ArticleSensors (Basel, Switzerland)2024
Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
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Who cites it
4 citing papers in PubMed.
- Examining care aides' perspectives and attitudes towards the adoption and sustainment of technology in care delivery: a scoping review.BMJ open · 2026Article
- Human-Centered AI in Sleep Health Management: Scoping Review of Stakeholder Perspectives and Co-Design Practices.Journal of medical Internet research · 2026Article
- Exploring bed sensor technology: interdisciplinary insights in a geriatric assessment inpatient setting.The Gerontologist · 2026Article
- Enhancing Quality of Resident Care and Staff Efficiency Through Implementation of Sensors in the Long-Term Care Setting: A Multi-Site Mixed-Methods Study.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Sleep is a crucial aspect of geriatric assessment for hospitalized older adults, and implementing AI-driven technology for sleep monitoring can significantly enhance the rehabilitation process. Sleepsense, an AI-driven sleep-tracking device, provides real-time data and insights, enabling healthcare professionals to tailor interventions and improve sleep quality. This study explores the perspectives of an interdisciplinary hospital team on implementing Sleepsense in geriatric hospital care. Using the interpretive description approach, we conducted focus groups with physicians, nurses, care aides, and an activity worker. The Consolidated Framework for Implementation Research (CFIR) informed our thematic analysis to identify barriers and facilitators to implementation. Among 27 healthcare staff, predominantly female (88.89%) and Asian (74.1%) and mostly aged 30-50 years, themes emerged that Sleepsense is perceived as a timesaving and data-driven tool that enhances patient monitoring and assessment. However, barriers such as resistance to change and concerns about trusting the device for patient comfort and safety were noted, while facilitators included training and staff engagement. The CFIR framework proved useful for analyzing implementation barriers and facilitators, suggesting future research should prioritize effective strategies for interdisciplinary team support to enhance innovation adoption and patient outcomes in rehabilitation settings.
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Registered trials
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.