Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 1 May 2022 / Approved: 5 May 2022 / Online: 5 May 2022 (09:34:09 CEST)

How to cite: Liang, Z. Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review. Preprints 2022, 2022050029 (doi: 10.20944/preprints202205.0029.v1). Liang, Z. Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review. Preprints 2022, 2022050029 (doi: 10.20944/preprints202205.0029.v1).

Abstract

The practice of quantified-self sleep tracking is increasingly common nowadays among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization, and are incapable of providing actionable recommendations that are tailored to users' physical, behavioral and environmental context. Here we coined the term context-aware sleep health recommender system (CASHRS) as an emerging multidisciplinary research field that bridges ubiquitous sleep computing and context-aware recommender systems. In this paper, we presented a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges in peer-reviewed publications that meet the characteristics of CASHRS. Analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS.

Keywords

Sleep tracking; Context aware recommender system; Quantified self; Personal informatics; Ubiquitous computing; Mobile computing; mHealth; CBI-I

Subject

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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