Exploring bed sensor technology: interdisciplinary insights in a geriatric assessment inpatient setting

Area of Science:

• Geriatric Medicine
• Health Informatics
• Sleep Science

Background:

• Sleep quality is vital for hospitalized older adults' recovery.
• Current sleep monitoring methods in hospitals are often inadequate.
• This study addresses the need for advanced sleep monitoring solutions.

Purpose of the Study:

• To assess the feasibility of using the AI-powered Sleepsense bed sensor for sleep monitoring.
• To evaluate patient and staff acceptance of Sleepsense in a geriatric inpatient setting.

Main Methods:

• Qualitative study using interviews with 22 patients and focus groups with 33 staff members.
• Data analyzed using interpretive description, guided by technology acceptance models.
• Explored feasibility and acceptance of Sleepsense bed sensors.

Main Results:

• Staff reported high acceptance due to nonintrusive nature and reduced nighttime checks.
• Identified themes: user acceptance, clinical integration, and implementation challenges.
• Challenges included consent, data interpretation, and sensor accuracy.

Conclusions:

• AI-powered sleep monitoring technologies have significant potential in subacute care.
• Addressing implementation barriers is crucial for successful adoption of new sleep technologies.
• Integrating Sleepsense can enhance patient care and maintain staff confidence.