278 Understanding Perspectives on Artificial Intelligence Technologies for Sleep Self-Management

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):

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review

“…The majority of research focusing on AI aid for informal caregivers primarily targets those caring for patients with Alzheimer's and dementia (35)(36)(37), neglecting the unique assistance needs of caregivers for patients with other chronic diseases. As the need for long-term care grows daily and informal caregivers play a pivotal role, we explored AIdriven solutions addressing informal caregiver burdens beyond those of dementia and Alzheimer's patients.…”

“…This is achieved through facilitating coordinated care and analyzing data from various facets of a patient's life and care, taking into account their frailty [36]. Such data encompass aspects like sleep quality [37] as well as seniors' mobility, cognitive functions, and communication abilities [38]; however, these tools predominantly serve patients or professional caregivers in hospitals and nursing centers [39][40][41], or solely collect patient data through smart homes and ambient assisted living, offering little to no guidance or information to informal caregivers [42][43][44]. These tools are crucial in caregiving, significantly easing the burden on caregivers; however, they primarily focus on the routine and daily care of elderly individuals [45,46], rather than addressing the specific needs of patients.…”