Distinguishing True Sedentary From Accelerometer Non-wearing Time: Accuracy Of Two Automated Wear-time Estimations

Winkler, Elisabeth; Gardiner, Paul A.; Healy, Genevieve; Clark, Bronwyn; Sugiyama, Takemi; Matthews, Charles E.; Owen, Neville

doi:10.1249/01.mss.0000354172.85505.33

Cited by 8 publications

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“…These methods were consistent with recommendations and common practices. 12,16 Data were scored with MeterPlus 4.3 software, with Freedson's cutpoint of 1952 cpm for moderate intensity to derive the outcome variable, mean minutes of MVPA per valid day. 17 Variables related to built environment were created with GIS software.…”

Section: Discussionmentioning

confidence: 99%

Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study

et al. 2016

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Section: Discussionmentioning

confidence: 99%

Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study

et al. 2016

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“…However, accuracy of these thresholds was not reported. Finally, Winkler et al (2009) compared the accuracy of a 60-min zero-count threshold allowing for no more than 2 min of active counts (either < 50 counts or < 100 counts) in free-living adults. Their findings showed that both approaches were similar in detecting nonwear time and misclassification of sedentary time when using the < 50 count criterion.…”

Section: Discussionmentioning

confidence: 99%

“…It is expected that greater variance will be found with differing population groups. Winkler et al (2009) reported substantial misclassification of sedentary time when using algorithms to identify nonwear time in adults classified as older, unemployed, or of a higher body mass. It is likely that more sophisticated techniques and modeling to classify sedentary behavior accurately will emerge in light of the independent relationship sedentary activities have with numerous chronic conditions (Hamilton, Hamilton, & Zderic, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…When there are extended consecutive epochs with zero counts, it is not possible to discern whether they result from sedentary behavior during accelerometer wear or if the unit was removed for a period of time (reasons may include forgetfulness, participation in contact or water sports, bathing, etc.). A limited number of studies have considered this issue, with inconsistent findings (Choi, Liu, Matthews, & Buchowski, 2010;Evenson & Terry, 2009;Song et al, 2010;Winkler, Gardiner, Healy, Clark, & Sugiyama, 2009). Combined, the results of these earlier studies suggest the most appropriate way to assess nonwear time may be to use a threshold of approximately 60-90 min of consecutive zero counts and allow up to 2 min of nonzero counts on either side.…”

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Identification of Accelerometer Nonwear Time and Sedentary Behavior

Oliver

Badland

Schofield

et al. 2011

Research Quarterly for Exercise and Sport

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“…The algorithm was not optimized to detect wear/nonwear during sleep; it would not be expected to perform well in that condition. Although to our knowledge the algorithm has never been independently validated in laboratory-based study, it has proven useful to automatically classify wear/nonwear time intervals (35). …”

Section: Introductionmentioning

confidence: 99%

Validation of Accelerometer Wear and Nonwear Time Classification Algorithm

Choi

Liu²,

Matthews³

et al. 2011

Medicine &Amp; Science in Sports &Amp; Exercise

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Introduction The use of movement monitors (accelerometers) for measuring physical activity (PA) in intervention and population-based studies is becoming a standard methodology for the objective measurement of sedentary and active behaviors and for validation of subjective PA self-reports. A vital step in PA measurements is classification of daily time into accelerometer wear and nonwear intervals using its recordings (counts) and an accelerometer-specific algorithm. Purpose To validate and improve a commonly used algorithm for classifying accelerometer wear and nonwear time intervals using objective movement data obtained in the whole-room indirect calorimeter. Methods We conducted a validation study of a wear/nonwear automatic algorithm using data obtained from 49 adults and 76 youth wearing accelerometers during a strictly monitored 24-h stay in a room calorimeter. The accelerometer wear and nonwear time classified by the algorithm was compared with actual wearing time. Potential improvements to the algorithm were examined using the minimum classification error as an optimization target. Results The recommended elements in the new algorithm are: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero/nonzero counts, and 3) allowance of 2-min interval of nonzero counts with the up/downstream 30-min consecutive zero counts window for detection of artifactual movements. Compared to the true wearing status, improvements to the algorithm decreased nonwear time misclassification during the waking and the 24-h periods (all P < 0.001). Conclusions The accelerometer wear/nonwear time algorithm improvements may lead to more accurate estimation of time spent in sedentary and active behaviors.

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Distinguishing True Sedentary From Accelerometer Non-wearing Time: Accuracy Of Two Automated Wear-time Estimations

Cited by 8 publications

References 0 publications

Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study

Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study

Identification of Accelerometer Nonwear Time and Sedentary Behavior

Validation of Accelerometer Wear and Nonwear Time Classification Algorithm

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