Background Obesity is a global health challenge and increases the risk of chronic diseases. Mobile health technologies (mHealth) have emerged as a personalized and multimodal intervention for weight management. This meta-analysis evaluated the effectiveness of mHealth-based lifestyle interventions on weight loss and BMI reduction in overweight and obese adults. Methods A total of 42 randomized controlled trials were identified and included from five major databases. Analyses were conducted using RevMan 5.4. Primary and secondary outcomes were changes in body weight and BMI, respectively. Results The meta-analysis showed that mHealth-based lifestyle interventions significantly reduced body weight (MD = − 1.79 kg, p < 0.00001) and BMI (MD = − 0.65 kg/m2, p < 0.00001). Subgroup analyses revealed that mobile applications interventions significantly declined both body weight (MD = − 1.97 kg, p < 0.00001) and BMI (MD = − 1.20 kg/m2, p = 0.002). Wearable-based technologies interventions also significantly reduced body weight (MD = − 1.08 kg, p = 0.0006) and BMI (MD = − 0.44 kg/m2, p < 0.00001). Web-based technologies interventions significantly decreased body weight loss (MD = − 1.99 kg, p < 0.00001), but no BMI (MD = − 0.44 kg/m2, p = 0.09). Health education based on mHealth interventions significantly lowered body weight (MD = − 0.78 kg, p = 0.01) and BMI (MD = − 0.44 kg/m2, p = 0.0003). Furthermore, intelligent management based on mHealth interventions significantly dropped body weight (MD = − 0.70 kg, p < 0.0001) and BMI (MD = − 0.38 kg/m2, p < 0.00001). No significant difference was found between mHealth-based online interventions and face-to-face interventions for body weight (MD = − 1.73 kg, p = 0.11) or BMI (MD = − 0.88 kg/m2, p = 0.10). Conclusion mHealth-based lifestyle interventions are effective in reducing body weight and BMI in overweight and obese adults. Further optimization is needed to enhance long-term effectiveness and user adherence.

Impact of mHealth-Based Lifestyle Interventions on Weight Loss in Overweight/Obese Adults: A Systematic Meta-Analysis

期刊论文
发表日期：

2025年9月23日

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Yuting Tang,
Xian Guo,
Xinyuan Liu,
Junqiang Qiu,
Junchao Yang

摘要

Background

Obesity is a global health challenge and increases the risk of chronic diseases. Mobile health technologies (mHealth) have emerged as a personalized and multimodal intervention for weight management. This meta-analysis evaluated the effectiveness of mHealth-based lifestyle interventions on weight loss and BMI reduction in overweight and obese adults.

Methods

A total of 42 randomized controlled trials were identified and included from five major databases. Analyses were conducted using RevMan 5.4. Primary and secondary outcomes were changes in body weight and BMI, respectively.

Results

The meta-analysis showed that mHealth-based lifestyle interventions significantly reduced body weight (MD = − 1.79 kg, p < 0.00001) and BMI (MD = − 0.65 kg/m², p < 0.00001). Subgroup analyses revealed that mobile applications interventions significantly declined both body weight (MD = − 1.97 kg, p < 0.00001) and BMI (MD = − 1.20 kg/m², p = 0.002). Wearable-based technologies interventions also significantly reduced body weight (MD = − 1.08 kg, p = 0.0006) and BMI (MD = − 0.44 kg/m², p < 0.00001). Web-based technologies interventions significantly decreased body weight loss (MD = − 1.99 kg, p < 0.00001), but no BMI (MD = − 0.44 kg/m², p = 0.09). Health education based on mHealth interventions significantly lowered body weight (MD = − 0.78 kg, p = 0.01) and BMI (MD = − 0.44 kg/m², p = 0.0003). Furthermore, intelligent management based on mHealth interventions significantly dropped body weight (MD = − 0.70 kg, p < 0.0001) and BMI (MD = − 0.38 kg/m², p < 0.00001). No significant difference was found between mHealth-based online interventions and face-to-face interventions for body weight (MD = − 1.73 kg, p = 0.11) or BMI (MD = − 0.88 kg/m², p = 0.10).

Conclusion

mHealth-based lifestyle interventions are effective in reducing body weight and BMI in overweight and obese adults. Further optimization is needed to enhance long-term effectiveness and user adherence.