Sleep Chatbot Intervention for Emerging Black/African American Adults
- Conditions
- Sleep DeprivationInsomniaMetabolic Syndrome
- Interventions
- Behavioral: sleep chatbot
- Registration Number
- NCT05956886
- Lead Sponsor
- University of Delaware
- Brief Summary
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.
- Detailed Description
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.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 24
- 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).
- 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.
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description sleep chatbot intervention sleep chatbot Using CBT-I principles, participants will receive a four-week intervention delivered through a chatbot. The self-administered intervention is comprised of personalized behavioral prescriptions based on stimulus control principles and sleep schedule modification goals using sleep efficiency (SE) criteria. Participants are allowed to self-adjust expectations and make realistic decisions on sleep schedules. Other CBT-I components will be used as on-demand content. The chatbot will facilitate sleep goal setting with the participant, communicate weekly behavioral prescription and CBT-I educational modules, collect sleep diary and provide adaptive feedback and reactive services (e.g. Q\&A conversations) 24/7.
- Primary Outcome Measures
Name Time Method Total sleep time Change from Baseline total sleep time in the end of intervention and 4-week follow-up. The total amount of sleep time (hours) will be estimated each night for seven consecutive days using a wrist-worn ActiGraph GT9X Link. The average sleep time over a week will be used in data analysis.
Sleep efficiency Change from Baseline sleep efficiency in the end of intervention and 4-week follow-up. Sleep efficiency (percentage of time spent asleep while in bed) will be estimated each night for seven consecutive days using a wrist-worn ActiGraph GT9X Link. The average sleep efficiency over a week will be used in data analysis. This variable indicates sleep quality.
Insomnia Severity Change from baseline score of Insomnia Severity Index in the end of intervention and 4-week follow-up. 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.
Intra-individual variability in midsleep times Change from baseline data of intra-individual variability in midsleep times in the end of intervention and 4-week follow-up. Sleep time and awakening time will be 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 will be 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 Outcome Measures
Name Time Method Metabolic health Change from baseline number of metabolic syndrome components in the end of intervention and 4-week follow-up. The total number of metabolic syndrome components, including high waist circumference, high blood pressure, high fasting triglycerides and glucose, and low HDL, will be calculated to indicate metabolic health (higher value, worse metabolic health). A point-of-care test will provide the fasting glucose and cholesterol panel.
Trial Locations
- Locations (1)
University of Delaware
🇺🇸Newark, Delaware, United States