About the Accuracy and Problems of Consumer Devices in the Assessment of Sleep

Ameen, Mohamed; Cheung, Lok Man; Hauser, Theresa; Hahn, Michael A.; Schabus, Manuel

doi:10.3390/s19194160

Cited by 49 publications

(36 citation statements)

References 18 publications

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“…Beyond adoption of sleep trackers by the general public, there is also growing interest from academic researchers and clinicians to better understand how to utilize sleep tracking data from consumer devices [ 6 , 10 ]. Early evidence indicated that consumer-grade sleep trackers have promise as tools for large scale sleep studies [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

Altini

Kinnunen²

2021

Sensors

111

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Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished.

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Section: Introductionmentioning

confidence: 99%

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

Altini

Kinnunen²

2021

Sensors

111

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“…Research around sleep tracking shows increased anxiety around sleep performance as a result is not uncommon (Baron et al, 2017). But research in sleep trackers also shows us how inaccurate they are (Ameen et al, 2019;Tuominen et al, 2019;Louzon et al, 2020). This is a conundrum for design, raised previously in a discussion about the "accuracy" of scales for supporting weight management (Kay et al, 2013): are we achieving what we hope to achieve for health with this dataled approach?…”

Section: Insourcing To Outsourcing Lens Of Interactive Health Techmentioning

confidence: 99%

“…In research, self-perceived sleep quality is considered to be a more important marker of success rather than duration (quantity) (Choe et al, 2015a) alone (Mary et al, 2013;Mander et al, 2017;Manzar et al, 2018). Thus, our heuristics/ experiments focused on practices that were assessed on a qualitative and experiential basis, rather than the currently dominant and possibly inaccurate (Ameen et al, 2019;Tuominen et al, 2019;Louzon et al, 2020) tracking approach of hours slept, number of interruptions, time assumed to be in a particular sleep state and so on. Our focus was: after trying this heuristic, how do you feel?…”

Section: Study Two: Sleepbetter-many Paths To Healthmentioning

confidence: 99%

Experiment in a Box (XB): An Interactive Technology Framework for Sustainable Health Practices

schraefel

Muresan

Hekler

2021

Front. Comput. Sci.

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This paper presents the Experiment in a Box (XB) framework to support interactive technology design for building health skills. The XB provides a suite of experiments—time-limited, loosely structured evaluations of health heuristics for a user-as-experimenter to select from and then test in order to determine that heuristic’s efficacy, and to explore how it might be incorporated into the person’s life and when necessary, to support their health and wellbeing. The approach leverages self-determination theory to support user autonomy and competence to build actionable, personal health knowledge skills and practice (KSP). In the three studies of XB presented, we show that with even the short engagement of an XB experiment, participants develop health practices from the interventions that are still in use long after the intervention is finished. To situate the XB approach relative to other work around health practices in HCI in particular, we contribute two design continua for this design space: insourcing to outsourcing and habits to heuristics. From this analysis, we demonstrate that XB is situated in a largely under-explored area for interactive health interventions: the insourcing and heuristic oriented area of the design space. Overall, the work offers a new scaffolding, the XB Framework, to instantiate time-limited interactive technology interventions to support building KSP that can thrive in that person, significantly both post-interventions, and independent of that technology.

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“…Participants wore an activity monitoring bracelet on the wrist of their nondominant hand every night. Daily sleep duration was monitored by a commercially available wrist-worn sleep/activity tracker, Mi band 2 [23]. Moderate intraclass correlation coefficients (ICC) of 0.62-0.75 have been reported for the Mi Band 2 sleep duration assessment [24].…”

Section: Sleep Monitoringmentioning

confidence: 99%

Daily Resting Heart Rate Variability in Adolescent Swimmers during 11 Weeks of Training

Kamandulis

Juodsnukis

Stanislovaitienė

et al. 2020

IJERPH

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Adolescent athletes are particularly vulnerable to stress. The current study aimed to monitor one of the most popular and accessible stress markers, heart rate variability (HRV), and its associations with training load and sleep duration in young swimmers during an 11-week training period to evaluate its relevance as a tool for monitoring overtraining. National-level swimmers (n = 22, age 14.3 ± 1.0 years) of sprint and middle distance events followed individually structured training programs prescribed by their swimming coach with the main intention of preparing for the national championships. HRV after awakening, during sleep and training were recorded daily. There was a consistent ~4.5% reduction in HRV after 3–5 consecutive days of high (>6 km/day) swimming volume, and an inverse relationship of HRV with large (>7.0 km/day) shifts in total training load (r = −0.35, p < 0.05). Day-to-day HRV did not significantly correlate with training volume or sleep duration. Taken together, these findings suggest that the value of HRV fluctuations in estimating the balance between the magnitude of a young athlete’s physical load and their tolerance is limited on a day-to-day basis, while under sharply increased or extended training load the lower HRV becomes an important indicator of potential overtraining.

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About the Accuracy and Problems of Consumer Devices in the Assessment of Sleep

Cited by 49 publications

References 18 publications

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

Experiment in a Box (XB): An Interactive Technology Framework for Sustainable Health Practices

Daily Resting Heart Rate Variability in Adolescent Swimmers during 11 Weeks of Training

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