利用组合数据分析,对研究报告实践进行系统性回顾,观察 24 小时运动行为与健康指标之间的关联。

Denver M Y Brown, Sarah Burkart, Claire I Groves, Guilherme Moraes Balbim, Christopher D Pfledderer, Carah D Porter, Christine St Laurent, Emily K Johnson, Chelsea L Kracht
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引用次数: 0

摘要

背景合成数据分析(CoDA)技术非常适合研究 24 小时运动行为(即睡眠、久坐行为、体力活动)与健康指标之间的关系,因为它们认识到这些行为是相互依存的,是构成一天的相对部分。因此,CoDA 技术在过去十年中得到了越来越多的采用,然而,研究报告实践中的异质性可能会阻碍通过荟萃分析来综合和量化这些关系的努力。本系统性综述描述了使用 CoDA 技术调查 24 小时运动行为与健康指标之间关系的研究报告实践:方法:对八个数据库进行了系统检索,此外还进行了补充检索(如正向/反向引用、专家咨询)。研究纳入了使用 CoDA 技术(涉及行为数据的对数比例转换)研究 24 小时运动行为的时间估计值与健康指标之间关系的观察性研究。研究人员提取了报告实践,并将其分为七个方面:(1) 方法论依据;(2) 行为测量和数据处理策略;(3) 构成构建;(4) 分析计划;(5) 构成特定描述性统计;(6) 模型结果;(7) 辅助信息。研究质量和偏倚风险由美国国立卫生研究院的观察性队列和横断面研究质量评估工具进行评估:102 项研究符合我们的纳入标准。不同领域的报告方法差异很大,大多数研究在方法学论证方面达到了较高的标准,但在所有其他领域的报告不一致。有些项目在所有研究中都有报告(例如,每天的组成分为几个部分),而其他项目则很少报告(例如,一天的定义:午夜到午夜与醒来到醒来)。大多数研究(85%)的研究质量和偏倚风险尚可:结论:目前的研究普遍存在报告方法不一致的问题。由于时间使用流行病学领域旨在准确捕捉和分析与健康结果相关的运动行为数据、促进不同研究间的比较以及为公共卫生干预和政策决策提供信息,因此显然需要一致、清晰和详细的报告方法。就报告建议达成共识是下一步的关键:在线版本包含补充材料,可查阅 10.1186/s44167-024-00062-8。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A systematic review of research reporting practices in observational studies examining associations between 24-h movement behaviors and indicators of health using compositional data analysis.

Background: Compositional data analysis (CoDA) techniques are well suited for examining associations between 24-h movement behaviors (i.e., sleep, sedentary behavior, physical activity) and indicators of health given they recognize these behaviors are co-dependent, representing relative parts that make up a whole day. Accordingly, CoDA techniques have seen increased adoption in the past decade, however, heterogeneity in research reporting practices may hinder efforts to synthesize and quantify these relationships via meta-analysis. This systematic review described reporting practices in studies that used CoDA techniques to investigate associations between 24-h movement behaviors and indicators of health.

Methods: A systematic search of eight databases was conducted, in addition to supplementary searches (e.g., forward/backward citations, expert consultation). Observational studies that used CoDA techniques involving log-ratio transformation of behavioral data to examine associations between time-based estimates of 24-h movement behaviors and indicators of health were included. Reporting practices were extracted and classified into seven areas: (1) methodological justification, (2) behavioral measurement and data handling strategies, (3) composition construction, (4) analytic plan, (5) composition-specific descriptive statistics, (6) model results, and (7) auxiliary information. Study quality and risk of bias were assessed by the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies.

Results: 102 studies met our inclusion criteria. Reporting practices varied considerably across areas, with most achieving high standards in methodological justification, but inconsistent reporting across all other domains. Some items were reported in all studies (e.g., how many parts the daily composition was partitioned into), whereas others seldom reported (e.g., definition of a day: midnight-to-midnight versus wake-to-wake). Study quality and risk of bias was fair in most studies (85%).

Conclusions: Current studies generally demonstrate inconsistent reporting practices. Consistent, clear and detailed reporting practices are evidently needed moving forward as the field of time-use epidemiology aims to accurately capture and analyze movement behavior data in relation to health outcomes, facilitate comparisons across studies, and inform public health interventions and policy decisions. Achieving consensus regarding reporting recommendations is a key next step.

Supplementary information: The online version contains supplementary material available at 10.1186/s44167-024-00062-8.

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