Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Jun 18, 2020
Date Accepted: Oct 28, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Effectiveness of rigorously evaluated behavior change interventions delivered via mobile application: Systematic review and meta-analysis
ABSTRACT
Background:
Background:
Mobile applications can deliver more efficiently behavior change interventions that are traditionally resource-intensive and difficult to scale.
Objective:
Objectives: To summarize the evidence of app-driven behavior change interventions on health outcomes and identify whether certain features contribute to improved outcomes.
Methods:
Methods:
We conducted a literature search in seven databases (MEDLINE; Scopus; PsycINFO, CINAHL, Global Index Medicus; Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews). Five reviewers independently screened and extracted sample characteristics. We used a random-effects model to calculate pooled effect size estimates for meta-analysis. Sensitivity analysis was conducted based on follow-up time, stand-alone app interventions, level of personalization, and pilot studies. Logistic regression was used to examine the structure of app features.
Results:
Results:
From the database searches, 8230 records were initially identified. Of these, 172 met inclusion criteria. Studies were predominantly conducted in high-income countries (164, 94.3%). The majority had follow-up periods of six months or less (143, 83.1%). Over half of the interventions were delivered by a standalone app (106, 61.6%). Static/one size fits all (97,56. 4.0%) was the most common level of personalization. Intervention frequency was mostly at least daily (123, 71.5%). A total of 156 studies involving 21,422 participants reported continuous health outcome data. The use of a behavior change app (either as a stand-alone or as part of a larger intervention) confers a slight/weak advantage over standard care in health interventions [standardized mean difference = 0.38 (95% CI: 0.31-0.45), I2 = 80%] though heterogeneity is high.
Conclusions:
Conclusion: Rigorous evaluation of behavior-change related intervention apps is increasing substantially. The evidence that behavior change apps improve health outcomes is weak, which may reflect the need for improved methodological and evaluative approaches to assessment of healthcare improvement apps. Clinical Trial: PROSPERO, CRD42018106868
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