Abstract
The activPAL monitor, often worn 24 h d−1, provides accurate classification of sitting/reclining posture. Without validated automated methods, diaries—burdensome to participants and researchers—are commonly used to ensure measures of sedentary behaviour exclude sleep and monitor non-wear.We developed, for use with 24 h wear protocols in adults, an automated approach to classify activity bouts recorded in activPAL ‘Events’ files as ‘sleep’/non-wear (or not) and on a valid day (or not). The approach excludes long periods without posture change/movement, adjacent low-active periods, and days with minimal movement and wear based on a simple algorithm. The algorithm was developed in one population (STAND study; overweight/obese adults 18–40 years) then evaluated in AusDiab 2011/12 participants (n = 741, 44% men, aged >35 years, mean ± SD 58.5 ± 10.4 years) who wore the activPAL3™ (7 d, 24 h d−1 protocol). Algorithm agreement with a monitor-corrected diary method (usual practice) was tested in terms of the classification of each second as waking wear (Kappa; κ) and the average daily waking wear time, on valid days. The algorithm showed ‘almost perfect’ agreement (κ > 0.8) for 88% of participants, with a median kappa of 0.94. Agreement varied significantly (p < 0.05, two-tailed) by age (worsens with age) but not by gender. On average, estimated wear time was approximately 0.5 h d−1 higher than by the diary method, with 95% limits of agreement of approximately this amount ±2 h d−1.In free-living data from Australian adults, a simple algorithm developed in a different population showed ‘almost perfect’ agreement with the diary method for most individuals (88%). For several purposes (e.g. with wear standardisation), adopting a low burden, automated approach would be expected to have little impact on data quality. The accuracy for total waking wear time was less and algorithm thresholds may require adjustments for older populations.
Highlights
Excessive time spent in sedentary behaviours—sitting or reclining while awake with low energy expenditure (⩽1.5 metabolic equivalents) (Sedentary Behaviour Research Network 2012)—has been associated with several chronic diseases and premature mortality (Thorp et al 2011, Wilmot et al 2012, Cong et al 2014, Shen et al 2014, Biswas et al 2015)
Evidence regarding the health consequences of sedentary behaviour and intervention effectiveness can be improved with the use of monitors that can assess time spent in sedentary behaviour objectively and accurately during free-living conditions
We considered our validity findings in light of an automated method that emerged after our review (Edwardson et al 2016, van der Berg et al 2016)
Summary
Excessive time spent in sedentary behaviours—sitting or reclining while awake with low energy expenditure (⩽1.5 metabolic equivalents) (Sedentary Behaviour Research Network 2012)—has been associated with several chronic diseases and premature mortality (Thorp et al 2011, Wilmot et al 2012, Cong et al 2014, Shen et al 2014, Biswas et al 2015). The methods researchers have applied for sleep and non-wear removal as identified in a recent review (Edwardson et al 2016) are varied and mostly high burden, limiting accuracy and the feasibility of collecting sedentary behaviour measures. For continuous (24 h) wear protocols, usual practice has involved excluding diary-reported sleeping periods (Ryan et al 2011, Alkhajah et al 2012, Craft et al 2012, Gorman et al 2013, Reid et al 2013, Berendsen et al 2014, Aguilar-Farias et al 2015). These low-burden methods have no published validity and key limitations (Edwardson et al 2016). Acceleration data have been used to this end (Harrington et al 2011, Barreira et al 2015a) with an unknown degree of validity
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