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Observational Study
. 2017 Nov;49(11):2176-2184.
doi: 10.1249/MSS.0000000000001362.

Physical Activity Dimensions Associated with Impaired Glucose Metabolism

Affiliations
Observational Study

Physical Activity Dimensions Associated with Impaired Glucose Metabolism

Hanan Amadid et al. Med Sci Sports Exerc. 2017 Nov.

Abstract

Purpose: Physical activity (PA) is important in the prevention of Type 2 diabetes, yet little is known about the role of specific dimensions of PA, including sedentary time in subgroups at risk for impaired glucose metabolism (IGM). We applied a data-driven decision tool to identify dimensions of PA associated with IGM across age, sex, and body mass index (BMI) groups.

Methods: This cross-sectional study included 1501 individuals (mean (SD) age, 65.6 (6.8) yr) at high risk for Type 2 diabetes from the ADDITION-PRO study. PA was measured by an individually calibrated combined accelerometer and heart rate monitor worn for 7 d. PA energy expenditure, time spent in different activity intensities, bout duration, and sedentary time were considered determinants of IGM together with age, sex, and BMI. Decision tree analysis was applied to identify subgroup-specific dimensions of PA associated with IGM. IGM was based on oral glucose tolerance test results and defined as a fasting plasma glucose level of ≥6.1 mmol·L and/or a 2-h plasma glucose level of ≥7.8 mmol·L.

Results: Among overweight (BMI ≥25 kg·m) men, accumulating less than 30 min·d of moderate-to-vigorous PA was associated with IGM, whereas among overweight women, sedentary time was associated with IGM. Among individuals older than 53 yr with normal weight (BMI <25 kg·m), time spent in light PA was associated with IGM. None of the dimensions of PA were associated with IGM among individuals ≤53 yr of age with normal weight.

Conclusions: We identified subgroups in which different activity dimensions were associated with IGM. Methodology and results from this study may suggest a preliminary step toward the goal of tailoring and targeting PA interventions aimed at Type 2 diabetes prevention.

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Conflict of interest statement

Conflict of Interest and Source of Funding

AB., N.J., M.J., T.L., D.V and D.W. own shares in Novo Nordisk A/S. No other potential conflicts of interest relevant to this article were reported for the remaining authors. H.A. has received scholarship funding from the Danish Ministry of Higher Education and Science, the Danish Heart Foundation and Aarhus University. D.W. is supported by the Danish Diabetes Academy, which is funded by the Novo Nordisk Foundation. K.F. is supported by the Novo Nordisk Foundation. The work of Soren Brage is supported by UK Medical Research Council [MC_UU_12015/3].

Figures

Figure 1
Figure 1
The Decision Tree depicts seven subgroups that emerged from the interplay/combination between retained physical activity determinants and sex, age and BMI. Following every split in each subgroup, the corresponding prevalence is given for normal glucose metabolism (NGM; light turquois) vs. impaired glucose metabolism (IGM; dark turquois). The relative risk of IGM in each subgroup is in reference to the subgroup with the lowest risk of IGM (subgroup 5). BMI, body mass index; LPA, light intensity physical activity (1.5-3.0METs); MVPA, moderate-to-vigorous physical activity of ≥ or < 30min (≥ 3.0 METs); SED-time, sedentary time while awake (≤ 1.5 MET, subtracting sleep); RR, relative risk.

References

    1. Assah FK, Brage S, Ekelund U, Wareham NJ. The association of intensity and overall level of physical activity energy expenditure with a marker of insulin resistance. Diabetologia. 2008;51(8):1399–407. - PMC - PubMed
    1. Batacan RB, Jr, Duncan MJ, Dalbo VJ, Tucker PS, Fenning AS. Effects of Light Intensity Activity on CVD Risk Factors: A Systematic Review of Intervention Studies. BioMed research international. 2015;2015:596367. - PMC - PubMed
    1. Besson H, Brage S, Jakes RW, Ekelund U, Wareham NJ. Estimating physical activity energy expenditure, sedentary time, and physical activity intensity by self-report in adults. The American journal of clinical nutrition. 2010;91(1):106–14. - PubMed
    1. Brage S, Brage N, Franks PW, Ekelund U, Wareham NJ. Reliability and validity of the combined heart rate and movement sensor Actiheart. European journal of clinical nutrition. 2005;59(4):561–70. - PubMed
    1. Brage S, Brage N, Franks PW, et al. Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. Journal of applied physiology (Bethesda, Md. : 1985) 2004;96(1):343–51. - PubMed

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