Automated detection of sleep-boundary times using wrist-worn accelerometry

O’Donnell, Johanna; Hollowell, Sven; Salimi‐Khorshidi, Gholamreza; Velardo, Carmelo; Sexton, Claire E.; Rahimi, Kazem; Johansen‐Berg, Heidi; Tarassenko, Lionel

doi:10.1101/225516

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…Our findings of the reasons for outlier values were in line with previous research [ 42 ], which have indicated that low activity before bedtime or after wake-up time makes it difficult to detect sleep period from the acceleration data. Also, wakefulness during the night was reported as a challenge in sleep duration detection from acceleration data [ 32 ].…”

Section: Discussionsupporting

confidence: 91%

“…Also, wakefulness during the night was reported as a challenge in sleep duration detection from acceleration data [ 32 ]. O’Donnell and colleagues have discussed that errors in self-reporting were a reason for the outliers [ 42 ]. Sleep period detection with a wearable device such as Polar Active or Ōura ring happens without any actions by the user, unlike in sleep diaries.…”

Section: Discussionmentioning

confidence: 99%

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Comparison and agreement between device-estimated and self-reported sleep periods in adults

et al. 2023

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Objectives Discriminating sleep period from accelerometer data remains a challenge despite many studies have adapted 24-h measurement protocols. We aimed to compare and examine the agreement among device-estimated and self-reported bedtime, wake-up time, and sleep periods in a sample of adults. Materials and methods Participants (108 adults, 61 females) with an average age of 33.1 (SD 0.4) were asked to wear two wearable devices (Polar Active and Ōura ring) simultaneously and record their bedtime and wake up time using a sleep diary. Sleep periods from Polar Active were detected using an in-lab algorithm, which is openly available. Sleep periods from Ōura ring were generated by commercial Ōura system. Scatter plots, Bland–Altman plots, and intraclass correlation coefficients (ICCs) were used to evaluate the agreement between the methods. Results Intraclass correlation coefficient values were above 0.81 for bedtimes and wake-up times between the three methods. In the estimation of sleep period, ICCs ranged from 0.67 (Polar Active vs. sleep diary) to 0.76 (Polar Active vs. Ōura ring). Average difference between Polar Active and Ōura ring was −1.8 min for bedtimes and −2.6 min for wake-up times. Corresponding values between Polar Active and sleep diary were −5.4 and −18.9 min, and between Ōura ring and sleep diary −3.6 min and −16.2 min, respectively. Conclusion Results showed a high agreement between Polar Active activity monitor and Ōura ring for sleep period estimation. There was a moderate agreement between self-report and the two devices in estimating bedtime and wake-up time. These findings suggest that potentially wearable devices can be interchangeably used to detect sleep period, but their accuracy remains limited. Key Messages Estimation of sleep period from different devices could be comparable. Difference between sleep periods from monitors and sleep diary are under 20 min. Device-based estimation of sleep period is encouraged in population-based studies.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Comparison and agreement between device-estimated and self-reported sleep periods in adults

et al. 2023

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“…Another strength was that both wrist acceleration and PAEE was assessed simultaneously, thus providing more accurate stratification by PAEE levels; however a limitation of our work is that we only measured physical activity during one week of monitoring, and this may not be representative of habitual behavior in this population. Another potential limitation is the separation between static and dynamic wrist acceleration; as has been previously addressed, the highand low-pass filter parameters does not perfectly discriminate between static and dynamic and a small proportion of real movement will be missed during rapid rotations (O'Donnell et al, 2017). Nonetheless, this is likely to only bias the movement differences we observe towards the null, since younger and slimmer individuals are more able to produce more rapid movements, and it will likely not impact much on the postural measures, as the gravitational acceleration component is several orders of magnitude larger than residual movement in the lowpass filtered signal, thus still returning a valid estimate of the relative distribution of gravity in the three axes.…”

Section: Discussionmentioning

confidence: 99%

Diurnal Profiles of Physical Activity and Postures Derived From Wrist-Worn Accelerometry in UK Adults

Perez-Pozuelo

White

Westgate

et al. 2020

Journal for the Measurement of Physical Behaviour

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Background: Wrist-worn accelerometry is the commonest objective method for measuring physical activity in large-scale epidemiological studies. Research-grade devices capture raw triaxial acceleration which, in addition to quantifying movement, facilitates assessment of orientation relative to gravity. No population-based study has yet described the interrelationship and variation of these features by time and personal characteristics. Methods: 2,043 United Kingdom adults (35–65 years) wore an accelerometer on the non-dominant wrist and a chest-mounted combined heart-rate-and-movement sensor for 7 days free-living. From raw (60 Hz) wrist acceleration, we derived movement (non-gravity acceleration) and pitch and roll (forearm) angles relative to gravity. We inferred physical activity energy expenditure (PAEE) from combined sensing and sedentary time from approximate horizontal arm angle coupled with low movement. Results: Movement differences by time-of-day and day-of-week were associated with forearm angles; more movement in downward forearm positions. Mean (SD) movement was similar between sexes ∼31 (42) mg, despite higher PAEE in men. Women spent longer with the forearm pitched >0°, above horizontal (53% vs 36%), and less time at <0° (37% vs 53%). Diurnal pitch was 2.5–5° above and 0–7.5°below horizontal during night and daytime, respectively; corresponding roll angles were ∼0° (hand flat) and ∼20° (thumb-up). Differences were more pronounced in younger participants. All diurnal profiles indicated later wake-times on weekends. Daytime pitch was closer to horizontal on weekdays; roll was similar. Sedentary time was higher (17 vs 15 hours/day) in obese vs normal-weight individuals. Conclusions: More movement occurred in forearm positions below horizontal, commensurate with activities including walking. Findings suggest time-specific population differences in behaviors by age, sex, and BMI.

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“…Another strength was that both wrist acceleration and PAEE was assessed simultaneously, thus providing more accurate stratification by PAEE levels; however a limitation of our work is that we only measured physical activity during one week of monitoring, and this may not be representative of habitual behaviour in this population. Another potential limitation is the separation between static and dynamic wrist acceleration; as has been previously addressed, the high-and low-pass filter parameters does not perfectly discriminate between static and dynamic and a small proportion of real movement will be missed during rapid rotations (34). Nonetheless, this is likely to only bias the movement differences we observe towards the null, since younger and slimmer individuals are more able to produce more rapid movements, and it will likely not impact much on the postural measures, as the gravitational acceleration component is several orders of magnitude larger than residual movement in the low-pass filtered signal, thus still returning a valid estimate of the relative distribution of gravity in the three axes.…”

Section: Discussionmentioning

confidence: 99%

Diurnal profiles of physical activity and postures derived from wrist-worn accelerometry in UK adults

Perez-Pozuelo

White

Westgate

et al. 2019

Preprint

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7Background 8 Wrist-worn accelerometry is the commonest objective method for measuring physical activity 9 in large-scale epidemiological studies. Research-grade devices capture raw triaxial 1 0 acceleration which, in addition to quantifying movement, facilitates assessment of orientation 1 1 relative to gravity. No population-based study has yet described the interrelationship and 1 2 variation of these features by time and personal characteristics. 1 3 Methods 1 4 2043 UK adults (35-65years) wore an accelerometer on the non-dominant wrist and a chest-1 5mounted combined heart-rate-and-movement sensor for 7days free-living. From raw (60Hz) 1 6 wrist acceleration, we derived movement (non-gravity acceleration) and pitch and roll (arm) 1 7 angles relative to gravity. We inferred physical activity energy expenditure (PAEE) from 1 8 combined sensing and sedentary time from approximate horizontal arm-angle coupled with 1 9 low movement. 2 0 Results 2 1 Movement differences by time-of-day and day-of-week were associated with arm-angles; 2 2 more movement in downward arm-positions. Mean(SD) movement was similar between 2 3 sexes ~31(42)mg, despite higher PAEE in men, 53(22) vs 48(19)J⋅min -1 ⋅ kg -1 . Women spent 2 4longer with the arm pitched >0° (53% vs 36%) and less time at <0° (37% vs 53%). Diurnal 2 5 pitch was 2.5-5° above and 0-7.5° below horizontal during night and daytime, respectively; 2 6 corresponding roll angles were ~0° and ~20° (thumb-up). Differences were more pronounced 2 7 in younger participants. All diurnal profiles indicated later wake-times on weekends. Daytime 2 8pitch was closer to horizontal on weekdays; roll was similar. Sedentary time was higher (17 2 9 vs 15hours/day) in obese vs normal-weight individuals. 3 0 Conclusions 3 1

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Automated detection of sleep-boundary times using wrist-worn accelerometry

Cited by 6 publications

References 21 publications

Comparison and agreement between device-estimated and self-reported sleep periods in adults

Comparison and agreement between device-estimated and self-reported sleep periods in adults

Diurnal Profiles of Physical Activity and Postures Derived From Wrist-Worn Accelerometry in UK Adults

Diurnal profiles of physical activity and postures derived from wrist-worn accelerometry in UK adults

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