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Sample size and statistical performance in studies of sedentary behaviour – a novel approach based on compositional data analysis
University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.ORCID iD: 0000-0003-1443-6211
University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.ORCID iD: 0000-0001-9327-6177
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Background. Sedentary behaviour studies often describe the extent of sitting by a time proportion; typically per cent time spent sitting. Proportions are examples of so-called “compositional data,” since they add up to a constrained sum (i.e. 100%). Compositional data differ from non-compositional data in aspects of essential importance to their analy-sis and interpretation, including how to address variability. Compositional data analysis (CDA) acts in a space of logarithmically transformed ratios of proportions, rather than on the proportions per se. We compared the statistical properties of confidence intervals (CI) of group mean values of sitting-time proportions obtained using standard procedures and CDA, exemplified by sample sizes required to obtain a specified precision.

Methods. Sitting and non-sitting time proportions calculated from whole-day accelerom-eter recordings in 25 office workers were used as a heuristic example. Variability between subjects was assessed using standard statistics and CDA. In both cases, the size and shape of a 95% CI on the estimated mean sitting-time proportion of n subjects was assessed for different sizes of the mean and values of n.

Results. While standard CIs at a specific n are independent of the mean value and sym-metric, CDA-derived CIs are asymmetric, except at a mean of 50%, and wider at “medium” than at “extreme” mean values. In the example, a 95% CI of ±5% around the mean was ob-tained using n=26 subjects according to standard procedures. However, using CDA, upper 95% CI limits of +5% were obtained with n=5 for a mean value of 90%, but required n=58 when the mean value was 60%. Similar-sized lower 95% CI limits of -5% were obtained with n=13 and n=63 at 90% and 60% means, respectively.

Discussion. CDA-based estimates of sample sizes differed markedly from estimates based on standard statistics. Properties and implications of CDA in sedentary behaviour research deserve further consideration.

Place, publisher, year, edition, pages
2016.
Keyword [en]
sedentary behaviour, compostional data, compositional data analysis
National Category
Environmental Health and Occupational Health
Identifiers
URN: urn:nbn:se:hig:diva-21897OAI: oai:DiVA.org:hig-21897DiVA: diva2:942393
Conference
Ninth International Conference on the Prevention of Work-Related Musculoskeletal Disorders (PREMUS), June 20-23, 2016, Toronto, Canada
Available from: 2016-06-23 Created: 2016-06-23 Last updated: 2017-09-12Bibliographically approved

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Mathiassen, Svend ErikSrinivasan, Divya
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CiteExportLink to record
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Cite
Citation style
  • apa
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