278 Understanding Perspectives on Artificial Intelligence Technologies for Sleep Self-Management. (3rd May 2021)

• Record Type: Journal Article
• Authors: Kearns, William; Laine, Megan; Oh, Esther; Thompson, Hilaire; Demiris, George
• Is Part Of: Sleep. Volume 44(2021)Supplement 2
• Page Start: A111
• Page End: A111
• Publication Date: 2021-05-03
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsab072.277
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Until recently, understanding one's sleep activity relied on technology only available in sleep labs with data analyzed by experts. Transitioning this technology from the lab to natural environments results in noisy data. Fortunately, advances in signal processing through Artificial Intelligence (AI) have made these technologies accessible to consumers. This study seeks to provide recommendations that address user preferences and concerns related to sleep self-management devices and software that leverage AI, as they have the potential to increase both the quantity and quality of sleep data available to researchers. Methods: We assigned adult participants (N=25) with Pittsburgh Sleep Quality Index scores ≥ 5 (indicating low sleep quality) to one of four focus group sessions based on their self-reported prior use of sleep technologies. After a short demonstration, the moderator solicited participant feedback on devices and software in each of the following four categories: headbands (Beddr, Dreem 2, Muse S) sleep tracking mats (Withings) snoring detectors (Smart Nora) mobile applications (Sleep Cycle Alarm Clock, Sleep Score, Do I Snore, Sleep Rate) Results: Participants anticipated discomfort from wearing headbands and placing snoring detectors under their pillow, although a subset of participants indicated that they would be willing to sacrifice comfort in exchange for improved accuracy. Conversely, participants were interested in sleep tracking pads since they could passively collect sleep data without additional burden. Similarly, participants viewed mobile applications positively due to their ability to collect sleep data from a nightstand rather than being attached to the participant; however, there were concerns about remembering to activate these applications. Conclusion: Based on these results, we recommend using sleep tracking mats to collect patient-generated sleep data due to their ease of use and relative comfort, the main concerns related to lab-based sleep study participation. As a passive sensor, these require the least setup and support consistent data collection. Other devices run the risk of participants forgetting to use the device or becoming removed during the night resulting in missing data. By leveraging these existing technologies for remote sleep studies, researchers can increase recruitment and accessibility to promote sleep research participant diversity. Support (if any):