Chronic Insomnia Characterized, Difficulty Falling Asleep, Difficulty Maintaining Sleep, Healthy Aging and Independent Living
Conditions
Keywords
precision health promotion, insomnia phenotyping, adaptive feedback loop, wearable technology
Brief summary
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
Detailed description
The DREAMS Study addresses the critical public health challenge of chronic insomnia among community-dwelling older adults (aged ≥60), which substantially impacts healthy aging and independent living. Traditional exercise interventions often suffer from suboptimal adherence and rely on subjective self-reporting that fails to capture the physiological "mismatch" between perceived and actual sleep. To get around these problems, this study uses a home-based, closed-loop, dual-track exercise recommendation model that combines wearable ActiGraph monitoring with AI-driven skeletal recognition technology (iMirror). This adaptive framework differentiates between insomnia phenotypes: daytime moderate-intensity training (HIIT or resistance exercise) is prescribed to enhance sleep drive for those with difficulty falling asleep (DFA), while nighttime relaxation training (yoga or Pilates) targets reduced hyperarousal for those with difficulty maintaining sleep (DMS). By utilizing continuous objective data, the system creates a feedback loop that dynamically adjusts exercise prescriptions (frequency, intensity, and timing), reducing the need for on-site professional supervision and ensuring safe implementation within the participant's familiar home environment. Ultimately, the DREAMS Study establishes a scalable, data-driven model for precision health promotion to inform future policies on sleep health in aging populations.
Interventions
DEVICEFit Mirror-Guided Home-Based Exercise Program
The DREAMS Study evaluates an AI-driven, home-based, dual-track exercise intervention for community-dwelling older adults (≥ 60) with chronic insomnia. Integrating FitMirror skeletal recognition for real-time guidance and ActiGraph wearable monitoring for continuous data collection, the system creates a closed-loop feedback mechanism to optimize sleep health. The intervention is tailored to insomnia phenotypes: daytime HIIT or resistance training is prescribed to enhance sleep drive (targeting sleep-onset difficulties), while nighttime yoga or Pilates targets reduced hyperarousal (targeting sleep-maintenance difficulties). Using a quasi-experimental design, the study measures improvements in multi-dimensional sleep health and functional fitness at baseline, post-intervention (12 weeks), and follow-up (24 weeks).
Sponsors
National Sun Yat-sen University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SUPPORTIVE_CARE
Masking
NONE
Intervention model description
The DREAMS Study addresses chronic insomnia in community-dwelling older adults (≥ 60) through an AI-driven, home-based, dual-track exercise model. Integrating continuous wearable monitoring (ActiGraph) with skeletal recognition technology (FitMirror), the system creates a closed-loop feedback mechanism to overcome subjective reporting biases and adherence barriers. Intervention is tailored to insomnia phenotypes: daytime HIIT or resistance training enhances sleep drive for those with sleep-onset difficulties (DFA), while nighttime yoga or Pilates reduces hyperarousal for those with sleep-maintenance difficulties (DMS). Using a quasi-experimental design with assessments at baseline, 12 weeks (post-intervention), and 24 weeks (follow-up), the study employs linear mixed-effects models to evaluate improvements in multi-dimensional sleep health and functional fitness.
Eligibility
Sex/Gender
ALL
Age
60 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Multi-dimensional Sleep Health Assessment via Objective Actigraphy and Subjective Diaries | Assessments are conducted at three key intervals: Baseline (T0), immediately post-intervention (12 weeks, T1), and at a follow-up (24 weeks, T2). | This study evaluates sleep using Buysse's (2018) Multidimensional Sleep Health framework. To address reviewer concerns regarding distinct units, the following parameters are reported separately: 1) Sleep Efficiency (%) and Duration (min), objectively monitored by ActiGraph wGT3X-BT (7-14 days per stage). 2) Sleep Regularity (min), defined as the standard deviation of the weekly sleep midpoint. 3) Sleep Timing (clock time), representing the average weekly midpoint. 4) Subjective Quality and Daytime Alertness (1-5 scale), captured via daily diaries where lower scores indicate better health. Actigraphy data are processed at 30-second epochs using ActiLife v6.13.3. This dual-track approach monitors how daytime HIIT (building sleep drive) and nighttime relaxation (reducing hyperarousal) affect specific insomnia phenotypes, overcoming the "mismatch" between subjective and objective perception. All metrics are summarized as weekly means |
| Multi-component Functional Physical Fitness Performance | Assessments are conducted at three key intervals: Baseline (T0), immediately post-intervention (12 weeks, T1), and at a follow-up (24 weeks, T2). | The specific components of the physical assessment include the 30-second chair stand test to evaluate lower body strength by counting full stands from a seated position, and the 30-second arm curl test to measure upper body strength using 5-pound weights for women or 8-pound weights for men. Flexibility is assessed through the chair sit-and-reach test for the lower body and the back scratch test for the upper body, with distances measured in centimeters to determine the range of motion in the limbs. Agility and dynamic balance are recorded using the 8-foot up-and-go test, which times how quickly a participant can rise from a chair, walk 2.44 meters around a marker, and return to a seated position. Cardiorespiratory endurance is measured using either the 2-minute knee-up test, which counts total repetitions of lifting the knees to a specified height, or the 6-minute walk test, which measures the total distance traveled in meters and can be used to estimate maximal aerobic capacity. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Body Composition Analysis via Multi-Frequency Bioelectrical Impedance Analysis | Assessments are conducted at three key intervals: Baseline (T0), immediately post-intervention (12 weeks, T1), and at a follow-up (24 weeks, T2). | Measurements are conducted in the morning, 3 hours post-meal, after emptying the bladder and avoiding vigorous exercise to prevent electrolyte and water fluctuations. Participants stand barefoot on the Tanita MC-780MA Multi-frequency Segmental Body Composition Analyzer, holding hand electrodes with arms positioned away from the torso. This assessment provides comprehensive data on basal metabolism, visceral fat index, body water, and segmental fat/muscle mass. Critically, it analyzes the Skeletal Muscle Mass Index (SMI), a key indicator for sarcopenia. |
| Self-Reported Demographic and Clinical Health Profile | Collected at Baseline (T0). | Participants self-complete a structured questionnaire covering demographic variables (gender, height, weight, education level) and clinical history, including comorbidities such as hypertension, diabetes, cardiovascular disease, COPD, asthma, cancer, sleep apnea, stroke, or brain injury. The profile also records the frequency of lifestyle habits (smoking, alcohol, and exercise categorized as none, weekly, monthly, or yearly) and any past diagnosis of psychiatric disorders. This data ensures proper screening and baseline characterization of the study population. |
Outcome results
None listed