Observational studyJMIR nursing2026
A One-Year Study Using Digital Biomarkers From Sensing Technologies to Assess Changes in Physical Activity Levels and Sleep Quality in Nursing Home Residents With Dementia: Observational Study.
Observational study in JMIR nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The trial behind it
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Authors and funding
6 authors.
Funding
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Abstract
Background: Proxy-rated questionnaires remain the standard for the assessment of activity and sleep for people with dementia living in nursing homes. Sensing technologies, such as wearables, can generate continuous data that provide quantitative insights into daily activities and behavioral and psychological symptoms, such as sleep disturbance. This study explores the use of sensing technologies in the detection of changes in physical activity levels and sleep behaviors over time. Objective: This study aims to explore the long-term capabilities of multimodal sensing technologies for assessing physical activity levels and sleep quality using selected digital biomarkers for nursing home residents with dementia. Objectives were for observation to be aligned with real-world conditions in which such sensing technologies would be applied within a nursing home environment and to assess whether distinct differences in selected digital biomarkers can be observed accurately and reliably longitudinally. Methods: This study included 11 participants (79-93 y) recruited from 2 dementia care units in Norway. A smartwatch (Garmin Vivoactive 5 or Garmin Venu 3) and a radar-based system (Vital Things, Somnofy) were used to collect 7 days and 6 nights of data on physical activity levels and sleep quality at baseline, 6 months, and 1 year. The Personal Self-Maintenance Score and Neuropsychiatric Inventory-Nursing Home Version (nighttime behaviors section K) were also administered. Digital biomarkers included Euclidean norm minus one (ENMO), sleep efficiency (SE), wake after sleep onset (WASO), sleep regulatory index (SRI), sleep fragmentation index (SFI), total sleep time (TST), and time out of bed (no presence). Results: A total of 9 participants were included in the final analysis. Differences were found in nighttime ENMO ( Conclusions: The use of sensing technologies could enable more objective, data-driven future care models for people with dementia residing in nursing homes; however, the results emphasized in this study require the recommendation for cautious, well-designed use of digital biomarkers for clinical decision-making.
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