As societies rapidly transition toward aging demographics, sleep issues among community-dwelling older adults have emerged as a critical concern affecting healthy aging and independent living. Current single-track exercise intervention models are often difficult to implement due to suboptimal adherence. Therefore, this study aims to utilize artificial intelligence technology combined with a dual-track residential exercise mode to improve sleep quality, thereby enhancing the self-care and independent living abilities of the elderly
No linked publications found in PubMed
Inclusion Criteria: * Age ≥ 60 years * Capable of independent mobility (without the use of assistive devices).4 * Meeting one of the following sleep disturbance criteria: Core symptoms of DSM-5 chronic insomnia (self-reported) for ≥ 3 months. Insomnia Severity Index (ISI) ≥ 15 (moderate-to-severe insomnia). Pittsburgh Sleep Quality Index (PSQI) \> 5 (poor sleep quality). * Basic ability to use a tablet or smartphone (caregivers may assist with login, but exercise must be performed by the participant). * Mini-Cog score ≥ 3. * Consent to wear wearable devices and participate in data collection. Exclusion Criteria: * Major cardiovascular events within the past 3 months (e.g., acute myocardial infarction, unstable angina), severe heart failure, or uncontrolled hypertension (e.g., SBP ≥ 180 or DBP ≥ 110 mmHg). * Severe osteoarticular or neuromuscular diseases that prevent the safe completion of exercise (e.g., recent hip fracture, severe Parkinsonian imbalance). * Severe psychiatric disorders or substance use disorders that may affect adherence. * Untreated moderate-to-severe obstructive sleep apnea (OSA) with extreme daytime sleepiness (the study will use objective measurements for preliminary screening). * Currently receiving structured psychotherapy for insomnia (e.g., CBT-I or BBTi) and not yet stabilized. * Severe visual or hearing impairment that prevents following voice or visual instructions.