why systematic literature review is important

• Search Menu
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

• < Previous

What makes a good systematic review and meta-analysis?

• Article contents
• Figures & tables
• Supplementary Data

A. M. Møller, P. S. Myles, What makes a good systematic review and meta-analysis?, BJA: British Journal of Anaesthesia , Volume 117, Issue 4, October 2016, Pages 428–430, https://doi.org/10.1093/bja/aew264

A systematic review (SR) aims to retrieve, synthesize, and appraise existing knowledge on a particular subject. Meta-analysis is the statistical method used to combine results from the relevant studies, and the resultant larger sample size provides greater reliability (precision) of the estimates of any treatment effect. 1 Clinical decisions should be based on the totality of the best evidence and not the results of individual studies. The value and credibility of an SR depends on the importance of the question, the quality of the original studies, the efforts undertaken to minimize bias, and the clinical applicability. 2

The number and quality of SRs appearing in anaesthesia journals has increased, in part because these provide up-to-date, reliable, and clinically relevant information for readers. 3 , 4 However, the acceptance rate for this journal is quite low, indicating a high proportion of low-quality manuscripts. This editorial has been written in order to help authors and readers understand the basic features of the SR and improve their ability to write and read them critically.

The value of any SR depends heavily on the quantity, quality, and heterogeneity of the included studies, yet a good meta-analysis methodology is at least as important. Key elements to increase chances of acceptance include a clear and detailed methodology, with a focus on generalizability and reproducibility. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist will help to include all essential elements ( http://www.prisma-statement.org/PRISMAStatement/PRISMAStatement.aspx ). 5 A good SR also includes a comprehensive and critical discussion of the results, including strengths and limitations, such as assessment of bias, heterogeneity, and used definitions and categorizations. Ideally, the importance of the study is highlighted, considering clinical usefulness and the need for future research (Table  1 ).

The author team for an SR should include at least one person with some experience in the performance of SRs, one person skilled in statistics, and one person with content knowledge of the topic being addressed. The last of these, ideally, should have led at least one of the clinical trials being included in the analysis. For the inexperienced, the PRISMA guidelines 5 can be useful, and in any case, it is strongly recommended that the conduct and reporting of the SR be in accordance with its principles.

Tips to improve the value of systematic reviews

Like any other paper, the SR has an introduction, a methods section, a results section, and a discussion. What makes the SR different is that the study data are derived from the reports of completed (and usually published) studies, and it does this in a very systematic way.

Before even starting the process of performing an SR, the authors should clarify their clinical question using the PICO (participants, intervention, comparison, and outcomes) approach. Recently, however many other types of SRs are being done that may not necessarily fit this formula. Examples include diagnostic reviews, prognostic reviews, and qualitative reviews. The methodology for these reviews is still under development and will not be considered further in this editorial.

The clinical question should be described in detail at the protocol stage. The participants are the group of patients to be included. It is important to consider the characteristics of these thoroughly in order to include the group of patients relevant to the question in focus. The intervention must likewise be well described, whereas the control can be placebo, no treatment, or standard care. Of course, two different treatments can also be compared. There needs to be a nominated primary end point in any trial, including SRs. 6 There is no fixed limit for secondary outcomes, but normally five to nine will be considered a maximum. The PICO is useful when designing the search strategy for the review. Subgroups and covariates should be carefully considered and prespecified in order to avoid data dredging. 7

The search strategy for SRs needs to be comprehensive and include all relevant databases. The most common databases to search are PubMed (Medline), Cochrane Library CENTRAL, Embase, Cinahl, and LiLacs. As the main interest is usually the reported effect size, it is worthwhile for meta-analyses to consider inclusion of abstracts from major conferences in recent years. The search strategy is part of the review methodology, although for some journals it can be described as supplementary material on the journal website. The search methods need to be written in such a way that the search can be repeated by the reader, and by the authors, in case of updating the review.

The review process will start by retrieving and selecting relevant papers for inclusion as described in the protocol. Every paper must be evaluated to determine whether it meets the inclusion criteria. It is recommended to make a table of all included papers, and that the search and screening be done independently by at least two investigators. Double-data extraction by two independently working researchers is recommended to prevent errors. 8 The papers need to fulfil inclusion criteria, specified in the methods section of the review. It is useful to provide a flow diagram describing the selection of papers for the review.

The SR protocol should be published before starting the review process. For Cochrane reviews, publication of the protocol has been standard procedure since the foundation of the Cochrane Collaboration in 1993. For other systematic reviews, it is now recommended to publish the protocol on PROSPERO ( http://www.crd.york.ac.uk/prospero/ ) 6 or another comparable publically accessible website.

After selection, the papers must be screened for bias. A useful tool for this process is the Cochrane risk of bias tool, 9 or AMSTAR. 10 Careful consideration must precede the performance of the meta-analysis in the review. Meta-analysis should be performed only when appropriate. There are two major factors that need to be evaluated before a decision about meta-analysis is made; one is heterogeneity between studies and the other is the existence of reporting bias.

Heterogeneity arises when the difference between trials is too big. The differences can be in the populations or in the interventions. The amount of heterogeneity can be quantified using the I 2 statistic. 11 Heterogeneity can also be evaluated visually, by inspecting a forest plot. 12 , 13 Although a random-effects meta-analysis can account for some heterogeneity, when significant heterogeneity exists, meta-analysis should not be performed. 14

Reporting bias is bias across trials. It arises when the result of a trial has an impact on the publications process. It is well known that a trial with a positive, significant result is more likely to be published faster (time lag bias), in a journal with a higher impact factor (publication bias), in English (language bias) than its non-significant counterpart, even if both trials are performed according to the highest standards of methodology. Reporting bias will therefore almost always tend to overestimate the treatment effect of an intervention. A funnel plot can be used to assess the amount of reporting bias, inducing asymmetry in the shape of the plot. 13 Likewise, small trial bias occurs because small trials tend to overestimate treatment effects, and these typically populate SRs in anaesthesia heavily. 15 Appropriate selection of treatment effects or risk estimates, and decisions regarding the use of fixed-effect or random-effects meta-analysis, and the software used, 16 are important.

Cochrane reviews are often published in a paper journal as a co-publication. This is most often done in order to reach a broader audience. Although the printed version of the Cochrane reviews in most instances will be shorter and more digestible, the overall methodology and the results and conclusion must remain the same.

In conclusion, SRs and meta-analyses synthesize and update knowledge on a topic of interest. The methodology should also be presented clearly and in sufficient detail, and the strength of the evidence should be evaluated cautiously.

Higgins JPT Green S . The Cochrane Collaboration . Cochrane Handbook for Systematic Reviews of Interventions . Version 510 . 2011 . Available from http://training.cochrane.org/handbook (accessed 4 August 2016)

Murad MH Montori VM Ioannidis JP et al.  How to read a systematic review and meta-analysis and apply the results to patient care: users’ guides to the medical literature . JAMA 2014 ; 312 : 171 – 9

Google Scholar

Lauritsen J Møller AM . Clinical relevance in anesthesia journals . Curr Opin Anaesthesiol 2006 ; 19 : 166 – 70

Stroup DF Thacker SB Olson CM Glass RM Hutwagner L . Characteristics of meta-analyses related to acceptance for publication in a medical journal . J Clin Epidemiol 2001 ; 54 : 655 – 60

Moher D Liberati A Tetzlaff J Altman DG Group P . Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement . J Clin Epidemiol 2009 ; 62 : 1006 – 12

Tricco AC Cogo E Page MJ et al.  A third of systematic reviews changed or did not specify the primary outcome: a PROSPERO register study . J Clin Epidemiol Advance Access published on April 11 , 2016 , doi: 10.1016/j.jclinepi.2016.03.025

Page MJ McKenzie JE Kirkham J et al.  Bias due to selective inclusion and reporting of outcomes and analyses in systematic reviews of randomised trials of healthcare interventions . Cochrane Database Syst Rev 2014 ; 10 : MR000035

Finding What Works in Health Care: Standards for Systematic Reviews . 2011 ; doi: 10.17226/13059

Higgins JP Altman DG Gotzsche PC et al.  The Cochrane Collaboration's tool for assessing risk of bias in randomised trials . Br Med J 2011 ; 343 : d5928

Shea BJ Hamel C Wells GA et al.  AMSTAR is a reliable and valid measurement tool to assess the methodological quality of systematic reviews . J Clin Epidemiol 2009 ; 62 : 1013 – 20

Higgins JP Thompson SG Deeks JJ Altman DG . Measuring inconsistency in meta-analyses . Br Med J 2003 ; 327 : 557 – 60

Sedgwick P . How to read a forest plot in a meta-analysis . Br Med J 2015 ; 351 : h4028

Sterne JA Egger M . Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis . J Clin Epidemiol 2001 ; 54 : 1046 – 55

Borenstein M Hedges LV Higgins JP Rothstein HR . A basic introduction to fixed-effect and random-effects models for meta-analysis . Res Synth Methods 2010 ; 1 : 97 – 111

Dechartres A Trinquart L Boutron I Ravaud P . Influence of trial sample size on treatment effect estimates: meta-epidemiological study . Br Med J 2013 ; 346 : f2304

Bax L Yu LM Ikeda N Moons KG . A systematic comparison of software dedicated to meta-analysis of causal studies . BMC Med Res Methodol 2007 ; 7 : 40

Citing articles via

Affiliations.

• Online ISSN 1471-6771
• Print ISSN 0007-0912
• Copyright © 2024 The British Journal of Anaesthesia Ltd
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Open access
• Published: 14 August 2018

Defining the process to literature searching in systematic reviews: a literature review of guidance and supporting studies

• Chris Cooper   ORCID: orcid.org/0000-0003-0864-5607 1 ,
• Andrew Booth 2 ,
• Jo Varley-Campbell 1 ,
• Nicky Britten 3 &
• Ruth Garside 4  

BMC Medical Research Methodology volume  18 , Article number:  85 ( 2018 ) Cite this article

203k Accesses

203 Citations

118 Altmetric

Metrics details

Systematic literature searching is recognised as a critical component of the systematic review process. It involves a systematic search for studies and aims for a transparent report of study identification, leaving readers clear about what was done to identify studies, and how the findings of the review are situated in the relevant evidence.

Information specialists and review teams appear to work from a shared and tacit model of the literature search process. How this tacit model has developed and evolved is unclear, and it has not been explicitly examined before.

The purpose of this review is to determine if a shared model of the literature searching process can be detected across systematic review guidance documents and, if so, how this process is reported in the guidance and supported by published studies.

A literature review.

Two types of literature were reviewed: guidance and published studies. Nine guidance documents were identified, including: The Cochrane and Campbell Handbooks. Published studies were identified through ‘pearl growing’, citation chasing, a search of PubMed using the systematic review methods filter, and the authors’ topic knowledge.

The relevant sections within each guidance document were then read and re-read, with the aim of determining key methodological stages. Methodological stages were identified and defined. This data was reviewed to identify agreements and areas of unique guidance between guidance documents. Consensus across multiple guidance documents was used to inform selection of ‘key stages’ in the process of literature searching.

Eight key stages were determined relating specifically to literature searching in systematic reviews. They were: who should literature search, aims and purpose of literature searching, preparation, the search strategy, searching databases, supplementary searching, managing references and reporting the search process.

Conclusions

Eight key stages to the process of literature searching in systematic reviews were identified. These key stages are consistently reported in the nine guidance documents, suggesting consensus on the key stages of literature searching, and therefore the process of literature searching as a whole, in systematic reviews. Further research to determine the suitability of using the same process of literature searching for all types of systematic review is indicated.

Peer Review reports

Systematic literature searching is recognised as a critical component of the systematic review process. It involves a systematic search for studies and aims for a transparent report of study identification, leaving review stakeholders clear about what was done to identify studies, and how the findings of the review are situated in the relevant evidence.

Information specialists and review teams appear to work from a shared and tacit model of the literature search process. How this tacit model has developed and evolved is unclear, and it has not been explicitly examined before. This is in contrast to the information science literature, which has developed information processing models as an explicit basis for dialogue and empirical testing. Without an explicit model, research in the process of systematic literature searching will remain immature and potentially uneven, and the development of shared information models will be assumed but never articulated.

One way of developing such a conceptual model is by formally examining the implicit “programme theory” as embodied in key methodological texts. The aim of this review is therefore to determine if a shared model of the literature searching process in systematic reviews can be detected across guidance documents and, if so, how this process is reported and supported.

Identifying guidance

Key texts (henceforth referred to as “guidance”) were identified based upon their accessibility to, and prominence within, United Kingdom systematic reviewing practice. The United Kingdom occupies a prominent position in the science of health information retrieval, as quantified by such objective measures as the authorship of papers, the number of Cochrane groups based in the UK, membership and leadership of groups such as the Cochrane Information Retrieval Methods Group, the HTA-I Information Specialists’ Group and historic association with such centres as the UK Cochrane Centre, the NHS Centre for Reviews and Dissemination, the Centre for Evidence Based Medicine and the National Institute for Clinical Excellence (NICE). Coupled with the linguistic dominance of English within medical and health science and the science of systematic reviews more generally, this offers a justification for a purposive sample that favours UK, European and Australian guidance documents.

Nine guidance documents were identified. These documents provide guidance for different types of reviews, namely: reviews of interventions, reviews of health technologies, reviews of qualitative research studies, reviews of social science topics, and reviews to inform guidance.

Whilst these guidance documents occasionally offer additional guidance on other types of systematic reviews, we have focused on the core and stated aims of these documents as they relate to literature searching. Table  1 sets out: the guidance document, the version audited, their core stated focus, and a bibliographical pointer to the main guidance relating to literature searching.

Once a list of key guidance documents was determined, it was checked by six senior information professionals based in the UK for relevance to current literature searching in systematic reviews.

Identifying supporting studies

In addition to identifying guidance, the authors sought to populate an evidence base of supporting studies (henceforth referred to as “studies”) that contribute to existing search practice. Studies were first identified by the authors from their knowledge on this topic area and, subsequently, through systematic citation chasing key studies (‘pearls’ [ 1 ]) located within each key stage of the search process. These studies are identified in Additional file  1 : Appendix Table 1. Citation chasing was conducted by analysing the bibliography of references for each study (backwards citation chasing) and through Google Scholar (forward citation chasing). A search of PubMed using the systematic review methods filter was undertaken in August 2017 (see Additional file 1 ). The search terms used were: (literature search*[Title/Abstract]) AND sysrev_methods[sb] and 586 results were returned. These results were sifted for relevance to the key stages in Fig.  1 by CC.

The key stages of literature search guidance as identified from nine key texts

Extracting the data

To reveal the implicit process of literature searching within each guidance document, the relevant sections (chapters) on literature searching were read and re-read, with the aim of determining key methodological stages. We defined a key methodological stage as a distinct step in the overall process for which specific guidance is reported, and action is taken, that collectively would result in a completed literature search.

The chapter or section sub-heading for each methodological stage was extracted into a table using the exact language as reported in each guidance document. The lead author (CC) then read and re-read these data, and the paragraphs of the document to which the headings referred, summarising section details. This table was then reviewed, using comparison and contrast to identify agreements and areas of unique guidance. Consensus across multiple guidelines was used to inform selection of ‘key stages’ in the process of literature searching.

Having determined the key stages to literature searching, we then read and re-read the sections relating to literature searching again, extracting specific detail relating to the methodological process of literature searching within each key stage. Again, the guidance was then read and re-read, first on a document-by-document-basis and, secondly, across all the documents above, to identify both commonalities and areas of unique guidance.

Results and discussion

Our findings.

We were able to identify consensus across the guidance on literature searching for systematic reviews suggesting a shared implicit model within the information retrieval community. Whilst the structure of the guidance varies between documents, the same key stages are reported, even where the core focus of each document is different. We were able to identify specific areas of unique guidance, where a document reported guidance not summarised in other documents, together with areas of consensus across guidance.

Unique guidance

Only one document provided guidance on the topic of when to stop searching [ 2 ]. This guidance from 2005 anticipates a topic of increasing importance with the current interest in time-limited (i.e. “rapid”) reviews. Quality assurance (or peer review) of literature searches was only covered in two guidance documents [ 3 , 4 ]. This topic has emerged as increasingly important as indicated by the development of the PRESS instrument [ 5 ]. Text mining was discussed in four guidance documents [ 4 , 6 , 7 , 8 ] where the automation of some manual review work may offer efficiencies in literature searching [ 8 ].

Agreement between guidance: Defining the key stages of literature searching

Where there was agreement on the process, we determined that this constituted a key stage in the process of literature searching to inform systematic reviews.

From the guidance, we determined eight key stages that relate specifically to literature searching in systematic reviews. These are summarised at Fig. 1 . The data extraction table to inform Fig. 1 is reported in Table  2 . Table 2 reports the areas of common agreement and it demonstrates that the language used to describe key stages and processes varies significantly between guidance documents.

For each key stage, we set out the specific guidance, followed by discussion on how this guidance is situated within the wider literature.

Key stage one: Deciding who should undertake the literature search

The guidance.

Eight documents provided guidance on who should undertake literature searching in systematic reviews [ 2 , 4 , 6 , 7 , 8 , 9 , 10 , 11 ]. The guidance affirms that people with relevant expertise of literature searching should ‘ideally’ be included within the review team [ 6 ]. Information specialists (or information scientists), librarians or trial search co-ordinators (TSCs) are indicated as appropriate researchers in six guidance documents [ 2 , 7 , 8 , 9 , 10 , 11 ].

How the guidance corresponds to the published studies

The guidance is consistent with studies that call for the involvement of information specialists and librarians in systematic reviews [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ] and which demonstrate how their training as ‘expert searchers’ and ‘analysers and organisers of data’ can be put to good use [ 13 ] in a variety of roles [ 12 , 16 , 20 , 21 , 24 , 25 , 26 ]. These arguments make sense in the context of the aims and purposes of literature searching in systematic reviews, explored below. The need for ‘thorough’ and ‘replicable’ literature searches was fundamental to the guidance and recurs in key stage two. Studies have found poor reporting, and a lack of replicable literature searches, to be a weakness in systematic reviews [ 17 , 18 , 27 , 28 ] and they argue that involvement of information specialists/ librarians would be associated with better reporting and better quality literature searching. Indeed, Meert et al. [ 29 ] demonstrated that involving a librarian as a co-author to a systematic review correlated with a higher score in the literature searching component of a systematic review [ 29 ]. As ‘new styles’ of rapid and scoping reviews emerge, where decisions on how to search are more iterative and creative, a clear role is made here too [ 30 ].

Knowing where to search for studies was noted as important in the guidance, with no agreement as to the appropriate number of databases to be searched [ 2 , 6 ]. Database (and resource selection more broadly) is acknowledged as a relevant key skill of information specialists and librarians [ 12 , 15 , 16 , 31 ].

Whilst arguments for including information specialists and librarians in the process of systematic review might be considered self-evident, Koffel and Rethlefsen [ 31 ] have questioned if the necessary involvement is actually happening [ 31 ].

Key stage two: Determining the aim and purpose of a literature search

The aim: Five of the nine guidance documents use adjectives such as ‘thorough’, ‘comprehensive’, ‘transparent’ and ‘reproducible’ to define the aim of literature searching [ 6 , 7 , 8 , 9 , 10 ]. Analogous phrases were present in a further three guidance documents, namely: ‘to identify the best available evidence’ [ 4 ] or ‘the aim of the literature search is not to retrieve everything. It is to retrieve everything of relevance’ [ 2 ] or ‘A systematic literature search aims to identify all publications relevant to the particular research question’ [ 3 ]. The Joanna Briggs Institute reviewers’ manual was the only guidance document where a clear statement on the aim of literature searching could not be identified. The purpose of literature searching was defined in three guidance documents, namely to minimise bias in the resultant review [ 6 , 8 , 10 ]. Accordingly, eight of nine documents clearly asserted that thorough and comprehensive literature searches are required as a potential mechanism for minimising bias.

The need for thorough and comprehensive literature searches appears as uniform within the eight guidance documents that describe approaches to literature searching in systematic reviews of effectiveness. Reviews of effectiveness (of intervention or cost), accuracy and prognosis, require thorough and comprehensive literature searches to transparently produce a reliable estimate of intervention effect. The belief that all relevant studies have been ‘comprehensively’ identified, and that this process has been ‘transparently’ reported, increases confidence in the estimate of effect and the conclusions that can be drawn [ 32 ]. The supporting literature exploring the need for comprehensive literature searches focuses almost exclusively on reviews of intervention effectiveness and meta-analysis. Different ‘styles’ of review may have different standards however; the alternative, offered by purposive sampling, has been suggested in the specific context of qualitative evidence syntheses [ 33 ].

What is a comprehensive literature search?

Whilst the guidance calls for thorough and comprehensive literature searches, it lacks clarity on what constitutes a thorough and comprehensive literature search, beyond the implication that all of the literature search methods in Table 2 should be used to identify studies. Egger et al. [ 34 ], in an empirical study evaluating the importance of comprehensive literature searches for trials in systematic reviews, defined a comprehensive search for trials as:

a search not restricted to English language;

where Cochrane CENTRAL or at least two other electronic databases had been searched (such as MEDLINE or EMBASE); and

at least one of the following search methods has been used to identify unpublished trials: searches for (I) conference abstracts, (ii) theses, (iii) trials registers; and (iv) contacts with experts in the field [ 34 ].

Tricco et al. (2008) used a similar threshold of bibliographic database searching AND a supplementary search method in a review when examining the risk of bias in systematic reviews. Their criteria were: one database (limited using the Cochrane Highly Sensitive Search Strategy (HSSS)) and handsearching [ 35 ].

Together with the guidance, this would suggest that comprehensive literature searching requires the use of BOTH bibliographic database searching AND supplementary search methods.

Comprehensiveness in literature searching, in the sense of how much searching should be undertaken, remains unclear. Egger et al. recommend that ‘investigators should consider the type of literature search and degree of comprehension that is appropriate for the review in question, taking into account budget and time constraints’ [ 34 ]. This view tallies with the Cochrane Handbook, which stipulates clearly, that study identification should be undertaken ‘within resource limits’ [ 9 ]. This would suggest that the limitations to comprehension are recognised but it raises questions on how this is decided and reported [ 36 ].

What is the point of comprehensive literature searching?

The purpose of thorough and comprehensive literature searches is to avoid missing key studies and to minimize bias [ 6 , 8 , 10 , 34 , 37 , 38 , 39 ] since a systematic review based only on published (or easily accessible) studies may have an exaggerated effect size [ 35 ]. Felson (1992) sets out potential biases that could affect the estimate of effect in a meta-analysis [ 40 ] and Tricco et al. summarize the evidence concerning bias and confounding in systematic reviews [ 35 ]. Egger et al. point to non-publication of studies, publication bias, language bias and MEDLINE bias, as key biases [ 34 , 35 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ]. Comprehensive searches are not the sole factor to mitigate these biases but their contribution is thought to be significant [ 2 , 32 , 34 ]. Fehrmann (2011) suggests that ‘the search process being described in detail’ and that, where standard comprehensive search techniques have been applied, increases confidence in the search results [ 32 ].

Does comprehensive literature searching work?

Egger et al., and other study authors, have demonstrated a change in the estimate of intervention effectiveness where relevant studies were excluded from meta-analysis [ 34 , 47 ]. This would suggest that missing studies in literature searching alters the reliability of effectiveness estimates. This is an argument for comprehensive literature searching. Conversely, Egger et al. found that ‘comprehensive’ searches still missed studies and that comprehensive searches could, in fact, introduce bias into a review rather than preventing it, through the identification of low quality studies then being included in the meta-analysis [ 34 ]. Studies query if identifying and including low quality or grey literature studies changes the estimate of effect [ 43 , 48 ] and question if time is better invested updating systematic reviews rather than searching for unpublished studies [ 49 ], or mapping studies for review as opposed to aiming for high sensitivity in literature searching [ 50 ].

Aim and purpose beyond reviews of effectiveness

The need for comprehensive literature searches is less certain in reviews of qualitative studies, and for reviews where a comprehensive identification of studies is difficult to achieve (for example, in Public health) [ 33 , 51 , 52 , 53 , 54 , 55 ]. Literature searching for qualitative studies, and in public health topics, typically generates a greater number of studies to sift than in reviews of effectiveness [ 39 ] and demonstrating the ‘value’ of studies identified or missed is harder [ 56 ], since the study data do not typically support meta-analysis. Nussbaumer-Streit et al. (2016) have registered a review protocol to assess whether abbreviated literature searches (as opposed to comprehensive literature searches) has an impact on conclusions across multiple bodies of evidence, not only on effect estimates [ 57 ] which may develop this understanding. It may be that decision makers and users of systematic reviews are willing to trade the certainty from a comprehensive literature search and systematic review in exchange for different approaches to evidence synthesis [ 58 ], and that comprehensive literature searches are not necessarily a marker of literature search quality, as previously thought [ 36 ]. Different approaches to literature searching [ 37 , 38 , 59 , 60 , 61 , 62 ] and developing the concept of when to stop searching are important areas for further study [ 36 , 59 ].

The study by Nussbaumer-Streit et al. has been published since the submission of this literature review [ 63 ]. Nussbaumer-Streit et al. (2018) conclude that abbreviated literature searches are viable options for rapid evidence syntheses, if decision-makers are willing to trade the certainty from a comprehensive literature search and systematic review, but that decision-making which demands detailed scrutiny should still be based on comprehensive literature searches [ 63 ].

Key stage three: Preparing for the literature search

Six documents provided guidance on preparing for a literature search [ 2 , 3 , 6 , 7 , 9 , 10 ]. The Cochrane Handbook clearly stated that Cochrane authors (i.e. researchers) should seek advice from a trial search co-ordinator (i.e. a person with specific skills in literature searching) ‘before’ starting a literature search [ 9 ].

Two key tasks were perceptible in preparing for a literature searching [ 2 , 6 , 7 , 10 , 11 ]. First, to determine if there are any existing or on-going reviews, or if a new review is justified [ 6 , 11 ]; and, secondly, to develop an initial literature search strategy to estimate the volume of relevant literature (and quality of a small sample of relevant studies [ 10 ]) and indicate the resources required for literature searching and the review of the studies that follows [ 7 , 10 ].

Three documents summarised guidance on where to search to determine if a new review was justified [ 2 , 6 , 11 ]. These focused on searching databases of systematic reviews (The Cochrane Database of Systematic Reviews (CDSR) and the Database of Abstracts of Reviews of Effects (DARE)), institutional registries (including PROSPERO), and MEDLINE [ 6 , 11 ]. It is worth noting, however, that as of 2015, DARE (and NHS EEDs) are no longer being updated and so the relevance of this (these) resource(s) will diminish over-time [ 64 ]. One guidance document, ‘Systematic reviews in the Social Sciences’, noted, however, that databases are not the only source of information and unpublished reports, conference proceeding and grey literature may also be required, depending on the nature of the review question [ 2 ].

Two documents reported clearly that this preparation (or ‘scoping’) exercise should be undertaken before the actual search strategy is developed [ 7 , 10 ]).

The guidance offers the best available source on preparing the literature search with the published studies not typically reporting how their scoping informed the development of their search strategies nor how their search approaches were developed. Text mining has been proposed as a technique to develop search strategies in the scoping stages of a review although this work is still exploratory [ 65 ]. ‘Clustering documents’ and word frequency analysis have also been tested to identify search terms and studies for review [ 66 , 67 ]. Preparing for literature searches and scoping constitutes an area for future research.

Key stage four: Designing the search strategy

The Population, Intervention, Comparator, Outcome (PICO) structure was the commonly reported structure promoted to design a literature search strategy. Five documents suggested that the eligibility criteria or review question will determine which concepts of PICO will be populated to develop the search strategy [ 1 , 4 , 7 , 8 , 9 ]. The NICE handbook promoted multiple structures, namely PICO, SPICE (Setting, Perspective, Intervention, Comparison, Evaluation) and multi-stranded approaches [ 4 ].

With the exclusion of The Joanna Briggs Institute reviewers’ manual, the guidance offered detail on selecting key search terms, synonyms, Boolean language, selecting database indexing terms and combining search terms. The CEE handbook suggested that ‘search terms may be compiled with the help of the commissioning organisation and stakeholders’ [ 10 ].

The use of limits, such as language or date limits, were discussed in all documents [ 2 , 3 , 4 , 6 , 7 , 8 , 9 , 10 , 11 ].

Search strategy structure

The guidance typically relates to reviews of intervention effectiveness so PICO – with its focus on intervention and comparator - is the dominant model used to structure literature search strategies [ 68 ]. PICOs – where the S denotes study design - is also commonly used in effectiveness reviews [ 6 , 68 ]. As the NICE handbook notes, alternative models to structure literature search strategies have been developed and tested. Booth provides an overview on formulating questions for evidence based practice [ 69 ] and has developed a number of alternatives to the PICO structure, namely: BeHEMoTh (Behaviour of interest; Health context; Exclusions; Models or Theories) for use when systematically identifying theory [ 55 ]; SPICE (Setting, Perspective, Intervention, Comparison, Evaluation) for identification of social science and evaluation studies [ 69 ] and, working with Cooke and colleagues, SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) [ 70 ]. SPIDER has been compared to PICO and PICOs in a study by Methley et al. [ 68 ].

The NICE handbook also suggests the use of multi-stranded approaches to developing literature search strategies [ 4 ]. Glanville developed this idea in a study by Whitting et al. [ 71 ] and a worked example of this approach is included in the development of a search filter by Cooper et al. [ 72 ].

Writing search strategies: Conceptual and objective approaches

Hausner et al. [ 73 ] provide guidance on writing literature search strategies, delineating between conceptually and objectively derived approaches. The conceptual approach, advocated by and explained in the guidance documents, relies on the expertise of the literature searcher to identify key search terms and then develop key terms to include synonyms and controlled syntax. Hausner and colleagues set out the objective approach [ 73 ] and describe what may be done to validate it [ 74 ].

The use of limits

The guidance documents offer direction on the use of limits within a literature search. Limits can be used to focus literature searching to specific study designs or by other markers (such as by date) which limits the number of studies returned by a literature search. The use of limits should be described and the implications explored [ 34 ] since limiting literature searching can introduce bias (explored above). Craven et al. have suggested the use of a supporting narrative to explain decisions made in the process of developing literature searches and this advice would usefully capture decisions on the use of search limits [ 75 ].

Key stage five: Determining the process of literature searching and deciding where to search (bibliographic database searching)

Table 2 summarises the process of literature searching as reported in each guidance document. Searching bibliographic databases was consistently reported as the ‘first step’ to literature searching in all nine guidance documents.

Three documents reported specific guidance on where to search, in each case specific to the type of review their guidance informed, and as a minimum requirement [ 4 , 9 , 11 ]. Seven of the key guidance documents suggest that the selection of bibliographic databases depends on the topic of review [ 2 , 3 , 4 , 6 , 7 , 8 , 10 ], with two documents noting the absence of an agreed standard on what constitutes an acceptable number of databases searched [ 2 , 6 ].

The guidance documents summarise ‘how to’ search bibliographic databases in detail and this guidance is further contextualised above in terms of developing the search strategy. The documents provide guidance of selecting bibliographic databases, in some cases stating acceptable minima (i.e. The Cochrane Handbook states Cochrane CENTRAL, MEDLINE and EMBASE), and in other cases simply listing bibliographic database available to search. Studies have explored the value in searching specific bibliographic databases, with Wright et al. (2015) noting the contribution of CINAHL in identifying qualitative studies [ 76 ], Beckles et al. (2013) questioning the contribution of CINAHL to identifying clinical studies for guideline development [ 77 ], and Cooper et al. (2015) exploring the role of UK-focused bibliographic databases to identify UK-relevant studies [ 78 ]. The host of the database (e.g. OVID or ProQuest) has been shown to alter the search returns offered. Younger and Boddy [ 79 ] report differing search returns from the same database (AMED) but where the ‘host’ was different [ 79 ].

The average number of bibliographic database searched in systematic reviews has risen in the period 1994–2014 (from 1 to 4) [ 80 ] but there remains (as attested to by the guidance) no consensus on what constitutes an acceptable number of databases searched [ 48 ]. This is perhaps because thinking about the number of databases searched is the wrong question, researchers should be focused on which databases were searched and why, and which databases were not searched and why. The discussion should re-orientate to the differential value of sources but researchers need to think about how to report this in studies to allow findings to be generalised. Bethel (2017) has proposed ‘search summaries’, completed by the literature searcher, to record where included studies were identified, whether from database (and which databases specifically) or supplementary search methods [ 81 ]. Search summaries document both yield and accuracy of searches, which could prospectively inform resource use and decisions to search or not to search specific databases in topic areas. The prospective use of such data presupposes, however, that past searches are a potential predictor of future search performance (i.e. that each topic is to be considered representative and not unique). In offering a body of practice, this data would be of greater practicable use than current studies which are considered as little more than individual case studies [ 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 ].

When to database search is another question posed in the literature. Beyer et al. [ 91 ] report that databases can be prioritised for literature searching which, whilst not addressing the question of which databases to search, may at least bring clarity as to which databases to search first [ 91 ]. Paradoxically, this links to studies that suggest PubMed should be searched in addition to MEDLINE (OVID interface) since this improves the currency of systematic reviews [ 92 , 93 ]. Cooper et al. (2017) have tested the idea of database searching not as a primary search method (as suggested in the guidance) but as a supplementary search method in order to manage the volume of studies identified for an environmental effectiveness systematic review. Their case study compared the effectiveness of database searching versus a protocol using supplementary search methods and found that the latter identified more relevant studies for review than searching bibliographic databases [ 94 ].

Key stage six: Determining the process of literature searching and deciding where to search (supplementary search methods)

Table 2 also summaries the process of literature searching which follows bibliographic database searching. As Table 2 sets out, guidance that supplementary literature search methods should be used in systematic reviews recurs across documents, but the order in which these methods are used, and the extent to which they are used, varies. We noted inconsistency in the labelling of supplementary search methods between guidance documents.

Rather than focus on the guidance on how to use the methods (which has been summarised in a recent review [ 95 ]), we focus on the aim or purpose of supplementary search methods.

The Cochrane Handbook reported that ‘efforts’ to identify unpublished studies should be made [ 9 ]. Four guidance documents [ 2 , 3 , 6 , 9 ] acknowledged that searching beyond bibliographic databases was necessary since ‘databases are not the only source of literature’ [ 2 ]. Only one document reported any guidance on determining when to use supplementary methods. The IQWiG handbook reported that the use of handsearching (in their example) could be determined on a ‘case-by-case basis’ which implies that the use of these methods is optional rather than mandatory. This is in contrast to the guidance (above) on bibliographic database searching.

The issue for supplementary search methods is similar in many ways to the issue of searching bibliographic databases: demonstrating value. The purpose and contribution of supplementary search methods in systematic reviews is increasingly acknowledged [ 37 , 61 , 62 , 96 , 97 , 98 , 99 , 100 , 101 ] but understanding the value of the search methods to identify studies and data is unclear. In a recently published review, Cooper et al. (2017) reviewed the literature on supplementary search methods looking to determine the advantages, disadvantages and resource implications of using supplementary search methods [ 95 ]. This review also summarises the key guidance and empirical studies and seeks to address the question on when to use these search methods and when not to [ 95 ]. The guidance is limited in this regard and, as Table 2 demonstrates, offers conflicting advice on the order of searching, and the extent to which these search methods should be used in systematic reviews.

Key stage seven: Managing the references

Five of the documents provided guidance on managing references, for example downloading, de-duplicating and managing the output of literature searches [ 2 , 4 , 6 , 8 , 10 ]. This guidance typically itemised available bibliographic management tools rather than offering guidance on how to use them specifically [ 2 , 4 , 6 , 8 ]. The CEE handbook provided guidance on importing data where no direct export option is available (e.g. web-searching) [ 10 ].

The literature on using bibliographic management tools is not large relative to the number of ‘how to’ videos on platforms such as YouTube (see for example [ 102 ]). These YouTube videos confirm the overall lack of ‘how to’ guidance identified in this study and offer useful instruction on managing references. Bramer et al. set out methods for de-duplicating data and reviewing references in Endnote [ 103 , 104 ] and Gall tests the direct search function within Endnote to access databases such as PubMed, finding a number of limitations [ 105 ]. Coar et al. and Ahmed et al. consider the role of the free-source tool, Zotero [ 106 , 107 ]. Managing references is a key administrative function in the process of review particularly for documenting searches in PRISMA guidance.

Key stage eight: Documenting the search

The Cochrane Handbook was the only guidance document to recommend a specific reporting guideline: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 9 ]. Six documents provided guidance on reporting the process of literature searching with specific criteria to report [ 3 , 4 , 6 , 8 , 9 , 10 ]. There was consensus on reporting: the databases searched (and the host searched by), the search strategies used, and any use of limits (e.g. date, language, search filters (The CRD handbook called for these limits to be justified [ 6 ])). Three guidance documents reported that the number of studies identified should be recorded [ 3 , 6 , 10 ]. The number of duplicates identified [ 10 ], the screening decisions [ 3 ], a comprehensive list of grey literature sources searched (and full detail for other supplementary search methods) [ 8 ], and an annotation of search terms tested but not used [ 4 ] were identified as unique items in four documents.

The Cochrane Handbook was the only guidance document to note that the full search strategies for each database should be included in the Additional file 1 of the review [ 9 ].

All guidance documents should ultimately deliver completed systematic reviews that fulfil the requirements of the PRISMA reporting guidelines [ 108 ]. The guidance broadly requires the reporting of data that corresponds with the requirements of the PRISMA statement although documents typically ask for diverse and additional items [ 108 ]. In 2008, Sampson et al. observed a lack of consensus on reporting search methods in systematic reviews [ 109 ] and this remains the case as of 2017, as evidenced in the guidance documents, and in spite of the publication of the PRISMA guidelines in 2009 [ 110 ]. It is unclear why the collective guidance does not more explicitly endorse adherence to the PRISMA guidance.

Reporting of literature searching is a key area in systematic reviews since it sets out clearly what was done and how the conclusions of the review can be believed [ 52 , 109 ]. Despite strong endorsement in the guidance documents, specifically supported in PRISMA guidance, and other related reporting standards too (such as ENTREQ for qualitative evidence synthesis, STROBE for reviews of observational studies), authors still highlight the prevalence of poor standards of literature search reporting [ 31 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 ]. To explore issues experienced by authors in reporting literature searches, and look at uptake of PRISMA, Radar et al. [ 120 ] surveyed over 260 review authors to determine common problems and their work summaries the practical aspects of reporting literature searching [ 120 ]. Atkinson et al. [ 121 ] have also analysed reporting standards for literature searching, summarising recommendations and gaps for reporting search strategies [ 121 ].

One area that is less well covered by the guidance, but nevertheless appears in this literature, is the quality appraisal or peer review of literature search strategies. The PRESS checklist is the most prominent and it aims to develop evidence-based guidelines to peer review of electronic search strategies [ 5 , 122 , 123 ]. A corresponding guideline for documentation of supplementary search methods does not yet exist although this idea is currently being explored.

How the reporting of the literature searching process corresponds to critical appraisal tools is an area for further research. In the survey undertaken by Radar et al. (2014), 86% of survey respondents (153/178) identified a need for further guidance on what aspects of the literature search process to report [ 120 ]. The PRISMA statement offers a brief summary of what to report but little practical guidance on how to report it [ 108 ]. Critical appraisal tools for systematic reviews, such as AMSTAR 2 (Shea et al. [ 124 ]) and ROBIS (Whiting et al. [ 125 ]), can usefully be read alongside PRISMA guidance, since they offer greater detail on how the reporting of the literature search will be appraised and, therefore, they offer a proxy on what to report [ 124 , 125 ]. Further research in the form of a study which undertakes a comparison between PRISMA and quality appraisal checklists for systematic reviews would seem to begin addressing the call, identified by Radar et al., for further guidance on what to report [ 120 ].

Limitations

Other handbooks exist.

A potential limitation of this literature review is the focus on guidance produced in Europe (the UK specifically) and Australia. We justify the decision for our selection of the nine guidance documents reviewed in this literature review in section “ Identifying guidance ”. In brief, these nine guidance documents were selected as the most relevant health care guidance that inform UK systematic reviewing practice, given that the UK occupies a prominent position in the science of health information retrieval. We acknowledge the existence of other guidance documents, such as those from North America (e.g. the Agency for Healthcare Research and Quality (AHRQ) [ 126 ], The Institute of Medicine [ 127 ] and the guidance and resources produced by the Canadian Agency for Drugs and Technologies in Health (CADTH) [ 128 ]). We comment further on this directly below.

The handbooks are potentially linked to one another

What is not clear is the extent to which the guidance documents inter-relate or provide guidance uniquely. The Cochrane Handbook, first published in 1994, is notably a key source of reference in guidance and systematic reviews beyond Cochrane reviews. It is not clear to what extent broadening the sample of guidance handbooks to include North American handbooks, and guidance handbooks from other relevant countries too, would alter the findings of this literature review or develop further support for the process model. Since we cannot be clear, we raise this as a potential limitation of this literature review. On our initial review of a sample of North American, and other, guidance documents (before selecting the guidance documents considered in this review), however, we do not consider that the inclusion of these further handbooks would alter significantly the findings of this literature review.

This is a literature review

A further limitation of this review was that the review of published studies is not a systematic review of the evidence for each key stage. It is possible that other relevant studies could help contribute to the exploration and development of the key stages identified in this review.

This literature review would appear to demonstrate the existence of a shared model of the literature searching process in systematic reviews. We call this model ‘the conventional approach’, since it appears to be common convention in nine different guidance documents.

The findings reported above reveal eight key stages in the process of literature searching for systematic reviews. These key stages are consistently reported in the nine guidance documents which suggests consensus on the key stages of literature searching, and therefore the process of literature searching as a whole, in systematic reviews.

In Table 2 , we demonstrate consensus regarding the application of literature search methods. All guidance documents distinguish between primary and supplementary search methods. Bibliographic database searching is consistently the first method of literature searching referenced in each guidance document. Whilst the guidance uniformly supports the use of supplementary search methods, there is little evidence for a consistent process with diverse guidance across documents. This may reflect differences in the core focus across each document, linked to differences in identifying effectiveness studies or qualitative studies, for instance.

Eight of the nine guidance documents reported on the aims of literature searching. The shared understanding was that literature searching should be thorough and comprehensive in its aim and that this process should be reported transparently so that that it could be reproduced. Whilst only three documents explicitly link this understanding to minimising bias, it is clear that comprehensive literature searching is implicitly linked to ‘not missing relevant studies’ which is approximately the same point.

Defining the key stages in this review helps categorise the scholarship available, and it prioritises areas for development or further study. The supporting studies on preparing for literature searching (key stage three, ‘preparation’) were, for example, comparatively few, and yet this key stage represents a decisive moment in literature searching for systematic reviews. It is where search strategy structure is determined, search terms are chosen or discarded, and the resources to be searched are selected. Information specialists, librarians and researchers, are well placed to develop these and other areas within the key stages we identify.

This review calls for further research to determine the suitability of using the conventional approach. The publication dates of the guidance documents which underpin the conventional approach may raise questions as to whether the process which they each report remains valid for current systematic literature searching. In addition, it may be useful to test whether it is desirable to use the same process model of literature searching for qualitative evidence synthesis as that for reviews of intervention effectiveness, which this literature review demonstrates is presently recommended best practice.

Abbreviations

Behaviour of interest; Health context; Exclusions; Models or Theories

Cochrane Database of Systematic Reviews

The Cochrane Central Register of Controlled Trials

Database of Abstracts of Reviews of Effects

Enhancing transparency in reporting the synthesis of qualitative research

Institute for Quality and Efficiency in Healthcare

National Institute for Clinical Excellence

Population, Intervention, Comparator, Outcome

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Setting, Perspective, Intervention, Comparison, Evaluation

Sample, Phenomenon of Interest, Design, Evaluation, Research type

STrengthening the Reporting of OBservational studies in Epidemiology

Trial Search Co-ordinators

Booth A. Unpacking your literature search toolbox: on search styles and tactics. Health Information & Libraries Journal. 2008;25(4):313–7.

Article   Google Scholar  

Petticrew M, Roberts H. Systematic reviews in the social sciences: a practical guide. Oxford: Blackwell Publishing Ltd; 2006.

Book   Google Scholar  

Institute for Quality and Efficiency in Health Care (IQWiG). IQWiG Methods Resources. 7 Information retrieval 2014 [Available from: https://www.ncbi.nlm.nih.gov/books/NBK385787/ .

NICE: National Institute for Health and Care Excellence. Developing NICE guidelines: the manual 2014. Available from: https://www.nice.org.uk/media/default/about/what-we-do/our-programmes/developing-nice-guidelines-the-manual.pdf .

Sampson M. MJ, Lefebvre C, Moher D, Grimshaw J. Peer Review of Electronic Search Strategies: PRESS; 2008.

Google Scholar  

Centre for Reviews & Dissemination. Systematic reviews – CRD’s guidance for undertaking reviews in healthcare. York: Centre for Reviews and Dissemination, University of York; 2009.

eunetha: European Network for Health Technology Assesment Process of information retrieval for systematic reviews and health technology assessments on clinical effectiveness 2016. Available from: http://www.eunethta.eu/sites/default/files/Guideline_Information_Retrieval_V1-1.pdf .

Kugley SWA, Thomas J, Mahood Q, Jørgensen AMK, Hammerstrøm K, Sathe N. Searching for studies: a guide to information retrieval for Campbell systematic reviews. Oslo: Campbell Collaboration. 2017; Available from: https://www.campbellcollaboration.org/library/searching-for-studies-information-retrieval-guide-campbell-reviews.html

Lefebvre C, Manheimer E, Glanville J. Chapter 6: searching for studies. In: JPT H, Green S, editors. Cochrane Handbook for Systematic Reviews of Interventions; 2011.

Collaboration for Environmental Evidence. Guidelines for Systematic Review and Evidence Synthesis in Environmental Management.: Environmental Evidence:; 2013. Available from: http://www.environmentalevidence.org/wp-content/uploads/2017/01/Review-guidelines-version-4.2-final-update.pdf .

The Joanna Briggs Institute. Joanna Briggs institute reviewers’ manual. 2014th ed: the Joanna Briggs institute; 2014. Available from: https://joannabriggs.org/assets/docs/sumari/ReviewersManual-2014.pdf

Beverley CA, Booth A, Bath PA. The role of the information specialist in the systematic review process: a health information case study. Health Inf Libr J. 2003;20(2):65–74.

Article   CAS   Google Scholar  

Harris MR. The librarian's roles in the systematic review process: a case study. Journal of the Medical Library Association. 2005;93(1):81–7.

PubMed   PubMed Central   Google Scholar  

Egger JB. Use of recommended search strategies in systematic reviews and the impact of librarian involvement: a cross-sectional survey of recent authors. PLoS One. 2015;10(5):e0125931.

Li L, Tian J, Tian H, Moher D, Liang F, Jiang T, et al. Network meta-analyses could be improved by searching more sources and by involving a librarian. J Clin Epidemiol. 2014;67(9):1001–7.

Article   PubMed   Google Scholar  

McGowan J, Sampson M. Systematic reviews need systematic searchers. J Med Libr Assoc. 2005;93(1):74–80.

Rethlefsen ML, Farrell AM, Osterhaus Trzasko LC, Brigham TJ. Librarian co-authors correlated with higher quality reported search strategies in general internal medicine systematic reviews. J Clin Epidemiol. 2015;68(6):617–26.

Weller AC. Mounting evidence that librarians are essential for comprehensive literature searches for meta-analyses and Cochrane reports. J Med Libr Assoc. 2004;92(2):163–4.

Swinkels A, Briddon J, Hall J. Two physiotherapists, one librarian and a systematic literature review: collaboration in action. Health Info Libr J. 2006;23(4):248–56.

Foster M. An overview of the role of librarians in systematic reviews: from expert search to project manager. EAHIL. 2015;11(3):3–7.

Lawson L. OPERATING OUTSIDE LIBRARY WALLS 2004.

Vassar M, Yerokhin V, Sinnett PM, Weiher M, Muckelrath H, Carr B, et al. Database selection in systematic reviews: an insight through clinical neurology. Health Inf Libr J. 2017;34(2):156–64.

Townsend WA, Anderson PF, Ginier EC, MacEachern MP, Saylor KM, Shipman BL, et al. A competency framework for librarians involved in systematic reviews. Journal of the Medical Library Association : JMLA. 2017;105(3):268–75.

Cooper ID, Crum JA. New activities and changing roles of health sciences librarians: a systematic review, 1990-2012. Journal of the Medical Library Association : JMLA. 2013;101(4):268–77.

Crum JA, Cooper ID. Emerging roles for biomedical librarians: a survey of current practice, challenges, and changes. Journal of the Medical Library Association : JMLA. 2013;101(4):278–86.

Dudden RF, Protzko SL. The systematic review team: contributions of the health sciences librarian. Med Ref Serv Q. 2011;30(3):301–15.

Golder S, Loke Y, McIntosh HM. Poor reporting and inadequate searches were apparent in systematic reviews of adverse effects. J Clin Epidemiol. 2008;61(5):440–8.

Maggio LA, Tannery NH, Kanter SL. Reproducibility of literature search reporting in medical education reviews. Academic medicine : journal of the Association of American Medical Colleges. 2011;86(8):1049–54.

Meert D, Torabi N, Costella J. Impact of librarians on reporting of the literature searching component of pediatric systematic reviews. Journal of the Medical Library Association : JMLA. 2016;104(4):267–77.

Morris M, Boruff JT, Gore GC. Scoping reviews: establishing the role of the librarian. Journal of the Medical Library Association : JMLA. 2016;104(4):346–54.

Koffel JB, Rethlefsen ML. Reproducibility of search strategies is poor in systematic reviews published in high-impact pediatrics, cardiology and surgery journals: a cross-sectional study. PLoS One. 2016;11(9):e0163309.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Fehrmann P, Thomas J. Comprehensive computer searches and reporting in systematic reviews. Research Synthesis Methods. 2011;2(1):15–32.

Booth A. Searching for qualitative research for inclusion in systematic reviews: a structured methodological review. Systematic Reviews. 2016;5(1):74.

Article   PubMed   PubMed Central   Google Scholar  

Egger M, Juni P, Bartlett C, Holenstein F, Sterne J. How important are comprehensive literature searches and the assessment of trial quality in systematic reviews? Empirical study. Health technology assessment (Winchester, England). 2003;7(1):1–76.

Tricco AC, Tetzlaff J, Sampson M, Fergusson D, Cogo E, Horsley T, et al. Few systematic reviews exist documenting the extent of bias: a systematic review. J Clin Epidemiol. 2008;61(5):422–34.

Booth A. How much searching is enough? Comprehensive versus optimal retrieval for technology assessments. Int J Technol Assess Health Care. 2010;26(4):431–5.

Papaioannou D, Sutton A, Carroll C, Booth A, Wong R. Literature searching for social science systematic reviews: consideration of a range of search techniques. Health Inf Libr J. 2010;27(2):114–22.

Petticrew M. Time to rethink the systematic review catechism? Moving from ‘what works’ to ‘what happens’. Systematic Reviews. 2015;4(1):36.

Betrán AP, Say L, Gülmezoglu AM, Allen T, Hampson L. Effectiveness of different databases in identifying studies for systematic reviews: experience from the WHO systematic review of maternal morbidity and mortality. BMC Med Res Methodol. 2005;5

Felson DT. Bias in meta-analytic research. J Clin Epidemiol. 1992;45(8):885–92.

Article   PubMed   CAS   Google Scholar  

Franco A, Malhotra N, Simonovits G. Publication bias in the social sciences: unlocking the file drawer. Science. 2014;345(6203):1502–5.

Hartling L, Featherstone R, Nuspl M, Shave K, Dryden DM, Vandermeer B. Grey literature in systematic reviews: a cross-sectional study of the contribution of non-English reports, unpublished studies and dissertations to the results of meta-analyses in child-relevant reviews. BMC Med Res Methodol. 2017;17(1):64.

Schmucker CM, Blümle A, Schell LK, Schwarzer G, Oeller P, Cabrera L, et al. Systematic review finds that study data not published in full text articles have unclear impact on meta-analyses results in medical research. PLoS One. 2017;12(4):e0176210.

Egger M, Zellweger-Zahner T, Schneider M, Junker C, Lengeler C, Antes G. Language bias in randomised controlled trials published in English and German. Lancet (London, England). 1997;350(9074):326–9.

Moher D, Pham B, Lawson ML, Klassen TP. The inclusion of reports of randomised trials published in languages other than English in systematic reviews. Health technology assessment (Winchester, England). 2003;7(41):1–90.

Pham B, Klassen TP, Lawson ML, Moher D. Language of publication restrictions in systematic reviews gave different results depending on whether the intervention was conventional or complementary. J Clin Epidemiol. 2005;58(8):769–76.

Mills EJ, Kanters S, Thorlund K, Chaimani A, Veroniki A-A, Ioannidis JPA. The effects of excluding treatments from network meta-analyses: survey. BMJ : British Medical Journal. 2013;347

Hartling L, Featherstone R, Nuspl M, Shave K, Dryden DM, Vandermeer B. The contribution of databases to the results of systematic reviews: a cross-sectional study. BMC Med Res Methodol. 2016;16(1):127.

van Driel ML, De Sutter A, De Maeseneer J, Christiaens T. Searching for unpublished trials in Cochrane reviews may not be worth the effort. J Clin Epidemiol. 2009;62(8):838–44.e3.

Buchberger B, Krabbe L, Lux B, Mattivi JT. Evidence mapping for decision making: feasibility versus accuracy - when to abandon high sensitivity in electronic searches. German medical science : GMS e-journal. 2016;14:Doc09.

Lorenc T, Pearson M, Jamal F, Cooper C, Garside R. The role of systematic reviews of qualitative evidence in evaluating interventions: a case study. Research Synthesis Methods. 2012;3(1):1–10.

Gough D. Weight of evidence: a framework for the appraisal of the quality and relevance of evidence. Res Pap Educ. 2007;22(2):213–28.

Barroso J, Gollop CJ, Sandelowski M, Meynell J, Pearce PF, Collins LJ. The challenges of searching for and retrieving qualitative studies. West J Nurs Res. 2003;25(2):153–78.

Britten N, Garside R, Pope C, Frost J, Cooper C. Asking more of qualitative synthesis: a response to Sally Thorne. Qual Health Res. 2017;27(9):1370–6.

Booth A, Carroll C. Systematic searching for theory to inform systematic reviews: is it feasible? Is it desirable? Health Info Libr J. 2015;32(3):220–35.

Kwon Y, Powelson SE, Wong H, Ghali WA, Conly JM. An assessment of the efficacy of searching in biomedical databases beyond MEDLINE in identifying studies for a systematic review on ward closures as an infection control intervention to control outbreaks. Syst Rev. 2014;3:135.

Nussbaumer-Streit B, Klerings I, Wagner G, Titscher V, Gartlehner G. Assessing the validity of abbreviated literature searches for rapid reviews: protocol of a non-inferiority and meta-epidemiologic study. Systematic Reviews. 2016;5:197.

Wagner G, Nussbaumer-Streit B, Greimel J, Ciapponi A, Gartlehner G. Trading certainty for speed - how much uncertainty are decisionmakers and guideline developers willing to accept when using rapid reviews: an international survey. BMC Med Res Methodol. 2017;17(1):121.

Ogilvie D, Hamilton V, Egan M, Petticrew M. Systematic reviews of health effects of social interventions: 1. Finding the evidence: how far should you go? J Epidemiol Community Health. 2005;59(9):804–8.

Royle P, Milne R. Literature searching for randomized controlled trials used in Cochrane reviews: rapid versus exhaustive searches. Int J Technol Assess Health Care. 2003;19(4):591–603.

Pearson M, Moxham T, Ashton K. Effectiveness of search strategies for qualitative research about barriers and facilitators of program delivery. Eval Health Prof. 2011;34(3):297–308.

Levay P, Raynor M, Tuvey D. The Contributions of MEDLINE, Other Bibliographic Databases and Various Search Techniques to NICE Public Health Guidance. 2015. 2015;10(1):19.

Nussbaumer-Streit B, Klerings I, Wagner G, Heise TL, Dobrescu AI, Armijo-Olivo S, et al. Abbreviated literature searches were viable alternatives to comprehensive searches: a meta-epidemiological study. J Clin Epidemiol. 2018;102:1–11.

Briscoe S, Cooper C, Glanville J, Lefebvre C. The loss of the NHS EED and DARE databases and the effect on evidence synthesis and evaluation. Res Synth Methods. 2017;8(3):256–7.

Stansfield C, O'Mara-Eves A, Thomas J. Text mining for search term development in systematic reviewing: A discussion of some methods and challenges. Research Synthesis Methods.n/a-n/a.

Petrova M, Sutcliffe P, Fulford KW, Dale J. Search terms and a validated brief search filter to retrieve publications on health-related values in Medline: a word frequency analysis study. Journal of the American Medical Informatics Association : JAMIA. 2012;19(3):479–88.

Stansfield C, Thomas J, Kavanagh J. 'Clustering' documents automatically to support scoping reviews of research: a case study. Res Synth Methods. 2013;4(3):230–41.

PubMed   Google Scholar  

Methley AM, Campbell S, Chew-Graham C, McNally R, Cheraghi-Sohi S. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv Res. 2014;14:579.

Andrew B. Clear and present questions: formulating questions for evidence based practice. Library Hi Tech. 2006;24(3):355–68.

Cooke A, Smith D, Booth A. Beyond PICO: the SPIDER tool for qualitative evidence synthesis. Qual Health Res. 2012;22(10):1435–43.

Whiting P, Westwood M, Bojke L, Palmer S, Richardson G, Cooper J, et al. Clinical effectiveness and cost-effectiveness of tests for the diagnosis and investigation of urinary tract infection in children: a systematic review and economic model. Health technology assessment (Winchester, England). 2006;10(36):iii-iv, xi-xiii, 1–154.

Cooper C, Levay P, Lorenc T, Craig GM. A population search filter for hard-to-reach populations increased search efficiency for a systematic review. J Clin Epidemiol. 2014;67(5):554–9.

Hausner E, Waffenschmidt S, Kaiser T, Simon M. Routine development of objectively derived search strategies. Systematic Reviews. 2012;1(1):19.

Hausner E, Guddat C, Hermanns T, Lampert U, Waffenschmidt S. Prospective comparison of search strategies for systematic reviews: an objective approach yielded higher sensitivity than a conceptual one. J Clin Epidemiol. 2016;77:118–24.

Craven J, Levay P. Recording database searches for systematic reviews - what is the value of adding a narrative to peer-review checklists? A case study of nice interventional procedures guidance. Evid Based Libr Inf Pract. 2011;6(4):72–87.

Wright K, Golder S, Lewis-Light K. What value is the CINAHL database when searching for systematic reviews of qualitative studies? Syst Rev. 2015;4:104.

Beckles Z, Glover S, Ashe J, Stockton S, Boynton J, Lai R, et al. Searching CINAHL did not add value to clinical questions posed in NICE guidelines. J Clin Epidemiol. 2013;66(9):1051–7.

Cooper C, Rogers M, Bethel A, Briscoe S, Lowe J. A mapping review of the literature on UK-focused health and social care databases. Health Inf Libr J. 2015;32(1):5–22.

Younger P, Boddy K. When is a search not a search? A comparison of searching the AMED complementary health database via EBSCOhost, OVID and DIALOG. Health Inf Libr J. 2009;26(2):126–35.

Lam MT, McDiarmid M. Increasing number of databases searched in systematic reviews and meta-analyses between 1994 and 2014. Journal of the Medical Library Association : JMLA. 2016;104(4):284–9.

Bethel A, editor Search summary tables for systematic reviews: results and findings. HLC Conference 2017a.

Aagaard T, Lund H, Juhl C. Optimizing literature search in systematic reviews - are MEDLINE, EMBASE and CENTRAL enough for identifying effect studies within the area of musculoskeletal disorders? BMC Med Res Methodol. 2016;16(1):161.

Adams CE, Frederick K. An investigation of the adequacy of MEDLINE searches for randomized controlled trials (RCTs) of the effects of mental health care. Psychol Med. 1994;24(3):741–8.

Kelly L, St Pierre-Hansen N. So many databases, such little clarity: searching the literature for the topic aboriginal. Canadian family physician Medecin de famille canadien. 2008;54(11):1572–3.

Lawrence DW. What is lost when searching only one literature database for articles relevant to injury prevention and safety promotion? Injury Prevention. 2008;14(6):401–4.

Lemeshow AR, Blum RE, Berlin JA, Stoto MA, Colditz GA. Searching one or two databases was insufficient for meta-analysis of observational studies. J Clin Epidemiol. 2005;58(9):867–73.

Sampson M, Barrowman NJ, Moher D, Klassen TP, Pham B, Platt R, et al. Should meta-analysts search Embase in addition to Medline? J Clin Epidemiol. 2003;56(10):943–55.

Stevinson C, Lawlor DA. Searching multiple databases for systematic reviews: added value or diminishing returns? Complementary Therapies in Medicine. 2004;12(4):228–32.

Suarez-Almazor ME, Belseck E, Homik J, Dorgan M, Ramos-Remus C. Identifying clinical trials in the medical literature with electronic databases: MEDLINE alone is not enough. Control Clin Trials. 2000;21(5):476–87.

Taylor B, Wylie E, Dempster M, Donnelly M. Systematically retrieving research: a case study evaluating seven databases. Res Soc Work Pract. 2007;17(6):697–706.

Beyer FR, Wright K. Can we prioritise which databases to search? A case study using a systematic review of frozen shoulder management. Health Info Libr J. 2013;30(1):49–58.

Duffy S, de Kock S, Misso K, Noake C, Ross J, Stirk L. Supplementary searches of PubMed to improve currency of MEDLINE and MEDLINE in-process searches via Ovid. Journal of the Medical Library Association : JMLA. 2016;104(4):309–12.

Katchamart W, Faulkner A, Feldman B, Tomlinson G, Bombardier C. PubMed had a higher sensitivity than Ovid-MEDLINE in the search for systematic reviews. J Clin Epidemiol. 2011;64(7):805–7.

Cooper C, Lovell R, Husk K, Booth A, Garside R. Supplementary search methods were more effective and offered better value than bibliographic database searching: a case study from public health and environmental enhancement (in Press). Research Synthesis Methods. 2017;

Cooper C, Booth, A., Britten, N., Garside, R. A comparison of results of empirical studies of supplementary search techniques and recommendations in review methodology handbooks: A methodological review. (In Press). BMC Systematic Reviews. 2017.

Greenhalgh T, Peacock R. Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. BMJ (Clinical research ed). 2005;331(7524):1064–5.

Article   PubMed Central   Google Scholar  

Hinde S, Spackman E. Bidirectional citation searching to completion: an exploration of literature searching methods. PharmacoEconomics. 2015;33(1):5–11.

Levay P, Ainsworth N, Kettle R, Morgan A. Identifying evidence for public health guidance: a comparison of citation searching with web of science and Google scholar. Res Synth Methods. 2016;7(1):34–45.

McManus RJ, Wilson S, Delaney BC, Fitzmaurice DA, Hyde CJ, Tobias RS, et al. Review of the usefulness of contacting other experts when conducting a literature search for systematic reviews. BMJ (Clinical research ed). 1998;317(7172):1562–3.

Westphal A, Kriston L, Holzel LP, Harter M, von Wolff A. Efficiency and contribution of strategies for finding randomized controlled trials: a case study from a systematic review on therapeutic interventions of chronic depression. Journal of public health research. 2014;3(2):177.

Matthews EJ, Edwards AG, Barker J, Bloor M, Covey J, Hood K, et al. Efficient literature searching in diffuse topics: lessons from a systematic review of research on communicating risk to patients in primary care. Health Libr Rev. 1999;16(2):112–20.

Bethel A. Endnote Training (YouTube Videos) 2017b [Available from: http://medicine.exeter.ac.uk/esmi/workstreams/informationscience/is_resources,_guidance_&_advice/ .

Bramer WM, Giustini D, de Jonge GB, Holland L, Bekhuis T. De-duplication of database search results for systematic reviews in EndNote. Journal of the Medical Library Association : JMLA. 2016;104(3):240–3.

Bramer WM, Milic J, Mast F. Reviewing retrieved references for inclusion in systematic reviews using EndNote. Journal of the Medical Library Association : JMLA. 2017;105(1):84–7.

Gall C, Brahmi FA. Retrieval comparison of EndNote to search MEDLINE (Ovid and PubMed) versus searching them directly. Medical reference services quarterly. 2004;23(3):25–32.

Ahmed KK, Al Dhubaib BE. Zotero: a bibliographic assistant to researcher. J Pharmacol Pharmacother. 2011;2(4):303–5.

Coar JT, Sewell JP. Zotero: harnessing the power of a personal bibliographic manager. Nurse Educ. 2010;35(5):205–7.

Moher D, Liberati A, Tetzlaff J, Altman DG, The PG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.

Sampson M, McGowan J, Tetzlaff J, Cogo E, Moher D. No consensus exists on search reporting methods for systematic reviews. J Clin Epidemiol. 2008;61(8):748–54.

Toews LC. Compliance of systematic reviews in veterinary journals with preferred reporting items for systematic reviews and meta-analysis (PRISMA) literature search reporting guidelines. Journal of the Medical Library Association : JMLA. 2017;105(3):233–9.

Booth A. "brimful of STARLITE": toward standards for reporting literature searches. Journal of the Medical Library Association : JMLA. 2006;94(4):421–9. e205

Faggion CM Jr, Wu YC, Tu YK, Wasiak J. Quality of search strategies reported in systematic reviews published in stereotactic radiosurgery. Br J Radiol. 2016;89(1062):20150878.

Mullins MM, DeLuca JB, Crepaz N, Lyles CM. Reporting quality of search methods in systematic reviews of HIV behavioral interventions (2000–2010): are the searches clearly explained, systematic and reproducible? Research Synthesis Methods. 2014;5(2):116–30.

Yoshii A, Plaut DA, McGraw KA, Anderson MJ, Wellik KE. Analysis of the reporting of search strategies in Cochrane systematic reviews. Journal of the Medical Library Association : JMLA. 2009;97(1):21–9.

Bigna JJ, Um LN, Nansseu JR. A comparison of quality of abstracts of systematic reviews including meta-analysis of randomized controlled trials in high-impact general medicine journals before and after the publication of PRISMA extension for abstracts: a systematic review and meta-analysis. Syst Rev. 2016;5(1):174.

Akhigbe T, Zolnourian A, Bulters D. Compliance of systematic reviews articles in brain arteriovenous malformation with PRISMA statement guidelines: review of literature. Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia. 2017;39:45–8.

Tao KM, Li XQ, Zhou QH, Moher D, Ling CQ, Yu WF. From QUOROM to PRISMA: a survey of high-impact medical journals' instructions to authors and a review of systematic reviews in anesthesia literature. PLoS One. 2011;6(11):e27611.

Wasiak J, Tyack Z, Ware R. Goodwin N. Jr. Poor methodological quality and reporting standards of systematic reviews in burn care management. International wound journal: Faggion CM; 2016.

Tam WW, Lo KK, Khalechelvam P. Endorsement of PRISMA statement and quality of systematic reviews and meta-analyses published in nursing journals: a cross-sectional study. BMJ Open. 2017;7(2):e013905.

Rader T, Mann M, Stansfield C, Cooper C, Sampson M. Methods for documenting systematic review searches: a discussion of common issues. Res Synth Methods. 2014;5(2):98–115.

Atkinson KM, Koenka AC, Sanchez CE, Moshontz H, Cooper H. Reporting standards for literature searches and report inclusion criteria: making research syntheses more transparent and easy to replicate. Res Synth Methods. 2015;6(1):87–95.

McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. PRESS peer review of electronic search strategies: 2015 guideline statement. J Clin Epidemiol. 2016;75:40–6.

Sampson M, McGowan J, Cogo E, Grimshaw J, Moher D, Lefebvre C. An evidence-based practice guideline for the peer review of electronic search strategies. J Clin Epidemiol. 2009;62(9):944–52.

Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ (Clinical research ed). 2017;358.

Whiting P, Savović J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, et al. ROBIS: a new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol. 2016;69:225–34.

Relevo R, Balshem H. Finding evidence for comparing medical interventions: AHRQ and the effective health care program. J Clin Epidemiol. 2011;64(11):1168–77.

Medicine Io. Standards for Systematic Reviews 2011 [Available from: http://www.nationalacademies.org/hmd/Reports/2011/Finding-What-Works-in-Health-Care-Standards-for-Systematic-Reviews/Standards.aspx .

CADTH: Resources 2018.

Download references

Acknowledgements

CC acknowledges the supervision offered by Professor Chris Hyde.

This publication forms a part of CC’s PhD. CC’s PhD was funded through the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme (Project Number 16/54/11). The open access fee for this publication was paid for by Exeter Medical School.

RG and NB were partially supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula.

The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

Authors and affiliations.

Institute of Health Research, University of Exeter Medical School, Exeter, UK

Chris Cooper & Jo Varley-Campbell

HEDS, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK

Andrew Booth

Nicky Britten

European Centre for Environment and Human Health, University of Exeter Medical School, Truro, UK

Ruth Garside

You can also search for this author in PubMed   Google Scholar

Contributions

CC conceived the idea for this study and wrote the first draft of the manuscript. CC discussed this publication in PhD supervision with AB and separately with JVC. CC revised the publication with input and comments from AB, JVC, RG and NB. All authors revised the manuscript prior to submission. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chris Cooper .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional file

Additional file 1:.

Appendix tables and PubMed search strategy. Key studies used for pearl growing per key stage, working data extraction tables and the PubMed search strategy. (DOCX 30 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Cooper, C., Booth, A., Varley-Campbell, J. et al. Defining the process to literature searching in systematic reviews: a literature review of guidance and supporting studies. BMC Med Res Methodol 18 , 85 (2018). https://doi.org/10.1186/s12874-018-0545-3

Download citation

Received : 20 September 2017

Accepted : 06 August 2018

Published : 14 August 2018

DOI : https://doi.org/10.1186/s12874-018-0545-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Literature Search Process
• Citation Chasing
• Tacit Models
• Unique Guidance
• Information Specialists

BMC Medical Research Methodology

ISSN: 1471-2288

• General enquiries: [email protected]

• Help & FAQ

Systematic reviews: what they are, why they are important, and how to get involved

• School of Health & Care Professions
• University of Portsmouth Ageing Network
• Centre for Interaction, Development and Diversity

Research output : Contribution to journal › Article › peer-review

Access to Document

• Systematic reviews - what they are Final published version, 421 KB
• http://www.jcpcarchives.org/abstract/systematic-reviews--what-they-are-why-they-are-important-73.php

Fingerprint

• reviews INIS 100%
• Systematic Literature Review Psychology 50%
• Systematic Review Nursing and Health Professions 50%
• planning INIS 25%
• resources INIS 25%
• Resources Psychology 25%

T1 - Systematic reviews: what they are, why they are important, and how to get involved

AU - MacKenzie, Heather

AU - Dewey, Ann

AU - Drahota, Amy

AU - Kilburn, Sally

AU - Kalra, P.

AU - Fogg, Carole

AU - Zachariah, D.

PY - 2012/10

Y1 - 2012/10

N2 - This article introduces systematic reviews and their purpose, and considerations in understanding and appraising reviews. The processes involved in planning and conducting a review are summarized, and resources for new reviewers are listed.

AB - This article introduces systematic reviews and their purpose, and considerations in understanding and appraising reviews. The processes involved in planning and conducting a review are summarized, and resources for new reviewers are listed.

M3 - Article

SN - 2250-3528

JO - Journal of Clinical and Preventive Cardiology

JF - Journal of Clinical and Preventive Cardiology

• Enroll & Pay
• Prospective Students
• Current Students
• Degree Programs

The Role of Systematic Literature Reviews

Systematic literature review is a basic scientific activity that allows scientists to view the “lay of the land” in a particular area. A systematic review identifies, evaluates, and synthesizes research results to create a summary of current evidence that can contribute to evidence-based practice. Systematic review methodology employs the same principles and rigor required in primary research. Typically, systematic literature reviews address narrow research questions (i.e., “Does the current literature prove that treatment ‘x’ works better than treatment ‘z”?) that are typically answered using meta-analysis techniques with evidence from randomized controlled trials using similar outcome measures. Three premises have been proposed for conducting systematic reviews: (a) to reduce large amounts of information into comprehensible units, (b) to aggregate critical information for decision-making and (c) to efficiently move from knowledge discovery to implementation. The first premise is especially significant given the research information “overload” that has developed in the last 20 years. Emergence of international groups to promote and disseminate systematic review results such as the Campbell Collaboration, focused on social and behavioral research and the Cochrane Collaboration, focused on medical and public health, emphasize the importance of these reviews.

Systematic Review Methodology

Rigorous methodology allows systematic reviews to include (a) quantitative studies, including randomized controlled trials, qualitative studies, and single-subject studies. Additionally, the methodology continues to evolve; for example, the Agency for Healthcare Research and Quality recently issued guidance for assessing the bias risk of individual studies in systematic reviews.  Adherence to established procedures permits replication, and inclusion of a peer review process strengthens the transparency, reduces bias, and ensures a quality systematic review product.  

Systematic Scoping Review Methodology

Another type of review, the  systematic   scoping  review, provides an alternative method to study problems that cannot be restricted to narrow research questions. Scoping reviews aim to answer broad questions about a topic, including what research questions have been asked, which groups have been studied, what types of research methodologies and measures have been used, and what the overall findings indicate.

Four common reasons for conducting a systematic scoping review are to: (a) examine the extent, range, and nature of research activity; (b) determine the value of undertaking a full systematic review; (c) summarize and disseminate research findings; and (d) identify research gaps in the literature.

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• Current issue
• Write for Us
• BMJ Journals More You are viewing from: Google Indexer

You are here

• Volume 19, Issue 1
• Reviewing the literature
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• Joanna Smith 1 ,
• Helen Noble 2
• 1 School of Healthcare, University of Leeds , Leeds , UK
• 2 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
• Correspondence to Dr Joanna Smith , School of Healthcare, University of Leeds, Leeds LS2 9JT, UK; j.e.smith1{at}leeds.ac.uk

https://doi.org/10.1136/eb-2015-102252

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Implementing evidence into practice requires nurses to identify, critically appraise and synthesise research. This may require a comprehensive literature review: this article aims to outline the approaches and stages required and provides a working example of a published review.

Are there different approaches to undertaking a literature review?

What stages are required to undertake a literature review.

The rationale for the review should be established; consider why the review is important and relevant to patient care/safety or service delivery. For example, Noble et al 's 4 review sought to understand and make recommendations for practice and research in relation to dialysis refusal and withdrawal in patients with end-stage renal disease, an area of care previously poorly described. If appropriate, highlight relevant policies and theoretical perspectives that might guide the review. Once the key issues related to the topic, including the challenges encountered in clinical practice, have been identified formulate a clear question, and/or develop an aim and specific objectives. The type of review undertaken is influenced by the purpose of the review and resources available. However, the stages or methods used to undertake a review are similar across approaches and include:

Formulating clear inclusion and exclusion criteria, for example, patient groups, ages, conditions/treatments, sources of evidence/research designs;

Justifying data bases and years searched, and whether strategies including hand searching of journals, conference proceedings and research not indexed in data bases (grey literature) will be undertaken;

Developing search terms, the PICU (P: patient, problem or population; I: intervention; C: comparison; O: outcome) framework is a useful guide when developing search terms;

Developing search skills (eg, understanding Boolean Operators, in particular the use of AND/OR) and knowledge of how data bases index topics (eg, MeSH headings). Working with a librarian experienced in undertaking health searches is invaluable when developing a search.

Once studies are selected, the quality of the research/evidence requires evaluation. Using a quality appraisal tool, such as the Critical Appraisal Skills Programme (CASP) tools, 5 results in a structured approach to assessing the rigour of studies being reviewed. 3 Approaches to data synthesis for quantitative studies may include a meta-analysis (statistical analysis of data from multiple studies of similar designs that have addressed the same question), or findings can be reported descriptively. 6 Methods applicable for synthesising qualitative studies include meta-ethnography (themes and concepts from different studies are explored and brought together using approaches similar to qualitative data analysis methods), narrative summary, thematic analysis and content analysis. 7 Table 1 outlines the stages undertaken for a published review that summarised research about parents’ experiences of living with a child with a long-term condition. 8

• View inline

An example of rapid evidence assessment review

In summary, the type of literature review depends on the review purpose. For the novice reviewer undertaking a review can be a daunting and complex process; by following the stages outlined and being systematic a robust review is achievable. The importance of literature reviews should not be underestimated—they help summarise and make sense of an increasingly vast body of research promoting best evidence-based practice.

• ↵ Centre for Reviews and Dissemination . Guidance for undertaking reviews in health care . 3rd edn . York : CRD, York University , 2009 .
• ↵ Canadian Best Practices Portal. http://cbpp-pcpe.phac-aspc.gc.ca/interventions/selected-systematic-review-sites / ( accessed 7.8.2015 ).
• Bridges J , et al
• ↵ Critical Appraisal Skills Programme (CASP). http://www.casp-uk.net / ( accessed 7.8.2015 ).
• Dixon-Woods M ,
• Shaw R , et al
• Agarwal S ,
• Jones D , et al
• Cheater F ,

Twitter Follow Joanna Smith at @josmith175

Competing interests None declared.

Read the full text or download the PDF:

A Systematic Review of Trajectories of Clinically Relevant Distress Amongst Adults with Cancer: Course and Predictors

• Open access
• Published: 05 May 2024

Cite this article

You have full access to this open access article

• Leah Curran   ORCID: orcid.org/0000-0002-6070-1568 1 , 2 ,
• Alison Mahoney 3 , 4 &
• Bradley Hastings 1  

67 Accesses

Explore all metrics

To improve interventions for people with cancer who experience clinically relevant distress, it is important to understand how distress evolves over time and why. This review synthesizes the literature on trajectories of distress in adult patients with cancer. Databases were searched for longitudinal studies using a validated clinical tool to group patients into distress trajectories. Twelve studies were identified reporting trajectories of depression, anxiety, adjustment disorder or post-traumatic stress disorder. Heterogeneity between studies was high, including the timing of baseline assessments and follow-up intervals. Up to 1 in 5 people experienced persistent depression or anxiety. Eight studies examined predictors of trajectories; the most consistent predictor was physical symptoms or functioning. Due to study methodology and heterogeneity, limited conclusions could be drawn about why distress is maintained or emerges for some patients. Future research should use valid clinical measures and assess theoretically driven predictors amendable to interventions.

Avoid common mistakes on your manuscript.

Introduction

Unsurprisingly, a diagnosis of cancer is often associated with psychological distress, such as symptoms of depression, anxiety, traumatic stress, fear of cancer progression or recurrence (FCR), or death anxiety. While longitudinal studies suggest that rates of distress generally improve over time, a substantial proportion of patients continue to experience clinically relevant distress (Calman et al., 2021 ; Henry et al., 2019 ; Krebber et al., 2014 ; Savard & Ivers, 2013 ). For mental health clinicians, understanding patients’ likely distress trajectory, specifically the timepoints at which distress is likely to emerge and whether the distress is likely to remain clinically relevant, is critical for the design and implementation of mental health and systemic interventions.

There are several gaps in the existing literature about how and why distress evolves amongst cancer patients. Firstly, longitudinal research has mainly reported prevalence data (Niedzwiedz et al., 2019 ), which conflates distress trajectories across different cancer cohorts and is not informative about how distress trajectories evolve for individual patients. For instance, there is evidence that distress increases longitudinally amongst younger patients and those with cancers that are more likely to recur, such as ovarian or oesophageal cancers (Liu et al., 2022 ; Starreveld et al., 2018 ; Watts et al., 2015 ). Additionally, there is scant longitudinal research regarding how distress evolves for individuals with advanced disease receiving novel therapies, who are likely at greater risk for psychological distress (Thewes et al., 2017 ). For example, amongst patients with advanced melanoma who achieved remission on immunotherapy, 64% had clinical levels of anxiety or depression at one point during the following year despite having no active disease (Rogiers et al., 2020 ). However, the distress trajectories and factors associated with recovered or persistent distress could not be ascertained by the data.

Secondly, longitudinal studies that group patients into distress trajectories vary in method. Some have used clinical cut-off scores and assessed individuals longitudinally at two time points (e.g. Linden et al., 2015 ), resulting in four clinically meaningful trajectories of distress: (1) non-cases (not meeting clinical criteria at either time point), (2) recovered (clinically significant distress at the first timepoint only), (3) persistent (meeting clinical criteria at both time points), and 4) emerging (clinically significant distress at the second timepoint only). In longitudinal studies conducted over more than two timepoints a fluctuating trajectory of distress may also be evident where a person meets clinical criteria at some, but not all, assessments (e.g., Mols et al., 2018 ). However, the majority of longitudinal studies of distress trajectories have used statistical methods, such as latent class growth analysis, to group participants into trajectories rather than utilising pre-determined clinical cut-off scores. Consequently, the number of trajectories identified, and the participants assigned to each trajectory, will differ according to the method used (e.g., Custers et al., 2020 ). Also, statistical analyses identify patterns in samples, so if the average outcome score is low, a statistically derived “persistently high” trajectory may include patients below a clinically meaningful threshold (e.g., Stanton et al., 2015 ). Since distress trajectories identified by statistical methods are difficult to interpret clinically, they have limited utility in informing clinical questions of how and why distress trajectories differ for individual patients.

Thirdly, it is unclear why some people are more likely to experience persistent or emerging distress over time. Theorists have suggested that intrapersonal and interpersonal constructs, such as metacognitions, cognitive appraisals, coping styles, physical symptom severity, social support, and the relationship with health care providers, may be important in understanding the evolution of distress and adjustment (Curran et al., 2017 ; Edmondson, 2014 ; Fardell et al., 2016 ; Kangas & Gross, 2020 ). However, the role of these constructs across the illness trajectory and within subgroups of patients is not clearly understood. For instance, a recent systematic review concluded that the only consistent psychological predictors of clinically relevant distress longitudinally are initial distress and neuroticism (Cook et al., 2018 ). However, this finding does not elucidate why some people had high distress scores at study entry or personality traits associated with experiencing more negative emotions. The reviewers called for better designed, theoretically informed longitudinal research to determine the psychological factors underpinning the aetiology and maintenance of cancer-related distress, which could then inform improvements in interventions. Separately, Kangas and Gross ( 2020 ) have called for further research to understand the trajectories of distress in a way that recognises cancer as a dynamic experience.

To address the above gaps, this study aims to summarize the literature regarding (1) the course of clinically relevant, individual trajectories of distress after a cancer diagnosis and (2) the psychological, sociodemographic and medical factors associated with different distress trajectories; and comment on how these findings relate to existing theories of cancer-related distress.

A systematic literature search was conducted following the PRISMA 2020 statement (Page et al., 2021 ). PsycINFO and Medline ALL were searched in December 2021 and updated in January 2023. Search terms related to clinical distress (depression, anxiety, trauma or stress related disorders), cancer AND longitudinal studies, were mapped to Medical Subject Headings (MeSH) and exploded where possible (see Supplementary Material for the full list of search items). The search was limited to peer-reviewed journals and articles in English, due to the unavailability of resources or funds to translate non-English articles. Reference lists of relevant articles were examined to identify further publications. Ethical approval was not required for this review of previously published data.

The first author (LC) inspected article titles and abstracts for the inclusion criteria: (1) written in English; (2) peer-reviewed; (3) adult patients with cancer; (4) mental health outcomes were assessed longitudinally with a validated clinically relevant measure and (5) results could be clustered according to clinical cut-off scores to identify distress trajectories. Articles were excluded if the sample (1) related to adult survivors of childhood cancer, (2) lacked statistical power to accurately determine the proportion of participants in each trajectory (power calculation in Supplementary Material), (3) only grouped patients into trajectories using statistical methods or (4) participants were enrolled in an intervention trial. A second reviewer (AM) independently examined 10% of article titles and abstracts, with disagreement resolved by consensus. The full text of retained articles were examined independently by both reviewers for further inclusion/exclusion with agreement reached by consensus.

The following data was obtained from the included studies by LC and fact-checked by AM: sample characteristics (sample size, age, gender, cancer type and stage, place of recruitment), time of study entry (T1), time since diagnosis, follow-up timepoints and the interval from T1, the proportion of patients in active treatment at follow-up, outcome measure used, trajectories identified, proportion of sample in each trajectory, and predictors of between group differences (if examined). When trajectories were not reported in the article, but data was available to calculate them, the proportion of patients in each trajectory was calculated from the completer sample. Results were tabulated according to the clinical outcome measured, and the number of assessments conducted longitudinally. Due to the heterogeneity of studies, a meta-analysis was not possible, and a narrative review was conducted.

The methodological quality of included studies was evaluated independently by LC and AM using four domains from the Quality in Prognosis Studies (QUIPS) tool (Hayden et al., 2013 ): study participation, attrition, outcome measurement and study confounding. A fifth domain, statistical analyses, was rated only if analyses of between group factors were conducted (some studies only reported descriptive statistics to calculate the proportion of the sample in each distress trajectory).

Figure  1 outlines the process of article selection and reasons for exclusion. After removing duplicates, 5509 articles were identified. Inter-rater reliability (Cohen’s Kappa) of 10% of the articles was 0.73. Title and abstract review yielded 82 articles for full-text review and two articles were identified via the ancestry method. Independent review of the articles resulted in full consensus to retain 14 articles, describing 12 samples. Studies were excluded where the proportion of patients in each distress trajectory could not be calculated from the published data (e.g., Lopes et al., 2022 ; Sutton et al., 2022 ).

PRISMA flow diagram of systematic search results

Table 1 provides the characteristics of the 12 study samples included in this review (N = 8566). Results are discussed according to the outcome measured. Also, the results are discussed according to the number of timepoints measured, as this impacts on the potential number of trajectories identified, and the proportion of patients assigned to each trajectory.

Depression Studies with Two Assessment Timepoints

Six studies assessed depression over two timepoints to identify four trajectories: non-cases, recovered, emerging and persistent (Alfonsson et al., 2016 ; Boyes et al., 2013 ; Hasegawa et al., 2019 ; Kim et al., 2012 ; Linden et al., 2015 ; Sullivan et al., 2016 ). Studies involved breast (Alfonsson et al., 2016 ; Kim et al., 2012 ), lung (Sullivan et al., 2016 ), or mixed cancer diagnoses (Boyes et al., 2013 ; Linden et al., 2015 ) or malignant Lymphoma or Multiple Myeloma (Hasegawa et al., 2019 ). In the non-breast cancer studies, male and female patients were equally represented. One study excluded patients with “secondary” disease (Kim et al., 2012 ) and three studies excluded terminally ill patients or those deemed unsuitable or too unwell by their physician to participate (Boyes et al., 2013 ; Hasegawa et al., 2019 ; Sullivan et al., 2016 ). Only three studies reported patients’ mean age, and participants were typically in their 50 s and 60 s (Alfonsson et al., 2016 ; Kim et al., 2012 ; Linden et al., 2015 ). Further analysis of one study sample (Kim et al., 2012 ) produced two additional publications (Kim et al., 2013 , 2018 ).

Caseness was usually determined by cut-off scores on self-report measures such as the depression subscale of the Hospital Anxiety and Depression Scale (HADS-D; Zigmond & Snaith, 1983 ), the depression subscale of the Psychosocial Screen for Cancer (PSSCAN; Linden et al., 2005 ), the Patient Health Questionnaire (Kroenke et al., 2001 ), and the Centre for Epidemiological Studies Depression Scale– short-form (Turvey et al., 1999 ). A structured diagnostic interview was only used in one sample and participants were classified as cases if they met criteria for minor or major depression (Kim et al., 2012 ). Assessment points varied considerably. Two studies assessed depression prior to treatment but the samples were followed up one month or 12 months later (Hasegawa et al.; Linden et al., 2015 ). One study assessed depression post-surgery and 12 months later (Kim et al., 2012 ). Three studies assessed depression 4–6 months post-diagnosis (on average) and followed up six (Boyes et al., 2013 ), seven (Sullivan et al., 2016 ), or 38 months later (Alfonsson et al., 2016 ). Most studies did not report whether patients were in active treatment throughout the course of the assessment period or had completed treatment.

In terms of depression trajectories, about half of patients with lung cancer were classified as non-cases (Sullivan et al., 2016 ), compared to two thirds of participants in other newly diagnosed cancer groups (Hasegawa et al., 2019 ; Kim et al., 2012 ; Linden et al., 2015 ). The highest rates of non-cases were reported in two studies that recruited patients 4–6 months post-diagnosis (80–82%; Alfonsson et al., 2016 ; Boyes et al., 2013 ). The proportion of recovered cases varied from 6 to 16% and was lowest in the sample that had low rates of depression at baseline (Alfonsson et al., 2016 ). The sample of patients with lung cancer had the highest rate of persistent depression (22%) measured over a seven-month period (Sullivan et al., 2016 ). The remaining studies reporting rates of persistent depression at 4–10%, measured over intervals ranging from 1–36 months. Emerging depression was most common in a mixed sample of newly diagnosed patients assessed before treatment and 12 months later (19%; Linden et al., 2015 ). The remaining studies reported emerging depression rates of 6–10%.

Group comparisons were conducted on three study samples to identify predictors associated with depression trajectories (Table  2 ). Amongst patients with newly diagnosed haematological malignancies, non-cases were more likely to be physically active compared to those with persistent depression (Hasegawa et al., 2019 ). No other demographic or medical variables were differentially associated with trajectories. Similarly, amongst newly diagnosed patients with breast cancer, recovery from depression was associated with greater improvements in general health, emotional, social and role functioning, fatigue, and insomnia over the 12-month follow-up period, compared to persistent cases (Kim et al., 2013 ). Conversely, persistent or emerging depression was associated with larger decrements in general health, and emotional and role functioning, and worsening physical symptoms compared to non-cases. Persistent depression was also associated with financial difficulties, personal or family history of depression, more metastatic axillary lymph nodes, larger tumour size, and specific genotypes compared to non-cases (Kim et al., 2012 , 2013 ). However, age, education, time since diagnosis or tumour stage were not associated with depression trajectories.

In contrast, younger age was associated with persistent depression in a study of people with various cancer diagnoses (Linden et al., 2015 ). Higher illness intrusiveness (i.e., the perceived impact of illness on functioning) was associated with persistent depression, although illness intrusiveness did not differentiate between persistent, recovered, or emerging groups. Persistent depression was also associated with higher baseline anxiety and depression scores. However, post-hoc analysis showed that baseline anxiety and depression scores were equally high for persistent and recovered cases and equally low for non-cases and emerging cases, suggesting that baseline anxiety and depression scores had limited utility in predicting depression trajectories.

Depression Studies with More Than Two Assessment Timepoints

Two studies assessed depression trajectories over three or more time points using a HADS-D score of > 8 to define caseness. Newly diagnosed patients with head and neck cancer, treated with curative intent, were assessed for depression before commencing treatment and 4 and 12 months later (Jansen et al., 2018 ). The number of patients still in active treatment was not reported. Five trajectories were identified: non-cases (63%), recovered (16%), persistent (7%), late emerging (12%), and recurrent (1%). Compared to non-cases and those who recovered, persistent, emerging or recurrent depression was associated with being single, widowed or divorced, lower education, lower income, later tumour stage at diagnosis, having chemotherapy, more co-morbidities, smoking and being a non-drinker or a hazardous drinker of alcohol. Age was associated with depression trajectories but was assessed as a categorical variable, making the results difficult to interpret.

Patients from a colorectal cancer registry who were one to ten years post-diagnosis were assessed yearly for depression over four years (Mols et al., 2018 ). Three depression trajectories were identified: non-cases (71%), persistent (8%) and fluctuating (21%). Those with persistent or fluctuating depression were more likely to have two or more comorbidities compared to non-cases. Fluctuating depression was also associated with older age, lower education, and stage IV disease (compared to stage III as assessed at study entry). Changes in disease status or treatment course were not assessed over time.

Anxiety Studies with Two Assessment Timepoints

Four studies assessed clinically relevant anxiety over two timepoints. Caseness was defined by a cut-score of ≥ 8 on the anxiety subscale of the HADS (HADS-A) or PSSCAN (Alfonsson et al., 2016 ; Boyes et al., 2013 ; Kim et al., 2020 ; Linden et al., 2015 ). Two studies involved women with breast cancer: women with stage 0-III cancer were assessed pre-surgery and 12 months later (Kim et al., 2020 ), and women with all cancer stages were assessed within 9 months of diagnosis and 3 years later (Alfonsson et al., 2016 ). The other two anxiety studies involved people with mixed cancer diagnoses assessed 6 months post-diagnosis and 6 months later (Boyes et al., 2013 ), and assessed before treatment commenced and 12 months later (Linden et al., 2015 ). Boyes et al. ( 2013 ) reported that 8% of their sample were receiving chemotherapy or radiotherapy at follow-up, but the proportion of patients in active treatment was not reported in the other studies.

Despite differences in samples and assessment timepoints across studies, some patterns emerged. Studies that recruited patients before commencing treatment had lower proportions of “non-cases” (48%-55%) and higher proportions of recovered cases (17–23%; Kim et al., 2020 ; Linden et al., 2015 ) compared to the studies that recruited on average 4–6 months after diagnosis (61–70% non-cases and 8–13% recovered cases; Alfonsson et al., 2016 ; Boyes et al., 2013 ). The rate of emerging anxiety was about 10% and was lowest in the study that recruited patients 6 months after diagnosis (7%; Boyes et al., 2013 ). The rate of persistent anxiety ranged from 14 to 21%, and was highest in a sample of newly diagnosed women with breast cancer (Boyes et al., 2013 ; Kim et al., 2020 ).

Two of these studies examined predictors of anxiety trajectories (Table  2 ). Persistent anxiety was associated with younger age and higher baseline anxiety or depression severity compared to non-cases; and recovered anxiety was associated with lower baseline anxiety compared to persistent cases (Linden et al., 2015 ). Persistent and emerging anxiety was also associated with more pain, breast and arm symptoms at follow-up compared to non-cases (Kim et al., 2020 ).

Anxiety Studies with More Than Two Assessment Timepoints

One study assessed patients with colorectal cancer for clinically relevant anxiety at diagnosis and yearly for three years (Mols et al., 2018 ). Using the HADS-A, patients were grouped into three trajectories: non-cases (68%), persistent (10%) and fluctuating (22%). As with depression, those with fluctuating or persistent anxiety were more likely to have two or more comorbidities compared to non-cases (Table  2 ). Fluctuating anxiety was also associated with being female, younger age, lower education, and stage IV disease (compared to stage I).

Mixed Anxiety and Depression

One study assessed patients who scored above the clinical cut-off on both the depression and anxiety HADS subscales (Boyes et al., 2013 ). Not surprisingly, the proportion of patients in the clinical range was much smaller than for anxiety or depression alone (5% emerging, 4% persistent).

Adjustment Disorder (AD)

One study examined AD (defined as marked distress not meeting criteria for another mental disorder) amongst patients with breast cancer who had completed treatment within the previous 5 years (Wijnhoven et al., 2022 ). Patients were assessed four times over 12 months using the HADS total score (HADS-T). Scores of 11–14 were defined as AD, and scores ≥ 15 were defined as “other mental disorder.” Participants were classified into 4 trajectories: non-cases (54%), fluctuating (38%), persistent “other mental disorder” (7%), and persistent AD (1%). Persistent or fluctuating trajectories (combined and compared to non-cases) were associated with younger age, more difficulties with daily activities, less social support than desired, lower optimism, and higher neuroticism.

Post-Traumatic Stress Disorder (PTSD)

Two studies examined PTSD trajectories. Patients with Non-Hodgkin’s Lymphoma (NHL) were assessed for PTSD two years post-diagnosis and five years later (Smith et al., 2011 ). 10% of the sample were receiving active treatment at study entry but the proportion receiving active treatment at follow-up is not reported. Male and female participants were equally represented, and sample mean age was 62 years. Using the PTSD Checklist-Civilian Version (PCL-C; Weathers et al., 1993 ), those at least moderately bothered by one re-experiencing, three avoidance and two arousal symptoms were classified as PTSD cases. Most patients in this cohort were non-cases for PTSD (90%), with small proportions of recovered (3%), emerging (4%) and persistent (3%) cases reported. The authors examined predictors of PCL-C scores not PTSD trajectories.

In the second study, patients with stage I-III breast cancer were assessed for PTSD on three occasions: 2–3, 4 and 6 months after diagnosis (Vin-Raviv et al., 2013 ). Whether participants were actively receiving cancer treatment was not assessed. Caseness was determined by Impact of Events Scale score ≥ 24 (Horowitz et al., 1979 ). At 4 months post-diagnosis, four trajectories were identifiable from the published data: non-cases (72%), recovered (12%), persistent (11%), and emerging (5%). At 6 months post-diagnosis, eight trajectories were identifiable. Four trajectories described those who did not meet criteria for PTSD at 6 months: non-cases (71%), sustained recovery (9%), PTSD emerged then resolved (4%), and late recovery (4%). Four trajectories described patients who met criteria for PTSD at 6 months: persistent (7%), late onset (2%), resolved but re-emerged (2%), and emerged and sustained (1%). Non-cases were compared to those who met PTSD criteria at two or more consecutive assessments. No clinical variables distinguished these groups, but non-cases were more likely to be aged over 50 and be White or Hispanic rather than Black or Asian.

FCR and Death Anxiety

No studies meeting our inclusion criteria examined trajectories of FCR or death anxiety.

Risk of Bias Assessment

Risk of bias assessment results are presented in Table  3 (see Supplementary Material for more detail). Inter-rater agreement was 84%. No studies were rated as having a low risk of bias across all domains. Of clinical importance, four studies were rated high on risk of bias for study participation due to low recruitment rates (38–51%; Boyes et al., 2013 ; Kim et al., 2020 ; Sullivan et al., 2016 ; Wijnhoven et al., 2022 ), while six studies were rated high on risk of bias for attrition, due to low completer rates (53–64%; Alfonsson et al., 2016 ; Jansen et al., 2018 ; Kim et al., 2020 ; Wijnhoven et al., 2022 ), non-completers were more likely to be anxious or depressed, 31 or key characteristics of non-completers were not described (Linden et al., 2015 ; Wijnhoven et al., 2022 ). In terms of outcome measurement, the window for baseline data collection post-diagnosis was wide in five studies (Alfonsson et al., 2016 ; Boyes et al., 2013 ; Mols et al., 2018 ; Smith et al., 2011 ; Wijnhoven et al., 2022 ). None of the studies examining why the trajectory groups differed (Hasegawa et al., 2019 ; Jansen et al., 2018 ; Kim et al., 2012 , 2020 ; Linden et al., 2015 ; Mols et al., 2018 ; Vin-Raviv et al., 2013 ; Wijnhoven et al., 2022 ) were informed by a theoretical model.

This is the first review to synthesise the current knowledge about longitudinal distress trajectories in patients with cancer. We identified 12 study samples assessing trajectories of depression (8), anxiety (5), PTSD (2) and AD (1). Unfortunately, due to heterogeneity between studies, we were unable to conduct a meta-analysis on the prevalence of trajectories or the predictors. Nevertheless, patterns did emerge in the findings which are discussed below.

Findings suggest that depression trajectories may be related to cancer cohorts, with higher levels of persistent depression evident in the study of patients with lung cancer (Sullivan et al., 2016 ). These findings contrast with a meta-analysis reporting that the average point-prevalence of depression amongst patients with lung cancer was similar to other cancer groups (Krebber et al., 2014 ). A key limitation of the cross-sectional prevalence studies included in the meta-analysis is that they do not inform who continued to have depression over time. For lung cancer, high rates of persistent depression have been associated with stigma (Cataldo & Brodsky, 2013 ), poor sleep quality (He et al., 2022 ), impacts on physical functioning (Hopwood & Stephens, 2000 ), and diagnosis commonly occurring at a later stage (Schabath & Cote, 2019 ). Within this context, early screening for depression may be a more robust indicator of persistent distress amongst lung cancer patients and flag the need for psychological intervention, compared to other cancer groups.

Findings also indicate that future studies should take greater account of survivor bias. Rates of non-cases of depression were highest amongst the two studies that recruited from large cancer registries (Alfonsson et al., 2016 ; Boyes et al., 2013 ) and were therefore more likely to include patients who had completed their initial treatment. Conversely, rates of recovery from depression were lower when initial assessments were conducted some months after diagnosis (Alfonsson et al., 2016 ; Boyes et al., 2013 ) compared to those studies that recruited close to diagnosis (Jansen et al., 2018 ; Kim et al., 2012 ). These data suggest that future research should commence longitudinal studies closer to diagnosis to allow comparisons across studies and identify patients who recover, and the protective factors associated with their recovery.

No conclusions could be drawn from the data about when depression is likely to emerge after a cancer diagnosis. Rates of emerging depression were highest amongst newly diagnosed patients with mixed cancer types assessed before treatment and 12 months later (19%; Linden et al., 2015 ). The 12-month assessment point was chosen to coincide with patients completing their initial treatment regime and adjusting to survivorship. However, some patients were in the palliative phase and about 5% of patients died during the follow-up period. Given the heterogeneity in the sample and the long interval between assessment timepoints, future studies should assess more frequently to determine when clinically relevant depression emerged, and for which patients, so that screening efforts can be directed accordingly.

Regarding predictors of depression trajectories, findings indicate that greater emphasis is needed on understanding physical symptom severity and psychological factors as predictors of depression. Physical symptom severity as measured by performance status (Hasegawa et al., 2019 ), the impact of physical symptoms on functioning (Kim et al., 2012 , 2013 ), or the presence of comorbidities (Jansen et al., 2018 ; Mols et al., 2018 ), was consistently associated with persistent depression. Demographic variables were not significant predictors (Jansen et al., 2018 ; Kim et al., 2013 ; Linden et al., 2015 ; Mols et al., 2018 ), and age inconsistently predicted depression trajectories (Hasegawa et al., 2019 ; Jansen et al., 2018 ; Linden et al., 2015 ). Clinical variables, such as time since diagnosis, treatment type, or tumour stage were also not significant predictors of persistent depression in most studies. Importantly, one study assessed changes in physical symptoms over time (Kim et al., 2013 ). Surprisingly, no studies examined whether being in active treatment or completing treatment explained distress trajectories. As adaptation to cancer evolves within a changing context of symptoms and functioning, contextual physical and treatment variables should also be assessed longitudinally and considered in future predictive models.

The two studies exploring psychological predictors of depression reported that a baseline or previous history of depression was associated with a persistent depression trajectory (Kim et al., 2012 ; Linden et al., 2015 ), suggesting that previous history should be included in early psychological screening processes. This also suggests that exploring the vulnerability factors that predisposed these individuals to depression is important in identifying psychological predictors amenable to treatment. For instance, theories suggest that habitual coping strategies and the quality of social support may impede or facilitate adjustment to cancer (Kangas & Gross, 2020 ) Separately, illness intrusiveness, or the subjective meaning and salience of symptoms, was argued to be a psychological predictor associated with persistent depression (Linden et al., 2015 ). The illness intrusiveness rating scale used in this study assesses the extent to which illness disrupts functioning in various quality-of-life domains (Devins et al., 2001 ), and may be a proxy for symptom severity. Consequently, more refined measures of illness meaning and salience are needed. Interestingly, there is evidence that related psychological constructs, such as illness representations and the impact of illness on self-schemas, are associated with depression amongst people with cancer (Carpenter et al., 2009 ; Richardson et al., 2017 ). These constructs are important to investigate in future research to identify psychological predictors of depression trajectories that may be amenable to interventions.

As with depression, anxiety trajectories were related to assessment timepoints. Studies that recruited at least 6 months after diagnosis from cancer registries had the highest proportion of non-cases (Boyes et al., 2013 ; Mols et al., 2018 ) while studies that recruited before treatment commenced had the lowest rate of non-cases and higher rates of persistent and recovered anxiety (Kim et al., 2020 ; Linden et al., 2015 ). The early phase of adjustment to diagnosis and treatment is an especially anxious period for patients who are experiencing uncertainty about treatment efficacy and the possible impacts on their roles and relationships (Thewes et al., 2017 ). However, a substantial proportion of patients did adjust without any specific intervention.

Findings regarding the emerging anxiety trajectory highlight the importance of assessing patients at multiple timepoints to identify patients in need of clinical intervention. While emerging anxiety was the least common trajectory, around 1 in 10 people did develop anxiety over time, even amongst those recruited up to nine months after diagnosis (Alfonsson et al., 2016 ). The rate of emerging anxiety occurred similarly across studies, suggesting this phenomenon was related to aspects of the cancer experience that are common across cancer cohorts, such as ongoing uncertainty and fears about the cancer progressing or recurring (Curran et al., 2017 ).

In terms of predictors of anxiety trajectories, demographic variables, such as education, income and age, were not consistent predictors (Kim et al., 2020 ; Linden et al., 2015 ; Mols et al., 2018 ). As with depression, cancer symptoms, comorbidities and/or general health functioning were associated with persistent anxiety (Kim et al., 2020 ; Mols et al., 2018 ). Higher illness intrusiveness and physical symptoms were also associated with emerging anxiety (Kim et al., 2020 ; Linden et al., 2015 ). This accords with the Enduring Somatic Threat Model, based on Terror Management Theory, that posits that physical symptoms remind a person of their mortality and inflate anxiety (Edmondson, 2014 ). Consequently, it is important that future studies assess contextual physical factors longitudinally to understand the course of anxiety and ensure effective physical symptom management to improve mental health outcomes.

Only one study examined predictors of recovered anxiety, and reported that, compared to persistent anxiety, baseline anxiety was lower (Linden et al., 2015 ). However, this does not inform us about why these patients had lower anxiety at baseline. Given the paucity of research on the predictors of anxiety trajectories, theoretically informed research is needed to understand the vulnerability and protective factors that underpin anxiety trajectories.

The two studies assessing PTSD trajectories also highlight a need for future studies to carefully consider assessment time-points (Smith et al., 2011 ; Vin-Raviv et al., 2013 ). Not surprisingly rates of persistent PTSD were lowest in the sample that included people on average 7 years post diagnosis (Smith et al., 2011 ). Neither of these studies examined predictors of trajectories specifically, although higher PTSD scores were associated with greater impacts of the cancer on appearance, life interference and worrying (Smith et al., 2011 ), and younger patients were more likely to meet criteria for PTSD on two consecutive longitudinal assessments (Vin-Raviv et al., 2013 ). Other possible predictors of persistent PTSD that are amenable to interventions warrant further investigation, such as a history of trauma or major life stressors prior to the cancer diagnosis (Silver-Aylaian & Cohen, 2001 ; Swartzman et al., 2017 ), avoidant coping (Jacobsen et al., 2002 ), greater illness uncertainty (Kuba et al., 2017 ), higher disease burden (Kuba et al., 2017 ; Shand et al., 2015 ), and lower social support (Jacobsen et al., 2002 ; Shand et al., 2015 ).

Since these studies, diagnostic criteria have changed so that PTSD is diagnosed only when there has been a sudden, catastrophic event in addition to the cancer diagnosis (such as a life-threatening haemorrhage), and the person experiences intrusive, hyperarousal and avoidance symptoms related to memories of that event (APA, 2013 ). Consequently, in the cancer context, where intrusions are generally related to future-orientated fears, PTSD should be rarely diagnosed; instead, patients with cancer who experience traumatic stress symptoms would likely meet criteria for AD (Kangas, 2013 ). Indeed, some researchers have argued that the classification of AD as a distinct stress disorder more appropriately describes the distress experienced in cancer settings and therefore may lead to improvements in research and interventions (Esser et al., 2019 ).

Only one study examined trajectories of AD (Wijnhoven et al., 2022 ), so no meaningful conclusions can be drawn from our review of the literature to date. The study was limited to patients with breast cancer who had completed treatment given with curative intent. Only 1.4% met the study criteria for persistent AD, that is, marked but not high levels of distress on the HADS-T. The low case-rate for persistent AD may relate to assessment validity or that the study included patients that would be expected to be past the initial post-diagnosis adjustment phase. More studies are needed to understand the trajectory of AD from diagnosis.

FCR or Death Anxiety

No studies examining trajectories of FCR met our inclusion criteria, principally because many studies of FCR have used statistical methods to group participants into trajectories rather than clinical cut-off scores. Investigating FCR is important because FCR is a pattern of worry, preoccupation and hypervigilance to body symptoms centred on fears about the cancer progressing or recurring, that can be distinguished from other anxiety disorders (Mutsaers et al., 2020 ). FCR is also likely to have a unique course and predictors with evidence that FCR persists over time, and may even increase in severity, especially for younger patients (Starreveld et al., 2018 ). Also, no studies of trajectories of death anxiety were reported. Death anxiety is important to consider as it is associated with, but distinct from other manifestations of anxiety, such as general anxiety, health anxiety or FCR (Curran et al., 2020 ; Menzies et al., 2021 ), and occurs across cancer cohorts, affecting people with metastatic or late-stage cancer (Lo et al., 2011 ; Neel et al., 2015 ) as well as people who have completed their cancer treatment and seem to be disease free (Cella & Tross, 1987 ; Lagerdahl et al., 2014 ). The lack of research on death anxiety may be due to there being no agreed definition of clinically relevant death anxiety, and the commonly used measures of death anxiety were developed for patients with incurable disease and their validity has not been tested in other cancer populations (Sharpe et al., 2018 ).

Study Limitations

While our review of the literature on distress trajectories was comprehensive, there were some limitations. The review was not pre-registered. Also, the findings were limited to articles published in English. While it is possible that some important non-English publications may have been missed, this approach is a standard methodology for reviews of cancer-related themes (e.g. Arring et al., 2023 ; Hasson-Ohayon et al., 2022 ). Further, given that our review aimed to identify the clinical utility of research to date, we decided to use clinical terms in our search strategy, such as anxiety, depression and adjustment disorder, rather than a generic search term, such as distress. We expected that the course, predictors, and potential intervention targets of various clinically meaningful psychological states, such as anxiety or depression, would differ and would not be captured if these constructs were conflated into a broader category of distress. This may have meant that we failed to capture the diversity of people’s experiences of distress after a cancer diagnosis.

Furthermore, the generalisability of our findings to the broader cancer population may be limited due to several considerations. Arguably, study quality criteria relating to rates of study participation and attrition are particularly important for indicating the clinical importance or meaningfulness of results because these criteria demonstrate how representative the study samples are of the general cancer population, and therefore how well results reflect clinical cohorts in naturalistic settings. Across studies, rates of participation and attrition were not robust, and studies typically excluded individuals with advanced cancers, those who were very unwell, and those with a previous cancer diagnosis. Bias due to attrition was only low in two studies, and individuals who did not complete follow-up assessments were generally more psychologically distressed and physical unwell at baseline compared to those who completed all study measures (Alfonsson et al., 2016 ; Boyes et al., 2013 ; Mols et al., 2018 ). Given that poorer physical health status was associated with persistent depression or anxiety, it is likely that our findings underestimate the prevalence of clinically meaningful distress amongst individuals with cancer. Future longitudinal research should maximise recruitment inclusivity across all stages of cancer and systematically account for study attrition.

Additionally, consistent findings regarding distress trajectories were difficult to determine due to methodological heterogeneity. Studies varied by the number of follow-up assessments conducted and the number of trajectories identified, with possible implications for the proportion of patients assigned to each trajectory. Also, initial assessments ranged from close to diagnosis (Jansen et al., 2018 ), when distress may be expected to be higher, to years after diagnosis (Mols et al., 2018 ). Follow-up assessments were generally conducted at pre-determined study time points rather than at times that are clinically meaningful or personally relevant to cancer patients such as prior to surveillance scans, or around diagnosis anniversary dates. Also, whether patients were receiving active treatment was generally not assessed. Incomplete clinical data about the context in which assessments occurred limits the clinical utility and ecological validity of findings and precludes conclusions regarding the optimal time to assess psychological distress in routine care. Future longitudinal research should consider taking measurements from the point of diagnosis, followed by meaningful points along the cancer care pathway.

Furthermore, it is unclear whether the psychological assessments conducted in the reviewed studies were optimal. Only one sample completed a structured diagnostic interview (Kim et al., 2012 , 2013 , 2018 ), but minor and major depression were merged to categorise caseness, potentially confounding the findings. While the most frequently used self-report measure was the HADS, there has been debate regarding optimal cut-off values (Annunziata et al., 2020 ; Vodermaier et al., 2009 ), and concern over its use as a case-finding instrument (Mitchell, 2010 ). This highlights the need for foundational research to establish valid case-finding measures so that clinically meaningful research can be conducted.

Lastly, there was limited assessment of psychological variables predicting distress trajectories. Studies generally investigated demographic and medical variables with little evaluation of psychological constructs that may explain the aetiology or maintenance of distress and are responsive to psychotherapy. For instance, one interesting area of research is coping strategies and how they may impact on emotion regulation differently at various phases of diagnosis, treatment, survivorship, recurrence and end of life (cf. Kangas & Gross, 2020 ). Future longitudinal studies need to assess therapeutically relevant constructs informed by theoretical models of cancer-related distress.

Conclusions

This study aimed to examine the course of clinically relevant, individual trajectories of distress after a cancer diagnosis and the psychological, sociodemographic and medical factors associated with different distress trajectories. The limited findings from this review suggest that depression screening efforts should be particularly directed at patients with lung cancer. Also, longitudinal approaches to screening are needed to detect emerging depression and anxiety, since up to 1 in 5 patients developed depression in the first year after diagnosis and about 1 in 10 developed clinically relevant anxiety (Kim et al., 2020 ; Linden et al., 2015 ). The review also highlighted that consistent assessment timepoints across studies are needed to establish a wider evidence base to inform screening efforts. Furthermore, since assessments conducted at diagnosis or during clinic visits capture distress that is understandable and often subsides, more finely grained assessments, such as utilising smart phone applications, would be useful in future studies to understand when high distress is likely to be sustained. This would ensure that interventions are utilised efficiently, do not pathologize understandable distress and allow natural adaptation to occur, while ensuring that unremitting distress is treated as early as possible.

Understanding why distress develops, resolves or continues is important clinically to elucidate the protective and maintaining factors underpinning distress trajectories, thereby providing intervention targets that can be tailored to individual trajectories. The review suggests that symptom burden was the most consistent predictor of persistent distress, highlighting that mental health interventions should be multi-disciplinary in their approach. Furthermore, prior history should be screened as a potential predictor of depression. Disappointingly, there was limited information about other psychosocial predictors that could guide interventions. Future research should be guided by theoretical models of cancer-related distress, such as cognitive processing and metacognitive approaches (Cook et al., 2015 ; Curran et al., 2017 ; Edmondson, 2014 ; Fardell et al., 2016 ; Kangas & Gross, 2020 ), that may identify targets for treatments.

Data Availability

More detail about QUIPs assessment is available in Supplementary Materials.

Code Availability

Not applicable.

Alfonsson, S., Olsson, E., Hursti, T., Lundh, M. H., & Johansson, B. (2016). Socio-demographic and clinical variables associated with psychological distress 1 and 3 years after breast cancer diagnosis. Supportive Care in Cancer, 24 , 4017–4023. https://doi.org/10.1007//s00520-016-3242y

Article   PubMed   Google Scholar  

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Association. https://doi.org/10.1176/appi.books.9780890425596

Book   Google Scholar  

Annunziata, M. A., Muzzatti, B., Bidoli, E., Flaiban, C., Bomben, F., Piccinin, M., Gipponi, K. M., Mariutti, G., Busato, S., & Mella, S. (2020). Hospital Anxiety and Depression Scale (HADS) accuracy in cancer patients. Supportive Care in Cancer, 28 , 3921–3926. https://doi.org/10.1007/s00520-019-05244-8

Arring, N., Barton, D. L., & Reese, J. B. (2023). Clinical practice strategies to address sesxual health in female cancer survivors. Journal of Clinical Oncology, 41 , 4927–4936. https://doi.org/10.1200/JCO.23.00523

Boyes, A. W., Girgis, A., D’Este, C. A., Zucca, A. C., Lecathelinais, C., & Carey, M. L. (2013). Prevalence and predictors of the short-term trajectory of anxiety and depression in the first year after a cancer diagnosis: A population-based longitudinal study. Journal of Clinical Oncology, 31 , 2724–2729. https://doi.org/10.1200/JCO.2012.44.7540

Calman, L., Turner, J., Fenlon, D., Permyakova, N. V., Wheelwright, S., Patel, M., . . . members of the, C. S. A. C. (2021). Prevalence and determinants of depression up to 5 years after colorectal cancer surgery: results from the ColoREctal Wellbeing (CREW) study. Colorectal Disease, 23 , 3234–3250. https://doi.org/10.1111/codi.15949 .

Carpenter, K., Andersen, B., Fowler, J., & Maxwell, G. (2009). Sexual self schema as a moderator of sexual and psychological outcomes for gynecologic cancer survivors. Archives of Sexual Behavior, 38 , 828–841. https://doi.org/10.1007/s10508-008-9349-6

Cataldo, J. K., & Brodsky, J. L. (2013). Lung cancer stigma, anxiety, depression and symptom severity. Oncology, 85 , 33–40. https://doi.org/10.1159/000350834

Cella, D. F., & Tross, S. (1987). Death anxiety in cancer survival: A preliminary cross-validation study. Journal of Personality Assessment, 51 , 451–461. https://doi.org/10.1207/s15327752jpa5103_12

Article   CAS   PubMed   Google Scholar  

Cook, S. A., Salmon, P., Dunn, G., Holcombe, C., Cornford, P., & Fisher, P. (2015). A prospective study of the association of metacognitive beliefs and processes with persistent emotional distress after diagnosis of cancer. Cognitive Therapy and Research, 39 , 51–60. https://doi.org/10.1007/s10608014-9640-x

Cook, S. A., Salmon, P., Hayes, G., Byrne, A., & Fisher, P. L. (2018). Predictors of emotional distress a year or more after diagnosis of cancer: A systematic review of the literature. Psycho-Oncology, 27 , 791–801. https://doi.org/10.1002/pon.4601

Article   PubMed   PubMed Central   Google Scholar  

Curran, L., Sharpe, L., & Butow, P. (2017). Anxiety in the context of cancer: A systematic review and development of an integrated model. Clinical Psychology Review, 56 , 40–54. https://doi.org/10.1016/j.cpr.2017.06.003

Curran, L., Sharpe, L., MacCann, C., & Butow, P. (2020). Testing a model of fear of cancer recurrence or progression: The central role of intrusions, death anxiety and threat appraisal. Journal of Behavioral Medicine, 43 , 225–236. https://doi.org/10.1007/s10865-019-00129-x

Custers, J. A., Kwakkenbos, L., van der Graaf, W. T., Prins, J. B., Gielissen, M. F., & Thewes, B. (2020). Not as stable as we think: A descriptive study of 12 monthly assessments of fear of cancer recurrence among curatively-treated breast cancer survivors 0–5 years after surgery. Frontiers in Psychology, 11 , 580979. https://doi.org/10.3389/fpsyg.2020.580979

Devins, G. M., Dion, R., Pelletier, L. G., Shapiro, C. M., Abbey, S., Raiz, L. R., Binik, Y. M., McGowan, P., Kutner, N. G., Beanlands, H., & Edworthy, S. M. (2001). Structure of lifestyle disruptions in chronic disease: A confirmatory factor analysis of the Illness Intrusiveness Ratings Scale. Medical Care, 39 , 1097–1104. https://doi.org/10.1097/00005650-200110000-00007

Edmondson, D. (2014). An enduring somatic threat model of posttraumatic stress disorder due to acute life-threatening medical events. Social and Personality Psychology Compass, 8 , 118–134. https://doi.org/10.1111/spc3.12089

Esser, P., Glaesmer, H., Faller, H., Koch, U., Härter, M., Schulz, H., Wegscheider, K., Weis, J., & Mehnert, A. (2019). Posttraumatic stress disorder among cancer patients—Findings from a large and representative interview-based study in Germany. Psycho-Oncology, 28 , 1278–1285. https://doi.org/10.1002/pon.5079

Fardell, J. E., Thewes, B., Turner, J., Gilchrist, J., Sharpe, L., Smith, A., Girgis, A., & Butow, P. (2016). Fear of cancer recurrence: A theoretical review and novel cognitive processing formulation. Journal of Cancer Survivorship, 10 , 663–673. https://doi.org/10.1007/s11764-015-0512-5

Hasegawa, T., Okuyama, T., Uchida, M., Aiki, S., Imai, F., Nishioka, M., Suzuki, N., Iida, S., Komatsu, H., Kusumoto, S., Ri, M., Osaga, S., & Akechi, T. (2019). Depressive symptoms during the first month of chemotherapy and survival in patients with hematological malignancies: A prospective cohort study. Psycho-Oncology, 28 , 1687–1694. https://doi.org/10.1002/pon.5143

Hasson-Ohayon, I., Goldzweig, G., Braun, M., & Hagedoorn, M. (2022). Beyond “being open about it”: A systematic review on cancer related communication within couples. Clinical Psychology Review, 96 , 102176. https://doi.org/10.1016/j.cpr.2022.102176

Hayden, J. A., van der Windt, D. A., Cartwright, J. L., Côté, P., & Bombardier, C. (2013). Assessing bias in studies of prognostic factors. Annals of Internal Medicine, 158 , 280–286. https://doi.org/10.7326/0003-4819-158-4-201302190-00009

He, Y., Sun, L. Y., Peng, K. W., Luo, M. J., Deng, L., Tang, T., & You, C. X. (2022). Sleep quality, anxiety and depression in advanced lung cancer: Patients and caregivers. BMJ Supportive & Palliative Care, 12 , e194–e200. https://doi.org/10.1136/bmjspcare-2018-001684

Article   Google Scholar  

Henry, M., Fuehrmann, F., Hier, M., Zeitouni, A., Kost, K., Richardson, K., … Frenkiel, S. (2019). Contextual and historical factors for increased levels of anxiety and depression in patients with head and neck cancer: A prospective longitudinal study. Head & Neck, 41 , 2538–2548. https://doi.org/10.1002/hed.25725 .

Hopwood, P., & Stephens, R. J. (2000). Depression in patients with lung cancer: prevalence and risk factors derived from quality-of-life data. Journal of Clinical Oncology, 18 , 893–903. https://doi.org/10.1200/jco.2000.18.4.893

Horowitz, M. M. D., Wilner, N. B. A., & Alvarez, W. M. A. (1979). Impact of event scale: A measure of subjective stress. Psychosomatic Medicine, 41 , 209–218. https://doi.org/10.1097/00006842-197905000-00004

Jacobsen, P. B., Sadler, I. J., Booth-Jones, M., Soety, E., Weitzner, M. A., & Fields, K. K. (2002). Predictors of posttraumatic stress disorder symptomatology following bone marrow transplantation for cancer. Journal of Consulting and Clinical Psychology, 70 , 235–240. https://doi.org/10.1037/0022-006X.70.1.235

Jansen, F., Verdonck-de Leeuw, I. M., Cuijpers, P., Leemans, C. R., Waterboer, T., Pawlita, M., Penfold, C., Thomas, A. W., & Ness, A. R. (2018). Depressive symptoms in relation to overall survival in people with head and neck cancer: A longitudinal cohort study. Psycho-Oncology, 27 , 2245–2256. https://doi.org/10.1002/pon.4816

Kangas, M. (2013). DSM-5 trauma and stress-related disorders: Implications for screening for cancer-related stress. Frontiers in Psychiatry, 4 , 122. https://doi.org/10.3389/fpsyt.2013.00122

Kangas, M., & Gross, J. (2020). The affect regulation in cancer framework: Understanding affective responding across the cancer trajectory. Journal of Health Psychology, 25 , 7–25. https://doi.org/10.1177/1359105317748468

Kim, J., Cho, J., Lee, S. K., Choi, E. K., Kim, I. R., Lee, J. E., Seok, W. K., & Nam, S. J. (2020). Surgical impact on anxiety of patients with breast cancer: 12-month follow-up prospective longitudinal study. Annals of Surgical Treatment and Research, 98 , 215–223. https://doi.org/10.4174/astr.2020.98.5.215

Kim, J. M., Kim, S. W., Stewart, R., Kim, S. Y., Shin, I. S., Park, M. H., Yoon, J. H., Lee, J. S., Park, S. W., Kim, Y. H., & Yoon, J. S. (2012). Serotonergic and BDNF genes associated with depression 1 week and 1 year after mastectomy for breast cancer. Psychosomatic Medicine, 74 , 8–15. https://doi.org/10.1097/PSY.0b013e318241530c

Kim, J. M., Stewart, R., Kim, S. Y., Kang, H. J., Jang, J. E., Kim, S. W., Shin, I. S., Park, M. H., Yoon, J. H., Park, S. W., Kim, Y. H., & Yoon, J. S. (2013). A one year longitudinal study of cytokine genes and depression in breast cancer. Journal of Affective Disorders, 148 , 57–65. https://doi.org/10.1016/j.jad.2012.11.048

Kim, S. Y., Kim, S. W., Shin, I. S., Park, M. H., Yoon, J. H., Yoon, J. S., & Kim, J. M. (2018). Changes in depression status during the year after breast cancer surgery and impact on quality of life and functioning. General Hospital Psychiatry, 50 , 33–37. https://doi.org/10.1016/j.genhosppsych.2017.09.009

Krebber, A. M. H., Buffart, L. M., Kleijn, G., Riepma, I. C., de Bree, R., Leemans, C. R., Becker, A., Burg, J., van Straten, A., Cuijpers, P., & Verdonck-de Leeuw, I. M. (2014). Prevalence of depression in cancer patients: A meta-analysis of diagnostic interviews and self-report instruments. Psycho-Oncology, 23 , 121–130. https://doi.org/10.1002/pon.3409

Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9. Journal of General Internal Medicine, 16 , 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kuba, K., Esser, P., Scherwath, A., Schirmer, L., Schulz-Kindermann, F., Dinkel, A., Balck, F., Koch, U., Kroger, N., Gotze, H., & Mehnert, A. (2017). Cancer-and-treatment-specific distress and its impact on posttraumatic stress in patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT). Psycho-Oncology, 26 , 1164–1171. https://doi.org/10.1002/pon.4295

Lagerdahl, A. S., Moynihan, M., & Stollery, B. (2014). An exploration of the existential experiences of patients following curative treatment for cancer: Reflections from a U.K. sample. Journal of Psychosocial Oncology, 32 , 555–575. https://doi.org/10.1080/07347332.2014.936647

Linden, W., MacKenzie, R., Rnic, K., Marshall, C., & Vodermaier, A. (2015). Emotional adjustment over 1 year post-diagnosis in patients with cancer: Understanding and predicting adjustment trajectories. Supportive Care in Cancer, 23 , 1391–1399. https://doi.org/10.1007/s00520-014-2492-9

Linden, W., Yi, D., Barroetavena, M. C., MacKenzie, R., & Doll, R. (2005). Development and validation of a psychosocial screening instrument for cancer. Health and Quality of Life Outcomes, 3 , 54. https://doi.org/10.1186/1477-7525-3-54

Liu, Y., Pettersson, E., Schandl, A., Markar, S., Johar, A., & Lagergren, P. (2022). Psychological distress after esophageal cancer surgery and the predictive effect of dispositional optimism: A nationwide population-based longitudinal study. Supportive Care in Cancer, 30 , 1315–1322. https://doi.org/10.1007/s00520-021-06517-x

Lo, C., Hales, S., Zimmermann, C., Gagliese, L., Rydall, A., & Rodin, G. (2011). Measuring death-related anxiety in advanced cancer: Preliminary psychometrics of the death and dying distress scale. Journal of Pediatric Hematology/oncology, 33 , S140–S145. https://doi.org/10.1097/MPH.0b013e318230e1fd

Lopes, C., Lopes-Conceicao, L., Fontes, F., Ferreira, A., Pereira, S., Lunet, N., & Araujo, N. (2022). Prevalence and persistence of anxiety and depression over five years since breast cancer diagnosis—the NEON-BC prospective study. Current Oncology, 29 , 2141–2153. https://doi.org/10.3390/curroncol29030173

Menzies, R. E., Sharpe, L., Helgadóttir, F. D., & Dar-Nimrod, I. (2021). Overcome death anxiety: The development of an online cognitive behaviour therapy programme for fears of death. Behaviour Change, 38 , 235–249. https://doi.org/10.1017/bec.2021.14

Mitchell, A. J. (2010). Short screening tools for cancer-related distress: A review and diagnostic validity meta-analysis. Journal of the National Comprehensive Cancer Network, 8 , 487–494. https://doi.org/10.6004/jnccn.2010.0035

Mols, F., Schoormans, D., de Hingh, I., Oerlemans, S., & Husson, O. (2018). Symptoms of anxiety and depression among colorectal cancer survivors from the population-based, longitudinal PROFILES Registry: Prevalence, predictors, and impact on quality of life. Cancer, 124 , 2621–2628. https://doi.org/10.1002/cncr.31369

Mutsaers, B., Butow, P., Dinkel, A., Humphris, G., Maheu, C., Ozakinci, G., . . . Lebel, S. (2020). Identifying the key characteristics of clinical fear of cancer recurrence: An international Delphi study. Psycho-Oncology, 29 , 430–436. https://doi.org/10.1002/pon.5283 .

Neel, C., Lo, C., Rydall, A., Hales, S., & Rodin, G. (2015). Determinants of death anxiety in patients with advanced cancer. BMJ Supportive and Palliative Care, 5 , 373–380. https://doi.org/10.1136/bmjspcare-2012-000420

Niedzwiedz, C. L., Knifton, L., Robb, K. A., Katikireddi, S. V., & Smith, D. J. (2019). Depression and anxiety among people living with and beyond cancer: A growing clinical and research priority. BMC Cancer, 19 , 943. https://doi.org/10.1186/s12885-019-6181-4

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., . . . Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Reviews, 10 , 89. https://doi.org/10.1186/s13643-021-01626-4 .

Richardson, E. M., Schüz, N., Sanderson, K., Scott, J. L., & Schüz, B. (2017). Illness representations, coping, and illness outcomes in people with cancer: A systematic review and meta-analysis. Psycho-Oncology, 26 , 724–737. https://doi.org/10.1002/pon.4213

Rogiers, A., Leys, C., De Cremer, J., Awada, G., Schembri, A., Theuns, P., De Ridder, M., & Neyns, B. (2020). Health-related quality of life, emotional burden, and neurocognitive function in the first generation of metastatic melanoma survivors treated with pembrolizumab: A longitudinal pilot study. Supportive Care in Cancer, 28 , 3267–3278. https://doi.org/10.1007/s00520-019-05168-3

Savard, J., & Ivers, H. (2013). The evolution of fear of cancer recurrence during the cancer care trajectory and its relationship with cancer characteristics. Journal of Psychosomatic Research, 74 , 354–360. https://doi.org/10.1016/j.jpsychores.2012.12.013

Schabath, M. B., & Cote, M. L. (2019). Cancer progress and priorities: lung cancer. Cancer Epidemiology, Biomarkers & Prevention, 28 , 1563–1579. https://doi.org/10.1158/1055-9965.Epi-19-0221

Shand, L. K., Cowlishaw, S., Brooker, J. E., Burney, S., & Ricciardelli, L. A. (2015). Correlates of post-traumatic stress symptoms and growth in cancer patients: A systematic review and meta-analysis. Psycho-Oncology, 24 , 624–634. https://doi.org/10.1002/pon.3719

Sharpe, L., Curran, L., Butow, P., & Thewes, B. (2018). Fear of cancer recurrence and death anxiety. Psycho-Oncology, 27 , 2559–2565. https://doi.org/10.1002/pon.4783

Silver-Aylaian, M., & Cohen, L. H. (2001). Role of major lifetime stressors in patients’ and spouses’ reactions to cancer. Journal of Traumatic Stress, 14 , 405–412. https://doi.org/10.1023/A:1011129321431

Smith, S. K., Zimmerman, S., Williams, C. S., Benecha, H., Abernethy, A. P., Mayer, D. K., Edwards, L. J., & Ganz, P. A. (2011). Post-traumatic stress symptoms in long-term non-Hodgkin’s lymphoma survivors: Does time heal? Journal of Clinical Oncology, 29 , 4526–4533. https://doi.org/10.1200/JCO.2011.37.2631

Stanton, A. L., Wiley, J. F., Krull, J. L., Crespi, C. M., Hammen, C., Allen, J. J., Barron, M., Jorge, A., & Weihs, K. L. (2015). Depressive episodes, symptoms, and trajectories in women recently diagnosed with breast cancer. Breast Cancer Research and Treatment, 154 , 105–115. https://doi.org/10.1007/s10549-015-3563-4

Starreveld, D. E. J., Markovitz, S. E., van Breukelen, G., & Peters, M. L. (2018). The course of fear of cancer recurrence: Different patterns by age in breast cancer survivors. Psycho-Oncology, 27 , 295–301. https://doi.org/10.1002/pon.4505

Sullivan, D. R., Forsberg, C. W., Ganzini, L., Au, D. H., Gould, M. K., Provenzale, D., & Slatore, C. G. (2016). Longitudinal changes in depression symptoms and survival among patients with lung cancer: A national cohort assessment. Journal of Clinical Oncology, 34 , 3984–3991. https://doi.org/10.1200/JCO.2016.66.8459

Sutton, T. L., Koprowski, M. A., Grossblatt-Wait, A., Brown, S., McCarthy, G., Liu, B., . . . Sheppard, B. C. (2022). Psychosocial distress is dynamic across the spectrum of cancer care and requires longitudinal screening for patient-centered care. Supportive Care in Cancer, 30 , 4255–4264. https://doi.org/10.1007/s00520-022-06814-z

Swartzman, S., Booth, J. N., Munro, A., & Sani, F. (2017). Posttraumatic stress disorder after cancer diagnosis in adults: A meta-analysis. Depression and Anxiety, 34 , 327–339. https://doi.org/10.1002/da.22542

Thewes, B., Husson, O., Poort, H., Custers, J. A. E., Butow, P. N., McLachlan, S. A., & Prins, J. B. (2017). Fear of cancer recurrence in an era of personalized medicine. Journal of Clinical Oncology, 35 , 3275–3278. https://doi.org/10.1200/JCO.2017.72.8212

Turvey, C. L., Wallace, R. B., & Herzog, R. (1999). A revised CES-D measure of depressive symptoms and a DSM-based measure of major depressive episodes in the elderly. International Psychogeriatrics, 11 , 139–148. https://doi.org/10.1017/S1041610299005694

Vin-Raviv, N., Hillyer, G. C., Hershman, D. L., Galea, S., Leoce, N., Bovbjerg, D. H., … Neugut, A. I. (2013). Racial disparities in posttraumatic stress after diagnosis of localized breast cancer: the BQUAL study. Journal of the National Cancer Institute, 105 , 563–572. https://doi.org/10.1093/jnci/djt024 .

Vodermaier, A., Linden, W., & Siu, C. (2009). Screening for emotional distress in cancer patients: A systematic review of assessment instruments. Journal of the National Cancer Institute, 101 , 1464–1488. https://doi.org/10.1093/jnci/djp336

Watts, S., Prescott, P., Mason, J., McLeod, N., & Lewith, G. (2015). Depression and anxiety in ovarian cancer: A systematic review and meta-analysis of prevalence rates. British Medical Journal Open, 5 , e007618. https://doi.org/10.1136/bmjopen-2015-007618

Weathers, F. W., Litz, B. T., Herman, D. S., Huska, J. A., & Keane, T. M. (1993). The PTSD Checklist (PCL): Reliability, validity, and diagnostic utility . Paper presented at the annual convention of the international society for traumatic stress studies, San Antonio, TX.

Wijnhoven, L., Custers, J., Kwakkenbos, L., & Prins, J. (2022). Trajectories of adjustment disorder symptoms in post-treatment breast cancer survivors. Supportive Care in Cancer, 30 , 3521–3530. https://doi.org/10.1007/s00520-022-06806-z

Zigmond, A., & Snaith, R. (1983). The Hospital Anxiety and Depression Scale. Acta Psychiatrica Scandinavica, 67 , 361–370. https://doi.org/10.1111/j.1600-0447.1983.tb09716.x

Download references

Open Access funding enabled and organized by CAUL and its Member Institutions. Partial financial support was provided by Health@Business Research Network, School of Management and Governance, University of New South Wales, Australia.

Author information

Authors and affiliations.

Health@Business Research Network, School of Management and Governance, University of New South Wales, High Street, Kensington, Australia

Leah Curran & Bradley Hastings

The Kinghorn Cancer Centre, St Vincent’s Hospital Network, 370 Victoria Street, Darlinghurst, Sydney, NSW, 2010, Australia

Leah Curran

Clinical Research Unit for Anxiety and Depression, St Vincent’s Hospital Network, 390 Victoria Street, Darlinghurst, Australia

Alison Mahoney

School of Psychiatry, University of New South Wales, High Street, Kensington, Australia

You can also search for this author in PubMed   Google Scholar

Contributions

LC designed the study, conducted the systematic literature review and independently rated included articles for bias. AM reviewed the selection of articles and independently conducted a full-text review of selected articles and a bias assessment. LC and AM wrote the first draft of the manuscript with detailed revisions provided by BH. All authors contributed to and approved the final manuscript.

Corresponding author

Correspondence to Leah Curran .

Ethics declarations

Conflict of interest.

Authors Leah Jane Curran, Alison Mahoney, and Bradley Hastings declare that they have no conflict of interest.

Human and Animal Rights

No new data was collected from human subjects for this review. All studies included in the review received ethics approval prior to initiation of study procedures.

Informed Consent

All studies included in the review ensured that informed consent was obtained from study participants.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 40 KB)

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Curran, L., Mahoney, A. & Hastings, B. A Systematic Review of Trajectories of Clinically Relevant Distress Amongst Adults with Cancer: Course and Predictors. J Clin Psychol Med Settings (2024). https://doi.org/10.1007/s10880-024-10011-x

Download citation

Accepted : 14 February 2024

Published : 05 May 2024

DOI : https://doi.org/10.1007/s10880-024-10011-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Distress trajectory
• Systematic review
• Find a journal
• Publish with us
• Track your research

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

Published on 29.4.2024 in Vol 26 (2024)

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

Authors of this article:

• Ana González-Castro 1 , PT, MSc   ; 
• Raquel Leirós-Rodríguez 2 , PT, PhD   ; 
• Camino Prada-García 3 , MD, PhD   ; 
• José Alberto Benítez-Andrades 4 , PhD  

1 Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain

2 SALBIS Research Group, Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain

3 Department of Preventive Medicine and Public Health, Universidad de Valladolid, Valladolid, Spain

4 SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, Universidad de León, León, Spain

Corresponding Author:

Ana González-Castro, PT, MSc

Nursing and Physical Therapy Department

Universidad de León

Astorga Ave

Ponferrada, 24401

Phone: 34 987442000

Email: [email protected]

Background: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis.

Objective: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk.

Methods: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices.

Results: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI.

Conclusions: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy.

Trial Registration: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv

Introduction

According to alarming figures reported by the World Health Organization in 2021, falls cause 37.3 million injuries annually that require medical attention and result in 684,000 deaths [ 1 ]. These figures indicate a significant impact of falls on the health care system and on society, both directly and indirectly [ 2 , 3 ].

Life expectancy has progressively increased over the years, leading to an aging population [ 4 ]. By 2050, it is estimated that 16% of the population will be >65 years of age. In this group, the incidence of falls has steadily risen, becoming the leading cause of accidental injury and death (accounting for 55.8% of such deaths, according to some research) [ 5 , 6 ]. It is estimated that 30% of this population falls at least once a year, negatively impacting their physical and psychological well-being [ 7 , 8 ].

Physically, falls are often associated with severe complications that can lead to extended hospitalizations [ 9 ]. These hospitalizations are usually due to serious injuries, often cranioencephalic trauma, fractures, or soft tissue injuries [ 10 , 11 ]. Psychologically, falls among the older adult population tend to result in self-imposed limitations due to the fear of falling again [ 10 , 12 ]. These limitations lead to social isolation as individuals avoid participating in activities or even individual mobility [ 13 ]. Consequently, falls can lead to psychological conditions such as anxiety and depression [ 14 , 15 ]. Numerous research studies on the risk of falls are currently underway, with ongoing investigations into various innovations and intervention ideas [ 16 - 19 ]. These studies encompass the identification of fall risk factors [ 20 , 21 ], strategies for prevention [ 22 , 23 ], and the outcomes following rehabilitation [ 23 , 24 ].

In the health care field, artificial intelligence (AI) is characterized by data management and processing, offering new possibilities to the health care paradigm [ 24 ]. Some applications of AI in the health care domain include assessing tumor interaction processes [ 25 ], serving as a tool for image-based diagnostics [ 26 , 27 ], participating in virus detection [ 28 ], and, most importantly, as a statistical and predictive method [ 29 - 32 ].

Several publications have combined AI techniques to address health care issues [ 33 - 35 ]. Within the field of predictive models, it is important to understand certain differentiations. In AI, we have machine learning and deep learning [ 36 - 38 ]. Machine learning encompasses a set of techniques applied to data and can be done in a supervised or unsupervised manner [ 39 , 40 ]. On the other hand, deep learning is typically used to work with larger data sets compared to machine learning, and its computational cost is higher [ 41 , 42 ].

Some examples of AI techniques include the gradient boosting machine [ 43 ], learning method, and the long short-term memory (LSTM) [ 44 ] and the convolutional neural network (CNN) [ 45 ], all of them are deep learning methods.

For all the reasons mentioned in the preceding section, it was considered necessary to conduct a systematic review to analyze the scientific evidence of AI applications in the analysis of data related to postural control and the risk of falls.

Data Sources and Searches

This systematic review and meta-analysis were prospectively registered on PROSPERO (ID CRD42023443277) and followed the Meta-Analyses of Observational Studies in Epidemiology checklist [ 46 ] and the recommendations of the Cochrane Collaboration [ 47 ].

The search was conducted in January 2024 on the following databases: PubMed, Scopus, ScienceDirect, Web of Science, CINAHL, and Cochrane Library. The Medical Subject Headings (MeSH) terms used for the search included machine learning , artificial intelligent , accidental falls , rehabilitation , and physical therapy specialty . The terms “predictive model” and “algorithms” were also used. These terms were combined using the Boolean operators AND and OR ( Textbox 1 ).

• (“machine learning”[MeSH Terms] OR “artificial intelligent”[MeSH Terms]) AND “accidental falls”[MeSH Terms]
• (“machine learning”[MeSH Terms] OR “artificial intelligent”) AND (“rehabilitation”[MeSH Terms] OR “physical therapy specialty”[MeSH Terms])
• “accidental falls” [Title/Abstract] AND “algorithms” [Title/Abstract]
• “accidental falls”[Title/Abstract] AND “predictive model” [Title/Abstract]
• TITLE-ABS-KEY (“machine learning” OR “artificial intelligent”) AND TITLE-ABS-KEY (“accidental falls”)
• TITLE-ABS-KEY (“machine learning” OR “artificial intelligent”) AND TITLE-ABS-KEY (“rehabilitation” OR “physical therapy specialty”)
• TITLE-ABS-KEY (“accidental falls” AND “algorithms”)
• TITLE-ABS-KEY (“accidental falls” AND “predictive model”)

ScienceDirect

• Title, abstract, keywords: (“machine learning” OR “artificial intelligent”) AND “accidental falls”
• Title, abstract, keywords: (“machine learning” OR “artificial intelligent”) AND (“rehabilitation” OR “physical therapy specialty”)
• Title, abstract, keywords: (“accidental falls” AND “algorithms”)
• Title, abstract, keywords: (“accidental falls” AND “predictive model”)

Web of Science

• TS=(“machine learning” OR “artificial intelligent”) AND TS=“accidental falls”
• TS=(“machine learning” OR “artificial intelligent”) AND TS= (“rehabilitation” OR “physical therapy specialty”)
• AB= (“accidental falls” AND “algorithms”)
• AB= (“accidental falls” AND “predictive model”)
• (MH “machine learning” OR MH “artificial intelligent”) AND MH “accidental falls”
• (MH “machine learning” OR MH “artificial intelligent”) AND (MH “rehabilitation” OR MH “physical therapy specialty”)
• (AB “accidental falls”) AND (AB “algorithms”)
• (AB “accidental falls”) AND (AB “predictive model”)

Cochrane Library

• (“machine learning” OR “artificial intelligent”) in Title Abstract Keyword AND “accidental falls” in Title Abstract Keyword
• (“machine learning” OR “artificial intelligent”) in Title Abstract Keyword AND (“rehabilitation” OR “physical therapy specialty”) in Title Abstract Keyword
• “accidental falls” in Title Abstract Keyword AND “algorithms” in Title Abstract Keyword
• “accidental falls” in Title Abstract Keyword AND “predictive model” in Title Abstract Keyword

Study Selection

After removing duplicates, 2 reviewers (AGC and RLR) independently screened articles for eligibility. In the case of disagreement, a third reviewer (JABA) finally decided whether the study should be included or not. We calculated the κ coefficient and percentage agreement scores to assess reliability before any consensus and estimated the interrater reliability using κ. Interrater reliability was estimated using κ>0.7 indicating a high level of agreement between the reviewers, κ of 0.5 to 0.7 indicating a moderate level of agreement, and κ<0.5 indicating a low level of agreement [ 48 ].

For the selection of results, the inclusion criteria were established as follows: (1) articles should have been published in the last 5 years (from 2018 to the present); (2) they must apply some AI method; (3) AI analyses should be applied to data from samples of humans; and (4) the sample analyzed should consist of people with independent walking, with or without the use of external orthopedic devices.

After screening the data, extracting, obtaining, and screening the titles and abstracts for inclusion criteria, the selected abstracts were obtained in full texts. Titles and abstracts lacking sufficient information regarding inclusion criteria were also obtained as full texts. Full-text articles were selected in case of compliance with inclusion criteria by the 2 reviewers using a data extraction form.

Data Extraction and Quality Assessment

The 2 reviewers mentioned independently extracting data from the included studies using a customized data extraction table in Excel (Microsoft Corporation). In case of disagreement, both reviewers debated until an agreement was reached.

The data extracted from the included articles for further analysis were: demographic information (title, authors, journal, and year), characteristics of the sample (age, inclusion and exclusion criteria, and number of participants), study-specific parameters (study type, AI techniques applied, and data analyzed), and the results obtained. Tables were used to describe both the studies’ characteristics and the extracted data.

Assessment of Risk of Bias

The methodological quality of the selected articles was evaluated using the Critical Review Form for Quantitative Studies [ 49 ]. The ROBINS-E (Risk of Bias in Nonrandomized Studies of Exposures) tool was used to evaluate the risk of bias [ 50 ].

Characteristics of the Selected Studies

A total of 3858 articles were initially retrieved, with 1563 duplicates removed. From the remaining 2295 articles, 2271 were excluded based on the initial selection criteria, leaving 24 articles for the subsequent analysis. In this second analysis, 2 articles were removed as they were systematic reviews, and 22 articles were finally selected [ 51 - 72 ] ( Figure 1 ). After the first reading of all candidate full texts, the kappa score for inclusion of the results of reviewers 1 and 2 was 0.98, indicating a very high level of agreement.

The methodological quality of the 22 analyzed studies (Table S1 in Multimedia Appendix 1 [ 51 , 52 , 54 , 56 , 58 , 59 , 61 , 63 , 64 , 69 , 70 , 72 ]) ranged from 11 points in 2 (9.1%) studies [ 52 , 65 ] to 16 points in 7 (32%) studies [ 53 , 54 , 56 , 63 , 69 - 71 ].

Study Characteristics and Risk of Bias

All the selected articles were cross-sectional observational studies ( Table 1 ).

In total, 34 characteristics affecting the risk of falls were extracted and classified into high fall-risk and low fall-risk groups with the largest sample sizes significantly differing from the rest. Studies based on data collected from various health care systems had larger sample sizes, ranging from 22,515 to 265,225 participants [ 60 , 65 , 67 ]. In contrast, studies that applied some form of evaluation test had sample sizes ranging from 8 participants [ 56 ] to 746 participants [ 55 ].

It is worth noting the various studies conducted by Dubois et al [ 54 , 72 ], whose publications on fall risk and machine learning started in 2018 and progressed until 2021. A total of 9.1% (2/22) of the articles by this author were included in the final selection [ 54 , 72 ]. Both articles used samples with the same characteristics, even though the first one was composed of 43 participants [ 54 ] and the last one had 30 participants [ 72 ]. All 86.4% (19/22) of the articles used samples of individuals aged ≥65 years [ 51 - 60 , 62 - 65 , 68 - 72 ]. In the remaining 13.6% (3/22) of the articles, the ages ranged between 16 and 62 years [ 61 , 66 , 67 ].

Althobaiti et al [ 61 ] used a sample of participants between the ages of 19 and 35 years for their research, where these participants had to reproduce examples of falls for subsequent analysis. In 2022, Ladios-Martin et al [ 67 ] extracted medical data from participants aged >16 years for their research. Finally, in 2023, the study by Maray et al [ 66 ] used 3 types of samples, with ages ranging from 21 to 62 years. Among the 22 selected articles, only 1 (4.5%) of them did not describe the characteristics of its sample [ 52 ].

Finally, regarding the sex of the samples, 13.6% (3/22) of the articles specified in the characteristics of their samples that only female individuals were included among their participants [ 53 , 59 , 70 ].

a AI: artificial intelligence.

b ML: machine learning.

c nd: none described.

d ADL: activities of daily living.

e TUG: Timed Up and Go.

f BBS: Berg Balance Scale.

g ASM: associative skill memories.

h CNN: convolutional neural network.

i FP: fall prevention.

j IMU: inertial measurement unit.

k AUROC: area under the receiver operating characteristic curve.

l AUPR: area under the precision-recall curve.

m MFS: Morse Fall Scale.

n XGB: extreme gradient boosting.

o MCT: motor control test.

p GBM: gradient boosting machine.

q RF: random forest.

r LOOCV: leave-one-out cross-validation.

s LSTM: long short-term memory.

Applied Assessment Procedures

All articles initially analyzed the characteristics of their samples to subsequently create a predictive model of the risk of falls. However, they did not all follow the same evaluation process.

Regarding the applied assessment procedures, 3 main options stood out: studies with tests or assessments accompanied by sensors or accelerometers [ 51 - 57 , 59 , 61 - 63 , 66 , 70 - 72 ], studies with tests or assessments accompanied by cameras [ 68 , 69 ], or studies based on medical records [ 58 , 60 , 65 , 67 ] ( Figure 2 ). Guillan et al [ 64 ] performed a physical and functional evaluation of the participants. In their study, they evaluated parameters such as walking speed, stride frequency and length, and the minimum space between the toes. Afterward, they asked them to record the fall events they had during the past 2 years in a personal diary.

In total, 22.7% (5/22) of the studies used the Timed Up and Go test [ 53 , 54 , 69 , 71 , 72 ]. In 18.2% (4/22) of them, the participants performed the test while wearing a sensor to collect data [ 53 , 54 , 71 , 72 ]. In 1 (4.5%) study, the test was recorded with a camera for later analysis [ 69 ]. Another commonly used method in studies was to ask participants to perform everyday tasks or activities of daily living while a sensor collected data for subsequent analysis. Specifically, 18.2% (4/22) of the studies used this method to gather data [ 51 , 56 , 61 , 62 ].

A total of 22.7% (5/22) of the studies asked participants to simulate falls and nonfalls while a sensor collected data [ 52 , 61 - 63 , 66 ]. In this way, the data obtained were used to create the predictive model of falls. As for the tests used, Eichler et al [ 68 ] asked participants to perform the Berg Balance Scale while a camera recorded their performance.

Finally, other authors created their own battery of tests for data extraction [ 55 , 59 , 64 , 70 ]. Gillain et al [ 64 ] used gait records to analyze speed, stride length, frequency, symmetry, regularity, and foot separation. Hu et al [ 59 ] asked their participants to perform normal walking, the postural reflexive response test, and the motor control test. In the study by Noh et al [ 55 ], gait tests were conducted, involving walking 20 m at different speeds. Finally, Greene et al [ 70 ] created a 12-question questionnaire and asked their participants to maintain balance while holding a mobile phone in their hand.

AI Techniques

The selected articles used various techniques within AI. They all had the same objective in applying these techniques, which was to achieve a predictive and classification model for the risk of falls [ 51 - 72 ].

In chronological order, in 2018, Nait Aicha et al [ 51 ] compared single-task learning models with multitask learning, obtaining better evaluation results through multitask learning. In the same year, Dubois et al [ 54 ] applied AI techniques that analyzed multiple parameters to classify the risk of falls in their sample. Qiu et al [ 53 ], also in the same year, used 6 machine learning models (logistic regression, naïve Bayes, decision tree, RF, boosted tree, and support vector machine) in their research.

In contrast, in 2019, Ferrete et al [ 52 ] compared the applicability of 2 different deep learning models: the classifier based on associative skill memories and a CNN classifier. In the same year, after confirming the applicability of AI as a predictive method for the risk of falls, various authors investigated through methods such as the RF to identify factors that can predict and quantify the risk of falls [ 63 , 65 ].

Among the selected articles, 5 (22.7%) were published in 2020 [ 58 - 62 ]. The research conducted by Tunca et al [ 62 ] compared the applicability of deep learning LSTM networks with traditional machine learning applied to the risk of falls. Hu et al [ 59 ] first used cross-validation, where algorithms were trained randomly, and then used the gradient boosting machine algorithm to classify participants as high or low risk. Ye et al [ 60 ] and Hsu et al [ 58 ] both used the extreme gradient boosting (XGBoost) algorithm based on machine learning to create their predictive model. In the same year, Althobaiti et al [ 61 ] trained machine learning models for their research.

In 2021, Lockhart et al [ 57 ] started using 3 deep learning techniques simultaneously with the same goal as before: to create a predictive model for the risk of falls. Specifically, they used the RF, RF with feature engineering, and RF with feature engineering and linear and nonlinear variables. Noh et al [ 55 ], again in the same year, used the XGBoost algorithm, while Roshdibenam et al [ 71 ], on the other hand, used the CNN algorithm for each location of the wearable sensors used in their research. Various machine learning techniques were used for classifying the risk of falls and for balance loss events in the research by Hauth et al [ 56 ]. Dubois et al [ 72 ] used the following algorithms: decision tree, adaptive boosting, neural net, naïve Bayes, k-nearest neighbors, linear support vector machine, radial basis function support vector machine, RF, and quadratic discriminant analysis. Hauth et al [ 56 ], on the other hand, used regularized logistic regression and bidirectional LSTM networks. In the research conducted by Greene et al [ 70 ], AI was used, but the specific procedure that they followed is not described.

Tang et al [ 69 ] published their research with innovation up to that point. In their study, they used a smart gait analyzer with the help of deep learning techniques to assess the diagnostic accuracy of fall risk through vision. Months later, in August 2022, Ladios-Martin et al [ 67 ] published their research, in which they compared 2 deep learning models to achieve the best results in terms of specificity and sensitivity in detecting fall risk. The first model used the Bayesian Point Machine algorithm with a fall prevention variable, and the second one did not use the variable. They obtained better results when using that variable, a mitigating factor defined as a set of care interventions carried out by professionals to prevent the patient from experiencing a fall during hospitalization. Particularly controversial, as its exclusion could obscure the model’s performance. Eichler et al [ 68 ], on the other hand, used machine learning–based classifier training and later tested the performance of RFs in score predictions.

Finally, in January 2023, Maray et al [ 66 ] published their research, linking the previously mentioned terms (AI and fall risk) with 3 wearable devices that are commonly used today. They collected data through these devices and applied transfer learning to generalize the model across heterogeneous devices.

The results of the 22 articles provided promising data, and all of them agreed on the feasibility of applying various AI techniques as a method for predicting and classifying the risk of falls. Specifically, the accuracy values obtained in the studies exceed 70%. Noh et al [ 55 ] achieved the “lowest” accuracy among the studies conducted, with a 70% accuracy rate. Ribeiro et al [ 52 ] obtained an accuracy of 92.7% when using CNN to differentiate between normal gait and fall events. Hsu et al [ 58 ] further demonstrated that the XGBoost model is more sensitive than the Morse Fall Scale. Similarly, in their comparative study, Nait Aicha et al [ 51 ] also showed that a predictive model created from accelerometer data with AI is comparable to conventional models for assessing the risk of falls. More specifically, Dubois et al [ 54 ] concluded that using 1 gait-related parameter (excluding velocity) in combination with another parameter related to seated position allowed for the correct classification of individuals according to their risk of falls.

Principal Findings

The aim of this research was to analyze the scientific evidence regarding the applications of AI in the analysis of data related to postural control and the risk of falls. On the basis of the analysis of results, it can be asserted that the following risk factors were identified in the analyzed studies: age [ 65 ], daily habits [ 65 ], clinical diagnoses [ 65 ], environmental and hygiene factors [ 65 ], sex [ 64 ], stride length [ 55 , 72 ], gait speed [ 55 ], and posture [ 55 ]. This aligns with other research that also identifies sex [ 73 , 74 ], age [ 73 ], and gait speed [ 75 ].

On the other hand, the “fear of falling” has been identified in various studies as a risk factor and a predictor of falls [ 73 , 76 ], but it was not identified in any of the studies included in this review.

As for the characteristics of the analyzed samples, only 9.1% (2/22) of the articles used a sample composed exclusively of women [ 53 , 59 ], and no article used a sample composed exclusively of men. This fact is incongruent with reality, as women have a longer life expectancy than men, and therefore, the number of women aged >65 years is greater than the number of men of the same age [ 77 ]. Furthermore, women experience more falls than men [ 78 ]. The connection between menopause and its consequences, including osteopenia, suggests a higher risk of falls among older women than among men of the same age [ 79 , 80 ].

Within the realm of analysis tools, the most frequently used devices to analyze participants were accelerometers [ 51 - 57 , 59 , 61 - 63 , 66 , 70 - 72 ]. However, only 36.4% (8/22) of the studies provided all the information regarding the characteristics of these devices [ 51 , 53 , 59 , 61 , 63 , 66 , 70 , 72 ]. On the other hand, 18.2% (4/22) of the studies used the term “inertial measurement unit” as the sole description of the devices used [ 55 - 57 , 71 ].

The fact that most of the analyzed procedures involved the use of inertial sensors reflects the current widespread use of these devices for postural control analysis. These sensors, in general (and triaxial accelerometers in particular), have demonstrated great diagnostic capacity for balance [ 81 ]. In addition, they exhibit good sensitivity and reliability, combined with their portability and low economic cost [ 82 ]. Another advantage of triaxial accelerometers is their versatility in both adult and pediatric populations [ 83 - 86 ], although the studies included in this review did not address the pediatric population.

The remaining studies extracted data from cameras [ 68 , 69 ], medical records [ 58 , 60 , 65 , 67 ], and other functional and clinical tests [ 59 , 64 , 70 ]. Regarding the AI techniques used, out of the 18.2% (4/22) of articles that used deep learning techniques [ 52 , 57 , 62 , 71 ], only 4.5% (1/22) did not provide a description of the sample characteristics [ 52 ]. In this case, the authors focused on the AI landscape, while the rest of the articles strike a balance between AI and health sciences.

Regarding the validity of the generated models, only 40.9% (9/22) of the articles assessed this characteristic [ 52 , 53 , 55 , 61 - 64 , 68 , 69 ]. The authors of these 9 (N=22, 40.9%) articles evaluated the validity of the models through accuracy. All the results obtained reflected accuracies exceeding 70%, with Ribeiro et al [ 52 ] achieving a notable accuracy of 92.7% and 100%. Specifically, they obtained a 92.7% accuracy through the CNN model for distinguishing normal gait, the prefall condition, and the falling situation, considering the step before the fall, and 100% when not considering it [ 52 ].

The positive results of sensitivity and specificity can only be compared between the studies of Qiu et al [ 53 ] and Gillain et al [ 64 ], as they were the only ones to take them into account, and in both investigations, they were very high. Similarly, in the case of the F 1 -score, only Althobaiti et al [ 61 ] examined this validity measure. This measure is the result of combining precision and recall into a single figure, and the outcome obtained by these researchers was promising.

Despite these differences, the 22 studies obtained promising results in the health care field [ 51 - 72 ]. Specifically, their outcomes highlight the potential of AI integration into clinical settings. However, further research is necessary to explore how health care professionals can effectively use these predictive models. Consequently, future research should focus on studying the application and integration of the already-developed models. In this context, fall prevention plans could be implemented for the target populations identified by the predictive models. This approach would allow for a retrospective analysis to determine if the combination of predictive models with prevention programs effectively reduces the prevalence of falls in the population.

Limitations

Regarding limitations, the articles showed significant variation in the sample sizes selected. Moreover, even in the study with the largest sample size (with 265,225 participants [ 60 ]), the amount of data analyzed was relatively small. In addition, several of the databases used were not generated specifically for the published research but rather derived from existing medical records [ 58 , 60 , 65 , 67 ]. This could explain the significant variability in the variables analyzed across different studies.

Despite the limitations, this research has strengths, such as being the first systematic review on the use of AI as a tool to analyze postural control and the risk of falls. Furthermore, a total of 6 databases were used for the literature search, and a comprehensive article selection process was carried out by 3 researchers. Finally, only cross-sectional observational studies were selected, and they shared the same objective.

Conclusions

The use of AI in the analysis of data related to postural control and the risk of falls proves to be a valuable tool for creating predictive models of fall risk. It has been identified that most AI studies analyze accelerometer data from sensors, with triaxial accelerometers being the most frequently used.

For future research, it would be beneficial to provide more detailed descriptions of the measurement procedures and the AI techniques used. In addition, exploring larger databases could lead to the development of more robust models.

Conflicts of Interest

None declared.

Quality scores of reviewed studies (Critical Review Form for Quantitative Studies tool results).

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

• Step safely: strategies for preventing and managing falls across the life-course. World Health Organization. 2021. URL: https://www.who.int/publications/i/item/978924002191-4 [accessed 2024-04-02]
• Keall MD, Pierse N, Howden-Chapman P, Guria J, Cunningham CW, Baker MG. Cost-benefit analysis of fall injuries prevented by a programme of home modifications: a cluster randomised controlled trial. Inj Prev. Feb 2017;23(1):22-26. [ CrossRef ] [ Medline ]
• Almada M, Brochado P, Portela D, Midão L, Costa E. Prevalence of falls and associated factors among community-dwelling older adults: a cross-sectional study. J Frailty Aging. 2021;10(1):10-16. [ CrossRef ] [ Medline ]
• Menéndez-González L, Izaguirre-Riesgo A, Tranche-Iparraguirre S, Montero-Rodríguez Á, Orts-Cortés MI. [Prevalence and associated factors of frailty in adults over 70 years in the community]. Aten Primaria. Dec 2021;53(10):102128. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Guirguis-Blake JM, Michael YL, Perdue LA, Coppola EL, Beil TL. Interventions to prevent falls in older adults: updated evidence report and systematic review for the US preventive services task force. JAMA. Apr 24, 2018;319(16):1705-1716. [ CrossRef ] [ Medline ]
• Pereira CB, Kanashiro AM. Falls in older adults: a practical approach. Arq Neuropsiquiatr. May 2022;80(5 Suppl 1):313-323. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Byun M, Kim J, Kim M. Physical and psychological factors affecting falls in older patients with arthritis. Int J Environ Res Public Health. Feb 09, 2020;17(3):1098. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Goh HT, Nadarajah M, Hamzah NB, Varadan P, Tan MP. Falls and fear of falling after stroke: a case-control study. PM R. Dec 04, 2016;8(12):1173-1180. [ CrossRef ] [ Medline ]
• Alanazi FK, Lapkin S, Molloy L, Sim J. The impact of safety culture, quality of care, missed care and nurse staffing on patient falls: a multisource association study. J Clin Nurs. Oct 12, 2023;32(19-20):7260-7272. [ CrossRef ] [ Medline ]
• Hossain A, Lall R, Ji C, Bruce J, Underwood M, Lamb SE. Comparison of different statistical models for the analysis of fracture events: findings from the Prevention of Falls Injury Trial (PreFIT). BMC Med Res Methodol. Oct 02, 2023;23(1):216. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Williams CT, Whyman J, Loewenthal J, Chahal K. Managing geriatric patients with falls and fractures. Orthop Clin North Am. Jul 2023;54(3S):e1-12. [ CrossRef ] [ Medline ]
• Gadhvi C, Bean D, Rice D. A systematic review of fear of falling and related constructs after hip fracture: prevalence, measurement, associations with physical function, and interventions. BMC Geriatr. Jun 23, 2023;23(1):385. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lohman MC, Fallahi A, Mishio Bawa E, Wei J, Merchant AT. Social mediators of the association between depression and falls among older adults. J Aging Health. Aug 12, 2023;35(7-8):593-603. [ CrossRef ] [ Medline ]
• Smith AD, Silva AO, Rodrigues RA, Moreira MA, Nogueira JD, Tura LF. Assessment of risk of falls in elderly living at home. Rev Lat Am Enfermagem. Apr 06, 2017;25:e2754. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Koh V, Matchar DB, Chan A. Physical strength and mental health mediate the association between pain and falls (recurrent and/or injurious) among community-dwelling older adults in Singapore. Arch Gerontol Geriatr. Sep 2023;112:105015. [ CrossRef ] [ Medline ]
• Soh SE, Morgan PE, Hopmans R, Barker AL, Ackerman IN. The feasibility and acceptability of a falls prevention e-learning program for physiotherapists. Physiother Theory Pract. Mar 18, 2023;39(3):631-640. [ CrossRef ] [ Medline ]
• Morat T, Snyders M, Kroeber P, De Luca A, Squeri V, Hochheim M, et al. Evaluation of a novel technology-supported fall prevention intervention - study protocol of a multi-centre randomised controlled trial in older adults at increased risk of falls. BMC Geriatr. Feb 18, 2023;23(1):103. [ FREE Full text ] [ CrossRef ] [ Medline ]
• You T, Koren Y, Butts WJ, Moraes CA, Yeh GY, Wayne PM, et al. Pilot studies of recruitment and feasibility of remote Tai Chi in racially diverse older adults with multisite pain. Contemp Clin Trials. May 2023;128:107164. [ CrossRef ] [ Medline ]
• Aldana-Benítez D, Caicedo-Pareja MJ, Sánchez DP, Ordoñez-Mora LT. Dance as a neurorehabilitation strategy: a systematic review. J Bodyw Mov Ther. Jul 2023;35:348-363. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jawad A, Baattaiah BA, Alharbi MD, Chevidikunnan MF, Khan F. Factors contributing to falls in people with multiple sclerosis: the exploration of the moderation and mediation effects. Mult Scler Relat Disord. Aug 2023;76:104838. [ CrossRef ] [ Medline ]
• Warren C, Rizo E, Decker E, Hasse A. A comprehensive analysis of risk factors associated with inpatient falls. J Patient Saf. Oct 01, 2023;19(6):396-402. [ CrossRef ] [ Medline ]
• Gross M, Roigk P, Schoene D, Ritter Y, Pauly P, Becker C, et al. Bundesinitiative Sturzprävention. [Update of the recommendations of the federal falls prevention initiative-identification and prevention of the risk of falling in older people living at home]. Z Gerontol Geriatr. Oct 11, 2023;56(6):448-457. [ CrossRef ] [ Medline ]
• Li S, Li Y, Liang Q, Yang WJ, Zi R, Wu X, et al. Effects of tele-exercise rehabilitation intervention on women at high risk of osteoporotic fractures: study protocol for a randomised controlled trial. BMJ Open. Nov 07, 2022;12(11):e064328. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. Dec 2017;2(4):230-243. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ye Y, Wu X, Wang H, Ye H, Zhao K, Yao S, et al. Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer. Ann Med. Dec 2023;55(1):2215541. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Fukui T, et al. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN Open. Apr 30, 2024;4(1):e267. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Yokote A, Umeno J, Kawasaki K, Fujioka S, Fuyuno Y, Matsuno Y, et al. Small bowel capsule endoscopy examination and open access database with artificial intelligence: the SEE-artificial intelligence project. DEN Open. Apr 22, 2024;4(1):e258. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ramalingam M, Jaisankar A, Cheng L, Krishnan S, Lan L, Hassan A, et al. Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses. Discov Nano. Dec 01, 2023;18(1):58. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Yerukala Sathipati S, Tsai MJ, Shukla SK, Ho SY. Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction. HGG Adv. Jul 13, 2023;4(3):100190. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Liu J, Dan W, Liu X, Zhong X, Chen C, He Q, et al. Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine. Health Inf Sci Syst. Dec 06, 2023;11(1):21. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Carou-Senra P, Ong JJ, Castro BM, Seoane-Viaño I, Rodríguez-Pombo L, Cabalar P, et al. Predicting pharmaceutical inkjet printing outcomes using machine learning. Int J Pharm X. Dec 2023;5:100181. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Li X, Zhu Y, Zhao W, Shi R, Wang Z, Pan H, et al. Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease. Ren Fail. Dec 2023;45(1):2212790. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bonnin M, Müller-Fouarge F, Estienne T, Bekadar S, Pouchy C, Ait Si Selmi T. Artificial intelligence radiographic analysis tool for total knee arthroplasty. J Arthroplasty. Jul 2023;38(7 Suppl 2):S199-207.e2. [ CrossRef ] [ Medline ]
• Kao DP. Intelligent artificial intelligence: present considerations and future implications of machine learning applied to electrocardiogram interpretation. Circ Cardiovasc Qual Outcomes. Sep 2019;12(9):e006021. [ FREE Full text ] [ CrossRef ] [ Medline ]
• van der Stigchel B, van den Bosch K, van Diggelen J, Haselager P. Intelligent decision support in medical triage: are people robust to biased advice? J Public Health (Oxf). Aug 28, 2023;45(3):689-696. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jakhar D, Kaur I. Artificial intelligence, machine learning and deep learning: definitions and differences. Clin Exp Dermatol. Jan 09, 2020;45(1):131-132. [ CrossRef ] [ Medline ]
• Ghosh M, Thirugnanam A. Introduction to artificial intelligence. In: Srinivasa KG, Siddesh GM, Sekhar SR, editors. Artificial Intelligence for Information Management: A Healthcare Perspective. Cham, Switzerland. Springer; 2021;88-44.
• Taulli T. Artificial Intelligence Basics: A Non-Technical Introduction. Berkeley, CA. Apress Berkeley; 2019.
• Patil S, Joda T, Soffe B, Awan KH, Fageeh HN, Tovani-Palone MR, et al. Efficacy of artificial intelligence in the detection of periodontal bone loss and classification of periodontal diseases: a systematic review. J Am Dent Assoc. Sep 2023;154(9):795-804.e1. [ CrossRef ] [ Medline ]
• Quek LJ, Heikkonen MR, Lau Y. Use of artificial intelligence techniques for detection of mild cognitive impairment: a systematic scoping review. J Clin Nurs. Sep 10, 2023;32(17-18):5752-5762. [ CrossRef ] [ Medline ]
• Tan D, Mohd Nasir NF, Abdul Manan H, Yahya N. Prediction of toxicity outcomes following radiotherapy using deep learning-based models: a systematic review. Cancer Radiother. Sep 2023;27(5):398-406. [ CrossRef ] [ Medline ]
• Rabilloud N, Allaume P, Acosta O, De Crevoisier R, Bourgade R, Loussouarn D, et al. Deep learning methodologies applied to digital pathology in prostate cancer: a systematic review. Diagnostics (Basel). Aug 14, 2023;13(16):2676. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Li K, Yao S, Zhang Z, Cao B, Wilson C, Kalos D, et al. Efficient gradient boosting for prognostic biomarker discovery. Bioinformatics. Mar 04, 2022;38(6):1631-1638. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chen T, Chen Y, Li H, Gao T, Tu H, Li S. Driver intent-based intersection autonomous driving collision avoidance reinforcement learning algorithm. Sensors (Basel). Dec 16, 2022;22(24):9943. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Huynh QT, Nguyen PH, Le HX, Ngo LT, Trinh NT, Tran MT, et al. Automatic acne object detection and acne severity grading using smartphone images and artificial intelligence. Diagnostics (Basel). Aug 03, 2022;12(8):1879. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Brooke BS, Schwartz TA, Pawlik TM. MOOSE reporting guidelines for meta-analyses of observational studies. JAMA Surg. Aug 01, 2021;156(8):787-788. [ CrossRef ] [ Medline ]
• Scholten RJ, Clarke M, Hetherington J. The Cochrane collaboration. Eur J Clin Nutr. Aug 28, 2005;59 Suppl 1(S1):S147-S196. [ CrossRef ] [ Medline ]
• Warrens MJ. Kappa coefficients for dichotomous-nominal classifications. Adv Data Anal Classif. Apr 07, 2020;15(1):193-208. [ CrossRef ]
• Law M, Stewart D, Letts L, Pollock N, Bosch J. Guidelines for critical review of qualitative studies. McMaster University Occupational Therapy Evidence-Based Practice Research Group. URL: https://www.canchild.ca/system/tenon/assets/attachments/000/000/360/original/qualguide.pdf [accessed 2024-04-05]
• Higgins JP, Morgan RL, Rooney AA, Taylor KW, Thayer KA, Silva RA, et al. Risk of bias in non-randomized studies - of exposure (ROBINS-E). ROBINS-E tool. URL: https://www.riskofbias.info/welcome/robins-e-tool [accessed 2024-04-02]
• Nait Aicha A, Englebienne G, van Schooten KS, Pijnappels M, Kröse B. Deep learning to predict falls in older adults based on daily-life trunk accelerometry. Sensors (Basel). May 22, 2018;18(5):1654. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ribeiro NF, André J, Costa L, Santos CP. Development of a strategy to predict and detect falls using wearable sensors. J Med Syst. Apr 04, 2019;43(5):134. [ CrossRef ] [ Medline ]
• Qiu H, Rehman RZ, Yu X, Xiong S. Application of wearable inertial sensors and a new test battery for distinguishing retrospective fallers from non-fallers among community-dwelling older people. Sci Rep. Nov 05, 2018;8(1):16349. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Dubois A, Bihl T, Bresciani JP. Automatic measurement of fall risk indicators in timed up and go test. Inform Health Soc Care. Sep 13, 2019;44(3):237-245. [ CrossRef ] [ Medline ]
• Noh B, Youm C, Goh E, Lee M, Park H, Jeon H, et al. XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes. Sci Rep. Jun 09, 2021;11(1):12183. [ CrossRef ] [ Medline ]
• Hauth J, Jabri S, Kamran F, Feleke EW, Nigusie K, Ojeda LV, et al. Automated loss-of-balance event identification in older adults at risk of falls during real-world walking using wearable inertial measurement units. Sensors (Basel). Jul 07, 2021;21(14):4661. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lockhart TE, Soangra R, Yoon H, Wu T, Frames CW, Weaver R. Prediction of fall risk among community-dwelling older adults using a wearable system. Sci Rep. 2021;11(1):20976. [ CrossRef ]
• Hsu YC, Weng HH, Kuo CY, Chu TP, Tsai YH. Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study. Sci Rep. Oct 08, 2020;10(1):16777. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hu Y, Bishnoi A, Kaur R, Sowers R, Hernandez ME. Exploration of machine learning to identify community dwelling older adults with balance dysfunction using short duration accelerometer data. In: Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. 2020. Presented at: EMBC '20; July 20-24, 2020;812-815; Montreal, QC. URL: https://ieeexplore.ieee.org/document/9175871 [ CrossRef ]
• Ye C, Li J, Hao S, Liu M, Jin H, Zheng L, et al. Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. Int J Med Inform. May 2020;137:104105. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Althobaiti T, Katsigiannis S, Ramzan N. Triaxial accelerometer-based falls and activities of daily life detection using machine learning. Sensors (Basel). Jul 06, 2020;20(13):3777. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tunca C, Salur G, Ersoy C. Deep learning for fall risk assessment with inertial sensors: utilizing domain knowledge in spatio-temporal gait parameters. IEEE J Biomed Health Inform. Jul 2020;24(7):1994-2005. [ CrossRef ]
• Kim K, Yun G, Park SK, Kim DH. Fall detection for the elderly based on 3-axis accelerometer and depth sensor fusion with random forest classifier. In: Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2019. Presented at: EMBC '19; July 23-27, 2019;4611-4614; Berlin, Germany. URL: https://ieeexplore.ieee.org/document/8856698 [ CrossRef ]
• Gillain S, Boutaayamou M, Schwartz C, Brüls O, Bruyère O, Croisier JL, et al. Using supervised learning machine algorithm to identify future fallers based on gait patterns: a two-year longitudinal study. Exp Gerontol. Nov 2019;127:110730. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lo Y, Lynch SF, Urbanowicz RJ, Olson RS, Ritter AZ, Whitehouse CR, et al. Using machine learning on home health care assessments to predict fall risk. Stud Health Technol Inform. Aug 21, 2019;264:684-688. [ CrossRef ] [ Medline ]
• Maray N, Ngu AH, Ni J, Debnath M, Wang L. Transfer learning on small datasets for improved fall detection. Sensors (Basel). Jan 18, 2023;23(3):1105. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ladios-Martin M, Cabañero-Martínez MJ, Fernández-de-Maya J, Ballesta-López FJ, Belso-Garzas A, Zamora-Aznar FM, et al. Development of a predictive inpatient falls risk model using machine learning. J Nurs Manag. Nov 30, 2022;30(8):3777-3786. [ CrossRef ] [ Medline ]
• Eichler N, Raz S, Toledano-Shubi A, Livne D, Shimshoni I, Hel-Or H. Automatic and efficient fall risk assessment based on machine learning. Sensors (Basel). Feb 17, 2022;22(4):1557. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tang YM, Wang YH, Feng XY, Zou QS, Wang Q, Ding J, et al. Diagnostic value of a vision-based intelligent gait analyzer in screening for gait abnormalities. Gait Posture. Jan 2022;91:205-211. [ CrossRef ] [ Medline ]
• Greene BR, McManus K, Ader LG, Caulfield B. Unsupervised assessment of balance and falls risk using a smartphone and machine learning. Sensors (Basel). Jul 13, 2021;21(14):4770. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Roshdibenam V, Jogerst GJ, Butler NR, Baek S. Machine learning prediction of fall risk in older adults using timed up and go test kinematics. Sensors (Basel). May 17, 2021;21(10):3481. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Dubois A, Bihl T, Bresciani JP. Identifying fall risk predictors by monitoring daily activities at home using a depth sensor coupled to machine learning algorithms. Sensors (Basel). Mar 11, 2021;21(6):1957. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Vo MT, Thonglor R, Moncatar TJ, Han TD, Tejativaddhana P, Nakamura K. Fear of falling and associated factors among older adults in Southeast Asia: a systematic review. Public Health. Sep 2023;222:215-228. [ CrossRef ] [ Medline ]
• Torun E, Az A, Akdemir T, Solakoğlu GA, Açiksari K, Güngörer B. Evaluation of the risk factors for falls in the geriatric population presenting to the emergency department. Ulus Travma Acil Cerrahi Derg. Aug 2023;29(8):897-903. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Son NK, Ryu YU, Jeong HW, Jang YH, Kim HD. Comparison of 2 different exercise approaches: Tai Chi versus Otago, in community-dwelling older women. J Geriatr Phys Ther. 2016;39(2):51-57. [ CrossRef ] [ Medline ]
• Sawa R, Doi T, Tsutsumimoto K, Nakakubo S, Kurita S, Kiuchi Y, et al. Overlapping status of frailty and fear of falling: an elevated risk of incident disability in community-dwelling older adults. Aging Clin Exp Res. Sep 11, 2023;35(9):1937-1944. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Calazans JA, Permanyer I. Levels, trends, and determinants of cause-of-death diversity in a global perspective: 1990-2019. BMC Public Health. Apr 05, 2023;23(1):650. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kakara R, Bergen G, Burns E, Stevens M. Nonfatal and fatal falls among adults aged ≥65 years - United States, 2020-2021. MMWR Morb Mortal Wkly Rep. Sep 01, 2023;72(35):938-943. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Dostan A, Dobson CA, Vanicek N. Relationship between stair ascent gait speed, bone density and gait characteristics of postmenopausal women. PLoS One. Mar 22, 2023;18(3):e0283333. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zheng Y, Wang X, Zhang ZK, Guo B, Dang L, He B, et al. Bushen Yijing Fang reduces fall risk in late postmenopausal women with osteopenia: a randomized double-blind and placebo-controlled trial. Sci Rep. Feb 14, 2019;9(1):2089. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Woelfle T, Bourguignon L, Lorscheider J, Kappos L, Naegelin Y, Jutzeler CR. Wearable sensor technologies to assess motor functions in people with multiple sclerosis: systematic scoping review and perspective. J Med Internet Res. Jul 27, 2023;25:e44428. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Abdollah V, Dief TN, Ralston J, Ho C, Rouhani H. Investigating the validity of a single tri-axial accelerometer mounted on the head for monitoring the activities of daily living and the timed-up and go test. Gait Posture. Oct 2021;90:137-140. [ CrossRef ] [ Medline ]
• Mielke GI, de Almeida Mendes M, Ekelund U, Rowlands AV, Reichert FF, Crochemore-Silva I. Absolute intensity thresholds for tri-axial wrist and waist accelerometer-measured movement behaviors in adults. Scand J Med Sci Sports. Sep 12, 2023;33(9):1752-1764. [ CrossRef ] [ Medline ]
• Löppönen A, Delecluse C, Suorsa K, Karavirta L, Leskinen T, Meulemans L, et al. Association of sit-to-stand capacity and free-living performance using Thigh-Worn accelerometers among 60- to 90-yr-old adults. Med Sci Sports Exerc. Sep 01, 2023;55(9):1525-1532. [ FREE Full text ] [ CrossRef ] [ Medline ]
• García-Soidán JL, Leirós-Rodríguez R, Romo-Pérez V, García-Liñeira J. Accelerometric assessment of postural balance in children: a systematic review. Diagnostics (Basel). Dec 22, 2020;11(1):8. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Leirós-Rodríguez R, García-Soidán JL, Romo-Pérez V. Analyzing the use of accelerometers as a method of early diagnosis of alterations in balance in elderly people: a systematic review. Sensors (Basel). Sep 09, 2019;19(18):3883. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 28.11.23; peer-reviewed by E Andrade, M Behzadifar, A Suárez; comments to author 09.01.24; revised version received 30.01.24; accepted 13.02.24; published 29.04.24.

©Ana González-Castro, Raquel Leirós-Rodríguez, Camino Prada-García, José Alberto Benítez-Andrades. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.04.2024.

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