成功支持减肥的数字干预方面的评估:系统评价与组成网络元分析。

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Michael Nunns, Samantha Febrey, Rebecca Abbott, Jill Buckland, Rebecca Whear, Liz Shaw, Alison Bethel, Kate Boddy, Jo Thompson Coon, G J Melendez-Torres
{"title":"成功支持减肥的数字干预方面的评估:系统评价与组成网络元分析。","authors":"Michael Nunns, Samantha Febrey, Rebecca Abbott, Jill Buckland, Rebecca Whear, Liz Shaw, Alison Bethel, Kate Boddy, Jo Thompson Coon, G J Melendez-Torres","doi":"10.2196/65443","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Obesity is a chronic complex disease associated with increased risks of developing several serious and potentially life-threatening conditions. It is a growing global health issue. Pharmacological treatment is an option for patients living with overweight or obesity. Digital technology may be leveraged to support patients with weight loss in the community, but it is unclear which of the multiple digital options are important for success.</p><p><strong>Objective: </strong>This systematic review and component network meta-analysis aimed to identify components of digital support for weight loss interventions that are most likely to be effective in supporting patients to achieve weight loss goals.</p><p><strong>Methods: </strong>We searched MEDLINE, Embase, APA PsycInfo, and Cochrane Central Register of Controlled Trials from inception to November 2023 for randomized controlled trials using any weight loss intervention with digital components and assessing weight loss outcomes in adults with BMI ≥25 kg/m<sup>2</sup> (≥23 kg/m<sup>2</sup> for Asian populations). Eligible trials were prioritized for synthesis based on intervention relevance and duration, and the target population. Trial arms with substantial face-to-face elements were deprioritized. Prioritized trials were assessed for quality using the Cochrane Risk of Bias Tool v1. We conducted intervention component analysis to identify key digital intervention features and a coding framework. All prioritized trial arms were coded using this framework and were included in component network meta-analysis.</p><p><strong>Results: </strong>Searches identified 6528 reports, of which 119 were included. After prioritization, 151 trial arms from 68 trials were included in the synthesis. Nine common digital components were identified from the 151 trial arms: provision of information or education, goal setting, provision of feedback, peer support, reminders, challenges or competitions, contact with a specialist, self-monitoring, and incentives or rewards. Of these, 3 components were identified as \"best bets\" because they were consistently and numerically, but not usually significantly, most likely to be associated with weight loss at 6 and 12 months. These were patient information, contact with a specialist, and incentives or rewards. An exploratory model combining these 3 components was significantly associated with successful weight loss at 6 months (-2.52 kg, 95% CI -4.15 to -0.88) and 12 months (-2.11 kg, 95% CI -4.25 to 0.01). No trial arms used this specific combination of components.</p><p><strong>Conclusions: </strong>Our findings indicate that the design of digital interventions to support weight loss should be carefully crafted around core components. On their own, no single digital component could be considered essential for success, but a combination of information, specialist contact, and incentives warrants further examination.</p><p><strong>Trial registration: </strong>PROSPERO CRD42023493254; https://tinyurl.com/ysyj8j8s.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e65443"},"PeriodicalIF":5.8000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141966/pdf/","citationCount":"0","resultStr":"{\"title\":\"Evaluation of the Aspects of Digital Interventions That Successfully Support Weight Loss: Systematic Review With Component Network Meta-Analysis.\",\"authors\":\"Michael Nunns, Samantha Febrey, Rebecca Abbott, Jill Buckland, Rebecca Whear, Liz Shaw, Alison Bethel, Kate Boddy, Jo Thompson Coon, G J Melendez-Torres\",\"doi\":\"10.2196/65443\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Obesity is a chronic complex disease associated with increased risks of developing several serious and potentially life-threatening conditions. It is a growing global health issue. Pharmacological treatment is an option for patients living with overweight or obesity. Digital technology may be leveraged to support patients with weight loss in the community, but it is unclear which of the multiple digital options are important for success.</p><p><strong>Objective: </strong>This systematic review and component network meta-analysis aimed to identify components of digital support for weight loss interventions that are most likely to be effective in supporting patients to achieve weight loss goals.</p><p><strong>Methods: </strong>We searched MEDLINE, Embase, APA PsycInfo, and Cochrane Central Register of Controlled Trials from inception to November 2023 for randomized controlled trials using any weight loss intervention with digital components and assessing weight loss outcomes in adults with BMI ≥25 kg/m<sup>2</sup> (≥23 kg/m<sup>2</sup> for Asian populations). Eligible trials were prioritized for synthesis based on intervention relevance and duration, and the target population. Trial arms with substantial face-to-face elements were deprioritized. Prioritized trials were assessed for quality using the Cochrane Risk of Bias Tool v1. We conducted intervention component analysis to identify key digital intervention features and a coding framework. All prioritized trial arms were coded using this framework and were included in component network meta-analysis.</p><p><strong>Results: </strong>Searches identified 6528 reports, of which 119 were included. After prioritization, 151 trial arms from 68 trials were included in the synthesis. Nine common digital components were identified from the 151 trial arms: provision of information or education, goal setting, provision of feedback, peer support, reminders, challenges or competitions, contact with a specialist, self-monitoring, and incentives or rewards. Of these, 3 components were identified as \\\"best bets\\\" because they were consistently and numerically, but not usually significantly, most likely to be associated with weight loss at 6 and 12 months. These were patient information, contact with a specialist, and incentives or rewards. An exploratory model combining these 3 components was significantly associated with successful weight loss at 6 months (-2.52 kg, 95% CI -4.15 to -0.88) and 12 months (-2.11 kg, 95% CI -4.25 to 0.01). No trial arms used this specific combination of components.</p><p><strong>Conclusions: </strong>Our findings indicate that the design of digital interventions to support weight loss should be carefully crafted around core components. On their own, no single digital component could be considered essential for success, but a combination of information, specialist contact, and incentives warrants further examination.</p><p><strong>Trial registration: </strong>PROSPERO CRD42023493254; https://tinyurl.com/ysyj8j8s.</p>\",\"PeriodicalId\":16337,\"journal\":{\"name\":\"Journal of Medical Internet Research\",\"volume\":\"27 \",\"pages\":\"e65443\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141966/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Internet Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/65443\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Internet Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/65443","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

摘要

背景:肥胖是一种慢性复杂疾病,与发展为几种严重和潜在危及生命的疾病的风险增加有关。这是一个日益严重的全球健康问题。药物治疗是超重或肥胖患者的一种选择。数字技术可以用来支持社区中的减肥患者,但目前尚不清楚多种数字选择中哪一种对成功很重要。目的:本系统综述和组成网络荟萃分析旨在确定数字支持减肥干预措施的组成部分,这些组成部分最有可能有效地支持患者实现减肥目标。方法:我们检索MEDLINE、Embase、APA PsycInfo和Cochrane中央对照试验注册库,从成立到2023年11月,检索使用任何数字分量的减肥干预措施的随机对照试验,并评估BMI≥25 kg/m2(亚洲人群≥23 kg/m2)的成年人的减肥结果。根据干预的相关性、持续时间和目标人群,对符合条件的试验进行综合排序。具有大量面对面元素的试验组被剥夺了优先权。使用Cochrane风险偏倚工具v1评估优先试验的质量。我们进行了干预成分分析,以确定关键的数字干预特征和编码框架。使用该框架对所有优先试验组进行编码,并纳入成分网络元分析。结果:搜索确定了6528份报告,其中119份被收录。经过优先排序,来自68个试验的151个试验臂被纳入综合。从151个试验项目中确定了9个常见的数字组成部分:提供信息或教育、目标设定、提供反馈、同伴支持、提醒、挑战或竞争、与专家联系、自我监控、激励或奖励。其中,3种成分被认为是“最佳选择”,因为它们在数字上是一致的,但通常不是显著的,最有可能与6个月和12个月的体重减轻有关。这些是患者信息,与专家的联系,以及奖励或奖励。结合这三个组成部分的探索性模型与6个月(-2.52 kg, 95% CI -4.15至-0.88)和12个月(-2.11 kg, 95% CI -4.25至0.01)的成功减肥显著相关。没有试验组使用这种特定的成分组合。结论:我们的研究结果表明,支持减肥的数字干预设计应该围绕核心组件精心设计。就其本身而言,没有任何一个数字组件可以被认为是成功的必要条件,但信息,专家联系和激励措施的组合值得进一步研究。试验注册:PROSPERO CRD42023493254;https://tinyurl.com/ysyj8j8s。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of the Aspects of Digital Interventions That Successfully Support Weight Loss: Systematic Review With Component Network Meta-Analysis.

Background: Obesity is a chronic complex disease associated with increased risks of developing several serious and potentially life-threatening conditions. It is a growing global health issue. Pharmacological treatment is an option for patients living with overweight or obesity. Digital technology may be leveraged to support patients with weight loss in the community, but it is unclear which of the multiple digital options are important for success.

Objective: This systematic review and component network meta-analysis aimed to identify components of digital support for weight loss interventions that are most likely to be effective in supporting patients to achieve weight loss goals.

Methods: We searched MEDLINE, Embase, APA PsycInfo, and Cochrane Central Register of Controlled Trials from inception to November 2023 for randomized controlled trials using any weight loss intervention with digital components and assessing weight loss outcomes in adults with BMI ≥25 kg/m2 (≥23 kg/m2 for Asian populations). Eligible trials were prioritized for synthesis based on intervention relevance and duration, and the target population. Trial arms with substantial face-to-face elements were deprioritized. Prioritized trials were assessed for quality using the Cochrane Risk of Bias Tool v1. We conducted intervention component analysis to identify key digital intervention features and a coding framework. All prioritized trial arms were coded using this framework and were included in component network meta-analysis.

Results: Searches identified 6528 reports, of which 119 were included. After prioritization, 151 trial arms from 68 trials were included in the synthesis. Nine common digital components were identified from the 151 trial arms: provision of information or education, goal setting, provision of feedback, peer support, reminders, challenges or competitions, contact with a specialist, self-monitoring, and incentives or rewards. Of these, 3 components were identified as "best bets" because they were consistently and numerically, but not usually significantly, most likely to be associated with weight loss at 6 and 12 months. These were patient information, contact with a specialist, and incentives or rewards. An exploratory model combining these 3 components was significantly associated with successful weight loss at 6 months (-2.52 kg, 95% CI -4.15 to -0.88) and 12 months (-2.11 kg, 95% CI -4.25 to 0.01). No trial arms used this specific combination of components.

Conclusions: Our findings indicate that the design of digital interventions to support weight loss should be carefully crafted around core components. On their own, no single digital component could be considered essential for success, but a combination of information, specialist contact, and incentives warrants further examination.

Trial registration: PROSPERO CRD42023493254; https://tinyurl.com/ysyj8j8s.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信