This study aims to assess the perspectives and usability of different consumer sleep technologies (CSTs) that leverage artificial intelligence (AI). We answer the following research questions: (1) what are user perceptions and ideations of CSTs (phase 1), (2) what are the users’ actual experiences with CSTs (phase 2), (3) and what are the design recommendations from participants (phases 1 and 2)? In this two-phase qualitative study, we conducted focus groups and usability testing to describe user ideations of desires and experiences with different AI sleep technologies and identify ways to improve the technologies. Results showed that focus group participants prioritized comfort, actionable feedback, and ease of use. Participants desired customized suggestions about their habitual sleeping environments and were interested in CSTs+AI that could integrate with tools and CSTs they already use. Usability study participants felt CSTs+AI provided an accurate picture of the quantity and quality of sleep. Participants identified room for improvement in usability, accuracy, and design of the technologies. We conclude that CSTs can be a valuable, affordable, and convenient tool for people who have issues or concerns with sleep and want more information. They provide objective data that can be discussed with clinicians.
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):
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