The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis.

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Yannik Terhorst, Johannes Knauer, Paula Philippi, Harald Baumeister
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引用次数: 0

Abstract

Background: The objective, unobtrusively collected GPS features (eg, homestay and distance) from everyday devices like smartphones may offer a promising augmentation to current assessment tools for depression. However, to date, there is no systematic and meta-analytical evidence on the associations between GPS features and depression.

Objective: This study aimed to investigate the between-person and within-person correlations between GPS mobility and activity features and depressive symptoms, and to critically review the quality and potential publication bias in the field.

Methods: We searched MEDLINE, PsycINFO, Embase, CENTRAL, ACM, IEEE Xplore, PubMed, and Web of Science to identify eligible articles focusing on the correlations between GPS features and depression from December 6, 2022, to March 24, 2023. Inclusion and exclusion criteria were applied in a 2-stage inclusion process conducted by 2 independent reviewers (YT and JK). To be eligible, studies needed to report correlations between wearable-based GPS variables (eg, total distance) and depression symptoms measured with a validated questionnaire. Studies with underage persons and other mental health disorders were excluded. Between- and within-person correlations were analyzed using random effects models. Study quality was determined by comparing studies against the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines. Publication bias was investigated using Egger test and funnel plots.

Results: A total of k=19 studies involving N=2930 participants were included in the analysis. The mean age was 38.42 (SD 18.96) years with 59.64% (SD 22.99%) of participants being female. Significant between-person correlations between GPS features and depression were identified: distance (r=-0.25, 95% CI -0.29 to -0.21), normalized entropy (r-0.17, 95% CI -0.29 to -0.04), location variance (r-0.17, 95% CI -0.26 to -0.04), entropy (r=-0.13, 95% CI -0.23 to -0.04), number of clusters (r=-0.11, 95% CI -0.18 to -0.03), and homestay (r=0.10, 95% CI 0.00 to 0.19). Studies reporting within-correlations (k=3) were too heterogeneous to conduct meta-analysis. A deficiency in study quality and research standards was identified: all studies followed exploratory observational designs, but no study referenced or fully adhered to the international guidelines for reporting observational studies (STROBE). A total of 79% (k=15) of the studies were underpowered to detect a small correlation (r=.20). Results showed evidence for potential publication bias.

Conclusions: Our results provide meta-analytical evidence for between-person correlations of GPS mobility and activity features and depression. Hence, depression diagnostics may benefit from adding GPS mobility and activity features as an integral part of future assessment and expert tools. However, confirmatory studies for between-person correlations and further research on within-person correlations are needed. In addition, the methodological quality of the evidence needs to improve.

Trial registration: OSF Registeries cwder; https://osf.io/cwder.

被动收集的 GPS 移动指标与抑郁症状之间的关系:系统回顾与元分析》。
背景:从智能手机等日常设备中客观、不显眼地收集 GPS 特征(如寄宿家庭和距离),可能会对目前的抑郁症评估工具起到很好的辅助作用。然而,迄今为止,还没有关于 GPS 特征与抑郁症之间关系的系统性荟萃分析证据:本研究旨在调查 GPS 移动性和活动特征与抑郁症状之间的人际相关性和人内相关性,并对该领域的研究质量和潜在的发表偏差进行严格审查:我们检索了MEDLINE、PsycINFO、Embase、CENTRAL、ACM、IEEE Xplore、PubMed和Web of Science,以确定从2022年12月6日至2023年3月24日期间关注GPS特征与抑郁症之间相关性的合格文章。纳入和排除标准由两名独立审稿人(YT 和 JK)分两阶段进行。符合条件的研究需要报告可穿戴式 GPS 变量(如总距离)与通过有效问卷测量的抑郁症状之间的相关性。涉及未成年人和其他心理健康疾病的研究除外。采用随机效应模型分析了人与人之间和人与人之间的相关性。根据 STROBE(加强流行病学观察性研究的报告)指南对研究进行比较,以确定研究质量。使用Egger检验和漏斗图调查发表偏倚:共有 19 项研究(涉及 2930 名参与者)被纳入分析。平均年龄为 38.42 岁(标准差为 18.96 岁),59.64%(标准差为 22.99%)的参与者为女性。GPS特征与抑郁之间存在显著的人际相关性:距离(r=-0.25,95% CI -0.29至-0.21)、归一化熵(r-0.17,95% CI -0.29至-0.04)、位置方差(r=-0.25,95% CI -0.29至-0.21)、熵(r=-0.17,95% CI -0.29至-0.04)、熵(r=-0.25,95% CI -0.29至-0.21)。04)、位置方差(r-0.17,95% CI -0.26至-0.04)、熵(r=-0.13,95% CI -0.23至-0.04)、聚类数(r=-0.11,95% CI -0.18至-0.03)和寄宿家庭(r=0.10,95% CI 0.00至0.19)。报告内部相关性(k=3)的研究过于分散,无法进行荟萃分析。研究质量和研究标准方面存在不足:所有研究都采用了探索性观察设计,但没有一项研究参考或完全遵守了国际观察性研究报告指南(STROBE)。共有 79% 的研究(k=15)检测到的相关性较小(r=.20)。结果显示存在潜在的发表偏倚:我们的研究结果为 GPS 移动性和活动特征与抑郁症的人际相关性提供了元分析证据。因此,将 GPS 移动性和活动特征作为未来评估和专家工具的一个组成部分,可能对抑郁症诊断有益。不过,还需要对人与人之间的相关性进行确证研究,并对人与人之间的相关性进行进一步研究。此外,证据的方法学质量也有待提高:OSF Registeries cwder; https://osf.io/cwder.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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.
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