Chiavarini Manuela, Giacchetta Irene, Rosignoli Patrizia, Fabiani Roberto
Department of Health Sciences, University of Florence, Viale GB Morgagni 48, 50134 Florence, Italy.
Local Health Unit of Bologna, Department of Hospital Network, Hospital Management of Maggiore and Bellaria, 40124 Bologna, Italy.
Nutrients. 2025 Jul 1;17(13):2200. doi: 10.3390/nu17132200.
BACKGROUND: Obesity in adults is a growing health concern. The principal interventions used in obesity management are lifestyle-change interventions such as diet, exercise, and behavioral therapy. Although they are effective, current treatment options have not succeeded in halting the global rise in the prevalence of obesity or achieving sustained long-term weight maintenance at the population level. E-health and m-health are both integral components of digital health that focus on the use of technology to improve healthcare delivery and outcomes. The use of eHealth/mHealth might improve the management of some of these treatments. Several digital health interventions to manage obesity are currently in clinical trials. OBJECTIVE: The aim of our systematic review is to evaluate whether digital health interventions (e-Health and m-Health) have effects on changes in anthropometric measures, such as weight, BMI, and waist circumference and behaviors such as energy intake, eating behaviors, and physical activity. METHODS: A search was conducted for randomized controlled trials (RCTs) conducted through 4 October 2024 through three databases (Medline, Web of Science, and Scopus). Studies were included if they evaluated digital health interventions (e-Health and m-Health) compared to control groups in overweight or obese adults (BMI ≥ 25 kg/m) and reported anthropometric or lifestyle behavioral outcomes. Study quality was assessed using the Cochrane Risk of Bias Tool (RoB 2). Meta-analyses were performed using random-effects or fixed-effects models as appropriate, with statistical significance set at < 0.05. RESULTS: Twenty-two RCTs involving diverse populations (obese adults, overweight individuals, postpartum women, patients with eating disorders) were included. Digital interventions included biofeedback devices, smartphone apps, e-coaching systems, web-based interventions, and mixed approaches. Only waist circumference showed a statistically significant reduction (WMD = -1.77 cm; 95% CI: -3.10 to -0.44; = 0.009). No significant effects were observed for BMI (WMD = -0.43 kg/m; = 0.247), body weight (WMD = 0.42 kg; = 0.341), or lifestyle behaviors, including physical activity (SMD = -0.01; = 0.939) and eating behavior (SMD = -0.13; = 0.341). Body-fat percentage showed a borderline-significant trend toward reduction (WMD = -0.79%; = 0.068). High heterogeneity was observed across most outcomes ( > 80%), indicating substantial variability between studies. Quality assessment revealed predominant judgments of "Some Concerns" and "High Risk" across the evaluated domains. CONCLUSIONS: Digital health interventions produce modest but significant benefits on waist circumference in overweight and obese adults, without significant effects on other anthropometric or behavioral parameters. The high heterogeneity observed underscores the need for more personalized approaches and future research focused on identifying the most effective components of digital interventions. Digital health interventions should be positioned as valuable adjuncts to, rather than replacements for, established obesity treatments. Their integration within comprehensive care models may enhance traditional interventions through continuous monitoring, real-time feedback, and improved accessibility, but interventions with proven efficacy such as behavioral counseling and clinical oversight should be maintained.
背景:成人肥胖是一个日益严重的健康问题。肥胖管理中使用的主要干预措施是生活方式改变干预,如饮食、运动和行为疗法。尽管这些措施有效,但目前的治疗选择未能阻止全球肥胖患病率的上升,也未能在人群层面实现长期持续的体重维持。电子健康(e-health)和移动健康(m-health)都是数字健康的组成部分,专注于利用技术改善医疗服务提供和结果。使用电子健康/移动健康可能会改善其中一些治疗的管理。目前有几种用于管理肥胖的数字健康干预措施正在进行临床试验。 目的:我们系统评价的目的是评估数字健康干预措施(电子健康和移动健康)是否对人体测量指标的变化有影响,如体重、体重指数(BMI)和腰围,以及对能量摄入、饮食行为和身体活动等行为有影响。 方法:通过三个数据库(Medline、科学网和Scopus)检索截至2024年10月4日进行的随机对照试验(RCT)。如果研究评估了超重或肥胖成年人(BMI≥25kg/m²)中数字健康干预措施(电子健康和移动健康)与对照组相比的情况,并报告了人体测量或生活方式行为结果,则纳入研究。使用Cochrane偏倚风险工具(RoB 2)评估研究质量。根据情况使用随机效应或固定效应模型进行荟萃分析,设定统计学显著性为<0.05。 结果:纳入了22项涉及不同人群(肥胖成年人、超重个体、产后妇女、饮食失调患者)的随机对照试验。数字干预措施包括生物反馈设备、智能手机应用程序、电子教练系统、基于网络的干预措施和混合方法。只有腰围显示出统计学上的显著降低(加权均数差=-1.
Cochrane Database Syst Rev. 2024-2-20
Cochrane Database Syst Rev. 2012-7-11
Cochrane Database Syst Rev. 2017-6-22
Cochrane Database Syst Rev. 2021-4-19
Cochrane Database Syst Rev. 2017-5-23
Cochrane Database Syst Rev. 2025-7-10
Cochrane Database Syst Rev. 2020-1-9