Sleep Chatbot Intervention for Emerging Black/African American Adults

University of Delaware

Status: Completed

Conditions: Insomnia; Sleep Deprivation; Metabolic Syndrome

Treatments: Behavioral: sleep chatbot

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT05956886

P20GM113125 (U.S. NIH Grant/Contract)

1901939-4

Details and patient eligibility

About

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.

Full 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.

Enrollment: 24 patients

Sex: All

Ages: 18 to 25 years old

Volunteers: No Healthy Volunteers

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.

Trial design

Primary purpose: Treatment

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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24 participants in 1 patient group

sleep chatbot intervention

Experimental group

Description:

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.

Treatment:

Behavioral: sleep chatbot