Not Just a Matter of Accuracy: A fNIRS Pilot Study into Discrepancy Between Sleep Data and Subjective Sleep Experience in Quantified-Self Sleep Tracking

Liang, Zilu

doi:10.1007/978-3-030-99197-5_7

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Popular consumer sleep-tracking wearables (e.g., Fitbit, Apple Watch, Oura Ring) and mobile apps (e.g., SleepAsAndroid, SleepCycle) have attracted increasing interest in the research community as well as among end users. A large body of research surrounding these technologies has intensively studied their accuracy and validity [7][8][9][10], as well as userperceived properties including usability and credibility [11][12][13]. Some studies have also attempted to develop novel sleep staging algorithms that work with the processed data available from consumer sleep trackers [14][15][16], as well as devising new analytic methods for personalized sleep data analysis and knowledge discovery [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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

Liang

2022

Electronics

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The practice of quantified-self sleep tracking has become increasingly common 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. A promising solution to address this gap is the context-aware sleep health recommender system (CASHRS), an emerging research field that bridges ubiquitous sleep computing and context-aware recommender systems. This paper presents 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 identified in peer-reviewed publications that meet the characteristics of CASHRS. The analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS.

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Section: Introductionmentioning

confidence: 99%

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

Liang

2022

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Popular consumer sleep-tracking wearables (e.g., Fitbit, Apple Watch, Oura Ring) and mobile apps (e.g., SleepAsAndroid, SleepCycle) have attracted increasing interest in the research community as well as among end users. A large body of research surrounding these technologies has intensively studied their accuracy and validity [7][8][9][10], as well as user perceived properties including usability and credibility [11][12][13]. Some studies have also attempted to develop novel sleep staging algorithms that work with the processed data available from consumer sleep trackers [14][15][16], as well as devising new analytic methods for personalized sleep data analysis and knowledge discovery [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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

Liang¹

2022

Preprint

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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.

show abstract

Not Just a Matter of Accuracy: A fNIRS Pilot Study into Discrepancy Between Sleep Data and Subjective Sleep Experience in Quantified-Self Sleep Tracking

Cited by 2 publications

References 21 publications

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

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

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

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