Potential associations between behavior change techniques and engagement with mobile health apps: a systematic review
Open accessAbstract
Introduction: Lack of engagement is a common challenge for digital health interventions. To achieve their potential, it is necessary to understand how best to support users’ engagement with interventions and target health behaviors. The aim of this systematic review was to identify the behavioral theories and behavior change techniques being incorporated into mobile health apps and how they are associated with the different components of engagement. Methods: The review was structured using the PRISMA and PICOS frameworks and searched six databases in July 2022: PubMed, Embase, CINAHL, APA PsycArticles, ScienceDirect, and Web of Science. Risk of bias was evaluated using the Cochrane Collaboration Risk of Bias 2 and the Mixed Methods Appraisal Tools. Analysis: A descriptive analysis provided an overview of study and app characteristics and evidence for potential associations between Behavior Change Techniques (BCTs) and engagement was examined. Results: The final analysis included 28 studies. Six BCTs were repeatedly associated with user engagement: goal setting, self-monitoring of behavior, feedback on behavior, prompts/cues, rewards, and social support. There was insufficient data reported to examine associations with specific components of engagement, but the analysis indicated that the different components were being captured by various measures. Conclusion: This review provides further evidence supporting the use of common BCTs in mobile health apps. To enable developers to leverage BCTs and other app features to optimize engagement in specific contexts and individual characteristics, we need a better understanding of how BCTs are associated with different components of engagement. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022312596.
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- Authors
- Milne-Ives, M., Homer, S. R., Andrade, J., & Meinert, E.
- Affiliations
- University of Plymouth (GB)
- Imperial College London (GB)
- Corresponding author
- Meinert, Edward
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2023-09-18
- Volume
- 14
- Pages
- 1227443-1227443
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC10545861
- OpenAlex
- W4386847941
- Cited by
- 69
- References
- 78
- Citations by year
- 2026: 32 · 2025: 29 · 2024: 8
- FWCI
- 3.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PEARL (University of Plymouth) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- Imperial College London
- Newcastle University
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Keywords
- behaviour change techniques, mobile health apps, user engagement, digital health interventions, goal setting, self-monitoring, feedback, prompts and cues, reward, social support, behavioral theory
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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