1. JAMA Pediatrics Journal Club Slides: Mobile Health Interventions for Improving Health Outcomes in Youth
Fedele DA, Cushing CC, Fritz A, Amaro CM, Ortega A. Mobile health interventions for improving health outcomes in youth: a meta-analysis. JAMA Pediatr. Published online March 20, doi: /jamapediatrics

2. Introduction Importance
Mobile telephone use is ubiquitous, especially among youth.
Technological advances have given rise to a proliferation in mobile health (mHealth) interventions in which mobile devices target a range of health promotion and disease management foci.
Critical steps toward facilitating the maturation of the mHealth ecosystem:
Increase our understanding of what existing mHealth interventions are most effective.
Elucidate ways that the interventions can be improved.
Objective
To determine the effectiveness of mobile health interventions for improving health outcomes in youth 18 years or younger.

3. Methods Study Design Meta-analysis. Data Sources
Studies published through November 30, 2016, in PubMed, CINAHL, ERIC, and PsychINFO. Search terms: telemedicine, eHealth, mobile health, mHealth, app, and mobile application.
Backward and forward literature searches were conducted on articles meeting study inclusion criteria.
Study Selection
Search results limited to infants, children, adolescents, or young adults when possible.
Inclusion criteria: Quantitative methods used to evaluate a mobile intervention app in a primary or secondary capacity to promote or modify health behavior in youth 18 years or younger.
Exclusion criteria: Unpublished dissertation or thesis, mean age of participants older than 18 years, study did not assess a health behavior and disease outcome, or article did not include sufficient statistics.

4. Methods
PRISMA Flow Diagram

5. Methods Outcomes Change in health behavior or disease control.
8 moderators assessed:
Intervention length
Whether caregivers were active recipients of the mHealth intervention
Text message–only interventions
Use of a theoretical framework to guide mHealth intervention components
Type of outcome (ie, behaviors occurring once vs were repeated)
Adolescent participants
Stakeholders involved in intervention development
Use of intent-to-treat analyses
Cochrane Collaboration risk of bias tool used to categorize risk of bias.
Limitations
Small number of studies makes it possible that some important moderators were not detected.

6. Results Participant and Study Characteristics
The final sample of 37 studies included 29 822 participants.
The sex of sample participants was relatively balanced (female, 53.2%).
Demographic information reporting was suboptimal across study sample:
Fifteen studies (40.5%) did not report mean participant age.
Fourteen studies (37.8%) did not report race/ethnicity.
Risk of Bias
Large proportion of studies rated as having a high risk of bias regarding the blinding of participants and personnel (n = 21) and attrition (n = 16).
Risk of selection bias was also relatively high (n = 14); however, 11 studies did not provide information to determine risk.
Most studies were coded as having a low risk of bias for selective reporting.

7. Results Aggregate Effect Size
Aggregate effect size of mHealth interventions compared with controls was small, but significant (n = 37; Cohen d = 0.22; 95% CI, ).
Significant heterogeneity was observed in effect sizes (Q = ; P < .001).
Systematic variability in study effect size was substantial (I2 = 66.17).
Significant Moderators
Studies that involved caregivers in the intervention produced larger effect sizes (n = 16; Cohen d = 0.28; 95% CI, ) compared with those that did not (n = 21; Cohen d = 0.13; 95% CI, ) (P = .05).
Nonsignificant Moderators
Use of theory to guide intervention development, use of text messaging, preadolescent vs adolescent participants, recurrence of the outcome, length of study, use of intent-to-treat analysis, and involvement of stakeholders in intervention development

8. Results
Summary of Point Estimates and Heterogeneity of Effect Sizes for Included mHealth Interventions

9. Comment
The primary objective of the present study was to determine the effectiveness of mHealth interventions in improving health-related outcomes in youth 18 years or younger.
Results suggest:
mHealth interventions can be effective in eliciting meaningful improvements in pediatric health behavior.
The aggregate effect size of mHealth interventions (Cohen d = .22) aligns well with the findings from previous meta-analyses of adherence promotion and eHealth behavior change interventions.
mHealth interventions may be a viable modality for health care professionals to effect health-related changes in pediatric populations.
mHealth interventions that include caregivers may have an advantage in producing positive health outcome changes in youth.

10. Comment
Risk of Bias
The use of randomized controlled trial designs (n = 24) and intent-to-treat analyses (n = 19) was relatively modest.
The use of less-stringent research designs (eg, pre-post) and the frequency of bias in the extant pediatric mHealth literature are limitations and introduce a degree of uncertainty regarding the efficacy of mHealth interventions and the possibility that the effect size will change as more studies are conducted.
Next Steps
Explicit incorporation and testing of the effect of competing and complementary theoretical mechanisms of behavior change.
Determine the optimal positioning of mHealth interventions within a larger ecological context.
Increase confidence in the robustness of the findings and reduce risk of bias by incorporating full extent of methodological rigor into mHealth studies.

11. Conflict of Interest Disclosures
Contact Information
If you have questions, please contact the corresponding author:
David Fedele, Department of Clinical & Health Psychology, University of Florida, 101 S Newell Dr, PO Box , Gainesville, FL
Funding/Support
None.
Conflict of Interest Disclosures
None disclosed.