Physical Activity and Television Viewing in Relation to Risk of Undiagnosed Abnormal Glucose Metabolism in Adults
Open accessAbstract
OBJECTIVE—The goal of this study was to assess the associations of physical activity time and television (TV) time with risk of “undiagnosed” abnormal glucose metabolism in Australian adults. RESEARCH DESIGN AND METHODS—This population-based cross-sectional study using a stratified cluster design involving 42 randomly selected Census Collector Districts across Australia included 8,299 adults aged 25 years or older who were free from new type 2 diabetes and self-reported ischemic disease and did not take lipid-lowering or antihypertensive drugs. Abnormal glucose metabolism (impaired fasting glycemia [IFG], impaired glucose tolerance [IGT], or new type 2 diabetes) was based on an oral glucose tolerance test. Self-reported physical activity time and TV time (previous week) were assessed using interviewer-administered questionnaires. RESULTS—After adjustment for known confounders and TV time, the odds ratio (OR) of having abnormal glucose metabolism was 0.62 (95% CI 0.41–0.96) in men and 0.71 (0.50–1.00) in women for those engaged in physical activity ≥2.5 h/week compared with those who were sedentary (0 h/week). The ORs of having abnormal glucose metabolism were 1.16 (0.79–1.70) in men and 1.49 (1.12–1.99) in women who watched TV >14 h/week compared with those who watched ≤7.0 h/week. Higher TV viewing (>14 h/week) was also associated with an increased risk of new type 2 diabetes in men and women and IGT in women compared with those watching <14 h/week. Total physical activity of ≥2.5 h/week was associated with a reduced risk of IFG, IGT, and new type 2 diabetes in both sexes; however, only the association with IGT in women was statistically significant. CONCLUSIONS—These findings suggest a protective effect of physical activity and a deleterious effect of TV time on the risk of abnormal glucose metabolism in adults. Population strategies to reduce risk of abnormal glucose metabolism should focus on reducing sedentary behaviors such as TV time, as well as increasing physical activity.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Data from several studies using accelerometers to objectively measure sedentary behavior show unfavourable associations with risk factors.
“There do exist data from several studies that used accelerometers to objectively measure sedentary behavior, showing unfavourable associations with risk factors (e.g., the Australian AusDiab study,[24] the Proactiv UK trial,[25] the US National Health and Nutrition Examination Survey[26]).”
From Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges (2014) · cites this paper as [24]
Most analyses to date integrate acceleration data from the vertical axis only into counts per user-specified time period and utilize cutpoints for classifying time spent in sedentary behaviour or physical activity, which are then related to health outcomes.
“Most analyses to date integrate acceleration data from the vertical axis only into counts per user-specified time period (e.g., counts per minute, cpm) and utilize cutpoints for classifying time spent in sedentary behaviour or physical activity (higher cpm reflects higher intensity activities), which are then related to health outcomes.[24-26]”
From Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges (2014) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 24 · Cited by 250
- Authors
- Dunstan, D. W., Salmon, J., Owen, N., Armstrong, T., Zimmet, P. Z., Welborn, T. A., Cameron, A. J., Dwyer, T., Jolley, D., Shaw, J. E., & on behalf of the AusDiab Steering Committee
- Affiliations
- International Diabetes Institute, Melbourne, Australia
- Deakin University (AU)
- The University of Queensland (AU)
- Australian Institute of Health and Welfare (AU)
- The University of Western Australia (AU)
- University of Tasmania (AU)
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2004-11-01
- Volume
- 27
- Issue
- 11
- Pages
- 2603-2609
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- OpenAlex
- W2104898709
- Cited by
- 250
- References
- 24
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 3 · 2022: 4 · 2021: 4 · 2020: 11 · 2019: 8 · 2018: 8 · 2017: 10 · 2016: 9 · 2015: 12 · 2014: 27 · 2013: 19 · 2012: 32
- FWCI
- 4.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- PubMed (publishedVersion)
- eCite Digital Repository (University of Tasmania) (submittedVersion)
- Research Bank (Australian Catholic University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- U.S. Department of Health and Human Services
- Bristol-Myers Squibb
- Eli Lilly and Company
- Pfizer
- AstraZeneca
- GlaxoSmithKline
- Sanofi
- Queensland Health
- Diabetes Australia
- Kidney Health Australia
- Novo Nordisk
- Servier
- Tasmanian Department of Health
- Medical Research Council
- National Health and Medical Research Council
- Cilag
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- physical activity, television viewing, sedentary behavior, abnormal glucose metabolism, impaired fasting glucose, impaired glucose tolerance, type 2 diabetes, oral glucose tolerance test, sex differences, diabetes risk
- MeSH terms
- Adult, Blood Glucose, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Fasting, Female, Humans, Life Style, Male, Middle Aged, Motor Activity, Surveys and Questionnaires, Risk Factors, Television, Time Factors, Odds Ratio, Glucose Intolerance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
