Reconsidering the Sedentary Behaviour Paradigm
Open accessAbstract
AIMS: Recent literature has posed sedentary behaviour as an independent entity to physical inactivity. This study investigated whether associations between sedentary behaviour and cardio-metabolic biomarkers remain when analyses are adjusted for total physical activity. METHODS: Cross-sectional analyses were undertaken on 4,618 adults from the 2003/04 and 2005/06 U.S. National Health and Nutrition Examination Survey. Minutes of sedentary behaviour and moderate-to-vigorous physical activity (MVPA), and total physical activity (total daily accelerometer counts minus counts accrued during sedentary minutes) were determined from accelerometry. Associations between sedentary behaviour and cardio-metabolic biomarkers were examined using linear regression. RESULTS: Results showed that sedentary behaviour was detrimentally associated with 8/11 cardio-metabolic biomarkers when adjusted for MVPA. However, when adjusted for total physical activity, the associations effectively disappeared, except for C-reactive protein, which showed a very small, favourable association (β = -0.06) and triglycerides, which showed a very small, detrimental association (β = 0.04). Standardised betas suggested that total physical activity was consistently, favourably associated with cardio-metabolic biomarkers (9/11 biomarkers, standardized β = 0.08-0.30) while sedentary behaviour was detrimentally associated with just 1 biomarker (standardized β = 0.12). CONCLUSION: There is virtually no association between sedentary behaviour and cardio-metabolic biomarkers once analyses are adjusted for total physical activity. This suggests that sedentary behaviour may not have health effects independent of physical activity.
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The models' fit to the data was in line with previous reports, with moderate amount of variance explained for systolic blood pressure (R2 adj = 0.21), HDL (R2 adj = 0.22), CRP (R2 adj = 0.26), plasma glucose (R2 adj = 0.33), insulin (R2 adj = 0.40) and HOMA (R2 adj = 0.42).
“The models’ fit to the data was in line with previous reports [1,6,10,14–17], with moderate amount of variance explained for systolic blood pressure (R2 adj = 0.21), HDL (R2 adj = 0.22), CRP (R2 adj = 0.26), plasma glucose (R2 adj = 0.33), insulin (R2 adj = 0.40) and HOMA (R2 adj = 0.42).”
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- Authors
- Maher, C., Olds, T., Mire, E., & Katzmarzyk, P. T.
- Affiliations
- University of South Australia (AU)
- Pennington Biomedical Research Center (US)
- Corresponding author
- Maher, Carol
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-01-15
- Volume
- 9
- Issue
- 1
- Pages
- e86403
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3893290
- OpenAlex
- W1974229194
- Cited by
- 114
- References
- 25
- Citations by year
- 2026: 3 · 2025: 2 · 2023: 4 · 2022: 5 · 2021: 5 · 2020: 7 · 2019: 6 · 2018: 11 · 2017: 17 · 2016: 18 · 2015: 17 · 2014: 19
- FWCI
- 15.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- UniSA Research Outputs Repository (University of South Australia) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Keywords
- sedentary behavior, physical inactivity, moderate-to-vigorous physical activity, total physical activity, C-reactive protein, triglycerides, accelerometer-measured activity
- MeSH terms
- Adult, Blood Glucose, Blood Pressure, Cross-Sectional Studies, Female, Humans, Insulin, Cholesterol, HDL, Male, Middle Aged, Motor Activity, Risk Factors, Triglycerides, Biomarkers, Linear Models, Waist Circumference, Young Adult, Sedentary Behavior, Accelerometry
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