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比利时社区居住老年人身体活动和久坐行为研究中的传感器触发式生态瞬时评估:从密集纵向研究中吸取的经验教训。

Sensor-triggered ecological momentary assessment in physical activity and sedentary behaviour research among Belgian community-dwelling elderly: lessons learnt from intensive longitudinal studies.

作者信息

Delobelle Julie, Lebuf Elien, Compernolle Sofie, Vetrovsky Tomas, Van Cauwenberg Jelle, Cimler Richard, Kuhnova Jitka, Van Dyck Delfien

机构信息

Physical Activity & Health Department of Movement and Sports Sciences, Ghent University, Gent, Belgium

Research Foundation Flanders, Brussels, Belgium.

出版信息

BMJ Open. 2025 Apr 3;15(4):e096327. doi: 10.1136/bmjopen-2024-096327.

DOI:10.1136/bmjopen-2024-096327
PMID:40180404
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11969600/
Abstract

OBJECTIVES

Regular physical activity (PA) and reduced sedentary behaviour (SB) have been associated with positive health outcomes, but many older adults do not comply with the current recommendations. Sensor-triggered ecological momentary assessment (EMA) studies allow capturing real-time data during or immediately after PA or SB, which can yield important insights into these behaviours. Despite the promising potential of sensor-triggered EMA, this methodology is still in its infancy. Addressing methodological challenges in sensor-triggered EMA studies is essential for improving protocol adherence and enhancing validity. Therefore, this study aimed to examine (1) the patterns in sensor-triggered EMA protocol adherence (eg, compliance rates), (2) the impact of specific settings (eg, event duration) on the number of prompted surveys, and (3) participants' experiences with engaging in a sensor-triggered EMA study.

DESIGN

Two longitudinal, sensor-triggered EMA studies-one focused on PA and the other on SB-were conducted using similar methodologies from February to October 2022. Participants' steps were monitored for seven days using a Fitbit activity tracker, which automatically prompted an EMA survey through the HealthReact smartphone application when specified (in)activity thresholds were reached. After the monitoring period, qualitative interviews were conducted. Data from both studies were merged.

SETTING

The studies were conducted among community-dwelling Belgian older adults.

PARTICIPANTS

The participants had a median age of 72 years, with 54.17% being females. The PA study included 88 participants (four dropped out), while the SB study included 76 participants (seven dropped out).

PRIMARY AND SECONDARY OUTCOME MEASURES

Descriptive methods and generalised logistic mixed models were employed to analyse EMA adherence patterns. Simulations were conducted to assess the impact of particular settings on the number of prompted EMA surveys. Additionally, qualitative interview data were transcribed verbatim and thematically analysed using NVivo.

RESULTS

Participants responded to 81.22% and 79.10% of the EMA surveys in the PA and SB study, respectively. The confirmation rate, defined as the percentage of EMA surveys in which participants confirmed the detected behaviour, was 94.16% for PA and 72.40% for SB. Logistic mixed models revealed that with each additional day in the study, the odds of responding to the EMA survey increased significantly by 1.59 times (OR=1.59, 95% CI: 1.36 to 1.86, p<0.01) in the SB study. This effect was not observed in the PA study. Furthermore, time in the study did not significantly impact the odds of participants confirming to be sedentary (OR=0.97, 95% CI: 0.92 to 1.02, p=0.28). However, it significantly influenced the odds of confirming PA (OR: 0.81, 95% CI: 0.68 to 0.97, p=0.02), with the likelihood of confirming decreasing by 19% with each additional day in the study. Furthermore, a one-minute increase in latency (ie, time between last syncing and starting the EMA survey) in the PA study decreased the odds of the participant confirming to be physically active by 20% (OR: 0.80, 95% CI: 0.72 to 0.89, p<0.01). Simulations of the specific EMA settings revealed that reducing the event duration and shorter minimum time intervals between prompts increased the number of EMA surveys. Overall, most participants found smartphone usage to be feasible and rated the HealthReact app as user-friendly. However, some reported issues, such as not hearing the notification, receiving prompts at an inappropriate time and encountering technical issues. While the majority reported that their behaviour remained unchanged due to study participation, some noted an increased awareness of their habits and felt more motivated to engage in PA.

CONCLUSIONS

This study demonstrates the potential of sensor-triggered EMA to capture real-time data on PA and SB among older adults, showing strong adherence potential with compliance rates of approximately 80%. The SB study had lower confirmation rates than the PA study, due to technical issues and discrepancies between self-perception and device-based measurements. Practical recommendations were provided for future studies, including improvements in survey timing, technical reliability and strategies to reduce latency.

摘要

目的

规律的体育活动(PA)和减少久坐行为(SB)与积极的健康结果相关,但许多老年人并未遵循当前的建议。传感器触发的生态瞬时评估(EMA)研究能够在体育活动或久坐行为期间或之后立即获取实时数据,这可为这些行为提供重要见解。尽管传感器触发的EMA具有广阔前景,但该方法仍处于起步阶段。解决传感器触发的EMA研究中的方法学挑战对于提高方案依从性和增强有效性至关重要。因此,本研究旨在探讨:(1)传感器触发的EMA方案依从性模式(如依从率);(2)特定设置(如事件持续时间)对提示调查数量的影响;(3)参与者参与传感器触发的EMA研究的体验。

设计

2022年2月至10月,采用相似方法进行了两项纵向、传感器触发的EMA研究,一项聚焦于体育活动,另一项聚焦于久坐行为。使用Fitbit活动追踪器对参与者的步数进行为期七天的监测,当达到指定的(不)活动阈值时,通过HealthReact智能手机应用程序自动提示进行EMA调查。监测期结束后,进行了定性访谈。两项研究的数据进行了合并。

设置

研究在比利时社区居住的老年人中开展。

参与者

参与者的年龄中位数为72岁,女性占54.17%。体育活动研究包括88名参与者(4人退出),久坐行为研究包括76名参与者(7人退出)。

主要和次要结局指标

采用描述性方法和广义逻辑混合模型分析EMA依从性模式。进行模拟以评估特定设置对提示EMA调查数量的影响。此外,对定性访谈数据进行逐字转录,并使用NVivo进行主题分析。

结果

在体育活动研究和久坐行为研究中,参与者分别对81.22%和79.10%的EMA调查做出了回应。确认率定义为参与者确认所检测到行为的EMA调查的百分比,体育活动研究中的确认率为94.16%,久坐行为研究中的确认率为72.40%。逻辑混合模型显示,在久坐行为研究中,研究每增加一天,参与者回应EMA调查的几率显著增加1.59倍(OR = 1.59,95%CI:1.36至1.86,p < 0.01)。在体育活动研究中未观察到这种效应。此外,研究时间对参与者确认久坐的几率没有显著影响(OR = 0.97,95%CI:0.92至1.02,p = 0.28)。然而,它对确认体育活动的几率有显著影响(OR:0.81,95%CI:0.68至0.97,p = 0.02),研究每增加一天,确认的可能性降低19%。此外,体育活动研究中延迟时间(即最后一次同步与开始EMA调查之间的时间)每增加一分钟,参与者确认进行体育活动的几率降低20%(OR:0.80,95%CI:0.72至0.89,p < 0.01)。对特定EMA设置的模拟显示,减少事件持续时间和缩短提示之间的最短时间间隔会增加EMA调查的数量。总体而言,大多数参与者认为使用智能手机是可行的,并将HealthReact应用程序评为用户友好型。然而,一些人报告了一些问题,如未听到通知、在不适当的时间收到提示以及遇到技术问题。虽然大多数人报告说他们的行为因参与研究而没有改变,但一些人指出他们对自己的习惯有了更高的认识,并更有动力参与体育活动。

结论

本研究证明了传感器触发的EMA在获取老年人体育活动和久坐行为实时数据方面的潜力,显示出约80%的依从率具有很强的依从潜力。由于技术问题以及自我认知与基于设备的测量之间的差异,久坐行为研究的确认率低于体育活动研究。为未来研究提供了实用建议,包括改善调查时间、技术可靠性以及减少延迟的策略。

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