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Identifying sedentary time using automated estimates of accelerometer wear time
  1. Elisabeth A H Winkler1,
  2. Paul A Gardiner1,
  3. Bronwyn K Clark1,
  4. Charles E Matthews2,
  5. Neville Owen1,3,
  6. Genevieve N Healy1,3
  1. 1School of Population Health, The University of Queensland, Brisbane, Australia
  2. 2Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
  3. 3Baker IDI Heart and Diabetes Institute, Melbourne, Australia
  1. Correspondence to Elisabeth A H Winkler, Cancer Prevention Research Centre, School of Population Health, The University of Queensland, Herston Road, Herston, QLD 4006, Australia; e.winkler{at}uq.edu.au

Abstract

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.

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Footnotes

  • Funding BKC, PAG, GNH, EAHW and NO are supported by a Queensland Health Core Research Infrastructure grant and by NHMRC Program Grant funding (#569940). PAG is supported by a Heart Foundation of Australia (# PP 06B 2889). BKC is supported by an Australian Post-graduate Award. GNH is also supported by a NHMRC (#569861)/National Heart Foundation of Australia (PH 374 08B 3905) Postdoctoral Fellowship.

  • Competing interests None.

  • Ethics approval The study uses secondary data from two studies, each of which obtained ethical approval from relevant parties. AusDiab substudy – The University of Queensland, Ethics Committee of the International Diabetes Institute. NHANES (publically available data) had ethical approval from NHANES Institutional Review Board/NCHS Research Ethics Review Board.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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