Calibration of an Accelerometer Activity Index Among Older Women and Its Association With Cardiometabolic Risk Factors

in Journal for the Measurement of Physical Behaviour

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Guangxing WangDivision of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA

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Sixuan WuInspur USA Inc., Bellevue, WA, USA

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Kelly R. EvensonDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

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Ilsuk KangDivision of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA

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Michael J. LaMonteDepartment of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo—SUNY, Buffalo, NY, USA

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John BellettiereDivision of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, USA

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I-Min LeeDivision of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Annie Green HowardDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

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Andrea Z. LaCroixDivision of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, USA

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Chongzhi DiDivision of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA

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Purpose: Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making it difficult to compare studies using different devices. Alternative summary metrics based on raw accelerometry data have been introduced in recent years. However, they were often not calibrated on ground truth measures of activity-related energy expenditure for direct translation into continuous activity intensity levels. Our purpose is to calibrate, derive, and validate thresholds among women 60 years and older based on a recently proposed transparent raw data-based accelerometer activity index (AAI) and to demonstrate its application in association with cardiometabolic risk factors. Methods: We first built calibration equations for estimating metabolic equivalents continuously using AAI and personal characteristics using internal calibration data (N = 199). We then derived AAI cutpoints to classify epochs into sedentary behavior and physical activity intensity categories. The AAI cutpoints were applied to 4,655 data units in the main study. We then utilized linear models to investigate associations of AAI sedentary behavior and physical activity intensity with cardiometabolic risk factors. Results: We found that AAI demonstrated great predictive accuracy for estimating metabolic equivalents (R2 = .74). AAI-Based physical activity measures were associated in the expected directions with body mass index, blood glucose, and high-density lipoprotein cholesterol. Conclusion: The calibration framework for AAI and the cutpoints derived for women older than 60 years can be applied to ongoing epidemiologic studies to more accurately define sedentary behavior and physical activity intensity exposures, which could improve accuracy of estimated associations with health outcomes.

Supplementary Materials

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