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Physical Activity Change in an RCT: Comparison of Measurement Methods.

  • Author(s): Nelson, Sandahl H
  • Natarajan, Loki
  • Patterson, Ruth E
  • Hartman, Sheri J
  • Thompson, Caroline A
  • Godbole, Suneeta V
  • Johnson, Eileen
  • Marinac, Catherine R
  • Kerr, Jacqueline
  • et al.

Published Web Location

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812571/
No data is associated with this publication.
Abstract

Objectives: We aimed to quantify the agreement between self-report, standard cut-point accelerometer, and machine learning accelerometer estimates of physical activity (PA), and exam- ine how agreement changes over time among older adults in an intervention setting. Methods: Data were from a randomized weight loss trial that encouraged increased PA among 333 postmenopausal breast cancer survivors. PA was estimated using accelerometry and a validated questionnaire at baseline and 6-months. Accelerometer data were processed using standard cut-points and a validated machine learning algorithm. Agreement of PA at each time-point and change was assessed using mixed effects regression models and concordance correlation. Results: At baseline, self-report and machine learning provided similar PA estimates (mean dif- ference = 11.5 min/day) unlike self-report and standard cut-points (mean difference = 36.3 min/ day). Cut-point and machine learning methods assessed PA change over time more similarly than other comparisons. Specifically, the mean difference of PA change for the cut-point versus machine learning methods was 5.1 min/day for intervention group and 2.9 in controls, whereas it was ≥ 24.7 min/day for other comparisons. Conclusions: Intervention researchers are facing the issue of self-report measures introducing bias and accelerometer cut-points being insensi- tive. Machine learning approaches may bridge this gap.

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