Personalized User Modelling for Sleep Insight

Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review

“…Lullaby [22] 2012 America -ShutEye [53] 2012 America -SleepCoacher [43] 2016 America Self-experimentation CBSR [42] 2018 America -PUM [41] 2020 America Self-knowledge discovery SleepBandit [54] 2020 America Self-experimentation PARIS [40] 2021 America -Clinical Sleepio [55] 2012 Europe -Sleepcare [56] 2017 Europe -SMSR [57] 2018 Asia -SRT [58] 2021 Oceania -Insomnia Coach [59] 2022 America -Figure 1. Architecture of CASHRS.…”

“…Especially, clinical CASHRS often relied on a user's average sleep quality in the past week, such as time in bed (TIB), total sleep time (TST), and sleep efficiency (SE), together with a user's preference, to adjust the recommended sleep window (either TST or bedtime/wake time) in a sleep restriction therapy [55][56][57][58][59]. On the other hand, temporal context (e.g., time of the day, weekday, meal time) was only considered in three CASHRS [22,41,53]. The environmental context describing the environmental situation when sleep takes place, such as ambient temperature, light, and noise levels, was considered in two CASHRS [22,41].…”

“…On the other hand, temporal context (e.g., time of the day, weekday, meal time) was only considered in three CASHRS [22,41,53]. The environmental context describing the environmental situation when sleep takes place, such as ambient temperature, light, and noise levels, was considered in two CASHRS [22,41]. In addition to monitoring the environmental context before bedtime or during sleep, the design of the Lullaby system also placed strong emphasis on monitoring these factors during daytime using the embedded sensors [22].…”

“…A problem of relying on population-level knowledge is that the recommendations may not ensure the desired homogeneous response from individual users due to interpersonal differences [83]. Only three systems implemented recommendation algorithms other than a knowledge-based algorithm: CBSR [42] and PARIS [40] used a collaborative algorithm to overcome the coldstart problem, while PUM [41] combined knowledge-based with content-based algorithms to leverage a user's self-knowledge extracted from self-tracking data. In a sense, the collaborative and content-based algorithms used in the three systems still relied on some kind of knowledge, but such knowledge was obtained using new approaches that deviated from the traditional large-sample experiment design.…”

“…On the other hand, sleep health RS for non-clinical populations is relatively new and has only started to attract research interest very recently [40][41][42][43]. Popular consumer sleep trackers such as Fitbit and Oura all provide generic sleep hygiene tips to facilitate stimulus control and relaxation before bed.…”