Deep Sleep in Older Adults

Proactive Life Inc

Status and phase: Begins enrollment this month; Phase 2

Conditions: Insomnia; Insomnia Chronic

Treatments: Behavioral: SleepEZ CBTi; Behavioral: CBT-I-IoT-AI; Behavioral: Sleep Hygiene IoT

Study type: Interventional

Funder types: Industry; NIH

Identifiers

NCT07316153

1R43AG097205-01 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 180 estimated patients

Sex: All

Ages: 65+ years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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.

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Quadruple Blind

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180 participants in 3 patient groups

CBT-I-IoT-AI

Experimental group

Description:

Participants randomized to this study arm will experience an attempted enhancement of standard video based digital CBT-I, that is facilitated with electronic device-based interventions (Internet of Things) and artificial intelligence (AI) customizations of content. Noninvasive ambulatory worn + bedside devices deliver real-time feedback of objective data to the participant on an application interface and this data is used to customize the software and promote healthy sleep routines. In addition, daily animated videos are customized to the users' challenges. Subjective data collected using the application are available live to the participant, when appropriate. Environmental cues and notifications are programmed into IoT devices to remind patients of their behavioral prescription and to create a living-space environment that is conducive to effective therapy. This will include smart lights and sounds that cue the participant.

Treatment:

Behavioral: CBT-I-IoT-AI

Sleep-EZ CBT-I Based Solution

Active Comparator group

Description:

Participants randomized to this study arm will experience standard digital-based CBT-I that is delivered via the Path to Better Sleep program called SleepEZ that was created by Veteran Affairs (VA). The program will be administered within the SleepSpace software, which will be used to track adherence and deliver sham interventions. Users will have access to a pared-down version of the SleepSpace electronic application that enables them to track sleep in a sleep diary, access the content found in SleepEZ, and integrate with smart light bulbs and sounds to receive certain sound and light interventions. For example, the lights will brighten during the users expected circadian dip.

Treatment:

Behavioral: SleepEZ CBTi

Sleep Hygiene + IoT

Active Comparator group

Description:

Participants randomized to this study arm will experience sleep hygiene education and training, a component of CBT-I, which will also occur within the SleepSpace software to ensure all participants receive the same software platform and adherence is tracked uniformly across solutions. Users will have access to a version of our electronic application that enables them to track sleep in a sleep diary, access the animated Sleep Hygiene content created in the same way as the videos in the CBTi-IoT-AI condition, and integrate with smart light bulbs and sounds to receive certain sound and light interventions.

Treatment:

Behavioral: Sleep Hygiene IoT