Evidence map›Paper›PMID 40456114›Full record

ArticleJMIR mHealth and uHealth2025

Behavioral Markers in Older Adults During COVID-19 Confinement: Secondary Analysis of In-Home Sensor Data.

Knoo Lee, Noah Marchal, Erin L Robinson, Kimberly R Powell

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Knoo LeeSinclair School of Nursing, University of Missouri, 915 Hitt St, S413, Columbia, MO, 65212, United States, 1 573-884-0421.ORCID 0000-0002-4339-9483
Noah MarchalInstitute for Data Science and Informatics, University of Missouri, Columbia, MO, United States.ORCID 0000-0003-2591-3984
Erin L RobinsonCollege of Health Professions, University of Missouri, Columbia, MO, United States.ORCID 0009-0005-3774-6492
Kimberly R PowellSinclair School of Nursing, University of Missouri, 915 Hitt St, S413, Columbia, MO, 65212, United States, 1 573-884-0421.ORCID 0000-0002-6144-1438

Funding

Customized Health Alerts and Consumer-Centered Interfaces Using In-Home and Wearable SensorsR01NR016423 · NINR · UNIVERSITY OF MISSOURI-COLUMBIA · PI SKUBIC, MARJORIE · 2018 to 2021
$2.4M
Linguistic Summarization of Sensor Data for Early Illness Recognition in EldercareR01LM012221 · NLM · UNIVERSITY OF MISSOURI-COLUMBIA · PI POPESCU, MIHAIL · 2016 to 2018
$912k
NINR NIH HHS R01 NR016423NLM NIH HHS R01 LM012221
6 · The paper itself

Abstract

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.

Indexed as

COVID-19QuarantineRemote Sensing TechnologySocial IsolationAgedAged, 80 and overFemaleHumansIndependent LivingMalePandemicsSARS-CoV-2aging-in-placecommunity dwellinghealthy lifestyleolder adultssensors

Identifiers

PMID40456114
PMCPMC12148247

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.