The evolution and effects of mobile health (mHealth) intervention on weight management among healthy overweight/obese populations in China: a systematic review and meta-analysis

Abstract

Background: China has experienced an alarming rise in obesity rates over the past decades. The rising rates of obesity and number of people affected, as well as the related health and economic consequences, place a huge burden on the China's healthcare system. Mobile health (mHealth) interventions provide promising options for low-cost and effective health promotion. This study aims to (I) describe the evolution of mHealth interventions in weight management among population in China; and (II) examine the effects of mHealth interventions on weight management among overweight/obese populations in China. Methods: Seven electronic databases (PubMed, MEDLINE, Web of Science, Embase, WANFANG, CINAHL Complete, and Google Scholar) were searched from June 1 to July 31, 2021, for eligible randomized controlled trials (RCTs) or quasi-experimental studies published in English or Chinese. All the selected studies (I) were conducted among healthy overweight/obese population in China, (II) used mHealth interventions to modify lifestyle and manage body weight (BW), and (III) reported weight-related measures as primary outcomes [BW, body mass index (BMI), waist circumference (WC), and/or body fat percentage (BFP)]. Two independent reviewers screened studies and extracted data. The modified Cochrane Collaboration tool was used to examine the risk of bias of the studies. A meta-analysis was conducted to examine the overall and subgroup (traditional and intelligent interventions) effect sizes of mHealth intervention on weight loss. Results: A total of 13 studies were included in this review, among which 2 studies used traditional mHealth intervention modalities (including Short Message Service and phone calls) and 11 studies used intelligent mHealth intervention modalities (including social media-based, self-management-based, web-based, and wearable devices). The pooled effects of mHealth interventions on weight loss were significant (BW: WMD =−1.84 kg, 95% CI: −1.94, −1.74, I2=0%; BMI: WMD =−1.98 kg/m2, 95% CI: −2.94, −1.03, I2=98%; WC: WMD =−2.32 cm, 95% CI: −2.83, −1.81, I2=27%; and BFP: WMD =−1.52, 95% CI: −2.31, −0.74, I2=99%), when compared to the control/usual care groups. The intelligent interventions appeared to be more effective in weight loss, especially in reducing BW (−2.66 vs. −1.84 kg), WC (−2.31 vs. −2.17 cm) and BFP (−2.41 vs. −1.02), when compared to the traditional mHealth interventions. Among the four subtypes of the intelligent mHealth interventions, wearable device-based interventions were found to be more effective in reducing BW and WC, while the social-media-based interventions seemed to yield more reduction in BMI and the self-management-based interventions appeared to be more effective in reducing BFP. Conclusions: Overall, mHealth interventions, especially those using intelligent modalities could be a promising approach for weight management among healthy overweight/obese Chinese populations. More studies that examine the effects of mHealth in weight management among vulnerable and elderly population are needed.