基于加速度计数据的生态瞬时评估验证：采用多水平模型的重复测量面板研究

Validation of Ecological Momentary Assessment With Reference to Accelerometer Data: Repeated-Measures Panel Study With Multilevel Modeling.

作者信息

Noh Jung Min, Im SongHyun, Park JooYong, Kim Jae Myung, Lee Miyoung, Choi Ji-Yeob

机构信息

Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea.

Department of Big Data Medical Convergence, Eulji University, Seongnam, Republic of Korea.

出版信息

J Med Internet Res. 2025 Apr 1;27:e59878. doi: 10.2196/59878.

DOI:10.2196/59878

PMID:40168069

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12000780/

Abstract

BACKGROUND

There is growing interest in the real-time assessment of physical activity (PA) and physiological variables. Acceleration, particularly those collected through wearable sensors, has been increasingly adopted as an objective measure of physical activity. However, sensor-based measures often pose challenges for large-scale studies due to their associated costs, inability to capture contextual information, and restricted user populations. Smartphone-delivered ecological momentary assessment (EMA) offers an unobtrusive and undemanding means to measure PA to address these limitations.

OBJECTIVE

This study aimed to evaluate the usability of EMA by comparing its measurement outcomes with 2 self-report assessments of PA: Global Physical Activity Questionnaire (GPAQ) and a modified version of Bouchard Physical Activity Record (BAR).

METHODS

A total of 235 participants (137 female, 98 male, and 94 repeated) participated in one or more 7-day studies. Waist-worn sensors provided by ActiGraph captured accelerometer data while participants completed 3 self-report measures of PA. The multilevel modeling method was used with EMA, GPAQ, and BAR as separate measures, with 6 subdomains of physiological activity (overall PA, overall excluding occupational, transport, exercise, occupational, and sedentary) to model accelerometer data. In addition, EMA and GPAQ were further compared with 6 domains of PA from the BAR as outcome measures.

RESULTS

Among the 3 self-reporting instruments, EMA and BAR exhibited better overall performance in modeling the accelerometer data compared to GPAQ (eg EMA daily: β=.387, P<.001; BAR daily: β=.394, P<.001; GPAQ: β=.281, P<.001, based on repeated-only participants with step counts from accelerometer as dependent variables).

CONCLUSIONS

Multilevel modeling on 3 self-report assessments of PA indicates that smartphone-delivered EMA is a valid and efficient method for assessing PA.

摘要

背景

人们对身体活动（PA）和生理变量的实时评估越来越感兴趣。加速度，尤其是通过可穿戴传感器收集的加速度，已越来越多地被用作身体活动的客观测量指标。然而，基于传感器的测量方法由于其相关成本、无法捕捉背景信息以及用户群体受限等问题，常常给大规模研究带来挑战。智能手机提供的生态瞬时评估（EMA）提供了一种不显眼且要求不高的测量PA的方法，以解决这些局限性。

目的

本研究旨在通过将EMA的测量结果与两种PA的自我报告评估方法进行比较，来评估EMA的可用性：全球身体活动问卷（GPAQ）和布沙尔身体活动记录（BAR）的修改版。

方法

共有235名参与者（137名女性，98名男性，94名重复参与者）参与了一项或多项为期7天的研究。在参与者完成3项PA自我报告测量时，由ActiGraph提供的腰部佩戴传感器捕获加速度计数据。采用多水平建模方法，将EMA、GPAQ和BAR作为单独的测量指标，以生理活动的6个子领域（总体PA、总体不包括职业、交通、锻炼、职业和久坐）对加速度计数据进行建模。此外，将EMA和GPAQ作为结果指标，进一步与BAR的6个PA领域进行比较。