Insomnia, Insomnia Chronic
Conditions
Keywords
cognitive behavioral therapy, internet of things, wearables, insomnia, older adults, cognitive impairment, cognition
Brief summary
This double blind randomized clinical trial on older independent-living healthy individuals with symptoms of insomnia will harness Cognitive Behavioral Therapy for Insomnia (CBT-I) and augment it with ambulatory data collection devices, personalized digital content, and smart sound and light cues (CBT-I +Internet of Things \[IoT\]+Artificial Intelligence \[AI\]). With this approach, the investigators aim to overcome many of the limitations that CBT-I in the clinic faces: the investigators can implement it in ambulatory settings while providing increased (remote) accessibility to therapy. The investigators will compare the CBT-I +IoT+AI to active controls that also integrate with smart phone devices, including SleepEZ, which is also based on CBT-I, and sleep hygiene education. These active controls will help determine whether CBT-I +IoT+AI is effective at treating insomnia based on the Insomnia Severity Index (ISI) (primary outcome), sleep metrics (secondary outcome), cognitive performance (secondary outcome), and additional outcomes like therapeutic adherence and other mental health assessments. Participants will be asked to track sleep with wearable and nearable devices, complete surveys, and complete cognitive assessments.
Detailed description
Insomnia is highly prevalent in older adults and is associated with impaired daytime functioning and increased risk for cognitive decline. Cognitive Behavioral Therapy for Insomnia (CBT-I) is the recommended first-line treatment, yet access, adherence, and scalability remain persistent barriers, particularly for older populations. Digital CBT-I programs address some access challenges but often demonstrate reduced adherence and diminished effectiveness in real-world use. This study evaluates a fully remote, automated digital CBT-I system that integrates mobile software with Internet of Things (IoT)-enabled environmental cues and artificial intelligence-driven personalization. The intervention is designed to promote adherence to CBT-I principles by passively supporting sleep-wake routines using adaptive sound, light, and behavioral prompts delivered through consumer electronic devices in the participant's home environment. The study is a randomized, double-blind, controlled trial conducted entirely remotely in community-dwelling older adults with clinically significant insomnia symptoms. Following screening and baseline assessment, participants are randomly assigned in equal allocation to one of three study arms: (1) an automated CBT-I system enhanced with IoT-based sound and light cues and personalized digital content, (2) an active digital CBT-I comparator, or (3) a sleep hygiene education active comparator condition. All participants receive comparable study devices and interaction time to maintain blinding and control for expectancy effects. The intervention period lasts six weeks and is preceded by a baseline assessment phase and followed by post-intervention and follow-up assessments. Throughout the study, participants complete standardized self-report measures of insomnia severity and engage in repeated, brief cognitive assessments administered via mobile devices. Objective sleep data are collected using non-invasive, ambulatory sensing technologies that operate passively in the home environment. The primary objective of the study is to compare changes in insomnia severity across study arms. Secondary objectives include evaluation of sleep characteristics, adherence to behavioral recommendations, and performance on cognitive tasks sensitive to sleep-related changes in older adults. The study is designed to assess feasibility, usability, and preliminary efficacy of an automated, home-based digital CBT-I approach that emphasizes adherence support and sleep quality enhancement. This trial will contribute evidence on whether an integrated digital and IoT-based behavioral intervention can improve insomnia outcomes and support cognitive functioning in older adults, informing future large-scale trials and potential clinical implementation.
Interventions
BEHAVIORALSleep Hygiene IoT
This intervention includes intractive videos regarding sleep hygiene, a component of CBT-I and leverages some IoT interventions.
BEHAVIORALCBT-I-IoT-AI
What distinguishes this condition is increased customization of the CBT-I content and increased usage of the Internet of Things (IoT) devices used to promote CBT-I directives.
BEHAVIORALSleepEZ CBTi
This condition includes interactive videos about CBT-I and leverages some IoT interventions.
Sponsors
National Institute on Aging (NIA)
Proactive Life Inc
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
QUADRUPLE (Subject, Caregiver, Investigator, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Fluent English speaker and reader. 2. Capable of providing one's own informed consent. 1. As determined by validated, abbreviated phone-based remote Montreal Cognitive Assessment (MoCA) testing score of ≥18, which is the score that separates Mild Cognitive Impairment (MCI) from Alzheimer's Disease and Related Dementia (ADRD). 3. Age 65+ years old (inclusive) at enrollment, but if recruitment is slow, the investigators may adjust the criteria to 60+, and if it is still slow, it could go as low as 55+ years old. 1. As self-reported on screening survey and later verified in video appointment (e.g. Zoom Health) 4. If residing in a community residence (such as a retirement community) in which a Medical Director designates living status categories, then the participant must be Independent Living status (or equivalent). a. As self-reported on screening survey 5. Insomnia Severity Index score ≥15 (i.e., at least clinical insomnia, that is moderate-to-severe) - but if recruitment is slow then the investigators will recruit with ISI score ≥11 mild-to-severe). a. As self-reported on the ISI screening survey 6. Willing to refrain from initiating new therapeutic interventions (e.g. medication; behavioral) that are not a part of this study protocol for issues pertaining to sleep for the duration of study participation. 1. By self-report 7. Willing to maintain any existing physician-directed pharmacologic intervention for issues pertaining to sleep for the duration of study participation. a. By self-report 8. Has a residence with WIFI. a. By self-report 9. Normal hearing with or without a hearing aid. a. By self-report 10. Difficulty falling asleep, staying asleep, or waking too early, occurring at least 3 nights/week for 3+months, causing significant daytime distress/impairment (e.g., fatigue, poor focus, mood issues), despite adequate sleep opportunity, and not better explained by another sleep disorder or substance. 1. By Sleep Condition Indicator \[SCI\]
Exclusion criteria
1. Illicit drug use in the past month (except for marijuana because it is legal in many States, and the investigators are recruiting nationwide). a. As self-reported on screening survey. Marijuana usage will be tracked via self-report and examined as a moderator. 2. Diagnosed serious mental health disorder. 1. Specifically, psychosis or bipolar depression, severe major depression, moderate to high risk of suicide, dementia 2. As self-reported on screening survey 3. Currently or recent engaged (past 1-year) in evidence-based psychotherapy for Insomnia (e.g., CBTi), in addition to ever receiving a full course of CBTi: a. By self-report 4. Cohabitating with a current or previous participant in this study. a. This criterion is to avoid cross-contamination of study condition awareness, if two cohabitating individuals are randomized into different study arms. 5. Initiation of any psychological treatment in the last 3-months. 6. A highly irregular schedule (e.g. shift work) that would prevent adoption of intervention strategies, as evaluated through the Shift Work Disorder Index. 7. Previous exposure to the SleepSpace software. 8. Medical conditions that are exacerbated by sleep restriction. 9. Planned major surgery during the trial.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Insomnia Severity Index (ISI) | Weekly for the first 8 weeks, then at week 10 and week 12 | Subjective patient completion of the Insomnia Severity Index survey. Sum of survey item responses; Minimum score: 0; Maximum score: 28. Higher sum score indicates a greater number of, or more severe, insomnia symptoms; reduction in sum score suggests improvement of insomnia. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in cognitive test battery performance | Daily for two weeks at baseline and two weeks post treatment | Objective test performance metrics on an ambulatory cognitive test battery delivered with a smartphone device that includes validated assessments: the Mobile Monitoring of Cognitive Change (M2C2) and DANA Brain Vitals. |
| Consensus Sleep Diary | Daily for the first eight weeks of the trial | Subjective sleep diary data will be used to determine perceived sleep features (duration, quality, time in bed, etc). |
Other
| Measure | Time frame | Description |
|---|---|---|
| Wearable Sleep Sensing (Heart Rate) | Daily from week two to week eight | Heart rate (up to .2 hertz) from the Apple Watch will be used to estimate time in bed, sleep/wake, sleep onset latency, wake after sleep onset, and other sleep related metrics. |
| Adherence | Daily during the intervention: Week three to Week eight | The frequency and duration of engagement with the content or other active aspctects of the intervention |
| Generalized Anxiety Symptoms | Baseline (Week 1) to post treatment (Week 12) | Change in anxiety symptoms measured by the Generalized Anxiety Disorder 7 item scale (GAD 7). |
| Nearable Sleep Sensing (Motion) | Daily from week two to week eight | Motion (up to 50 hertz x, y, z actigraphy) from the iPhone will be used to estimate time in bed, sleep/wake, sleep onset latency, wake after sleep onset, and other sleep related metrics. |
| Post Traumatic Stress Symptoms | Baseline (Week 1) to post treatment (Week 12) | Change in post traumatic stress symptoms measured by the Primary Care PTSD Screen (PC PTSD). |
| Social Connectedness From Voice Features | Baseline (Week 1) to post treatment (Week 12) | Exploratory changes in social connectedness metrics derived from speech and voice features collected during study interactions. |
| Depressive Symptoms | Baseline (Week 1) to post treatment (Week 12) | Change in depressive symptoms measured by the Patient Health Questionnaire 8 item scale (PHQ 8). |
| Nearable Sleep Sensing (Sound) | Daily from week two to week eight | Sound data (decibels) from the iPhone will be used to estimate time in bed, sleep/wake, sleep onset latency, wake after sleep onset, and other sleep related metrics. |
| Wearable Sleep Sensing (Motion) | Daily from week two to week eight | Motion (up to 50 hertz x, y, z actigraphy) from the Apple Watch will be used to estimate time in bed, sleep/wake, sleep onset latency, wake after sleep onset, and other sleep related metrics. |
Countries
United States
Contacts
Primary ContactDaniel Gartenberg, PhD
Backup ContactMelissa Markovitz
Outcome results
None listed