Evidence map›Paper›PMID 33427672›Full record

SynthesisJMIR mHealth and uHealth2021

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

Sarah J Iribarren, Tokunbo O Akande, Kendra J Kamp, Dwight Barry, Yazan G Kader, Elizabeth Suelzer

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
114citing papers in PubMed, 14 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

114 citing papers in PubMed, 14 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Pooled it
  11. Pooled it
  12. Pooled it
  13. Pooled it
  14. Pooled it
  15. Trial
  16. Trial
  17. Trial
  18. Trial
  19. Trial
  20. Trial

54 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sarah J IribarrenDepartment of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, WA, United States.ORCID 0000-0003-2980-0717
Tokunbo O Akande *Department of Pediatrics, Sanford Health, Bemidji, MN, United States.ORCID 0000-0003-3371-9217
Kendra J Kamp *Division of Gastroenterology, School of Medicine, University of Washington, Seattle, WA, United States.ORCID 0000-0002-7753-3564
Dwight BarryEnterprise Analytics, Seattle Children's Hospital, Seattle, WA, United States.ORCID 0000-0003-0938-6037
Yazan G KaderDepartment of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, WA, United States.ORCID 0000-0003-0082-7385
Elizabeth SuelzerMedical College of Wisconsin Libraries, Medical College of Wisconsin, Milwaukee, WI, United States.ORCID 0000-0002-1809-8080

Funding

UNIVERSITY OF WASHINGTON GI TRAINING GRANTT32DK007742 · NIDDK · UNIVERSITY OF WASHINGTON · PI GRADY, WILLIAM MALLORY · 1996 to 2022
$4.7M
TB-TST (TB treatment support tools): Refinement and evaluation of an interactive mobile app and direct adherence monitoring on TB treatment outcomesR01AI147129 · NIAID · INSTITUTO DE EFECTIVIDAD CLINICA Y SANIT · PI IRIBARREN, SARAH JO HANNAH, RUBINSTEIN, FERNANDO ADRIÁN · 2019 to 2023
$2.3M
Aging and Informatics Training ProgramT32NR014833 · NINR · UNIVERSITY OF WASHINGTON · PI THOMPSON, HILAIRE J · 2014 to 2018
$982k
Development and Evaluation of an Interactive Mobile Health Intervention to Support Patients with Active TuberculosisK23NR017210 · NINR · UNIVERSITY OF WASHINGTON · PI IRIBARREN, SARAH JO HANNAH · 2017 to 2019
$437k
NIAID NIH HHS R01 AI147129NIDDK NIH HHS T32 DK007742NINR NIH HHS K23 NR017210NINR NIH HHS T32 NR014833
6 · The paper itself

Abstract

backgroundInterventions 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.

objectiveThis 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.

methodsA 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.

resultsFrom 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.

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

Indexed as

Cell PhoneDisease ManagementMobile ApplicationsRandomized Controlled Trials as TopicAdolescentAgedChildHealth PromotionHumansPilot ProjectsQuality of Lifemobile appsmobile phonesystematic review

Identifiers

PMID33427672
PMCPMC7834932

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.