The major focus of this project is to develop novel statistical methods for objectively measured physical activity (PA) data by accelerometers or other wearable monitors, and to apply these methods to understand how speci?c patterns of activity intensity, frequency and duration are associated with health outcomes, such as cardiovascu- lar diseases. With rapid technological advances, it is now increasingly common to record accelerometer data in large-scale epidemiological studies. While the size and complexity of this type of data is exploding, the de- velopment of analytic methods is largely lacking. Novel analytic methods that are interpretable, robust, and yet computationally ef?cient are urgently needed, especially with the technology rapidly evolving. Motivated by the Women's Health Initiative (WHI) and other PA studies, we propose novel statistical meth- ods to summarize and effectively utilize objective physical activity measurements for health association analysis. Speci?cally, we will ?rst develop a functional data analysis framework to extract novel summary indices from standard resolution accelerometer data and ?exibly model the association between PA patterns and health. We next consider high-resolution raw accelerometer data and propose a systematic analytical framework for identifying walking in free-living environment, describing walking patterns and studying their association with health outcomes. Finally, we propose novel measurement error correction approaches for large-scale studies with complex sampling designs, where objective measurement is available only in a small subset of the study population. The proposed methodological research is motivated by PA studies of the WHI and will be directly applied to these projects. Applications of these methods to these studies will improve our understanding of PA, which will eventually help lay the foundation for future PA guidelines. These methods are generally applicable to other epidemiological studies that collect objective PA measurements, and software will be made publicly accessible.
The major focus of this proposal is the development of novel statistical methods for analyzing objectively mea- sured physical activity data to improve our understanding of the association between physical activity and health outcomes, such as cardiovascular diseases.
| LaMonte, Michael J; Buchner, David M; Rillamas-Sun, Eileen et al. (2018) Accelerometer-Measured Physical Activity and Mortality in Women Aged 63 to 99. J Am Geriatr Soc 66:886-894 |
| Huang, Yijian; Wang, Ching-Yun (2018) Cox regression with dependent error in covariates. Biometrics 74:118-126 |
| Yu, Hsiang; Cheng, Yu-Jen; Wang, Ching-Yun (2018) Methods for multivariate recurrent event data with measurement error and informative censoring. Biometrics 74:966-976 |
| Bellettiere, John; Healy, Genevieve N; LaMonte, Michael J et al. (2018) Associations of sedentary time and diabetes in 6166 older women: The Objective Physical Activity and Cardiovascular Health Study. J Gerontol A Biol Sci Med Sci : |
| Buchner, David M; Rillamas-Sun, Eileen; Di, Chongzhi et al. (2017) Accelerometer-Measured Moderate to Vigorous Physical Activity and Incidence Rates of Falls in Older Women. J Am Geriatr Soc 65:2480-2487 |
| Su, Yu-Ru; Di, Chong-Zhi; Hsu, Li (2017) Hypothesis testing in functional linear models. Biometrics 73:551-561 |
| Rosenberg, Dori; Godbole, Suneeta; Ellis, Katherine et al. (2017) Classifiers for Accelerometer-Measured Behaviors in Older Women. Med Sci Sports Exerc 49:610-616 |
| Shadyab, Aladdin H; LaMonte, Michael J; Kooperberg, Charles et al. (2017) Association of Accelerometer-Measured Physical Activity With Leukocyte Telomere Length Among Older Women. J Gerontol A Biol Sci Med Sci 72:1532-1537 |
| Cooper, Rachel; Huang, Lei; Hardy, Rebecca et al. (2017) Obesity History and Daily Patterns of Physical Activity at Age 60-64 Years: Findings From the MRC National Survey of Health and Development. J Gerontol A Biol Sci Med Sci 72:1424-1430 |
| Urbanek, Jacek K; Harezlak, Jaroslaw; Glynn, Nancy W et al. (2017) Stride variability measures derived from wrist- and hip-worn accelerometers. Gait Posture 52:217-223 |
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