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Title: | Comparing cadence-based and machine learning based estimates for physical activity intensity classification: The UK Biobank | Authors: | Wei, Le Ahmadi, Matthew N. Hamer, Mark Blodgett, Joanna M. Small, Scott Trost, Stewart Stamatakis, Emmanuel |
Issue Date: | 2024 | Source: | Journal of science and medicine in sport, 2024 (27) 8 p.551-556 | Pages: | 551-556 | Journal Title: | Journal of science and medicine in sport | Abstract: | Objectives: Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based intensity schema in 84,315 participants (≥40 years) with wrist-worn accelerometers.; Design: Validity study.; Methods: Both cadence-based methods (one-level cadence, two-level cadence) calculated intensity-specific time based on cadence-thresholds while the two-level cadence identified stepping behaviors first. We used an overlapping plot, mean absolute error, and Spearman's correlation coefficient to examine agreements between the cadence-based and machine-learning methods. We also evaluated agreements between methods based on practically-important-difference (moderate-to-vigorous-physical activity: ±20 min/day, moderate-physical activity: ±15, vigorous-physical activity: ±2.5, light-physical activity: ±30).; Results: The group-level (median) minutes of moderate-to-vigorous- and moderate-physical activity estimated by one-level cadence were within the range of practically-important-difference compared to the machine-learning method (bias of median: moderate-to-vigorous-physical activity, -3.5, interquartile range [-15.8, 12.2]; moderate-physical activity, -6.0 [-17.2, 4.1]). The group-level vigorous- and light-physical activity minutes derived by two-level cadence were within practically-important-difference range (vigorous-physical activity: -0.9 [-3.1, 0.5]; light-physical activity, -1.3 [-28.2, 28.9]). The individual-level differences between the cadence-based and machine learning methods were high across intensities (e.g., moderate-to-vigorous-physical activity: mean absolute error [one-level cadence: 24.2 min/day; two-level cadence: 26.2]), with the proportion of participants within the practically-important-difference ranging from 8.4 % to 61.6 %.; Conclusions: One-level cadence showed acceptable group-level estimates of moderate-to-vigorous and moderate-physical activity while two-level cadence showed acceptable group-level estimates of vigorous- and light-physical activity. The cadence-based methods might not be appropriate for individual-level intensity-specific time estimation.; Competing Interests: Declaration of interest statement We confirm that all authors declare that they have no conflicts of interest. (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.) | DOI: | 10.1016/j.jsams.2024.05.002 | Resources: | https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,athens&db=mdc&AN=38852004&site=ehost-live |
Appears in Sites: | Children's Health Queensland Publications Queensland Health Publications |
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