Dataset Information

Effectiveness of Mobile Apps to Promote Health and Manage Disease: Systematic Review and Meta-analysis of Randomized Controlled Trials.

ABSTRACT:

Background

Interventions aimed at modifying behavior for promoting health and disease management are traditionally resource intensive and difficult to scale. Mobile health apps are being used for these purposes; however, their effects on health outcomes have been mixed.

Objective

This study aims to summarize the evidence of rigorously evaluated health-related apps on health outcomes and explore the effects of features present in studies that reported a statistically significant difference in health outcomes.

Methods

A literature search was conducted in 7 databases (MEDLINE, Scopus, PsycINFO, CINAHL, Global Index Medicus, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews). A total of 5 reviewers independently screened and extracted the study characteristics. We used a random-effects model to calculate the 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

From the database searches, 8230 records were initially identified. Of these, 172 met the inclusion criteria. Studies were predominantly conducted in high-income countries (164/172, 94.3%). The majority had follow-up periods of 6 months or less (143/172, 83.1%). Over half of the interventions were delivered by a stand-alone app (106/172, 61.6%). Static/one-size-fits-all (97/172, 56.4%) was the most common level of personalization. Intervention frequency was daily or more frequent for the majority of the studies (123/172, 71.5%). A total of 156 studies involving 21,422 participants reported continuous health outcome data. The use of an app to modify behavior (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%), although heterogeneity was high.

Conclusions

The evidence in the literature demonstrates a steady increase in the rigorous evaluation of apps aimed at modifying behavior to promote health and manage disease. Although the literature is growing, the evidence that apps can improve health outcomes is weak. This finding may reflect the need for improved methodological and evaluative approaches to the development and assessment of health care improvement apps.

Trial registration

PROSPERO International Prospective Register of Systematic Reviews CRD42018106868; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=106868.

SUBMITTER: Iribarren SJ

PROVIDER: S-EPMC7834932 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effectiveness of Mobile Apps to Promote Health and Manage Disease: Systematic Review and Meta-analysis of Randomized Controlled Trials.

Iribarren Sarah J SJ Akande Tokunbo O TO Kamp Kendra J KJ Barry Dwight D Kader Yazan G YG Suelzer Elizabeth E

JMIR mHealth and uHealth 20210111 1

<h4>Background</h4>Interventions aimed at modifying behavior for promoting health and disease management are traditionally resource intensive and difficult to scale. Mobile health apps are being used for these purposes; however, their effects on health outcomes have been mixed.<h4>Objective</h4>This study aims to summarize the evidence of rigorously evaluated health-related apps on health outcomes and explore the effects of features present in studies that reported a statistically significant di ...[more]

PMID: 33427672

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Dataset Information

Effectiveness of Mobile Apps to Promote Health and Manage Disease: Systematic Review and Meta-analysis of Randomized Controlled Trials.

Background

Objective

Methods

Results

Conclusions

Trial registration

Publications

Effectiveness of Mobile Apps to Promote Health and Manage Disease: Systematic Review and Meta-analysis of Randomized Controlled Trials.

Similar Datasets

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