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Updated: Sep 4, 2026
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
A One-Year Study Using Digital Biomarkers From Sensing Technologies to Assess Changes in Physical Activity Levels and
Lydia D Boyle1, Monica Patrascu1, Bettina S Husebo1
1Center for Elderly and Nursing Home Medicine, University of Bergen, Årstadveien 17, Bergen, Vestland, 5009, Norway, 47 040518081.
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 (P=.01) and in 4 of the sleep biomarkers: TST (P=.02), SE (P=.02), WASO (P=.01), and SRI (P=.01). The long-term reliability of the group ENMO was poor (intraclass correlation coefficient 0.00-0.02); however, it was moderate to strong (0.58-0.79) between individuals at each time point. The adherence and acceptability of the technologies was high (88%-96%), and the application of the devices was well tolerated by the participants, with no adverse events.
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.
