. , Aarts, H. , & Van Knippenberg, A. (1997). Habit, information acquisition, and the process of making travel mode choices. European Journal of Social Psychology , 27 (5), 539–560.
Verplanken, B. , & Orbell, S. (2003). Reflections on past behavior: A self-report index of habit strength . Journal of Applied Social Psychology , 33 (6), 1313–1330.
Verplanken, B. , & Roy, D. (2016). Empowering interventions to promote sustained lifestyles: Testing the habit discontinuity hypothesis in a field experiment . Journal of Environmental Psychology , 45 , 127–134.
Verplanken, B. , & Wood, W. (2006). Interventions to break and create consumer habits . Journal of Public Policy & Marketing , 25 (1), 90–103.
Walker, I. , Thomas, G. O. , & Verplanken, B. (2015). Old habits die hard: Travel habit formation and decay during an office relocation. Environment and Behavior , 47 (10), 1089–1106.
Webb, T. L. , & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin , 132 , 249–268.
Webb, T. L. , Sheeran, P. , & Luszczynska, A. (2009). Planning to break unwanted habits: Habit strength moderates implementation intention effects on behavior change. British Journal of Social Psychology , 48 (3), 507–523.
White, I. , Smith, L. , Aggio, D. , Shankar, S. , Begum, S. , Matei, R. , . . . Jefferis, B. J. (2017). On Your Feet to Earn Your Seat: Pilot RCT of a theory-based sedentary behavior reduction intervention for older adults . Pilot and Feasibility Studies , 3 (1), 23.
Wind, M. , Kremers, S. , Thijs, C. , & Brug, J. (2005). Toothbrushing at school: Effects on toothbrushing behavior, cognitions and habit strength . Health Education , 105 (1), 53–61.
Wood, W. , Labrecque, J. S. , Lin, P.-Y. , & Rünger, D. (2014). Habits in dual process models. In J. W. Sherman , B. Gawronski , & Y. Trope (Eds.), Dual process theories of the social mind (pp. 371–385). New York, NY: Guilford Press.
Wood, W. , & Neal, D. T. (2007). A new look at habits and the habit-goal interface . Psychological Review , 114 (4), 843–863.
Wood, W. , & Rünger, D. (2016). Psychology of habit. Annual Review of Psychology , 67 , 289–314.
World Health Organization . (2015). Global status report on road safety 2015 . Geneva, Switzerland: World Health Organization.
World Health Organization . (2017). Global health observatory data . Geneva, Switzerland: World Health Organization.

1. Rhodes and colleagues have extended this line of thinking by incorporating preparatory actions into the process, showing that habitual preparation for an activity (e.g., packing a gym bag) can influence frequency of engagement in the focal behavior (in this case, exercise; Kaushal, Rhodes, Meldrum, & Spence, 2017 ). However, this differs from the instigation–execution distinction in that it focuses on the role of habit in different behaviors (preparatory actions vs. focal actions) rather than different roles of habit in the same behavior.

2. This is perhaps inevitable given the present review criteria, which excluded studies that used context-dependent repetition to study the habit formation process itself. However, real-world studies of the formation of health habits have not been based on context-dependent repetition alone; both Lally et al. ( 2010 ) and Fournier et al. ( 2017 ) instructed participants to use prompts and cues and set action plans or implementation intentions (see also Judah et al., 2013 ).

Methods of Health Behavior Change
Theoretical Approaches to Physical Activity Promotion
Use of Wearable Activity Trackers for Physical Activity Promotion

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Changing behaviour: an essential component of tackling health inequalities

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Peer review
Theresa M Marteau , professor 1 ,
Harry Rutter , professor 2 ,
Michael Marmot , professor 3
1 Department of Public Health and Primary Care, Behaviour and Health Research Unit, University of Cambridge, UK
2 Department of Social and Policy Sciences, University of Bath, Bath, UK
3 Institute of Health Equity, University College London, UK
Correspondence to: T M Marteau tm388{at}cam.ac.uk

Theresa Marteau and colleagues argue that behavioural and social causes of poor health must be tackled in parallel to reduce inequalities

Life expectancy in England is stalling, while at the same time health inequalities are widening. The 2020 Marmot review of health inequalities in England showed that between 2010 and 2018 the gap in life expectancy at birth between those living in the least and most deprived areas increased. 1 For men the gap increased from 9.1 to 9.5 years and for women from 6.8 to 7.7 years.

The time people spend in poor health has also increased across England since 2010, from 15.8 to 16.2 years for men, and 18.7 to 19.4 years for women. But these average figures mask an even steeper social gradient than that seen for life expectancy, meaning that those in more deprived areas spend a larger proportion of their already shorter lives in poor health.

The covid-19 pandemic is set to widen these inequalities yet further. 2 For example, the age standardised mortality rate associated with covid-19 in the most deprived areas in July 2020 was 3.1 deaths/100 000 population, more than double the rate in the least deprived areas (1.4 deaths/100 000 population). 3

Both the 2010 and 2020 Marmot reviews 1 4 outline actions in five priority areas for health equity: giving every child the best start in life; good education and lifelong learning to maximise capabilities; fair employment and good working conditions; healthy standard of living for all; and healthy and sustainable places and environments in which to live. However, the 2010 Marmot review included a sixth area—strengthening prevention of ill health—that was omitted from the 2020 review on the basis that it had received more policy focus over the past decade than the other areas. Preventing ill health requires a focus on the behaviours that follow the social gradient and contribute most to chronic disease, including smoking and unhealthy diets. How effective have the policies over the past decade been and how can we do better?

Policies focusing on behavioural causes

In England, the four leading behavioural causes of years of life lost are tobacco use, unhealthy diet, alcohol consumption, and physical inactivity. 5 Importantly, all of these behaviours are socioeconomically patterned. Changing them therefore has the potential to increase not only life expectancy but healthy life expectancy, which has a 19 year gap between rich and poor. 6 Yet despite England announcing some policies on these behaviours in England over the past decade, there has been little effective action.

Most of the relevant policies have centred on childhood obesity. At national level this includes the publication of the childhood obesity plan in 2016 followed by two further chapters in 2018 and 2019. These documents proposed important population level measures, including several that target commercial determinants of health such as advertising and marketing. A soft drinks industry levy was introduced in 2018, and is showing promising effects in both driving reformulation and reducing sales of sugary drinks. 7 But the other major measures proposed have yet to move beyond the consultation stage. The outgoing chief medical officer for England stated in 2019 that we are “nowhere near achieving” the government ambition to halve childhood obesity by 2030.” 8

Policy activity across the three other behavioural causes has been uneven, with some strong action on tobacco control but little on alcohol and physical activity. The 2012 ban on tobacco displays at point of sale was followed by the introduction of standardised packaging in 2016, and the introduction of a minimum excise tax in 2017. The last measure in particular would be predicted to reduce inequalities in smoking, as price based interventions most consistently lead to greater declines in smoking among adults and young people in lower socioeconomic positions than higher. 9 10 But although the prevalence of smoking in England has fallen over the past decade, the gap in smoking between those in routine and manual occupations compared with those in other occupations has widened substantially. 11 Smoking remains the single largest behavioural contributor to the gap in life expectancy between poor and rich people. 12

English governments have introduced no new policies in the past decade on alcohol control. Minimum unit price, which early findings in Scotland suggest has potential to reduce the social gradient in alcohol harm, 13 was eschewed in England in 2013. Since then government policies have served to increase rather than decrease the affordability of alcohol, with no notable cuts or freezes in alcohol duty, including in the most recent budget. 14

The four UK chief medical officers published an update in 2019 of their 2011 guidelines for physical activity, 15 but no formal policies have been introduced at national level.

What would effective policy comprise?

Changing behaviours equitably requires multiple interventions in multiple systems. Policy makers need to work at local, national, and international levels, engaging with the communities they serve. Interventions should include approaches that target high risk individuals as well as those aimed at whole populations. For example, smoking cessation and weight management services can support behaviour change for smokers and people who are overweight, while tobacco taxation and food advertising restrictions influence the behaviours of entire populations.

Two complementary types of interventions can change behaviour: those that target conscious processes and those that target non-conscious processes. Providing personalised risk profiles, for example, requires conscious effort to influence smoking and eating behaviour. 16 By contrast, changing the context or choice architecture within which a behaviour occurs—for example, by increasing the proportion of healthier foods offered, 17 requires less conscious effort by an individual to make healthier decisions.

Conscious processes generally make higher demands on people’s cognitive, social, and material resources. These resources are not evenly distributed across society, so interventions that rely on them can widen health inequalities. 18 19 Such effects are known as intervention generated inequalities. Unfortunately, interventions that rely on conscious processes have dominated policy responses to health inequalities in England since the 1970s. 20

Interventions with most promise for both improving population health and reducing the gap between the poorest and the richest are those aimed at whole populations using interventions that largely target non-conscious processes. They include fiscal and economic interventions, marketing approaches, and interventions altering the availability of products that harm health. 21 22 23

Tackling behavioural and social causes in parallel

The behavioural causes of health inequalities—tobacco use, unhealthy diet, alcohol consumption, and physical inactivity 5 —share several drivers with the social causes. These include factors such as unequal distribution of income, goods and services, education, employment, and power, 24 and, importantly, poverty—with its attentional, emotional, and material consequences.

Intervening on the social determinants can therefore also have a positive effect on the behavioural determinants. For example, increasing household incomes in the poorest households can increase spending on fruit and vegetables 25 and reduce spending on tobacco and alcohol, 25 26 27 perhaps by reducing stress in these households. 26 27

But such effects, while welcome, are insufficient on their own to change behaviours at the scale needed to reduce the health inequalities. It is also necessary to tackle the drivers of the behavioural causes that are not shared with the social causes.

One set of drivers that shapes much of the routine, habitual, and impulsive behaviour contributing to health inequalities is the stimuli or cues that surround us in physical, economic, digital, social, and commercial environments. Cues that encourage unhealthy behaviours such as the presence of tobacco, alcohol, and fast food outlets are generally much more prevalent in areas of high deprivation. 28

Higher densities of tobacco retailers are associated with higher levels of smoking, 29 including smoking in pregnancy, 30 31 and lower quit attempts. 30 Equivalent patterns are seen for densities of fast food outlets and the prevalence of obesity in both adults and children, 32 33 and for alcohol retailers and alcohol consumption and harm. 34 Conversely, cues for healthier behaviour such as physical activity include green spaces, which are twice as likely to be found in towns that are least deprived as in those that are most deprived. 35 Green spaces are associated with higher self-reported health and mental wellbeing, 36 37 both outcomes that are lower in more deprived groups.

Removing or reducing the environmental drivers of unhealthy behaviours and replacing them with drivers for healthier behaviours would have beneficial effects across populations, with the largest effects in the areas of highest deprivation. For example, reducing volume and speed of traffic and providing segregated infrastructure such as cycle lanes are associated with increased physical activity through cycling. 38 Reducing the size of wine glasses and wine bottles reduces alcohol consumption. 39 40

Other important environmental drivers of behaviour include price and marketing, for which effective interventions include taxes and regulation. 21 22 23 The strongest evidence for the largest improvements in population health and reductions in inequalities is for interventions that reduce the affordability of tobacco and alcohol. 23 Similar effects seem likely for interventions that reduce the affordability of foods high in fat, sugar, or salt. 7 41 Price based interventions with the most promise to increase physical activity are those that increase the affordability of walking, cycling, and public transport, and disincentivise driving. 42

Price based interventions tend to have larger effects on people with low incomes—that is, those most likely to experience the harms that result from unhealthy products and practices. 43 Effective marketing interventions include bans or restrictions on advertising and marketing designed to persuade people to consume health damaging products. Reducing exposure of children and adults to alcohol and unhealthy food marketing reduces their consumption of these products, 44 45 and anti-tobacco campaigns reduce smoking prevalence and increase quitting rates. 46

Achieving effective policy action requires strong political and public support to overcome powerful lobbying from commercial organisations that profit at the expense of population health. 47 48 Tackling behavioural and social causes together is particularly important for price based interventions. Incomes in the poorest families in the UK fell during the financial crisis of 2008-09, leaving them no higher in 2018-19 than in 2001-02. 49 But introducing price based interventions without steps to alleviate poverty will rightly lack public support. Calls to address the inequalities revealed and worsened by the covid-19 pandemic 50 51 52 have the potential to raise support for effective action.

The large and growing health inequalities in England described in the Marmot 2020 review can be both stalled and reversed. Although greater policy focus has been given to behavioural causes than social causes of inequalities over the past decade, this focus has not been matched by effective action at the scale needed. Given behavioural and social causes share some but not all drivers, effectively tackling health inequalities requires addressing both behavioural and social causes, in parallel and at a scale commensurate with this huge and growing problem. Tackling health inequalities should now form the core of all policies to build resilient societies post covid-19.

Key messages

The 2020 Marmot review showed that health inequalities in England have widened since 2010

Prevention of ill health was omitted from the 2020 review on the basis that it had received greater policy focus than social causes

This policy focus was, however, unmatched by effective action

Behavioural causes of ill health and inequality—tobacco use, unhealthy diet, alcohol consumption, and physical inactivity—share only some drivers with the social causes

Effectively tackling health inequalities requires addressing both behavioural and social causes in parallel

Contributors and sources: TMM conceived the idea for this paper following discussions with HR and MM. TMM and HR prepared the first draft of the paper to which MM added conceptual ideas and salient evidence. All authors edited the manuscript before approving the final version. TMM is guarantor of the article.

Competing interests: We have read and understood BMJ policy on declaration of interests and have no relevant interests to declare.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

Goldblatt P ,
Riordan R ,
↵ Office for National Statistics. Deaths involving COVID-19 by local area and socioeconomic deprivation: deaths occurring between 1 March and 31 July 2020. 2020. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsinvolvingcovid19bylocalareasanddeprivation/deathsoccurringbetween1marchand31july2020
Newton JN ,
↵ Office for National Statistics. Health state life expectancies by national deprivation deciles, England and Wales: 2015-2017. 2017. https://cam.ldls.org.uk/vdc_100063278677.0x000001
Scarborough P ,
Adhikari V ,
Harrington RA ,
↵ Davies S. Time to solve childhood obesity: an independent report by the chief medical officer. 2019. https://www.webarchive.org.uk/access/resolve/20191014215140/https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/837907/cmo-special-report-childhood-obesity-october-2019.pdf
↵ Office for National Statistics. Adult smoking habits in the UK: 2019. 2020. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/healthandlifeexpectancies/bulletins/adultsmokinghabitsingreatbritain/2019
O’Donnell A ,
Anderson P ,
Jané-Llopis E ,
Manthey J ,
↵ Alcohol Health Alliance. AHA responds to budget 2020. Press release, 11 Mar 2020. https://ahauk.org/aha-responds-to-budget-2020/
↵ Department of Health & Social Care, UK chief medical officers’ physical activity guidelines. 2019. https://www.webarchive.org.uk/access/resolve/20190923233125/https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/832868/uk-chief-medical-officers-physical-activity-guidelines.pdf
Hollands GJ ,
French DP ,
Griffin SJ ,
Cartwright E ,
Pilling M ,
Veinot TC ,
Mitchell H ,
Kriznik NM ,
Kinmonth AL ,
Marteau TM ,
World Health Organization
Bloomberg MR ,
Summers LH ,
Houweling TA ,
Commission on Social Determinants of Health
Waldfogel J ,
Washbrook E
Strully KW ,
Rehkopf DH ,
Milligan K ,
Macdonald L ,
Shortt NK ,
Barnett R ,
Chaiton MO ,
Mecredy G ,
Clemens T ,
↵ Public Health England. Patterns and trends in child obesity: national and regional data. https://www.gov.uk/government/publications/child-obesity-patterns-and-trends/patterns-and-trends-in-child-obesity-national-and-regional-data
Burgoine T ,
Forouhi NG ,
Wareham NJ ,
Monsivais P
Mitchell R ,
↵ Goodair B, Kenny M, Marteau TM. Townscapes: England’s health inequalities. Bennett Institute for Public Policy, 2020. https://www.bennettinstitute.cam.ac.uk/publications/townscapes-englands-health-inequalities/
Twohig-Bennett C ,
Wheeler BW ,
Fleming LE ,
Depledge MH
Codling S ,
Mantzari E ,
Scheelbeek PFD ,
Cornelsen L ,
Suhrcke M ,
Yeatman H ,
Kuipers MAG ,
Kickbusch I ,
Reynolds JP ,
van der Linden S ,
↵ Tomlinson D, Corlett A, Handscomb K, McCurdy C, Brewer M. The living standards audit 2020. Resolution Foundation, 2020. https://www.resolutionfoundation.org/publications/the-living-standards-audit-2020/
↵ Raleigh V. Covid-19 has exposed ‘deep inequalities and stark differences in life expectancy.’ King’s Fund press release, 12 Jun 2020. https://www.kingsfund.org.uk/press/press-releases/covid-19-stark-differences-life-expectancy
↵ OECD. Building back better: a sustainable, resilient recovery after covid-19. 2020. http://www.oecd.org/coronavirus/policy-responses/building-back-better-a-sustainable-resilient-recovery-after-covid-19-52b869f5/

This paper is in the following e-collection/theme issue:

Published on 24.4.2024 in Vol 26 (2024)

Behavior Change Approaches in Digital Technology–Based Physical Rehabilitation Interventions Following Stroke: Scoping Review

Authors of this article:

Helen J Gooch, BSc ;
Kathryn A Jarvis, PhD ;
Rachel C Stockley, PhD

Stroke Research Team, School of Nursing and Midwifery, University of Central Lancashire, Preston, United Kingdom

Corresponding Author:

Helen J Gooch, BSc

Stroke Research Team

School of Nursing and Midwifery

University of Central Lancashire

BB247 Brook Building

Victoria Street

Preston, PR1 2HE

United Kingdom

Phone: 44 1772894956

Email: [email protected]

Background: Digital health technologies (DHTs) are increasingly used in physical stroke rehabilitation to support individuals in successfully engaging with the frequent, intensive, and lengthy activities required to optimize recovery. Despite this, little is known about behavior change within these interventions.

Objective: This scoping review aimed to identify if and how behavior change approaches (ie, theories, models, frameworks, and techniques to influence behavior) are incorporated within physical stroke rehabilitation interventions that include a DHT.

Methods: Databases (Embase, MEDLINE, PsycINFO, CINAHL, Cochrane Library, and AMED) were searched using keywords relating to behavior change, DHT, physical rehabilitation, and stroke. The results were independently screened by 2 reviewers. Sources were included if they reported a completed primary research study in which a behavior change approach could be identified within a physical stroke rehabilitation intervention that included a DHT. Data, including the study design, DHT used, and behavior change approaches, were charted. Specific behavior change techniques were coded to the behavior change technique taxonomy version 1 (BCTTv1).

Results: From a total of 1973 identified sources, 103 (5%) studies were included for data charting. The most common reason for exclusion at full-text screening was the absence of an explicit approach to behavior change (165/245, 67%). Almost half (45/103, 44%) of the included studies were described as pilot or feasibility studies. Virtual reality was the most frequently identified DHT type (58/103, 56%), and almost two-thirds (65/103, 63%) of studies focused on upper limb rehabilitation. Only a limited number of studies (18/103, 17%) included a theory, model, or framework for behavior change. The most frequently used BCTTv1 clusters were feedback and monitoring (88/103, 85%), reward and threat (56/103, 54%), goals and planning (33/103, 32%), and shaping knowledge (33/103, 32%). Relationships between feedback and monitoring and reward and threat were identified using a relationship map, with prominent use of both of these clusters in interventions that included virtual reality.

Conclusions: Despite an assumption that DHTs can promote engagement in rehabilitation, this scoping review demonstrates that very few studies of physical stroke rehabilitation that include a DHT overtly used any form of behavior change approach. From those studies that did consider behavior change, most did not report a robust underpinning theory. Future development and research need to explicitly articulate how including DHTs within an intervention may support the behavior change required for optimal engagement in physical rehabilitation following stroke, as well as establish their effectiveness. This understanding is likely to support the realization of the transformative potential of DHTs in stroke rehabilitation.

Introduction

Digital health technologies (DHTs) comprise apps, programs, or software used in the health and social care systems [ 1 ]. They are considered to have almost unlimited potential to transform health care interventions and delivery and empower people to take a greater role in their own care and well-being [ 2 , 3 ].

Stroke is one of the leading causes of acquired disability worldwide, with around 12 million people experiencing a stroke each year [ 4 ]. Rehabilitation is a complex, multifaceted process [ 5 ] that facilitates those with health conditions and disabilities to participate in and gain independence in meaningful life roles [ 6 ]. It is considered an essential aspect of health care provision following a stroke [ 7 ] as a means to address poststroke impairments, which can involve motor, sensory, and cognitive functions. Changes in the ability to move due to impairment of both movement and sensory function are commonly experienced by people following a stroke [ 8 ] and are addressed by physical rehabilitation comprising regular, intensive practice and repetition of movements and tasks [ 9 , 10 ]. Conventional physical rehabilitation often struggles to deliver the intensity required to optimize recovery [ 11 ], and over recent years, there has been significant interest in the use of DHTs, such as virtual reality (VR), telerehabilitation, robotics, and activity monitors [ 12 - 16 ], to enhance and increase the intensity of rehabilitation. DHTs can provide a whole intervention or be used as a component of a wider intervention; the term DHT-based intervention has been used within this review to refer to both situations.

For many people who survive a stroke, rehabilitation requires individuals to engage in regular and frequent rehabilitative activities to achieve improvements in function and realize their optimal recovery. This necessitates adjustments to an individual’s behavior [ 17 ] over a sustained period of time. Changing behavior is a complex process and is underpinned by a variety of different theories, models, and frameworks [ 18 ], such as social cognitive theory [ 19 ] or the behavior change wheel framework [ 20 ]. Individual activities within a complex intervention that are designed to change behavior can be separated into replicable active components widely referred to as behavior change techniques (BCTs) [ 21 ]. Historically, labels applied to BCTs have lacked consensus, resulting in uncertainty and difficulty in comparing interventions. This has been addressed in the behavior change technique taxonomy version 1 (BCTTv1) [ 22 ], a classification system of 93 distinct BCTs clustered into 16 groups, which is a well-recognized tool to provide consistency with BCT reporting in interventions. DHTs provide an emerging opportunity to support the behavior change required within physical stroke rehabilitation interventions through facilitators that are embedded within the technology itself that aim to form, alter, or reinforce behaviors [ 23 ]. Understanding of this area is limited, with most literature exploring the use of DHTs to support behavior change focused on specific health-related behaviors such as physical activity or healthy eating [ 24 ] rather than as a core component of a type of rehabilitation intervention. Motivation is acknowledged to play an integral role in behavior change [ 25 ], and it is often assumed that DHTs provide motivation to engage with rehabilitation [ 26 ]. However, for this assumption to be realized, the DHTs must be able to support and deliver interventions that facilitate the vital changes in behavior needed to promote prolonged and sustained engagement in stroke rehabilitative activities. Imperative to this is understanding the theories, models, and frameworks that underpin interventions and the BCTs (active components) within the interventions [ 27 - 29 ]. The theories, models, and frameworks alongside the BCTs will be referred to hereinafter as approaches. Within the context of DHT-based physical stroke rehabilitation interventions, approaches to behavior change warrant further investigation.

Aim and Objectives

This scoping review aimed to identify if and how behavior change approaches are incorporated within DHT-based physical stroke rehabilitation interventions. Specifically, it sought to:

Establish if behavior change theories, models, and frameworks, or BCTs, are described when reporting on DHT-based interventions that have been developed or evaluated for use in poststroke physical rehabilitation.
Identify if behavior change theories, models, or frameworks underpin the interventions and which of these are being used.
Identify if the BCTTv1 is being used to report BCTs within interventions.
Determine which BCTs (based on the BCTTv1) can be identified within the interventions.
Explore whether the type of technology influences the techniques used to change behaviors.

Review Methodology

A scoping review was completed and reported following established guidelines [ 30 , 31 ] and the Preferred Reporting of Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR; Multimedia Appendix 1 ) [ 32 ]. The protocol was registered with the Open Science Framework [ 33 ].

Eligibility Criteria

Any published sources that reported a completed primary research study in which a behavior change approach could be identified within a DHT-based physical stroke rehabilitation intervention were included ( Multimedia Appendix 2 ). Physical rehabilitation comprised interventions that addressed an impairment, or sequela of impairment, of sensory function and pain, neuromusculoskeletal and movement-related functions, or voice and speech, as defined by the International Classification of Functioning, Disability, and Health [ 34 ]. Completed primary research included all types of studies, both quantitative and qualitative, and no minimum sample size or intervention length was set. The BCTTv1 [ 22 ] was used to support the identification of BCTs within the interventions.

Information Sources and Search Strategy

A systematic database search was conducted in Embase, MEDLINE, PsycINFO, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL Register of Controlled Trials, and AMED on March 21, 2023. The search was completed in collaboration with an information specialist who provided support with the development of the free text and thesaurus search terms, created the final search, adjusted the searches for the different databases, and ran the search. It consisted of 4 distinct search streams: behavior change, DHT, physical rehabilitation, and stroke, which were then combined ( Multimedia Appendix 3 ). Searches were restricted to the English language (due to review resources) and by date to search from 2001; the date restriction acknowledges the main time period of DHT growth [ 35 ], captures sources reported in systematic reviews of DHTs in stroke rehabilitation [ 12 - 16 ], and is reflected in other scoping literature exploring DHTs [ 24 ]. Additional sources were identified by hand searching, including scrutiny of the included source reference lists.

Selection of Sources of Evidence

The titles and abstracts of deduplicated sources from database searches and hand searches were independently screened by 2 reviewers, 1 of whom had completed the BCTTv1 web-based training package [ 36 ] to inform decisions made around the use of BCTs. Any conflicts were discussed, and if a consensus was not reached, the source was included for full-text screening. Attempts were made to locate a completed study publication from eligible conference abstracts, protocols, and trial registry entries. Full-text sources were screened independently by 2 reviewers, and disagreements were resolved by a third reviewer. Reasons for full-text exclusion were recorded. EndNote X9 software (Clarivate) and the Rayyan web tool software (Qatar Computing Research Institute) [ 37 ] were used to facilitate the source selection process.

Data Charting Process

A review-specific data charting tool was developed and initially piloted using 3 sources by 3 reviewers, and then further developed iteratively throughout the process [ 30 ]. Data charting was completed collectively by 2 reviewers. When several sources referred to a single study, these sources were grouped together for data charting, and if a source identified additional sources for further detail of the intervention (eg, a protocol or supplementary material), then this information was also used to support data charting.

The data charting tool was developed with reference to the Template for Intervention Description and Replication (TIDieR) checklist [ 27 ] and with a focus on the DHT-based intervention and behavior change approaches ( Multimedia Appendix 4 [ 14 , 38 - 40 ]). In the absence of a recognized predefined taxonomy for DHTs, the DHTs used in the sources were charted iteratively by the type of technology [ 41 ] from the information provided about the intervention. Over time, DHT categories emerged and were defined ( Multimedia Appendix 4 ). Discrete BCTs were identified from the intervention detail provided using the BCTTv1 [ 22 ] ( Multimedia Appendix 5 [ 42 ]). A pragmatic decision was made that the single reviewer who had completed the BCTTv1 web-based training package [ 36 ] would code the interventions to the BCTTv1. Any areas of uncertainty were discussed in detail among the review team.

Synthesis of Results

In accordance with the aims of a scoping review, formal assessments of methodological quality were not completed [ 30 , 31 ]. Findings were synthesized using descriptive statistics facilitated by SPSS Statistics 28.0.0.0 (IBM Corp) and Microsoft Excel (version 2208; Microsoft Corporation) and presented in text, table, and chart formats. The characteristics of the included sources, specifically participant numbers, age, and time since stroke, and intervention details, were summarized to provide contextual information for the review. Time since stroke was based on a published timeline framework [ 43 ], which describes the following phases: acute (1-7 days), early subacute (7 days to 3 months), late subacute (3-6 months), and chronic (greater than 6 months).

The behavior change theories, models, or frameworks underpinning the DHT-based interventions and sources where interventions had already been coded to the BCTTv1 were summarized. The use of individual BCTs, as coded by reviewers from intervention descriptions, was briefly summarized; however, the main focus of the BCT synthesis was completed by grouping the BCTs into the 16 BCTTv1 clusters, in order to provide an overview of their use across the sources and allow comparison with other reviews [ 44 , 45 ]. A cluster was only identified once per source, irrespective of the number of individual BCTs within that cluster. Relationships between BCTTv1 clusters and between DHT type and BCTTv1 clusters were descriptively explored. A relationship map was used to visually represent the strength of the connections between the BCTTv1 clusters, with a thicker line indicating that variables were more frequently reported together. No inferential statistical analysis was used.

From a total of 1973 sources screened, 357 full-text sources were assessed for eligibility, then after grouping sources that referred to a single study, 103 (5%) distinct sources were included in the review [ 46 - 148 ] ( Figure 1 ). Of the 245 sources excluded at full-text screening, 165 (67%) were excluded due to a lack of a behavior change approach.

Characteristics of Sources of Evidence

All sources of evidence were studies and will be referred to as such hereinafter. The number of studies in this field has rapidly increased over time ( Figure 2 ), from a single study in 2004 to 8 in 2022, with a peak of 15 in 2021. The majority (86/103, 83%) [ 47 - 51 , 53 - 56 , 58 , 59 , 61 , 63 - 68 , 71 - 86 , 89 - 95 , 97 - 105 , 107 , 109 , 111 , 112 , 114 , 115 , 117 - 126 , 128 - 136 , 138 - 148 ] were published in the past 10 years. Most studies took place in North America (41/103, 40%) [ 46 - 49 , 52 , 55 , 56 , 60 , 64 - 67 , 69 , 70 , 72 , 74 , 76 - 78 , 80 , 85 - 88 , 92 , 93 , 97 , 99 , 101 , 108 - 110 , 126 - 129 , 137 , 138 , 141 , 142 , 145 ] and Europe (35/103, 34%) [ 51 , 53 , 54 , 57 , 58 , 62 , 63 , 68 , 71 , 79 , 81 - 84 , 89 , 111 , 113 - 125 , 132 , 136 , 140 , 143 , 146 , 147 ], with the remainder in Asia (16/103, 16%) [ 50 , 59 , 61 , 91 , 94 , 95 , 98 , 100 , 102 - 104 , 107 , 135 , 139 , 144 , 148 ], Australasia (9/103, 9%) [ 75 , 96 , 105 , 106 , 112 , 130 , 131 , 133 , 134 ], Africa (1/103, 1%) [ 90 ], and a single multicontinental study (1/103, 1%) [ 73 ]. Almost half (45/103, 44%) the studies are reported as feasibility or pilot studies [ 49 , 56 , 58 , 64 , 66 , 68 , 69 , 72 - 74 , 76 , 77 , 79 , 82 - 84 , 89 , 90 , 92 , 93 , 95 , 97 , 100 - 104 , 106 , 108 , 114 , 116 , 117 , 119 , 122 , 124 - 126 , 131 , 134 , 136 , 138 , 139 , 141 , 143 , 147 ]. Other study designs included randomized controlled trials (20/103, 19%) [ 50 , 51 , 60 , 61 , 65 , 75 , 80 , 85 , 86 , 91 , 107 , 109 , 112 , 128 - 130 , 137 , 144 , 146 , 148 ], single session investigations (19/103, 18%) [ 47 , 52 , 57 , 59 , 71 , 78 , 87 , 88 , 98 , 110 , 115 , 118 , 120 , 123 , 127 , 132 , 133 , 135 , 142 ], nonrandomized experimental designs (13/103, 13%) [ 53 - 55 , 62 , 63 , 67 , 81 , 94 , 96 , 99 , 105 , 113 , 145 ], case studies (4/103, 4%) [ 46 , 48 , 70 , 140 ], and realist evaluations (2/103, 2%) [ 111 , 121 ].

Participants

There were a total of 2825 participants in the 103 included studies. Studies tended to be small, with a median of 16 participants and a range of 1-188. Only half (55/103, 53%) the studies [ 46 - 48 , 50 , 56 , 57 , 59 , 61 , 64 , 67 , 69 - 72 , 78 , 79 , 82 , 87 , 88 , 92 , 93 , 95 - 99 , 101 , 102 , 105 , 106 , 108 , 111 - 121 , 123 - 127 , 134 , 138 - 140 , 142 , 143 , 145 , 147 ] reported the minimum and maximum age of participants, which ranged from 17 to 99 years. Over three-quarters (83/103, 81%; 2508 participants) of studies reported the time since the onset of stroke. Of these 83 studies, 1 (1%; 48 participants) study [ 91 ] was conducted in the acute phase, 14 (17%; 504 participants) studies [ 60 , 61 , 68 , 74 , 79 , 92 , 100 , 102 , 109 , 114 , 133 , 144 , 146 , 148 ] were conducted in the early subacute phase, 11 (13%; 316 participants) studies [ 59 , 65 , 66 , 72 , 75 , 76 , 81 , 104 , 107 , 121 , 134 ] were conducted in the late subacute phase, and 57 (69%; 1640 participants) studies [ 46 , 48 , 49 , 51 , 53 , 54 , 57 , 63 , 64 , 67 , 69 , 70 , 73 , 78 , 80 , 82 , 84 , 85 , 88 , 89 , 93 - 99 , 101 , 103 , 105 , 106 , 108 , 111 - 113 , 117 - 120 , 122 - 125 , 127 - 131 , 136 - 142 , 145 , 147 ] were conducted in the chronic phase [ 43 ].

Study Intervention

An overview of study intervention characteristics is provided ( Table 1 ). Interventions were focused on upper limb rehabilitation in almost two-thirds (65/103, 63%) of the studies [ 46 - 49 , 51 , 54 - 59 , 62 - 65 , 68 , 71 , 72 , 74 , 75 , 77 - 81 , 85 - 88 , 92 , 95 , 96 , 99 , 101 - 103 , 105 - 108 , 110 , 112 , 113 , 116 - 118 , 121 , 123 - 125 , 127 , 128 , 132 , 133 , 135 - 137 , 139 - 142 , 144 - 147 ]. Nearly all interventions (96/103, 93%) [ 46 - 80 , 84 - 94 , 96 - 117 , 119 - 121 , 124 - 148 ] were delivered to individual participants, with over half (62/103, 60%) [ 46 - 50 , 53 - 58 , 60 , 61 , 64 - 70 , 72 , 74 - 77 , 79 , 80 , 82 - 86 , 89 , 90 , 93 , 94 , 96 , 97 , 99 , 101 , 105 , 111 , 112 , 116 , 117 , 119 - 122 , 126 , 129 - 131 , 134 , 136 , 138 , 139 , 141 , 143 - 145 , 147 ] delivered fully or partly in the participant’s homes. Two-thirds (70/103, 68%) of studies [ 46 - 50 , 52 - 54 , 57 , 60 , 62 , 63 , 65 - 74 , 76 - 84 , 86 - 93 , 98 , 100 , 102 , 104 , 108 , 109 , 112 - 115 , 117 , 118 , 120 , 122 - 125 , 129 - 131 , 135 - 138 , 140 - 142 , 144 - 146 , 148 ] included partial or full supervision of the intervention, with this predominately being provided face-to-face (48/70, 69%) [ 46 , 47 , 52 , 57 , 60 , 62 , 63 , 67 , 68 , 71 , 73 , 78 , 81 - 84 , 86 - 89 , 91 , 92 , 98 , 100 , 102 , 104 , 108 , 109 , 112 - 115 , 117 , 118 , 120 , 122 - 125 , 135 - 137 , 140 , 142 , 144 - 146 , 148 ]. Interventions lasted between a single session and 26 weeks.

Of the 103 studies, over half (n=57, 55%) of the studies [ 46 , 47 , 51 - 54 , 57 , 61 , 63 , 67 , 68 , 70 , 71 , 73 , 75 - 78 , 81 , 84 - 86 , 88 - 91 , 93 , 95 , 96 , 98 , 100 , 102 - 104 , 106 , 109 , 112 , 114 , 115 , 123 - 126 , 129 , 130 , 132 , 133 , 135 - 138 , 140 , 143 - 147 ] included 1 type of DHT, 30 (29%) studies [ 48 , 49 , 55 , 56 , 58 - 60 , 62 , 64 , 69 , 83 , 92 , 94 , 97 , 99 , 101 , 105 , 107 , 108 , 110 , 111 , 113 , 116 , 118 , 121 , 122 , 127 , 128 , 139 , 142 ] included 2 types, and 16 (16%) studies [ 50 , 65 , 66 , 72 , 74 , 79 , 80 , 82 , 87 , 117 , 119 , 120 , 131 , 134 , 141 , 148 ] included 3 types. VR was the most frequently used DHT (58/103, 56%) [ 46 - 49 , 51 - 53 , 57 , 59 , 62 , 63 , 65 , 66 , 69 , 71 , 72 , 74 , 77 , 78 , 80 , 81 , 84 - 89 , 92 , 95 , 96 , 98 , 102 - 104 , 106 , 112 , 113 , 115 , 117 - 120 , 123 - 128 , 132 , 135 - 137 , 140 , 142 , 143 , 146 - 148 ] followed by apps (31/103, 30%) [ 50 , 55 , 56 , 58 , 61 , 64 - 66 , 72 , 74 , 75 , 79 , 82 , 83 , 94 , 97 , 99 , 101 , 105 , 108 , 111 , 114 , 116 , 119 - 122 , 131 , 134 , 139 , 141 ]. Further information on intervention characteristics with detail on associated citations is available ( Multimedia Appendix 6 [ 46 - 148 ]).

a F2F: face-to-face.

b DHT: digital health technology.

c VR: virtual reality.

Behavior Change Theories, Models, and Frameworks

Most studies (93/103, 90%) [ 46 - 49 , 51 - 62 , 64 - 73 , 75 - 89 , 91 - 93 , 96 - 106 , 108 - 115 , 117 - 137 , 139 , 140 , 142 - 148 ] endeavored to link the intervention to behavior change; however, in the majority of these studies (75/93, 81%) [ 46 , 51 - 56 , 58 - 62 , 64 - 69 , 71 - 73 , 75 , 77 - 89 , 91 - 93 , 96 , 97 , 99 - 101 , 103 - 106 , 108 , 110 , 112 , 114 , 115 , 117 - 120 , 123 , 124 , 127 , 128 , 131 - 137 , 139 , 140 , 142 - 144 , 146 - 148 ], this explanation was centered on the reporting of the techniques perceived to change behaviors without direct reference to use of the BCTTv1 or on the reporting of a component of the intervention or the whole of the intervention as motivating. These explanations lack detail on how or why this influences behavior change. Examples of this included “the app also provided performance feedback, allowing the user to compare their current performance against their score from the previous session” (Bhattacharjya et al [ 56 ]) and “games motivate patients to engage in enjoyable play behavior” (Cramer et al [ 66 ]). A limited number of studies (18/103, 17%) [ 47 - 49 , 57 , 70 , 76 , 98 , 102 , 109 , 111 , 113 , 121 , 122 , 125 , 126 , 129 , 130 , 145 ] articulated 1 or more theories, models, or frameworks of behavior change. While it is acknowledged that the BCTTv1 is a taxonomy framework rather than a theoretical framework, for the purpose of this review, it has been included as a framework for behavior change. A total of 13 different theories, models, or frameworks were identified within these 18 studies, with social cognitive theory being the most frequently reported (6/18, 33%) [ 76 , 109 , 111 , 121 , 129 , 130 ], followed by the behavior change technique taxonomy (4/18, 22%) [ 48 , 49 , 122 , 129 ], game design theory (3/18, 17%) [ 47 , 57 , 125 ], operant conditioning (3/18, 17%) [ 47 , 98 , 121 ], and self-determination theory (3/18, 17%) [ 48 , 49 , 126 ]. Further information on behavior change theories, models, and frameworks, with details on associated citations, is available ( Multimedia Appendix 7 [ 47 - 49 , 57 , 70 , 76 , 98 , 102 , 109 , 111 , 113 , 121 , 122 , 125 , 126 , 129 , 130 , 145 ]).

Behavior Change Techniques

Despite 4 studies acknowledging the BCTTv1, explicit BCTTv1 codes were only reported in 2 studies (2/103, 2%) [ 48 , 122 ]. However, a third study (1/103, 1%) mapped the techniques used to change behavior directly to the transtheoretical model [ 145 ]. There was a median of 3 (range 1-14) individual BCTs coded per study, with a total of 383 BCTs across the 103 studies. The most frequently identified individual BCTs were feedback on behavior and nonspecific reward ( Multimedia Appendix 8 ).

There was also a median of 3 (range 1-8) BCTTv1 clusters per study, with a total of 288 clusters coded across the 103 studies. The most frequently used of the 16 possible clusters were feedback and monitoring (88/103, 85%) [ 46 - 60 , 62 - 69 , 71 - 74 , 76 , 78 - 80 , 82 - 92 , 94 - 106 , 108 - 113 , 116 , 117 , 119 - 129 , 134 - 146 , 148 ], reward and threat (56/103, 54%) [ 46 - 49 , 51 - 53 , 55 - 57 , 62 , 65 , 69 , 71 , 72 , 74 , 77 , 80 , 81 , 85 , 86 , 88 , 89 , 91 , 92 , 95 , 96 , 98 , 102 , 103 , 106 - 108 , 112 , 113 , 115 , 117 - 119 , 121 - 125 , 128 , 132 , 134 - 137 , 140 , 142 , 143 , 146 - 148 ], goals and planning (33/103, 32%) [ 49 , 58 , 60 , 65 - 68 , 70 , 72 , 74 , 76 , 79 , 80 , 82 , 83 , 90 , 91 , 93 , 94 , 97 , 100 , 109 , 111 , 112 , 121 , 122 , 126 , 129 , 130 , 134 , 138 , 141 , 145 ], and shaping knowledge (33/103, 32%) [ 46 , 48 , 50 , 53 - 56 , 58 , 60 , 61 , 64 - 72 , 74 , 75 , 86 , 94 , 97 , 101 - 103 , 108 , 111 , 113 , 114 , 120 , 129 - 131 , 139 - 141 ]. Other BCTTv1 clusters used were social support (24/103, 23%) [ 48 , 49 , 58 , 60 , 64 , 67 , 70 , 72 , 73 , 79 , 80 , 82 , 84 , 90 , 93 , 101 , 108 , 117 , 119 , 129 - 131 , 134 , 141 ], comparison of behavior (23/103, 22%) [ 46 , 50 , 53 , 54 , 60 , 61 , 64 - 66 , 74 , 75 , 81 , 86 , 101 , 104 , 111 , 114 , 118 , 122 , 123 , 125 , 131 , 139 ], associations (16/103, 15%) [ 58 , 60 , 65 , 66 , 68 , 75 , 80 , 83 , 87 , 90 , 110 , 120 , 131 , 133 , 139 , 144 ], repetition and substitution (6/103, 6%) [ 60 , 82 , 109 , 122 , 129 , 130 ], scheduled consequences (3/103, 3%) [ 47 , 80 , 88 ], natural consequences (2/103, 2%) [ 129 , 138 ], comparison of outcomes (2/103, 2%) [ 47 , 133 ], antecedents (1/103, 1%) [ 60 ], and self-belief (1/103, 1%) [ 70 ]. The clusters of regulation, identity, and covert learning were not identified. Within the context of the review, it was noted that the reward and threat cluster only included reward-based BCTs. A tabulated summary and graphical representation of the BCTTv1 clusters is available ( Multimedia Appendix 9 [ 46 - 148 ]).

The exploration of clusters that were reported together in an intervention ( Figure 3 ) identified the strongest relationship between the clusters of feedback and monitoring and reward and threat. Clear links were also identified between feedback and monitoring and 4 other clusters: goals and planning, shaping knowledge, social support, and comparison of behavior, and between the shaping knowledge and comparison of behavior clusters.

Behavior Change Techniques and Digital Health Technology

The feedback and monitoring cluster was reported most frequently for all types of DHT ( Figure 4 ), with the greatest proportion of this cluster in robotics (11/25, 44%) [ 59 , 62 , 87 , 92 , 110 , 113 , 117 , 127 , 128 , 142 , 148 ], VR (52/148, 35%) [ 46 - 49 , 51 - 53 , 57 , 59 , 62 , 63 , 65 , 66 , 69 , 71 , 72 , 74 , 78 , 80 , 84 - 89 , 92 , 95 , 96 , 98 , 102 - 104 , 106 , 112 , 113 , 117 , 119 , 120 , 123 - 135 - 137 , 140 , 142 , 143 , 146 , 148 ], and sensors (17/48, 35%) [ 50 , 55 , 56 , 87 , 94 , 99 , 101 , 105 , 108 , 110 , 111 , 116 , 119 - 121 , 134 , 141 ]. Robotics and VR also often used the reward and threat cluster (9/25, 36% [ 62 , 92 , 107 , 113 , 117 , 118 , 128 , 142 , 148 ] and 48/148, 32% [ 46 - 49 , 51 - 53 , 57 , 62 , 65 , 69 , 71 , 72 , 74 , 77 , 80 , 81 , 85 , 86 , 88 , 89 , 92 , 95 , 96 , 98 , 102 , 103 , 106 , 112 , 113 , 115 , 117 - 119 , 123 - 125 , 128 , 132 , 135 - 137 , 140 , 142 , 143 , 146 - 148 ], respectively), while the goals and planning cluster was a dominant second cluster in activity monitors (13/53, 25%) [ 67 , 68 , 76 , 79 , 80 , 82 , 91 , 100 , 109 , 122 , 129 , 138 , 145 ].

Summary of Evidence

This scoping review provides a comprehensive overview of approaches used to support changes in behavior in DHT-based physical stroke rehabilitation interventions. Research in this field is in its infancy, with the predominance of studies in this review being described as pilot or feasibility studies with limited participants.

Despite using comprehensive behavior change search terms, only a limited number (103/1973, 6%) of screened sources were included. Over two-thirds of full-text sources were excluded as they did not describe or refer to any behavior change theories, models, or frameworks or BCTs, suggesting that in general, explicit behavior change approaches are not reported as being integral to DHT-based physical stroke rehabilitation.

Only 18 (17%) of the 103 included studies articulated a theory, model, or framework to underpin the intervention, which aimed to change behavior, despite widely published recommendations about the importance of overt use of theory when developing, evaluating, and reporting interventions [ 27 , 29 ], including those related to behavior change [ 28 ]. The proportion of studies articulating a behavior change theory, model, or framework in this work is significantly lower than review findings in non-rehabilitation DHT-based interventions that have sought to influence specific behaviors such as physical activity or weight control [ 24 , 44 ]. These reviews have identified up to two-thirds of sources reporting a theory, model, or framework. However, our findings mirror the relative absence of behavior change theories, models, and frameworks in rehabilitation interventions more generally, irrespective of whether they use digital technology [ 149 ] or not [ 45 ], and it is widely recognized that the complex nature of rehabilitation often results in the essential characteristics of interventions being poorly defined [ 150 ]. Consistent with our findings in these other reviews, a variety of theories, models, and frameworks were found to underpin interventions, with social cognitive theory being the most frequently reported [ 24 , 44 , 45 , 149 ]. The explicit description of BCTs using the BCTTv1 within DHT-based physical stroke rehabilitation interventions is also poorly reported (2%), despite a significant proportion of the sources being dated after the publication of the BCTTv1 in 2013 [ 22 ]. This lack of acknowledgment of behavior change approaches impedes the accumulation of knowledge within this field.

It is important that both the underpinning theory and BCTs are reported so the mechanisms by which the BCTs elicit change can be better understood [ 21 ]. The general assumption that the motivational and captivating aspects of DHTs will promote prolonged and repeated engagement with rehabilitative activities, in particular in those DHTs that incorporate game design [ 151 ], risks suboptimal outcomes for patients and wasted investment of time and money if the mechanisms by which the DHT elicits change are not considered.

When exploring which BCT clusters featured within the reviewed DHT-based interventions, the findings relating to the commonly used clusters of feedback and monitoring, goals and planning, and shaping knowledge are consistent with findings from DHT-based interventions to change a specific behavior [ 44 ] and non-DHT–based rehabilitation [ 45 ]. However, a novel finding in our review was the frequent identification of the reward and threat cluster, although it was noted that all techniques related to reward and none to threat. A large number of studies in this review used VR technology, which frequently incorporates gamified tasks or gameplay. Reward is an integral part of game design theory alongside feedback [ 152 ], and so it is perhaps unsurprising that the feedback and monitoring, and reward and threat clusters dominated and an association between these 2 clusters was seen.

Limitations

Rehabilitation is a process that comprises multiple behaviors and so exploring approaches to change behavior within this context was complicated. There were challenges in searching and screening sources for inclusion as few studies explicitly reported approaches to change behavior, and there is a similarity in the vocabulary used within behavior change and other theoretical approaches (eg, “feedback,” which is used within motor learning). Similarly, only a very small proportion of studies explicitly reported BCTs within interventions. The lack of clear reporting of behavior change introduces the risk that sources may be omitted during both the searching and screening process highlighting the difficulty of comprehensively reviewing this field of work. An inclusive approach to screening reduced the risk of erroneously excluding sources, but it is perhaps inevitable that the sources included reflect those studies that have reported a behavior change approach rather than all studies that have used one.

This lack of clear BCT reporting also posed challenges for intervention coding. The use of the BCTTv1 aimed to ensure the review used a generalizable nomenclature to describe BCTs, and the 1 reviewer who had completed BCTTv1 training coded all the interventions. It is acknowledged that decisions made in the application of the BCTTv1 within the context of the review will have introduced some subjectivity in intervention coding, which will ultimately influence the review findings. Although the coding process could have been made more robust by having a second reviewer trained in the BCTTv1 also code the interventions, regular and extensive discussions between all members of the review team took place with the aim of ensuring consistency with the coding process. Clear documentation as to how the BCTTv1 was used within this review ( Multimedia Appendix 5 ) supports transparency as to the decisions made and the reproducibility of the review.

The absence of a recognized predefined taxonomy for DHTs posed a challenge when categorizing the DHT interventions, acknowledging that the distinction between the categories used to present the results is open to interpretation. A description of how the reviewers interpreted these categories is provided ( Multimedia Appendix 4 ).

Implications for Research

Future studies aimed at developing and evaluating DHT-based rehabilitation interventions, including those relating to physical stroke rehabilitation, need to ensure there is explicit recognition and reporting of the specific approaches used to change behavior, articulating both the theory on which the intervention is based and how the intervention plans to deliver the change in behavior using universally recognized terminology. This should be reported as part of a program theory and potential mechanisms of action, which are key parts of developing and evaluating complex interventions [ 29 ]. This detailed reporting would further support an understanding of how changes in behavior could be best enabled by DHT-based rehabilitation interventions and how this contributes to changes in patient outcomes. It would also enable further evaluation of the optimal behavioral components of interventions, enabling patients to use and clinicians to deliver the most effective DHT-based rehabilitative interventions. More generally, as the use of DHTs expands, there is an urgent need for some form of taxonomy to categorize and clearly define the different types of DHTs to facilitate consistent reporting, replication, and comparison of DHT-based interventions.

This novel and original review is the first to explore if and how approaches to change behavior are incorporated within DHT-based physical stroke rehabilitation. It demonstrates that a minority of studies report using approaches to change behavior within this context, despite these changes in behavior being vital to meet the demands of rehabilitative activities. Those who do report behavior change often lack the underpinning detail as to how the DHT-based intervention will facilitate these changes. In order for DHT-based interventions to realize their potential within rehabilitation and their impact on patient outcomes, approaches to change behavior must be embedded in the intervention and appropriately reported.

Acknowledgments

The authors would like to thank Catherine Harris (Information Specialist, University of Central Lancashire) for her assistance in developing the search strategy and running the searches, and Rebekah Murray (Undergraduate Research Intern, University of Central Lancashire) for her support with aspects of the screening and data charting process.

This work was funded by a UK Research and Innovation Future Leaders Fellowship (grant MR/T022434/1).

Data Availability

All data supporting this study are openly available from the University of Central Lancashire repository [ 153 ].

Authors' Contributions

RCS conceived the review focus and oversaw the work. HJG developed the review design and search strategy. HJG, KAJ, and RCS completed the screening of the identified sources. HJG and KAJ piloted the data charting tool. HJG completed the data charting, data analysis, and the initial manuscript draft. KAJ and RCS reviewed and made substantial contributions to the manuscript. All authors approved the final manuscript.

Conflicts of Interest

None declared.

Preferred Reporting of Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist.

Inclusion and exclusion criteria.

Full search strategy as used in Medline.

Data charting tool.

Review-specific behavior change technique taxonomy coding decisions.

Intervention characteristics (with associated references).

Behavior change theories, models, and frameworks reported (with associated references).

Individual behavior change techniques coded.

Behavior change technique taxonomy clusters identified (with associated references).

Evidence standards framework for digital health technologies. National Institute of Clinical Excellence (NICE). 2018. URL: https://tinyurl.com/mpmrwrwx [accessed 2022-01-25]
Castle-Clark S. The NHS at 70: What will new technology mean for the NHS and its patients? Kings Fund. 2018. URL: https://tinyurl.com/44k9fmat [accessed 2024-04-05]
Topel E. The Topol review: preparing the healthcare workforce to deliver the digital future. Health Education England, NHS. 2019. URL: https://topol.hee.nhs.uk/ [accessed 2022-01-19]
GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795-820. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wade DT. What is rehabilitation? An empirical investigation leading to an evidence-based description. Clin Rehabil. 2020;34(5):571-583. [ FREE Full text ] [ CrossRef ] [ Medline ]
Rehabilitation. World Health Organisation. 2023. URL: https://www.who.int/news-room/fact-sheets/detail/rehabilitation [accessed 2023-05-04]
Grefkes C, Fink GR. Recovery from stroke: current concepts and future perspectives. Neurol Res Pract. 2020;2:17. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lawrence ES, Coshall C, Dundas R, Stewart J, Rudd AG, Howard R, et al. Estimates of the prevalence of acute stroke impairments and disability in a multiethnic population. Stroke. 2001;32(6):1279-1284. [ FREE Full text ] [ CrossRef ] [ Medline ]
National clinical guideline for stroke for the United Kingdom and Ireland. Intercollegiate Stroke Working Party. 2023. URL: https://tinyurl.com/yxy6b9tp [accessed 2023-05-02]
Veerbeek JM, van Wegen E, van Peppen R, van der Wees PJ, Hendriks E, Rietberg M, et al. What is the evidence for physical therapy poststroke? a systematic review and meta-analysis. PLoS One. 2014;9(2):e87987. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lang CE, Macdonald JR, Reisman DS, Boyd L, Kimberley TJ, Schindler-Ivens SM, et al. Observation of amounts of movement practice provided during stroke rehabilitation. Arch Phys Med Rehabil. 2009;90(10):1692-1698. [ FREE Full text ] [ CrossRef ] [ Medline ]
Laver KE, Lange B, George S, Deutsch JE, Saposnik G, Crotty M. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev. 2017;11(11):CD008349. [ FREE Full text ] [ CrossRef ] [ Medline ]
Laver KE, Adey-Wakeling Z, Crotty M, Lannin NA, George S, Sherrington C. Telerehabilitation services for stroke. Cochrane Database Syst Rev. 2020;1(1):CD010255. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lynch EA, Jones TM, Simpson DB, Fini NA, Kuys SS, Borschmann K, et al. Activity monitors for increasing physical activity in adult stroke survivors. Cochrane Database Syst Rev. 2018;7(7):CD012543. [ FREE Full text ] [ CrossRef ] [ Medline ]
Mehrholz J, Pohl M, Platz T, Kugler J, Elsner B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2018;9(9):CD006876. [ FREE Full text ] [ CrossRef ] [ Medline ]
Mehrholz J, Thomas S, Kugler J, Pohl M, Elsner B. Electromechanical-assisted training for walking after stroke. Cochrane Database Syst Rev. 2020;10(10):CD006185. [ FREE Full text ] [ CrossRef ] [ Medline ]
Jones F, Riazi A. Self-efficacy and self-management after stroke: a systematic review. Disabil Rehabil. 2011;33(10):797-810. [ CrossRef ] [ Medline ]
Behaviour change: general approaches. National Institute of Clinical Excellence. UK. National Institute of Clinical Excellence (NICE); 2007. URL: https://tinyurl.com/yfthycch [accessed 2024-04-05]
Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ. Prentice-Hall; 1986.
Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42. [ FREE Full text ] [ CrossRef ] [ Medline ]
Carey RN, Connell LE, Johnston M, Rothman AJ, de Bruin M, Kelly MP, et al. Behavior change techniques and their mechanisms of action: a synthesis of links described in published intervention literature. Ann Behav Med. 2019;53(8):693-707. [ FREE Full text ] [ CrossRef ] [ Medline ]
Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46(1):81-95. [ FREE Full text ] [ CrossRef ] [ Medline ]
Oinas-Kukkonen H. A foundation for the study of behavior change support systems. Pers Ubiquit Comput. 2012;17(6):1223-1235. [ CrossRef ]
Taj F, Klein MCA, van Halteren A. Digital health behavior change technology: bibliometric and scoping review of two decades of research. JMIR Mhealth Uhealth. 2019;7(12):e13311. [ FREE Full text ] [ CrossRef ] [ Medline ]
West R, Michie S. A Guide to Development and Evaluation of Digital Behaviour Change Interventions in Healthcare. London. Silverback Publishing; 2016.
Lewis GN, Rosie JA. Virtual reality games for movement rehabilitation in neurological conditions: how do we meet the needs and expectations of the users? Disabil Rehabil. 2012;34(22):1880-1886. [ CrossRef ] [ Medline ]
Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. [ FREE Full text ] [ CrossRef ] [ Medline ]
Michie S, Prestwich A. Are interventions theory-based? development of a theory coding scheme. Health Psychol. 2010;29(1):1-8. [ FREE Full text ] [ CrossRef ] [ Medline ]
Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM, et al. A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. BMJ. 2021;374:n2061. [ FREE Full text ] [ CrossRef ] [ Medline ]
Peters MDJ, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. 2015;13(3):141-146. [ FREE Full text ] [ CrossRef ] [ Medline ]
Peters MDJ, Godfrey CM, McInerney P, Munn Z, Tricco AC, Khalil H. Scoping Reviews (2020 version). In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis. Adelaide, Australia. JBI; 2020. [ CrossRef ]
Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-473. [ FREE Full text ] [ CrossRef ] [ Medline ]
Gooch H, Stockley R, Jarvis K. Behaviour change approaches within digital health technology-based interventions in physical rehabilitation following stroke: a scoping review protocol Version 2 081122. OSF Registries. 2022. URL: https://osf.io/yjn5g [accessed 2022-11-15]
ICF checklist. World Health Organisation. 2003. URL: https://tinyurl.com/5fz6krew [accessed 2022-01-19]
Hillyer M. How has technology changed—and changed us—in the past 20 years? World Economic Forum. 2020. URL: https://tinyurl.com/5n74wsym [accessed 2023-10-18]
Online training. BCT Taxonomy v1. 2022. URL: http://www.bct-taxonomy.com/ [accessed 2022-03-08]
Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wadley M, Bradbury M, Stockley R. Virtual reality. The Chartered Society of Physiotherapy. URL: https://www.csp.org.uk/professional-clinical/digital-physiotherapy/virtual-reality [accessed 2024-03-29]
Cambridge dictionary. Cambridge University Press and Assessment. URL: https://tinyurl.com/2f2mx6kz [accessed 2024-03-29]
Weber LM, Stein J. The use of robots in stroke rehabilitation: A narrative review. NeuroRehabilitation. 2018;43(1):99-110. [ CrossRef ] [ Medline ]
English C, Ceravolo MG, Dorsch S, Drummond A, Gandhi DB, Green JH, et al. Telehealth for rehabilitation and recovery after stroke: state of the evidence and future directions. Int J Stroke. 2022;17(5):487-493. [ CrossRef ] [ Medline ]
Taub E, Crago JE, Burgio LD, Groomes TE, Cook EW, DeLuca SC, et al. An operant approach to rehabilitation medicine: overcoming learned nonuse by shaping. J Exp Anal Behav. 1994;61(2):281-293. [ FREE Full text ] [ CrossRef ] [ Medline ]
Bernhardt J, Hayward KS, Kwakkel G, Ward NS, Wolf SL, Borschmann K, et al. Agreed definitions and a shared vision for new standards in stroke recovery research: the Stroke Recovery and Rehabilitation Roundtable taskforce. Int J Stroke. 2017;12(5):444-450. [ FREE Full text ] [ CrossRef ] [ Medline ]
Asbjørnsen RA, Smedsrød ML, Solberg Nes L, Wentzel J, Varsi C, Hjelmesæth J, et al. Persuasive system design principles and behavior change techniques to stimulate motivation and adherence in electronic health interventions to support weight loss maintenance: scoping review. J Med Internet Res. 2019;21(6):e14265. [ FREE Full text ] [ CrossRef ] [ Medline ]
Bayly J, Wakefield D, Hepgul N, Wilcock A, Higginson IJ, Maddocks M. Changing health behaviour with rehabilitation in thoracic cancer: a systematic review and synthesis. Psychooncology. 2018;27(7):1675-1694. [ CrossRef ] [ Medline ]
Alankus G, Proffitt R, Kelleher C, Engsberg J. Stroke therapy through motion-based games: a case study. ACM Trans Access Comput. 2011;4(1):1-35. [ CrossRef ]
Alankus G, Kelleher C. Reducing compensatory motions in motion-based video games for stroke rehabilitation. Hum Comput Interact. 2015;30(3-4):232-262. [ CrossRef ]
Allegue DR, Kairy D, Higgins J, Archambault PS, Michaud F, Miller W, et al. A personalized home-based rehabilitation program using exergames combined with a telerehabilitation app in a chronic stroke survivor: mixed methods case study. JMIR Serious Games. 2021;9(3):e26153. [ FREE Full text ] [ CrossRef ] [ Medline ]
Allegue DR, Higgins J, Sweet SN, Archambault PS, Michaud F, Miller W, et al. Rehabilitation of upper extremity by telerehabilitation combined with exergames in survivors of chronic stroke: preliminary findings from a feasibility clinical trial. JMIR Rehabil Assist Technol. 2022;9(2):e33745. [ FREE Full text ] [ CrossRef ] [ Medline ]
Asano M, Tai BC, Yeo FY, Yen SC, Tay A, Ng YS, et al. Home-based tele-rehabilitation presents comparable positive impact on self-reported functional outcomes as usual care: the Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) randomised controlled trial. J Telemed Telecare. 2021;27(4):231-238. [ CrossRef ] [ Medline ]
Ballester BR, Maier M, Mozo RMSS, Castañeda V, Duff A, Verschure PFMJ. Counteracting learned non-use in chronic stroke patients with reinforcement-induced movement therapy. J Neuroeng Rehabil. 2016;13(1):74. [ FREE Full text ] [ CrossRef ] [ Medline ]
Bardack A, Bhandari P, Doggett J, Epstein M, Gagliolo N, Graff S, et al. EMG biofeedback videogame system for the gait rehabilitation of hemiparetic individuals. Digital Repository at the University of Maryland. 2010. URL: https://drum.lib.umd.edu/bitstream/handle/1903/10082/CHIP.pdf?sequence=1&isAllowed=y [accessed 2023-02-27]
Bellomo RG, Paolucci T, Saggino A, Pezzi L, Bramanti A, Cimino V, et al. The WeReha Project for an innovative home-based exercise training in chronic stroke patients: a clinical study. J Cent Nerv Syst Dis. 2020;12:1179573520979866. [ FREE Full text ] [ CrossRef ] [ Medline ]
Benvenuti F, Stuart M, Cappena V, Gabella S, Corsi S, Taviani A, et al. Community-based exercise for upper limb paresis: a controlled trial with telerehabilitation. Neurorehabil Neural Repair. 2014;28(7):611-620. [ FREE Full text ] [ CrossRef ] [ Medline ]
Bhattacharjya S, Stafford MC, Cavuoto LA, Yang Z, Song C, Subryan H, et al. Harnessing smartphone technology and three dimensional printing to create a mobile rehabilitation system, mRehab: assessment of usability and consistency in measurement. J Neuroeng Rehabil. 2019;16(1):127. [ FREE Full text ] [ CrossRef ] [ Medline ]
Bhattacharjya S, Cavuoto LA, Reilly B, Xu W, Subryan H, Langan J. Usability, usefulness, and acceptance of a novel, portable rehabilitation system (mRehab) using smartphone and 3D printing technology: mixed methods study. JMIR Hum Factors. 2021;8(1):e21312. [ FREE Full text ] [ CrossRef ] [ Medline ]
Burke JW, McNeill MDJ, Charles DK, Morrow PJ, Crosbie JH, McDonough SM. Optimising engagement for stroke rehabilitation using serious games. Vis Comput. 2009;25(12):1085-1099. [ CrossRef ]
Burridge JH, Lee ACW, Turk R, Stokes M, Whitall J, Vaidyanathan R, et al. Telehealth, wearable sensors, and the internet: will they improve stroke outcomes through increased intensity of therapy, motivation, and adherence to rehabilitation programs? J Neurol Phys Ther. 2017;41(Suppl 3):S32-S38. [ CrossRef ] [ Medline ]
Cai S, Wei X, Su E, Wu W, Zheng H, Xie L. Online compensation detecting for real-time reduction of compensatory motions during reaching: a pilot study with stroke survivors. J Neuroeng Rehabil. 2020;17(1):58. [ FREE Full text ] [ CrossRef ] [ Medline ]
Chumbler NR, Quigley P, Li X, Morey M, Rose D, Sanford J, et al. Effects of telerehabilitation on physical function and disability for stroke patients: a randomized, controlled trial. Stroke. 2012;43(8):2168-2174. [ FREE Full text ] [ CrossRef ] [ Medline ]
Chung BPH, Chiang WKH, Lau H, Lau TFO, Lai CWK, Sit CSY, et al. Pilot study on comparisons between the effectiveness of mobile video-guided and paper-based home exercise programs on improving exercise adherence, self-efficacy for exercise and functional outcomes of patients with stroke with 3-month follow-up: a single-blind randomized controlled trial. Hong Kong Physiother J. 2020;40(1):63-73. [ FREE Full text ] [ CrossRef ] [ Medline ]
Colombo R, Pisano F, Mazzone A, Delconte C, Micera S, Carrozza MC, et al. Design strategies to improve patient motivation during robot-aided rehabilitation. J Neuroeng Rehabil. 2007;4:3. [ FREE Full text ] [ CrossRef ] [ Medline ]
Colomer C, Llorens R, Noé E, Alcañiz M. Effect of a mixed reality-based intervention on arm, hand, and finger function on chronic stroke. J Neuroeng Rehabil. 2016;13(1):45. [ FREE Full text ] [ CrossRef ] [ Medline ]
Conroy SS, Harcum S, Keldsen L, Bever CT. Novel use of existing technology: a preliminary study of patient portal use for telerehabilitation. J Telemed Telecare. 2022;28(5):380-388. [ CrossRef ] [ Medline ]
Cramer SC, Dodakian L, Le V, See J, Augsburger R, McKenzie A, et al. Efficacy of home-based telerehabilitation vs in-clinic therapy for adults after stroke: a randomized clinical trial. JAMA Neurol. 2019;76(9):1079-1087. [ FREE Full text ] [ CrossRef ] [ Medline ]
Cramer SC, Dodakian L, Le V, McKenzie A, See J, Augsburger R, et al. A feasibility study of expanded home-based telerehabilitation after stroke. Front Neurol. 2020;11:611453. [ FREE Full text ] [ CrossRef ] [ Medline ]
Danks KA, Roos MA, McCoy D, Reisman DS. A step activity monitoring program improves real world walking activity post stroke. Disabil Rehabil. 2014;36(26):2233-2236. [ FREE Full text ] [ CrossRef ] [ Medline ]
Da-Silva RH, Moore SA, Rodgers H, Shaw L, Sutcliffe L, van Wijck F, et al. Wristband accelerometers to motivate arm exercises after Stroke (WAVES): a pilot randomized controlled trial. Clin Rehabil. 2019;33(8):1391-1403. [ CrossRef ] [ Medline ]
Deng H, Durfee WK, Nuckley DJ, Rheude BS, Severson AE, Skluzacek KM, et al. Complex versus simple ankle movement training in stroke using telerehabilitation: a randomized controlled trial. Phys Ther. 2012;92(2):197-209. [ FREE Full text ] [ CrossRef ] [ Medline ]
Deutsch JE, Maidan I, Dickstein R. Patient-centered integrated motor imagery delivered in the home with telerehabilitation to improve walking after stroke. Phys Ther. 2012;92(8):1065-1077. [ FREE Full text ] [ CrossRef ] [ Medline ]
Dias P, Silva R, Amorim P, Lains J, Roque E, Pereira ISF, et al. Using virtual reality to increase motivation in poststroke rehabilitation. IEEE Comput Graph Appl. 2019;39(1):64-70. [ CrossRef ] [ Medline ]
Dodakian L, McKenzie AL, Le V, See J, Pearson-Fuhrhop K, Quinlan EB, et al. A home-based telerehabilitation program for patients with stroke. Neurorehabil Neural Repair. 2017;31(10-11):923-933. [ FREE Full text ] [ CrossRef ] [ Medline ]
Dorsch AK, Thomas S, Xu X, Kaiser W, Dobkin BH, SIRRACT investigators. SIRRACT: an international randomized clinical trial of activity feedback during inpatient stroke rehabilitation enabled by wireless sensing. Neurorehabil Neural Repair. 2015;29(5):407-415. [ FREE Full text ] [ CrossRef ] [ Medline ]
Edwards D, Kumar S, Brinkman L, Ferreira IC, Esquenazi A, Nguyen T, et al. Telerehabilitation initiated early in post-stroke recovery: a feasibility study. Neurorehabil Neural Repair. 2023;37(2-3):131-141. [ FREE Full text ] [ CrossRef ] [ Medline ]
Emmerson KB, Harding KE, Taylor NF. Home exercise programmes supported by video and automated reminders compared with standard paper-based home exercise programmes in patients with stroke: a randomized controlled trial. Clin Rehabil. 2017;31(8):1068-1077. [ CrossRef ] [ Medline ]
Ezeugwu VE, Manns PJ. The feasibility and longitudinal effects of a home-based sedentary behavior change intervention after stroke. Arch Phys Med Rehabil. 2018;99(12):2540-2547. [ CrossRef ] [ Medline ]
Fluet GG, Qiu Q, Patel J, Cronce A, Merians AS, Adamovich SV. Autonomous use of the home virtual rehabilitation system: a feasibility and pilot study. Games Health J. 2019;8(6):432-438. [ FREE Full text ] [ CrossRef ] [ Medline ]
Foreman MH, Engsberg JR. A virtual reality tool for measuring and shaping trunk compensation for persons with stroke: design and initial feasibility testing. J Rehabil Assist Technol Eng. 2019;6:2055668318823673. [ FREE Full text ] [ CrossRef ] [ Medline ]
Fusari G, Gibbs E, Hoskin L, Lawrence-Jones A, Dickens D, Crespo RF, et al. What is the feasibility and patient acceptability of a digital system for arm and hand rehabilitation after stroke? a mixed-methods, single-arm feasibility study of the 'OnTrack' intervention for hospital and home use. BMJ Open. 2022;12(9):e062042. [ FREE Full text ] [ CrossRef ] [ Medline ]
Gauthier LV, Nichols-Larsen DS, Uswatte G, Strahl N, Simeo M, Proffitt R, et al. Video game rehabilitation for outpatient stroke (VIGoROUS): a multi-site randomized controlled trial of in-home, self-managed, upper-extremity therapy. EClinicalMedicine. 2022;43:101239. [ FREE Full text ] [ CrossRef ] [ Medline ]
Goršič M, Cikajlo I, Goljar N, Novak D. A multisession evaluation of an adaptive competitive arm rehabilitation game. J Neuroeng Rehabil. 2017;14(1):128. [ FREE Full text ] [ CrossRef ] [ Medline ]
Grau-Pellicer M, Lalanza JF, Jovell-Fernández E, Capdevila L. Impact of mHealth technology on adherence to healthy PA after stroke: a randomized study. Top Stroke Rehabil. 2020;27(5):354-368. [ CrossRef ] [ Medline ]
Guidetti S, Gustavsson M, Tham K, Andersson M, Fors U, Ytterberg C. F@ce: a team-based, person-centred intervention for rehabilitation after stroke supported by information and communication technology—a feasibility study. BMC Neurol. 2020;20(1):387. [ FREE Full text ] [ CrossRef ] [ Medline ]
Held JP, Ferrer B, Mainetti R, Steblin A, Hertler B, Moreno-Conde A, et al. Autonomous rehabilitation at stroke patients home for balance and gait: safety, usability and compliance of a virtual reality system. Eur J Phys Rehabil Med. 2018;54(4):545-553. [ FREE Full text ] [ CrossRef ] [ Medline ]
Hernandez A, Bubyr L, Archambault PS, Higgins J, Levin MF, Kairy D. Virtual reality-based rehabilitation as a feasible and engaging tool for the management of chronic poststroke upper-extremity function recovery: randomized controlled trial. JMIR Serious Games. 2022;10(3):e37506. [ FREE Full text ] [ CrossRef ] [ Medline ]
Hung NT, Paul V, Prakash P, Kovach T, Tacy G, Tomic G, et al. Wearable myoelectric interface enables high-dose, home-based training in severely impaired chronic stroke survivors. Ann Clin Transl Neurol. 2021;8(9):1895-1905. [ FREE Full text ] [ CrossRef ] [ Medline ]
Johnson MJ, Shakya Y, Strachota E, Ahamed SI. Low-cost monitoring of patients during unsupervised robot/computer assisted motivating stroke rehabilitation. Biomed Tech (Berl). 2011;56(1):5-9. [ CrossRef ] [ Medline ]
Johnson MJ, Van der Loos HFM, Burgar CG, Shor P, Leifer LJ. Experimental results using force-feedback cueing in robot-assisted stroke therapy. IEEE Trans Neural Syst Rehabil Eng. 2005;13(3):335-348. [ CrossRef ] [ Medline ]
Jonsdottir J, Baglio F, Gindri P, Isernia S, Castiglioni C, Gramigna C, et al. Virtual reality for motor and cognitive rehabilitation from clinic to home: a pilot feasibility and efficacy study for persons with chronic stroke. Front Neurol. 2021;12:601131. [ FREE Full text ] [ CrossRef ] [ Medline ]
Kamwesiga JT, Eriksson GM, Tham K, Fors U, Ndiwalana A, von Koch L, et al. A feasibility study of a mobile phone supported family-centred ADL intervention, F@ce, after stroke in Uganda. Global Health. 2018;14(1):82. [ FREE Full text ] [ CrossRef ] [ Medline ]
Kanai M, Izawa KP, Kobayashi M, Onishi A, Kubo H, Nozoe M, et al. Effect of accelerometer-based feedback on physical activity in hospitalized patients with ischemic stroke: a randomized controlled trial. Clin Rehabil. 2018;32(8):1047-1056. [ CrossRef ] [ Medline ]
Keeling AB, Piitz M, Semrau JA, Hill MD, Scott SH, Dukelow SP. Robot Enhanced Stroke Therapy Optimizes Rehabilitation (RESTORE): a pilot study. J Neuroeng Rehabil. 2021;18(1):10. [ FREE Full text ] [ CrossRef ] [ Medline ]
Kessler D, Anderson ND, Dawson DR. Occupational performance coaching for stroke survivors delivered via telerehabilitation using a single-case experimental design. Br J Occup Ther. 2021;84(8):488-496. [ FREE Full text ] [ CrossRef ] [ Medline ]
Kim DY, Kwon H, Nam KW, Lee Y, Kwon HM, Chung YS. Remote management of poststroke patients with a smartphone-based management system integrated in clinical care: prospective, nonrandomized, interventional study. J Med Internet Res. 2020;22(2):e15377. [ FREE Full text ] [ CrossRef ] [ Medline ]
Kim J, Lee M, Kim Y, Eun SD, Yoon B. Feasibility of an individually tailored virtual reality program for improving upper motor functions and activities of daily living in chronic stroke survivors: a case series. Eur J Integr Med. 2016;8(5):731-737. [ CrossRef ]
King M, Hijmans JM, Sampson M, Satherley J, Hale L. Home-based stroke rehabilitation using computer gaming. N Z J Physiother. 2012;40(3):128-134. [ FREE Full text ]
Kringle EA, Setiawan IMA, Golias K, Parmanto B, Skidmore ER. Feasibility of an iterative rehabilitation intervention for stroke delivered remotely using mobile health technology. Disabil Rehabil Assist Technol. 2020;15(8):908-916. [ FREE Full text ] [ CrossRef ] [ Medline ]
Kumar D, Sinha N, Dutta A, Lahiri U. Virtual reality-based balance training system augmented with operant conditioning paradigm. Biomed Eng Online. 2019;18(1):90. [ FREE Full text ] [ CrossRef ] [ Medline ]
Langan J, Bhattacharjya S, Subryan H, Xu W, Chen B, Li Z, et al. In-home rehabilitation using a smartphone app coupled with 3D printed functional objects: single-subject design study. JMIR Mhealth Uhealth. 2020;8(7):e19582. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lawrie S, Dong Y, Steins D, Xia Z, Esser P, Sun S, et al. Evaluation of a smartwatch-based intervention providing feedback of daily activity within a research-naive stroke ward: a pilot randomised controlled trial. Pilot Feasibility Stud. 2018;4:157. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lawson S, Tang Z, Feng J. Supporting stroke motor recovery through a mobile application: a pilot study. Am J Occup Ther. 2017;71(3):7103350010p1-7103350010p5. [ CrossRef ] [ Medline ]
Lee M, Pyun SB, Chung J, Kim J, Eun SD, Yoon BC. A further step to develop patient-friendly implementation strategies for virtual reality-based rehabilitation in patients with acute stroke. Phys Ther. 2016;96(10):1554-1564. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lee M, Son J, Kim J, Pyun SB, Eun SD, Yoon BC. Comparison of individualized virtual reality- and group-based rehabilitation in older adults with chronic stroke in community settings: a pilot randomized controlled trial. Eur J Integr Med. 2016;8(5):738-746. [ CrossRef ]
Lee MM, Shin DC, Song CH. Canoe game-based virtual reality training to improve trunk postural stability, balance, and upper limb motor function in subacute stroke patients: a randomized controlled pilot study. J Phys Ther Sci. 2016;28(7):2019-2024. [ FREE Full text ] [ CrossRef ] [ Medline ]
Levy T, Crotty M, Laver K, Lannin N, Killington M. Does the addition of concurrent visual feedback increase adherence to a home exercise program in people with stroke: a single-case series? BMC Res Notes. 2020;13(1):361. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lewis GN, Woods C, Rosie JA, McPherson KM. Virtual reality games for rehabilitation of people with stroke: perspectives from the users. Disabil Rehabil Assist Technol. 2011;6(5):453-463. [ CrossRef ] [ Medline ]
Li X, Wang L, Miao S, Yue Z, Tang Z, Su L, et al. Sensorimotor rhythm-brain computer interface with audio-cue, motor observation and multisensory feedback for upper-limb stroke rehabilitation: a controlled study. Front Neurosci. 2022;16:808830. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lum PS, Taub E, Schwandt D, Postman M, Hardin P, Uswatte G. Automated Constraint-Induced Therapy Extension (AutoCITE) for movement deficits after stroke. J Rehabil Res Dev. 2004;41(3A):249-258. [ FREE Full text ] [ CrossRef ] [ Medline ]
Mansfield A, Wong JS, Bryce J, Brunton K, Inness EL, Knorr S, et al. Use of accelerometer-based feedback of walking activity for appraising progress with walking-related goals in inpatient stroke rehabilitation: a randomized controlled trial. Neurorehabil Neural Repair. 2015;29(9):847-857. [ FREE Full text ] [ CrossRef ] [ Medline ]
Matarić MJ, Eriksson J, Feil-Seifer DJ, Winstein CJ. Socially assistive robotics for post-stroke rehabilitation. J Neuroeng Rehabil. 2007;4:5. [ FREE Full text ] [ CrossRef ] [ Medline ]
Mawson S, Nasr N, Parker J, Davies R, Zheng H, Mountain G. A personalized self-management rehabilitation system with an intelligent shoe for stroke survivors: a realist evaluation. JMIR Rehabil Assist Technol. 2016;3(1):e1. [ FREE Full text ] [ CrossRef ] [ Medline ]
McNulty PA, Thompson-Butel AG, Faux SG, Lin G, Katrak PH, Harris LR, et al. The efficacy of Wii-based Movement Therapy for upper limb rehabilitation in the chronic poststroke period: a randomized controlled trial. Int J Stroke. 2015;10(8):1253-1260. [ CrossRef ] [ Medline ]
Mihelj M, Novak D, Milavec M, Ziherl J, Olenšek A, Munih M. Virtual rehabilitation environment using principles of intrinsic motivation and game design. Presence: Teleop Virt Environ. 2012;21(1):1-15. [ CrossRef ]
Mitchell C, Bowen A, Tyson S, Conroy P. A feasibility randomized controlled trial of ReaDySpeech for people with dysarthria after stroke. Clin Rehabil. 2018;32(8):1037-1046. [ FREE Full text ] [ CrossRef ] [ Medline ]
Moan ME, Vonstad EK, Su X, Vereijken B, Solbjør M, Skjæret-Maroni N. Experiences of stroke survivors and clinicians with a fully immersive virtual reality treadmill exergame for stroke rehabilitation: a qualitative pilot study. Front Aging Neurosci. 2021;13:735251. [ FREE Full text ] [ CrossRef ] [ Medline ]
Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, et al. Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des. 2010;21(2-3):223-236. [ CrossRef ]
Nijenhuis SM, Prange GB, Amirabdollahian F, Sale P, Infarinato F, Nasr N, et al. Feasibility study into self-administered training at home using an arm and hand device with motivational gaming environment in chronic stroke. J Neuroeng Rehabil. 2015;12:89. [ FREE Full text ] [ CrossRef ] [ Medline ]
Novak D, Nagle A, Keller U, Riener R. Increasing motivation in robot-aided arm rehabilitation with competitive and cooperative gameplay. J Neuroeng Rehabil. 2014;11:64. [ FREE Full text ] [ CrossRef ] [ Medline ]
Olafsdottir SA, Jonsdottir H, Bjartmarz I, Magnusson C, Caltenco H, Kytö M, et al. Feasibility of ActivABLES to promote home-based exercise and physical activity of community-dwelling stroke survivors with support from caregivers: a mixed methods study. BMC Health Serv Res. 2020;20(1):562. [ FREE Full text ] [ CrossRef ] [ Medline ]
Olafsdottir SA, Jonsdottir H, Magnusson C, Caltenco H, Kytö M, Maye L, et al. Developing ActivABLES for community-dwelling stroke survivors using the Medical Research Council framework for complex interventions. BMC Health Serv Res. 2020;20(1):463. [ FREE Full text ] [ CrossRef ] [ Medline ]
Parker J, Mawson S, Mountain G, Nasr N, Zheng H. Stroke patients' utilisation of extrinsic feedback from computer-based technology in the home: a multiple case study realistic evaluation. BMC Med Inform Decis Mak. 2014;14:46. [ FREE Full text ] [ CrossRef ] [ Medline ]
Paul L, Wyke S, Brewster S, Sattar N, Gill JMR, Alexander G, et al. Increasing physical activity in stroke survivors using STARFISH, an interactive mobile phone application: a pilot study. Top Stroke Rehabil. 2016;23(3):170-177. [ CrossRef ] [ Medline ]
Pereira F, Badia SBI, Jorge C, Cameirão MS. The use of game modes to promote engagement and social involvement in multi-user serious games: a within-person randomized trial with stroke survivors. J Neuroeng Rehabil. 2021;18(1):62. [ FREE Full text ] [ CrossRef ] [ Medline ]
Perez-Marcos D, Chevalley O, Schmidlin T, Garipelli G, Serino A, Vuadens P, et al. Increasing upper limb training intensity in chronic stroke using embodied virtual reality: a pilot study. J Neuroeng Rehabil. 2017;14(1):119. [ FREE Full text ] [ CrossRef ] [ Medline ]
Popović MD, Kostić MD, Rodić SZ, Konstantinović LM. Feedback-mediated upper extremities exercise: increasing patient motivation in poststroke rehabilitation. Biomed Res Int. 2014;2014:520374. [ FREE Full text ] [ CrossRef ] [ Medline ]
Proffitt R, Lange B. Feasibility of a customized, in-home, game-based stroke exercise program using the Microsoft Kinect® sensor. Int J Telerehabil. 2015;7(2):23-34. [ FREE Full text ] [ CrossRef ] [ Medline ]
Rosati G, Oscari F, Reinkensmeyer DJ, Secoli R, Avanzini F, Spagnol S, et al. Improving robotics for neurorehabilitation: enhancing engagement, performance, and learning with auditory feedback. IEEE Int Conf Rehabil Robot. 2011;2011:5975373. [ CrossRef ] [ Medline ]
Rowe JB, Chan V, Ingemanson ML, Cramer SC, Wolbrecht ET, Reinkensmeyer DJ. Robotic assistance for training finger movement using a Hebbian model: a randomized controlled trial. Neurorehabil Neural Repair. 2017;31(8):769-780. [ FREE Full text ] [ CrossRef ] [ Medline ]
Sakakibara BM, Lear SA, Barr SI, Goldsmith CH, Schneeberg A, Silverberg ND, et al. Telehealth coaching to improve self-management for secondary prevention after stroke: a randomized controlled trial of Stroke Coach. Int J Stroke. 2022;17(4):455-464. [ FREE Full text ] [ CrossRef ] [ Medline ]
Saywell NL, Vandal AC, Mudge S, Hale L, Brown P, Feigin V, et al. Telerehabilitation after stroke using readily available technology: a randomized controlled trial. Neurorehabil Neural Repair. 2021;35(1):88-97. [ FREE Full text ] [ CrossRef ] [ Medline ]
Scrivener K, Sewastenko J, Bouvier-Farrell A, MacDonald K, Van Rijn T, Tezak J, et al. Feasibility of a self-managed, video-guided exercise program for community-dwelling people with stroke. Stroke Res Treat. 2021;2021:5598100. [ FREE Full text ] [ CrossRef ] [ Medline ]
Shah N, Amirabdollahian F, Basteris A. Designing motivational games for stroke rehabilitation. New Jersey. IEEE; 2014. Presented at: 7th International Conference on Human System Interactions (HSI); June 16-18, 2014;166-171; Costa da Caparica, Portugal. [ CrossRef ]
Signal NEJ, McLaren R, Rashid U, Vandal A, King M, Almesfer F, et al. Haptic nudges increase affected upper limb movement during inpatient stroke rehabilitation: multiple-period randomized crossover study. JMIR Mhealth Uhealth. 2020;8(7):e17036. [ FREE Full text ] [ CrossRef ] [ Medline ]
Simpson DB, Bird ML, English C, Gall SL, Breslin M, Smith S, et al. "Connecting patients and therapists remotely using technology is feasible and facilitates exercise adherence after stroke". Top Stroke Rehabil. 2020;27(2):93-102. [ CrossRef ] [ Medline ]
Song X, van de Ven SS, Chen S, Kang P, Gao Q, Jia J, et al. Proposal of a wearable multimodal sensing-based serious games approach for hand movement training after stroke. Front Physiol. 2022;13:811950. [ FREE Full text ] [ CrossRef ] [ Medline ]
Standen PJ, Threapleton K, Richardson A, Connell L, Brown DJ, Battersby S, et al. A low cost virtual reality system for home based rehabilitation of the arm following stroke: a randomised controlled feasibility trial. Clin Rehabil. 2017;31(3):340-350. [ FREE Full text ] [ CrossRef ] [ Medline ]
Subramanian SK, Lourenço CB, Chilingaryan G, Sveistrup H, Levin MF. Arm motor recovery using a virtual reality intervention in chronic stroke: randomized control trial. Neurorehabil Neural Repair. 2013;27(1):13-23. [ FREE Full text ] [ CrossRef ] [ Medline ]
Sullivan JE, Espe LE, Kelly AM, Veilbig LE, Kwasny MJ. Feasibility and outcomes of a community-based, pedometer-monitored walking program in chronic stroke: a pilot study. Top Stroke Rehabil. 2014;21(2):101-110. [ CrossRef ] [ Medline ]
Toh SFM, Gonzalez PC, Fong KNK. Usability of a wearable device for home-based upper limb telerehabilitation in persons with stroke: a mixed-methods study. Digit Health. 2023;9:20552076231153737. [ FREE Full text ] [ CrossRef ] [ Medline ]
Tsekleves E, Paraskevopoulos IT, Warland A, Kilbride C. Development and preliminary evaluation of a novel low cost VR-based upper limb stroke rehabilitation platform using Wii technology. Disabil Rehabil Assist Technol. 2016;11(5):413-422. [ CrossRef ] [ Medline ]
Uswatte G, Taub E, Lum P, Brennan D, Barman J, Bowman MH, et al. Tele-rehabilitation of upper-extremity hemiparesis after stroke: proof-of-concept randomized controlled trial of in-home Constraint-Induced Movement therapy. Restor Neurol Neurosci. 2021;39(4):303-318. [ CrossRef ] [ Medline ]
Valdés BA, Van der Loos HFM. Biofeedback vs. game scores for reducing trunk compensation after stroke: a randomized crossover trial. Top Stroke Rehabil. 2018;25(2):96-113. [ CrossRef ] [ Medline ]
Wairagkar M, McCrindle R, Robson H, Meteyard L, Sperrin M, Smith A, et al. MaLT—combined motor and language therapy tool for brain injury patients using kinect. Methods Inf Med. 2017;56(2):127-137. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wei WXJ, Fong KNK, Chung RCK, Cheung HKY, Chow ESL. "Remind-to-Move" for promoting upper extremity recovery using wearable devices in subacute stroke: a multi-center randomized controlled study. IEEE Trans Neural Syst Rehabil Eng. 2019;27(1):51-59. [ CrossRef ] [ Medline ]
Whitford M, Schearer E, Rowlett M. Effects of in home high dose accelerometer-based feedback on perceived and actual use in participants chronic post-stroke. Physiother Theory Pract. 2020;36(7):799-809. [ CrossRef ] [ Medline ]
Widmer M, Held JPO, Wittmann F, Valladares B, Lambercy O, Sturzenegger C, et al. Reward during arm training improves impairment and activity after stroke: a randomized controlled trial. Neurorehabil Neural Repair. 2022;36(2):140-150. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wittmann F, Held JP, Lambercy O, Starkey ML, Curt A, Höver R, et al. Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system. J Neuroeng Rehabil. 2016;13(1):75. [ FREE Full text ] [ CrossRef ] [ Medline ]
Yuan Z, Peng Y, Wang L, Song S, Chen S, Yang L, et al. Effect of BCI-controlled pedaling training system with multiple modalities of feedback on motor and cognitive function rehabilitation of early subacute stroke patients. IEEE Trans Neural Syst Rehabil Eng. 2021;29:2569-2577. [ FREE Full text ] [ CrossRef ] [ Medline ]
Chong MS, Sit JWH, Karthikesu K, Chair SY. Effectiveness of technology-assisted cardiac rehabilitation: a systematic review and meta-analysis. Int J Nurs Stud. 2021;124:104087. [ CrossRef ] [ Medline ]
Hart T, Dijkers MP, Whyte J, Turkstra LS, Zanca JM, Packel A, et al. A theory-driven system for the specification of rehabilitation treatments. Arch Phys Med Rehabil. 2019;100(1):172-180. [ CrossRef ] [ Medline ]
Deterding S, Dixon D, Khaled R, Nacke L. From game design elements to gamefulness: defining "gamification". New York, NY, US. Association for Computing Machinery; 2011. Presented at: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments; September 28-30, 2011;9-15; Tampere, Finland. [ CrossRef ]
Barrett N, Swain I, Gatzidis C, Mecheraoui C. The use and effect of video game design theory in the creation of game-based systems for upper limb stroke rehabilitation. J Rehabil Assist Technol Eng. 2016;3:2055668316643644. [ FREE Full text ] [ CrossRef ] [ Medline ]
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Edited by A Mavragani; submitted 15.05.23; peer-reviewed by M Broderick, G Sweeney, E Crayton, D Pogrebnoy; comments to author 11.10.23; revised version received 14.11.23; accepted 26.12.23; published 24.04.24.

©Helen J Gooch, Kathryn A Jarvis, Rachel C Stockley. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Decolonising global health research: Shifting power for transformative change

Roles Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations United Nations University-International Institute for Global Health, Kuala Lumpur, Malaysia, Department of Community and Family Medicine, Faculty of Medicine, University of Jaffna, Jaffna, Sri Lanka

Roles Conceptualization, Writing – review & editing

Affiliation United Nations University-International Institute for Global Health, Kuala Lumpur, Malaysia

Roles Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing

Ramya Kumar,
Rajat Khosla,
David McCoy

Published: April 24, 2024

https://doi.org/10.1371/journal.pgph.0003141
Reader Comments

Recent debates on decolonizing global health have spurred interest in addressing the power asymmetries and knowledge hierarchies that sustain colonial ideas and relationships in global health research. This paper applies three intersecting dimensions of colonialism (colonialism within global health; colonisation of global health; and colonialism through global health) to develop a broader and more structural understanding of the policies and actions needed to decolonise global health research. It argues that existing guidelines and checklists designed to make global health research more equitable do not adequately address the underlying power asymmetries and biases that prevail across the global health research ecosystem. Beyond encouraging fairer partnerships within individual research projects, this paper calls for more emphasis on shifting the balance of decision-making power, redistributing resources, and holding research funders and other power-holders accountable to the places and peoples involved in and impacted by global health research.

Citation: Kumar R, Khosla R, McCoy D (2024) Decolonising global health research: Shifting power for transformative change. PLOS Glob Public Health 4(4): e0003141. https://doi.org/10.1371/journal.pgph.0003141

Editor: Ananya Banerjee, McGill University, CANADA

Copyright: © 2024 Kumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Inequity within international research partnerships has troubled the field of global health for decades. In particular, power asymmetries between actors from wealthier and historically-privileged countries and their counterparts in the Global South (GS) have led to paternalistic ways of working, unequal sharing of resources, skewed distribution of benefits, and limited commitments to capacity strengthening [ 1 ]. Recent debates on decolonizing global health have brought renewed attention to addressing these problems in global health research. In addition to highlighting equity concerns, these discussions draw attention to the epistemic injustice and “white saviour” mentalities that underpin research collaborations [ 2 – 8 ].

Recognising that power asymmetries in global health are produced by both historical and current exploitation and resource extraction, our approach to decolonizing global health involves three intersecting dimensions: 1) colonialism within global health; 2) colonisation of global health; and 3) colonialism through global health [ 9 ]. The first dimension speaks to power differentials and resource disparities between different actors within the field of global health. The second deals with the dominance of certain powerful actors and vested interests over the overall complex of global health structures, systems, policies and practices. The third dimension refers to exploitative and extractive practices that occur through the health sector [ 9 ].

This paper uses this framework of three dimensions to arrive at a broader understanding of the scope of policies and actions needed to decolonise global health research. We begin by briefly outlining persisting inequities within research partnerships- already addressed by a large body of literature. Next, we draw attention to issues that are underexplored, specifically who controls the agenda of global health research (i.e., colonisation of global health research), and who benefits from such research (i.e., colonialism through global health research) ( Fig 1 ). We then present a brief review of recent guidelines and checklists that seek to decolonize global health research and/or centre the needs and aspirations of the GS in research, revealing an emphasis on addressing inequity within research partnerships. We end by recommending policies and actions that would decolonize the field of global health research in an effective and comprehensive manner.

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https://doi.org/10.1371/journal.pgph.0003141.g001

This paper employs the terminology “Global North” (GN) and “Global South” (GS) to reflect asymmetries in power and access to resources between not just countries but also population groups. This terminology only partly corresponds to the classification of countries according to per capita gross national income, i.e., low-income, middle-income, and high-income countries (LIC, MIC, and HIC) [ 10 ]. However, where we quote from sources that explicitly refer to LICs, MICs or HICs, these terms are retained. We borrow from Garcia-Basteiro and Abimbola [ 11 ] to define global health research as research that seeks to address health inequity within and across countries, aiming to improve health in what they call “low-resource settings” described as regions weighed down by financial constraints, suboptimal service delivery, underdeveloped physical and knowledge infrastructure, historical, political and sociocultural contexts/specificities, and geographical, environmental and human resource limitations.

2. Colonialism within global health research: Who leads?

According to the World Health Organization (WHO), in 2020, of the USD 37 billion spent worldwide on ‘biomedical research’, 98.7% went to HICs [ 12 ]. Perhaps more reflective of the global health research landscape, in 2021, 82% of the Bill and Melinda Gates Foundation’s (BMGF) grant funding went to HIC recipients [ 13 ]. This unequal distribution of funding is striking when one considers that much global health research is carried out in GS settings.

The inequitable global health research funding patterns reflect not only the wider socio-economic disparity between GN and GS, but also the biases within the global health research system. For example, grant calls, either explicitly or through eligibility criteria or capacity requirements, favour GN-based institutions [ 14 ] with research funding agencies of key donor countries often requiring principal investigators (PI) to be based in their country or compel PIs from the GS to partner with a researcher based in the donor country [ 15 , 16 ]. Eligibility criteria based on geographic location and experience may further restrict applications from GS-based researchers [ 17 ]. GN-based researchers are also better able to navigate the funding terrain with their training, networks and resources [ 18 , 19 ].

Although most global health funding agencies require GN-based researchers to “collaborate” with local “partners,” the terms of collaboration are usually set by the former who typically conceptualise the research before inviting others onboard [ 20 ]. This gives GS-based researchers limited influence over the research, despite their expertise and familiarity with the context [ 7 , 21 , 22 ], thus supporting what has been called “parachute” research, where GN-based collaborators fly in for weeks at a time for onsite “supervision” [ 23 , 24 ]. As grant cycles are usually short, the urgency to meet deadlines results in lopsided decision-making, hasty administrative approvals and, at times, the undermining of local administrative and ethics procedures [ 8 ].

Much grant funding goes towards the salaries of GN-based researchers with substantially less dedicated to research systems and capacity strengthening in GS settings [ 2 , 14 ]. This lack of long-term commitment to the development of GS-based institutions sustains the status quo [ 25 ]. Meanwhile, extant capacity strengthening initiatives are often uni-directional and paternalistic, involving assumptions about what competencies GS collaborators may lack [ 26 ].

Inequity is further reinforced by authorship patterns that are biased towards GN-based researchers [ 27 , 28 ]. Authorship guidelines of prominent journals systematically exclude non-native English writers [ 29 ] by giving weight to written contributions over field work [ 30 ]. Representation at conferences and symposia is similarly unequal, although research collaborations do enable participation for some GS-based researchers. Even so, visa and other barriers challenge researchers from travelling to meeting destinations [ 31 ].

3. Colonisation of global health research: Who controls?

Global health funding agencies wield significant power in defining global health problems and the approaches taken to addressing them [ 7 , 32 ]. Under the current system, researchers based at universities and other research institutions respond to grant calls, crafting their research to fit with the agendas and ideologies of global health funders rather than vice versa [ 33 ].

Extreme wealth concentration under neoliberal globalization and the rise of ‘philanthrocapitalism’ by which global health problems are framed as market opportunities, has seen a shift from public to private financing in global health [ 34 , 35 ]. However, private actors have interests and priorities that may be at odds with the public interest or with achieving equity in health. For instance, the shift from publicly-funded to industry-funded research has distorted scientific evidence on infant formula with detrimental effects on infant and child health [ 36 ]. Moreover, the funding decisions of corporations and foundations are ultimately approved by a handful of largely GN-based board members, who are not subjected to any independent mechanisms of accountability for their funding decisions or their impact on people affected by these decisions [ 32 , 37 ]. Although some funders have recently instituted measures to address diversity within their leadership [ 13 , 38 ], such change will not be transformative without redistributing power and resources, and genuine efforts to improve accountability [ 39 ].

Research funders favour specific thematic areas, not always based on the health problems prevailing in specific GS settings [ 19 , 40 ]. They tend to promote technology-based solutions and favour innovation and entrepreneurship in projects that yield quick and quantifiable results [ 41 ]. The preference for short-term impact over longer-term improvements in health results in grant proposals that centre “magic bullets” (e.g., vaccines, medicines, bed nets, mobile apps) rather than systems building, local capacity strengthening and unblocking the social and political barriers to the scale up of proven and more sustainable alternatives [ 42 , 43 ].

Academic programmes in global health continue to be characterised by what has been called a “white saviour complex” or a depoliticized, patronizing and charity-based approach shaped, in part, by a wider aid industry [ 44 , 45 ]. Global health curricula remain largely disconnected from the many realities and locales of the GS, both in geography and lived experience. Dominant Eurocentric epistemologies, which are embraced and propagated by powerful global health institutions, are usually given primacy in research training, even as heterodox methodologies that interrogate power and inequality are marginalized [ 45 , 46 ].

The inability of countries of the GS to weigh in on the global health research agenda and define their own priorities is perpetuated by their minimal contributions to research funding [ 25 , 47 , 48 ]. While domestic investment is critical to shift the balance of power, debt-ridden governments of lower-income countries may have limited leeway with their health and R&D budgets owing to fiscal constraints [ 19 ]. For these countries, HIC-driven global health research collaborations may present a welcome source of foreign currency. Too often however, external funding for health research takes place with little coordination among granting agencies [ 49 , 50 ], facilitating duplication, and making impact assessment difficult.

4. Colonialism through global health research: Who benefits?

The asymmetric global health research funding structure also gives powerful states and private actors opportunities to craft research in the GS in ways that they benefit from financially or economically. These benefits are primarily driven by the commercialization of research and publishing, supported by imperatives to expand markets, unfair intellectual property rights (IPR) regimes, and predatory academic journals.

Arguably, the biggest profits are made by commercial entities that hold patents for global health technologies often tested through research carried out in GS settings. Such research aids market expansion for medicines, vaccines, diagnostic tests, mobile devices, etc. benefitting big pharma, biotechnology, and big tech companies, while doing little to strengthen public health infrastructure and services or reduce dependency [ 19 , 41 ]. Indeed, some private foundations are routing a growing proportion of their tax-subsidised grants to private for-profit organisations, in both GN and GS settings [ 37 , 51 ].

Current IPR regimes which provide private companies with extensive monopoly rights over new and modified technologies despite much basic research being funded publicly is one aspect of an R&D ecosystem biased in favour of private financial interests at the expense of public health. This was seen with the billions of dollars of private profits generated from COVID-19 vaccines despite vast amounts of public and charitable funds that went into their development [ 52 ].

The unequal benefits accrued through authorship in global health journals have been widely studied [ 27 , 28 ] but less is known about their commercial dimensions. The revenue of academic publishers is estimated to be about USD 19 billion annually, where about half the market share is controlled by five transnational companies, with Elsevier alone accounting for 16% of the market share, with profit margins in the order of 40 per cent [ 53 ]. These corporations are all headquartered in the GN and maximise profits through article processing charges (APCs), subscriptions, and the uncompensated labour of authors and peer-reviewers. Ever-increasing APCs are required to publish ‘open access’ in prestigious journals, implemented in the name of equity, but barring most GS-based researchers through stringent waiver criteria [ 54 ]. Global spending on APCs alone is estimated to exceed USD 2 billion annually [ 55 ]. Academic journals are, in turn, linked to bibliometric platforms that track the ‘impact’ of research communications, which feed into commercialised university ranking systems [ 56 ]. With research funding and citations in ‘high-impact’ journals being key elements of performance indices, the top-twenty universities, as ranked by Academic Ranking of World Universities and Time Higher Education, are all located in the GN [ 57 ].

The current system of global health education supports extraction of wealth and other resources from GS to GN. A recent analysis of masters in global health degrees revealed that 95% of them are based in HICs, costing on average USD 37,732 in tuition [ 58 ]. Given the location and cost of global health postgraduate programmes, their graduates, including those from GS settings, are likely to be drawn to work with global or GN-based institutions both to repay the debt incurred and because of the lack of well-remunerated positions back home [ 58 ]. Ultimately, career trajectories in global health are skewed towards the GN and not “low-resource settings” where global health work and resources are much needed [ 23 ].

In sum, whether in terms of leadership, control or benefits, GN-based actors and institutions are privileged within the broader global health research ecosystem, often to the detriment of researchers, institutions and ‘beneficiaries’ in GS settings. It appears that global health research supports a renewed form of extractivism, where resources in the GS, including funding, knowledge and researchers, are drawn to the GN. In the next section, we examine whether and to what extent recent guidelines on decolonising global health research address the three intersecting dimensions of colonialism in global health research.

5. Recent guidelines that aim to decolonise global health research

We searched the literature for tools that either explicitly or in their framing seek to decolonise global health research and/or centre the needs and aspirations of the GS in research. As searches on PubMed and Scopus [(“decol*” OR “colonial*”) AND “global health” AND “research” AND (manual OR guideline OR checklist)] yielded less than 10 publications, we also searched Google Scholar, Google, and pursued reference lists of identified publications. Criteria for inclusion were: addressing equity in global health research with reference to colonialism or explicit attention to making research fairer for peoples and institutions in the GS; including a set of standards or guidelines; targeting researchers, research institutions or funders; published within the five-year period of 2019 to 2023. We identified eight tools that fit our criteria as described below.

Hodson et al. [ 40 ] offer a set of “practical measures” for global health researchers, underpinned by four principles: “1) seek locally derived and relevant solutions to global health issues, 2) create paired collaborations between HIC and LMIC institutions at all levels of training, 3) provide funding for both HIC and LMIC team members, [and] 4) assign clear roles and responsibilities to value, leverage, and share the strengths of all team members.” This guideline addresses specific challenges experienced in GS settings by advocating for: educating all team members on global health history; early engagement of GN-based researchers with local administrations; capacity strengthening to support independent research in GS settings; protected research time for all team members; preventing GS-based researchers being drawn away from regular work; and ensuring knowledge translation to local communities, among other measures. Despite the commitment to long-term capacity strengthening, the guideline focuses primarily on research processes within partnerships.

Kumar et al. [ 26 ] propose a set of individual and institutional level actions to advance equity in global health research. Those at the individual level include questioning “notions of absolute scientific objectivity” (p.146), adopting a decolonial approach towards global health concepts and implicit hierarchies, cultivating respect and humility, promoting fairness at all levels (including at the level of global health leadership), and going beyond ‘equality’ to recognize ‘equity’ within collaborations. At the institutional level, they support decentring the GN in global health efforts (including the location of centres of knowledge), promoting solidarity, investing in researchers from LMICs, bi-directional capacity strengthening, evaluating partnerships by “measures of fairness” and “ethical and culturally responsive engagement,” and correcting “colonising and unethical practices” (p.146). While some of these actions aim to rectify power asymmetries well beyond research partnerships, they do not include specific guidance on implementation.

Embracing a feminist decolonial approach, Singh et al. [ 59 ] offer a guideline for researchers working in situations of forced displacement that centres participant agency, voice, and experience; it aims to address power hierarchies through a set of recommendations targeting various stages of research. The guideline demands: consideration to “political, social, economic, and historical contexts and power hierarchies of the research setting” (p.561); involving marginalised groups in the research design; reflecting on how coloniality and gendered power relations may be reinforced during data collection; an intersectional analysis of gendered power relations; collaboration in analysis and knowledge dissemination; and using research to “challenge unjust systems and policies and deliver gender transformative and equitable programmes” (p.561). Although the guideline aims to reconfigure power within individual research projects, it offers no direction on how to redistribute power.

Rashid [ 8 ] offers guidance for researchers in LICs to “[navigate] the violent process of decolonisation in global health research.” The guideline includes a list of dos and don’ts to help researchers in LICs contend with power asymmetries in international research collaborations. They recommend carefully reviewing agreements, clarifying systems of reporting and accountability, insisting on inclusion in communications with funders, meticulous documentation, boosting one’s profile, expanding networks, and building solidarity. However, this guideline focuses on change at the individual level on the part of researchers in GS settings rather than systemic change.

The TRUST Code–“A Global Code of Conduct for Equitable Research Partnerships” is based on the core values of fairness, respect, care, and honesty [ 60 ]. Compiled by a team with wide representation from the GS, the TRUST Code consists of 23 articles. Apart from conventional ethical standards, the tool emphasises: bona fide involvement of local communities in research, fairness in the transfer and ownership of data and biological materials, and fair compensation of local collaborators. It emphasises cultural acceptability, community assent, respect for local ethics review and giving consideration to the impact of research on local human resources, animal welfare, and the environment. It calls for clarity on roles, responsibilities, capacity strengthening, transparency, and integrity of the research process. Although broadly framed around justice for communities and researchers in the GS, the tool primarily concentrates on making individual research partnerships more equitable.

The Research for Health Justice Framework proposed by Pratt and colleagues [ 61 ] offers two sets of guidelines, one for health researchers and another for granting agencies. Developed through an iterative process and fine-tuned through case studies in GS settings, the guidelines emphasize equity, justice, and inclusion, with accompanying explanations on implementation. The guideline for researchers addresses: selection of the research population and research problem, research capacity development, delivery of ancillary care, and knowledge translation practices. With respect to granting agencies, it asks that they prioritise the health concerns of the worst-off, promote ownership of the research agenda by LMIC researchers and support projects that seek to advance equity within healthcare systems, atop measures to support equitable research practices. While this framework is comprehensive in scope, the guidelines are still largely limited to the research process and do not explicitly seek to transform the global health granting system and the power asymmetries within it.

Focusing specifically on global health research funding, Charani et al. [ 19 ] outline eight areas of action for funders: 1) developing situational awareness, including an understanding of institutional dynamics and who benefits from grants; 2) formulating a mission statement that pledges equity in research; 3) equitable allocation of funds to cover differential needs of HIC- and LMIC-based researchers; 4) funding structures that encourage local ownership and leadership; 5) bi-directional capacity strengthening that enables all partners to engage with funders; 6) diversity and inclusion across the grant cycle, including in design, knowledge dissemination, access to training etc.; 7) knowledge generation, including methodologies, frameworks, tools and clarity on data ownership; and 8) reflection and feedback involving HIC and LMIC researchers on equal terms. Encouraging funders to include specific requirements for grant recipients to comply with participatory approaches and fair sharing of resources and benefits, the guideline also speaks to what should be funded, who should be funded and how. Moreover, among its recommendations—albeit with no details provided—are “a transparent process for tracking the progress of funding” and “a code of ethics for global health funders”.

The Global Health Decolonisation Movement Africa [ 17 ], self-described as a collective of African citizens, has published a guideline called, Pragmatic Approaches to Decolonising Global Health in Africa . What is unique about this guideline is that it addresses multiple “stakeholders” in HICs, including individual practitioners, funding agencies, academic and training institutions, scientific publishers, and event conveners and organisers, among others. The guideline broadly seeks to address racism against Africans within global health, and promotes African leadership and self-determination. The section for funders calls for diversifying grant review panels, rejecting “parachute” proposals, and removing requirements for researchers based in Africa to collaborate with HIC-based institutions. For academic and training institutions, the guideline recommends diversifying leadership and recruitment practices, and addressing coloniality in global health curricula. And for scientific journals, it demands diversifying authorship and peer-review panels. While this guideline emphasises diversity, equity, and inclusion, it remains constrained by the limitations of the current system of global health research funding.

In sum, there is considerable variation in guidance on improving equity in research partnerships and decolonising global health research. All reviewed sources strive to make the research process fairer and rectify power asymmetries through diversity, equity and inclusion measures, but only some engage with historical imbalances in power, interrogate dominant knowledge paradigms, centre the concerns of marginalized groups, and create space for self-determination. The guidelines for funders go beyond research partnerships to address who and what is funded. However, for the most part, these guidelines neglect the wider contextual factors that shape agenda-setting in global health research, as well as the actors and institutions that control and benefit from them.

6. Shifting the balance of power in global health research: Going forward

In this section, we draw on the three intersecting dimensions of colonialism in global health research to present seven action areas that we call for to mitigate inequitable, exploitative and extractive arrangements in global health research ( Fig 2 ).

https://doi.org/10.1371/journal.pgph.0003141.g002

First, and most fundamentally, we call for a critical examination of the epistemological and ideological underpinnings of global health research. While current debates engage to some extent with the marginalization of indigenous perspectives, few question the dominance of positivist approaches and the biomedical paradigm. Guided by a biased hierarchy of evidence that favours quantitative assessments, global health research remains over-occupied with testing the efficacy of discrete, downstream and often clinical technologies and interventions, taking attention away from the social and structural determinants of health, which are more challenging to measure [ 41 , 43 , 62 ]. Shaped by neoliberal ideology, understandings of health and healthcare have evolved from collectivist to individualist interpretations, giving way to economistic evaluations based on assumptions that resource constraints in low-income settings are inevitable [ 63 ]. Global health education could challenge dominant paradigms and mainstream approaches that advance social justice and equity in health [ 64 ].

Second, we need a better and more detailed analysis of the overall pattern and performance of research funding: where it comes from, where it goes, how it is spent, and its impact. A few of the guidelines reviewed earlier do address the global health research funding system. For instance, Charani et al. [ 19 ] recommend that funding agencies self-monitor whom they fund and also call for a code of ethics for funders, while the Global Health Decolonisation Movement Africa [ 17 ] asks funders to remove requirements for researchers based in Africa to collaborate with GN-based institutions. Even so, these measures remain couched within the current structure and system of grant funding that lacks transparency and leaves power concentrated in the hands of largely GN-based donors. The problematic norm of donors funding favoured research areas over those that are identified locally remains largely unchallenged. At the very least, information should be available by funder, recipient, research area, and research setting, possibly through a centralized system that requires funders to provide information on their funding practices. Auditing such data should enable analysis of not only where research funding comes from and who receives it but also its impact.

Third, efforts to address power asymmetries in global health research must compel reform at the highest levels of global governance. By virtue of their funding contributions, powerful states, their bilateral agencies, private foundations, and corporate actors, among others, shape the global health research agenda. Bilateral agencies tend to push foreign policy and other domestic interests [ 65 , 66 ], while corporate actors are driven by profit, and many private foundations by the creed that the private sector can more effectively tackle intractable global health problems [ 67 ]. Bilateral and multilateral agencies should be held accountable for what they fund with taxpayer contributions, while private funders—who are primarily accountable to their boards—must be appropriately regulated and prevented from having undue influence on the shaping of research priorities [ 68 , 69 ].

A comprehensive guideline for research funders that promotes fairer distribution of resources and improved accountability is needed. Such a guideline could incorporate the measures proposed by Charani et al. [ 19 ], Pratt et al. [ 61 ] and the Global Health Decolonisation Movement Africa [ 17 ]. An international agreement, akin to the Declaration of Helsinki [ 70 ]—the World Medical Association’s ethical principles for medical research—could encourage and eventually normalise funding of equity-oriented research and local ownership. Decision-making on funding priorities must be shared with the GS, not just with governments but also with researchers, institutions, and the beneficiaries of research [ 26 ].

Fourth, national research systems should be supported and strengthened with in-built mechanisms of accountability. While there are calls for LMIC governments to invest more in R&D [ 25 ], the onus for change cannot be placed on these countries alone. Rather donors must also commit to investing in local research infrastructure, human resources, and higher education systems, all key to building research capacities. Meanwhile, government allocations for health research in GS settings should be guided by appropriate needs assessments and strategic plans to strengthen national research capacity [ 71 ] as once encouraged by the Commission on Health Research for Development (COHRED), an independent global initiative that supported research for heath and development in LMICs [ 72 ]. Systemic investments in research capacity strengthening with long-term budget commitments and harmonised mechanisms should be established [ 73 ] to replace the current piecemeal manner in which health research is conducted, often subject to the whims of external funders. Bi-directional scholarships for postgraduate training in research, with service requirements in GS settings, could target specific human resource gaps. Fifth, a global fund for research [ 74 ], guided by a multilateral framework that pools donor funds and channels them based on national health priorities may help to harmonise external funding, avoid duplication, and enable greater transparency and accountability.

Sixth, given acute human resource constraints in many GS countries, brain drain must be stemmed. The WHO Global Code of Practice on the International Recruitment of Health Personnel [ 75 ] provides a multilateral framework but fails to hold the GN to account for their unethical recruitment practices. Instead, the Code focuses on the rights of migrating health workers and places the onus on ‘developing countries’ to retain them. It does not recognize the vast amounts of (often public) resources invested in health worker training in GS settings, nor does it recommend compensation to source countries for this training. Academic global health programmes should re-orient their curricula [ 76 ] so that the primary career pathways for global health practitioners are viewed to be in GS settings.

Lastly, interventions to promote fairer distribution of benefits should look beyond authorship and academic credit, to address extractivist practices within the research industry that impede access to knowledge and technologies in the GS. The current IPR regime upholds patent protection, allowing big pharma to control product pricing and restrict market entry of generic manufacturers who could drive down the cost of medicines and other health products [ 77 , 78 ]. IPR regimes need to be revised to enhance fairness in the distribution of the benefits of science rather than support industry benefits and profit over public health.

In this paper, we applied three intersecting dimensions (colonialism within global health; colonisation of global health; and colonialism through global health) to develop a broader and more structural understanding of the policies and actions needed to decolonise global health research. We highlighted the tendency of existing guidelines that seek to make research partnerships more equitable and less colonial, to target the behaviour of researchers and research institutions within the boundaries of individual research projects. Following such guidance should result in better and more appropriate global health research. However, efforts to decolonise global health research should go beyond addressing equity within research partnerships to reconfiguring power arrangements within the global health research ecosystem. This means re-orienting research along social justice and equity lines, building research capacities in GS settings, and moving away from the existing donor-driven model.

Of critical concern is the prevailing system of research funding that functions with little transparency or downward accountability. Data should be made available to scrutinize and evaluate the funding processes of research funders and the appropriateness and impact of funding patterns and practices. It would be important to examine not just the specific outputs and outcomes of individual grant programmes and research projects, but also the impact of the entire global health research portfolio on the overall functioning of health research systems at global and national levels and, in particular, how research outputs contribute towards advancing health equity. Quick fixes and half-hearted measures would simply not work. Time is now for the global health community to come together and demand a complete overhaul of the competitive global health research funding system, and its replacement or accompaniment with a more strategic and publicly-driven pooling and harmonised allocation of resources aimed at correcting the many deep and structural inequalities across the global health research ecosystem. This would also require fostering equity-oriented research approaches, grounded in local ownership, with systems of accountability built in.

Acknowledgments

The authors thank Emer Breen, Tiffany Nassiri-Ansari, and Emma Rhule, for helpful feedback on earlier versions of the paper.

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Google Scholar
10. World Bank Group [Internet]. World Bank country and lending groups; n.d. [cited 2023 Sept 27]. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
12. World Health Organization [Internet]. Investments on grants for biomedical research by funder, type of grant, health category and recipient; 2023 [cited 2023 Sept 27]. https://www.who.int/observatories/global-observatory-on-health-research-and-development/monitoring/investments-on-grants-for-biomedical-research-by-funder-type-of-grant-health-category-and-recipient
13. Bill and Melinda Gates Foundation [Internet]. DEI progress report 2021; n.d. [cited 2023 Sept 27]. https://docs.gatesfoundation.org/documents/bill_and_melinda_gates_foundation_2021_dei_progress_report.pdf
15. National Institutes of Health [Internet]. NIH global health funding; 2023 [cited 2023 Sept 27]. https://www.fic.nih.gov/Funding/Pages/NIH-funding-opportunities.aspx
16. National Institute for Health and Care Research [Internet]. Eligibility; 2023 [cited 2023 Sept 27]. https://www.nihr.ac.uk/researchers/i-need-help-funding-my-research/eligibility/
17. Global Health Decolonisation Movement Africa [Internet]. Pragmatic approaches to decolonizing global health in Africa; 2021 [cited 2023 Sept 27]. https://ghdmafrica.org/wp-content/uploads/2021/02/GHDM_brochure_web.pdf
30. International Committee of Medical Journal Editors ICJME. Defining the Role of Authors and Contributors; 2023 [cited 2023 Sept 27]. https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html
32. Rushton S, Williams OD. Private actors in global health governance. In: Rushton S, Williams OD, editors. Partnerships and foundations in global health governance. Hampshire: Palgrave MacMillan; 2011. pp.1–25.
34. McCoy D, McGoey L. Global health and the Gates Foundation—in perspective. In: Rushton S, Williams OD, editors. Partnerships and foundations in global health governance. Hampshire: Palgrave MacMillan; 2011. pp.143–163.
35. Wilson J. Philanthrocapitalism and global health. In: Benatar S, Brock G, editors. Global health: ethical challenges. Cambridge: Cambridge University Press; 2021. pp.416–428.
38. Wellcome Trust [Internet]. Diversity and inclusion: helping more ideas thrive; n.d. [cited 2023 Sept 28]. https://wellcome.org/what-we-do/diversity-and-inclusion
51. Breen E, Kumar, R [Internet]. Private foundations and their global health grant-making patterns: a rapid analysis of the Rockefeller Foundation, Wellcome Trust, and Bill and Melinda Gates Foundation; 2023 [cited 2024 March 29]. https://www.globalpolicy.org/en/publication/private-foundations-and-their-global-health-grant-making-patterns
56. Chen G, Chan L. University rankings and governance by metrics and algorithms. In: Hazelkorn E, editor. Research handbook on university rankings: theory, methodology, influence and impact. Cheltenham: Edward Elgar; 2021. pp. 424–441.
57. Nassir-Ansari T, McCoy D. World-class universities? Interrogating the biases and coloniality of global university rankings [Internet]. Kuala Lumpur: United Nations University-International Institute for Global Health; 2023 [cited 2023 Sept 27]. https://collections.unu.edu/eserv/UNU:9082/UNI-Ranking-paper_February-2023.pdf
60. TRUST (2018). The TRUST Code–A global code of conduct for equitable research partnerships. https://www.globalcodeofconduct.org/the-code/
63. Adams V. Metrics: what counts in global health. Durham: Duke University Press; 2016.
65. Oxfam. Sick development: how rich-country government and World Bank funding to for-profit private hospitals causes harm, and why it should be stopped; 2023 [cited 2023 Sept 28]. https://oxfamilibrary.openrepository.com/bitstream/handle/10546/621529/bp-sick-development-funding-for-profit-private-hospitals-260623-en.pdf?sequence=14
72. Commission on Health Research for Development. Health research: essential link to equity in development; 1990 [cited 2023 Sept 28]. http://www.cohred.org/downloads/open_archive/ComReports_0.pdf
73. WHO Council on the Economics of Health for All [Internet]. Health for all: transforming economies to deliver what matters: Final report; 2023 [cited 2023 Sept 28]. https://cdn.who.int/media/docs/default-source/council-on-the-economics-of-health-for-all/council-eh4a_finalreport_web.pdf?sfvrsn=a6505c22_5&download=true
75. WHO. WHO Global Code of Practice on the International Recruitment of Health Personnel; 2010 [cited 2023 Sept 28]. https://cdn.who.int/media/docs/default-source/health-workforce/migration-code/code_en.pdf?sfvrsn=367f7d35_7&download=true

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Rewiring the Brain: Poverty Linked With Neurological Changes That Affect Behavior, Illness, and Development

By De Gruyter April 24, 2024

A new review highlights how poverty and low socioeconomic status significantly influence cognitive development, mental health, and educational outcomes, suggesting that these factors contribute to a cycle of generational poverty. It calls for comprehensive interventions to address these far-reaching impacts.

Research connects low socioeconomic status to brain alterations impacting educational achievement, mental health, and language development.

What influences mental health, academic achievement, and cognitive growth? A recent review published in De Gruyter’s Reviews in the Neurosciences indicates that poverty and low socioeconomic status (SES) are significant contributing factors. While previous research has explored the individual impacts of poverty on the brain and behavior, this review introduces the first integrated framework. It synthesizes evidence from various studies to directly connect brain alterations caused by low SES with behavioral, pathological, and developmental outcomes.

SES refers to the social standing of an individual or family, and involves factors such as wealth, occupation, educational attainment, and living conditions. As well as affecting day-to-day life, perhaps surprisingly SES can also have far-reaching consequences for our brains that begin in childhood and persist into adulthood.

So, how can poverty and low SES change the brain? The review examines the negative effects of poor nutrition, chronic stress, and environmental hazards (such as pollution and inadequate housing conditions), which are more likely to affect low-SES families. These factors can impair the brain development of children, which in turn can influence their language skills, educational attainment, and risk of psychiatric illness.

Stress and Its Impact on Learning

For instance, families with low SES are more likely to experience increased stress levels, and these can affect their children from an early age. Sustained stress can reduce levels of neurogenesis — the growth of new neurons — in the hippocampus, which may impair learning abilities and negatively affect educational attainment and career opportunities in later life.

Integrative Framework of the Links Between Brain and Behavioral Abnormalities Due to Poverty

A framework of poverty-related factors and future consequences, such as delay language development, poor educational attainment, and neural abnormalities. Credit: Eid Abo Hamza et al./De Gruyter

The unified framework proposed by the researchers also helps to explain generational poverty, which can leave the children of SES families unable to escape their situation when they grow up and become parents themselves. This vicious cycle can be hard to break.

Interestingly, the researchers provide an extensive list of proposed studies that could test the validity of their framework and find new ways to break the generational poverty cycle. These include focusing on the effects of low SES in specific brain regions, and identifying techniques to enhance the performance of affected children in school.

The review is timely, as inequalities in society widen. Identifying specific mechanisms behind generational poverty could help researchers and policymakers to develop new early interventions. The new framework takes account of the multifactorial nature of generational poverty, and could pave the way for more holistic and sophisticated societal interventions that acknowledge this complexity.

“This research sheds light on the profound ways in which poverty and SES affect not just the present living conditions of individuals, but also their cognitive development, mental health, and future opportunities,” said Dr. Eid Abo Hamza of Al Ain University in the United Arab Emirates, who is first author of the review. “By understanding these relationships, society can better address inequalities and support those in disadvantaged situations, potentially leading to interventions that can help break the cycle of poverty.”

Reference: “The impact of poverty and socioeconomic status on brain, behaviour, and development: a unified framework” by Eid Abo Hamza, Richard Tindle, Simon Pawlak, Dalia Bedewy and Ahmed A. Moustafa, 15 April 2024, Reviews in the Neurosciences . DOI: 10.1515/revneuro-2023-0163

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Understanding behaviour change in relation to agroecological transition: A novel approach

From The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT)
Published on 19.04.24
Challenges Nutrition, health & food security

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A systems perspective is crucial to understanding behavior change processes in the agroecological transformation of food systems. Systems-level frameworks around behavior in agricultural systems are commonly used to assess the uptake of agricultural innovations, and in doing so, acknowledge the role of individual demographics and economic and governance institutions in shaping farmers’, fishers’, and pastoralists’ technology use decisions. While some behavior change frameworks further acknowledge the role of social networks in transmitting agricultural knowledge and innovations, they rarely account for wider social and relational structures that govern individual and collective behaviors, and are thus relatively gender-blind. Social and relational factors are especially relevant to understanding behavior change among women and marginalized groups whose opportunity spaces are often constrained by social norms and unequal power relations. Building on the social-ecological systems framework for resource governance, we offer a conceptual framework for analyzing behavior change for agroecological transformation in several countries engaged in the CGIAR Agroecological Initiative. The framework integrates concepts related to social relations, behavioral norms, power, agency, and opportunity spaces with those from agricultural innovation systems and social and behavior change research. We thereby propose a systematic means of integrating gender relations and equity concerns into behavioral analysis in the context of food systems transformation.

Rietveld, A.; Freed, S.; Voss, R.C.; Falk, T.

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PSYCH 424 blog

Challenges and Strategies in Changing Health Behaviors: A Personal Journey Through Kinetics

Embarking on a journey to enhance our health behaviors can be a daunting and unfamiliar path with psychological barriers that seem almost impossible to achieve at times. However, by leveraging insights from established theories like the Health Belief Model (HB<) and the Theory of Planned Behavior (TPB), we can discover effective strategies for initiating and maintain health behavior changes. These models clarify how our personal beliefs about health risks and the potential benefits of change, combined with social influences and our perceived control over behaviors, play critical roles in shaping our health-related decisions (Gruman et al., 2016).

This semester’s venture into a kinetics class for stress management was a leap into the unknown. With no clear expectations (and a decent injury), I found myself navigating through the complexities of managing anxiety with exercise. According to the Health Belief Model, my recognition of stress’s severe potential repercussions, along with a very stressful few months, motivated me to adopt effective coping mechanisms (Gruman et al., 2016). The class provided structure opportunities to explore these mechanisms, fundamentally reshaping my daily routine and approach to stress management.

The transition into new health behaviors was anything but smooth. The Theory of Planned Behavior, highlights that our actions are influenced by our attitudes towards the behavior, the social norms surrounding us, and our perceived ability to effect change (Gruman et al., 2016). Initially hesitant, I found solace and encouragement in the supportive social setting of the class. Keeping a mandatory stress log because a pivotal strategy, allowing me to pinpoint and systematically address my anxiety triggers. This reflective practice reinforced my behavioral changes, mirroring the TPB’s emphasis on the importance of intentionally and planned action in overcoming habitual barriers.

Beyond the physical activity, this class reacquainted me with the power of visualization techniques. This mental practice became a cornerstone of my anxiety management, enhancing my physical activities and provide a robust mental health boost. This synergy between mental and physical health exercises underscored the interconnected nature of our well-being, illustrating how visualization can act as a powerful too in our mental health toolkit (Gruman et al., 2016).

One of the most transformative revelations was learning to harness anxiety as a driving force rather than a hinderance. This paradigm shift, supported by the Health Belief Model’s focus on recognizing the benefits of behavior change, allowed me to view exercise as a powerful and empowering tool rather than a burdensome chore. It catalyzed a positive feedback loop where the more I moved, the more control I felt over my well-being. This highlights the profound impact of mindset in the realm of health behavior change (Gruman et al., 2016).

Navigating through kinetics class was a vivid, practical application of Health Behavior Model and the Theory of Planned Behavior. It proved that with the right psychological tools and a bit of determination, transforming health behaviors is not only feasible but also enriching. These strategies and insights gained from this class have armed me with the resilience and knowledge to sustain healthier habits, affirming the incredible potential for personal growth and change when we align our mindset and our methods.

Gruman, J. A., Schneider, F. W., & Coutts, L. M. (Eds.). (2016). Applied social psychology: Understanding and addressing social and practical problems. SAGE Publications, Incorporated.

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Feasibility of a quality-improvement program based on routinely collected health outcomes in Dutch primary care physical therapist practice: a mixed-methods study

LSF Smeekens 1 ,
AC Verburg 1 ,
MJM Maas 1 , 2 ,
R van Heerde 1 ,
A van Kerkhof 3 &
PJ van der Wees 1

BMC Health Services Research volume 24 , Article number: 509 ( 2024 ) Cite this article

Metrics details

This study evaluates the feasibility of a nine-month advanced quality-improvement program aimed at enhancing the quality of care provided by primary care physical therapists in the Netherlands. The evaluation is based on routinely collected health outcomes of patients with nonspecific low back pain, assessing three feasibility domains: (1) appropriateness, feasibility, and acceptability for quality-improvement purposes; (2) impact on clinical performance; and (3) impact on learning and behavioral change.

A mixed-methods quality-improvement study using a concurrent triangulation design was conducted in primary care physical therapist practice. Feedback reports on the processes and outcomes of care, peer assessment, and self-assessment were used in a Plan-Do-Study-Act cycle based on self-selected goals. The program’s appropriateness, feasibility, and acceptability, as well as the impact on clinical performance, were evaluated using the Intervention Appropriate Measure, Feasibility Intervention Measure, Acceptability Intervention Measure (for these three measure, possible scores range from 4 to 20), and with a self-assessment of clinical performance (scored 0–10), respectively. The impact on learning and behavioral change was evaluated qualitatively with a directed content analysis.

Ten physical therapists from two practices participated in this study. They rated the program with a mean of 16.5 (SD 1.9) for appropriateness, 17.1 (SD 2.2) for feasibility, and 16.4 (SD 1.5) for acceptability. Participants gave their development in clinical performance a mean score of 6.7 (SD 1.8). Participants became aware of the potential value of using outcome data and gained insight into their own routines and motivations. They changed their data collection routines, implemented data in their routine practice, and explored the impact on their clinical behavior.

Conclusions

This explorative study demonstrated that a quality-improvement program, using health outcomes from a national registry, is judged to be feasible.

Impact statement

This study provides preliminary evidence on how physical therapists may use health outcomes to improve their quality, which can be further used in initiatives to improve outcome-based care in primary physical therapy.

Peer Review reports

High-quality health care is defined as care that is safe, timely, equitable, effective, efficient, and patient centered [ 1 ]. Against a background of rapidly increasing healthcare costs, service restrictions, and differences in quality, there is an increasing need for initiatives to improve quality of care [ 2 ]. This has led the Royal Dutch Society for Physical Therapy (KNGF) to initiate the ‘Quality in Motion’ program, which aims to improve the effectiveness and patient centeredness of care in physical therapist practice by providing therapists with feedback on health outcomes [ 3 ]. Outcome measures include patient-reported outcomes (PROs), which are used to assess aspects of a patient’s health status coming directly from the patient. Patient-reported outcome measures (PROMs) are questionnaires or single-item scales used to assess PROs [ 4 ], and can be used to support quality improvement [ 3 ]; however, there is a clear lack of understanding about how physical therapists can best utilize feedback about PROs to improve quality of care [ 5 , 6 , 7 ].

Nonspecific low back pain (NSLBP) is one of the most common health conditions in primary physical therapist practice [ 8 , 9 ]. Based on health outcomes from a clinical registry and consensus among stakeholders (i.e., physical therapists, researchers, patients, and health insurers), Verburg et al. [ 4 ] developed a core set of PRO-based quality indicators for patients with NSLBP in primary physical therapist practice. The set was found to be useful for quality-improvement initiatives, and stakeholders reported that it added value for routine practice [ 3 , 4 ]. These outcomes can be aggregated across patients in clinical registries, providing data for managing clinical quality, benchmarking and public reporting across organizations, and in clinical research; however, their aggregated use for quality improvement was found to be suboptimal [ 10 , 11 , 12 ]. An earlier study found that electronic health record (EHR) compatibility and therapist knowledge of the PROMs are the two key barriers to wider PROM use [ 13 ], with similar issues reported in other professions [ 14 , 15 ].

Feedback interventions, particularly when provided by a colleague both verbally and in writing [ 16 ], have shown promise in improving physical therapist practice [ 17 , 18 ]. Correspondingly, feedback reporting on processes and outcomes of care has been identified as an effective intervention that can support the exchange of best practices and mutual learning [ 16 , 18 , 19 ]. Additionally, involving peers as feedback providers in peer assessment creates meaningful learning experiences and is associated with behavioral change and measurable performance improvement in healthcare professionals [ 20 , 21 , 22 ]. Maas et al. [ 23 ] showed that peer assessment using video recordings of client communication and clinical records is an effective feedback intervention method in enhancing commitment to change and improving the clinical performance of physical therapists. Furthermore, feedback interventions seem to be more effective in changing clinical behavior when including clear targets and an action plan [ 16 ]. Accordingly, the Plan-Do-Study-Act facilitates systematic testing of changes in real-world settings, allowing for rapid learning and adaptation. This approach has been effectively utilized in various healthcare studies to enhance clinical outcomes and process efficiencies [ 24 ]; however, most physical therapists are not familiar with such quality-improvement interventions based on health outcomes [ 25 ].

The aim of this study is therefore to evaluate the feasibility of an advanced quality-improvement program for physical therapists in primary care. The evaluation involves feedback, peer assessment, and self-assessment in a rapid improvement Plan-Do-Study-Act cycle, using the routinely collected health outcome data of patients with NSLBP.

Study design and setting

The program feasibility was evaluated through an explorative quality-improvement study using a mixed-methods approach in a concurrent triangulation design [ 26 ]. The following program feasibility domains were addressed [ 27 ]: (1) appropriateness, feasibility, and acceptability for quality-improvement; (2) impact on clinical performance; and (3) impact on learning and behavioral change. We used the Standards for QUality Improvement Reporting Excellence (SQUIRE) Guidelines [ 28 ]. The evaluation was conducted between January and October 2022. We tested our program in a convenience sample of Dutch primary care physical therapists organized in a regional network of communities of practice (the Cooperation of Physical Therapists Nijmegen; CFN).

Participants

All physical therapy practices within the CFN network ( n = 30) were approached to recruit therapists for the study. Invitations were extended via a digital newsletter, which included the goals of the study and contact details of the first author (LS). Physical therapists willing to participate received detailed study information by email and were screened for eligibility using the inclusion criteria below. Participation was voluntary. All participants provided written informed consent.

Inclusion criteria

Licensed Dutch physical therapists were eligible to participate in this study if they provided primary care to patients with NSLBP aged 18 years or older [ 3 , 4 ]. They also had to evaluate selected outcomes as part of a standard clinical routine in patients with NSLBP using the following PROMs (associated domain): Numeric Pain Rating Scale (NPRS) (pain intensity), Patient Specific Functioning Scale (PSFS) (physical activity), Quebec Pain Disability Scale (QBPDS) (physical functioning), Global Perceived Effect (GPE-DV) (perceived treatment effect), and STarT Back Screening Tool (SBST) (profile grouping based on risk of poor outcome) [ 3 , 4 ]. Physical therapists collected outcomes using these PROMs, which were directly recorded into their EHRs. These data were transferred to the national data registry of the Royal Dutch Society for Physical Therapy (KNGF). Additionally, to facilitate meaningful participation in the quality-improvement program, particularly during peer assessment sessions and outcome discussions, it was essential for participants to have contributed sufficient data to the national clinical registry from January 2021 to November 2021 (a minimum requirement of five patients with a closed treatment episode). An episode was considered closed when the physical therapist closed the episode in the EHR, or if six weeks had passed after the last visit. Informed consent for delivering data to the national clinical registry was obtained from every patient. This approach ensured that participants could engage with actual data reflective of their clinical practices rather than hypothetical scenarios, fostering deeper learning and reflection on professional conduct and patient care. The requirement for therapists to have already been actively collecting and submitting data as part of their clinical routine underlines the study’s aim to engage therapists who were not only familiar with the use of PROMs, but who also had sufficient data to enable a meaningful analysis and discussion within the context of the quality-improvement program.

The quality-improvement program content

The nine-month program consisted of a rapid improvement cycle comprising multiple consecutive steps and quality-improvement interventions. In step 1, participants were offered the opportunity to complete an e-learning module on using data in clinical practice [ 29 ]. In step 2, personal data exports were extracted from the national clinical registry. Participants received feedback reports on the processes and outcomes of their care in step 3 [ 30 , 31 , 32 ], then attended peer assessment meetings in step 4 [ 18 , 23 , 33 ], In step 4, the therapists drafted a rapid improvement Plan-Do-Study-Act cycle and individual quality-improvement goals [ 6 , 34 , 35 ], and in step 5, they performed a self-assessment of their clinical performance [ 36 ]. See Additional File 1 for further details of the program. The process and outcome indicators of the PROMs for patients with NSLBP were used in the program (see Additional File 2 ) [ 3 , 4 ].

Evaluation of program feasibility and outcome measures

The program’s perceived appropriateness, acceptability, and feasibility for quality-improvement purposes were evaluated using the Dutch versions of the Intervention Appropriate Measure (IAM), the Feasibility Intervention Measure (FIM), and the Acceptability Intervention Measure (AIM), respectively [ 37 ], which have been demonstrated to be valid and reliable tools [ 37 ]. Each measure consists of four items scored on a five-point Likert scale, with higher scores indicating better appropriateness, acceptability, and feasibility, respectively (scoring range: 4–20 for each tool). The impact on clinical performance was evaluated using self-assessment checklists [ 36 ] (steps 5 and 7 of the quality-improvement program), while the impact on learning and behavioral change was qualitatively determined during the peer assessment (steps 4 and 6). We used a parallel approach in collecting the quantitative and qualitative data, giving equal weight to both methods.

Data collection

Participants were invited by email to attend the peer assessment meetings. A script (see Additional Files 5 and 6 ) for each meeting was designed by the research team, addressing different quality-improvement interventions. A participatory evaluation strategy was used, allowing an assessment of the impact of the program on learning and behavioral change during the actual implementation [ 38 ]. The peer assessment meetings lasted 100–120 min and were conducted face-to-face by an external coach (RvH) using open-ended questions, which facilitated group discussion and knowledge development. A safe environment was encouraged within each peer group [ 20 , 22 ]. The peer assessment meetings were audio-taped, video-recorded, and subsequently transcribed verbatim. Written informed consent was obtained from all participants. The identities of the participants were considered confidential; therefore, the transcripts of the meetings were processed anonymously. Participants were asked to complete a self-assessment checklist halfway through the program, at the end, and six months after via email. Likewise, participants completed the IAM, FIM, and AIM at the end of the study, following the second peer assessment meeting.

Data analysis

Quantitative analysis.

The mean scores and standard deviations (SDs) of the IAM, FIM, and IAM were calculated. For the quality-improvement program to be considered appropriate, feasible, and acceptable [ 37 ], a minimum mean score of 15 out of 20, averaged over all participants, was required for each measure. The mean scores and SDs were calculated separately for the self-assessment checklists at three timepoints. For the quality-improvement program to be considered to impact the development of clinical performance, a minimum mean score of 5 out of 10 was required [ 36 ], averaged over all competed self-assessment checklists. Our comparative analysis focused on the mean scores and differences in process and outcome indicators between two periods: the pre-improvement period (the 12 months before the start of the study) and the quality-improvement period (the nine months after the study began). The latter period integrates data from both the initial and subsequent phases of the quality-improvement program, reflecting insights consolidated from the two feedback reports received by the participants during the program (Fig. 1 ). Our analysis focused exclusively on complete case episodes with both baseline and endpoint measurements to ensure the integrity and applicability of the data for participation in the quality-improvement program. All quantitative data were analyzed using SPSS Statistics, version 26 (IBM, Armonk, New York, USA).

The structure of the quality-improvement (QI) program QI = quality improvement; PDSA = Plan-Do-Study-Act

Qualitative analysis

Transcripts of the peer assessment meetings were read in detail, and a directed content analysis was used to study them [ 39 , 40 ]. A codebook was developed in advance, informed by the research questions. Text fragments were labeled according to these a priori codes, which were further refined during the coding process. Meaningful text fragments that could not be labeled were coded inductively. The transcript analysis was supported by ATLAS.ti version 8.4 [ 41 ]. Two researchers (LS and AvK) independently coded the transcripts, discussed the codes to reach consensus, and created the codebook, allocating codes into categories based on their similarities [ 42 ]. A researcher (MM) with ample experience in peer assessment and qualitative research guided this process. Kirkpatrick’s model, which was designed to evaluate the impact of an educational program, was used to allocate the identified categories to four domains: reaction, learning, behavior, and results (see Additional File 7 ) [ 43 ]. Preliminary findings after both peer assessment meetings and the final codes, categories, and the allocation of categories to the domains were discussed by the research team (LS, MM, RvH, AV, and PvdW) in several meetings. A member checking procedure was conducted by sending a summary with preliminary results to all participants after the first meeting to increase the credibility of the results. To optimize the transferability of the results, we aimed to saturate the information by recruiting at least three peer groups.

In total, 10 physical therapists from two different practices participated in the program. Two mixed-practice peer groups were formed, each consisting of five participants. The participants’ characteristics are outlined in Table 1 .

Quantitative results

Table 2 provides an overview of the appropriateness, feasibility, and acceptability of the program, as well as the perceived development in clinical performance. All predefined criteria regarding the minimum score on the IAM, FIM, AIM, and the self-assessment checklists were met.

The mean process and outcome indicator scores for the three data periods are compared in Table 3 . All process indicators improved substantially during and after the quality-improvement cycle, with mean improvements ranging from 9 to 26%.

Qualitative results

We conducted four peer assessment meetings, two for each peer group. After analyzing the qualitative data, the codes were classified into eight major categories. These categories were allocated to the four domains of Kirkpatrick’s model of evaluation (see Table 4 ). Quotes are numbered and labeled by peer group (see Table 5 ).

Domsain: reaction

Program appreciation; suggestions for program improvement.

Participating in a quality-improvement program based on routinely collected health outcomes was novel for most participants. In general, the therapists considered the program’s content meaningful, pleasant, acceptable, and accessible (Q1-G2), and proposed several advancements to increase future program experiences and satisfaction (Q2-G1)(Q3-G1).

Domain: learning

Awareness and insight.

Most participants became more aware of the existing data and the possibilities for analyzing and comparing them. They developed an understanding of the clinical relevance of the data presented, and identified possible explanatory factors by interpreting and clarifying the data (Q4-G1). Participants also gained insight into how to appropriately design data collection, the importance of proper data collection methods (Q5-G1), and potential areas for implementing data in routine practice.

Participants became more aware of data collection throughout the quality-improvement cycle, but acknowledged the lack of a standardized, valid, and reliable data collection method (Q6-G1). Before the quality-improvement program, most participants did not routinely use data to guide and improve their practice, despite dedicating considerable effort to its collection (Q7-G1).

The evolving knowledge gained from the quality-improvement cycle led participants to realize that routinely implementing data can enhance their clinical practice, and more importantly can significantly benefit patients (Q8-G2). Some participants openly argued that using data will not improve the quality of their physical therapy. They challenged the perceived value of the data in comparison with their own expertise and discussed the required time investment in relation to the perceived returns.

Motivational change

Collecting and using data with the objective of improving quality of care for the patient was not a common mindset among participants. Instead, data collection was performed to meet obligatory external requirements and was not considered a priority. However, as the quality-improvement cycle continued, most participants reported a shift to more intrinsically motivated efforts for collecting data (Q9-G2).

Domain: Behavior

Intentions for behavioral change.

Participants were encouraged to reflect on their own clinical behavior and reported feeling motivated to change their routine practice. All participants planned to improve their process indicators and data collection routines, particularly by allowing patients to complete their own questionnaires. Some participants proposed integrating the data into their practice and investigating its impact on their clinical behavior (Q10-G2).

Demonstrated behavioral change

All participants revised the extent and approach of their data collection. Most participants successfully applied some form of data use in routine practice, such as to evaluate treatment progress, to guide treatment and decision-making processes, as input for taking patient histories, for patient empowerment, for goal setting with the patient, and to complement or contradict their own assumptions (Q11-G2). Although they changed their data collection routines, two participants admitted they still rarely used data to support their clinical behavior (Q12-G1).

Barriers to and facilitators of behavioral change

Participants identified several barriers and facilitators that hindered or helped them to achieve their intended behavioral changes (Q13-G2)(Q14-G2)(Q15-G1) (see Table 6 ). These factors impacted the quantity of data collected, influenced the data collection protocols used, and shaped efforts to integrate data into routine practice.

Domain: results

Goal attainment.

The majority of participants set goals related to processes and collection routines. Seven of the 10 participants accomplished their personal targets regarding improving process indicators (Q16-G2). All participants achieved their objectives around changing data collection routines. One participant openly debated the benefit of goal attainment on the added value and quality of care for the patient (Q17-G1).

This study explored the feasibility of a quality-improvement program designed to enhance the quality of primary care physical therapists. The program uses health outcomes from a national registry and incorporates feedback, peer assessment, and self-assessment in a Plan-Do-Study-Act cycle. We found that the participants considered the program an appropriate, feasible, and acceptable intervention for quality-improvement purposes, and found it beneficial for improving their clinical performance. All participants improved the completeness of the data they collected. They also gained insights into the potential value of using outcome data in clinical practice, as well as in examining their routines and motivation. Participants recognized the importance of handling data, revised their data collection methods, began to implement data use into their routine practice, and observed the impact on their clinical behavior. They acknowledged the added value of using data when formulating clear treatment targets, monitoring treatment processes, motivating patients, and, on an aggregated level, improving the quality of care. While most participants reacted positively to the program and acknowledged its added value, they faced significant challenges, such as the complexity of integrating systematic data collection into daily practice, external pressures to meet specific outcome benchmarks, and the need for more knowledge and skills in data interpretation and application. These factors sometimes hindered the full realization of the program’s benefits and highlighted areas for improvement that should be addressed to improve the program before wider implementation.

Comparison to similar studies

This study builds upon previous research that highlighted the potential value of outcome data in quality-improvement initiatives [ 4 , 24 ]. When evaluating the potential value of feedback, peer assessment, self-assessment, and Plan-Do-Study-Act cycles in physical therapist care, most previous studies did not use aggregated real-world data from clinical registries. Maas et al. [ 23 ] and Steenbruggen et al. [ 36 ] incorporated feedback, peer assessment, and self-assessment in comprehensive quality-improvement programs aimed at the professional development of physical therapists, using client records, video recordings of client communication, and the tracer methodology, respectively. Both programs were found to be feasible and led to improvements in clinical performance [ 23 , 36 ]. The results of the present study support and extend previous findings of these quality-improvement strategies in physical therapist practice.

During the initial peer assessment meeting, the participants gained new knowledge and became more conscious of their own behavior. These findings are consistent with previous research indicating that peer assessment promotes learning, increases self-awareness [ 22 , 44 ], and builds self-concept [ 45 , 46 ]. Additionally, participants developed a critical perspective regarding their daily routines and expressed a desire to change their behavior. A similar enhanced commitment to change was reported by physical therapists who underwent cycles of peer assessment and self-assessment [ 23 ]. These findings are in line with theories of health behavior, which suggest that all behavioral change begins with recognizing one’s own behavior [ 47 ], and with the intention to change [ 48 ].

Another important finding was the observed shift in motivation for collecting data. Prior to the quality-improvement program, data were often collected in a non-validated manner, driven by external factors such as health insurers, and were not used to improve patient care. This is consistent with previous findings that the use of feedback in quality improvement is hindered by a perceived political motive for public reporting rather than improved patient care [ 7 ], by financial incentives from health insurers [ 49 ], and by a lack of experience and skills [ 7 , 50 ]. Instead of collecting data to meet an external goal, most participants moving along the quality-improvement cycle reported a shift to a more intrinsic motivation. This could be attributed to participants giving new meaning to collecting and handling data in their daily practice, and establishing their own personal values. These findings are consistent with Ryan and Deci’s self-determination theory, which states that the basis for intrinsic motivation and behavior is formed by people finding a rationale within themselves [ 51 ]. Indeed, participants in the current study emphasized the importance of having clear self-directed motives for data collection as a key driver of behavioral change. Consistent with this, healthcare providers previously reported being more likely to take steps for quality improvement in response to the feedback of aggregated PROMs if they perceived these data to be credible and beneficial for improving patient care [ 19 ]. Throughout the quality-improvement cycle, learning and understanding of data management continued to be developed through experience and reflection, in line with Dewey’s experiential learning theory [ 52 ].

All participants made self-initiated behavioral changes during the program, which was believed to be supported by the application of knowledge gained by following the Plan-Do-Study-Act cycle [ 24 ]. Setting specific targets and making an action plan may increase the effectiveness of feedback and facilitate behavioral changes [ 18 ]. In the present study, feedback was provided by a colleague, more than once, both verbally and in writing to further increase its effectiveness. The participants were largely successful in changing their data-collection procedures; however, there is still room for improvement in the use of data in routine daily practice. Previous studies have shown that clinicians find PROMs useful for supporting the therapeutic process [ 19 ]; however, it took more time or effort to develop these application skills than was available within the timespan of the program. This assumption is supported by the feedback intervention theory, which assumes that the effectiveness of feedback is lower when the ‘task novelty’ and ‘task complexity’ are higher [ 53 ]. Indeed, participants mentioned a lack of knowledge and skills regarding data application as important barriers to its use. Feeling competent is very important for accomplishing behavioral change, according to the self-determination theory [ 51 ]. Correspondingly, previous research indicated that healthcare providers need more support and guidance on how to structurally implement data into their daily practice [ 19 ].

Strengths and limitations

In this explorative study, an innovative theory- and evidence-based quality-improvement program was developed and implemented in daily physical therapy practice. Integrating multiple proven quality-improvement interventions, combined and informed by outcome data, clearly contributed to the inventive character of this program. Using a participatory strategy for the evaluation of program feasibility during the implementation enhanced the evaluation relevance, as well as providing valuable information regarding the program’s beneficial features and suggestions for improvements from the direct perspectives of the intended end-users. Using both qualitative and quantitative data in a concurrent triangulation design also contributed to the rigor of this study.

This study has several limitations. First, although we intended to include three peer groups for data saturation, only two were ultimately recruited. This could have impacted the validity and transferability of the results. Despite this, the two peer groups provided us with rich data that were deemed sufficient for program evaluation and feasibility study purposes [ 54 ]. Second, the peer groups were comprised of physical therapists selected based on the amount of data they collected. As all participants needed to meet external requirements regarding data collection, they could be seen as early adopters. The voluntary participation and external motivation of the participants may have influenced the results and may limit generalizability to other physical therapists. Third, indicative of its exploratory nature, the study’s sample size was limited, but was deemed sufficient to address our research questions. Additionally, the gender distribution among participants, with nine out of 10 being male, does not reflect the typical gender distribution in primary care physiotherapy in the Netherlands. This discrepancy was unintentional, emerging from the recruitment process, but could nevertheless constitute a selection bias, and underscores the need for caution when generalizing findings across diverse physiotherapy contexts. Lastly, although the coach promoted a safe environment during the group meetings, they were not anonymous, and participants may have felt unable to talk openly. Alongside the fact that the assessments could not be blinded, this may have introduced social desirability bias.

Implications for research and practice

Our findings can be used by national physical therapist bodies and other stakeholders in the field to develop initiatives for improving outcome-based care. This program is well suited for use in primary physical therapy care as it integrates with the peer assessment methodology commonly used in many practices. Such integration minimizes the opportunity costs usually associated with new initiatives by leveraging existing peer-learning and feedback structures, making it a feasible and cost-effective strategy for quality improvement [ 55 ]. Additionally, recommendations for advancing the national clinical data registry may further improve the usability for end-users and future researchers, who may wish to study whether the findings are also generalizable to other primary care physical therapist practices. In this study, feedback reporting appeared to support the establishment of quality-improvement goals, and future research could investigate the value of these strategies in evaluating results and changing clinical practices. The sustainability of the observed participant’s behavioral changes and their translation of their revised data-collection routines into quality improvements in care require further consideration. Future studies could improve the program’s feasibility by directly addressing the identified facilitators. Additionally, the program’s impact on patient outcomes should be explored in a full-scale study with long-term follow up.

This explorative study demonstrated that a quality-improvement program incorporating feedback, peer assessment, and self-assessment in a Plan-Do-Study-Act cycle, and using health outcomes from a national registry, was deemed feasible for quality improvement. The implementation of the program led to knowledge development, perceived improvements in clinical performance, and a change in the behavior of the physical therapists regarding data handling in their routine practice.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Acceptability Intervention Measure

Cooperation of Physical Therapists Nijmegen

Electronic health system

Feasibility Intervention Measure

Global Perceived Effect

Intervention Appropriate Measure

Royal Dutch Society for Physical Therapy

Nonspecific low back pain

Numeric Pain Rating Scale

Patient-reported outcome measures

Patient-reported outcomes

Patient Specific Functioning Scale

Quebec Pain Disability Scale

STarT Back Screening Tool

Standard deviation

QUality Improvement Reporting Excellence

Spath P, Kelly DL. Applying quality management in healthcare: a systems approach. Health Administration Press Chicago; 2017.

Porter ME. Value-based Health care Delivery. 2008;248:503–9.

Google Scholar

Meerhoff GA, Verburg AC, Schapendonk RM, Cruijsberg J, Nijhuis-van der Sanden MW, van Dulmen SA et al. Reliability, validity and discriminability of patient reported outcomes for non-specific low back pain in a nationwide physical therapy registry: a retrospective observational cohort study 2021;16:e0251892.

Verburg AC, van Dulmen SA, Kiers H, Nijhuis-van der Sanden MW, van der Wees PJ. Patient-reported outcome-based quality indicators in Dutch primary care physical therapy for patients with nonspecific low back Pain. A Cohort Study; 2021.

Swinkels RA, Meerhoff GM, Custers JW, van Peppen RP, Beurskens AJ, Wittink H. Using outcome measures in daily practice: development and evaluation of an implementation strategy for physiotherapists in. Neth. 2015;67:357–64.

van Dulmen S, van der Wees P. Eindrapport toepassing van minimale dataset en kwaliteitsindicatoren voor lage rugklachten in de fysiotherapie n.d.

Greenhalgh J, Dalkin S, Gibbons E, Wright J, Valderas JM, Meads D et al. How do aggregated patient-reported outcome measures data stimulate health care improvement? A realist synthesis. 2018;23(1):57–65.

Koes BW, Van Tulder M, Lin C-WC, Macedo LG, McAuley J, Maher C. An updated overview of clinical guidelines for the management of non-specific low back pain in primary care. 2010;19(12):2075–94.

Koninklijk Nederlands Genootschap voor Fysiotherapie (KNGF). / vereniging van Oefentherapeuten Cesar en Mensendieck (VvOCM). KNGF-richtlijn Lage rugpijn en lumbosacraal radiculair syndroom. Amersfoort/Utrecht: KNGF/VvoCM; 2021. n.d.

Copeland JM, Taylor WJ, Dean SG. Factors influencing the use of outcome measures for patients with low back pain: a survey of New. Z Phys Therapists. 2008;88(12):1492–505.

Jette DU, Halbert J, Iverson C, Miceli E, Shah P. Use of standardized outcome measures. Phys Therapist Practice: Perceptions Appl. 2009;89(2):125–35.

van Dulmen SA, van der Wees PJ, Staal JB, Braspenning J, Nijhuis-van der Sanden MW. Patient reported outcome measures (PROMs) for goalsetting and outcome measurement in primary care physiotherapy. Explorative Field Study. 2017;103:66–72.

Meerhoff GA, van Dulmen SA, Cruijsberg JK, Nijhuis-van der Sanden MW, Van der Wees PJ. Which factors influence the use of patient-reported outcome measures in Dutch physiotherapy practice? A cross-sectional study. 2020;72(1):63–70.

Irizarry T, DeVito Dabbs A, Curran CR. Patient portals and patient engagement: a state of the science review. 2015;17(6):e148.

Sorondo B, Allen A, Bayleran J, Doore S, Fathima S, Sabbagh I et al. Using a patient portal to transmit patient reported health information into the electronic record: workflow implications and user experience. 2016;4(3).

Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD et al. Audit and feedback: effects on professional practice and healthcare outcomes. 2012.

van der Wees P, van der Vleuten C. Marjo Maas Ria Nijhuis–van der Sanden Geert Rutten Yvonne Heerkens n.d.:187.

Gibbons C, Porter I, Gonçalves-Bradley DC, Stoilov S, Ricci-Cabello I, Tsangaris E et al. Routine provision of feedback from patient-reported outcome measurements to healthcare providers and patients in clinical practice 2021.

Greenhalgh J, Dalkin S, Gooding K, Gibbons E, Wright J, Meads D et al. Functionality and feedback: a realist synthesis of the collation, interpretation and utilisation of patient-reported outcome measures data to improve patient care. 2017;5:1–280.

Maas MJ, Nijhuis-van der Sanden MW, Driehuis F, Heerkens YF, van der Vleuten CP, van der Wees PJ. Feasibility of peer assessment and clinical audit to self-regulate the quality of physiotherapy services: a mixed methods study 2017;7:e013726.

Maas MJ, van der Wees PJ, Braam C, Koetsenruijter J, Heerkens YF, van der Vleuten CP et al. An innovative peer assessment approach to enhance guideline adherence in physical therapy: single-masked, cluster-randomized controlled trial. 2015;95(4):600–12.

Phillips Fox D. Peer review of health care professionals: a systematic review of the literature. Melbourne. 2009. https://www.safetyandquality.gov.au/wp-content/uploads/2012/01/25738-LitReview.pdf (accessed June 2022).

Maas MJ, Driehuis F, Meerhoff GA, Heerkens YF, van der Vleuten CP, Nijhuis-van der Sanden MW et al. Impact of self-and peer assessment on the clinical performance of physiotherapists in primary care: a cohort study 2018;70:393–401.

Kampstra NA, Zipfel N, van der Nat PB, Westert GP, van der Wees PJ, Groenewoud AS. Health outcomes measurement and organizational readiness support quality improvement: a. Syst Rev. 2018;18:1–14.

McDonald PL, Foley TJ, Verheij R, Braithwaite J, Rubin J, Harwood K et al. Data to knowledge to improvement: creating the learning health system. 2024;384.

Creswell JW, Plano Clark VL, Gutmann ML, Hanson WE. An expanded typology for classifying mixed methods research into designs 2003:209–40.

Bowen DJ, Kreuter M, Spring B, Cofta-Woerpel L, Linnan L, Weiner D, et al. How we Des Feasibility Stud. 2009;36:452–7.

Ogrinc G, Armstrong GE, Dolansky MA, Singh MK, Davies L. SQUIRE-EDU (standards for QUality Improvement Reporting Excellence in Education): publication guidelines for educational improvement. 2019;94(10):1461.

https://www.npi.nl/cursussen-en-opleidingen/e-learning-en-toetsen/13025-e-learning-hoe-gebruik-ik-data-in-de-praktijk . n.d.

Krägeloh CU, Czuba KJ, Billington DR, Kersten P, Siegert RJ. Using feedback from patient-reported outcome measures in mental health services: a scoping study and typology. 2015;66:224–41.

Hildon Z, Neuburger J, Allwood D, van der Meulen J, Black N. Clinicians’ and patients’ views of metrics of change derived from patient reported outcome measures (PROMs) for comparing providers’. Perform Surg. 2012;12:1–13.

Hildon Z, Allwood D, Black N. Making data more meaningful: patients’ views of the format and content of quality indicators comparing health. care Providers. 2012;88:298–304.

Pronovost PJ, Hudson DW. Improving healthcare quality through organisational peer-to-peer assessment: lessons from the nuclear power industry. 2012;21:872–5.

Wakefield J, Herbert CP, Maclure M, Dormuth C, Wright JM, Legare J, et al. Commit Change Statements can Predict Actual Change Pract. 2003;23:81–92.

Rehring SMA, Steiner JF, Reifler LM, Glenn KA, Daley MF. Commitment to change statements and actual practice change after a Continuing Medical. Educ Intervention. 2021;41:145–52.

Steenbruggen RA, van Heusden-Scholtalbers LA, Hoogeboom TJ, Maas M, Brand P, van der Wees P. Impact and feasibility of a tailor-made patient communication quality improvement programme for hospital-based physiotherapists: a mixed-methods study. 2021;10:e001286.

Weiner BJ, Lewis CC, Stanick C, Powell BJ, Dorsey CN, Clary AS, et al. Psychometric Assess Three New Developed Implement Outcome Measures. 2017;12:1–12.

Cousins JB, Whitmore E. Framing participatory evaluation 1998;1998:5–23.

van Staa A, de Vries K. Directed content analysis: een meer deductieve dan inductieve aanpak bij kwalitatieve analyse 2014;19.

Kiger ME, Varpio L. Thematic analysis of qualitative data. AMEE Guide 131. 2020;42:846–54.

Friese S. Qualitative data analysis with ATLAS. Ti. Sage; 2019.

O’Connor C, Joffe H. Intercoder reliability in qualitative research: debates and practical guidelines 2020;19:1609406919899220.

Kirkpatrick D. Great Ideas Revisit. 1996;50:54–60.

van Dulmen SA, Maas M, Staal JB, Rutten G, Kiers H, Nijhuis-van der Sanden M, et al. Effectiveness of peer assessment for implementing a Dutch physical therapy low back pain guideline. Cluster Randomized Controlled Trial. 2014;94:1396–409.

Epstein RM, Siegel DJ, Silberman J. Self-monitoring in clinical practice: a challenge for medical educators 2008;28:5–13.

Daves DA, Mazmanian PE, Fordis M, Van Harrision R, Thorpe KE, Perrier L. Accuracy of physician self-assessment compared with observed measures of competence 2006;296:1094–102.

Prochaska JO, Velicer WF. Transtheoretical Model Health Behav Change. 1997;12:38–48.

CAS Google Scholar

Ajzen I. The theory of planned behavior 1991;50:179–211.

Scholte M, Neeleman-van der Steen CW, van der Wees PJ, Nijhuis-van der Sanden MW, Braspenning J. The reasons behind the (non) use of feedback reports for quality improvement in physical therapy: a mixed-method study. 2016;11(8):e0161056.

Alvarado N, McVey L, Elshehaly M, Greenhalgh J, Dowding D, Ruddle R, et al. Analysis of a web-based dashboard to support the Use of National Audit Data in Quality Improvement. Realist Evaluation. 2021;23(11):e28854.

Ryan RM, Deci EL. Self-determination theory 2017.

Dewey J, Authentic IEL. Experiential learning 1938.

Kluger AN, DeNisi A. The effects of feedback interventions on performance: a historical review, a meta-analysis, and a preliminary feedback intervention theory. 1996;119:254.

Anderson R. New MRC guidance on evaluating complex interventions. 2008;337.

Maas MJ, van Poelgeest E, Weymans C, PJvd W. Module 3 feedback op proces en uitkomsten. KNGF; 201.

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Acknowledgements

The authors express their gratitude to the participating physical therapists from the regional network of the Cooperation of Physical Therapists Nijmegen (CFN).

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Contributions

Concept/idea/research design: LS, AV and PvdW Data analysis: LS, AvK and MM Project management: LS and AV Writing of the manuscript: LS and AV Peer assessment coach: RvH Approval of final manuscript: LS, MM, RvH, AV and PvdW.

Corresponding author

Correspondence to LSF Smeekens .

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The study protocol was approved by the Medical Ethical Committee of Radboud university medical center (registration #2021–13329). This study complies with the Declaration of Helsinki. All participants provided written informed consent.

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Not applicable.

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The authors declare that they have no competing interests.

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Smeekens, L., Verburg, A., Maas, M. et al. Feasibility of a quality-improvement program based on routinely collected health outcomes in Dutch primary care physical therapist practice: a mixed-methods study. BMC Health Serv Res 24 , 509 (2024). https://doi.org/10.1186/s12913-024-10958-5

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Enhancing the translation of health behaviour change research into practice: a selective conceptual review of the synergy between implementation science and health psychology

Affiliations.

1 Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.
2 School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada.
3 School of Psychology, University of Ottawa, Ottawa, Canada.
4 Division of Medical Education, University of Manchester, Manchester, UK.
5 Centre for Health Services Studies, University of Kent, Canterbury, UK.
6 Centre for Behaviour Change, University College London, London, UK.
7 Department of Social Work, Education, and Community Wellbeing, Northumbria University, Newcastle upon Tyne, UK.
8 School of Psychology, Aston University, Birmingham, UK.
9 Research Centre for Health, Psychology and Communities, Manchester Metropolitan University, Manchester, UK.
10 Health Services and Performance Research, University Claude Bernard Lyon 1, Lyon, France.
11 Health Sciences, University of York, York, UK.
12 School of Health Sciences & Manchester Centre for Health Psychology, University of Manchester, Manchester, UK.
13 Social Psychology, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland.
14 Dept of Work & Social Psychology, Maastricht University, Maastricht, The Netherlands.
15 Dept of Health Promotion and Behavioral Sciences, The University of Texas School of Public Health, Houston, TX, USA.
16 Centre for Aging and Rehabilitation, Bradford Institute for Health Research, Bradford, UK.
17 Faculty of Psychology, SWPS University of Social Sciences and Humanities, Wroclaw, Poland.
18 NHMRC CRE in Digital Technology to Transform Chronic Disease Outcomes, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
19 Team Advies & Onderzoek, Municipal Health Service (GGD) Kennemerland, Haarlem, the Netherlands.
20 Health Behaviour Change Research Group, National University of Ireland, Galway, Ireland.
21 Centre for Maternal and Child Health Research, School of Health Sciences, City, University of London, London, United Kingdom.
22 School of Allied Health, University of Limerick, Limerick, Ireland.
PMID: 33446062
DOI: 10.1080/17437199.2020.1866638

Health psychology is at the forefront of developing and disseminating evidence, theories, and methods that have improved the understanding of health behaviour change. However, current dissemination approaches may be insufficient for promoting broader application and impact of this evidence to benefit the health of patients and the public. Nevertheless, behaviour change theory/methods typically directed towards health behaviours are now used in implementation science to understand and support behaviour change in individuals at different health system levels whose own behaviour impacts delivering evidence-based health behaviour change interventions. Despite contributing to implementation science, health psychology is perhaps doing less to draw from it. A redoubled focus on implementation science in health psychology could provide novel prospects for enhancing the impact of health behaviour change evidence. We report a Health Psychology Review -specific review-of-reviews of trials of health behaviour change interventions published from inception to April 2020. We identified 34 reviews and assessed whether implementation readiness of behaviour change interventions was discussed. We then narratively review how implementation science has integrated theory/methods from health psychology and related discipline. Finally, we demonstrate how greater synergy between implementation science and health psychology could promote greater follow-through on advances made in the science of health behaviour change.

Keywords: Implementation science; behaviour change; behaviour change wheel; intervention development; intervention mapping; theoretical domains framework.

Publication types

Research Support, Non-U.S. Gov't
Behavioral Medicine*
Health Behavior
Implementation Science*

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The 2023 Latin America report of the Lancet Countdown on health and climate change: the imperative for health-centred climate-resilient development

External links.

Climate change is a public health issue. The Lancet Countdown: Health and Climate Change in Latin America launched in May 2020 and brings together academic institutions and UN agencies to track how climate change is affecting health across the continent, how countries are responding, and the health benefits of an accelerated response.

Building from the first report, the 2023 report of the Lancet Countdown Latin America, presents 34 indicators that track the relationship between health and climate change up to 2022, aiming at providing evidence to public decision-making with the purpose of improving the health and wellbeing of Latin American populations and reducing social inequities through climate actions focusing on health.

Stella M. Hartinger, Yasna K. Palmeiro-Silva, Camila Llerena-Cayo, Luciana Blanco-Villafuerte, Luis E. Escobar, Avriel Diaz, Juliana Helo Sarmiento, Andres G. Lescano, Oscar Melo, David Rojas-Rueda, Bruno Takahashi, Max Callaghan, Francisco Chesini, Shouro Dasgupta, Carolina Gil Posse, Nelson Gouveia, Aline Martins de Carvalho, Zaray Miranda-Chacón, Nahid Mohajeri, Chrissie Pantoja, Elizabeth J.Z. Robinson, Maria Fernanda Salas, Raquel Santiago, Enzo Sauma, Mauricio Santos-Vega, Daniel Scamman, Milena Sergeeva, Tatiana Souza de Camargo, Cecilia Sorensen, Juan D. Umaña, Marisol Yglesias-González, Maria Walawender, Daniel Buss, Marina Romanello, The 2023 Latin America report of the Lancet Countdown on health and climate change: the imperative for health-centred climate-resilient development, The Lancet Regional Health – Americas, 2024, 100746, ISSN 2667-193X, https://doi.org/10.1016/j.lana.2024.10 0746 .

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9 facts about bullying in the U.S.

Many U.S. children have experienced bullying, whether online or in person. This has prompted discussions about schools’ responsibility to curb student harassment , and some parents have turned to home-schooling or other measures to prevent bullying .

Here is a snapshot of what we know about U.S. kids’ experiences with bullying, taken from Pew Research Center surveys and federal data sources.

Pew Research Center conducted this analysis to understand U.S. children’s experiences with bullying, both online and in person. Findings are based on surveys conducted by the Center, as well as data from the Bureau of Justice Statistics, the National Center for Education Statistics and the Centers for Disease Control and Prevention. Additional information about each survey and its methodology can be found in the links in the text of this analysis.

Bullying is among parents’ top concerns for their children, according to a fall 2022 Center survey of parents with children under 18 . About a third (35%) of U.S. parents with children younger than 18 say they are extremely or very worried that their children might be bullied at some point. Another 39% are somewhat worried about this.

Of the eight concerns asked about in the survey, only one ranked higher for parents than bullying: Four-in-ten parents are extremely or very worried about their children struggling with anxiety or depression.

A bar chart showing that bullying is among parents' top concerns for their children.

About half of U.S. teens (53%) say online harassment and online bullying are a major problem for people their age, according to a spring 2022 Center survey of teens ages 13 to 17 . Another 40% say it is a minor problem, and just 6% say it is not a problem.

Black and Hispanic teens, those from lower-income households and teen girls are more likely than those in other groups to view online harassment as a major problem.

Nearly half of U.S. teens have ever been cyberbullied, according the 2022 Center survey of teens . The survey asked teens whether they had ever experienced six types of cyberbullying. Overall, 46% say they have ever encountered at least one of these behaviors, while 28% have experienced multiple types.

A bar chart showing that nearly half of teens have ever experienced cyberbullying, with offensive name-calling being the type most commonly reported.

The most common type of online bullying for teens in this age group is being called an offensive name (32% have experienced this). Roughly one-in-five teens have had false rumors spread about them online (22%) or were sent explicit images they didn’t ask for (17%).

Teens also report they have experienced someone other than a parent constantly asking them where they are, what they’re doing or who they’re with (15%); being physically threatened (10%); or having explicit images of them shared without their consent (7%).

Older teen girls are especially likely to have experienced bullying online, the spring 2022 survey of teens shows. Some 54% of girls ages 15 to 17 have experienced at least one cyberbullying behavior asked about in the survey, compared with 44% of boys in the same age group and 41% of younger teens. In particular, older teen girls are more likely than the other groups to say they have been the target of false rumors and constant monitoring by someone other than a parent.

They are also more likely to think they have been harassed online because of their physical appearance: 21% of girls ages 15 to 17 say this, compared with about one-in-ten younger teen girls and teen boys.

A horizontal stacked bar chart showing that older teen girls stand out for experiencing multiple types of cyberbullying behaviors.

White, Black and Hispanic teens have all encountered online bullying at some point, but some of their experiences differ, the spring 2022 teens survey found. For instance, 21% of Black teens say they’ve been targeted online because of their race or ethnicity, compared with 11% of Hispanic teens and 4% of White teens.

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A bar chart showing that black teens more likely than those who are Hispanic or White to say they have been cyberbullied because of their race or ethnicity

During the 2019-2020 school year, around two-in-ten U.S. middle and high school students said they were bullied at school . That year, 22% of students ages 12 to 18 said this, with the largest shares saying the bullying occurred for one day only (32%) or for between three and 10 days (29%), according to the most recent available data from the Bureau of Justice Statistics (BJS) and the National Center for Education Statistics (NCES).

Certain groups of students were more likely to experience bullying at school. They include girls, middle schoolers (those in sixth, seventh or eighth grade), and students in rural areas.

The most common types of at-school bullying for all students ages 12 to 18 were being made the subject of rumors (15%) and being made fun of, called names or insulted (14%).

A bar chart showing that girls, middle schoolers and rural students are among the most likely to say they were bullied at school in 2019-2020.

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Fewer than half (46%) of middle and high schoolers who were bullied at school in 2019-2020 said they notified a teacher or another adult about it, according to the BJS and NCES data. Younger students were more likely to tell an adult at school. Around half or more of sixth, seventh and eighth graders said they did so, compared with 28% of 12th graders.

Students who reported more frequent bullying were also more likely to notify an adult at school. For instance, 60% of those who experienced bullying on more than 10 days during the school year told an adult, compared with 35% of those who experienced it on one day.

In 2021, high schoolers who are gay, lesbian or bisexual were about twice as likely as their heterosexual counterparts to say they’d been bullied, both at school and online, according to the Centers for Disease Control and Prevention . In the 12 months before the survey, 22% of high school students who identify as gay, lesbian or bisexual – and 21% of those who identify as questioning or some other way – said they were bullied on school property. That compares with 10% of heterosexual students. The data does not include findings for transgender students.

A dot plot showing that high schoolers' experiences with bullying vary widely by sexual orientation.

The trend is similar when it comes to electronic bullying through text or social media: 27% of high school students who identify as lesbian, gay or bisexual say they experienced this in the 12 months before the survey, as did 23% of those who identify as questioning or some other way. That compares with 11% of those who identify as heterosexual.

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Hyper kids? Research shows sugar isn’t the culprit

Parents long have blamed their children’s “bouncing off the wall” behavior on eating too much sugar, but experts say there’s no truth to it. “It’s a myth that sugar causes hyperactivity,” says Mark Wolraich, professor emeritus in developmental and behavioral pediatrics at the University of Oklahoma Health Sciences Center. Yet, he acknowledges, “it’s still a strong belief. … Sometimes it’s very hard to change embedded impressions of what affects behavior.”

Wolraich conducted studies in the 1990s that disproved the notion that sugar causes attention-deficit/hyperactivity disorder in children. These included a double-blind randomized controlled trial that found that neither sugar nor the artificial sweetener aspartame affected behavior or cognitive function among children whose parents perceived them as high energy “sugar sensitive,” compared with those with “normal” behavior, even when sugar intake exceeded typical dietary levels.

“It was pretty definitive,” Wolraich says.

The Centers for Disease Control and Prevention also states that sugar doesn’t make kids hyper, saying “research doesn’t support the popularly held views that ADHD is caused by eating too much sugar, watching too much television, parenting, or social and environmental factors such as poverty or family chaos.”

Parents probably continue to make this association because children tend to become overly excited during specific events – birthday parties, for example – when the menu contains high-sugar items, such as ice cream, birthday cake and goody bags.

Also, “kids tend to get a lot of sugar around the holidays, when there are other things that rev them up,” Wolraich says. “So it looks like they are getting overactive when they are eating a lot of sugary foods.”

How did this belief start?

Some experts trace its origins to 1973, when allergist Benjamin Feingold linked children’s hyperactivity to ingesting artificial food colors; additives; preservatives; and salicylates, substances found in plants and foods and also used in many medicines, such as aspirin. He also wrote a popular book on the topic.

Although sugar was not among the dietary culprits Feingold criticized, many parents mistakenly made the connection, since high amounts of sugar go hand in hand with foods containing dyes and other additives.

In recent years, studies have connected several artificial dyes, including red dye No. 3, to hyperactivity and other behavioral problems in children. A 2021 report by the California Office of Environmental Health Hazard Assessment concluded that some children who consume food dyes exhibit these health effects, although sensitivity to them varies among children.

What else you should know

Even though sugar is absolved in this one case, it doesn’t mean kids can eat it with abandon, experts warn.

“Sugar is not vindicated from other adverse health effects,” says Donald Hensrud, associate professor of nutrition and preventive medicine at the Mayo Clinic College of Medicine. “It provides extra calories and increases weight, contributing to obesity and possibly later heart disease. It can cause cavities. It has no nutrients and displaces other foods that do.”

So, what’s the bottom-line message to parents? “I don’t promote giving children a lot of sugar,” Wolraich says. “Sugar can be a negative factor in a balanced diet because its taste is so attractive. But sugar does not have a high nutritive value. So eating a lot of sugary foods that are low in other important dietary nutrients is not a good idea – but not because of hyperactivity.”

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IMAGES

The Integrated Theory of Health Behavior Change
The 6 Stages of Behavior Change
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Change4Health
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Examples of Health Behaviors and Concepts

VIDEO

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COMMENTS

Understanding and Predicting Health Behaviour Change: A Contemporary View Through the Lenses of Meta-Reviews
To move the health behaviour change field forward, the SOBC Research Network (funded by the U.S. National Institutes of Health) seeks to improve the understanding of underlying mechanisms of human behaviour change by promoting and a basic mechanism of action research by use of an experimental medicine method (Nielsen et al., 2018; Suls et al ...
Methods of Health Behavior Change
1. Knowledge and outcome expectancies (improving people's knowledge about the health consequences of their behaviors), 2. Personal relevance (drawing people's attention to what health behavior change would mean for them), 3. Positive affective attitudes (promoting positive feelings about behavior change), 4.
Health Behavior Change: Theories, Methods, and Interventions
Interests: her main areas of research are health psychology and behavioral medicine with interests in health behavior motivation, self-regulation, and change; she is particularly interested in understanding the multiple effects of motivational, volitional, and automatic processes on health behavior and the translation of research findings into ...
Full article: Developing habit-based health behaviour change
Developing habit-based health behaviour change interventions: twenty-one questions to guide future research. Benjamin Gardner a Department of Psychology, ... generated pertinent questions to guide further research into habit and health behaviour. Results . In line with the four topics discussed at the meeting, 21 questions were identified ...
Full article: Health behaviour: Current issues and challenges
For example, recent research (Conner et al., Citation 2016) has suggested that prioritising changing one health behaviour over another may lead to stronger intention-behaviour relationships which, in turn, may increase the impact of interventions designed to change behaviour through targeting intentions. This would suggest that interventions ...
Encouraging Health Behavior Change: Eight Evidence-Based Strategies
Effectively encouraging patients to change their health behavior is a critical skill for primary care physicians. Modifiable health behaviors contribute to an estimated 40 percent of deaths in the ...
Habit Formation and Behavior Change
Research around the application of habit formation to health behavior change interventions is reviewed, drawn from two sources: extant theory and evidence regarding how habit forms, and previous interventions that have used habit formation principles and techniques to change behavior. ... Physical exercise habit: On the conceptualization and ...
Changing behaviour: an essential component of tackling health ...
The 2020 Marmot review of health inequalities in England showed that between 2010 and 2018 the gap in life expectancy at birth between those living in the least and most deprived areas increased. 1 For men the gap increased from 9.1 to 9.5 years and for women from 6.8 to 7.7 years. The time people spend in poor health has also increased across ...
Health Behavior Change: Moving from Observation to Intervention
How can progress in research on health behavior change be accelerated? Experimental medicine (EM) offers an approach that can help investigators specify the research questions that need to be addressed and the evidence needed to test those questions. Whereas current research draws predominantly on multiple overlapping theories resting largely ...
Enhancing the translation of health behaviour change research into
Methods. We were interested in whether the reviews of trials of health behaviour change interventions published in Health Psychology Review make mention of the extent to which the evidence synthesised is ready for implementation research, delivery and evaluation beyond the trials in which they were tested. We identified and screened all articles published and in press in Health Psychology ...
Shaping the future of behavioral and social research at NIA
A leading example is the Health and Retirement Study (HRS), which tracks the health of Americans ages 50+ and includes robust data on physical, cognitive, and psychological health. ... In parallel, we lead NIH's Science of Behavior Change program and are bolstering the foundation for implementation science through the NIH Stage Model, a ...
Journal of Medical Internet Research
Background: Digital health technologies (DHTs) are increasingly used in physical stroke rehabilitation to support individuals in successfully engaging with the frequent, intensive, and lengthy activities required to optimize recovery. Despite this, little is known about behavior change within these interventions. Objective: This scoping review aimed to identify if and how behavior change ...
Decolonising global health research: Shifting power for transformative
1. Introduction. Inequity within international research partnerships has troubled the field of global health for decades. In particular, power asymmetries between actors from wealthier and historically-privileged countries and their counterparts in the Global South (GS) have led to paternalistic ways of working, unequal sharing of resources, skewed distribution of benefits, and limited ...
Rewiring the Brain: Poverty Linked With Neurological Changes That
While previous research has explored the individual impacts of poverty on the brain and behavior, this review introduces the first integrated framework. It synthesizes evidence from various studies to directly connect brain alterations caused by low SES with behavioral, pathological, and developmental outcomes.
Understanding behaviour change in relation to agroecological transition
A systems perspective is crucial to understanding behavior change processes in the agroecological transformation of food systems. Systems-level frameworks around behavior in agricultural systems are commonly used to assess the uptake of agricultural innovations, and in doing so, acknowledge the role of individual demographics and economic and governance institutions in shaping farmers ...
Challenges and Strategies in Changing Health Behaviors: A Personal
This highlights the profound impact of mindset in the realm of health behavior change (Gruman et al., 2016). Navigating through kinetics class was a vivid, practical application of Health Behavior Model and the Theory of Planned Behavior. It proved that with the right psychological tools and a bit of determination, transforming health behaviors ...
Feasibility of a quality-improvement program based on routinely
Background This study evaluates the feasibility of a nine-month advanced quality-improvement program aimed at enhancing the quality of care provided by primary care physical therapists in the Netherlands. The evaluation is based on routinely collected health outcomes of patients with nonspecific low back pain, assessing three feasibility domains: (1) appropriateness, feasibility, and ...
Enhancing the translation of health behaviour change research into
A redoubled focus on implementation science in health psychology could provide novel prospects for enhancing the impact of health behaviour change evidence. We report a Health Psychology Review-specific review-of-reviews of trials of health behaviour change interventions published from inception to April 2020. We identified 34 reviews and ...
The 2023 Latin America report of the Lancet Countdown on health and
Building from the first report, the 2023 report of the Lancet Countdown Latin America, presents 34 indicators that track the relationship between health and climate change up to 2022, aiming at providing evidence to public decision-making with the purpose of improving the health and wellbeing of Latin American populations and reducing social inequities through climate actions focusing on health.
Latest science news, discoveries and analysis
Find breaking science news and analysis from the world's leading research journal.
9 facts about bullying in the U.S.
Pew Research Center conducted this analysis to understand U.S. children's experiences with bullying, both online and in person. ... Some 54% of girls ages 15 to 17 have experienced at least one cyberbullying behavior asked about in the survey, compared with 44% of boys in the same age group and 41% of younger teens. In particular, older teen ...
Hyper kids? Research shows sugar isn't the culprit
Sometimes it's very hard to change embedded impressions of what affects behavior." Wolraich conducted studies in the 1990s that disproved the notion that sugar causes attention-deficit ...

Understanding and Predicting Health Behaviour Change: A Contemporary View Through the Lenses of Meta-Reviews

Urte Scholz

Acknowledgments

Contributor Information

CROSS-BEHAVIOR TECHNIQUES

BEHAVIOR-SPECIFIC TECHNIQUES

GETTING STARTED

Continue Reading

More in FPM

Article contents

What Are Habits and Habitual Behaviors ?

How Does Habit Form?

Which Behavior Change Techniques Should Be Used to Form Habit?

Which Behavior Change Techniques Have Been Used to Form Habit, and with What Effect?

Diet and Physical Activity Interventions

Physical Activity and Sedentary Behavior Interventions

Dietary Interventions

Oral Hygiene

Food Safety

Behavior Change Techniques Used in Previous Interventions

Table 1. Behavior Change Techniques Identified in 15 Habit Formation Interventions

Table 2. Behavior Change Techniques Documented in 15 Habit Formation Interventions

How Should Habit-Based Interventions Be Evaluated?

Related Articles

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Changing behaviour: an essential component of tackling health inequalities

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What would effective policy comprise?

Tackling behavioural and social causes in parallel

Key messages

This paper is in the following e-collection/theme issue:

Behavior Change Approaches in Digital Technology–Based Physical Rehabilitation Interventions Following Stroke: Scoping Review

Corresponding Author:

Introduction

Aim and Objectives

Review Methodology

Eligibility Criteria

Information Sources and Search Strategy

Selection of Sources of Evidence

Data Charting Process

Synthesis of Results

Characteristics of Sources of Evidence

Participants

Study Intervention

Behavior Change Theories, Models, and Frameworks

Behavior Change Techniques

Behavior Change Techniques and Digital Health Technology

Summary of Evidence

Limitations

Implications for Research

Acknowledgments

Data Availability

Authors' Contributions

Conflicts of Interest

Abbreviations

Decolonising global health research: Shifting power for transformative change

1. Introduction

2. Colonialism within global health research: Who leads?

3. Colonisation of global health research: Who controls?

4. Colonialism through global health research: Who benefits?

5. Recent guidelines that aim to decolonise global health research

6. Shifting the balance of power in global health research: Going forward

Acknowledgments

Rewiring the Brain: Poverty Linked With Neurological Changes That Affect Behavior, Illness, and Development

Research connects low socioeconomic status to brain alterations impacting educational achievement, mental health, and language development.

Stress and Its Impact on Learning

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Feasibility of a quality-improvement program based on routinely collected health outcomes in Dutch primary care physical therapist practice: a mixed-methods study