Sleep DeprivationNCT05956886
Sleep Chatbot Intervention for Emerging Black/African American Adults
A study testing sleep chatbot in people with Sleep Deprivation and Insomnia, run by University of Delaware at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 25 years
- Conditions the study is for
- Sleep Deprivation, Insomnia, Metabolic Syndrome
- What is being tested?
- sleep chatbot
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 26 participants
- Where
- 1 siteUnited States
- Study dates
- 4 Sep 2023to 5 Feb 2025
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 25 years
- All sexes
- No healthy volunteers
- Adult
Who may be able to join
- 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).
Who cannot join
- 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 locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence Sleep Chatbot in Emerging Black/African American Adults With Cardiometabolic Risk Factors: a Feasibility Study
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.
Read the 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.
- Conditions
- Sleep Deprivation, Insomnia, Metabolic Syndrome
- Being tested
- sleep chatbot
- Keywords
- sleep health, cardiometabolic health, emerging adults
- Medical subject headings
- Sleep Deprivation, Sleep Initiation and Maintenance Disorders, Metabolic Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
sleep chatbotBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Behavioral: sleep chatbot |
What is being measured
The primary outcome is the main question the study is designed to answer.
Acceptability
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)."
Measured: End of intervention (at week 4)
Retention Rate
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.
Measured: End of intervention (week 4) and one-month follow-up (week 8)
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.
Measured: End of intervention (at week 4)
PSQI
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.
Measured: End of intervention (at week 4)
Total Sleep Time
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.
Measured: End of intervention (at week 4)
Sleep Efficiency
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.
Measured: End of intervention (at week 4)
Intra-individual Variability in Midsleep Times
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.
Measured: End of intervention (at week 4)
Secondary outcomes (2)
Sleep Self Efficacy
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.
Measured: End of intervention (at week 4)
Composite Metabolic Health
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.
Measured: End of intervention (at week 4)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jul 2023
- Study start4 Sep 2023
- Primary completion5 Feb 2025
- Study completion5 Feb 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Sleep DeprivationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- 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).
- Masking
- None (open label)
- Enrolment
- 26 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Delaware
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute of General Medical Sciences (NIGMS)
- Responsible party
- Sponsor
- Investigators
- Xiaopeng Ji, PhD, Principal investigator, University of Delaware
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05956886
- Sponsor's protocol ID
- 1901939-4
- Other IDs
- P20GM113125
- Status last verified
- Jun 2026
- First submitted
- 9 May 2023
- Last updated
- 31 Jul 2026
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 28794957Raynor 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.
PubMed 23889858Kocevska 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.
PubMed 33199855Matricciani 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.
PubMed 33515457Griggs 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.
PubMed 32731152Stock 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.
PubMed 31753739Nicol 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.
PubMed 36413390Stephens 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.
PubMed 31094445Edinger 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.
PubMed 33164742
Study at a glance
From the registry record, last updated by the sponsor on 31 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Sleep Chatbot in Emerging Black/African American Adults With Cardiometabolic Risk Factors: a Feasibility Study
- ClinicalTrials.gov ID
- NCT05956886
- Acronym / sponsor ID
- 1901939-4
- Conditions
- Sleep Deprivation, Insomnia, Metabolic Syndrome
- Keywords
- sleep health, cardiometabolic health, emerging adults
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 26 participants (actual)
- Lead sponsor
- University of DelawareUniversity, hospital or other
- Collaborators
- National Institute of General Medical Sciences (NIGMS)
- Study start
- 4 Sep 2023 (actual)
- Primary completion
- 5 Feb 2025 (actual)
- Study completion
- 5 Feb 2025 (actual)
- First posted
- 24 Jul 2023 (actual)
- Results
- Posted 31 Jul 2026
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- 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).
| Arm | Type | Receives |
|---|---|---|
| sleep chatbot intervention | Experimental | Behavioral: sleep chatbotUsing 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. |
Who may qualify
- Ages
- 18 to 25 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (5 listed)
- 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).
Key exclusion criteria (7 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| sleep chatbotPersonalized 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. | Behavioural | sleep chatbot intervention |
Primary outcome
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)."
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.
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.
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.
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.
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.
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.
2 secondary outcomes
Sleep Self Efficacy
Time frame: End of intervention (at week 4)
Composite Metabolic Health
Time frame: End of intervention (at week 4)
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Jul 2026.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 28794957Nolan 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.
- Background: PubMed 23889858Raynor 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.
- Background: PubMed 33199855Kocevska 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.
- Background: PubMed 33515457Matricciani 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.
- Background: PubMed 32731152Griggs 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.
- Background: PubMed 31753739Stock 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.
- Background: PubMed 36413390Nicol 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.
- Background: PubMed 31094445Stephens 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.
- Background: PubMed 33164742Edinger 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Xiaopeng Ji, PhD principal investigator
University of Delaware
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing sleep chatbot in people with Sleep Deprivation and Insomnia, run by University of Delaware at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 31 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 31 Jul 2026). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Acceptability (time frame: End of intervention (at week 4)). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05956886, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Jul 2026.