Generative AI for Sleep Promotion
ConditionSleep Problems
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18-75
SponsorNew York University
About this trial
Sleep is a major public health concern in the United States. Major guidelines recommend that adults obtain at least 7 hours of sleep per night. More than 25% of U.S. adults do not meet this recommendation, and 14.5% report difficulty falling asleep on most days or every day during the past month. The investigators developed a low-cost, easily accessible sleep intervention using artificial intelligence (AI) technologies. The purpose of this study is to examine the effect of this intervention on sleep outcomes among U.S. adults.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Aged 18-75 years
had a raw score of 23 or higher on the Patient-Reported Outcomes Measurement Information System (PROMIS™) Sleep Disturbance
had a smartphone and be able to access the internet
willing and able to participate through text messages on a smartphone
Disqualifiers
pregnant or lactating
concurrently participating in other research studies focused on lifestyle modifications
Trial design
Design model
Parallel
Treatments tested in this trial
AI-based chatbot
BehavioralParticipants engage with an AI chatbot for two weeks to learn personalized education and skills regarding sleep promotion
Treatment groups
60 Participants
are divided into 2 treatment groupsGroup A: InterventionExperimental treatment 1 intervention
Group B: Waitlist controlActive comparator 1 intervention
Trial outcomes
Primary outcomes
1
Fitbit-measured sleep duration
Sleep duration will be measured objectively using Fitbit and defined as the total amount of time spent asleep during the primary sleep period, expressed in minutes.
Time frame
Baseline, 2-week post-intervention, and 3- and 6-month follow-upsSecondary outcomes
1
Fitbit-measured sleep onset
Sleep onset will be measured objectively using Fitbit and defined as the time from attempting to fall asleep to sleep onset, expressed in minutes.
Time frame
Baseline, 2-week post-intervention, and 3- and 6-month follow-ups2
Fitbit-measured sleep efficiency
Sleep efficiency will be measured objectively using Fitbit and defined as the percentage of time spent asleep while in bed, expressed as a percentage (%).
Time frame
Baseline, 2-week post-intervention, and 3- and 6-month follow-ups3
Fitbit-measured wake after sleep onset
Wake after sleep onset will be measured objectively using Fitbit and defined as the total amount of time participants are awake after initially falling asleep until the final awakening, expressed in minutes.
Time frame
Baseline, 2-week post-intervention, and 3- and 6-month follow-ups4
Perceived sleep quality
Perceived sleep quality measured by the Pittsburgh Sleep Quality Index. The total score ranges from 0 to 21, with higher scores indicating worse sleep quality.
Time frame
Baseline, 2-week post-intervention, and 3- and 6-month follow-upsSponsors and contacts
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Source ClinicalTrials.gov