Fritz Roschelle, Cook Diane
Betty Irene Moore School of Nursing, University of California Davis Health, 4610 X St, Sacramento, CA, 95817, United States, 1 9167344349.
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States.
JMIR Nurs. 2025 May 1;8:e69052. doi: 10.2196/69052.
Older adults manage multiple impacts on health, including chronic conditions and adverse external events. Smart homes are positioned to have a positive impact on older adults' health by (1) allowing new understandings of behavior change so risks associated with external events can be assessed, (2) quantifying the impact of social determinants on health, and (3) designing interventions that respond appropriately to detected behavior changes. Information derived from smart home sensors can provide objective data about behavior changes to support a learning health care system. In this paper, we introduce a smart home capable of detecting behavior changes that occur during adverse external events like pandemics and wildfires.
Examine digital markers collected before and during 2 events (the COVID-19 pandemic and wildfires) to determine whether clinically relevant behavior changes can be observed and targeted upstream interventions suggested.
Secondary analysis of historic ambient sensor data collected on 39 adults managing one or more chronic conditions was performed. Interrupted time series analysis was used to extract behavior markers related to external events. Comparisons were made to examine differences between exposures using machine learning classifiers.
Behavior changes were detected for 2 adverse external events (the COVID-19 pandemic and wildfire smoke) initially and over time. However, the direction and magnitude of change differed between participants and events. Significant pandemic-related behavior changes ranked by impact included a decrease in time (3.8 hours/day) spent out of home, an increase in restless sleep (946.74%), and a decrease in indoor activity (38.89%). Although participants exhibited less restless sleep during exposure to wildfire smoke (120%), they also decreased their indoor activity (114.29%). Sleep duration trended downward during the pandemic shutdown. Time out of home and sleep duration gradually decreased while exposed to wildfire smoke. Behavior trends differed across exposures. In total, two key discoveries were made: (1) using retrospective analysis, the smart home was capable of detecting behavior changes related to 2 external events; and (2) older adults' sleep efficiency, time out of home, and overall activity levels changed while experiencing external events. These behavior markers can inform future sensor-based monitoring research and clinical application.
Sensor-based findings could support individualized interventions aimed at sustaining the health of older adults during events like pandemics and wildfires. Creating care plans that directly respond to sensor-derived health information, like adding guided indoor exercise, web-based socialization sessions, and mental health-promoting activities, would have practical impacts on wellness. The smart home's novel, evidence-based information could inform future management of chronic conditions, allowing nurses to understand patients' health-related behaviors between the care points so timely, individualized interventions are possible.
老年人应对多种对健康的影响，包括慢性病和不良外部事件。智能家居有望通过以下方式对老年人的健康产生积极影响：（1）使人们对行为改变有新的认识，从而能够评估与外部事件相关的风险；（2）量化社会决定因素对健康的影响；（3）设计能够对检测到的行为改变做出适当反应的干预措施。从智能家居传感器获得的信息可以提供有关行为改变的客观数据，以支持学习型医疗保健系统。在本文中，我们介绍了一种能够检测在大流行和野火等不利外部事件期间发生的行为改变的智能家居。
检查在两个事件（新冠疫情和野火）之前及期间收集的数字标记，以确定是否可以观察到临床相关的行为改变，并提出上游干预措施。
对收集到的39名患有一种或多种慢性病的成年人的历史环境传感器数据进行二次分析。使用中断时间序列分析来提取与外部事件相关的行为标记。使用机器学习分类器进行比较，以检查不同暴露之间的差异。
最初以及随着时间的推移，检测到了两个不利外部事件（新冠疫情和野火烟雾）导致的行为改变。然而，参与者和事件之间的变化方向和幅度有所不同。按影响程度排序的与疫情相关的显著行为改变包括离家时间减少（每天3.8小时）、不安稳睡眠增加（946.74%）以及室内活动减少（38.89%）。虽然参与者在接触野火烟雾期间不安稳睡眠较少（120%），但他们的室内活动也减少了（114.29%）。在疫情封锁期间，睡眠时间呈下降趋势。在接触野火烟雾期间，离家时间和睡眠时间逐渐减少。不同暴露的行为趋势有所不同。总共得出了两个关键发现：（1）通过回顾性分析，智能家居能够检测到与两个外部事件相关的行为改变；（2）老年人的睡眠效率、离家时间和总体活动水平在经历外部事件时发生了变化。这些行为标记可为未来基于传感器的监测研究和临床应用提供参考。
基于传感器的研究结果可以支持旨在在大流行和野火等事件期间维持老年人健康的个性化干预措施。制定直接响应传感器衍生健康信息的护理计划，如增加有指导的室内锻炼、基于网络的社交活动和促进心理健康的活动，将对健康产生实际影响。智能家居提供的新颖的、基于证据的信息可以为未来慢性病的管理提供参考，使护士能够了解患者在护理点之间与健康相关的行为，从而有可能进行及时的个性化干预。
