The Future of Sleep Measurements

Arnardóttir, Erna Sif; Íslind, Anna Sigríður; Óskarsdóttir, María

doi:10.1016/j.jsmc.2021.05.004

Cited by 30 publications

(26 citation statements)

References 94 publications

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“…Although the sleep research community welcomes the advancement of consumer-grade self-trackers, including wearables and nearables, they are also widely aware of the numerous challenges that remain, especially regarding the need to validate the devices to ensure their accuracy and reliability [ 8 ]. Although many of these barriers are for wearable and nearable technology companies to solve, it is the responsibility of the sleep research community and the medical informatics community to make a collective effort and decide upon necessary and sufficient requirements for validating the devices [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

“…The gold standard measurement setting for clinical assessment of sleep quality and sleep disturbances is the use of polysomnography for 1 night, typically performed in a hospital environment [ 6 ]. Polysomnography is expensive and requires individuals to sleep with several sensors attached to their face, scalp, and body, which is cumbersome [ 7 , 8 ]. Furthermore, data from such a study gives no indication on important routine aspects of sleep quality such as the average total sleep time (TST) of individuals, when they normally go to bed and wake up, whether they are affected by seasonal changes, or whether they have insomnia [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…More recently, consumer-grade self-tracking technologies that facilitate sleep data collection over longer periods have emerged [ 12 ]. Wearable technology (wearables) is an umbrella term for body-worn connected sensors [ 8 ]. Smartwatches are an example of such wearables and can capture information similar to actigraphy.…”

Section: Introductionmentioning

confidence: 99%

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

Óskarsdóttir¹,

Íslind²,

August³

et al. 2022

JMIR Form Res

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Óskarsdóttir¹,

Íslind²,

August³

et al. 2022

JMIR Form Res

Self Cite

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“…Not surprisingly, one promising application of MRI-based brain age prediction is early detection of future neuropsychiatric disorders in children and/or adolescents (Chung et al, 2018). The relative stability of structural MRI measures bound the temporal resolution of brain age estimates using that modality (Cole & Franke, 2017;Karch et al, 2019), while the significantly higher night-to-night variability of sleep EEG-based brain age estimates is both a challenge to overcome if looking for stability, but also a potential additional source of meaningful signal to exploit in future work (Arnal et al, 2020;Arnardottir et al, 2021;Hogan et al, 2021).…”

Section: Section Iv: Brain Health As Assessed By Deviations From Heal...mentioning

confidence: 99%

How Machine Learning is Powering Neuroimaging to Improve Brain Health

et al. 2022

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This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, “Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application”, co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

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“…The average agreement for sleep stages is, for example, only 82.6% [3]. There is therefore a growing need for automation and large-scale analysis in the field of sleep medicine [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Non-Invasive Isolation of Respiratory Cycles

Holm¹,

ÓskarsdÓttir²,

ArnardÓttir³

et al. 2022

Preprint

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In this paper, we introduce a novel algorithm designed to isolate individual respiratory cycles on a thoracic respiratory inductance plethysmography signal. The algorithm locates breaths using signal processing and statistical methods and enables the analysis of sleep data on an individual breath level. The algorithm was evaluated on 7.3 hours of hand-annotated data, or 8782 individual breaths in total, and was estimated to correctly isolate 94% of respiratory cycles while producing false positives that amount to only 5% of the total number of detections. The algorithm was specifically evaluated on data containing a great number of sleep-disordered breathing events. We found that the algorithm did not suffer in terms of accuracy when detecting breaths in the presence of sleep-disordered breathing. The algorithm was also evaluated across a large set of participants, and we found that the accuracy of the algorithm was consistent across participants. This algorithm is finally made public via an open-source Python library.

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The Future of Sleep Measurements

Cited by 30 publications

References 94 publications

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

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

How Machine Learning is Powering Neuroimaging to Improve Brain Health

Automatic Non-Invasive Isolation of Respiratory Cycles

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