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Modeling Temporal Variation in Physical Activity Using Functional Principal Components Analysis

Author

Listed:
  • Selene Yue Xu

    (UC San Diego)

  • Sandahl Nelson

    (San Diego State University
    UC San Diego)

  • Jacqueline Kerr

    (UC San Diego
    UC San Diego
    UC San Diego)

  • Suneeta Godbole

    (UC San Diego)

  • Eileen Johnson

    (UC San Diego
    UC Berkeley)

  • Ruth E. Patterson

    (UC San Diego
    UC San Diego)

  • Cheryl L. Rock

    (UC San Diego
    UC San Diego)

  • Dorothy D. Sears

    (UC San Diego
    UC San Diego)

  • Ian Abramson

    (UC San Diego)

  • Loki Natarajan

    (UC San Diego
    UC San Diego)

Abstract

Accelerometers are person-worn sensors that provide objective measurements of movement based on minute-level activity counts, thus providing a rich framework for assessing physical activity patterns. New statistical approaches and computational tools are needed to exploit these densely sampled time-series data. We implement a functional principal component mixed model approach to ascertain temporal activity patterns in 578 overweight women (60% cancer survivors) and summarize individual patterns with unique personalized principal component scores. We then test if these patterns are associated with health by performing multiple regression of health outcomes (including biomarkers, namely, insulin, C-reactive protein, and quality of life) on activity patterns represented by these scores. Our model elucidates the most important patterns/modes of variation in physical activities. Results show that health outcomes including biomarkers and quality of life are strongly associated with the total volume, as well as temporal variation in activity. In addition, associations between physical activity and health outcomes are not modified by cancer status. Our findings suggest that employing a multilevel functional principal component analysis approach can elicit important temporal patterns in physical activity. It further allows us to study the relationship between health outcomes and activity patterns, and thus could be a valuable modeling approach in behavioral research.

Suggested Citation

  • Selene Yue Xu & Sandahl Nelson & Jacqueline Kerr & Suneeta Godbole & Eileen Johnson & Ruth E. Patterson & Cheryl L. Rock & Dorothy D. Sears & Ian Abramson & Loki Natarajan, 2019. "Modeling Temporal Variation in Physical Activity Using Functional Principal Components Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 403-421, July.
  • Handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-019-09237-3
    DOI: 10.1007/s12561-019-09237-3
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    References listed on IDEAS

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    1. Haochang Shou & Vadim Zipunnikov & Ciprian M. Crainiceanu & Sonja Greven, 2015. "Structured functional principal component analysis," Biometrics, The International Biometric Society, vol. 71(1), pages 247-257, March.
    2. Morris, Jeffrey S. & Arroyo, Cassandra & Coull, Brent A. & Ryan, Louise M. & Herrick, Richard & Gortmaker, Steven L., 2006. "Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1352-1364, December.
    3. Francesco Sera & Lucy J Griffiths & Carol Dezateux & Marco Geraci & Mario Cortina-Borja, 2017. "Using functional data analysis to understand daily activity levels and patterns in primary school-aged children: Cross-sectional analysis of a UK-wide study," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.
    4. Jeffrey S. Morris & Raymond J. Carroll, 2006. "Wavelet‐based functional mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 179-199, April.
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    Cited by:

    1. Wenyi Lin & Jingjing Zou & Chongzhi Di & Dorothy D. Sears & Cheryl L. Rock & Loki Natarajan, 2023. "Longitudinal Associations Between Timing of Physical Activity Accumulation and Health: Application of Functional Data Methods," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 309-329, July.

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