老年人在COVID-19隔离期间的行为标记:家庭传感器数据的二次分析

IF 6.2 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Knoo Lee, Noah Marchal, Erin L Robinson, Kimberly R Powell
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引用次数: 0

摘要

背景:老年人受到COVID-19大流行的影响不成比例,这一年龄组的死亡人数很高。尽管2019冠状病毒病大流行已经结束,但社会隔离和居家隔离的影响继续影响老年人的心理和情绪健康。据报道,不健康的生活方式行为,包括身体和社交活动不足,以及睡眠质量差。改变健康生活方式的建议主要针对普通人群,强调需要为脆弱的老年人提供个性化建议。遥感技术可为了解老年人的行为变化和提供个性化建议提供机会。目的:本研究旨在描述COVID-19疫情期间居家隔离和社会隔离对社区居住老年人的影响,并研究安装在家中的遥感器等综合计算技术如何帮助提供安全和健康生活方式的建议。方法:作为一项大型研究和正在进行的社区老年人研究的一部分,在研究样本的家中安装了远程传感器,包括床传感器、3D深度相机和被动红外(PIR)运动传感器。我们比较了2019冠状病毒病爆发前大约一个月(2020年1月14日至2020年2月13日)和大流行爆发后一个月(2020年3月14日至2020年4月13日)的传感器特征。我们采用描述性统计和配对样本t检验对covid -19前期和早期两个时间段进行比较。结果:分析了64名老年人的传感器数据,其中大多数为女性(n=51, 80%),年龄在60 - 76岁之间(n=58, 92%),独居(n=50, 78%)。配对样本t检验结果显示,在covid -19前和covid -19早期时间段之间,传感器特征存在显著差异。我们发现床上不安有统计学意义(covid前:平均值14.98,SD 5.10;早期:平均15.56,SD 5.25;t554 = -4.10;结论:本研究强调,大流行期间居家隔离显著影响老年人的行为和健康,导致久坐不动的生活方式增加,睡眠质量下降。这些变化可能导致身心健康下降,增加患抑郁症的风险,缺乏社会接触,以及功能下降。研究结果强调了老年人在未来传染病爆发时的必要性,并建议将家庭传感器技术作为一种潜在的工具,用于监测老年人的健康状况,并在禁闭期间指导决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Behavioral Markers in Older Adults During COVID-19 Confinement: Secondary Analysis of In-Home Sensor Data.

Background: Older adults were disproportionately affected by the COVID-19 pandemic, with a high number of deaths occurring in this age group. The impact of social isolation and home confinement continues to impact the mental and emotional health of older adults, despite the end of the COVID-19 pandemic. Unhealthy lifestyle behaviors, including physical and social inactivity, and poor sleep quality, have been reported. Recommendations for healthy lifestyle changes have primarily targeted the general population, highlighting the need for personalized recommendations for vulnerable older adults. Remote sensing technologies may offer an opportunity to understand behavior changes among older adults and provide personalized recommendations.

Objective: This study aims to describe the effects of home confinement and social isolation on community-dwelling older adults during the COVID-19 outbreak and investigate how integrated computing technologies, such as remote sensors installed in homes, can help inform recommendations for safe and healthy lifestyles.

Methods: As part of a larger study and ongoing research with community-dwelling older adults, remote sensors including bed transducers, 3D depth cameras, and passive infrared (PIR) motion sensors were installed in the homes of the study sample. We compared features derived from sensors for approximately one month before the COVID-19 outbreak (January 14, 2020-February 13, 2020) and one month after the onset of the pandemic (March 14, 2020-April 13, 2020). We used descriptive statistics and paired-sample t tests to compare the 2 time periods, pre-COVID-19 and early-COVID-19.

Results: Sensor data from 64 older adults were analyzed, the majority identifying as female (n=51, 80%), aged >76 years (n=58, 92%), and living alone (n=50, 78%). Results from paired-sample t tests demonstrated significant differences in sensor features between the pre-COVID-19 and early-COVID-19 time periods. We found statistically significant differences in bed restlessness (pre-COVID: mean 14.98, SD 5.10; early-COVID: mean 15.56, SD 5.25; t554=-4.10; P<.001), time spent in bed (pre-COVID: mean 32,547.41, SD 9269.96; early-COVID: mean 33,494.73, SD 10,887.33; t554=-2.81; P=.005), pulse (pre-COVID: mean 68.45, SD 3.30; early-COVID: mean 68.10, SD 3.36; t554=3.66; P<.001), respiration (pre-COVID: mean 14.54, SD 1.32; early-COVID: mean 14.41, SD 1.31; t553=3.72; P<.001), and stride length (pre-COVID: mean 29.10, SD 4.813; early-COVID: mean 28.76, SD 5.016; t595=2.17; P=.03). Among the study sample, bed restlessness and time spent in bed increased between the 2 time periods, while pulse, respiration, and stride length decreased.

Conclusions: This study highlights that home confinement during the pandemic significantly impacted the behavior and health of older adults, leading to more sedentary lifestyles and poorer sleep quality. These changes may contribute to a decline in physical and mental health, increasing the risk of depression, lack of social contact, and diminished functional capacity. The findings underscore the need for older adults in future infectious disease outbreaks and suggest in-home sensor technology as a potential tool for monitoring their health and guiding decisions during periods of confinement.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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