{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:46:15Z","timestamp":1782845175191,"version":"3.54.5"},"reference-count":53,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n 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\n t<\/jats:italic>\n tests to compare the 2 time periods, pre-COVID-19 and early-COVID-19.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n 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\n t<\/jats:italic>\n tests demonstrated significant differences in sensor features between the pre\u2013COVID-19 and early\u2013COVID-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;\n t<\/jats:italic>\n 554<\/jats:sub>\n =\u22124.10;\n P<\/jats:italic>\n <.001), time spent in bed (pre-COVID: mean 32,547.41, SD 9269.96; early-COVID: mean 33,494.73, SD 10,887.33;\n t<\/jats:italic>\n 554<\/jats:sub>\n =\u22122.81;\n P<\/jats:italic>\n =.005), pulse (pre-COVID: mean 68.45, SD 3.30; early-COVID: mean 68.10, SD 3.36;\n t<\/jats:italic>\n 554<\/jats:sub>\n =3.66;\n P<\/jats:italic>\n <.001), respiration (pre-COVID: mean 14.54, SD 1.32; early-COVID: mean 14.41, SD 1.31;\n t<\/jats:italic>\n 553<\/jats:sub>\n =3.72;\n P<\/jats:italic>\n <.001), and stride length (pre-COVID: mean 29.10, SD 4.813; early-COVID: mean 28.76, SD 5.016;\n t<\/jats:italic>\n 595<\/jats:sub>\n =2.17;\n P<\/jats:italic>\n =.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.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/56678","type":"journal-article","created":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:46:47Z","timestamp":1742928407000},"page":"e56678-e56678","source":"Crossref","is-referenced-by-count":1,"title":["Behavioral Markers in Older Adults During COVID-19 Confinement: Secondary Analysis of In-Home Sensor Data"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4339-9483","authenticated-orcid":false,"given":"Knoo","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2591-3984","authenticated-orcid":false,"given":"Noah","family":"Marchal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3774-6492","authenticated-orcid":false,"given":"Erin","family":"L Robinson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6144-1438","authenticated-orcid":false,"given":"Kimberly R","family":"Powell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"R1","unstructured":"WHO coronavirus (COVID-19) dashboard. 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