{"title":"行为改变资源用于基于移动应用程序的干预措施,解决超重和肥胖成人的体重、行为和代谢结果:随机对照试验的系统评价和荟萃分析。","authors":"Sijia Li, You Zhou, Ying Tang, Haoming Ma, Yuying Zhang, Aoqi Wang, Xingyi Tang, Runyuan Pei, Meihua Piao","doi":"10.2196/63313","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Overweight and obesity have become a public health issue. Lifestyle modifications delivered through mobile devices, especially mobile phones, present an opportunity to support weight loss efforts. However, evidence regarding the effects of mobile apps on other outcomes, such as blood pressure and physical activity (PA), remains limited. Recent studies on this topic require a systematic review and updating, and the active elements that promote behavior change remain unclear.</p><p><strong>Objective: </strong>The meta-analysis aimed to explore the effects of mobile phone apps on weight-related outcomes (weight, BMI, waist circumference [WC], fat mass, fat mass percentage), behavioral outcomes (moderate-to-vigorous physical activity [MVPA], energy intake), and metabolic outcomes (systolic blood pressure [SBP], diastolic blood pressure [DBP], triglycerides, hemoglobin A1c [HbA1c]) among adults with overweight and obesity. Behavior change techniques (BCTs), the smallest replicable intervention elements, were also identified to clarify the components used in current studies, along with associated resources, including facilitating, boosting, and nudging. In addition, factors influencing the effectiveness of these interventions were explored.</p><p><strong>Methods: </strong>Six databases (PubMed, Embase, CENTRAL, Web of Science, PsycINFO, and CINAHL) were searched for relevant randomized controlled trials (RCTs) published in English from inception to May 20, 2024. Two independent authors conducted study selection, data extraction, and quality assessment. The effect size of interventions was calculated using the mean difference (MD), and a random-effects model was applied for data analysis. Subgroup and sensitivity analyses were conducted to explore potential influencing factors and identify possible sources of heterogeneity.</p><p><strong>Results: </strong>A total of 29 studies were included. The results indicated that mobile phone app interventions significantly reduced weight (MD=-1.45 kg, 95% CI -2.01 to -0.89; P<.001), BMI (MD=-0.35 kg/m2, 95% CI -0.57 to -0.13; P=.002), WC (MD=-1.98 cm, 95% CI -3.42 to -0.55; P=.007), fat mass (MD=-1.32 kg, 95% CI -1.94 to -0.69; P<.001), DBP (MD=-1.76 mm Hg, 95% CI -3.47 to -0.04; P=.04), and HbA1c (MD=-0.13%, 95% CI -0.22 to -0.04; P=.005). However, nonsignificant effects were observed for other outcomes. The most frequently used BCTs included 2.3 \"self-monitoring of behavior\" (n=25), 4.1 \"instruction on how to perform the behavior\" (n=24), 2.2 \"feedback on behavior\" (n=20), 1.1 \"goal setting (behavior)\" (n=19), and 1.4 \"action planning\" (n=15). Fifty-nine percent of included studies used 3 resource types (ie, facilitating, boosting, and nudging). Subgroup analyses identified combined diet and PA interventions, medium-term intervention duration, and the use of ≥8 BCTs as potential reference interventions for improving outcomes.</p><p><strong>Conclusions: </strong>This meta-analysis demonstrates that mobile phone app interventions significantly reduce weight, BMI, WC, fat mass, DBP, and HbA1c in adults with overweight and obesity. However, future studies should explore ways to optimize app interventions by incorporating behavior change strategies and resources to further enhance their overall effectiveness.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e63313"},"PeriodicalIF":6.2000,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12392691/pdf/","citationCount":"0","resultStr":"{\"title\":\"Behavior Change Resources Used in Mobile App-Based Interventions Addressing Weight, Behavioral, and Metabolic Outcomes in Adults With Overweight and Obesity: Systematic Review and Meta-Analysis of Randomized Controlled Trials.\",\"authors\":\"Sijia Li, You Zhou, Ying Tang, Haoming Ma, Yuying Zhang, Aoqi Wang, Xingyi Tang, Runyuan Pei, Meihua Piao\",\"doi\":\"10.2196/63313\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Overweight and obesity have become a public health issue. Lifestyle modifications delivered through mobile devices, especially mobile phones, present an opportunity to support weight loss efforts. However, evidence regarding the effects of mobile apps on other outcomes, such as blood pressure and physical activity (PA), remains limited. Recent studies on this topic require a systematic review and updating, and the active elements that promote behavior change remain unclear.</p><p><strong>Objective: </strong>The meta-analysis aimed to explore the effects of mobile phone apps on weight-related outcomes (weight, BMI, waist circumference [WC], fat mass, fat mass percentage), behavioral outcomes (moderate-to-vigorous physical activity [MVPA], energy intake), and metabolic outcomes (systolic blood pressure [SBP], diastolic blood pressure [DBP], triglycerides, hemoglobin A1c [HbA1c]) among adults with overweight and obesity. Behavior change techniques (BCTs), the smallest replicable intervention elements, were also identified to clarify the components used in current studies, along with associated resources, including facilitating, boosting, and nudging. In addition, factors influencing the effectiveness of these interventions were explored.</p><p><strong>Methods: </strong>Six databases (PubMed, Embase, CENTRAL, Web of Science, PsycINFO, and CINAHL) were searched for relevant randomized controlled trials (RCTs) published in English from inception to May 20, 2024. Two independent authors conducted study selection, data extraction, and quality assessment. The effect size of interventions was calculated using the mean difference (MD), and a random-effects model was applied for data analysis. Subgroup and sensitivity analyses were conducted to explore potential influencing factors and identify possible sources of heterogeneity.</p><p><strong>Results: </strong>A total of 29 studies were included. The results indicated that mobile phone app interventions significantly reduced weight (MD=-1.45 kg, 95% CI -2.01 to -0.89; P<.001), BMI (MD=-0.35 kg/m2, 95% CI -0.57 to -0.13; P=.002), WC (MD=-1.98 cm, 95% CI -3.42 to -0.55; P=.007), fat mass (MD=-1.32 kg, 95% CI -1.94 to -0.69; P<.001), DBP (MD=-1.76 mm Hg, 95% CI -3.47 to -0.04; P=.04), and HbA1c (MD=-0.13%, 95% CI -0.22 to -0.04; P=.005). However, nonsignificant effects were observed for other outcomes. The most frequently used BCTs included 2.3 \\\"self-monitoring of behavior\\\" (n=25), 4.1 \\\"instruction on how to perform the behavior\\\" (n=24), 2.2 \\\"feedback on behavior\\\" (n=20), 1.1 \\\"goal setting (behavior)\\\" (n=19), and 1.4 \\\"action planning\\\" (n=15). Fifty-nine percent of included studies used 3 resource types (ie, facilitating, boosting, and nudging). Subgroup analyses identified combined diet and PA interventions, medium-term intervention duration, and the use of ≥8 BCTs as potential reference interventions for improving outcomes.</p><p><strong>Conclusions: </strong>This meta-analysis demonstrates that mobile phone app interventions significantly reduce weight, BMI, WC, fat mass, DBP, and HbA1c in adults with overweight and obesity. 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引用次数: 0
摘要
背景:超重和肥胖已经成为一个公共卫生问题。通过移动设备,尤其是移动电话,改变生活方式为减肥提供了机会。然而,关于移动应用程序对血压和身体活动(PA)等其他结果的影响的证据仍然有限。最近关于这一主题的研究需要系统的回顾和更新,而促进行为改变的积极因素仍不清楚。目的:本荟萃分析旨在探讨手机应用程序对超重和肥胖成人体重相关结局(体重、BMI、腰围[WC]、脂肪质量、脂肪质量百分比)、行为结局(中高强度体力活动[MVPA]、能量摄入)和代谢结局(收缩压[SBP]、舒张压[DBP]、甘油三酯、血红蛋白A1c [HbA1c])的影响。行为改变技术(bct)是最小的可复制干预元素,也被确定为澄清当前研究中使用的组成部分,以及相关资源,包括促进、促进和推动。此外,还探讨了影响这些干预措施有效性的因素。方法:检索PubMed、Embase、CENTRAL、Web of Science、PsycINFO、CINAHL 6个数据库,检索自成立至2024年5月20日发表的相关英文随机对照试验(RCTs)。两位独立作者进行了研究选择、数据提取和质量评估。采用均值差(MD)计算干预措施的效应量,采用随机效应模型进行数据分析。进行亚组分析和敏感性分析,以探索潜在的影响因素并确定可能的异质性来源。结果:共纳入29项研究。结果显示,手机应用干预显著降低体重(MD=-1.45 kg, 95% CI -2.01 ~ -0.89);结论:本荟萃分析表明,手机应用干预显著降低超重和肥胖成人的体重、BMI、WC、脂肪量、DBP和HbA1c。然而,未来的研究应该探索如何通过结合行为改变策略和资源来优化app干预,进一步提高其整体效果。
Behavior Change Resources Used in Mobile App-Based Interventions Addressing Weight, Behavioral, and Metabolic Outcomes in Adults With Overweight and Obesity: Systematic Review and Meta-Analysis of Randomized Controlled Trials.
Background: Overweight and obesity have become a public health issue. Lifestyle modifications delivered through mobile devices, especially mobile phones, present an opportunity to support weight loss efforts. However, evidence regarding the effects of mobile apps on other outcomes, such as blood pressure and physical activity (PA), remains limited. Recent studies on this topic require a systematic review and updating, and the active elements that promote behavior change remain unclear.
Objective: The meta-analysis aimed to explore the effects of mobile phone apps on weight-related outcomes (weight, BMI, waist circumference [WC], fat mass, fat mass percentage), behavioral outcomes (moderate-to-vigorous physical activity [MVPA], energy intake), and metabolic outcomes (systolic blood pressure [SBP], diastolic blood pressure [DBP], triglycerides, hemoglobin A1c [HbA1c]) among adults with overweight and obesity. Behavior change techniques (BCTs), the smallest replicable intervention elements, were also identified to clarify the components used in current studies, along with associated resources, including facilitating, boosting, and nudging. In addition, factors influencing the effectiveness of these interventions were explored.
Methods: Six databases (PubMed, Embase, CENTRAL, Web of Science, PsycINFO, and CINAHL) were searched for relevant randomized controlled trials (RCTs) published in English from inception to May 20, 2024. Two independent authors conducted study selection, data extraction, and quality assessment. The effect size of interventions was calculated using the mean difference (MD), and a random-effects model was applied for data analysis. Subgroup and sensitivity analyses were conducted to explore potential influencing factors and identify possible sources of heterogeneity.
Results: A total of 29 studies were included. The results indicated that mobile phone app interventions significantly reduced weight (MD=-1.45 kg, 95% CI -2.01 to -0.89; P<.001), BMI (MD=-0.35 kg/m2, 95% CI -0.57 to -0.13; P=.002), WC (MD=-1.98 cm, 95% CI -3.42 to -0.55; P=.007), fat mass (MD=-1.32 kg, 95% CI -1.94 to -0.69; P<.001), DBP (MD=-1.76 mm Hg, 95% CI -3.47 to -0.04; P=.04), and HbA1c (MD=-0.13%, 95% CI -0.22 to -0.04; P=.005). However, nonsignificant effects were observed for other outcomes. The most frequently used BCTs included 2.3 "self-monitoring of behavior" (n=25), 4.1 "instruction on how to perform the behavior" (n=24), 2.2 "feedback on behavior" (n=20), 1.1 "goal setting (behavior)" (n=19), and 1.4 "action planning" (n=15). Fifty-nine percent of included studies used 3 resource types (ie, facilitating, boosting, and nudging). Subgroup analyses identified combined diet and PA interventions, medium-term intervention duration, and the use of ≥8 BCTs as potential reference interventions for improving outcomes.
Conclusions: This meta-analysis demonstrates that mobile phone app interventions significantly reduce weight, BMI, WC, fat mass, DBP, and HbA1c in adults with overweight and obesity. However, future studies should explore ways to optimize app interventions by incorporating behavior change strategies and resources to further enhance their overall effectiveness.
期刊介绍:
JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636.
The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.
JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.