A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer

Hees, Vincent T. van; Sabia, Séverine; Anderson, Kelly; Denton, Sarah J.; Oliver, James; Catt, Michael; Abell, Jessica; Kivimäki, Mika; Trenell, Michael I.; Singh‐Manoux, Archana

doi:10.1371/journal.pone.0142533

Cited by 531 publications

(497 citation statements)

References 34 publications

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“…Time periods with low variability in the z‐angle of the accelerometer (ie, <5° over 5 minutes) were defined as sustained inactivity periods . Then, an automatized algorithm was used to detect sleep onset and offset, and sustained inactivity periods within these times were classified as sleep .…”

Section: Methodsmentioning

confidence: 99%

Twenty four‐hour activity cycle in older adults using wrist‐worn accelerometers: The seniors‐ENRICA‐2 study

Cabanas‐Sánchez

Esteban‐Cornejo

Migueles

et al. 2020

Scandinavian Med Sci Sports

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Objectives This study aimed: (a) to provide a detailed description of sleep, sedentary behavior (SED), light physical activity (LPA), and moderate‐to‐vigorous physical activity (MVPA) over the complete 24‐hours period using raw acceleration data in older adults; and (b) to examine the differences in the 24‐hours activity cycle by sex, age, education, and body mass index (BMI). Methods Population‐based cohort comprising 3273 community‐dwelling individuals (1739 women), aged 71.8 ± 4.5 years, participating in the Seniors‐ENRICA‐2 study. Participants wore a wrist‐worn ActiGraph GT9X accelerometer for 7 consecutive days, and the raw signal was processed using the R‐package GGIR. Results Participants reached 21.5 mg as mean acceleration over the whole day; 32.3% (7.7 h/d) of time was classified as sleep, 53.2% (12.7 h/d) as SED, 10.4% (148.6 min/d) as LPA, and 4.1% (59.0 min/d) as MVPA. No marked differences were found in sleep‐related variables between socio‐demographic and BMI groups. However, women showed higher LPA but lower SED and MVPA than men. Moreover, SED increased whereas LPA and MVPA decreased with age. Participants with obesity (BMI ≥ 30 kg/m2) accumulated more SED and less LPA and MVPA than those without obesity. As expected, adherence to physical activity recommendations varied widely (9.2%‐76.6%) depending on the criterion of MVPA accumulation. Conclusion Objective assessment of the 24‐hour activity cycle provides extensive characterization of daily activities distribution in older adults and may inform health‐promotion interventions in this population. Women, the oldest old, and those with obesity offer relevant targets of strategies to improve lifestyle patterns.

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Section: Methodsmentioning

confidence: 99%

Twenty four‐hour activity cycle in older adults using wrist‐worn accelerometers: The seniors‐ENRICA‐2 study

Cabanas‐Sánchez

Esteban‐Cornejo

Migueles

et al. 2020

Scandinavian Med Sci Sports

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“…As part of the accelerometer measurement, sleep periods were detected using a previously validated algorithm34. Data from the first waking to waking on the penultimate day were extracted, corresponding to 7 full days.…”

Section: Methodsmentioning

confidence: 99%

Accelerometer assessed moderate-to-vigorous physical activity and successful ageing: results from the Whitehall II study

Menai

Hees

Elbaz

et al. 2017

Sci Rep

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130

129

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Physical activity is key for successful ageing, but questions remain regarding the optimal physical activity pattern. We examined the cross-sectional association between physical activity and successful ageing using data on 3,749 participants (age range = 60–83years) of the Whitehall II study. The participants underwent a clinical assessment, completed a 20-item physical activity questionnaire, and wore a wrist-mounted accelerometer for 9 days. Successful ageing was defined as good cognitive, motor, and respiratory functioning, along with absence of disability, mental health problems, and major chronic diseases. Time spent in moderate-to-vigorous physical activity (MVPA) episodes assessed by accelerometer was classified as “short” (1–9.59 minutes) and “long” (≥10 minutes) bouts. Linear multivariate regression showed that successful agers (N = 789) reported 3.79 (95% confidence interval (CI): 1.39–6.19) minutes more daily MVPA than other participants. Accelerometer data showed this difference to be 3.40 (95% CI:2.44–4.35) minutes for MVPA undertaken in short bouts, 4.16 (95% CI:3.11–5.20) minutes for long bouts, and 7.55 (95% CI:5.86–9.24) minutes for all MVPA bouts lasting 1 minute or more. Multivariate logistic regressions showed that participants undertaking ≥150 minutes of MVPA per week were more likely to be successful agers with both self-reported (Odd Ratio (OR) = 1.29,95% (CI):1.09–1.53) and accelerometer data (length bout ≥1 minute:OR = 1.92, 95%CI:1.60–2.30). Successful agers practice more MVPA, having both more short and long bouts, than non-successful agers.

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“…Sleep was estimated within the GGIR R package (version 1.2–11; ). Briefly, nocturnal periods of time where there was no change in arm angle greater than 5 degrees over at least 5 min, were classified as sleep periods [53]. …”

Section: Methodsmentioning

confidence: 99%

Predictors of Segmented School Day Physical Activity and Sedentary Time in Children from a Northwest England Low-Income Community

Taylor

Curry

Knowles

et al. 2017

IJERPH

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Background: Schools have been identified as important settings for health promotion through physical activity participation, particularly as children are insufficiently active for health. The aim of this study was to investigate the child and school-level influences on children′s physical activity levels and sedentary time during school hours in a sample of children from a low-income community; Methods: One hundred and eighty-six children (110 boys) aged 9–10 years wore accelerometers for 7 days, with 169 meeting the inclusion criteria of 16 h∙day−1 for a minimum of three week days. Multilevel prediction models were constructed to identify significant predictors of sedentary time, light, and moderate to vigorous physical activity during school hour segments. Child-level predictors (sex, weight status, maturity offset, cardiorespiratory fitness, physical activity self-efficacy, physical activity enjoyment) and school-level predictors (number on roll, playground area, provision score) were entered into the models; Results: Maturity offset, fitness, weight status, waist circumference-to-height ratio, sedentary time, moderate to vigorous physical activity, number of children on roll and playground area significantly predicted physical activity and sedentary time; Conclusions: Research should move towards considering context-specific physical activity and its correlates to better inform intervention strategies.

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A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer

Cited by 531 publications

References 34 publications

Twenty four‐hour activity cycle in older adults using wrist‐worn accelerometers: The seniors‐ENRICA‐2 study

Twenty four‐hour activity cycle in older adults using wrist‐worn accelerometers: The seniors‐ENRICA‐2 study

Accelerometer assessed moderate-to-vigorous physical activity and successful ageing: results from the Whitehall II study

Predictors of Segmented School Day Physical Activity and Sedentary Time in Children from a Northwest England Low-Income Community

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