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成功支持减肥的数字干预措施各方面评估:基于成分网络荟萃分析的系统评价

Evaluation of the Aspects of Digital Interventions That Successfully Support Weight Loss: Systematic Review With Component Network Meta-Analysis.

作者信息

Nunns Michael, Febrey Samantha, Abbott Rebecca, Buckland Jill, Whear Rebecca, Shaw Liz, Bethel Alison, Boddy Kate, Thompson Coon Jo, Melendez-Torres G J

机构信息

Isca Evidence, University of Exeter Medical School, Faculty of Health & Life Sciences, University of Exeter, Exeter, United Kingdom.

NIHR Applied Research Collaboration South West Peninsula (PenARC), University of Exeter Medical School, University of Exeter, St Lukes Campus, Exeter, United Kingdom.

出版信息

J Med Internet Res. 2025 May 22;27:e65443. doi: 10.2196/65443.

Abstract

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/m (≥23 kg/m 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.

摘要

背景

肥胖是一种慢性复杂疾病,与多种严重且可能危及生命的疾病风险增加相关。它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。数字技术可用于支持社区中的减肥患者,但尚不清楚多种数字选项中哪些对成功减肥很重要。

目的

本系统评价和成分网络荟萃分析旨在确定减肥干预措施中数字支持的成分,这些成分最有可能有效支持患者实现减肥目标。

方法

我们检索了MEDLINE、Embase、APA PsycInfo和Cochrane对照试验中央注册库,从数据库建立至2023年11月,查找使用任何包含数字成分的减肥干预措施并评估体重指数(BMI)≥25 kg/m²(亚洲人群为≥23 kg/m²)的成年人减肥结果的随机对照试验。根据干预相关性、持续时间和目标人群,对符合条件的试验进行优先排序以进行综合分析。具有大量面对面元素的试验组被降序排列。使用Cochrane偏倚风险工具v1对优先试验进行质量评估。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。所有优先试验组均使用该框架进行编码,并纳入成分网络荟萃分析。

结果

检索共识别出6528篇报告,其中119篇被纳入。优先排序后,来自68项试验的151个试验组被纳入综合分析。从这151个试验组中识别出9个常见的数字成分:提供信息或教育、设定目标、提供反馈、同伴支持、提醒、挑战或竞赛、与专家联系、自我监测以及激励或奖励。其中,3个成分被确定为“最佳选择”,因为它们在数字上始终且最有可能与6个月和12个月时的体重减轻相关,但通常无显著差异。这些成分是患者信息、与专家联系以及激励或奖励。一个结合这3个成分的探索性模型与6个月(-2.52 kg,95%置信区间-4.15至-0.88)和12个月(-2.11 kg,95%置信区间-4.25至0.01)时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措施设计应围绕核心成分精心构建。就其本身而言,没有单一的数字成分可被视为成功的关键,但信息、与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

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcd1/12141966/c4dae0b9a6f1/jmir_v27i1e65443_fig1.jpg

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