Unobtrusive Nighttime Movement Monitoring to Support Nursing Home Continence Care: Algorithm Development and Validation Study

• Published in: JMIR nursing Vol. 7; p. e58094
• Main Authors: Strauven, Hannelore, Wang, Chunzhuo, Hallez, Hans, Vanden Abeele, Vero, Vanrumste, Bart
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 24.12.2024
• Subjects: Accelerometry - methods; Aged; Aged, 80 and over; Aging in Place; AI in Older Adult Care; Algorithms; Assisted Living for the Elderly and Nursing Home Care; Female; Humans; Male; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Movement - physiology; Nursing Homes; Nursing in a Long-Term Care Facility / Nursing Home for the Elderly; Original Paper; Sensors at Home and Domotics; Textile Sensors and Smart Textiles; Urinary Incontinence - diagnosis; agitation; incontinence; unobtrusive; accelerometer; nursing home; enuresis; sensor technology
• ISSN: 2562-7600; 2562-7600
• DOI: 10.2196/58094

The rising prevalence of urinary incontinence (UI) among older adults, particularly those living in nursing homes (NHs), underscores the need for innovative continence care solutions. The implementation of an unobtrusive sensor system may support nighttime monitoring of NH residents' movements and, more specifically, the agitation possibly associated with voiding events. This study aims to explore the application of an unobtrusive sensor system to monitor nighttime movement, integrated into a care bed with accelerometer sensors connected to a pressure-redistributing care mattress. A total of 6 participants followed a 7-step protocol. The obtained dataset was segmented into 20-second windows with a 50% overlap. Each window was labeled with 1 of the 4 chosen activity classes: in bed, agitation, turn, and out of bed. A total of 1416 features were selected and analyzed with an XGBoost algorithm. At last, the model was validated using leave one subject out cross-validation (LOSOCV). The trained model attained a trustworthy overall F1-score of 79.56% for all classes and, more specifically, an F1-score of 79.67% for the class "Agitation." The results from this study provide promising insights in unobtrusive nighttime movement monitoring. The study underscores the potential to enhance the quality of care for NH residents through a machine learning model based on data from accelerometers connected to a viscoelastic care mattress, thereby driving progress in the field of continence care and artificial intelligence-supported health care for older adults.