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Meta-Analysis
. 2024 Apr 30:12:e51478.
doi: 10.2196/51478.

Effectiveness of mHealth App-Based Interventions for Increasing Physical Activity and Improving Physical Fitness in Children and Adolescents: Systematic Review and Meta-Analysis

Jun-Wei Wang  1   2 , Zhicheng Zhu  3 , Zhang Shuling  1 , Jia Fan  1 , Yu Jin  1 , Zhan-Le Gao  1 , Wan-Di Chen  4 , Xue Li #  1
Affiliations
Meta-Analysis

Effectiveness of mHealth App-Based Interventions for Increasing Physical Activity and Improving Physical Fitness in Children and Adolescents: Systematic Review and Meta-Analysis

Jun-Wei Wang et al. JMIR Mhealth Uhealth. .

Abstract

Background: The COVID-19 pandemic has significantly reduced physical activity (PA) levels and increased sedentary behavior (SB), which can lead to worsening physical fitness (PF). Children and adolescents may benefit from mobile health (mHealth) apps to increase PA and improve PF. However, the effectiveness of mHealth app-based interventions and potential moderators in this population are not yet fully understood.

Objective: This study aims to review and analyze the effectiveness of mHealth app-based interventions in promoting PA and improving PF and identify potential moderators of the efficacy of mHealth app-based interventions in children and adolescents.

Methods: We searched for randomized controlled trials (RCTs) published in the PubMed, Web of Science, EBSCO, and Cochrane Library databases until December 25, 2023, to conduct this meta-analysis. We included articles with intervention groups that investigated the effects of mHealth-based apps on PA and PF among children and adolescents. Due to high heterogeneity, a meta-analysis was conducted using a random effects model. The Cochrane Risk of Bias Assessment Tool was used to evaluate the risk of bias. Subgroup analysis and meta-regression analyses were performed to identify potential influences impacting effect sizes.

Results: We included 28 RCTs with a total of 5643 participants. In general, the risk of bias of included studies was low. Our findings showed that mHealth app-based interventions significantly increased total PA (TPA; standardized mean difference [SMD] 0.29, 95% CI 0.13-0.45; P<.001), reduced SB (SMD -0.97, 95% CI -1.67 to -0.28; P=.006) and BMI (weighted mean difference -0.31 kg/m2, 95% CI -0.60 to -0.01 kg/m2; P=.12), and improved muscle strength (SMD 1.97, 95% CI 0.09-3.86; P=.04) and agility (SMD -0.35, 95% CI -0.61 to -0.10; P=.006). However, mHealth app-based interventions insignificantly affected moderate to vigorous PA (MVPA; SMD 0.11, 95% CI -0.04 to 0.25; P<.001), waist circumference (weighted mean difference 0.38 cm, 95% CI -1.28 to 2.04 cm; P=.65), muscular power (SMD 0.01, 95% CI -0.08 to 0.10; P=.81), cardiorespiratory fitness (SMD -0.20, 95% CI -0.45 to 0.05; P=.11), muscular endurance (SMD 0.47, 95% CI -0.08 to 1.02; P=.10), and flexibility (SMD 0.09, 95% CI -0.23 to 0.41; P=.58). Subgroup analyses and meta-regression showed that intervention duration was associated with TPA and MVPA, and age and types of intervention was associated with BMI.

Conclusions: Our meta-analysis suggests that mHealth app-based interventions may yield small-to-large beneficial effects on TPA, SB, BMI, agility, and muscle strength in children and adolescents. Furthermore, age and intervention duration may correlate with the higher effectiveness of mHealth app-based interventions. However, due to the limited number and quality of included studies, the aforementioned conclusions require validation through additional high-quality research.

Trial registration: PROSPERO CRD42023426532; https://tinyurl.com/25jm4kmf.

Keywords: children and adolescents; mHealth apps; meta-analysis; mobile health; mobile phone; physical activity; physical fitness; systematic review.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Flowchart of the study selection.
Figure 2
Figure 2
Risk bias assessment of the included studies. (A) Risk of bias graph and (B) risk of bias summary.
Figure 3
Figure 3
Forest plot of the effect of mobile health app–based interventions on increasing total physical activity.
Figure 4
Figure 4
Forest plot of the effect of mobile health app–based interventions on decreasing sedentary behavior.
Figure 5
Figure 5
Forest plot of the effect of mobile health app–based interventions on increasing moderate to vigorous physical activity.
Figure 6
Figure 6
Forest plot of the effect of mobile health app–based interventions on BMI.
Figure 7
Figure 7
Forest plot of the effect of mobile health app–based interventions on waist circumference.
Figure 8
Figure 8
Forest plot of the effect of mobile health app–based interventions on cardiorespiratory fitness.
Figure 9
Figure 9
Forest plot of the effect of mobile health app–based interventions on muscular strength.
Figure 10
Figure 10
Forest plot of the effect of mobile health app–based interventions on muscular power.
Figure 11
Figure 11
Forest plot of the effect of mobile health app–based interventions on muscular endurance.
Figure 12
Figure 12
Forest plot of the effect of mobile health app–based interventions on agility.
Figure 13
Figure 13
Forest plot of the effect of mobile health app–based interventions on flexibility.
See this image and copyright information in PMC

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