Sleep Chatbot Intervention for Emerging Black/African American Adults

NCT05956886 | Completed DMC

• 2 other identifiers: 1901939-4P20GM113125
• Study Type: interventional
• Target: 24
• Locations: 1 country
• Sites: 1

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.

Conditions

Sleep Deprivation; Insomnia; Metabolic Syndrome

Keywords

sleep health; cardiometabolic health; emerging adults

Outcome Measures

Primary Outcomes (4)

• Total sleep time

  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.

  Change from Baseline total sleep time in the end of intervention and 4-week follow-up.

• Sleep efficiency

  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.

  Change from Baseline sleep efficiency in the end of intervention and 4-week follow-up.

• Intra-individual variability in midsleep times

  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.

  Change from baseline data of intra-individual variability in midsleep times in the end of intervention and 4-week follow-up.

• Insomnia Severity

  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.

  Change from baseline score of Insomnia Severity Index in the end of intervention and 4-week follow-up.

Secondary Outcomes (1)

• Metabolic health

  Change from baseline number of metabolic syndrome components in the end of intervention and 4-week follow-up.

Other Outcomes (3)

• Chronotype (Morningness or eveningness)

  Change from baseline score of Horne and Ostberg Morningness/Eveningness Questionnaire in the end of intervention and 4-week follow-up.

• Daytime sleepiness

  Change from baseline score of Epworth Sleepiness Scale in the end of intervention and 4-week follow-up.

• Sleep beliefs

  Change from baseline scores of Dysfunctional Beliefs and Attitudes about Sleep Scare in the end of intervention and 4-week follow-up.

Study Arms (1)

sleep chatbot intervention

EXPERIMENTAL

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.

Behavioral: sleep chatbot

Interventions

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.

sleep chatbot intervention

Eligibility Criteria

• Age: 18 Years - 25 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)

You may qualify if:

• 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).

You may not qualify if:

• 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.

Sponsors & Collaborators

• University of Delaware: lead
• National Institute of General Medical Sciences (NIGMS): collaborator

Study Sites (1)

University of Delaware

Newark, Delaware, 19716, United States

Location

Related Publications (9)

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  PMID: 28794957 BACKGROUND

• Raynor LA, Schreiner PJ, Loria CM, Carr JJ, Pletcher MJ, Shikany JM. Associations of retrospective and concurrent lipid levels with subclinical atherosclerosis prediction after 20 years of follow-up: the Coronary Artery Risk Development in Young Adults (CARDIA) study. Ann Epidemiol. 2013 Aug;23(8):492-7. doi: 10.1016/j.annepidem.2013.06.003.

  PMID: 23889858 BACKGROUND

• Kocevska D, Lysen TS, Dotinga A, Koopman-Verhoeff ME, Luijk MPCM, Antypa N, Biermasz NR, Blokstra A, Brug J, Burk WJ, Comijs HC, Corpeleijn E, Dashti HS, de Bruin EJ, de Graaf R, Derks IPM, Dewald-Kaufmann JF, Elders PJM, Gemke RJBJ, Grievink L, Hale L, Hartman CA, Heijnen CJ, Huisman M, Huss A, Ikram MA, Jones SE, Velderman MK, Koning M, Meijer AM, Meijer K, Noordam R, Oldehinkel AJ, Groeniger JO, Penninx BWJH, Picavet HSJ, Pieters S, Reijneveld SA, Reitz E, Renders CM, Rodenburg G, Rutters F, Smith MC, Singh AS, Snijder MB, Stronks K, Ten Have M, Twisk JWR, Van de Mheen D, van der Ende J, van der Heijden KB, van der Velden PG, van Lenthe FJ, van Litsenburg RRL, van Oostrom SH, van Schalkwijk FJ, Sheehan CM, Verheij RA, Verhulst FC, Vermeulen MCM, Vermeulen RCH, Verschuren WMM, Vrijkotte TGM, Wijga AH, Willemen AM, Ter Wolbeek M, Wood AR, Xerxa Y, Bramer WM, Franco OH, Luik AI, Van Someren EJW, Tiemeier H. Sleep characteristics across the lifespan in 1.1 million people from the Netherlands, United Kingdom and United States: a systematic review and meta-analysis. Nat Hum Behav. 2021 Jan;5(1):113-122. doi: 10.1038/s41562-020-00965-x. Epub 2020 Nov 16.

  PMID: 33199855 BACKGROUND

• Matricciani L, Paquet C, Fraysse F, Grobler A, Wang Y, Baur L, Juonala M, Nguyen MT, Ranganathan S, Burgner D, Wake M, Olds T. Sleep and cardiometabolic risk: a cluster analysis of actigraphy-derived sleep profiles in adults and children. Sleep. 2021 Jul 9;44(7):zsab014. doi: 10.1093/sleep/zsab014.

  PMID: 33515457 BACKGROUND

• Griggs S, Conley S, Batten J, Grey M. A systematic review and meta-analysis of behavioral sleep interventions for adolescents and emerging adults. Sleep Med Rev. 2020 Dec;54:101356. doi: 10.1016/j.smrv.2020.101356. Epub 2020 Jul 8.

  PMID: 32731152 BACKGROUND

• Stock AA, Lee S, Nahmod NG, Chang AM. Effects of sleep extension on sleep duration, sleepiness, and blood pressure in college students. Sleep Health. 2020 Feb;6(1):32-39. doi: 10.1016/j.sleh.2019.10.003. Epub 2019 Nov 19.

  PMID: 31753739 BACKGROUND

• Nicol G, Wang R, Graham S, Dodd S, Garbutt J. Chatbot-Delivered Cognitive Behavioral Therapy in Adolescents With Depression and Anxiety During the COVID-19 Pandemic: Feasibility and Acceptability Study. JMIR Form Res. 2022 Nov 22;6(11):e40242. doi: 10.2196/40242.

  PMID: 36413390 BACKGROUND

• Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med. 2019 May 16;9(3):440-447. doi: 10.1093/tbm/ibz043.

  PMID: 31094445 BACKGROUND

• Edinger JD, Arnedt JT, Bertisch SM, Carney CE, Harrington JJ, Lichstein KL, Sateia MJ, Troxel WM, Zhou ES, Kazmi U, Heald JL, Martin JL. Behavioral and psychological treatments for chronic insomnia disorder in adults: an American Academy of Sleep Medicine clinical practice guideline. J Clin Sleep Med. 2021 Feb 1;17(2):255-262. doi: 10.5664/jcsm.8986.

  PMID: 33164742 BACKGROUND

MeSH Terms

Conditions

Sleep Deprivation; Sleep Initiation and Maintenance Disorders; Metabolic Syndrome

Condition Hierarchy (Ancestors)

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

Study Officials

• Xiaopeng Ji, PhD

  University of Delaware

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Masking Details: This is a feasibility study aimed at developing a new intervention strategy.
• Purpose: TREATMENT
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Using the pretest-posttest design, the investigators will test the efficacy of the 4-week sleep chatbot intervention on improving sleep health (primary) and metabolic syndrome factors (exploratory).

Study Record Dates

• First Submitted: May 9, 2023
• First Posted: July 24, 2023
• Study Start: September 4, 2023
• Primary Completion: February 5, 2025
• Study Completion: February 5, 2025
• Last Updated: April 4, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)