cbt for depression literature review

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• v.62(Suppl 2); 2020 Jan

Cognitive Behavioral Therapy for Depression

Manaswi gautam.

Consultant Psychiatrist Gautam Hospital and Research Center, Jaipur, Rajasthan, India

Adarsh Tripathi

1 Department of Psychiatry, King George's Medical University, Lucknow, Uttar Pradesh, India

Deepanjali Deshmukh

2 MGM Medical College, Aurangabad, Maharashtra, India

Manisha Gaur

3 Consultant Psychologist, Gaur Mental Health Clinic, Ajmer, Rajasthan, India

INTRODUCTION

Depressive disorders are one of the most common psychiatric disorders that occur in people of all ages across all world regions. Although it may present at any age however adolescence to early adults is the most common age of onset, and females are affected two times more in comparison to the males. Depressive disorders can occur as heterogeneous conditions in clinical scenario ranging from transient minor symptoms to severe and debilitating clinical conditions, causing severe social and occupational impairments. Usually, it presents with constellations of cognitive, emotional, behavioral, physiological, interpersonal, social, and occupational symptoms. The illness can be of various severities, and a significant proportion of the patients can have recurrent illness. Depression is also highly comorbid with several psychiatric and medical illnesses such as anxiety disorders, substance use, obsessive–compulsive disorder, diabetes, hypertension, and cardiovascular illnesses.

Major depressive disorders accounted for around 8.2% global years lived with disability (YLD) in 2010, and it was the second leading cause of the YLDs. In addition, they also contribute to the burden of several other disorders indirectly such as suicide and ischemic heart disease.[ 1 ]

EVIDENCE BASE FOR COGNITIVE BEHAVIORAL THERAPY IN DEPRESSION

Cognitive behavioral therapy (CBT) is one of the most evidence-based psychological interventions for the treatment of several psychiatric disorders such as depression, anxiety disorders, somatoform disorder, and substance use disorder. The uses are recently extended to psychotic disorders, behavioral medicine, marital discord, stressful life situations, and many other clinical conditions.

A sufficient number of researches have been conducted and shown the efficacy of CBT in depressive disorders. A meta-analysis of 115 studies has shown that CBT is an effective treatment strategy for depression and combined treatment with pharmacotherapy is significantly more effective than pharmacotherapy alone.[ 2 ] Evidence also suggests that relapse rate of patient treated with CBT is lower in comparison to the patients treated with pharmacotherapy alone.[ 3 ]

Treatment guidelines for the depression suggest that psychological interventions are effective and acceptable strategy for treatment. The psychological interventions are most commonly used for mild-to-moderate depressive episodes. As per the prevailing situations of India with regards to significant lesser availability of trained therapist in most of the places and patients preferences, the pharmacological interventions are offered as the first-line treatment modalities for treatment of depression.

Indication for Cognitive behavior therapy as enlisted in table 1 .

Indications for cognitive behavioral therapy (situations that can call for preferred use of the psychological interventions) are

CONTRAINDICATIONS FOR COGNITIVE BEHAVIORAL THERAPY

There is no absolute contraindication to CBT; however, it is often reported that clients with comorbid severe personality disorders such as antisocial personality disorders and subnormal intelligence are difficult to manage through CBT. Special training and expertise may be needed for the treatment of these clients.

Patient with severe depression with psychosis and/or suicidality might be difficult to manage with CBT alone and need medications and other treatment before considering CBT. Organicity should be ruled out using clinical evaluation and relevant investigations, as and when required.

There are many advantages of CBT in depression as given in table 2

Advantages of cognitive behavioral therapy in depression

CHOICE OF TREATMENT SETTINGS

CBT can be done on an Out Patient Department (OPD) basis with regular planned sessions. Each session lasts for about 45 min–1 h depending on the suitability for both patients and therapists. In specific situations, the CBT can be delivered in inpatient settings along with treatment as usual such as adjuvant treatment in severe depression, high risk for self-harm or suicidal patients, patients with multiple medical or psychiatric comorbidities and in patients hospitalized due to social reasons.

ASSESSMENT AND EVALUATION FOR THE THERAPY

A detail diagnostic assessment is needed for the assessment of psychopathology, premorbid personality, diagnosis, severity, presence of suicidal ideations, and comorbidities. Baseline assessment of severity using a brief scale will be helpful in mutual understanding of severity before starting therapy and also to track the progress. Clients during depressive illness often fail to recognize early improvement and undermine any positive change. Objective rating scale hence helps in pointing out the progress and can also help in determining agenda during therapy process. Beck Depression Inventory (A. T. Beck, Steer, and Brown, 1996), the Depression Anxiety Stress Scales (Lovibond and Lovibond, 1995), Montgomery-Asberg Depression Rating Scale, Hamilton Rating Scale for Depression are useful rating scales for this purpose. The assessment for CBT in depression is, however, different from diagnostic assessment.

THE USE OF COGNITIVE BEHAVIORAL THERAPY ACCORDING TO SEVERITY OF DEPRESSION

Various trials have shown the benefit of combined treatment for severe depression.

Combined therapy though costlier than monotherapy it provides cost-effectiveness in the form of relapse prevention.

Number of sessions depends on patient responsiveness.

Booster sessions might be required at the intervals of the 1–12 th month as per the clinical need.

A model for reference is given in table 3

The use of cognitive behavioral therapy according to the severity of depression

The general outline of CBT for depression has been discussed in table 4

Overview of cognitive behavioral therapy for depression

CBT – Cognitive behavioral therapy

COGNITIVE MODEL FOR DEPRESSION

Cognitive theory conceptualizes that people are not influenced by the events rather the view they take of the events. It essentially means that individual differences in the maladaptive thinking process and negative appraisal of the life events lead to the development of dysfunctional cognitive reactions. This cognitive dysfunction is in turn is responsible for the rest of the symptoms in affective and behavioral domains.

Aaron beck proposed a cognitive model of depression, and it is detailed in Figure 1 . Cognitive dysfunctions are of the following categories.

Cognitive behavioral therapy model of depression

• Schema - stable internal structure of information usually formed during early life, also include core belief about self
• information processing and intermediate belief are usually interpreted as rules of living and usually expressed in terms of “if and then” sentences
• Automatic thoughts - proximally related to everyday events and in depression, often reflects cognitive triad, i.e., negative view of oneself, world, and future.

Negative cognitive triad of depression as given beck is as following:

• I am helpless (helplessness)
• The future is bleak (hopelessness)
• I am worthless (worthlessness).

CHOICE OF THE PATIENT

Patient-related factors that facilitated response are.

• Psychological mindedness of patients: Patients who are able to understand and label their feelings and emotions generally respond better to CBT. Although some patients in the course of treatment learn those skills during treatment
• Intellectual level of the patient might also affect the overall effectiveness of the treatment
• Willingness and motivation on the part of patients: Although it is not prerequisite, patients who are motivated to analyze their feelings and ready to undergo various homework show a better response to treatment
• Patient preference is single most important factor: After initial assessment of the patient those who prefer psychological treatment can be offered CBT alone or in combination depending on type of depression
• Those with mild to moderate depression CBT can be recommended as a first line of treatment
• Patients with severe depression might need combination of both CBT and medications (and or other treatments)
• Special situations such as children and adolescents, pregnancy, lactation, female in fertile age group planning for pregnancy, medical comorbidities
• Inability to tolerate psychopharmacological treatment
• The presence of significant psychosocial factors, intrapsychic conflicts, and interpersonal difficulties.

Therapist related factors

• Availability of cognitive behavioral therapist/psychiatrist
• The ability of therapist to form therapeutic alliance with the patient.

CLINICAL INTERVIEW FOR COGNITIVE BEHAVIORAL THERAPY

Symptoms and associated cognitions.

Negative automatic thoughts both trigger and enhance depression. It might be helpful to identify unhealthy automatic thoughts associated with symptoms of depression.

Some common symptoms and associated automatic thoughts are given in table 5 .

Symptoms of depression and associated cognitions

Impact on functioning

it is important to know the extent and effect of depression on the overall functioning and interpersonal relationships.

Coping strategies

Sometimes patients with depression might have adapted a coping strategies which make them feel good for short duration (e.g., alcohol consumption) but might be unhealthy in long term.

Onset of current symptoms

Patient's perception about the situation at the onset of symptoms might provide useful information about underlying cognitive distortions.

Background information

Detailed history of patient is necessary, including patients premorbid personality.

The therapist should be able to do the cognitive case conceptualization for the patient as given in Figure 2 .

Case conceptualization for the cognitive model of depression

MANAGING TREATMENT

An outline of the breakup of typical session of CBT is given in table 6 .

Session structure of cognitive behavioral therapy

Starting treatment

First treatment interview has mainly four objectives:

• To establish a warm collaborative therapeutic alliance
• To list specific problem set and associated goals
• To psycho-educate patient regarding the cognitive model and vicious cycle that maintains the depression
• Give the patient idea about further treatment procedures.

CBT can be explained in the following headings

• Behavioral interventions

Working with negative automatic thoughts

• Ending session.

The first treatment interview has four main objectives:

• To establish a warm, collaborative therapeutic alliance
• To list specific problems and associated goals, and select a first problem to tackle
• To educate the patient about the cognitive model, especially the vicious circle that maintains depression
• To give the patient first-hand experience of the focused, workman-like, empirical style of CBT.

These convey two important messages: (1) It is possible to make sense of depression; (2) there is something the patient can do about it. These messages directly address hopelessness and helplessness.

• Identifying problems and goals:-The various problems faced by patients should be included in a list which can include symptoms of depression or social problems (e.g., family conflict). Developing this list at the end of the first session helps in planning treatment goals
• Introducing cognitive model of depression:- In the first session at least a basic idea about how our cognitions affect our emotions and behavior is taught to the patient. The data provided by patient can be used to give insight into behaviors
• Where to start:-Common treatment goal is agreed upon by patient and therapist, therapeutic alliance is of key importance in CBT. Appropriate homework assignment should be given to patient according to predecided goal.

Behavioural interventions

Reducing ruminations.

It has been seen that depressed patients spend a significant amount of time and attention focusing on their shortcomings. Making patient aware of those negative ruminations and consciously diverting attention toward certain positive aspects can be taught to patients.

Monitoring activities

Loss of interest in day to day activities is central to the depression. It has been seen that early behavioral intervention has been increased sense of autonomy in the patients.

Patients are taught to record each and every activity hour by hour on the activity schedule. Each activity is rated 0–10 for Pleasure (P) and Mastery (M). P ratings indicate how enjoyable the activity was, and M ratings how much of an achievement it was. Mostly depressed patients feel low on achievement all the time. Hence, M should be explained as “achievement how you felt at the time of doing.” Patients are instructed to rate activities immediately and not retrospectively.

Example of activity schedule is

Activity Chart Write in each box, activity performed and depression rating from 0-100% (0-minimal, 100-maximum)

Planning activities

Once the patient learns to self-monitor activities each day is planned in advance.

This helps patients by:

• This provides a structure and helps with setting priorities
• This avoids the need to keep making decisions about what to do next
• This changes perception from chaos to manageable tasks
• This increases the chances that activities will be carried out
• This enhances patients’ sense of control.

A plan for activities is made in such a way that both pleasure and mastery are balanced (e.g., ironing cloths followed by listening to music). The tasks which are generally avoided by patient can be divided into graded tasks.

The patient is taught to evaluate each and every day in detail also encouraged to keep the record of unhelpful negative thoughts regarding tasks.

Other important behavioral activities are:-

• Mindfulness meditation: Helps people stay grounded in the present by keeping away from ruminations
• Successive approximation: Breaking larger tasks into smaller tasks which are easy to accomplish
• Visualizing the best part of the day
• Pleasant activity scheduling.

Scheduling an activity in near future which one can look on with mastery and with sense of achievement.

The main tool for this negative automatic thought record.

Thought Record -1

Thought Record – 2

Identifying negative automatic thoughts

Patients learn to record upsetting incidents as soon as possible after they occur (delay makes it difficult to recall thoughts and feelings accurately). They learn:

• To identify unpleasant emotions (e.g., despair, anger, guilt), signs that negative thinking is present. Emotions are rated for intensity on a 0–100 scale. These ratings (though the patient may initially find them difficult) help to make small changes in emotional state obvious when the search for alternatives to negative thoughts begins. This is important since change is rarely all-or-nothing, and small improvements may otherwise be missed
• To identify the problem situation. What was the patient doing or thinking about when the painful emotion occurred (e.g., “waiting at the supermarket checkout,” “worrying about my husband being late home”)?
• To identify negative automatic thoughts associated with the unpleasant emotions. Sessions direct the therapist towards asking: “And what went through your mind at that moment?” Patients become aware of thoughts, images, or implicit meanings that are present when emotional shifts occur, and record. Belief in each thought is also rated on a 0%–100%.

Questioning negative automatic thoughts

Therapist can help patient to discover dysfunctional automatic thoughts through “guided discovery.”

• What is evidence?
• What are alternative views?
• What are advantages and disadvantages of this way of thinking?
• What are my thinking biases?

Common cognitive distortions are

• Black– and– white (also called all– or– nothing, polarized, or dichotomous thinking): Situations viewed in only two categories instead of on a continuum. Example: “If I don’t top the exams. I’m a failure”
• Fortune-telling (also called catastrophizing): Future is predicted negatively without considering other possible, more likely outcomes. Example: “I ll be so upset, i won’t be able to function at all”
• Disqualifying or discounting the positive: The person unreasonably tell oneself that positive experiences, deeds, or qualities do not count. Example: “I cracked the examl, but that doesn’t mean I’m competent; It was a fluke”
• Emotional reasoning: One thinks something must be true because he/she “feels” (actually believe) it so strongly, ignoring or discounting evidence to the contrary. Example: “I know I successfully complete most of my tasks, but I still feel like I’ m incompetent”
• Labeling: One puts a fixed, global label on oneself or others without considering that the evidence might more reasonably lead to a less disastrous conclusion. Example: “I’m a failure. He's not good enough”
• Magnification/minimization: When one evaluates oneself, another person, or a situation, one unreasonably magnifies the negative and/or minimizes the positive. Example: “Getting a C Grade in exams proves how mediocre I am. Getting high marks doesn’t mean I’m smart”
• Selective abstraction (also called mental filter): One pays undue attention to one's negative detail instead of seeing the whole picture. Example: “Because I got just passing marks in one subject in my examinations (which also contained distinctions in other subjects) it means I’m not a good student”
• Mind reading: One believes that he/she knows what others are thinking, failing to consider other, more likely possibilities. Example: “He assumes that his boss thinks that he is a novice for this assignment”
• Overgeneralization: One makes a negative conclusion that goes far beyond the current situation. Example: “(Because I felt uncomfortable at the meeting) I don’t have what it takes to be a group leader”
• Personalization: O ne believes others are behaving negatively because of him/her, without exploring alternative explanations for their behavior. Example: “The watchman didn’t smile at me because I did something wrong”
• Imperatives (also called “Should” and “must” statements): One has a precise, fixed idea of how one or others should behave, and they overestimate how bad it is that these expectations are not met with. Example: “It's terrible that I sneeze as I am a Gym Trainer”
• Tunnel vision: One only views the negative aspects of a situation. Example: “My subordinate can’t do anything right. He's callous, casual and insensitive towards his job.”

Testing negative automatic thoughts: What can I do now?

It is important that cognitive changes that are brought out by questioning are consolidated by behavior experiments.

Ending the treatment

CBT is time-limited goal-directed form of therapy. Hence, the patient is made aware about end of treatment in advance. This can be done through the following stages.

Dysfunctional assumptions identification

Consolidating learning blueprint.

• Preparation for the setback.

Once the patient is able to identify negative automatic thoughts. Before ending treatment patient patients should be made aware about dysfunctional assumptions.

• Where did this rule come from? Identifying the source of a dysfunctional assumption (e.g., parental criticism) often helps to encourage distance by suggesting that its development is understandable, though it may no longer be relevant or useful
• In what ways is the rule unrealistic? Dysfunctional assumptions do not fit the way the world works. They operate by extremes, which are reflected in their language (always/never rather than some of the time; must/should/ought rather than want/prefer/would like)
• In what ways is the rule helpful? Dysfunctional assumptions are not usually wholly negative in their effects. For example, perfectionism may lead to genuine, high-quality performance. If such advantages are not recognized and taken into account when new assumptions are formulated, the patient may be reluctant to move forward
• In what ways is the rule unhelpful? The advantages of dysfunctional assumptions are normally outweighed by their costs. Perfectionism leads to rewards, but it also undermines satisfaction with achievements and stops people learning from constructive criticism
• What alternative rule might be more realistic and helpful? Once the old assumption has been undermined, it is helpful to formulate an explicit alternative (e.g., "It is good to do things well, but I am only human-sometimes I make mistakes"). This provides a new guideline for living, rather than simply undermining the old system
• What needs to be done to consolidate the new rule? As with negative automatic thoughts, re-evaluation is best made real through experience: Behavioral experiments.

The patient should be able to summarize whatever he has learned throughout the sessions.

The following questions might help to set the framework:

• How did my problems develop? (unhelpful beliefs and assumptions, the experiences that led to their formation, events precipitating onset)
• What kept them going? (maintenance factors)
• What did I learn from therapy that helped? Techniques (e.g., activity scheduling) and Ideas (e.g., "I can do something to influence my mood")
• What were my most unhelpful negative thoughts and assumptions? What alternatives did I find to them? (summarized in two columns)
• How can I build on what I have learned? (a solid, practical, clearly specified action plan).

Preparation for the setback

Since depression is recurring illness patient should be made aware about the possibility of relapse.

• What might lead to a setback for me? For example, future losses (e.g., children leaving home) and stresses (e.g., financial difficulties), i.e., events which impinge on patients’ vulnerabilities and are thus liable to be interpreted negatively
• What early warning signs do I need to be alert for?
• Feelings, behaviors, and symptoms that might indicate the beginning of another depression are identified and listed
• If I notice that I am becoming depressed again, what should I do? Clear simple instructions, which will make sense despite low mood, are needed here. Specific ideas and techniques summarized earlier in the blueprint should be referred to.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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• Review Article
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• Published: 15 September 2022

A systematic review of digital and face-to-face cognitive behavioral therapy for depression

• Lana Kambeitz-Ilankovic 1 , 2   na1 ,
• Uma Rzayeva 1   na1 ,
• Laura Völkel 1 ,
• Julian Wenzel 1 ,
• Johanna Weiske 2 ,
• Frank Jessen 1 ,
• Ulrich Reininghaus 3 , 4 , 5 ,
• Peter J. Uhlhaas 6 , 7 ,
• Mario Alvarez-Jimenez 8 , 9 &
• Joseph Kambeitz   ORCID: orcid.org/0000-0002-8988-3959 1 , 10  

npj Digital Medicine volume  5 , Article number:  144 ( 2022 ) Cite this article

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Cognitive behavioral therapy (CBT) represents one of the major treatment options for depressive disorders besides pharmacological interventions. While newly developed digital CBT approaches hold important advantages due to higher accessibility, their relative effectiveness compared to traditional CBT remains unclear. We conducted a systematic literature search to identify all studies that conducted a CBT-based intervention (face-to-face or digital) in patients with major depression. Random-effects meta-analytic models of the standardized mean change using raw score standardization (SMCR) were computed. In 106 studies including n  = 11854 patients face-to-face CBT shows superior clinical effectiveness compared to digital CBT when investigating depressive symptoms ( p  < 0.001, face-to-face CBT: SMCR = 1.97, 95%-CI: 1.74–2.13, digital CBT: SMCR = 1.20, 95%-CI: 1.08–1.32) and adherence ( p  = 0.014, face-to-face CBT: 82.4%, digital CBT: 72.9%). However, after accounting for differences between face-to-face and digital CBT studies, both approaches indicate similar effectiveness. Important variables with significant moderation effects include duration of the intervention, baseline severity, adherence and the level of human guidance in digital CBT interventions. After accounting for potential confounders our analysis indicates comparable effectiveness of face-to-face and digital CBT approaches. These findings underline the importance of moderators of clinical effects and provide a basis for the future personalization of CBT treatment in depression.

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Introduction.

Cognitive behavioral therapy (CBT) is the gold-standard intervention for major depression besides pharmacotherapy 1 . Since its emergence nearly fifty years ago, a large number of studies has underlined the effectiveness of CBT in improving depressive symptoms, anxiety symptoms and psychosocial functioning 2 , 3 . In order to increase accessibility to CBT, recent digital CBT approaches have been developed by incorporating technological tools such as emails, smartphone apps or internet-guided therapy 4 . These approaches hold a number of potential advantages such as cost effectiveness, improved accessibility to evidence-based care for patients living in remote areas, patients living abroad or patients with immobility and - most recently - to face the challenge of providing CBT during the COVID-19 pandemic 5 .

A number of studies suggest that CBT can effectively reduce depressive symptoms, anxiety or psychosocial functioning 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 . In line with these promising aspects, healthcare professionals 14 and especially young patients report to be open towards the adoption of digital treatments 15 . For patients and clinicians there is a strong preference for blended approaches which combine face-to-face CBT with digital interventions 16 , 17 . However, the majority of patients with depression seem to prefer face-to-face CBT 18 and adherence to digital interventions is often low 19 , 20 .

Previous meta-analyses compare face-to-face with digital CBT for different conditions 21 , 22 and report inconsistent results, possibly due to small samples of studies and heterogeneous interventions. Despite robust evidence for the clinical effectiveness of face-to-face and digital CBT, the equivalence of these treatments remains an open question. This represents a critical challenge for mental health professionals that need to decide which intervention should be recommended to patients and which factors should be considered when making this decision.

Our primary aim of this systematic review is to compare the effects of face-to-face vs. digital CBT interventions. The secondary aim is to investigate the moderating factors for these interventions. Overall our results indicate that after controlling for a number of potential confounders, face-to-face and digital CBT might be comparable in terms of clinical effectiveness for treating depression. We identify a number relevant factors that moderate the treatment response such as the duration of the intervention, baseline severity, adherence and the level of human guidance in digital CBT interventions.

Literature search

We identified 682 potential studies out of which 239 studies were retrieved and assessed in full-text according to our inclusion criteria. Of the included studies, 22 face-to-face studies and 63 digital CBT studies had more than one patient sample that was eligible for inclusion due to multiple study arms. For the face-to-face CBT studies, we identified a small number of studies with a very long treatment duration ( n  = 5 studies between 1 and 6 years of treatment duration). In order to make face-to-face and digital studies more comparable, we restricted all following analyses to studies that had a treatment duration of not more than 1 year. Thus, in total n  = 106 studies with a total of n  = 161 samples and n  = 11854 patients were included in the present meta-analysis (Supplementary Tables 5 and 6 ). This resulted in n  = 81 samples ( n  = 3257 patients) receiving face-to-face CBT and n  = 80 samples ( n  = 8597 patients) receiving digital CBT (see Fig. 1 ).

Flow-chart of the literature search according to the recommendation of the PRISMA guidelines.

We observed significant differences between face-to-face and digital CBT samples with respect to multiple patient characteristics and other aspects of the intervention (see Table 1 ).

The assessment of risk of bias indicated an overall high risk of bias and comparable risk for studies investigating face-to-face CBT and studies investigating digital CBT approaches. For both interventions, the main risk of bias resulted from insufficient blinding of participants and insufficient blinding of the outcome assessment. A direct comparison indicated higher risk of selection bias (due to insufficient allocation concealment) in face-to-face CBT studies ( p  = 0.005) whereas digital CBT studies showed higher potential detection bias (blinding of outcome assessment, p  = 0.017, Supplementary Figs. 2 and 3 , Supplementary Table 4 ).

Effectiveness of face-to-face vs. digital CBT

In the analysis of depressive symptoms, face-to-face interventions (SMCR = 1.97, 95%-CI: 1.74–2.13) showed significantly stronger reductions ( p  < 0.001) as compared to digital interventions (SMCR = 1.20, 95%-CI: 1.08–1.32, Fig. 2 ). The difference between digital and face-to-face CBT studies remained significant after applying the trim-and-fill method to compensate for putatively missing studies ( p  < 0.001) and after controlling for differences in study design by using number of sessions and duration of intervention as covariates in the meta-analytic models ( p  = 0.010). However, there were no significant differences between digital and face-to-face CBT samples after controlling for differences in patient characteristics (mean age, gender ratio, antidepressant treatment, severity of depressive symptoms at baseline) using moderator analysis ( p  = 0.068) or when employing propensity score matching to control for differences in study design and patient characteristics ( p  = 0.700, Supplement page 5 and 6 ). In a subanalysis of samples based on BDI-II scores ( n  = 102 samples from 62 studies), depression scores were significantly higher in face-to-face studies as compared to digital studies at baseline ( p  = 0.048, independent t -test) but no differences after the intervention ( p  = 0.708, independent t-test) or at follow-up ( p  = 0.384, independent t -test) yielded significance (Fig. 2 and Table 1 ). The analysis of adherence indicated significantly fewer drop-outs in face-to-face (82.4%) as compared to digital CBT studies (72.9%, p  = 0.014, Fig. 3 , Supplement page 7 and 8 ). When accounting for these differences in adherence, face-to-face CBT showed stronger improvements of depressive symptoms as compared to digital CBT ( p  < 0.001).

a Effects of CBT on anxiety symptoms, depression symptoms and psychosocial functioning. b Results of the meta-analyses of long-term stability of treatment gains. c Subanalysis of samples based on depression severity based on BDI-II scores. P values indicate significance of differences between digital and face-to-face interventions tested by moderator analysis. Error bars indicate lower and upper limits of the 95% confidence interval. Effect sizes and p values are presented without correction for differences in patient samples or study design characteristics and without correction for potential publication bias.

a Clinical outcomes following the CBT intervention. b Comparisons of adherence. P values indicate significance of differences between digital and face-to-face interventions tested by moderator analysis in the meta-analytic model. Error bars indicate lower and upper limits of the 95% confidence interval.

Face-to-face studies (SMCR = 1.29, 95%-CI: 0.87–1.71) showed significantly stronger improvement in psychosocial functioning ( p  < 0.001) as compared to digital studies (SMCR = 0.49, 95%-CI: 0.39–0.58, Fig. 2 ). This difference remained significant after controlling for potential publication bias ( p  < 0.001) and after controlling for differences in study design by using number of sessions and duration of intervention as covariates ( p  = 0.013). However, there were no significant differences between digital and face-to-face CBT samples after controlling for differences in patient characteristics (mean age, gender ratio, antidepressant treatment, severity of depressive symptoms at baseline) using moderator analysis ( p  = 0.091) or when employing propensity score matching to control for differences in study design ( p  = 0.068, see supplement page 4 and 5 ).

In addition, face-to-face studies (SMCR = 1.30, 95%-CI: 0.65–1.95) showed no significant difference with regard to anxiety ( p  < 0.240) as compared to digital studies (SMCR = 0.90, 95%-CI: 0.78–1.03, see Fig. 2 ). These results remained unchanged when accounting for potential publication bias ( p  < 0.240). There were too few studies to conduct further analyses while controlling for additional potentially confounding variables.

All results were robust with respect to different estimates of the correlations between pre- and post-intervention assessments ( r  = 0 to r  = 1 in steps of 0.1, Supplementary Fig. 1 ).

In the analysis of the long-term stability of treatment gains, face-to-face and digital interventions showed no statistical difference in depressive symptoms ( p  = 0.550), psychosocial functioning ( p  = 0.078) or anxiety symptoms ( p  = 0.820, Fig. 2 , Table 1 and Supplement page 5 and 6 ).

Moderator analysis

Face-to-face CBT treatments were superior to guided digital CBT treatments regarding improvement of depressive symptoms ( p  < 0.001), improvement of psychosocial functioning ( p  < 0.001) and in adherence ( p  < 0.001, see Fig. 3 ). At the same time, guided digital CBT was superior to unguided digital CBT regarding depressive symptoms ( p  < 0.001) and psychosocial functioning ( p  = 0.043) but there was no difference in adherence ( p  = 0.207). No differences between face-to-face CBT, guided digital CBT and unguided digital CBT were found regarding anxiety symptoms (all p  > 0.1).

The effect of CBT on depressive symptoms was moderated by the number of sessions ( p  = 0.017) and the treatment intensity ( p  < 0.001) in face-to-face studies whereas in digital studies there was a moderation effect of the duration of the intervention ( p  = 0.034). Baseline symptom severity moderated effects of CBT on depressive symptoms in face-to-face studies ( p  = 0.038) and in digital studies ( p  = 0.029).

The effect of CBT on psychosocial functioning was moderated by age of onset of depression ( p  = 0.004) but there were too few studies to investigate this effect in digital studies. Mean age was a significant moderator in face-to-face ( p  < 0.001) but not in digital studies ( p  = 0.058). Presence of antidepressant treatment and comorbid anxiety disorder were significant moderators in face-to-face studies ( p  < 0.001 and p  = 0.013, respectively) but not in digital studies ( p  > 0.05).

In the analysis of anxiety symptoms, the effect of CBT was moderated by the baseline severity of depressive symptoms in digital studies ( p  = 0.001) but not in face-to-face studies ( p  = 0.714).

Digital CBT interventions are becoming increasingly relevant for the treatment of depressive disorders. Despite the rapid proliferation of these approaches, a systematic assessment of the clinical effectiveness of CBT as compared to traditional (face-to-face) approaches, is still lacking. In the present meta-analysis we compared a total of 106 studies and over 11000 patients. To the best of our knowledge the current analysis represents the largest and most comprehensive analysis of the comparative clinical effectiveness of face-to-face and digital CBT interventions for depression. Overall, our results indicate that face-to-face approaches show superior clinical effectiveness in reducing depressive symptoms and psychosocial functioning but not in reducing comorbid anxiety symptoms. In a supplementary analysis of BDI-II equivalent scores, we largely confirmed the findings of our main analysis. Importantly, face-to-face studies were associated with higher treatment adherence. However, there were significant differences in sample-characteristics and interventions between face-to-face and digital CBT studies. Informed by knowledge that multiple factors including age, gender or disease severity at baseline may moderate treatment response ( 23 , 24 , 25 , 26 but see 27 , 28 ), we employed covariate analysis and propensity score matching to control for these differences. These analyses revealed no significant differences between the face-to-face and digital interventions, suggesting that these approaches might have more comparable clinical effectiveness when accounting for moderators. Further controlled studies conducted in more comparable populations, interventions and study designs are needed to confirm these findings. Our results provide a strong foundation to initiate these efforts.

Motivated by the recent calls for precision psychiatry approaches, a number of studies have investigated potential moderators of clinical effects of face-to-face 29 , 30 and digital CBT treatments 23 , 30 , 31 with the aim to increase clinical effectiveness and to facilitate the adoption of digital tools for clinical scenarios or populations in which they are most successful.

For digital CBT, some studies indicated that high baseline severity of depressive symptoms predicts improvement of depressive symptoms 24 , 31 , 32 , 33 , 34 , 35 or psychological distress 36 . Conversely, other studies reported no such effect 28 , 37 , 38 or even a better response to a CBT intervention delivered by trained clinicians via internet in patients with lower baseline severity of symptoms 39 . Interestingly, our findings show a significant moderation effect of baseline severity on the improvement of depressive symptoms in face-to-face CBT studies and a moderation effect of similar size in digital CBT studies (see Fig. 4 ). This suggests that both digital and face-to-face CBT may be suitable interventions for patients with more severe forms of depression.

Strength of moderation was quantified by the beta-coefficient of the meta-analytic moderation model and moderation effects are plotted as absolute and sqrt values for better visualization. “*” indicates significant moderation effects ( p  < 0.05) in the meta-analytic model.

In line with our findings, a recent study indicated that concurrent use of antidepressant medication is common in digital CBT trials of depression and anxiety 40 . In this analysis, digital CBT showed equivalent efficacy for patients with antidepressant medication and patients not using them 40 . Another study focused on psychological distress and found significantly higher improvements in patients on antidepressants after participating in a digital CBT programme 36 . Importantly, a high number of studies investigating face-to-face CBT, antidepressant medication was an exclusion criterion whereas this was not the case for most digital CBT studies. Thus, antidepressant medication represents a potential confound for the identified differences between digital and face-to-face CBT studies.

Treatment adherence is another important challenge for the successful implementation of digital mental health 41 , 42 . Previous studies investigated the role of adherence and identified adherence as a predictor of faster treatment response to digital CBT 28 , 35 . In the current analysis, patient characteristics and the design of the intervention were not related to adherence. However, face-to-face CBT was associated with higher adherence compared to digital CBT and no difference between guided and unguided digital CBT with respect to adherence was observed. Interestingly, our results indicate that adherence is related to the reduction of depressive symptoms in digital CBT interventions (but not in face-to-face interventions) whereas improvement of functioning was moderated by adherence in face-to-face interventions (but not in digital interventions).

In line with these findings, a higher number of sessions is an important positive predictor of the success of digital CBT treatment 39 . Interestingly, previous meta-regression analysis on the effect of the duration of CBT on treatment outcome revealed only minor effects but this analysis underlined the importance of treatment intensity (e.g. the number of treatment sessions per week) 43 .

A number of potential limitations need to be considered in the interpretation of our current findings. First, the result that face-to-face and digital CBT show similar clinical effects after the statistical correction of potential confounds remains to be confirmed in trials designed specifically to test this hypothesis. Second, we acknowledge that in the present analysis the main outcome measures are pre-post difference scores which need to be interpreted carefully as they include other effects besides the intervention such as placebo effects or the natural course of the depressive disorder. However, our main results focus on the comparison of face-to-face and digital CBT which should not lead to confounded results. Lastly, our analysis of potential biases indicated several potential risks for the majority of the included studies. This was mainly a result of insufficient blinding of participants and raters.

Face-to-face and digital CBT are effective therapy approaches for the treatment of major depression. While currently available evidence suggests robust effectiveness of face-to-face approaches, digital CBT might show comparable effects when controlling for moderators. In particular, additional human support, longer interventions and high adherence were associated with favorable treatment effects of digital CBT. Our results emphasize the potential of digital CBT to be integrated as a valuable tool in specific clinical scenarios including more severe presentations of major depression. Finally, specific moderators might guide clinicians as well as future studies in the personalization of CBT treatment for patients with depression.

Search strategy and selection criteria

We conducted a systematic literature search in the PubMed database to identify all relevant studies published until January 11th, 2021. In addition, primary studies in existing meta-analyses were checked for eligibility 2 , 7 , 12 , 22 , 44 . The search terms were: ((“cognitive behavioral therapy“) OR (“digital psychotherapy“ OR “psychotherapy app“ OR “mobile” OR “internet”)) AND (“major depression“) NOT (“bulimia“ OR “anorexia“ OR “psychosis” OR “bipolar“ OR “OCD“ OR “anxiety“)) NOT (“review”[Publication Type])).

We included studies that: (1) investigated patients with Major Depressive Disorder as diagnosed by the Diagnostic Statistical Manual (DSM) or International Classification of Diseases ICD, (2) employed an individual, CBT-based intervention (including second- and third-wave CBT approaches such as schema therapy, mindfulness therapy and interpersonal psychotherapy), (3) reported measures of either depressive symptoms, anxiety symptoms or psychosocial functioning (4) before and after the intervention in a (5) randomized controlled study design. We included CBT interventions administered in a face-to-face manner and CBT in a digital setting. Digital CBT could be administered in a guided or unguided manner and we included computer-based approaches (internet-based, computerized CBT-modules or email-based) as well as smartphone-based approaches.

Studies were excluded if they: (1) included less than five participants, (2) included children or adolescents (<18 years), (3) focused exclusively on a more specific depression diagnosis (i.e. postpartum depression or late-life depression), or primarily investigated somatic (e.g. HIV, diabetes) or psychiatric main diagnose preceding depressive symptomatology (e.g. panic disorder), (4) employed a psychotherapeutic intervention based on psychoanalysis or culturally-adapted psychotherapy as well as therapy delivered by a telephone or group therapy of any therapy direction.

In case some relevant data was not reported in the published manuscripts of the studies identified during the literature search, we contacted authors via email in order to obtain the missing data. In some cases we did not receive any response or the needed data was not available. Studies were excluded from our meta-analysis, if data was not sufficient to calculate effect sizes as specified in the methods section.

The procedure for this meta-analysis has been publicly registered at https://osf.io/z45xr . We follow the PRISMA reporting guidelines 45 and additional details regarding the literature search are provided in the supplementary methods. Approval from the local ethics committee was waived as no original data was acquired in the context of this study.

Data extraction

Depressive and anxiety symptoms were assessed by self- or observer-rated clinical scales (e.g. Beck’s Depression Inventory, Hamilton Depression Scale, State Trait Anxiety Inventory-STAI, Hamilton Anxiety Scale). In order to compare depressive symptom severity at baseline across studies, reported symptom measures were converted to BDI-II using published conversion procedures 46 , 47 . Psychosocial functioning was assessed using measures of global functioning (e.g. Global Assessment of Functioning), work-related functioning (e.g. Well-Being Inventory), social functioning (e.g. Social and Occupational Functioning Assessment Scale), health-related functioning (e.g. World Health Organization Quality of Life) and life quality (e.g. Quality of life scale). Adherence was quantified for all samples by the ratio of patients that did not drop out of the study and underwent an assessment after the intervention.

Literature search and data extraction were conducted independently by two researchers (L.V. and UM.R.). Discrepancies were resolved in a consensus conference (L.K.I, L.V. and UM.R.). All information was checked for potential extraction errors independently by two researchers (N.D., J.W.).

Outcome measures

We computed the standardized mean change using raw score standardization (SMCR) describing changes between measures before and after the intervention 48 .

Here, Mean Pre and Mean Post refer to the mean of clinical measures before and after the intervention and SD Pre refers to the standard deviation before the intervention. As compared to the widely used standardized mean difference (SMD), SMCR accounts for the dependence of groups in pre-post study designs in the calculation of the sampling variances.

SMCRs were computed separately for the three outcome dimensions (depressive symptoms, anxiety symptoms, psychosocial functioning). In case studies reported more than one measure for a specific outcome, these measures were averaged. Long-term stability of treatment gains following CBT were analyzed by calculating changes between the post-intervention time point and the follow-up assessment. As the calculation of SMCRs requires the correlation between baseline and follow-up measures, we estimated a correlation of r  = 0.65 based on several previous studies 49 , 50 . We conducted sensitivity analyses using the entire spectrum of possible correlations (0–1 with steps of 0.05) to test whether the overall effects are robust to different correlation coefficients (supplementary materials).

Meta-analytic procedures

The main outcome was the difference in clinical effectiveness between face-to-face and digital CBT interventions. This was assessed by conducting a meta-analysis including all effect sizes (SMCR) and testing for the relevance of the factor “intervention” (face-to-face vs. digital CBT). Potential confounders including characteristics of the patient samples (mean age, gender ratio, severity of depressive symptoms at baseline, antidepressant treatment) or by differences in interventions (number of sessions, duration of intervention in weeks) was assessed by including these factors in our meta-analysis. Moreover, we investigated the moderating effect of treatment intensity which was defined as the number of CBT sessions divided by the duration of the intervention in weeks. In addition, we employed propensity score matching of face-to-face and digital CBT studies to control for differences in potentially confounding variables. In case studies did not report values for these factors, we employed median imputation. Lastly, moderator analysis was conducted to assess the role of additional factors for the clinical effectiveness of CBT interventions. Moderator analysis was conducted separately for face-to-face and digital CBT studies Table 2 .

For all meta-analyses, heterogeneity was assessed using I 2 statistics to describe the percentage of variation across studies 51 . Higher values indicate larger heterogeneity, with I 2 values of 25%, 50% and 75% representing low, moderate and high heterogeneity respectively 51 . Publication bias was assessed by visual inspection of funnel plots and by employing Egger’s test for funnel plot asymmetry for each meta-analysis. In case of significant Egger’s test, we used the trim-and-fill method to estimate the number of missing studies and report corrected estimated effect sizes 52 . A significance level of p  < 0.05 (two-tailed) was used for all analyses. All reported p values describe summary effect sizes or moderation effects of meta-analytic models unless stated otherwise.

Quality assessment

Two independent authors (U.M.R. and L.K.I.) assessed risk of bias using the Cochrane Risk of Bias tool 53 . We used four previously established classification criteria to quantify the risk of bias each study (high, low or unclear risk of bias): (1) random sequence generation, (2) allocation concealment, (3) selective outcome reporting (4) incomplete outcome data (5) blinding of participants and study personnel (6) blinding of outcome assessment.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All data analyzed in this meta-analysis is available upon reasonable request from the corresponding author.

Code availability

All code for analysis is available upon reasonable request from the corresponding author. All analyses were performed using R version 4.1.1 54 and the package metafor 55 .

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Acknowledgements

We thank Nils Dreschke for his help during data extraction and we acknowledge the helpful input from several authors of the publications analyzed in the context of this meta-analysis. JK obtained funding from the German Research Foundation (DFG, grant agreement No KA 4413/1-1) and from the EC (European Collaboration Project funded under the 7th Framework Programme under grant agreement no 602152). JK received honoraria for talks presented at educational meetings organized by Janssen-Cilag and Otsuka/Lundbeck, outside the submitted work. LKI was supported by NARSAD Brain and behavior Research Foundation, Young Investigator Award No° 28474.

Open Access funding enabled and organized by Projekt DEAL. We acknowledge support for the Article Processing Charge from the DFG (German Research Foundation, 491454339).

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These authors contributed equally: Lana Kambeitz-Ilankovic, Uma Rzayeva.

Authors and Affiliations

Department of Psychiatry and Psychotherapy, University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany

Lana Kambeitz-Ilankovic, Uma Rzayeva, Laura Völkel, Julian Wenzel, Frank Jessen & Joseph Kambeitz

Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany

Lana Kambeitz-Ilankovic & Johanna Weiske

Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany

Ulrich Reininghaus

ESRC Centre for Society and Mental Health, King’s College London, London, UK

Centre for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany

Peter J. Uhlhaas

Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK

Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia

Mario Alvarez-Jimenez

Orygen, Parkville, VIC, Australia

Research Center Jülich, Institute for Cognitive Neuroscience (INM-3), Jülich, Germany

Joseph Kambeitz

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Contributions

L.K.-I. and U.R. contributed equally to this work. L.K.-I. & J.K. designed the study. U.R., L.V., J.W. and J.W. contributed to literature search, data extraction and quality control. L.K.-I., J.K. and U.R. conducted the analysis. F.J., U.R., P.U. and M.A.-J. advised during data analysis. L.K.-I., J.K., U.R., F.J., U.R., P.U. and M.A.-J. contributed to the interpretation of the data and the writing of the manuscript. All authors contributed to the critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. All authors are accountable for all aspects of the work.

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Correspondence to Joseph Kambeitz .

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Kambeitz-Ilankovic, L., Rzayeva, U., Völkel, L. et al. A systematic review of digital and face-to-face cognitive behavioral therapy for depression. npj Digit. Med. 5 , 144 (2022). https://doi.org/10.1038/s41746-022-00677-8

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Article contents

Cognitive behavioral therapy for depression.

• Stirling Moorey Stirling Moorey South London and Maudsley NHS Foundation Trust, Centre for Anxiety Disorders and Trauma
•  and  Steven D. Hollon Steven D. Hollon Department of Psychology, Vanderbilt University
• https://doi.org/10.1093/acrefore/9780190236557.013.837
• Published online: 23 February 2021

Cognitive behavioral therapy (CBT) has the strongest evidence base of all the psychological treatments for depression. It has been shown to be effective in reducing symptoms of depression and preventing relapse. All models of CBT share in common an assumption that emotional states are created and maintained through learned patterns of thoughts and behaviors and that new and more helpful patterns can be learned through psychological interventions. They also share a commitment to empirical testing of the theory and clinical practice. Beck’s Cognitive Therapy sees negative distorted thinking as central to depression and is the most established form of CBT for depression. Behavioral approaches, such as Behavioral Activation, which emphasize behavioral rather than cognitive change, also has a growing evidence base. Promising results are emerging from therapies such as Mindfulness Based Cognitive Therapy (MBCT) and rumination-focused therapy that focus on the process of managing thoughts rather than their content. Its efficacy-established CBT now faces the challenge of cost-effective dissemination to depressed people in the community.

• cognitive behavior therapy
• cognitive therapy
• behavior therapy
• evidence-based therapy

Origins and Development of Behavioral and Cognitive Models of Depression

Behavioral models of depression have been largely based on Skinnerian or operant conditioning theory. Ferster ( 1973 ) proposed a model that saw depression as characterized by a decrease in the frequency of positively reinforced activities. Factors such as decreased environmental reward (e.g., resulting from a significant loss), avoidance or escape from aversive stimuli, schedules of reinforcement, and suppressed anger contribute to a reduction in the depressed person’s behavioral repertoire which in turn leads to less rewarding experiences. Lewinsohn ( 1974 ) developed this model further, as did Staats and Helby ( 1985 ) (see Dimidjian, Barrera, Martell, Muñoz, & Lewinsohn, 2011 ). However, this did not lead to significant developments in treatment or in outcome research, partly due to the surge in interest in Beck’s cognitive approach to depression that resulted from publication of the first randomized controlled trial to show that a psychological treatment could be as effective as antidepressants in depression (Rush, Beck, Kovacs, & Hollon, 1977 ). Beck first identified the importance of thoughts in depression in the early 1960s (Beck, 1963 , 1964 ). In contrast to behavioral approach that saw “internal” self-talk as a covert behavior, Beck suggested that cognition was central to depression. Beck noted that the dreams and self-reports of depressed patients were pervasively negative: They experienced a stream of negative automatic thoughts in response to events. In depression, he hypothesized, there was a shift in information processing such that stimuli which might usually be perceived as neutral or positive are seen as negative: a systematic cognitive bias. Underlying this bias are cognitive structures or schemas, often expressed as dysfunctional attitudes which, when activated by an event or accumulation of events, skew the interpretations and evaluations the person makes about the world. Examples of these include beliefs such as, “If I fail at something it means I’m a complete failure” or “If I don’t have someone to love and accept me it means I’m unlovable.” This results in an increasingly negative view of the self (“I am a failure; I am unlovable”), the world (“the world is unrewarding; others will reject me”), and the future (“I will never achieve my goals”) during the course of a depressive episode. Reduced expectations of being valued or succeeding at what the depressed person undertakes lead to avoidance and passivity that further reinforces the depressed mood and negative beliefs (Beck, 1967 , 1987 ).

Adverse life events and experiences in childhood lead to underlying assumptions, often expressed in conditional form: “If . . . then . . . .” For instance, the belief “If I fail at something, I’m a complete failure” may be laid down over years of being on the receiving end of demanding parental expectations. A significant failure experience in adult life, such as not passing an exam, will lead to activation of this schema and consequent depression (see Figure 1 ). Evidence for the cognitive model has accumulated since its original presentation (Beck & Alford, 2009 ; Clark & Beck, 1999 ). The association between negative thoughts and depression is particularly robust and seems to apply across cultures (Beshai, Dobson, Adel, & Hanna, 2016 ). Beck has modified the model to take account of research findings to include the concept of cognitive reactivity. People who are prone to depression will have a greater activation of negative beliefs than those who are not when they experience mood shifts in response to the vicissitudes of life (Scher, Ingram, & Segal, 2005 ). While major life events may be needed to trigger first-onset depression, repeated episodes make it easier for mild events to produce depression: the so-called kindling effect (Kendler, Thornton, & Gardner, 2000 ).

Figure 1. Developmental formulation.

Cognitive approaches such as Beck’s and Alloy and Abramson’s hopelessness model of depression (Abramson et al., 1989 ) generated the most research in the last decades of the 20th century , but in the first decades of the 21st century , behavioral models of depression experienced a resurgence, initially stimulated by the finding in a dismantling trial that the behavioral component of cognitive therapy was as effective as the full package (Jacobson et al., 1996 ). Contemporary behavioral activation models, based on Lewinsohn’s more integrative model (Lewinsohn, Hoberman, Teri, & Hautzinger, 1985 ), have a more sophisticated account of positive reinforcement, pay more attention to cognition by targeting ruminations, and emphasize the importance of avoidance of interpersonal situations in maintaining depression. There has also been a shift away from cognitive content (i.e., negative thoughts) to an interest in cognitive processes such as ruminations. Post-Beckian cognitive models emphasize the importance of how one relates to one’s thoughts as a factor in maintaining depression. Trying to analyze why one is depressed or fix one’s perceived inadequacies leads to cycles of rumination that dig one deeper into depression. Metacognitive therapy, rumination-focused cognitive behavioral therapy (CBT), and mindfulness-based cognitive therapy are examples of these more process-oriented forms of CBT (Segal, Williams, & Teasdale, 2013 ; Watkins, 2018 ; Wells, 2011 ). Table 1 summarizes the CBT models for depression in chronological order.

Table 1. Current Cognitive Behavior Therapies for Depression

Beck’s cognitive therapy, outline of treatment.

This form of cognitive behavioral therapy (CBT) is the best known and most researched, so it is described here in some detail. Cognitive therapy for depression (CT) is a relatively brief (20 sessions), structured, problem-focused treatment, firmly based on the cognitive model of depression. It can be understood to have a hierarchy of aims:

to reduce hopelessness and suicidality

to resolve target problems related to depression by teaching strategies to manage mood

to reduce vulnerability to future depression by modifying underlying beliefs and developing a relapse prevention plan

Target problems and goals are established at the beginning of therapy and each session is structured to use time as effectively as possible; an agenda is set which generally follows the plan:

bridge to last session with review of risk and current mood

review of homework

two to three agreed topics to address

setting homework

summary and feedback

Treatment is based on an individualized formulation which is developed in partnership with the patient. This initially focuses on the way in which thoughts, feelings, and behaviors interact to maintain the depression. The patient learns to identify situations that trigger a lowering of mood and the link between their negative thoughts and the mood shift. Similarly, the resulting patterns of behavior, such as withdrawal, are recognized. As therapy progresses, this conceptualization is deepened: Repeating sets of negative automatic thoughts reveal themes of underlying beliefs. The developmental conceptualization (Figure 1 ) links past learning experiences to these underlying beliefs or schemas and helps the patient see how these have made them vulnerable to depression. Because patients will be asked to examine deeply held beliefs, therapy tries to be as collaborative as possible. Rather than telling the patient their beliefs are maladaptive, the therapist encourages the patient to enter into a partnership to explore the validity and usefulness of them. Beliefs are turned into hypotheses that can then be tested through verbal discussion (Socratic questioning) or direct action (behavioral experiments). Depressed patients discover that their thoughts may be biased by their mood and learn to identify cognitive distortions or thinking errors. This process of putting beliefs to the test is referred to as “collaborative empiricism.” Therapy consists of a variety of cognitive and behavioral techniques. At the beginning of therapy, particularly if the patient is more deeply depressed, techniques will be more behavioral. These often begin with monitoring activities and rating them for the degree to which they are pleasurable or give a sense of achievement (mastery). Patients are then encouraged to engage in activities that promote pleasure or mastery and to note the effect on their mood. In contrast to Behavioral Activation that seeks behavioral change for its own sake, the activity work in cognitive therapy is always used in the service of cognitive change and, wherever possible, framed as an experiment to test negative thoughts. For instance, someone may predict that if they call a friend, they won’t be interested in them. The therapist can help them devise an experiment in which they take the risk of telephoning and evaluate the result: They may find that it took them half an hour to end the call because the friend was so pleased to hear from them! The next phase of therapy is for the patient to learn to recognize and evaluate their thoughts. This begins with monitoring of negative automatic thoughts as they arise in everyday situations. Patients learn to recognize how the depression biases their thinking in a negative direction. The therapist then uses Socratic questioning to evaluate the thoughts with the patient in the session, asking questions to help them examine their view of the situation. The touchstone for evaluating the thoughts is their logical consistency and the evidence available. Patients then practice identifying thoughts, asking questions such as: “What’s the evidence for and against this thought?”; “What’s the effect of thinking in this way? Is it helpful to me?”; and “Could there be an alternative explanation or way of testing my thoughts?” as homework between sessions. In the third phase of therapy, beliefs are elicited and tested that underlie the distorted thinking and make the patient vulnerable to future depression. So, for instance, a belief that “I must always succeed” or “I’m a failure” may be associated with perfectionistic behavior. The person may stay late at work, spend twice as long as their colleagues writing reports, and check them several times. The belief that “If I don’t do things perfectly, I’ll be found out and seen as a failure” can be tested through experiments where the patient spends less time preparing and checking reports and discovers that the result is just as good. They can then move on to deliberately making small mistakes and may discover that no one notices. In this final phase of therapy, the patient is encouraged to develop a blueprint or relapse prevention plan that summarizes as follows:

what she has learned from therapy

what techniques she needs to continue practicing (e.g., “make sure I structure my week so I don’t have long periods where I can ruminate”)

what risk factors and early warning signs to look out for

what she can do if her mood starts to drop

Efficacy of Cognitive Therapy for Depression

The first randomized controlled trial of CT (Rush et al., 1977 ) demonstrated a slight superiority of psychological treatment over tricyclic antidepressants with respect to acute response, but largely because the medications were tapered too soon such that early relapse was confounded with a lack of response. In the succeeding 40 years, numerous studies have compared Beck’s therapy with tricyclics and with specific serotonin reuptake inhibitors (SSRIs) and consistently found the two approaches to be equally effective (see reviews by Butler, Chapman, Forman, & Beck, 2006 ; Cuijpers et al., 2013a ; Cuijpers, Cristea, Karyotaki, Reijnders, & Huibers, 2016 ), though an individual patient data meta-analysis suggests there may be a slight advantage of medication over CBT (Weitz et al., 2015 ). There is evidence that combining CBT and medication adds to the effects of both (Cuijpers et al., 2014 ), although that effect appears to be heavily moderated (Hollon et al., 2014 ) and may come at the expense of undercutting CBT’s enduring effect (DeRubeis et al., 2020 ). CBT is significantly more effective than waiting list controls, treatment as usual, or placebo (effect size 0.71; Cuipers et al., 2013a ), while head to head comparisons of CBT with other evidence-based therapies, such as interpersonal therapy, tend to show both therapies to be equally effective (e.g., Luty et al., 2007 ). CBT is not only effective with mild-moderate levels of depression but also for the moderate-severe range when delivered by well-trained therapists (DeRubeis et al., 2005 ). Despite these encouraging findings that place CBT as the psychological treatment with the most robust empirical support, only 60% of patients achieve remission. When publication bias and use of waiting list controls are accounted for, the effect size of studies reduces considerably (Cuijpers, Cristea, Karyotaki, Reijnders, & Huibers, 2016 ; Driessen, Hollon, Bockting, Cuijpers, & Turner, 2015 ), as for antidepressant medications (Turner, Matthews, Linardatos, Tell, & Rosenthal, 2008 ). Table 2 summarizes comparisons between CBT (not exclusively Beck’s cognitive therapy), antidepressant medication, waiting list control, treatment as usual, and other psychotherapies.

Table 2. Efficacy of CBT for Depression

Notes : WL = waiting list; TAU = treatment as usual; ADM = antidepressant medication; NNT = number needed to treat.

Source : Data adapted from Cuijpers et al. ( 2013a ).

Relapse Prevention

Early randomized controlled trials comparing CBT with antidepressant medication that was withdrawn at the end of the trial reported relapse rates of 15–28% for CBT compared to 50–60% with a tricyclic (Evans et al., 1992 ; Simons, Murphy, Levine, & Wetzel, 1986 ). Biological psychiatrists argued that the antidepressant may have been withdrawn too soon for a fair comparison, since the recommendation is that medication be continued for 6–9 months after symptoms remit, but the differential relapse does indicate that CBT has an enduring effect. Later studies then compared CBT with maintenance medication. The relapse rates for patients receiving continuation medication were equivalent at 30% to patients receiving CBT alone (Cuijpers et al., 2013b ). In effect, CBT cuts risk of relapse among remitted patients by more than half relative to prior medications, and the two studies that compared prior CBT found that the enduring effect extended to the prevention of recurrence relative to recovered patients withdrawn after a year of continuation medication (Dobson et al., 2008 ; Hollon et al., 2005 ). In partially recovered depressed outpatients, adding CT to maintenance medication reduces relapse rates more than maintenance medication alone, and the beneficial effects of CBT persist for up to 3½ years (Paykel et al., 1999 , 2005 ). There is strong support in these studies for an enduring relapse prevention effect from CBT (Clarke, Mayo-Wilson, Kenny, & Pilling, 2015 ). However, it has been argued that rather than CBT preventing relapse, it is antidepressant discontinuation that promotes it (Andrews, Kornstein, Halberstadt, Gardner, & Neale, 2011 ). SSRIs increase serotonin available in the synapse by blocking reuptake but over time the system responds by reducing serotonin synthesis in the presynaptic neurone and reducing postsynaptic receptor sensitivity. This would explain why it seems to be so difficult to take patients off SSRIs without triggering a relapse (Hollon et al., 2019 ). Further research will hopefully answer this question.

Mediating Factors

Research into the factors that mediate outcome of CBT for depression fall into two categories: dismantling studies that attempt to identify active elements of treatment, and correlational studies that assess the relationship between treatment variables and reduction in depressive symptoms. Cuijpers, Cristea, Karyotaki, Reijnders, and Hollon ( 2019a ) recently carried out a meta-analysis of component studies to date and concluded that few had sufficient power to detect differences. Hundt, Mignogna, Underhill, and Cully ( 2013 ) reviewed the evidence for the impact of CBT skills on outcome and found that the small number of studies to date provided evidence that the frequency and quality of skill use influenced outcome. Click or tap here to enter text.Segal et al. ( 2019 ) found that the use of CBT skills post therapy was linked to reduced relapse and that this was mediated by the extent to which patients “decentered” from their negative thinking. Strunk and colleagues found that those patients who best mastered the skills taught in CBT were those least likely to relapse following treatment termination (Strunk, DeRubeis, Chiu, & Alvarez, 2007 ). The inclusion of homework has a significant effect on therapy outcome (Kazantzis, Whittington, & Dattilio, 2010 ). The therapeutic alliance is associated with therapy outcome across a range of different therapies (see Moorey & Lavender [ 2018 ] for a discussion of the importance of the therapeutic relationship in CBT). In CBT for depression, it may be the agreement on tasks and goals of therapy that is the most important aspect of this. Patients who accept the cognitive model and experience early symptom gains are likely to report a better therapeutic alliance and to make greater gains in therapy (Webb et al., 2011 ).

Behavioral Treatments for Depression

In 1996 , Neil Jacobson and colleagues reported the results of a three-way dismantling study that compared the behavioral activation (BA) component of Beck’s cognitive therapy (CT) for depression with BA plus thought challenging (AT), and with the full CT package. Each proved equally effective and the results held up at follow-up (Jacobson et al., 1996 ; Gortner et al., 1998 ). This revitalized the interest in behavioral models of treatment for depression and led to the development of a new therapy: BA. Like earlier behavioral approaches, BA sees depression as a result of a reduction in positive reinforcement which leads to a reduction in behavior and further low mood. In contrast to earlier models, this approach emphasizes the role of negative reinforcement of avoidance behavior: Social withdrawal and avoidance of responsibility and rumination bring temporary relief from painful affect but lead to more passivity and inactivity. BA uses activity monitoring and scheduling to encourage healthy behaviors and teaches patients to do their own functional analysis. Patients identify triggers for avoidance (Triggers, Reactions, and Avoidance Patterns—TRAPs) and replace them with coping responses (Triggers, Reactions, and Coping response—TRACs). A range of other techniques, including graded task assignment, mental rehearsal, problem-solving, and skills training, may all be employed (Martell, Addis, & Jacobson, 2001 ; Martell, Dimidjian, & Herman-Dunn, 2010 ). Behavioral activation is simpler and easier to teach than cognitive therapy (Ekers, Dawson, & Bailey, 2013 ) and there is a growing body of evidence for its effectiveness. Meta-analysis has found that there is a large effect size in comparison with controls (standardized mean difference [ SMD ] of −0.74) and a moderate superiority of BA over medication ( SMD −0.42) (Ekers et al., 2014 ).

Behavioral couple therapy (BCT) is a brief (12–20 sessions) intervention that can be applied when there is relationship distress and at least one partner is depressed. There is an interaction between the couple’s behavior and the depression such that intimacy and support is reduced and conflict increased. BCT seeks to improve the relationship through communication training, fostering positive exchanges between partners and teaching joint problem-solving skills. The approach is based on the groundbreaking work of Neil Jacobson (Jacobson et al., 1991 , 1993 ) but has developed over the subsequent 20 years. BCT improves both depression and the quality of the relationship (Christensen, Atkins, Yi, Baucom, & George, 2006 ) and is recommended in a number of guidelines such as the NICE guidelines for depression. A recent Cochrane review advised caution since the quality of randomized controlled trials (RCTs) of couples therapies and sample sizes are relatively low (Barbato, D’Avanzo, & Parabiaghi, 2018 ).

Process-Oriented Cognitive Behavioral Therapies

In contrast to cognitive behavioral therapy (CBT) for anxiety disorders, which has progressed through delineating specific models for the subgroups of anxiety diagnoses (panic, social phobia, etc.), depression has resisted this type of subcategorization beyond perhaps the distinction between acute and chronic depression. The research has therefore focused on refining the methodology of trials using Beck’s manualized cognitive therapy and more latterly behavioral activation (BA). Alternative cognitive approaches that have developed over the past 20 years have moved the focus from cognitive content (i.e., distorted negative thinking) to cognitive processes (e.g., rumination): the “third wave” behavior therapies. Well’s metacognitive therapy was first applied to anxiety and then later depression. It addresses the positive beliefs (“If I can understand why I am depressed I will be able to find a way out”) and negative (“I can’t control this rumination”) beliefs that drive worry and rumination and associated attentional processes (Papageorgiou & Wells, 2009 ; Wells, 2011 ). A meta-analysis suggests this approach may be more effective than standard CBT (Normann, van Emmerik, & Morina, 2014 ). A related approach is Watkins’ rumination-focused CBT which helps depressed patients shift their thinking style from abstract, overgeneralized thinking that maintains depression to more concrete, problem-focused thinking (Watkins, 2018 ). A randomized controlled trial has demonstrated its superiority over treatment as usual in residual depression (Watkins et al., 2011 ). One of the most influential developments in CBT in recent years has been mindfulness-based cognitive therapy (MBCT). This was originally developed as a relapse prevention program for recurrent depression. Relapse is understood to involve “a reactivation, at times of lowering mood, of patterns of negative thinking similar to the thought patterns that were active during previous episodes of depression” (Segal, Williams, & Teasdale, 2013 , p. 65). Rather than working with the cognitive appraisals, MBCT seeks to help people develop a “meta-awareness” of thoughts, feelings, and physical sensations so that there is a decentering or defusion from these patterns rather than identification with them. Mindfulness is the awareness that arises when one pays attention to one’s experiences in the present moment and in an accepting, nonjudgmental way. MBCT is delivered in groups of from 8 to 15 people and uses a combination of regular formal and informal meditation practices and insights from CBT. Meta-analysis suggests there is a relative risk reduction of 43% for those with three or more depressive episodes (Piet & Hougard, 2011 ) and that MBCT may be more effective for those with residual or fluctuating depressive symptoms (Kuyken et al., 2016 ; Segal et al., 2010 ). Acceptance and Commitment Therapy (ACT) is another “third wave” approach that is now being applied to depression with evidence for its efficacy (Bai, Luo, Zhang, Wu, & Chi, 2020 ; Zettle, 2004 ). The initial results from these process-oriented therapies are very encouraging, but sample sizes are small and more research is needed to determine what benefits they may have over the established behavioral and cognitive therapies for depression.

Application of Cognitive Behavioral Therapy to Various Populations

Cognitive behavioral therapy (CBT) has been successfully applied across the life cycle. CBT for adolescent depression is an effective intervention and in many ways similar to individual CBT for adults; it has also been used in a group format and with parental involvement. Parental engagement is understandably more important with the younger depressed patient (see David-Ferdon & Kaslow, 2008 ) for a meta-analysis of CBT for depression in children and adolescents, and Amberg & Ost [ 2014 ] in children from 8 to 12 years of age). CBT has also been successfully adapted for older people (Chand & Grossberg, 2013 ; Pinquart, Duberstein, & Lyness, 2007 ). Studies generally support the delivery of CBT to people with physical illness and associated depression (Beltman, Voshaar, & Speckens, 2010 ). Adaptations may be required to take account of difficulties in carrying out behavioral activation strategies that require physical exertion, and sensitivity in the way therapists help patients manage negative thoughts that may often have some basis in reality (Moorey, 1997 ). CBT appears to be effective across a range of health conditions (Okuyama, Akechi, Mackenzie, & Furukawa, 2017 ), including life-threatening illnesses such as cancer (Anderson, Watson, & Davidson, 2008 ; Moorey & Greer, 2011 ). Many of these trials, however, have small samples and a recent large-scale RCT comparing CBT with treatment as usual in patients with depression and advanced cancer failed to find an effect of therapy (Serfaty et al., 2020 ). CBT originated in a Western context, and the concept of collaborative empiricism assumes a relationship of equals in which clients share their thoughts and feelings and work toward solving problems and achieving their goals. In Eastern cultures, however, relationships may be structured more hierarchically. People may be less used to openly expressing and sharing their thoughts and feelings, and they may have a far more interdependent view of their goals. Adaptations of CBT in non-Western countries have tended to keep the content of the intervention relatively unchanged but have modified the forms of language used, the context, and the mode of delivery (Chowdhary et al., 2014 ). Preliminary evidence suggests that CBT can be transported cross-culturally with no loss of its effectiveness (see, e.g., a discussion of CBT in Japan: Ono et al. [ 2011 ]; Kobori et al. [ 2014 ]).

Disseminating Cognitive Behavioral Therapy

Much of the research in cognitive behavioral therapy (CBT) has been in the form of efficacy trials carried out in academic settings delivered by well-trained therapists. More effectiveness studies are needed to establish its usefulness in depression in “real world settings,” but perhaps more importantly, ways are needed to disseminate the techniques to the wider population. Freud’s model of the weekly 50-minute hour consultation has persisted into the 21st century . The prevalence of depression means it will never be possible to train enough therapists to deliver face-to-face CBT to those who need it. One solution is to move the treatment out of the one-to-one setting using groups or technology to improve cost-effectiveness. Another innovation in the United Kingdom has been the Improving Access to Psychological Therapies program that attempts to standardize evidence-based therapy nationwide. Briefer CBT delivered by nonprofessionals has been trialed in low- and middle-income countries. These three areas are described here as examples of alternative ways to deliver CBT more widely.

Alternative Formats to Individual CBT: Group, Computer, Internet, and Telephone

Group CBT is widely practiced but has not received as much research attention as individual therapy. It is usually delivered in a psychoeducational structured format (Scott, 2011 ). It may not be acceptable to about one third of patients, and the need for individual orientation sessions to prepare and engage patients means that it may not be as cost-effective as it appears on the surface. A naturalistic study, however, found that individual CBT was 1.5 times more expensive than groups that included 8–12 participants (Brown et al., 2011 ). A meta-analysis found that individual CBT was slightly superior post-treatment, but there was no difference at 3 months follow-up (Huntley, Araya, & Salisbury, 2012 ). Computerized CBT (cCBT) has become very popular because of its potential cost-effectiveness. Hofman, Pollitt, Broeks, Stewart, and Van Stolk ( 2017 ) carried out a systematic review of the available cCBT platforms and their effectiveness. They found large within-group effect sizes averaging 1.23. The findings overall do support its use in depression, but it may not be reaching groups who are less computer literate: The average cCBT participant was a female in her late 30s with a university degree who was in full-time employment. There should also be caution in assuming that participants will make full use of the program without any assistance: Reviews have consistently found guided self-help to be more effective than unguided (Andersson & Cuijpers, 2009 ). With the increased availability of the internet, online CBT programs are also being used more widely. For instance, a web-based program for depression has been shown to be more effective than treatment as usual (Farrer, Christensen, Griffiths, & Mackinnon, 2011 ). Finally, telephone CBT also appears to be an effective treatment for depression (Castro et al., 2020 ). Cuijpers and colleagues carried out a network meta-analysis comparing individual, group, telephone-administered, guided self-help, and unguided self-help for people with depression (Cuijpers, Noma, Karyotaki, Cipriani, & Furukawa, 2019b ). All approaches were equally effective and superior to a waiting list and care as usual. Guided self-help appeared to be less acceptable than individual, group, or telephone formats.

The U.K. Improving Access to Psychological Therapies Initiative (IAPT)

Psychotherapy has traditionally been something of a “cottage industry,” with an emphasis on the individual skill and discretion of the therapist, but not organized in a systematic, nationwide fashion. Provision has been patchy and many patients have not had access to evidence-based therapies. The U.K. Improving Access to Psychological Therapies (IAPT) program has been developed to redress this balance and to show that locally based therapy services that have clear targets, the means to evaluate outcomes, and are cost effective can work. In 2007 , the economist Richard Layard and the psychologist David Clark joined forces to lobby for a much-needed expansion of psychological therapies in the United Kingdom. They argued that anxiety and depression had significant deleterious effects on the economy (Layard, 2006 ). They suggested that the costs of increasing psychological therapies services would be outweighed by the benefits in savings to the health service and treasury through increased tax revenues and reduced spending on benefits. The IAPT program implements psychological treatments that have been shown to be effective and monitors their impact. The services set challenging targets for access (16% of the community prevalence of anxiety and depression) and outcomes (50% recovery: defined as PHQ-9 and GAD-7 scores falling below 10). Treatment follows a stepped care model. Low-intensity (LI) therapy is delivered by Personal Wellbeing Practitioners (PWPs). LI treatment includes guided self-help, computerized CBT, behavioral activation, and psychoeducational groups.

High-intensity therapy (HI) involves weekly face-to-face therapy delivered by fully trained CBT therapists. Patients with less severe problems are initially treated with LI and stepped up to HI if necessary, while more severe problems are treated with HI as the first intervention. A total of 36% of people receive only LI, 28% HI, and 34% both (Clark, 2018 ). IAPT services now treat nearly one million patients a year and achieve recovery in 50% of cases as well as reliable improvement in 66% (Clark, 2018 ), with evidence of substantial change in depression scores and a moderate impact on functioning (Wakefield et al., 2020 ). Over the 10 years IAPT has been operating services, recovery rates have been improving year by year. IAPT has received criticism on the grounds that it relies too heavily on quantitative measures that may give a falsely optimistic indication of improvement: There may be a mismatch between outcome measures and the client’s reported experience of distress (Bendall & McGrath, 2020 ), and also for its “managerialism” and perceived emphasis on efficiency over person-centered care (Dalal, 2018 ). Services do not always deliver the full “dose” of CBT for depression recommended in the NICE guidelines, and there is evidence that comorbid personality difficulties and complexity affect outcome and re-referral after treatment (Cairns, 2014 ; Goddard, Wingrove, & Moran, 2015 ). That being said, recovery rates have climbed from a percentage in the mid-30s to over 50% over the past decade (Clark, 2018 ). There is nothing like these rates elsewhere in the world.

CBT in Low- and Middle-Income Countries

The challenge of delivering CBT in developing countries where there are few psychiatrists and psychotherapists is substantial, but a number of programs are rising to the challenge. Community mental health workers can be trained to carry out brief CBT interventions with beneficial effects (e.g., Rahman, Malik, Sikander, Roberts, & Creed’s [ 2008 ] study of CBT for perinatal depression in rural Pakistan, and Bolton et al.’s [ 2014 ] study of CBT for depression, anxiety, and PTSD in Burmese refugees). The World Health Organisation (WHO) is rolling out a program called Problem Management Plus which trains lay helpers to deliver five weekly individual face-to-face sessions of 90 minutes for a range of problems, including depression. They teach simple evidence-based strategies such as relaxation, problem-solving, behavioral activation, and ways to strengthen social support (Rahman et al., 2016 ; WHO, 2016 ). Patel and colleagues found that from six to eight sessions of a culturally adapted version of behavioral activation, called the Healthy Activity Program delivered by lay counselors with no prior psychiatric training, was more efficacious than enhanced treatment as usual in a general practice setting in rural India (Patel et al., 2017 ), and that gains made in treatment largely held across a 9-month follow-up (Weobong et al., 2017 ).

Future Directions

The cognitive and behavioral interventions (if adequately implemented) can be as efficacious as medications in the treatment of even more severe depression (DeRubeis et al., 2005 ; Dimidjian et al., 2006 ) and have an enduring effect that medications simply lack (Dobson et al., 2008 ; Hollon et al., 2005 ). That being said, not everyone responds to either intervention, and there is emerging evidence that differential response to CBT versus medications can be predicted in advance. DeRubeis and colleagues used regression equations to combine multiple predictors of differential response into a single Personalized Advantage Index (PAI) and found that overall response could have been improved by as much as the typical drug-placebo difference if each patient had been given his or her optimal intervention (DeRubeis et al., 2014 ). This group has now moved on to using machine learning to generate precision treatment rules (PTRs) that can predict the optimal treatment for a given patient, and it should revolutionize the field (Cohen & DeRubeis, 2018 ). Even in the absence of making treatments better, overall efficiency of mental health delivery can be improved by getting each patient what he or she most needs.

Dissemination can be improved as well. Efforts to task-shift to lay counselors in low- and middle-income countries (LMIC) have shown that lay counselors with no prior psychiatric experience can be trained to deliver cognitive and behavioral therapies in an efficacious manner (Singla et al., 2017 ). The treatment gap is clearly largest in LMICs, but too few resources are available in high-income countries as well and, as IAPT has shown so well, a stepped-care approach can extend resources in a most salubrious fashion. It may well be that task-sharing approaches developed out of necessity in LMICs may readily transfer to other parts of the world also.

Finally, there is reason to think that nonpsychotic common mental disorders (including depression and anxiety) may represent adaptations that evolved to increased inclusive fitness (the propagation of one’s gene line) in our ancestral past (Hollon, Cohen, Singla, & Andrews, 2019 ). Most such “disorders” revolve around negative affects that motivate a differentiated response to different environmental challenges (Hollon, DeRubeis, Andrews, & Thompson, in press). To the extent that that is true, then simply “anesthetizing the pain” with medications may do little to resolve the problems that brought the symptoms about. Those psychosocial interventions (cognitive and behavior therapies and interpersonal psychotherapy) that teach problem-solving and interpersonal skills are likely to have broader and more enduring effects that sole reliance on pharmacological interventions (Hollon, in press).

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The effects of cognitive behavioural therapy on depression and quality of life in patients with maintenance haemodialysis: a systematic review

• Chen Ling   ORCID: orcid.org/0000-0003-4658-354X 1 , 2 ,
• Debra Evans 3 ,
• Yunfang Zhang 1 , 2 ,
• Jianying Luo 4 ,
• Yanping Hu 4 ,
• Yuxia Ouyang 4 ,
• Jiamin Tang 1 &
• Ziqiao Kuang 5  

BMC Psychiatry volume  20 , Article number:  369 ( 2020 ) Cite this article

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Depression is highly prevalent among Haemodialysis (HD) patients and is known to results in a series of adverse outcomes and poor quality of life (QoL). Although cognitive behavioural therapy (CBT) has been shown to improve depressive symptoms and QoL in other chronic illness, there is uncertainty in terms of the effectiveness of CBT in HD patients with depression or depressive symptoms.

All randomised controlled trials relevant to the topic were retrieved from the following databases: CINHAL, MEDLINE, PubMed, PsycINFO and CENTRAL. The grey literature, specific journals, reference lists of included studies and trials registers website were also searched. Data was extracted or calculated from included studies that had measured depression and quality of life using valid and reliable tools –this included mean differences or standardised mean differences and 95% confidence intervals. The Cochrane risk of bias tool was used to identify the methodological quality of the included studies.

Six RCTs were included with varying methodological quality. Meta-analysis was undertaken for 3 studies that employed the CBT versus usual care. All studies showed that the depressive symptoms significantly improved after the CBT. Furthermore, CBT was more effective than usual care (MD = − 5.28, 95%CI − 7.9 to − 2.65, P  = 0.37) and counselling (MD = − 2.39, 95%CI − 3.49 to − 1.29), while less effective than sertraline (MD = 2.2, 95%CI 0.43 to 3.97) in alleviating depressive symptoms. Additionally, the CBT seems to have a beneficial effect in improving QoL when compared with usual care, while no significant difference was found in QoL score when compared CBT with sertraline.

Conclusions

CBT may improve depressive symptoms and QoL in HD patients with comorbid depressive symptoms. However, more rigorous studies are needed in this field due to the small quantity and varied methodological quality in the identified studies.

Peer Review reports

End stage renal disease (ESRD) is a leading cause of morbidity and mortality worldwide, and it has a sharply increasing incidence and prevalence. Globally, the number of ESRD patients was 2.62 million in 2010 [ 1 ] and it is predicted to increase to more than double by 2030 to 5.4 million [ 2 ]. The increased ESRD prevalence is predominantly due to the incidence of diabetes and hypertension stay high and show an increasing trend [ 3 ]. Currently, HD is the mainstream treatment for ESRD patients, and 90% of them are receiving this therapy worldwide [ 4 ].

Depression is a prominent psychological problem in HD patients. It is estimated that HD patients have an approximately four-fold incidence of depression compared to the general population [ 5 ]. A multinational cross-sectional study found that the prevalence of depression was up to 46% from 2278 HD participants [ 6 ]. The depression symptoms of HD patients are associated with a series of adverse outcomes, for instance, lower treatment compliance [ 7 , 8 ], malnutrition, increased morbidity [ 9 ], decreased quality of life, higher rates of hospitalisation and mortality among HD patients [ 10 , 11 , 12 ]. However, depression issues are often under recognized and untreated [ 13 ]. Therefore, these severe outcomes indicated the importance of monitoring the mental state of the patients as well as the necessity of providing effective treatments for patients with HD.

CBT is one of the most widely practised therapeutic approaches in psychology. CBT reduces depressive symptoms by identifying inaccurate and maladaptive cognitions, testing the cognitions against reality, and modifying the dysfunctional thoughts, emotions and behaviours through different strategies accordingly [ 14 ]. The standard techniques of CBT which are utilised in treating depression are divided into two parts. The cognitive techniques include cognition identification, thought recording, cognition restructuring, thought testing and distraction strategy training [ 15 , 16 ]. The behavioural techniques consist of goal setting, activity scheduling, relaxation training and relapse prevention [ 17 ].

NICE clinical guideline [ 18 ] recommended CBT as a therapy for depression in people with chronic diseases. Subsequently, growing evidence has been shown that CBT is a well-established intervention in depression in different chronic diseases, such as diabetes, hypertension, heart failure co-morbid depression patients [ 19 , 20 , 21 ]. It also has a promising effect on some patients’ QoL. However, the effects of CBT on HD patients with depression remains unclear because there is no systematic review that specifically targets this issue.

Previously, there were three systematic reviews [ 22 , 23 , 24 ] that investigated the effects of psychological therapies on depression in HD and Chronic kidney disease patients. While these reviews included CBT studies, due to small quantity of the included articles of CBT and the included patients were not required to be assessed by the validated depression scales, there is a lack of conclusion which specifically emphasises the effect of CBT. The authors of the systematic reviews also recommended that certain types of psychological interventions could be investigated to reach more reliable conclusions [ 22 ]. Given that new RCTs have emerged after these three systematic reviews, there is a need to upgrade the evidence to assess the impact of CBT on patients’ reported measures of depression and QoL in individuals with HD.

In the present systematic review, randomised controlled trials (RCTs) were included exclusively. A randomised controlled trial is a type of scientific experiment that randomly allocating subjects to two or more groups, treating them differently, and then comparing them with respect to a measured response. Due to the randomised allocating process, this type of trial can reduce certain sources of bias, such as selection bias, when testing the effectiveness of treatments.

This article adherences to the PRISMA guidelines [ 25 ] for systematic review. The PRISMA checklist for this systematic review is presented in Additional file  1 (supplementary material).

Criteria for considering studies for this systematic review

The type of studies conducted.

Randomised controlled trials.

The type of participants involved

Participants were limited to adult patients (aged 18 years and over) with HD treatment (more than 3 months) and depression or depressive symptoms. Studies were included if participants who had depression or depressive symptoms were assessed by investigators using structured clinic interview (DSM) or validated depression scales. Studies whose patients had cognitive dysfunction were excluded because they could not understand and follow the procedures of CBT.

The type of interventions and comparison intervention used

The intervention of interest in this systematic review was CBT or CBT-based intervention. The included studies had to entail both cognitive and behavioural components, such as cognitive restructuring, behavioural activation, muscle relaxation and deep breathing. Studies which solely comprise cognitive therapy or behavioural therapy were excluded because they did not belong to the definition of CBT.

The intervention in included articles was CBT conducted by therapists or professional nurse or in a computerised CBT. The formats of CBT could be delivered individually (by telephone or face-to-face) or in groups. The comparison interventions could include no treatment, usual care, waiting lists and any other therapies.

The type of outcome measured

The outcomes of interest in this systematic review were depression and QoL among HD patients. There was no limitation on the types of validated scales relevant to depression and QoL.

Language, full-text availability and the timeline of the studies

Studies included in this review were required to be the English language and full-text articles. Only studies undertaken from January 1976 were included in this systematic review. According to Silverstein [ 26 ], thrice-weekly HD treatment has over four decades of routine access and clinical experience for adult HD patients. This means that the regular maintenance HD was started in 1976. The history of CBT can be traced back to the 1960s [ 27 ], which was longer than the maintenance HD treatment. Therefore, the present author identified the search dates range from January 1976 to July 2019.

Search strategy

Electronic database search.

Index term, such as Medical Subject Headings (MeSH) and free texts were used to ensure a comprehensive and specific search. The identified key search terms were “haemodialysis”, “cognitive behaviour therapy”, “cognitive therapy”, “behavioural therapy” and “depression”. The corresponding synonyms, abbreviations and truncations were utilised to expand the search range also. The full electronic search strategy is presented in Additional file 2 (supplementary material).

The following electronic databases were visited to identify the relevant RCTs: CINHAL, MEDLINE, PsycINFO, PubMed, CENTRAL (from 1st April 2019 up to 4th July 2019). The search record of CINHAL is attached in Additional file 3 (supplementary material)

Complementary search

The present author searched some specialist journals, such as Journal of Renal Care ; BMC Nephrology; International Urology and Nephrology; American Journal of Kidney Diseases; Hemodialysis International. Also, the present author browsed the reference lists of relevant systematic reviews and all included studies to identify additional articles that might have been missed from an electronic search.

Grey literature

To find as much evidence as possible, http://ethos.bl.uk/ , www.opengrey.eu/ and https://scholar.google.com/ were searched to identify relevant dissertations, conference abstracts or other research papers.

To ascertain the conclusions of the systematic review were as up to date as possible, the present author searched the trials registers website, such as www.ClinicalTrials.gov .

Study selection procedures

There were two stages of selection work. The first stage was reviewing the title and abstract. Initially, all the search results from different databases were downloaded into Endnote Version 9.0 software. Duplicate literature records were removed by the software. Then, all the titles and abstracts of the imported literature were scanned by the present author. The standard of the reviewing was based on the population, intervention, comparative intervention, outcome and type of study. Articles that were not relevant to the topic of the systematic review were excluded. For those articles that met the inclusion criteria, or they did not provide enough information in the abstract, the full-text articles were required. If the full text of research could not be obtained after contacting the article author, applying for the inter-library loans service, or using any other methods, the articles were excluded. Those obtained full-text articles were brought into the next stage of selection.

The second stage was reviewing the full-text paper. The standard of the reviewing was based on the inclusion criteria and exclusion criteria. For the studies which could not be determined by the author, they were discussed with the second author to achieve a consensus result. The selection of articles was followed with the PRISMA flowcharts and presented with a diagram.

Quality assessment

The Cochrane risk of bias tool was used to assess the potential bias in the studies included in the present systematic review. Included studies were assessed via six domains, including selection bias, performance bias, detection bias, attrition bias, reporting bias and other bias. The results of the assessment were expressed as low bias risk, high bias risk and unclear bias risk. RevMan 5.3 software was used to present the results of the quality assessment more visually.

Data extraction

A pre-designed data extraction form was employed to collect relevant and necessary information of included studies. The data to be extracted include details of study information (authors, published year country and publication), methods (aims of the study, study design, setting), participants (sample size and allocation, drop out, mean age, gender, inclusion criteria, and exclusion criteria), interventions (including descriptions of the implementation process of CBT and counter-intervention, frequency and length of intervention, length of follow-up, amount of contact, adverse effects and deliverers), outcomes (primary and secondary outcomes specified and collected), results (the depression and QoL scores at baseline, post-intervention and follow-up), conclusions and the results of the assessment of the risk of biases.

Data synthesis

In this systematic review, the included comparison interventions were usual care, no intervention and any other therapies. Due to the diversity of interventions included, narrative synthesis combined with meta-analyses may be used in the present review. To measure the clinical effectiveness of the intervention, mean differences (MD) and the corresponding 95% confidence intervals (CI) were calculated. To assess the heterogeneity among studies, chi-square test and I 2 were utilised. If the tested heterogeneity is not significant ( P  ≥ 0.1, I 2  ≤ 50), the fixed-effect model can be used. If the tested heterogeneity is distinct ( P <0.1, I 2 >50), the random effect model can be used in meta-analysis [ 28 ]. The amount of heterogeneity was evaluated visually by conducting a forest plot [ 29 ].

Results of the search strategy

The initial search of electronic databases yielded a total of 1056 records, and 3 records were identified through other resources. After the removal of duplicate studies and careful appraisal of titles, abstracts and full-text, 6 articles were included in the present systematic review. The process of literature retrieval is summarised in Fig.  1 below. The characteristics of excluded studies are summarised in Additional file 4 (supplementary material).

PRISMA Flowchart for search result. Detailed legend: The initial search of electronic databases yielded a total of 1056 records, and 3 records were identified through other resources. After the removal of duplicate studies and careful appraisal of titles, abstracts and full-text, 6 articles were included in the present systematic review

Characteristics of included studies

A total of six RCTs and 479 participants were included in the current review (248 in CBT groups, 231 in control groups). The studies all published between 2009 and 2019. The sample sizes ranging from 49 to 116 patients per study. In this population, 51.6% of the participants were males whilst 48.4% of them were females. Studies specifically recruited adult patients over 18 years old, and the mean age of this population ranged from 41.7 to 54.0.

All studies included HD participants with depressive symptoms, while with different criteria. The inclusion criteria, characteristics of the population and baseline are summarised in Table  1 below. According to the scoring instructions of different depression scales and the baseline depression score, the included participants were assessed as mild to moderate depression before the treatment in Lerma et al.’s study [ 30 ]; moderate depression in four studies [ 31 , 32 , 33 , 34 ]; and moderate to severe depression in Al saraireh et al.’s study [ 35 ]. The depressive symptoms in above studies were measured by the Beck Depression Inventory (BDI), Hamilton Depression Rating Scale (HDRS), Mini International Neuropsychiatric Interview (MINI), Hospital Anxiety and Depression Scale (HADS) and Quick Inventory of Depressive Symptoms-Clinician-rated (QIDS-C).

Details of study interventions and comparisons

All the intervention groups included both the cognitive and behaviour elements. Moreover, all of the studies used a face-to-face method to conduct CBT. However, these CBT were varied in format, delivery and duration. In four studies, the CBT interventions were conducted by individual format [ 32 , 33 , 34 , 35 ]. The remaining two studies evaluated group CBT programmes, each group consisting of 3–6 patients [ 30 , 31 ]. Overall, the duration of CBT varied from 5 weeks to 12 weeks, and the study follow-up period ranged from 1 month to 6 months after the post-treatment. Each weekly session lasted 1 h to 2 h. The interventions were delivered by psychologists, therapists without description, or nurses who had CBT expertise.

In the comparison groups, three studies compared CBT against usual care (also sometimes described in trials as treatment as usual or waiting list) [ 30 , 31 , 32 ]. The remaining three studies compared CBT with active comparisons groups comprising counselling [ 33 ], psychoeducation [ 35 ] and antidepressants [ 34 ]. Table  2 provides the detailed characteristics of the included studies below.

Results of study quality assessment

Figure  2 and Fig.  3 below present a summary of the risk of bias across studies.

Risk of bias graph: review authors’ judgements about each risk of bias item presented as percentages across all included studies

Risk of bias summary: review authors’ judgements about each risk of bias item for each included study. Detailed legend: Read the main text --Results of study quality assessment (Page 19–20)

Random sequence generation

All studies were described as “randomised”, and five of the six studies reported adequate information about randomisation. However, one study [ 32 ] was rated as unclear because there were insufficient details about the methods of randomisation.

Allocation concealment

Four studies [ 30 , 32 , 34 , 35 ] failed to state the detailed information of allocation concealment. Therefore, these four studies were rated unclear by default. Two studies [ 31 , 33 ] used sealed envelopes to conceal the assignments, which in turn avoids selection bias. Hence, these two studies were rated as at low risks of allocation concealment.

Blinding of participants and personnel

Given the nature and method of implementation of CBT, it was impossible to keep the persons receiving or delivering the intervention or usual care blinded. Therefore, all studies were at high risk of performance bias.

Blinding of outcome assessment

In the six studies, four articles explicitly stated the blinding of outcome assessors [ 30 , 31 , 32 , 34 ]. Hence, they were at low risk of detection bias. There was no description of the blinding of outcome assessment in the remaining two studies [ 33 , 35 ]. Hence, the detection bias was rated as unclear in these two studies.

Incomplete outcome data

Four studies [ 30 , 31 , 32 , 34 ] were rated as low risk of attrition bias due to the relatively low and balanced dropout rates, and clearly stated reasons. Valsara et al.’s study [ 33 ] failed to report the reasons for dropout. Therefore, the attrition bias of Valsara et al.’s study was considered as unclear. One study had higher attrition rates (CBT group was 21.6%, while 25.9% in the psychoeducation group) [ 35 ]. Therefore, Al saraireh et al.’s study [ 35 ] was rated as at high attrition risk.

Selective reporting

One trial protocol was published in Mehrotra et al.’s study [ 34 ]. All the outcomes were reported as planned. For the other five articles, selective reporting bias was not able to be assessed due to a lack of published protocols. Therefore, the methodologies and results sections of these five studies were carefully scanned to find incomplete data reports. All of the articles reported the pre-set outcomes. Hence, the rest of the five studies were rated as at low reporting bias.

Effects of the intervention

The summary of the outcomes and effects of the interventions are elaborated in Table  3 below.

CBT vs usual care

Three studies compared CBT versus usual care at post-treatment and follow-up.

Reduction in depressive symptoms

Post-treatment

The meta-analyses of the three CBT versus usual care studies for depression are shown in Fig.  4 . The CBT studies favoured the direction of the intervention, showing improvements in symptoms of depression (MD = − 5.28, 95% CI − 7.9 to − 2.65, p  = 0.37).

Forest plot of CBT vs usual care in the reduction of depressive symptoms after post-treatment. Detailed legend: Read the main text --Effects of the intervention (Page 22–23)

Lerma et al.’s study [ 30 ] conducted five weekly CBT sessions. The calculated MD was − 4.8 (95%CI − 10.6 to 1.00), meaning that the difference in depressive symptoms mean scores between the CBT and usual care was not statistically significant (Fig.  5 ). In Cukor et al.’s [ 32 ] and Duarte et al.’s [ 31 ] studies, they all conducted 12 weeks of CBT. Duarte et al.’s study demonstrated the significant differences in favour of CBT (MD = − 7.1, 95%CI − 10.88 to − 3.32). Upon a closer looking in Duarte et al.’s study and compared the data between baseline (Table 2 above) and post-treatment, the depression level gradually decreased from moderate depression to mild depression in CBT group (baseline:24.2 ± 9.7, post-treatment: 14.1 ± 8.7, P <0.001). Conversely, the patients in the usual care group stayed in moderate depression level after the treatment (baseline:27.3 ± 10.7, post-treatment: 21.2 ± 9.1, P <0.001).

Forest plot of CBT vs usual care in the reduction of depressive symptoms after follow-up. Detailed legend: Lerma et al.’s [ 28 ] study reported the significant difference (MD = -7.6, 95%CI − 12.75 to − 2.45) between two groups during the 4 weeks follow-up after treatment. Similarly, in Duarte et al.’s [ 29 ] study, the difference between CBT compared with usual care was also be found during the 6 months follow-up after treatment (MD = -6.8, 95%CI − 11.07 to − 2.53). In contrast, in Cukor et al.’s [ 30 ] study, there was a non-significant effect in reducing the depression symptoms between the CBT and usual care during the 3 months follow-up

However, Cukor et al. ‘s [ 32 ] study showed no difference between the CBT and usual care (MD = − 2.8, 95%CI − 7.47 to 1.87) (Fig. 4 ). A more in-depth look at the baseline and post-treatment depression scores, the depression level of both groups changed from moderate to mild depression (post-treatment in CBT group: 11.7 ± 9.8; post-treatment in usual care group: 14.5 ± 8.5). Additionally, Cukor et al.’s [ 32 ] study also used the HAM-D scale to test the effectiveness of CBT. Compared with the non-significant results measured by BDI, the results measured by HAM-D scales showed a significant difference in favour of CBT compared with usual care (MD = -4.4, 95%CI − 7.51 to − 1.29). Furthermore, the depression level reduced significantly from moderate depression to normal condition in the CBT group, while the participants in the control group stayed a mild degree of depression using the HAM-D tool.

The meta-analyses of the three CBT versus usual care studies for depression are shown in Fig. 5 . The CBT studies favoured the direction of the intervention, showing improvements in symptoms of depression (MD = − 4.37, 95% CI − 9.90 to 1.16, p  = 0.008). Statistically significant heterogeneity was found in this analyse (I 2  = 79%).

Three studies reported the depressive scores at follow-up (Fig. 5 ). Lerma et al.’s [ 30 ] study reported the significant difference (MD = -7.6, 95%CI − 12.75 to − 2.45) between two groups during the 4 weeks follow-up after treatment. Similarly, in Duarte et al.’s [ 31 ] study, the difference between CBT compared with usual care was also be found during the 6 months follow-up after treatment (MD = -6.8, 95%CI − 11.07 to − 2.53). In contrast, in Cukor et al.’s [ 32 ] study, there was a non-significant effect in reducing the depression symptoms between the CBT and usual care during the 3 months follow-up. (Fig. 5 ).

Improvement in QoL

Three studies demonstrated QoL outcomes between CBT with usual care. Duarte et al.’s [ 31 ] study stated that CBT had a positive effect of improving the mental component summary in the KDQOL scale ( P <0.001 in the CBT group, P  = 0.451 in usual care group), whilst the difference in physical component summary in the KDQOL scale was not significant ( P  = 0.577 in the CBT group, P  = 0.604 in the control group).

Lerma et al.’s [ 30 ] study showed a significant difference between the CBT and usual care on QoL at post-treatment and 5 weeks follow-up (SMD = 0.73, 95%CI 0.13 to1.33; SMD = 0.89, 95%CI 0.28 to1.50). In contrast, in Cukor et al.’ [ 32 ] study, no statistically significant differences were found at post-treatment and follow-up.

CBT vs non-directed counselling

One study (67 participants) contributed to this outcome [ 33 ]. Compared to baseline, the two groups all decreased depression level from moderate to mild. Nevertheless, the difference in depression scores between the CBT group and the non-directed counselling was significant, favouring CBT. (MD -2.39, 95%CI − 3.49 to − 1.29). Similarly, there was also a significant difference (MD -3.01, 95%CI − 4.06 to − 1.96) after 3 months of follow-up. This study did not investigate the QoL outcome at post-treatment or follow-up.

CBT vs antidepressant

Mehrotra et al.’s [ 34 ] study (114 participants) compared the effectiveness between CBT and sertraline, and the depression symptoms were measured by QIDS-C. The two groups all showed significant effects in reducing depressive symptoms from moderate to mild. However, the results demonstrated that sertraline groups were more effective than CBT in reducing depressive symptoms immediately post-treatment (MD 2.2, 95%CI 0.43 to 3.97). The follow-up data of depressive symptoms was not reported. Regarding the QoL, the difference in QoL improvement between the CBT group and sertraline group was non-significant (Effect estimate with 95% CI: − 0.6 (− 0.2 to 1.4)).

CBT vs psychoeducation

Only Al saraireh et al.’s [ 35 ] study (105 participants) reported that psychoeducation reduced the HAM-D score significantly compared to CBT (MD 3.9, 95%CI 2.27 to 5.52). Compared to baseline, the severity of depression in the psychoeducation group decreased from severe to moderate, while the severity of depression in CBT group did not change. The change of depression scores at follow-up and QoL were not reported in their study.

Summary of the main findings

All studies showed that depressive symptoms improved with CBT. Upon a closer look, the results demonstrated a beneficial effect of CBT on depressive symptoms and QoL when compared to usual care and non-directive counselling. It also stated that CBT was less effective than sertraline and psychoeducation in improving depressive symptoms.

Discussion of the main findings

CBT seems to be more effective than usual care in alleviating depression. As mentioned before, three studies compared CBT with usual care, and they were varied in the quality of the evidence and results. Duarte et al.’s [ 31 ] study had the least risk of bias among these three studies (only had performance bias, which was unavoidable in conducting CBT). Given the strong evidence from Duarte et al.’s study, CBT appears to more effective than usual care in improving depressive symptoms.

Due to the sparse experiments on this topic, globally, there is no specific guidance of depression in HD patients. However, the finding of the present review is relatively consistent with the NICE guideline [ 18 ] on chronic disease patients with depression. This guideline recommends CBT for mild to moderate depression patients with a chronic illness condition [ 18 ]. Similarly, this finding is in line with the systematic review [ 19 ] indicating that CBT was more effective than usual care in heart failure patients with depression.

However, in the current review, it seems that HD patients with depression did not benefit from short-term CBT. In Lerma et al.’s [ 30 ] study, after 5 weeks CBT, the depressive score between the two groups did not show statistically significant difference. The possible reason might be that depression is a chronic condition; patients could not recover with limited psychological treatments. Likewise, NICE guidelines [ 18 ] also suggest that nine to 12 weeks CBT were needed for chronic disease patients with depression. However, the result of Lerma et al.’s (2014) study needs to be interpreted with caution due to the small sample size and relatively low quality of the evidence.

Interestingly, in the present review it was also found that CBT has a long-term sustainable effect among HD patients with depression. In Duarte et al.’s [ 31 ] study, at 6 months follow-up after the treatment of CBT, the depression scores decreased in CBT group and showed a significant difference between the comparison and intervention groups. This point is also supported by Cuijpers, Hollon [ 36 ]. The possible reasons for this effect could be explained in that patients in CBT groups are taught the skills and knowledge to identify maladaptive thinking and deal with the depressive symptoms. Since the patients were equipped with the coping strategies, they could take preventative methods to alleviate depressive symptoms [ 37 ]. Indeed, one of the aims of CBT is to empower clients to become their own therapist [ 17 ]. In that way, CBT could help patients prevent depression recurrence [ 38 ].

CBT vs counselling

In the present review, one study showed that CBT was more effective than non-directive counselling at post-treatment and 3 months of follow-up in HD patients [ 33 ]. The possible reasons for this result might be the different strategies used between CBT and counselling. CBT is task-oriented, focusing on changing the clients’ thinking and behaviour patterns, and finding solutions to the practical issues. In contrast, counselling is less directive. Counsellors use active listening and empathetic attitude strategies to help the patients to understand themselves better [ 39 ]. Valsara et al.’s [ 33 ] result supports the statements of NICE guidelines for depression in adults [ 18 ]. In this guideline, CBT is recommended as a frontline treatment, while counselling is suggested as a second-line intervention.

However, in recent years, a growing number of studies suggest that CBT and counselling have comparable effects [ 40 , 41 ]. Therefore, it is unknown whether the recommendations of NICE guidance would be revised based on these current studies. As the number of studies on this topic was sparse, and the quality of Valsaraj et al.’s [ 33 ] study was not high, there is no firm conclusion for these two therapies. Hence, better-designed RCTs which improve on the methodology used by Valsaraj et al.’s study are needed in the future. However, evidence-based medicine is not only about the effectiveness of the intervention but also the preferences of the patients where possible [ 42 ]. Therefore, further studies could conduct not only quantitative studies to investigate the effectiveness of these two therapies but also qualitative research to explore the preferences and experiences of HD patients in these two kinds of psychotherapies.

CBT vs sertraline

It is noteworthy that, in the present review, the newest study conducted by Mehrotra et al. [ 34 ] reported that sertraline was slightly more effective than CBT in HD patients with moderate depression. Mehrotra et al.’s [ 34 ] study had a relatively high methodological quality. The multicentre design could balance the confounding factors, promoting generalisation. Moreover, compared to other studies in this review, the depressive symptoms in their studies are measured by clinician-rated validated depression scale. This could increase the reliability of the outcome measurements.

This finding is consistent with an RCT, which compared the effectiveness of CBT with sertraline in diabetes patients with depression [ 43 ]. In comparison to CBT, the rapid therapeutic effect is the most advantageous to antidepressants. However, compared to diabetes patients, the safety of the antidepressants should be emphasised among HD patients due to their limited renal function and the possibility of drug-drug interactions. Indeed, in Mehrotra et al.’s [ 34 ] study, the rates of adverse events were higher in the sertraline group. Therefore, for moderate depressive HD patients, both treatments could be considered, while the pharmacological therapies need to be taken into account carefully.

In addition, for HD patients with severe depression, the combination of CBT with antidepressants is worthy to further investigation. According of NICE (2009) [ 18 ], the guideline suggests that CBT with antidepressants can be utilised among severe depression patients with a chronic illness. However, most of the participants in the present systematic review were diagnosed with moderate depression. Hence, further study could investigate the efficacy of the combined function of CBT with antidepressants.

Regarding QoL, CBT might have a positive influence in improving QoL. In the present review, four studies all showed that the QoL scores increased after the CBT when comparing to baseline QoL scores. However, comparing CBT with usual care and sertraline, different results were reported. Owing to the varied number of risk of biases of these four studies, the present author could not reach a convincing conclusion. Nevertheless, considering the methodological quality of Duarte et al.’s [ 31 ] study is higher than the other three studies, CBT could be suggested as an effective treatment in improving QoL among HD patients with comorbid depression.

The applicability of evidence

The scope of the current systematic review was limited to adult HD patients with depressive symptoms. The literature on therapy for depression in paediatric HD was not reviewed. Furthermore, the majority of the adult patients were middle-aged population, which was inappropriate to apply the conclusion to the geriatric HD patients with depression. Lastly, most of the participants included in the present systematic review were assessed as having moderate depression. Hence, the conclusions of the current review may not be applicable to HD patients with severe depression.

The applicability of CBT

Given that CBT could be considered as an efficient, safe treatment option for HD patients, renal department healthcare providers should consider CBT as a treatment option. According to Goh et al. (2018) [ 7 ], the CBT might hard to embed in standard care in terms of insufficient access for participants to this therapy and limited CBT providers [ 44 ]. Hence, the present author discussed the solutions of this issue in two ways, which is elaborated as follows.

Internet-based CBT can be considered as an effective treatment for HD patients with depression. In the present review, all studies used traditional face-to-face CBT. Barriers of face-to-face CBT include geographic distance, limited professional therapists and high cost of therapy [ 45 ]. To bridge these treatment gaps, Internet-based CBT has been proved as one kind of methods to resolve the barriers mentioned above. Furthermore, according to an updated meta-analysis conducted by Carlbring et al. [ 46 ], internet-guided CBT and traditional face-to-face CBT have equivalent effects. However, for HD patients with comorbid depression, there was an absence of evidence which used internet-based CBT. Therefore, further study could investigate this type of CBT in HD patients.

Nurses can be considered as deliverers of CBT. Generally, CBT is conducted by professional therapists or psychologists. Interestingly, one study conducted in the US after hurricanes Katrina and Rita demonstrates that CBT may not need to be performed by psychologists. In their research, twenty-two social workers used CBT to care ESRD patients after the disaster. The depressive symptoms were significantly improved after the therapy [ 47 ]. Truly, in the present review, two of the included studies showed that the CBT which was conducted by nurses also had a promising effect on decreasing depression scores. Likewise, an RCT with 279 chronic obstructive pulmonary disease patients with diagnosed anxiety, a nurse-led CBT has been proved to be a clinically and cost-effective treatment to alleviate anxiety symptoms [ 48 ]. Therefore, further study could investigate the effectiveness of nurse-led CBT in HD patients.

Strengths and limitation of this systematic review

Only HD patients diagnosed with depressive symptoms were included in the present review. This is inconsistent with the previous three relevant systematic reviews [ 22 , 23 , 49 ] which failed to include participants diagnosed with depressive symptoms at baseline. The number of included studies was decreased due to this rigorous criterion. Nevertheless, the conclusions of the present review serve the most relevant population.

Only six RCTs with 479 participants were included in the current systematic review; the handful quantity of studies and small sample size limited generalisation. Secondly, the diagnostic criteria of depression, the definition of CBT components, format, duration, as well as the outcome measurements were varied in included studies. Thirdly, the quality of the included studies was varied. Only one study was rated as low risk of bias in most of the domains. Therefore, firm conclusions could not be identified due to the reasons above.

Fourth, most of the outcome measurements (depression and QoL) were self-reported questionnaires, which involved patients’ subjective feeling; this may also produce biases. In addition, publication bias might be generated due to merely English articles were included in the present review. Lastly, there were insufficient studies that investigated the long-term maintained effects of CBT. Only one study assessed the depressive symptoms and QoL at 6 months follow-up. Therefore, the long-lasting effect of CBT was unknown.

Implications for practice

Depression screening and early intervention of depression might be essential in routine HD nursing. In the current review, the present author found that most of the included patients had moderate depression at baseline, while the proportion of mild depression patients was small. This condition indicates that healthcare providers need to find approaches to prevent depressive symptoms from deteriorating in the early stage of depression. Hence, screening and integrating the knowledge and skills of CBT with patients’ education might be an effective way to improve HD patients’ well-being.

Implications for future research

At present, the quality and number of studies investigated in this field were insufficient. Therefore, more rigorous studies comparing the CBT with usual care and other treatments (for example, antidepressant) in HD patients with depression are needed in the future. In terms of the methodological quality or the existing evidence, future studies can focus on recruiting larger sample size, utilising allocation concealment and recruiting blinded outcome assessors to improve the quality of the studies. In regard to the gaps of the present review, future research can work toward the different approaches in CBT among HD patients with depression, such as internet-based CBT, CBT combined with antidepressants or nurse-led CBT. Additionally, more studies should focus on the long-term effects of CBT on depressive symptoms and QoL.

HD patients diagnosed with depression could be investigated in the future. Generally, depression should be diagnosed by professionals according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). In the present review, none of the participants was diagnosed with depression according to DSM; most of them are screened by different depression questionnaires. Duarte et al’ s research used the MINI International Neuropsychiatric Interview to screen out the participants instead of depression questionnaires. However, MINI is applied to meet the need for a short but accurate structured psychiatric interview for multicentre clinical trials and epidemiology studies and to be used as the first step in outcome tracking in non-research clinical settings [ 50 , 51 ]. Thus, MINI should not be used to officially diagnose depression. Given this status, the present author suggests that researchers could pay attention to this type of person.

In summary, CBT has shown an encouraging effect on depressive symptoms and mental summary of QoL among HD patients with depressive symptoms. Twelve weeks of intervention can be recommended in HD clinical practice. However, due to the mixed quality and small quantity of the existing studies, firm conclusions were prevented.

Availability of data and materials

Not applicable since no new data involved.

Abbreviations

End Stage Renal Disease

• Haemodialysis

Cognitive Behavioural Therapy

• Quality of life

Beck Depression Inventory

Hamilton Depression Rating Scale

Mini International Neuropsychiatric Interview

Hospital Anxiety and Depression Scale

Quick Inventory of Depressive Symptoms-Clinician-rated

Mean Difference

Confidence Intervals

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Acknowledgements

We are grateful to Alice May, Simon Cook, Hongyan Li and Tianbiao Zhou for their help in reviewing this paper.

This study was funded by the Guangzhou Medical Key Subject.

Construction Project (grant no. 2017–2020) and the Program of.

Huadu District Science and Technology, Guangzhou, China (grant no. 15-HDWS2017).

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CL and DE contributed to the conception and design of the review. CL and DE undertook and contributed to the systematic search, screening, selecting articles and assessed study quality. CL revised all versions of the manuscript. DE, YZ, JL, YH, YO, JT and ZK critically reviewed the manuscript. All authors have read and approved the final manuscript, and ensure that this is the case.

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Chen Ling: Registered nurse (RGN). Master of Nursing of Guangzhou Medical University (China), Master of Science Professional Practice-Healthcare programme in the Birmingham City University (United Kingdom).

Debra Evans: PhD in Physiology, Senior Lecturer in research methodology of Birmingham City University. Senior Fellow of Higher Education Academy (HEA).

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PRISMA Checklist for the present systematic review.

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Characteristics of excluded studies.

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Ling, C., Evans, D., Zhang, Y. et al. The effects of cognitive behavioural therapy on depression and quality of life in patients with maintenance haemodialysis: a systematic review. BMC Psychiatry 20 , 369 (2020). https://doi.org/10.1186/s12888-020-02754-2

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• Cognitive behavioural therapy

BMC Psychiatry

ISSN: 1471-244X

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• Introduction
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eTable 1. Search Strategy and Number of Hits per Search Engine

eTable 2. Characteristics of Included Studies

eTable 3. Sensitivity Analysis of Treatment Effects Based on Symptom Outcome Measures

eTable 4. Subgroup Analyses Across Treatment Approaches

eTable 5. Subgroup Analyses Across Comparison Groups

eFigure 1. Study Design Quality, Therapy Quality, and Researcher Allegiance per Study

eFigure 2. Standardized Effect Sizes of Comparisons Between CBT and Comparison Groups on Symptoms for Generalized Anxiety Disorder

eFigure 3. Standardized Effect Sizes of Comparisons Between CBT and Comparison Groups on Symptoms for Panic Disorder With or Without Agoraphobia

eFigure 4. Standardized Effect Sizes of Comparisons Between CBT and Comparison Groups on Symptoms for Social Anxiety Disorder

eFigure 5. Standardized Effect Sizes of Comparisons Between CBT and Comparison Groups on Symptoms for Specific Phobia

eFigure 6. Standardized Effect Sizes of Comparisons Between CBT and Comparison Groups on Symptoms for Posttraumatic Stress Disorder

eFigure 7. Standardized Effect Sizes of Comparisons Between CBT and Comparison Groups on Symptoms for Obsessive Compulsive Disorder

eFigure 8. Funnel Plots of Standard Error by Hedges g of Symptom Level After Cognitive Behavioral Therapy Relative to Comparison Groups for Generalized Anxiety Disorder

eFigure 9. Funnel Plots of Standard Error by Hedges g of Symptom Level After Cognitive Behavioral Therapy Relative to Comparison Groups for Panic Disorder With or Without Agoraphobia

eFigure 10. Funnel Plots of Standard Error by Hedges g of Symptom Level After Cognitive Behavioral Therapy Relative to Comparison Groups for Social Anxiety Disorder

eFigure 11. Funnel Plots of Standard Error by Hedges g of Symptom Level After Cognitive Behavioral Therapy Relative to Comparison Groups for Specific Phobia

eFigure 12. Funnel Plots of Standard Error by Hedges g of Symptom Level After Cognitive Behavioral Therapy Relative to Comparison Groups for Posttraumatic Stress Disorder

• Error in Abstract, Text, Table, and Supplemental Content JAMA Psychiatry Correction July 1, 2020

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van Dis EAM , van Veen SC , Hagenaars MA, et al. Long-term Outcomes of Cognitive Behavioral Therapy for Anxiety-Related Disorders : A Systematic Review and Meta-analysis . JAMA Psychiatry. 2020;77(3):265–273. doi:10.1001/jamapsychiatry.2019.3986

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Long-term Outcomes of Cognitive Behavioral Therapy for Anxiety-Related Disorders : A Systematic Review and Meta-analysis

• 1 Department of Clinical Psychology, Utrecht University, Utrecht, the Netherlands
• 2 Department of Psychiatry, Amsterdam Universitair Medisch Centrum, location Vrije Universiteit medisch centrum, Amsterdam, the Netherlands
• 3 Department of Psychiatry, Amsterdam Universitair Medisch Centrum, location Academisch Medisch Centrum, University of Amsterdam, Amsterdam, the Netherlands
• 4 Department of Clinical, Neuro, and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
• Correction Error in Abstract, Text, Table, and Supplemental Content JAMA Psychiatry

Question   What is the long-term outcome of cognitive behavioral therapy for anxiety disorders, posttraumatic stress disorder, and obsessive-compulsive disorder?

Findings   In this systematic review and meta-analysis of 69 randomized clinical trials including 4118 patients, cognitive behavioral therapy was associated with better outcomes compared with control conditions among patients with anxiety symptoms within 12 months after treatment completion. At longer follow-up, significant associations were found only for generalized anxiety disorder, social anxiety disorder, and posttraumatic stress disorder; relapse rates (predominantly for panic disorder with or without agoraphobia) after 3 to 12 months were 0% to 14%.

Meaning   The findings suggest that compared with control conditions, cognitive behavioral therapy was generally associated with lower anxiety symptoms within 12 months after treatment completion, but few studies have examined longer-term outcomes.

Importance   Cognitive behavioral therapy is recommended for anxiety-related disorders, but evidence for its long-term outcome is limited.

Objective   This systematic review and meta-analysis aimed to assess the long-term outcomes after cognitive behavioral therapy (compared with care as usual, relaxation, psychoeducation, pill placebo, supportive therapy, or waiting list) for anxiety disorders, posttraumatic stress disorder (PTSD), and obsessive-compulsive disorder (OCD).

Data Sources   English-language publications were identified from PubMed, PsycINFO, Embase, Cochrane, OpenGrey (1980 to January 2019), and recent reviews. The search strategy included a combination of terms associated with anxiety disorders (eg, panic or phobi* ) and study design (eg, clinical trial or randomized controlled trial ).

Study Selection   Randomized clinical trials on posttreatment and at least 1-month follow-up effects of cognitive behavioral therapy compared with control conditions among adults with generalized anxiety disorder, panic disorder with or without agoraphobia, social anxiety disorder, specific phobia, PTSD, or OCD.

Data Extraction and Synthesis   Researchers independently screened records, extracted statistics, and assessed study quality. Data were pooled using a random-effects model.

Main Outcomes and Measures   Hedges g was calculated for anxiety symptoms immediately after treatment and at 1 to 6 months, 6 to 12 months, and 12 months or more after treatment completion.

Results   Of 69 randomized clinical trials (4118 outpatients) that were mainly of low quality, cognitive behavioral therapy compared with control conditions was associated with improved outcomes after treatment completion and at 1 to 6 months and at 6 to 12 months of follow-up for a generalized anxiety disorder (Hedges g , 0.07-0.40), panic disorder with or without agoraphobia (Hedges g , 0.22-0.35), social anxiety disorder (Hedges g , 0.34-0.60), specific phobia (Hedges g , 0.49-0.72), PTSD (Hedges g , 0.59-0.72), and OCD (Hedges g , 0.70-0.85). At a follow-up of 12 months or more, these associations were still significant for generalized anxiety disorder (Hedges g , 0.22; number of studies [ k ] = 10), social anxiety disorder (Hedges g , 0.42; k  = 3), and PTSD (Hedges g , 0.84; k  = 5), but not for panic disorder with or without agoraphobia ( k  = 5) and could not be calculated for specific phobia ( k  = 1) and OCD ( k  = 0). Relapse rates after 3 to 12 months were 0% to 14% but were reported in only 6 randomized clinical trials (predominantly for panic disorder with or without agoraphobia).

Conclusions and Relevance   The findings of this meta-analysis suggest that cognitive behavioral therapy for anxiety-related disorders is associated with improved outcomes compared with control conditions until 12 months after treatment completion. At a follow-up of 12 months or more, effects were small to medium for generalized anxiety disorder and social anxiety disorder, large for PTSD, and not significant or not available for other disorders. High-quality randomized clinical trials with 12 months or more of follow-up and reported relapse rates are needed.

Anxiety disorders, posttraumatic stress disorder (PTSD), and obsessive-compulsive disorder (OCD) are highly prevalent 1 , 2 and are associated with substantial personal 3 and societal costs. 4 - 6 Clinical practice guidelines recommend psychological and pharmacological interventions for anxiety-related disorders, 7 - 13 but most patients favor psychotherapy over pharmacotherapy. 14 Cognitive behavioral therapy (CBT) for these disorders has been associated with reduced symptoms at short term, 15 , 16 with small to medium effect sizes adjusted for publication bias and when studies with waiting list comparisons were not taken into account. 15 However, regarding its long-term outcome, little meta-analytic evidence is available. Such evidence is important because the course of anxiety-related disorders is typically chronic. 17 Evidence on the long-term outcome is particularly vital for researchers to prioritize research directions (eg, further examining variables associated with treatment success and ways to optimize treatment) and for clinicians to give patients realistic information.

Four recent meta-analyses have addressed the long-term outcome of CBT for anxiety-related disorders, and they generally indicate a medium symptom reduction up to 2 years following treatment completion. 18 - 21 However, in 2 of these, 18 , 21 CBT outcome was only calculated over time (pretreatment vs posttreatment vs follow-up) and not relative to a control condition. Therefore, these meta-analyses could not disentangle treatment outcome from placebo effects or spontaneous remission. Moreover, because pretreatment and posttreatment correlations of individual studies are often unknown, there may be substantial errors in these effect size estimations. 22 The other 2 meta-analyses did use control conditions, but these were limited to placebo, 19 resulting in 23 studies, or relaxation, 20 resulting in 27 studies. The numbers of studies would be at least twice as large if other comparison conditions were also included (eg, a care-as-usual group). In addition, no meta-analysis has examined the association between CBT and relapse rates in anxiety-related disorders, to our knowledge. Cross-sectional findings indicate that approximately 31% to 55% of patients with remitted anxiety meet diagnostic criteria of the same or another disorder within 4 years. 23 Research on relapse and the return of fear has become a major focus of fundamental fear and anxiety research, 24 but the evidence for clinical relapse after psychotherapy in anxiety-related disorders is limited.

Our aim was to conduct a comprehensive meta-analysis to establish a reliable estimate of the long-term outcome of CBT relative to passive and active comparison groups in anxiety disorders, PTSD, and OCD. We examined (1) long-term effects (≥1-month posttreatment) and (2) relapse rates after successful treatment in patients with generalized anxiety disorder (GAD), panic disorder with or without agoraphobia (PD), social anxiety disorder (SAD), specific phobia, PTSD, and OCD.

The systematic review and meta-analysis was preregistered at PROSPERO, 25 and it adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) reporting guideline. 26

Relevant English-language publications were identified by systematically searching PubMed, PsycINFO, Embase, Cochrane, and OpenGrey (from 1980 until January 2019). The search strategy included a combination of terms related to anxiety disorders (eg, panic or phobi* ) and study design (eg, clinical trial or randomized controlled trial ). eTable 1 in the Supplement provides the exact search strategies. The electronic database search was supplemented with a bibliography screening of 4 relevant meta-analyses 18 - 21 and 1 systematic review. 27

Randomized clinical trials were included that examined effects of CBT (ie, any therapy with cognitive restructuring and/or a behavioral therapy, such as exposure, as core component), 15 including third generation CBTs (ie, acceptance and commitment therapy and metacognitive therapy), at least 1 month after treatment completion, in an individual, group, or internet treatment format. Comparison groups included care as usual (ie, anything patients would normally receive as long as it was not a structured type of psychotherapy, such as primary care at medical centers or case management with educational groups), 15 relaxation, psychoeducation, pill placebo, supportive therapy, or waiting list. Studies were included if they tested adult patients (or samples consisting mostly of adults but also some adolescents aged ≥16 years) who received a diagnosis of GAD, PD, SAD, specific phobia, PTSD, or OCD based on results of a structured diagnostic interview.

Studies were excluded if they did not use CBT (eg, applied relaxation, eye movement desensitization and reprocessing, or interpersonal therapy) or did not report symptoms separately for each disorder. To reduce clinical heterogeneity, studies were also excluded if they had done any of the following: (1) used self-guided therapy without any guidance, (2) used CBT combined with medication or pill placebo, or (3) tested inpatients.

Titles and abstracts of the records were independently screened by two of us (E.A.M.vD. and S.C.vV.) with the use of the Covidence systematic review tool. 28 The full-text screening and data extraction were independently performed by two of us (E.A.M.vD. and R.M.vdH.). In case of disagreements during the screening or data extraction process, a consensus was reached through discussion or by the decision of a third person (P.C.). If full-text records were inaccessible, authors and/or libraries were contacted ( k  = 12; response rate, 33%). If crucial statistics were missing, study authors were contacted ( k  = 8; response rate, 38%).

To assess the quality of the included studies, 5 criteria of the Cochrane Collaboration’s risk of bias tool were used: adequate generation of allocation sequence, concealment of allocation to conditions, blinding of outcome assessment, adequately dealing with incomplete outcome data (this was evaluated as being of high quality when we could use intention-to-treat analyses), and no selective outcome reporting (based on whether authors referred to trial registrations or study design publications). 29 In addition, quality of treatment implementation was evaluated according to the following 4 criteria outlined by Chambless and Hollon 30 : (1) the use of a treatment protocol, (2) training of therapists, (3) monitoring of therapy (integrity check), and (4) researcher allegiance. Researcher allegiance was defined as 1 of the authors’ involvement in developing the treatment under investigation, except when collaborators had mixed allegiances. 31 All quality assessments were independently completed by two of us (E.A.M.vD. and R.M.vdH.), and disagreement was solved through discussion or by the decision of a third person (P.C.).

Comprehensive Meta-analysis software, version 3 (Biostat) 32 was used to calculate the pooled effect sizes separately for each disorder. If studies used multiple symptom measures, these outcomes were pooled within studies, 33 except for a sensitivity analysis that included 1 outcome measure (based on a frequency ranking). Random-effects models were selected in all analyses and available intention-to-treat data were used. Power analyses were conducted with the online Power Calculator Tool. 34 The primary outcome variable was anxiety symptoms. Hedges g was calculated to indicate differences between treatment and comparison groups at posttreatment and follow-up. Follow-up measurements were categorized into 3 periods: 1 to 6 months, 6 to 12 months, and 12 months or more of posttreatment follow-up. Relapse rates were defined as the percentage of relapse after treatment response at follow-up (treatment group vs comparison group). Relative risk was calculated to indicate dropout differences between treatment and comparison groups. Subgroup analyses were performed on treatment approaches, comparison groups, and study quality using a mixed-effects model and meta-regression. Analyses with at least 3 studies per subgroup are reported.

To assess potential publication bias, the Egger test of the intercept was used, which is a significance test based on the asymmetry of funnel plots. 35 The funnel-plot–based method of Duval and Tweedie 36 was used to test and adjust for publication bias through a trim and fill technique. To estimate heterogeneity across studies, the I 2 statistic with 95% CIs (using the HETEROGI module for Stata, version 8 [StataCorp]) 37 was calculated, which displays the proportion of the observed variance that would remain if we could remove the sampling error. A common benchmark for interpretation is 25% for small, 50% for medium, and 75% for large heterogeneity. 33 We also calculated 95% prediction intervals to estimate the effect size range in future studies. 38

Figure 1 displays the PRISMA flowchart of the selection and inclusion process. We screened 10 857 titles and abstracts and retrieved 715 full-text records, of which 69 published studies (reported in 73 records) met our inclusion criteria: 14 studies on GAD, 13 studies on PD, 7 studies on SAD, 3 studies on specific phobia, 30 studies on PTSD, and 2 studies on OCD (eTable 2 in the Supplement presents characteristics of these studies). A total of 4118 unique patients were enrolled (age and sex not available in the final analyses). The studies examined CBT (number of studies [ k ] = 42), exposure therapy, ( k  = 26), cognitive therapy ( k  = 10), cognitive reprocessing ( k  = 1), metacognitive therapy ( k  = 1), applied tension ( k  = 1), and acceptance and commitment therapy ( k  = 1). Comparison groups consisted of care as usual ( k  = 13), relaxation ( k  = 24), psychoeducation ( k  = 2), pill placebo ( k  = 5), supportive therapy ( k  = 14), waiting list ( k  = 12), and tension only ( k  = 1). Multiple treatment or comparison groups within 1 study were pooled together ( k  = 9). We found 41 studies reporting outcomes at 1 to 6 months, 34 studies at 6 to 12 months, and 24 studies at 12 months or more of follow-up. Groups did generally not differ in dropout (relative risk range, 0.97-1.03; P  > .50), but for PTSD, there was slightly more dropout in the comparison group (relative risk, 0.95; P  = .01).

Figure 2 and eFigure 1 in the Supplement present the study and treatment quality assessments. Only 12 studies met criteria for high quality (ie, ≥4 of 5 criteria). Nineteen of the studies (27.5%) applied random sequence generation and allocation concealment. In 44 studies (63.8%), the outcome assessments were blinded and 35 studies (50.7%) applied intention-to-treat analyses. Only 21 studies (30.4%) reported a preregistration or a design protocol, and in 13 cases, the outcomes were not reported in accordance with their preregistration. The overall treatment implementation quality was high and most studies had a high risk of researchers’ allegiance.

Table 1 presents effect sizes, heterogeneity indices, and adjusted effect sizes for risk of publication bias based on the trim and fill procedure of Duval and Tweedie 36 for all disorders across time (eFigures 2-7 in the Supplement provide forest plots and eFigures 8-12 in the Supplement provide funnel plots). A sensitivity analysis with 1 outcome measure yielded similar results (eTable 3 in the Supplement ). After treatment, the pooled effect size of CBT relative to control conditions was small for PD (Hedges g , 0.22; 95% CI, 0.01-0.43); medium for GAD (Hedges g , 0.39; 95% CI, 0.12-0.66), SAD (Hedges g , 0.38; 95% CI, 0.19-0.57), and specific phobia (Hedges g , 0.49; 95% CI, 0.13-0.84); and medium to large for PTSD (Hedges g , 0.72; 95% CI, 0.52-0.93) and OCD (Hedges g , 0.70; 95% CI, 0.29-1.12). The Egger test of the intercept was only significant for PTSD (intercept β, 3.13; 95% CI, 1.78-4.49, P  < .01; all others, β < 2.34; P  > .20). The trim and fill procedure 36 yielded lower adjusted effect sizes for all disorders except OCD ( Table 1 ). Heterogeneity was low to moderate for PD, SAD, specific phobia, and OCD, and it was moderate to large for GAD and PTSD.

At 1 to 6 months of follow-up, the relative pooled estimate of CBT was small for GAD (Hedges g , 0.07; 95% CI, −0.50 to 0.63) and PD (Hedges g , 0.27; 95% CI, −0.01 to 0.55), medium for SAD (Hedges g , 0.60; 95% CI, 0.36-0.85), and medium to large for specific phobia (Hedges g , 0.72; 95% CI, 0.01-1.44), PTSD (Hedges g , 0.67; 95% CI, 0.46-0.88), and OCD (Hedges g , 0.85; 95% CI, 0.47-1.22). The Egger test of the intercept was significant for GAD (intercept β, −10.45; 95% CI, −16.15 to 4.76, P  = .03) and PTSD (intercept β, 3.10; 95% CI, 1.28-4.92, P  = .002; all others: β < 4.22, P  > .08), and the trim and fill procedure resulted in a lower adjusted effect size only for PTSD (Hedges g , 0.50; 95% CI, 0.27-0.73). Heterogeneity was low for PD, SAD, and OCD; moderate for specific phobia; and moderate to large for GAD and PTSD.

At 6 to 12 months of follow-up, the pooled effect size of CBT relative to control conditions was small to medium for GAD (Hedges g , 0.40; 95% CI, 0.13-0.67), PD (Hedges g , 0.35; 95% CI, 0.11-0.59), and SAD (Hedges g , 0.34; 95% CI, 0.07-0.61) and medium for PTSD (Hedges g , 0.59; 95% CI, 0.42-0.77). No pooled effect sizes could be calculated for specific phobia ( k  = 0) and OCD ( k  = 0). The Egger test of the intercept did not indicate a risk of publication bias for any disorder (all β < 2.74, P  > .06). The trim and fill procedure resulted in lower adjusted effect sizes only for SAD and PTSD ( Table 1 ). Heterogeneity was low for PD, SAD, and PTSD and moderate for GAD.

After a follow-up of 12 months or more, CBT was still associated with a better outcome than control conditions for GAD (Hedges g , 0.22; 95% CI, 0.02-0.42; k  = 10), SAD (Hedges g , 0.42; 95% CI, 0.04-0.79; k  = 3), and PTSD (Hedges g , 0.84; 95% CI, 0.03-1.64; k  = 5), but this effect was not significant for PD (Hedges g , 0.14; 95% CI, –0.19 to 0.47; k  = 5) and could not be calculated for specific phobia ( k  = 1) and OCD ( k  = 0). The Egger test of the intercept did not indicate a risk of publication bias (β < 3.51 for all, P  > .09), but the trim and fill procedure yielded a lower nonsignificant effect for PTSD (Hedges g , 0.54; 95% CI, –0.20 to 1.29). Heterogeneity was low for PD, SAD, and GAD but large for PTSD.

eTables 4 and 5 in the Supplement present exploratory subgroup analyses for treatment approaches and comparison groups. For specific phobia and OCD, subgroup analyses could not be performed (<2 studies per comparison group). Meta-regression analyses revealed no significant differences across treatment approaches for any disorder at any time (all Q < 1.92; P  > .38).

For GAD and SAD, the comparison groups did not significantly differ at any time. For PD, subgroup analyses showed a significant medium treatment effect of CBT relative to pill placebo at posttreatment (Hedges g , 0.42) and at 6 to 12 months of follow-up (Hedges g , 0.73). There were no significant treatment effects relative to any other active comparison group at any time (all P  > .06; eTable 5 in the Supplement ). For PTSD, CBT appeared to be generally more effective relative to all comparison groups until 12 months of follow-up (Hedges g , >0.73; all P  < .02), but not compared with supportive therapy after 12 months or more (Hedges g , 0.08; P  = .44). At treatment completion, studies that used a waiting list comparison group yielded significantly ( P  < .01) larger effect sizes (Hedges g , 1.25), while studies using a supportive therapy comparison condition yielded significantly lower effect sizes (Hedges g , 0.27) ( P  = .02).

Exploratory subgroup analyses on study quality could only be performed for PTSD (high-quality studies: k  = 8) and showed larger effect sizes at all times for high-quality studies (Hedges g , 0.65-2.10) compared with the other studies (Hedges g , 0.51-0.57). There were no high-quality studies for SAD and specific phobia and only a few for PD ( k  = 1), GAD ( k  = 2), and OCD ( k  = 1).

A total of 6 studies (7 comparisons) reported relapse rates after successful treatment. Of these, 5 studies were about PD 39 - 43 and 1 was about OCD. 44 An additional study described relapse of PD as a comorbid condition after PTSD treatment, and this study was not included. 45 All 6 studies used small sample sizes (n < 28), and most operationalized successful treatment using ambiguous treatment response criteria rather than reliable remission criteria (eg, the absence of a disorder based on a clinical interview). Therefore, we refrained from statistically pooling these results and instead presented outcomes per study in Table 2 . Overall, relapse rates were relatively low: in 3 of 7 comparisons, relapse occurred after successful CBT and relapse rates ranged from 0% to 14%.

This systematic review and meta-analysis examined the long-term outcome of CBT for anxiety disorders, PTSD, and OCD across 69 randomized clinical trials. Overall, CBT was associated with moderate symptom reductions up to 12 months after treatment. Longer effects were still significant for GAD, SAD, and PTSD, but not for PD and could not be calculated for specific phobia and OCD. Because this meta-analysis included a limited number of high-quality studies and English language articles only, our reported effect estimates should be interpreted with caution. Because statistical heterogeneity was considerable in GAD and PTSD studies, our effect estimates for these disorders are uncertain. Future meta-analyses should aim to explain this heterogeneity as more studies become available. Although post hoc power analyses generally showed sufficient statistical power of our main analyses, simulation studies showed that at least 40 studies per analysis are needed to reach sufficient power. 47 Therefore, nonsignificant findings, especially of the subgroup analyses, should be interpreted as the absence of evidence rather than evidence of absence.

Our overall findings were in line with CBT outcomes for depression 48 and suggest that skills and insights acquired during CBT are relatively stable until 12 months after treatment but do not improve further. Nevertheless, evidence for CBT outcomes at 12 months or more after treatment is scarce. Given the chronic trajectories of anxiety-related disorders 17 and because longer illness duration may increase the odds of developing comorbidity, 49 it is important to examine whether treatment effects are maintained 12 months or more after treatment. Thus, more research on CBT efficacy at 12 months or more of follow-up and on ways to optimize effects is needed.

Relapse rates after successful CBT were relatively low (0%-14%) compared with uncontrolled trials that indicated a maximum relapse of 13% for SAD 50 and 23% for PD. 51 However, only a few studies reported them (5 studies for PD and 1 for OCD), in contrast to studies on pharmacotherapy for anxiety-related disorders that frequently report clinical relapse after treatment discontinuation. 52 Also, these studies calculated relapse rates based on ambiguous response criteria rather than relative to complete remission. Therefore, future research should carefully define and report relapse criteria (eg, a return of the full symptomatology 24 , 53 based on a structured interview). Future research may also give insight into risk factors for relapse, which could identify patients at risk who may benefit from additional or more intensive therapy or from pharmacotherapy to prevent relapse. Relapse prevention after psychotherapy is still relatively uncharted in the field of anxiety-related disorders but is quite common and effective in depressive disorders. 54 For example, studies have shown the efficacy of well-being therapy 55 , 56 as second-line relapse prevention strategy in patients with GAD. 57

For PD, when corrected for publication bias, CBT outcome did not significantly differ from control conditions (except for a small to medium effect at 6-12 months of follow-up). This may be explained by the frequent use of applied relaxation as a control condition, which may involve some exposure. 39 Relaxation appeared to be as effective as CBT in a previous meta-analysis. 20 Subgroup analyses across comparison groups revealed a medium treatment effect for PD within 12 months after treatment when CBT was compared with pill placebo, but not relative to other active comparison groups. However, the subgroup analyses should be interpreted with caution because of the small subsample sizes.

For specific phobia and OCD, only a few studies met our inclusion criteria, and treatment effect estimates could not be calculated beyond a 6-month follow-up. Most previous studies on OCD treatment with long-term assessments have tested the efficacy of pharmacotherapy (augmented with CBT). 58 , 59 Because approximately 50% of patients with OCD do not respond to pharmacotherapy and many patients relapse after medication discontinuation, 58 more research is needed on the long-term efficacy of CBT as an alternative stand-alone treatment.

Regarding PTSD, after correcting for publication bias, we observed medium treatment effects favoring CBT over control conditions at posttreatment until 12 months of follow-up. At 12 months or more of follow-up, there was a nonsignificant medium effect adjusted for publication bias, which probably did not reach statistical significance because of limited statistical power.

Strengths of this meta-analysis are the inclusion of more comparison groups, which yielded more studies than previous meta-analyses, 18 - 21 and the investigation of long-term outcomes (including relapse rates) after CBT for anxiety-related disorders. Furthermore, we conducted a comprehensive literature search, an independent screening and data extraction, and treatment and study quality assessments. Several limitations should also be noted. First, meta-analyses are inherently associated with heterogeneity regarding methodological aspects (eg, outcome measures) and clinical aspects (eg, CBT approaches and samples). Therefore, future research is needed to test which specific methodological or treatment factors explain the reported effects. 60 Second, because of limited experimental control during follow-up periods, confounding factors may have threatened the validity of our long-term effect estimates (eg, because of additional treatment or adverse life events). Third, symptom outcome measures were averaged to handle dependent outcomes, which may have resulted in overestimated SEs. 61 Fourth, most studies had suboptimal designs (or these criteria were poorly reported) and a high risk of researcher allegiance bias, which may have affected the reliability of our effect estimates.

Anxiety-related disorders are characterized by a chronic course, thus sustainable treatment effects are important. The results of this meta-analysis suggest that, on average, CBT was associated with moderate symptom reductions in anxiety disorders, PTSD, and OCD until 12 months after treatment completion. At a follow-up of 12 months or more, these effects were still present for GAD, SAD, and PTSD, but not for PD. For specific phobia and OCD, no follow-up data beyond 6 months after treatment completion were available. Studies on relapse were scarce but gave the preliminary impression that relapse rates after successful treatment, predominantly for PD, may be relatively low (0%-14% at 3-12 months following treatment completion). More high-quality randomized clinical trials on long-term treatment effects (preferably ≥12 months after treatment completion) and relapse are warranted to facilitate more reliable long-term effect size estimations.

Accepted for Publication: September 18, 2019.

Published Online: November 23, 2019. doi:10.1001/jamapsychiatry.2019.3986

Correction: This article was corrected on May 13, 2020, to fix the description of the last category of follow-up time throughout the Abstract, the text, Table 1, and the Supplement.

Corresponding Author: Eva A. M. van Dis, MSc, Department of Clinical Psychology, Utrecht University, PO Box 80140, 3508 TC Utrecht, the Netherlands ( [email protected] ).

Author Contributions: Ms van Dis had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: van Dis, van Veen, Hagenaars, Batelaan, Bockting, Cuijpers, Engelhard.

Acquisition, analysis, or interpretation of data: van Dis, van Veen, Hagenaars, Bockting, van den Heuvel.

Drafting of the manuscript: van Dis, Engelhard.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: van Dis.

Obtained funding: Bockting, Engelhard.

Administrative, technical, or material support: van Dis, Engelhard.

Supervision: Hagenaars, Batelaan, Bockting, Cuijpers, Engelhard.

Full-text screening, data extraction, quality assessments of articles: van den Heuvel.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by Vici grant 453-15-005 from the Netherlands Organization for Scientific Research (Dr Engelhard). Ms van Veen was supported by TOP grant 40-00812-98-12030 (Dr Engelhard) from the Netherlands Organization for Health Research and Development. Dr Bockting was supported by the fellowship of the Institute for Advanced Studies of the University of Amsterdam.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Meeting Presentation: This paper was presented at the Association for Cognitive and Behavioral Therapies meeting; November 23, 2019; Atlanta, Georgia.

Additional Contributions: We thank Paulien Wiersma, MSc (Utrecht University Library), for her assistance in designing the search strategy.

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• Published: 03 October 2021

Cognitive–behavioral therapy for management of mental health and stress-related disorders: Recent advances in techniques and technologies

• Mutsuhiro Nakao 1 ,
• Kentaro Shirotsuki 2 &
• Nagisa Sugaya 3  

BioPsychoSocial Medicine volume  15 , Article number:  16 ( 2021 ) Cite this article

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Cognitive–behavioral therapy (CBT) helps individuals to eliminate avoidant and safety-seeking behaviors that prevent self-correction of faulty beliefs, thereby facilitating stress management to reduce stress-related disorders and enhance mental health. The present review evaluated the effectiveness of CBT in stressful conditions among clinical and general populations, and identified recent advances in CBT-related techniques. A search of the literature for studies conducted during 1987–2021 identified 345 articles relating to biopsychosocial medicine; 154 (45%) were review articles, including 14 systemic reviews, and 53 (15%) were clinical trials including 45 randomized controlled trials. The results of several randomized controlled trials indicated that CBT was effective for a variety of mental problems (e.g., anxiety disorder, attention deficit hypersensitivity disorder, bulimia nervosa, depression, hypochondriasis), physical conditions (e.g., chronic fatigue syndrome, fibromyalgia, irritable bowel syndrome, breast cancer), and behavioral problems (e.g., antisocial behaviors, drug abuse, gambling, overweight, smoking), at least in the short term; more follow-up observations are needed to assess the long-term effects of CBT. Mental and physical problems can likely be managed effectively with online CBT or self-help CBT using a mobile app, but these should be applied with care, considering their cost-effectiveness and applicability to a given population.

History of cognitive–behavioral therapy (CBT)

CBT is a type of psychotherapeutic treatment that helps people to identify and change destructive or disturbing thought patterns that have a negative influence on their behavior and emotions [ 1 ]. Under stressful conditions, some individuals tend to feel pessimistic and unable to solve problems. CBT promotes more balanced thinking to improve the ability to cope with stress. The origins of CBT can be traced to the application of learning theory principles, such as classical and operant conditioning, to clinical problems. So-called “first-wave” behavioral therapy was developed in the 1950s [ 2 ]. In the US, Albert Ellis founded rational emotive therapy to help clients modify their irrational thoughts when encountering problematic events, and Aaron Beck employed cognitive therapy for depressed clients using Ellison’s model [ 3 ]. Behavioral therapy and cognitive therapy were later integrated in terms of theory and practice, leading to the emergence of “second-wave” CBT in the 1960s. The first- and second-wave forms of CBT arose via attempts to develop well-specified and rigorous techniques based on empirically validated basic principles [ 4 ]. From the 1960s onward, the dominant psychotherapies worldwide have been second-wave forms of CBT. Recently, however, a third-wave form of CBT has attracted increasing attention, leading to new treatment approaches such as acceptance and commitment therapy, dialectical behavior therapy, mindfulness-based cognitive therapy, functional analytic psychotherapy, and extended behavioral activation; other forms may also exist, although this is subject to conjecture [ 4 ]. In a field of psychosomatic medicine, it has been reported that cognitive restructuring is effective in improving psychosomatic symptoms [ 5 ], exposure therapy is suitable for a variety of anxious disease conditions like panic disorder and agoraphobia [ 6 ], and mindfulness reduces stress-related pain in fibromyalgia [ 7 ]. Several online and personal computer-based CBT programs have also been developed, with or without the support of clinicians; these can also be accessed by tablets or smartphones [ 8 ]. Against this background, this review focused on the effectiveness of CBT with a biopsychosocial approach, and proposed strategies to promote CBT application to both patient and non-patient populations.

Research on CBT

Using “CBT “and “biopsychosocial” as PubMed search terms, 345 studies published between January 1987 and May 2021 were identified (Fig.  1 ); 14 of 154 review articles were systemic reviews, and 45 of 53 clinical trials were randomized controlled trials. Most clinical trials recruited the samples from patient populations in order to assess specific diseases, but some targeted at those from non-patient populations like a working population in order to assessing mind-body conditions relating to sick leave [ 9 ]. The use of biopsychosocial approaches to treat chronic pain is shown to be clinically and economically efficacious [ 10 ]; for example, CBT is effective for chronic low-back pain [ 11 ]. The prevalence of chronic low-back pain, defined as pain lasting for more than 3 months, was reported to be 9% in primary-care settings and 7–29% in community settings [ 12 ]. Chronic low-back pain is not only prevalent, but is a source of significant physical disability, role impairment, and diminished psychological well-being and quality of life [ 11 ]. Interestingly, according to the results of our own study [ 13 ], CBT was effective among hypochondriacal patients without chronic low-back pain, but not in hypochondriacal patients with chronic low-back pain. These group differences did not seem to be due to differences in the baseline levels of hypochondriasis. Although evidence has suggested that both hypochondriasis and chronic low-back pain can be treated effectively with CBT [ 10 , 11 , 14 ], this has not yet been validated. Chronic low-back pain may be associated with a variety of conditions, including anxiety, depression, and somatic disorders such as illness conviction, disease phobia, and bodily preoccupation. The core psychopathology of hypochondriacal chronic low-back pain should be clarified to promote adequate symptom management [ 13 ].

Number of articles per year identified by a PubMed search from 1989 to the present

Since 2000, Cochrane reviews have evaluated the effectiveness of CBT for a variety of mental, physical, and behavioral problems. Through a search of the Cochrane Library database up to May 2021 [ 15 ], 124 disease conditions were assessed to clarify the effects of CBT in randomized controlled trials; the major conditions for which CBT showed efficacy are listed in Table  1 . These include a broad range of medical problems such as psychosomatic illnesses (e.g., chronic fatigue syndrome, irritable bowel syndrome, and fibromyalgia), psychiatric disorders (e.g., anxiety, depression, and developmental disability), and socio-behavioral problems (drug abuse, smoking, and problem gambling). For most of these conditions, CBT proved effective in the short term after completion of the randomized controlled trial. Although the number of literature was still limited, some studies have reported significant and long-term treatment effects of CBT on some aspects of mental health like obsessive-compulsive disorder [ 16 ] 1 year after the completion of intervention. Future research should investigate the duration of CBT’s effects and ascertain the optimal treatment intensity, including the number of sessions.

Future directions for CBT application in biopsychosocial domains

In Japan, CBT for mood disorders was first covered under the National Health Insurance (NHI) in 2010, and CBT for the following psychiatric disorders was subsequently added to the NHI scheme: obsessive–compulsive disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, and bulimia nervosa [ 17 ]. The treatment outcomes and health insurance costs for these six disorders should be analyzed as the first step, for appropriate allocation of medical resources according to disease severity and complexity [ 18 ]. In Japan, health insurance coverage is provided only when physicians apply for remuneration. A system promoting nurse involvement in CBT delivery [ 19 ], as well as shared responsibility between the CBT instructor and certified psychologists (or even a complete shift from physicians to psychologists), has yet to be established. Information and communication technology (ICT) devices may allow CBT delivery to be shared between medical staff and psychologists, in medical, community and self-help settings [ 8 ]. The journal BioPsychoSocial Medicine published 334 relevant articles up to the end of May 2021, 112 (33.5%) of which specifically addressed CBT [ 20 ]. CBT is a hot topic in biopsychosocial medicine, and more research is required to encourage its application to clinical and general populations.

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Abbreviations

• Cognitive–behavioral therapy

Information and communication technology

National Health Insurance

Post-traumatic stress disorder

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Acknowledgments

The study was supported in part by a Research Grant (Kiban C) from the Japanese Ministry of Education, Culture, Sports, Science and Technology.

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Department of Psychosomatic Medicine, School of Medicine, International University of Health and Welfare, 4-3, Kozunomori, Narita-shi, Chiba, 286-8686, Japan

Mutsuhiro Nakao

Graduate School of Human and Social Sciences, Musashino University, Tokyo, Japan

Kentaro Shirotsuki

Unit of Public Health and Preventive Medicine, School of Medicine, Yokohama City University, Yokohama, Japan

Nagisa Sugaya

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Nakao, M., Shirotsuki, K. & Sugaya, N. Cognitive–behavioral therapy for management of mental health and stress-related disorders: Recent advances in techniques and technologies. BioPsychoSocial Med 15 , 16 (2021). https://doi.org/10.1186/s13030-021-00219-w

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Some forms of augmented brain stimulation recommended for major depression

by Wolters Kluwer Health

According to a review published in Harvard Review of Psychiatry , certain combinations of medication or psychotherapy in conjunction with transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS) are supported by clinical studies for treatment of major depressive disorder (MDD). The authors do caution that, overall, the research has important limitations.

TMS was approved by the Food and Drug Administration for treatment of MDD in 2008. It uses pulsed magnetic fields to induce an electric current in the left dorsolateral prefrontal cortex (dlPFC). tDCS conducts weaker electrical currents to the dlPFC via electrodes placed on the scalp. While not yet FDA-approved, tDCS is quite promising, especially since the equipment is portable and therefore more accessible.

Co-senior authors Tina Chou, Ph.D., and Darin Dougherty, MD, Director of Research and Director of the Division of Neurotherapeutics at Massachusetts General Hospital in Boston, and colleagues reviewed medical literature on whether combining TMS or tDCS with traditional MDD treatment can lead to greater symptom reduction. They searched PubMed, PsycInfo, and Cochrane Library through December 5, 2023, reviewing 58 studies that incorporated outcome measures for MDD.

The reviewers found support for several augmented strategies:

• Pairing mindfulness-based stress reduction with TMS can be more effective for MDD than mindfulness training or general psychological care alone. However, mindfulness interventions should not occur during a TMS session.
• The combination of standard (not shortened or adapted) mindfulness-based cognitive behavioral therapy and tDCS can reduce MDD symptoms more than the combination of general relaxation exercises and tDCS.
• Adding TMS or tDCS to a stable dose of pharmacotherapy can decrease MDD symptoms; however, benzodiazepines may interfere with treatment response, and antipsychotics may interfere with response to TMS. (There are no studies on combining antipsychotics and tDCS.)
• When adding TMS to an ongoing medication regimen, clinicians should consider a phased approach, starting with 1 or 2 Hz (which has fewer side effects and may be more tolerable than higher frequencies) and progressing to 10 Hz if necessary.
• Starting citalopram at 20–40 mg/day along with TMS can accelerate symptom reduction one to two weeks into treatment, a reduction that can be maintained through the end of treatment.
• Combining sertraline 50 mg/day with 30-minute sessions of tDCS can significantly reduce MDD symptoms, especially for patients with more severe MDD.

Drs. Chou, Dougherty, and co-authors discuss notable limitations of the papers they reviewed:

• The majority of studies had small sample sizes, even as few as four or five participants.
• Most larger studies lacked a control group or were open-label, naturalistic, or retrospective.
• Few TMS studies were comparable because they used different stimulation protocols.
• Most studies were short-term.

"Open-label trials are useful during initial, exploratory phases, or if it is not feasible to blind participants to treatments," the authors note. "In these particular studies, however, the degree to which they are used is peculiar considering the literature on neuromodulation augmentation spans nearly 20 years."

Drs. Chou and Dougherty's group continues, "Given the potential side effects of adding medications, and the effort and time required to engage in psychotherapy, such additional interventions need to confer benefits beyond what TMS or tDCS offers alone. Crucially, randomized controlled trials are necessary to move the field forward."

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Systematic review article, dropout in cognitive behavioral treatment in adults living with overweight and obesity: a systematic review.

• 1 Human Nutrition and Eating Disorder Research Center, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Pavia, Italy
• 2 Laboratory of Food Education and Sport Nutrition, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Pavia, Italy

Obesity is a chronic, complex, and multifactorial disease resulting from the interaction of genetic, environmental, and behavioral factors. It is characterized by excessive fat accumulation in adipose tissue, which damages health and deteriorates the quality of life. Although dietary treatment can significantly improve health, high attrition is a common problem in weight loss interventions with serious consequences for weight loss management and frustration. The strategy used to improve compliance has been combining dietary prescriptions and recommendations for physical activity with cognitive behavioral treatment (CBT) for weight management. This systematic review determined the dropout rate and predictive factors associated with dropout from CBT for adults with overweight and obesity. The data from the 37 articles selected shows an overall dropout rate between 5 and 62%. The predictive factors associated with attrition can be distinguished by demographics (younger age, educational status, unemployed status, and ethnicity) and psychological variables (greater expected 1-year Body Mass Index loss, previous weight loss attempts, perceiving more stress with dieting, weight and shape concerns, body image dissatisfaction, higher stress, anxiety, and depression). Common reasons for dropping out were objective (i.e., long-term sickness, acute illness, and pregnancy), logistical, poor job conditions or job difficulties, low level of organization, dissatisfaction with the initial results, lack of motivation, and lack of adherence. According to the Mixed Methods Appraisal quality analysis, 13.5% of articles were classified as five stars, and none received the lowest quality grade (1 star). The majority of articles were classified as 4 stars (46%). At least 50% of the selected articles exhibited a high risk of bias. The domain characterized by a higher level of bias was that of randomization, with more than 60% of the articles having a high risk of bias. The high risk of bias in these articles can probably depend on the type of study design, which, in most cases, was observational and non-randomized. These findings demonstrate that CBT could be a promising approach for obesity treatment, achieving, in most cases, lower dropout rates than other non-behavioral interventions. However, more studies should be conducted to compare obesity treatment strategies, as there is heterogeneity in the dropout assessment and the population studied. Ultimately, gaining a deeper understanding of the comparative effectiveness of these treatment strategies is of great value to patients, clinicians, and healthcare policymakers.

Systematic review registration : PROSPERO 2022 CRD42022369995 Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022369995 .

Introduction

It is well known that obesity is a significant public health burden ( 1 ), affecting both physical and psychological status. According to the recently published evidence-based practice guide of the Academy of Nutrition and Dietetics, obesity is recognized as excess adiposity. It is correlated with many adverse health outcomes, such as mortality risk, prediabetes, type 2 Diabetes Mellitus (T2DM), cardiovascular disease, obstructive sleep apnea, and certain types of cancer ( 2 , 3 ). Dietary administration combined with physical activity is the most recommended treatment for weight loss ( 4 ).

Although dietary treatment can significantly improve health, dietary modifications are difficult to make on an individual basis, and obstacles to changing behavior may also be influenced by psychological factors in addition to biological ones. For this reason, high attrition is a common problem in weight loss interventions with serious consequences for weight loss management and frustration ( 5 ). Understanding the factors contributing to attrition could allow the identification of patients at the highest risk of dropout, supporting them during the intervention, or identifying more suitable intervention options.

Previous studies have associated high attrition rates with many variables, such as demographics (age, age at the onset of obesity, sex, occupational status, education) ( 6 – 9 ), anthropometrics (body-mass index BMI) ( 9 ), psychological aspects (high weight loss expectations, health status, self-esteem, perception of one’s body image, social or family support, anxiety, depression) ( 7 , 9 – 13 ), behavior (eating habits and behavior, binge eating, physical activity level, alcohol consumption, lack of motivation, stress, and smoking) ( 9 ), and treatment-related factors (early nutritional interventions, previous dietary treatments, type of treatments, initial response, and expectation of weight loss) ( 8 , 13 – 16 ). A consistent set of predictors has not yet been identified because of the large variety of study settings and methodologies used.

The initial response to treatment has emerged among the predictive factors related to treatment ( 16 , 17 ). In fact, in most cases, the dropout percentage increases if the initial weight loss is unsatisfactory for the patient. Patients discontinue the program in the first weeks (early dropout) because of “failure to achieve the expected goal” and “feeling frustrated and disappointed.”

Regarding the psychological profile of patients before treatment, the dropout rate is higher when there is a greater state of anxiety and depression or, in general, a compromised state of mental health. These factors correlate with a lack of trust in healthcare personnel, lower motivation to undertake the path, or greater difficulty tolerating possible failure ( 10 ).

According to a different position statement from the Obesity Management Task Force of the European Association for the Study of Obesity ( 18 ) and from the Brazilian Association for the Study of Obesity and metabolic syndrome (ABESO), Cognitive Behavioral Therapy (CBT) should be used for weight management in patients with overweight and obesity (class of recommendation I; level of evidence A) ( 18 , 19 ). CBT is the oldest and most studied behavior change theory used in nutrition counseling. It provides a theoretical basis for most structured diet, exercise, and behavioral therapy programs. It is based on the premise of CBT theory that behavioral and emotional reactions are learned using cognitive and behavioral strategies. CBT focuses on external factors, such as environmental stimuli and reinforcement, and internal factors, such as thoughts and mood changes. CBT aims to help patients acquire specific cognitive and behavioral skills to increase adherence to the dietary and physical activity changes required for body weight management that can be used going forward to support their mental health and wellness.

Dietitians apply strategies on both factors to unlearn undesirable eating patterns and behaviors and replace them with more functional thoughts and actions ( 20 , 21 ). CBT strategies include goal setting, self-monitoring, problem-solving, social support, stress management, stimulus control, cognitive restructuring, relapse prevention, rewards, and contingency management ( 20 ).

This study systematically reviewed the dropout rate and predictive factors of dropout in cognitive behavioral treatment (CBT) in adult patients with overweight or obesity in order to provide a comprehensive assessment of the literature about this topic.

Materials and methods

This systematic review was performed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method ( 22 ). The following electronic databases were searched: PubMed, Scopus, and the Cochrane Library. The language used was English. Only full-text articles published in the last 20 years and full-text articles available were included.

Clinical and observational trials, case reports, and case series were included to investigate the dropout rate and predictive factors associated with the dropout rate in adults living with overweight and obesity undergoing cognitive behavioral therapy (CBT). In-vitro and animal studies, guidelines, letters, editorials, comments, news articles, conference abstracts, theses, and dissertations were excluded.

The study protocol was registered on the PROSPERO platform (registration number: CRD42022369995).

Literature research strategy

An electronic search was conducted with subject index terms, including “patient dropouts,” “weight loss,” “diet reducing,” “weight loss therapy,” and “diet therapy.” Google Scholar was used to search for gray literature, and some references found in the review articles were included manually. The study population consisted of adults aged 18–65 years old with overweight or obesity (Body Mass Index (BMI) ≥25 kg/m2). The intervention was cognitive behavioral therapy (CBT), and the comparison was standard dietary treatment. The research question, and specific inclusion and exclusion criteria based on PICOS strategy are presented in Table 1 .

Table 1 . PICOS criteria of inclusion and exclusion.

Study selection

Two authors (LCLN and FM) independently performed the research and study selection. The articles found in the electronic database were organized using the Mendeley reference manager and Rayyan software ( 23 ), following two steps: (1) reading the titles and abstracts, and (2) evaluation of the complete articles selected in the previous stage and inclusion of other studies present in the references of the selected articles. The decision to include the articles was based on the PICOS strategy: population (P) – adult (18–65 years) patients with overweight or obesity (BMI ≥ 25 kg/m 2 ); intervention (I) – cognitive behavioral treatment; control (C) – standard dietary treatment; outcome (O) – attrition rate and weight loss; and study type (S) – clinical and observational trials, case reports, and case series. These inclusion criteria were used to identify potentially relevant abstracts, and if abstracts were coherent with them, full papers were obtained and assessed. In cases of disagreement, a third author (CF) reviewed the full-text articles to make a decision. Studies meeting the specified inclusion criteria were included in the qualitative analysis. Additionally, relevant articles were manually added to the search. Study sample characteristics, design, intervention, dropout rate, results, and quality were extracted. The risk of bias was assessed using the RoB 2.0 Cochrane tool ( 24 ), and the quality of evidence was assessed using the Mixed Methods Appraisal (MMAT) system ( 25 ).

After searching the databases using search strings, 5,509 articles were identified. Figure 1 describes the selection phase and the retained articles in each phase. After selection, 37 articles that used the CBT approach in weight loss interventions addressed the dropout rate and associated predictive factors. The details of each selected article are listed in Table 2 .

Figure 1 . Flowchart of included studies ( 18 ). From: Page et al. ( 26 ).

Table 2 . Description in details of the included articles, 2023.

As shown in Table 2 , the articles were categorized based on their origin, as follows: 20 studies were from Italy (54%), 6 from the USA, 2 from Germany, 2 from Sweden, 2 from Switzerland, 2 from Spain, 2 from the Netherlands, and the other countries with one article each (Denmark, Japan, Portugal, Lebanon, Bahrain). The selected sample, according to the inclusion criteria, comprised adult patients (aged 18–65 years) with overweight or obesity (BMI ≥25 kg/m 2 ). During the analysis, it emerged that the predominant gender was female. The samples included in the various studies did not exhibit any other associated pathologies, except in some specific studies. For example, some studies have included breast cancer survivors ( 34 ), patients with polycystic ovary syndrome (PCOS) ( 25 ), patients diagnosed with T2DM ( 34 ), and patients with binge eating disorder (BED) ( 31 , 39 , 54 ).

Overall, dropout rates were between 5 to 62%. Dropout results were associated with several predictive factors, including demographic and psychological variables. In terms of demographic variables, some studies showed that younger age ( 35 , 61 ), educational status ( 38 ), unemployed status ( 55 ), and ethnicity ( 38 ) could influence the dropout rate. Regarding psychological factors, attrition has been correlated with a higher expectation of 1-year BMI loss ( 28 ), weight and shape concerns ( 43 , 55 ), body image dissatisfaction ( 58 ), higher stress ( 51 ), anxiety ( 58 ) and depression ( 43 , 58 ). Other studies have associated an increased dropout rate with an increasing number of previous weight loss attempts ( 27 ), age at first diet attempt ( 52 ) and perceived stress with dieting ( 44 ). In the study by Sasdelli et al. ( 58 ), the dropout rate decreased with an increase in concern for present health, motivation, and consciousness about the importance of physical activity. The most frequent predictive factors are age and baseline BMI/weight referred to in 31 and 19% studies, respectively. Table 3 presents all the reported predictive factors analyzed in the selected studies.

Table 3 . Reported predictive factors to dropout in the selected articles with CBT in patients with overweight or obesity.

Common reasons for dropping out are reported in Table 4 . Generally, the articles reported objective reasons ( 27 , 36 , 48 , 51 ), such as long-term sickness, acute illness, pregnancy, logistics ( 28 , 30 ), poor job conditions or job difficulties ( 36 , 51 , 55 ), low level of organization ( 55 ), dissatisfaction with initial results ( 27 , 28 , 31 , 48 , 52 ), lack of motivation ( 28 , 39 , 48 , 52 ) and lack of adherence ( 59 ).

Table 4 . Motivations to dropout in CBT patients with overweight or obesity, according to selected articles reporting this aspect.

Twenty-three of the included studies used CBT as the only approach ( 27 – 29 , 32 , 33 , 35 , 37 – 40 , 44 – 46 , 49 – 51 , 53 – 56 , 58 , 60 – 62 ), three as a control group ( 31 , 57 , 59 ), and eleven as an intervention group ( 30 , 34 , 36 , 41 – 43 , 47 , 48 , 51 , 52 , 63 , 64 ). Different strategies were used for the intervention group (IG) in trials where CBT was the control group. In the study by Muñoz et al. ( 59 ) IG was characterized by a Cognitive Training Intervention, which consisted of a hypocaloric diet and 12 cognitive training sessions via Brain Exercise ( 59 ), while in other studies by Laparoscopic Sleeve Gastrectomy (LSG) ( 57 ) or sibutramine administration ( 31 ). Most trials were conducted by dietitians or certified nutritionists, often CBT experts. Other professionals often involved included physicians, psychologists, and physical therapists.

In addition to analyzing dropout rate and weight loss, biochemical parameters ( 32 , 34 , 42 , 59 , 60 , 62 ), such as glycaemic and lipid profiles, or cognitive and psychological variables, were assessed using specific questionnaires, such as the Goals and Relative Weights Questionnaire (GRWQ), Body Uneasiness Test (BUT), Symptom Checklist (SCL-90) and Binge Eating Scale (BES) ( 31 , 35 , 39 , 40 , 48 , 49 , 52 , 54 , 57 , 59 , 60 , 62 , 63 ).

According to the MMAT quality analysis, 13.5% of articles were classified as 5 stars, and none received the lowest quality grade (1 star). The majority of articles were classified as 4 stars (46%). Seventeen studies were included in the intention-to-treat analysis ( Figure 2 ), and 20 studies were included in the per-protocol analysis ( Figure 3 ). The highest domains with risk of bias in both the intention-to-treat and per-protocol analysis were domain 1, which pertained to the randomization process, and domain 2, which addressed deviations from the intended interventions. All other domains (D5: selection of the reported result, D4: measurement of the outcome; and D3: missing outcome data) had a low risk of bias. The overall valuation in the intention-to-treat analysis revealed 7 articles at high risk of bias (40%), while in the per-protocol analysis, there were 15 articles (75%). Also, in the intention-to-treat analysis, two articles (11.8%) were at low risk of bias, while in the per-protocol, there was one. In summary, more than half of the selected articles had a high risk of bias. The domain characterized by a higher level of bias was randomization, which was caused by the type of study design.

Figure 2 . Results of risk of bias analysis of intention-to-treat studies ( 24 ). (A) Risk of Bias by article included on each domain. (B) Overall risk of Bias percentage on each domain. From Sterne et al. ( 65 ).

Figure 3 . Results of risk of bias analysis of per protocol studies ( 24 ). (A) Risk of Bias by article included on each domain. (B) Overall risk of Bias percentage on each domain. From Sterne et al. ( 65 ).

This study systematically reviewed the dropout rate and predictive factors associated with dropout from cognitive behavioral treatment (CBT) in adult patients with overweight or obesity. The review demonstrated that the dropout rate ranges between 5 and 62%. As indicated in the results, some demographic and psychological factors could be the predictors.

Most included studies considered psychological variables, revealing a significant association with the dropout rate. These findings support the hypothesis that analyzing the psychological profile of patients with overweight or obesity through specific questionnaires can help identify individuals at higher dropout risk. Furthermore, such assessments can assist in providing appropriate support during the intervention or determining suitable intervention.

There were common reasons among these studies for patients to discontinue treatment. Some of these issues, such as organization and logistics, can be resolved by providing practical tools to address the barriers to successful treatment. Others, such as dissatisfaction with initial results and lack of motivation, could be managed by helping patients to understand that an improvement in general health is already obtained with a weight loss of 5–10% of the initial weight, thereby reducing their weight loss expectations and weight loss targets. According to Dalle Grave et al., regardless of the degree of weight loss, people with obesity have a high prevalence of body dissatisfaction, which improves at the 6-month follow-up following treatment ( 66 ).

With this in mind, it is important to resize the patient’s expectations about weight loss (often overestimated with respect to the real therapeutic goal) through better communication by the healthcare professional or the multidisciplinary team and to pay more attention during the initial phase of treatment.

Moreover, this review revealed that most of the included articles showed that CBT led to significant improvements in psychological variables and BED episodes. In 2016, Calugi et al. ( 54 ) concluded that although the BED group maintained higher psychological impairment than the group without BED at 6 months, more than half of the BED patients were no longer diagnosable at 5 years follow-up.

In studies where CBT was used as the only approach, the dropout rate ranged between 10 and 62%. Not all studies considered the same follow-up period, and in studies with longer follow-up periods, the dispersion increased exponentially. Nevertheless, in the study by Brambilla et al. ( 39 ) where CBT was used in both the intervention and control groups, the dropout rate was low (16%). Since CBT was the only therapy common to all three interventions, it is probably effective in preventing dropout independently of the results obtained, as shown by the authors.

Furthermore, there were several studies where CBT was used in the intervention group, and the dropout rate was higher than or equal to the control group. In the study by Mefferd et al. ( 34 ) dropouts (10.6%) were assigned to the intervention group. However, considering that the control group consisted of a wait-list, it is difficult to conclude the effectiveness of CBT. In a study by Stahre et al. ( 36 ) there were no significant differences between the two groups. Although, the percentage of completers was very high in both cases (87% in the intervention group with CBT and 80% in the control group). Donini et al. ( 41 ), instead, showed higher treatment duration in Nutritional Psycho-Physical Reconditioning (NPPR) and a significantly lower dropout rate (5.5% vs. 54.4% in standard diet intervention). In addition, weight loss and fat mass reduction were higher in NPPR. The authors of this study hypothesized that the low dropout rate could be ascribed to the multidisciplinary and cognitive-behavioral approach, which provides effective tools to address barriers that usually hinder compliance (e.g., establishing acceptable goals) and increase patients’ motivation to adhere to the procedure. They also affirmed that the improvement in anxiety and depression in the NPPR group allowed one to maintain an adequate lifestyle and sustain the achieved results.

The data from this review have clinical implications as they could help clinicians identify those at a higher risk of dropping out by investigating specific factors as best as possible. In fact, the importance of motivation in the failure of weight loss treatment makes the assessment of motivation a core procedure for all patients with obesity and overweight, both before and during treatment. It has been recently suggested that the importance of motivation in the failure of weight loss programs makes the assessment of motivation a core procedure for all patients living with obesity, both before and during treatment. Armstrong et al. suggested that a motivational interview (a directive, patient-centered counseling approach focused on exploring and resolving ambivalence) appears to enhance weight loss in people with overweight or obesity ( 67 ). Moreover, the motivational interview could be used as a separate intervention throughout the course of treatment, when the motivation of obese patients decreases ( 68 ). Furthermore, the dissatisfaction with the initial results of the treatment association with dropouts indicates that intensive treatment in the first part of the program might be useful. For example, increasing the number of sessions, offering them closer together, or even offering intermediate telephone contacts could be a potentially effective way to increase the initial weight loss rate and consequently reduce the dropout rate.

This study has several limitations. Most of the studies included, especially those added manually, had an observational and non-randomized design, which resulted in a high risk of bias, as shown in Figure 3 . This data could probably be because of the decision to add several articles published by the same research group ( 28 , 29 , 33 , 40 , 50 , 53 , 54 , 56 , 60 , 61 ), notwithstanding that this is a leading expert team and permitted a better understanding of the advantages and considerations of CBT. Despite the high risk of bias, the observational design allows clinicians to analyze and comprehend the complex phenomenon of obesity and evaluate numerous variables. Another limitation may be derived from the search strategy because the acronym CBT can be used for both “cognitive behavioural therapy” and “cognitive behavioural treatment” or “cognitive behavioural theory.” Moreover, most of the time, these terms are also reported with “-” divisors. Furthermore, it’s not possible to define the exact approach used in the selected studies; in particular, if the authors used generic forms of CBT or specific form of CBT for obesity management. Indeed, a specific form of CBT, called personalized CBT for obesity (CBT-OB), has been developed and widely studied in recent decades. The main goals of CBT-OB are to help patients to (i) reach, accept and maintain a healthy amount of weight loss (i.e., 5–10% of their starting body weight); (ii) adopt and maintain a lifestyle conducive to weight control; and (iii) develop a stable “weight-control mind-set.” Specific integrations enable the treatment to be personalized, and help patients address with specific strategies and procedures the processes that could be, respectively, associated with drop-out, the amount of weight lost, and maintaining a lower weight in the long term treatment. CBT-OB therapists adopt a therapeutic style designed to develop and nurture a collaborative working relationship (the therapist and patient(s) work together as a team) ( 69 , 70 ). Given the prevalence of long lasting eating disorders (ED) and their association with high attrition from weight management programs, the search strategy could have included specific terms and amplified the results, this could be addressed in future studies. The strength of the search lies in being systemic and in including all articles concerning CBT in treating obesity, regardless of other correlated pathologies.

Future research with well-designed randomized clinical trials involving different behavioral approaches could focus on answering how it could affect the adherence to the treatment and prevent the dropping out of adults living with overweight and obesity.

High attrition is a common problem in weight loss interventions and seriously affects weight loss management and frustration. The purpose of this current systematic review was to determine the predictive factors of dropout in treatment of people with overweight and obesity. The main predictive factors are younger age and baseline BMI/weight; and the common motivations of dropping out are dissatisfaction with the result or treatment, personal issues and health problems. Moreover, this review provides additional evidence with respect to CBT leading to significant improvements in psychological variables. These findings have important clinical implications as they could help clinicians identify those at a higher risk of dropping out, support them during the intervention, or find more suitable intervention options.

However, this review highlights the need for more rigorous and well-designed clinical trials to provide more definitive evidence. Ultimately, a deeper understanding of the comparative effectiveness of these treatment strategies is of great value to patients, clinicians, and healthcare policymakers.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

CF and FM: conceptualization. LN, CF, and AT: methodology. LN, MG, CF, and FM: investigation and writing – original draft preparation. LN, CF, FM, MG, SF, and AT: data curation and writing—review and editing. AT and CF: supervision. All authors have read and agreed to the present version of the manuscript.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

We thank Dr. Riccardo Dalle Grave for the precious contributors for the paper.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

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Keywords: dropout, predictive factors, cognitive behavioral therapy, cognitive behavioral treatment, nutritional counseling, attrition, overweight, systematic review

Citation: Neri LdCL, Mariotti F, Guglielmetti M, Fiorini S, Tagliabue A and Ferraris C (2024) Dropout in cognitive behavioral treatment in adults living with overweight and obesity: a systematic review. Front. Nutr . 11:1250683. doi: 10.3389/fnut.2024.1250683

Received: 10 August 2023; Accepted: 26 April 2024; Published: 09 May 2024.

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Copyright © 2024 Neri, Mariotti, Guglielmetti, Fiorini, Tagliabue and Ferraris. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Cinzia Ferraris, [email protected]

† These authors share first authorship

This article is part of the Research Topic

Nutritional Counseling for Lifestyle Modification