Cited by 317Mobile Health and mHealth ApplicationsDigital Mental Health InterventionsMobile Learning in Education

Mobile Health Interventions for Improving Health Outcomes in Youth: A Meta-analysis

Open access
Fedele, David A., Cushing, Christopher C., Fritz, Alyssa, Amaro, Christina M., Ortega, Adrian·2017·JAMA Pediatrics

Abstract

Importance: Mobile health interventions are increasingly popular in pediatrics; however, it is unclear how effective these interventions are in changing health outcomes. Objective: To determine the effectiveness of mobile health interventions for improving health outcomes in youth 18 years or younger. Data Sources: Studies published through November 30, 2016, were collected through PubMed, Cumulative Index to Nursing and Allied Health Literature, Educational Resources Information Center, and PsychINFO. Backward and forward literature searches were conducted on articles meeting study inclusion criteria. Search terms included telemedicine, eHealth, mobile health, mHealth, app, and mobile application. Study Selection: Search results were limited to infants, children, adolescents, or young adults when possible. Studies were included if quantitative methods were used to evaluate an application of mobile intervention technology in a primary or secondary capacity to promote or modify health behavior in youth 18 years or younger. Studies were excluded if the article was an unpublished dissertation or thesis, the mean age of participants was older than 18 years, the study did not assess a health behavior and disease outcome, or the article did not include sufficient statistics. Inclusion and exclusion criteria were applied by 2 independent coders with 20% overlap. Of 9773 unique articles, 36 articles (containing 37 unique studies with a total of 29 822 participants) met the inclusion criteria. Data Extraction and Synthesis: Of 9773 unique articles, 36 articles (containing 37 unique studies) with a total of 29 822 participants met the inclusion criteria. Effect sizes were calculated from statistical tests that could be converted to standardized mean differences. All aggregate effect sizes and moderator variables were tested using random-effects models. Main Outcomes and Measures: Change in health behavior or disease control. Results: A total of 29 822 participants were included in the studies. In studies that reported sex, the total number of females was 11 226 (53.2%). Of those reporting age, the average was 11.35 years. The random effects aggregate effect size of mobile health interventions was significant (n = 37; Cohen d = 0.22; 95% CI, 0.14-0.29). The random effects model indicated that providing mobile health intervention to a caregiver increased the strength of the intervention effect. Studies that involved caregivers in the intervention produced effect sizes (n = 16; Cohen d = 0.28; 95% CI, 0.18-0.39) larger than those that did not include caregivers (n = 21; Cohen d = 0.13; 95% CI, 0.02-0.25). Other coded variables did not moderate study effect size. Conclusions and Relevance: Mobile health interventions appear to be a viable health behavior change intervention modality for youth. Given the ubiquity of mobile phones, mobile health interventions offer promise in improving public health.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1001/jamapediatrics.2017.0042

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. [2]Smith A. Chapter one: a portrait of smartphone ownership. PewResearchCenter. http://www.pewinternet.org/2015/04/01/chapter-one-a-portrait-of-smartphone-ownership/. Published April 1, 2015. Accessed September 10, 2016.
  2. [14]Lu F, Turner KMB. Reducing adolescent obesity with a mobile fitness application: study results of youth age 15 to 17. Paper presented at: 2013 Institute of Electrical and Electronics Engineers 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013); Lisbon, Portugal. [Google Scholar]
  3. [15]Higgins J, Green S. Cochrane Handbook for Systematic Reviews of Interventions. Chichester, England: Wiley-Blackwel; 2011. [Google Scholar]
  4. [32]Hashemian TS, Kritz-Silverstein D, Baker R. Text2Floss: the feasibility and acceptability of a text messaging intervention to improve oral health behavior and knowledge. J Public Health Dent. 2015;75(1):34-41. [DOI] [PubMed] [Google Scholar]

Cited by

Sorted by citations

Authors
Fedele, D. A., Cushing, C. C., Fritz, A., Amaro, C. M., & Ortega, A.
Affiliations
  • University of Florida Health (US)
  • University of Kansas (US)
Corresponding author
Fedele, David A.
Venue
JAMA Pediatrics
Published
2017-05-01
Volume
171
Issue
5
Pages
461
ISSN
  • 1072-4710 (ISSN-L)
  • 2168-6203
OpenAlex
W2601896593
Cited by
316
References
56
Citations by year
2026: 19 · 2025: 28 · 2024: 24 · 2023: 29 · 2022: 48 · 2021: 58 · 2020: 38 · 2019: 31 · 2018: 34 · 2017: 6
FWCI
48.32
Type
journal article
Language
en
Open access
Locations
  • Archives of Pediatrics and Adolescent Medicine (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
Keywords
mobile health intervention, mobile health, youth health, health behavior change, disease control, adolescents, children, caregivers, telemedicine, eHealth apps, intervention effectiveness, effect size, child health promotion, disease prevention, health disparities, healthcare workers
MeSH terms
Adolescent, Child, Female, Health Promotion, Humans, Infant, Male, Health Behavior, Telemedicine, Young Adult

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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review