A5111242051- Sleep and related disorders
- Frailty in Older Adults
- Pharmacy and Medical Practices
- Obstructive Sleep Apnea Research
- Balance, Gait, and Falls Prevention
- Health and Well-being Studies
- Sepsis Diagnosis and Treatment
- Sleep and Wakefulness Research
- Pharmaceutical Practices and Patient Outcomes
- Sleep and Work-Related Fatigue
- Artificial Intelligence in Healthcare
- Chronic Disease Management Strategies
- Machine Learning in Healthcare
Osaka University
2024-2025
Osaka Health Science University
2024
Objective: Uncoupled sleep is a phenomenon characterized by discrepancy between patterns and complaints. This study aimed to evaluate the effect of report feedback utilizing information communication technology combined with health guidance on improving subjective objective outcomes in community-dwelling older people without uncoupled sleep. Methods: was conducted Sakai City, Japan. The Athens Insomnia Scale (AIS) employed outcomes. Participants were categorized as complaining sleepers if...
Objectives: To evaluate the effectiveness of sleep monitor device, feedback from report, and regular advice for community-dwelling older people. Methods: Randomized controlled trial evaluator blinded. Subjects are over 65-year-old who live in community or living alone households requiring support under long-term care insurance Japan. They divided into three groups: A) For 6 months, send monthly report conduct telephone intervention; B) first 3 months is same intervention as A, then next...
<title>Abstract</title> Purpose To construct a prediction model for early post-discharge falls among older adults in Japan using machine learning, leveraging patient information collected during hospitalization. Methods This prospective cohort study was conducted at an acute care hospital Osaka, Japan. Participants were inpatients aged ≥ 65 years admitted to the geriatric ward between February 2022 and July 2023. At admission discharge, 83 items from electronic medical records. The outcome,...
The study aimed to develop a machine learning (ML) model predict early postdischarge falls in older adults using data that are easy collect acute care hospitals. This may reduce the burden imposed by complex measures on patients and health staff.