Insomnia, Metabolic Syndrome, Sleep Deprivation
Conditions
Keywords
sleep health, cardiometabolic health, emerging adults
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.
Interventions
BEHAVIORALsleep chatbot
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.
Sponsors
University of Delaware
National Institute of General Medical Sciences (NIGMS)
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
TREATMENT
Masking
NONE
Masking description
This is a feasibility study aimed at developing a new intervention strategy.
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).
Eligibility
Sex/Gender
ALL
Age
18 Years to 25 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Acceptability | 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 | 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 | 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 | 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 | 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 | 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 | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sleep Self Efficacy | End of intervention (at week 4) | 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. |
| Composite Metabolic Health | End of intervention (at week 4) | 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. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORXiaopeng Ji, PhD
University of Delaware
Participant flow
Recruitment details
Participants were recruited across the campuses of the University of Delaware (UD) and Delaware State University (DSU). After IRB approval, three strategies were used to enhance enrollment: (1) Flyers were placed on approved university bulletin boards, (2) Web advertisements were posted on university websites and registered student organizations, (3) Research staff participated in undergraduate student events at UD and DSU to offer information about the study.
Pre-assignment details
There were no significant events that occurred between the enrollment and assignment of participants to an arm or group.
Baseline characteristics
| Characteristic | — |
|---|---|
| Age, Continuous | 19.91 years STANDARD_DEVIATION 1.49 |
| Ethnicity (NIH/OMB) Hispanic or Latino | 1 Participants |
| Ethnicity (NIH/OMB) Not Hispanic or Latino | 25 Participants |
| Ethnicity (NIH/OMB) Unknown or Not Reported | 0 Participants |
| Insomnia severity | 12.65 point STANDARD_DEVIATION 3.79 |
| Intra-individual variability in midsleep times | 1.28 Hour STANDARD_DEVIATION 0.69 |
| Metabolic health | 1.42 number of abnormal metabolic components STANDARD_DEVIATION 0.94 |
| PSQI total score | 8.69 point STANDARD_DEVIATION 2.84 |
| Race (NIH/OMB) American Indian or Alaska Native | 0 Participants |
| Race (NIH/OMB) Asian | 0 Participants |
| Race (NIH/OMB) Black or African American | 26 Participants |
| Race (NIH/OMB) More than one race | 0 Participants |
| Race (NIH/OMB) Native Hawaiian or Other Pacific Islander | 0 Participants |
| Race (NIH/OMB) Unknown or Not Reported | 0 Participants |
| Race (NIH/OMB) White | 0 Participants |
| Sex: Female, Male Female | 18 Participants |
| Sex: Female, Male Male | 8 Participants |
| Sleep efficiency | 81.40 % STANDARD_DEVIATION 5.14 |
| Sleep self-efficacy | 25.65 scores on a scale STANDARD_DEVIATION 5.19 |
| Total sleep time | 5.92 hours/night STANDARD_DEVIATION 1.19 |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | 0 / 26 |
| other Total, other adverse events | 0 / 26 |
| serious Total, serious adverse events | 0 / 26 |
Outcome results
None listed