A Personalized Approach for Detecting Unusual Sleep from Time Series Sleep-Tracking Data

Liang, Zilu; Martell, Mario Alberto Chapa; Nishimura, Takuichi

doi:10.1109/ichi.2016.99

Cited by 20 publications

(24 citation statements)

References 20 publications

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“…We identified 23 publications making theoretical contributions, including models of personal informatics [4,77,168,204], frameworks describing how tracking technology can best support a domain or practice (e.g., transformative reflection [266], serious mental illness [202], diabetes [128], productivity [290]), and defining terms (e.g., adherence [275]). The 11 methodological contributions offered new strategies for analyzing selftracked data (e.g., Bayesian analysis for self-experimentation [256], personalized models for event detection from self-tracked data [169]) and approaches for using self-tracked data to understand people's everyday experiences [48,99,117].…”

Section: Rq4: Fewer Artifact Contributions In Later Yearsmentioning

confidence: 99%

Mapping and Taking Stock of the Personal Informatics Literature

Epstein

Caldeira

Figueiredo

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

105

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The research community on the study and design of systems for personal informatics has grown over the past decade. To take stock of what the topics the field has studied and methods the field has used, we map and label 523 publications from ACM's library, IEEE Xplore, and PubMed. We surface that the literature has focused on studying and designing for health and wellness domains, an emphasis on understanding and overcoming barriers to data collection and reflection, and progressively fewer contributions involving artifacts being made. Our mapping review suggests directions future research could explore, such as identifying and resolving barriers to tracking stages beyond collection and reflection, engaging more with domain experts, and further discussing the privacy and ethical concerns around tracked data.

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Section: Rq4: Fewer Artifact Contributions In Later Yearsmentioning

confidence: 99%

Mapping and Taking Stock of the Personal Informatics Literature

Epstein

Caldeira

Figueiredo

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

105

View full text Add to dashboard Cite

show abstract

“…There is increasing evidence that consumer sleep-monitoring wristbands raise awareness of sleep health and have a positive impact on personal sleep hygiene [4][5][6], though the long-term impact of these technologies has not been elucidated [7]. In the meantime, researchers and clinicians are increasingly adopting consumer wristbands, such as Fitbit devices, as outcome measurement tools in research studies [6,[8][9][10][11][12][13][14]. Compared with traditional polysomnography (PSG), Fitbit devices significantly reduce the time and monetary cost for longitudinal sleep data collection, and they could provide rich information that was not possible to collect outside sleep laboratories or clinics in the past.…”

Section: Importance Of Consumer Sleep Tracking Devicesmentioning

confidence: 99%

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors (Preprint)

Liang¹,

Martell²

2019

Preprint

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BACKGROUND It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. OBJECTIVE This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. METHODS A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night’s sleep in participants’ homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed–rank test was performed to investigate the effect of user-specific factors. RESULTS Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics. CONCLUSIONS Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

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“…Most of the sleep staging algorithms are proprietary and are not made available to the public. These devices are also increasingly used in scientific studies to measure sleep outcomes [17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Not All Errors Are Created Equal: Influence of User Characteristics on Measurement Errors of Consumer Wearable Devices for Sleep Tracking

Liang¹,

Martell²

2018

EAI Endorsed Transactions on Pervasive Health and Technology

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Consumer sleep tracking devices are known to be inaccurate, but there is a lack of understanding of how user characteristics may affect the accuracy of these devices. This study aims to examine the effect of age, gender, subjective sleep quality, sleep hygiene and sleep structure on the accuracy of two consumer sleep trackers, i.e. Fitbit Charge 2 and Neuroon. Sleep data were collected from 27 healthy participants using consumer devices and a medical device concurrently. Analysis found that age, sleep hygiene and sleep structure were significantly associated to the accuracy of consumer sleep trackers, whereas no association was found on gender and subjective sleep quality. Both consumer devices had improved accuracy on total sleep time and sleep efficiency for participants who had longer, deeper and less interrupted sleep. Our findings suggest that consumer devices may not be suited for young adults and for people with short and fragmented sleep.

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A Personalized Approach for Detecting Unusual Sleep from Time Series Sleep-Tracking Data

Cited by 20 publications

References 20 publications

Mapping and Taking Stock of the Personal Informatics Literature

Mapping and Taking Stock of the Personal Informatics Literature

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors (Preprint)

Not All Errors Are Created Equal: Influence of User Characteristics on Measurement Errors of Consumer Wearable Devices for Sleep Tracking

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