Validation of the Withings Sleep Analyzer, an under-the-mattress device for the detection of moderate-severe sleep apnea syndrome

Edouard, Paul; Campo, David Gutiérrez Díaz del; Bartet, Pierre; Yang, Rui-Yi; Bruyneel, Marie; Roisman, Gabriel; Escourrou, P.

doi:10.5664/jcsm.9168

Cited by 77 publications

(64 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This requires further investigation. Nonetheless, recognizing these potential caveats, published validation data (16) and our own validation assessment (online supplement) show reasonably good agreement in AHI quantified via the under-mattress device compared to in-laboratory polysomnography.…”

Section: Discussionmentioning

confidence: 74%

“…These signals are used to estimate sleep macrostructure (total sleep time), sleep timing (bedtime and wake-time) and the AHI using automated algorithms (see supplementary material for a more detailed technology description). Clinical validation shows good agreement with polysomnography-derived AHI (16,17) with high predictive performance (88% sensitivity and 88% specificity) to classify moderate-to-severe OSA (≥15 events/h sleep). A further internal validation study in 32 participants (26 men and 6 women) independently studied at the Adelaide Institute for Sleep Health laboratory (see Supplementary Material), showed similar diagnostic performance characteristics.…”

Section: Monitoring Equipmentmentioning

confidence: 80%

“…Accordingly, this study sought to use validated ( 16 ), novel noninvasive under-mattress sensor technology in a large global population sample to estimate OSA prevalence from multinight recordings and to assess the impact of night-to-night variability in OSA severity on diagnostic classification.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea

Lechat

Naik

Reynolds

et al. 2022

Am J Respir Crit Care Med

Self Cite

103

View full text Add to dashboard Cite

At a Glance Commentary (191/200) Scientific Knowledge on the SubjectRecent studies that have assessed obstructive sleep apnea (OSA) over several consecutive nights indicate that OSA severity can vary markedly from night-to-night which may have important implications for diagnosis, management, and prevalence estimates. Prior to recent advances in non-invasive home sleep monitoring technology, it was not feasible to examine night-to-night variation in OSA severity and its potential impact on diagnostic classification and prevalence estimates over extended periods in the home setting at scale.

show abstract

Section: Discussionmentioning

confidence: 74%

Section: Monitoring Equipmentmentioning

confidence: 80%

See 1 more Smart Citation

Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea

Lechat

Naik

Reynolds

et al. 2022

Am J Respir Crit Care Med

Self Cite

103

View full text Add to dashboard Cite

show abstract

“…Nasal cannula and thermistor cannot measure inspiratory effort, and nasal cannula cannot measure mouth breathing. Though, there are also other nonwearable alternatives like using sound [24] (subject to audio noise from surroundings), visionbased solutions (affected by bed sheets), infrared solutions [25], or under-the-mattress solutions [26,27]. To the authors' knowledge, neither of these alternatives has been shown to reliably classify individual sleep apnea type, apnea duration, or temporal distribution of events.…”

Section: Discussionmentioning

confidence: 96%

A Pilot Study of Detecting Individual Sleep Apnea Events Using Noncontact Radar Technology, Pulse Oximetry, and Machine Learning

Toften¹,

Kjellstadli²,

Tyvold³

et al. 2021

Journal of Sensors

View full text Add to dashboard Cite

The gold standard for assessing sleep apnea, polysomnography, is resource intensive and inconvenient. Thus, several simpler alternatives have been proposed. However, validations of these alternatives have focused primarily on estimating the apnea-hypopnea index (apnea events per hour of sleep), which means information, clearly important from a physiological point of view such as apnea type, apnea duration, and temporal distribution of events, is lost. The purpose of the present study was to investigate if this information could also be provided with the combination of radar technology and pulse oximetry by classifying sleep apnea events on a second-by-second basis. Fourteen patients referred to home sleep apnea testing by their medical doctor were enrolled in the study (6 controls and 8 patients with sleep apnea; 4 mild, 2 moderate, and 2 severe) and monitored by Somnofy (radar-based sleep monitor) in parallel with respiratory polygraphy. A neural network was trained on data from Somnofy and pulse oximetry against the polygraphy scorings using leave-one-subject-out cross-validation. Cohen’s kappa for second-by-second classifications of no event/event was 0.81, or almost perfect agreement. For classifying no event/hypopnea/apnea and no event/hypopnea/obstructive apnea/central apnea/mixed apnea, Cohen’s kappa was 0.43 (moderate agreement) and 0.36 (fair agreement), respectively. The Bland-Altman 95% limits of agreement for the respiratory event index (apnea events per hour of recording) were -8.25 and 7.47, and all participants were correctly classified in terms of sleep apnea severity. Furthermore, the results showed that the combination of radar and pulse oximetry could be more accurate than the two technologies separately. Overall, the results indicate that radar technology and pulse oximetry could reliably provide information on a second-by-second basis for no event/event which could be valuable for management of sleep apnea. To be clinically useful, a larger study is necessary to validate the algorithm on a general population.

show abstract

“…At a second home visit, the participants were equipped with the wearable Withings Steel HR smartwatch (Withings Inc), tracking the number of steps and heart rate on a per-minute basis. Participants were also equipped with the nearable Withings Aura sleep mattress (Withings Inc), tracking the various phases of sleep (sleep onset latency, wake, light sleep, deep sleep, rapid eye movement [REM] sleep, and waking up times) on a per-minute basis [ 34 ]. The first day of data logging for the participants spanned from June 7, 2017, to September 25, 2017.…”

Section: Methodsmentioning

confidence: 99%

Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study

Óskarsdóttir¹,

Íslind²,

August³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

Background The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used. Objective The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary. Methods Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels. Results Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time. Conclusions We demonstrate that >2 months’ worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future.

show abstract

Validation of the Withings Sleep Analyzer, an under-the-mattress device for the detection of moderate-severe sleep apnea syndrome

Cited by 77 publications

References 37 publications

Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea

Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea

A Pilot Study of Detecting Individual Sleep Apnea Events Using Noncontact Radar Technology, Pulse Oximetry, and Machine Learning

Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study

Contact Info

Product

Resources

About