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Completed

NCT Number: NCT05956886

Sleep Chatbot Intervention for Emerging Black/African American Adults

Unhealthy sleep and cardiometabolic risk are two major public health concerns in emerging Black/African American (BAA) adults. Evidence-based sleep interventions such as cognitive-behavioral therapy for insomnia (CBT-I) are available but not aligned with the needs of this at-risk group. Innovative work on the development of an artificial intelligence sleep chatbot using CBT-I guidelines will provide scalable and efficient sleep interventions for emerging BAA adults.

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Key information

Conditions

Sleep Deprivation Dyssomnias Glucose Metabolism Disorders Hyperinsulinism Insomnia Insulin Resistance Mental Disorders Metabolic Diseases Metabolic Syndrome Nervous System Diseases Neurologic Manifestations Nutritional and Metabolic Diseases Pathological Conditions, Signs and Symptoms Signs and Symptoms Sleep Disorders, Intrinsic Sleep Initiation and Maintenance Disorders Sleep Wake Disorders

Age range

18 year–25 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

University of Delaware

Newark, Delaware, 19716, United States

About this study

Abnormal metabolic syndrome (MetS) components affect up to 40% of emerging adults (18-25 years), particularly Black/African Americans (BAA). MetS risk in early life tracks into adulthood and predicts cardiovascular diseases and type 2 diabetes mellitus later in life. Unhealthy sleep is a known modifiable factor for MetS components. However, the prevalence of unhealthy sleep (up to 60%) in emerging adults is alarming, potentially exacerbating downstream future cardiometabolic health. Cognitive-behavioral therapy for insomnia (CBT-I) is an evidence-based intervention for unhealthy sleep that improves both sleep quantity and quality. Compared with traditional in-person intervention paradigms, digital CBT-I has comparable efficacy with enhanced accessibility and affordability. However, current digital CBT-I based programs are unable to deliver tailored content and interactive services in a humanlike way, thus are unable to meet the needs of emerging BAA adults at risk for MetS. Building on prior work by the team, the investigators will leverage artificial intelligence (AI) technologies and refine an AI sleep chatbot using CBT-I guidelines and examine its feasibility and efficacy in a 4-week clinical trial in short-or-poor sleeping, emerging BAA adults with at least one MetS factor.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • male or female ages 18-25 years old
  • self-identified as Black/African Americans (BAA),
  • poor sleep [Insomnia severity index (ISI) >10]
  • having at least one of the cardiometabolic risk factors on the Life's Essential 8 checklist for cardiovascular health, as defined by the American Heart Association, including health factors confirmed by fasting blood testing during the first lab visit (fasting blood glucose ≥110mg/dL, high-density lipoprotein (good cholesterol) ≤ 40 mg/dL for males and ≤ 50 mg/dL for females, triglycerides ≥150mg/dL, total cholesterol ≥200 mg/dL, blood pressure ≥130/85mmHg, waist circumference≥40 inches for males, ≥35 inches for females) or healthy behaviors such as short sleep (<7 hours), smoking or inactive (<150 minutes/week of moderate aerobic activity such as gardening, social dancing, or < 75 minutes/week of vigorous aerobic activity such as running, swimming laps, jumping rope), and (e) own a smartphone (iPhone or Android).
  • own a smartphone (iPhone or Android).

Exclusion criteria

  • self-report medical conditions [i.e., major depressive disorder [Patient Health Questionnaire-9 (PHQ-9) ≥15)
  • diagnosed obstructive apnea] that may affect sleep
  • regular use of medications with substantial impact on sleep and cardio-metabolic markers
  • shift worker
  • smoker
  • alcohol abuse (Alcohol Use Disorders Identification Test--short form score ≥7 for males and ≥5 for females)
  • self-report pregnancy/lactation.

Treatment and study plan

sleep chatbot

Behavioral

Personalized intervention algorithms will be developed based on CBT-I guidelines, focus group data, individual sleep baseline information and self-reported prioritized sleep goals. The CBT-I intervention will focus on principles of sleep restriction and stimulus control, with other CBT-I components used as on-demand content. The sleep chatbot system will facilitate sleep goal-setting with the participant and communicate weekly behavioral prescriptions and educational modules. After baseline data collection, the research coordinator will provide intervention orientation and set up the first-week sleep modification goal during the in-person/Zoom meeting. Sleep modification goals in the remaining weeks will be developed through the participant-chatbot interaction. The Chatbot system will send sleep-related information and behavioral reminders/feedback based on the interactive conversation with participants. Participants will also complete a sleep diary prompted by a chatbot.

Primary outcomes

  1. Acceptability

    Time frame: End of intervention (at week 4)

    Results were report as # of participants reporting Acceptable and Completely acceptable. Acceptability question: "Overall, how acceptable was the sleep chat bot intervention to you? (Completely unacceptable; Unacceptable; No opinion; Acceptable; Completely acceptable)."

  2. Retention Rate

    Time frame: End of intervention (week 4) and one-month follow-up (week 8)

    Percentage of enrolled participants completed the intervention, completed end-of-intervention assessment, and completed one-month follow-up assessment; among those who received intervention modules (that is, excluding those who withdrew before intervention began), the rate of core module completion.

  3. Insomnia Severity

    Time frame: End of intervention (at week 4)

    The Insomnia Severity Index is composed of 7 items measuring insomnia-related sleep disturbance and daytime dysfunction. The seven answers are added up to get a total score (0-28), with higher scores indicating severer insomnia.

  4. PSQI

    Time frame: End of intervention (at week 4)

    The Pittsburgh Sleep Quality Index (PSQI) is a widely-used, self-rated questionnaire that assesses sleep quality and disturbances over a 1-month period.The scores from all seven components are summed to yield a single Global PSQI Score, ranging from 0 to 21. Greater scores mean worse sleep.

  5. Total Sleep Time

    Time frame: End of intervention (at week 4)

    The total amount of sleep time (hours) was estimated each night for seven consecutive days using a wrist-worn ActiGraph GT9X Link. The average sleep time over a week were used in data analysis.

  6. Sleep Efficiency

    Time frame: End of intervention (at week 4)

    Sleep efficiency (percentage of time spent asleep while in bed) were estimated each night for seven consecutive days using a wrist-worn ActiGraph GT9X Link. The average sleep efficiency over a week were used in data analysis. This variable indicates sleep quality.

  7. Intra-individual Variability in Midsleep Times

    Time frame: End of intervention (at week 4)

    Sleep time and awakening time were estimated for seven consecutive days using a wrist-worn ActiGraph GT9X Link. Mid-sleep time each night refers to the mid-point between sleep time and awakening time. Intra-individual variability in midsleep times were calculated as the standard deviation of the mid-sleep time over a week for each participant. This variable reflects the regularity of sleep, with higher values showing greater irregularity.

Secondary outcomes

  1. Sleep Self Efficacy

    Time frame: End of intervention (at week 4)

    The Sleep Self-Efficacy Scale, a 9-item scale assessing participants' beliefs in their ability to engage in productive sleep behaviors, measured sleep-related self efficacy. Each item was scored on a standard Likert scale from 1 (Not confident at all) to 5 (Very confident).The total scores ranged from 9 to 45, with higher scores indicating greater sleep self-efficacy.

  2. Composite Metabolic Health

    Time frame: End of intervention (at week 4)

    Composite metabolic health was calculated by the total number of metabolic syndrome components, including high BMI, high blood pressure, high fasting triglycerides and glucose, and low HDL, were calculated to indicate metabolic health (higher value, worse metabolic health). A point-of-care test provided the fasting glucose and cholesterol panel.

Sponsors and collaborators

Lead sponsor

University of Delaware

Other

Collaborators

  • National Institute of General Medical Sciences (NIGMS)

Registry information

Official study title

Artificial Intelligence Sleep Chatbot in Emerging Black/African American Adults With Cardiometabolic Risk Factors: a Feasibility Study

Important dates

Study start
2023
Primary completion
2025
Study completion
2025
First posted
Jul 24, 2023
Registry last updated
Jul 31, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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