ArticleThe Gerontologist2026
Exploring bed sensor technology: interdisciplinary insights in a geriatric assessment inpatient setting.
Article in The Gerontologist, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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.
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Who cites it
1 citing paper in PubMed.
- Human-Centered AI in Sleep Health Management: Scoping Review of Stakeholder Perspectives and Co-Design Practices.Journal of medical Internet research · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND AND
objectivesSleep quality is a critical component of health and recovery for hospitalized older adults, yet current monitoring practices often lack the precision and detail required for effective intervention. This qualitative study aimed to evaluate the feasibility and acceptance of implementing the artificial intelligence-powered Sleepsense bed sensor for sleep monitoring in a geriatric inpatient hospital setting. RESEARCH DESIGN AND
methodsThis qualitative study involved interviews with 22 patients and focus groups with 33 interdisciplinary staff members. Data were analyzed using an interpretive description approach, guided by the technology acceptance model and unified theory of acceptance and use of technology, to explore the feasibility and acceptance of Sleepsense bed sensors in a geriatric inpatient setting.
resultsKey findings from thematic analysis emerged in three main themes representing the feasibility and acceptance of Sleepsense bed sensors among hospitalized older adults: user acceptance, integration with somnolog into clinical practice, and implementation barriers and practical challenges. Staff reported high acceptance of Sleepsense technology due to its nonintrusiveness and ability to reduce disruptive nighttime checks. However, challenges such as the need for consent, data interpretation, and occasional inaccuracies were also identified. Integrating Sleepsense with existing care practices was recommended to enhance patient care while maintaining staff confidence. DISCUSSION AND IMPLICATIONS: This study underscores the potential of advanced sleep monitoring technologies in subacute care settings and highlights the importance of addressing implementation barriers for effective adoption.
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