Identifying sedentary time using automated estimates of accelerometer wear time
Purpose: The authors evaluated the accuracy of three automated accelerometer wear-time estimation algorithms against self-report. Direct effects on sedentary time (<100 cpm) and indirect effects on moderate-to-vigorous physical activity (MVPA, =1952 cpm) time were examined. Methods: A subsamp...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/27201 |
| _version_ | 1848752197845647360 |
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| author | Winkler, E. Gardiner, P. Clark, B. Matthews, C. Owen, N. Healy, Genevieve |
| author_facet | Winkler, E. Gardiner, P. Clark, B. Matthews, C. Owen, N. Healy, Genevieve |
| author_sort | Winkler, E. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Purpose: The authors evaluated the accuracy of three automated accelerometer wear-time estimation algorithms against self-report. Direct effects on sedentary time (<100 cpm) and indirect effects on moderate-to-vigorous physical activity (MVPA, =1952 cpm) time were examined. Methods: A subsample from the 2004/2005 Australian Diabetes, Obesity and Lifestyle Study (n=148) completed activity logs and wore accelerometers for a total of 987 days. A published algorithm that allows movement within non-wear periods (Algorithm 1) was compared with one that allows less movement (Algorithm 2) or no movement (Algorithm 3). Implications for population estimates were examined using 2003/2004 US National Health and Nutrition Examination Survey data. Results: Mean difference per day between the criterion and estimated wear time was negligible for all three algorithms (=11 min), but 95% limits of agreement (LOA) were wide (±=2 h). Respectively, the algorithms (1, 2 and 3) misclassified sedentary time as non-wear on 31.9%, 19.4% and 18% of days and misclassified non-wear time as sedentary on 42.8%, 43.7% and 51.3% of days. Use of Algorithm 2 (compared with Algorithm 1) affected population estimates of sedentary time (higher by 20 min/day) but not MVPA time. Agreement between Algorithms 1 and 2 was good for MVPA time (mean difference -0.08, LOA: -2.08, 1.91 min), but not for wear time or sedentary time. Conclusion: Accelerometer wear time can be estimated accurately on average; however, misclassification can be substantial for individuals. Algorithm choice affects estimates of sedentary time. Allowing very limited movement within non-wear periods can improve accuracy. |
| first_indexed | 2025-11-14T08:04:48Z |
| format | Journal Article |
| id | curtin-20.500.11937-27201 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:04:48Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-272012018-03-29T09:09:00Z Identifying sedentary time using automated estimates of accelerometer wear time Winkler, E. Gardiner, P. Clark, B. Matthews, C. Owen, N. Healy, Genevieve Purpose: The authors evaluated the accuracy of three automated accelerometer wear-time estimation algorithms against self-report. Direct effects on sedentary time (<100 cpm) and indirect effects on moderate-to-vigorous physical activity (MVPA, =1952 cpm) time were examined. Methods: A subsample from the 2004/2005 Australian Diabetes, Obesity and Lifestyle Study (n=148) completed activity logs and wore accelerometers for a total of 987 days. A published algorithm that allows movement within non-wear periods (Algorithm 1) was compared with one that allows less movement (Algorithm 2) or no movement (Algorithm 3). Implications for population estimates were examined using 2003/2004 US National Health and Nutrition Examination Survey data. Results: Mean difference per day between the criterion and estimated wear time was negligible for all three algorithms (=11 min), but 95% limits of agreement (LOA) were wide (±=2 h). Respectively, the algorithms (1, 2 and 3) misclassified sedentary time as non-wear on 31.9%, 19.4% and 18% of days and misclassified non-wear time as sedentary on 42.8%, 43.7% and 51.3% of days. Use of Algorithm 2 (compared with Algorithm 1) affected population estimates of sedentary time (higher by 20 min/day) but not MVPA time. Agreement between Algorithms 1 and 2 was good for MVPA time (mean difference -0.08, LOA: -2.08, 1.91 min), but not for wear time or sedentary time. Conclusion: Accelerometer wear time can be estimated accurately on average; however, misclassification can be substantial for individuals. Algorithm choice affects estimates of sedentary time. Allowing very limited movement within non-wear periods can improve accuracy. 2012 Journal Article http://hdl.handle.net/20.500.11937/27201 10.1136/bjsm.2010.079699 restricted |
| spellingShingle | Winkler, E. Gardiner, P. Clark, B. Matthews, C. Owen, N. Healy, Genevieve Identifying sedentary time using automated estimates of accelerometer wear time |
| title | Identifying sedentary time using automated estimates of accelerometer wear time |
| title_full | Identifying sedentary time using automated estimates of accelerometer wear time |
| title_fullStr | Identifying sedentary time using automated estimates of accelerometer wear time |
| title_full_unstemmed | Identifying sedentary time using automated estimates of accelerometer wear time |
| title_short | Identifying sedentary time using automated estimates of accelerometer wear time |
| title_sort | identifying sedentary time using automated estimates of accelerometer wear time |
| url | http://hdl.handle.net/20.500.11937/27201 |