Clinical trial NCT05956886: Sleep Chatbot Intervention for Emerging Black/African American Adults
Official title: Artificial Intelligence Sleep Chatbot in Emerging Black/African American Adults With Cardiometabolic Risk Factors: a Feasibility Study
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Sleep Deprivation
Insomnia
Metabolic Syndrome
Keywords: sleep health
cardiometabolic health
emerging adults
Lead sponsor: University of Delaware
Sponsor class: OTHER
Collaborators: name: National Institute of General Medical Sciences (NIGMS)
First posted: 2023-07-24
Enrollment: 24
Sex: ALL
Minimum age (years): 18
Maximum age (years): 25
Standard ages: ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: 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).
Primary purpose: TREATMENT

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Total sleep time
timeFrame: Change from Baseline total sleep time in the end of intervention and 4-week follow-up.
measure: Sleep efficiency
timeFrame: Change from Baseline sleep efficiency in the end of intervention and 4-week follow-up.
measure: Intra-individual variability in midsleep times
timeFrame: Change from baseline data of intra-individual variability in midsleep times in the end of intervention and 4-week follow-up.
measure: Insomnia Severity
timeFrame: Change from baseline score of Insomnia Severity Index in the end of intervention and 4-week follow-up.
Secondary outcomes:
measure: Metabolic health
timeFrame: Change from baseline number of metabolic syndrome components in the end of intervention and 4-week follow-up.
Other outcomes:
measure: Chronotype (Morningness or eveningness)
timeFrame: Change from baseline score of Horne and Ostberg Morningness/Eveningness Questionnaire in the end of intervention and 4-week follow-up.
measure: Daytime sleepiness
timeFrame: Change from baseline score of Epworth Sleepiness Scale in the end of intervention and 4-week follow-up.
measure: Sleep beliefs
timeFrame: Change from baseline scores of Dysfunctional Beliefs and Attitudes about Sleep Scare in the end of intervention and 4-week follow-up.
Locations:
city: Newark
country: United States
facility: University of Delaware
state: Delaware
Study officials:
affiliation: University of Delaware
name: Xiaopeng Ji, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Nolan PB, Carrick-Ranson G, Stinear JW, Reading SA, Dalleck LC. Prevalence of metabolic syndrome and metabolic syndrome components in young adults: A pooled analysis. Prev Med Rep. 2017 Jul 19;7:211-215. doi: 10.1016/j.pmedr.2017.07.004. eCollection 2017 Sep.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.