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Updated: Sep 11, 2026
Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
Published on: March 7, 2019
Objective Measures of Physical Activity and Cardiometabolic and Endocrine Biomarkers
Hala B Alessa1, Andrea K Chomistek, Susan E Hankinson
11Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA; 2Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN; 3Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; 4Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA; 5Nutritional Immunology Laboratory, Human Nutrition Research Center on Aging and Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA; 6Clinical Chemistry Laboratory and Stable Isotope Library, Pennington Biomedical Research Center, Baton Rouge, LA; 7Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD; 8Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, and 9Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Purpose:
Although physical activity is an established risk factor for chronic disease prevention, the specific mechanisms underlying these relationships are poorly understood. We examined the associations between total activity counts and moderate-vigorous physical activity (MVPA) measured by accelerometer, and physical activity energy expenditure measured by doubly labeled water, with plasma levels of proinsulin, insulin, c-peptide, insulin growth factor binding protein-3, insulin growth factor-1, adiponectin, leptin, and leptin-sR.
Methods:
We conducted a cross-sectional analysis of 526 healthy US women in the Women's Lifestyle Validation Study, 2010 to 2012. We performed multiple linear regression models adjusting for potential lifestyle and health-related confounders to assess the associations between physical activity, measured in quartiles (Q) and biomarkers.
Results:
Participants in Q4 versus Q1 of total activity counts had lower proinsulin (-20%), c-peptide (-7%), insulin (-31%), and leptin (-46%) levels, and higher adiponectin (55%), leptin-sR (25%), and insulin growth factor-1 (9.6%) levels (all P trend ≤ 0.05). Participants in Q4 versus Q1 of MVPA had lower proinsulin (-26%), c-peptide (-7%), insulin (-32%), and leptin (-40%) levels, and higher adiponectin (31%) and leptin-sR (22%) levels (all P trend ≤ 0.05). Further adjustment for body mass index (BMI) attenuated these associations, but the associations with adipokines remained significant. Those in Q4 versus Q1 of physical activity energy expenditure had lower leptin (-21%) and higher leptin-sR (10%) levels (all P trend ≤ 0.05), after additional adjustment for BMI. In the sensitivity analysis, the associations were similar but attenuated when physical activity was measured using the subjective physical activity questionnaire.
Conclusions:
Our data suggest that greater physical activity is modestly associated with favorable levels of cardiometabolic and endocrine biomarkers, where the strongest associations were found with accelerometer-measured physical activity. These associations may be only partially mediated through BMI, further supporting the role of physical activity in the reduction of cardiometabolic and endocrine disease risk, independent of adiposity.
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