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.
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http://dx.doi.org/10.1016/j.jsams.2024.05.002 | DOI Listing |
Scand J Med Sci Sports
September 2024
Mackenzie Wearables Research Hub, Charles Perkins Centre, The University of Sydney, Sydney, Australia.
Step cadence-based and machine-learning (ML) methods have been used to classify physical activity (PA) intensity in health-related research. This study examined the association of intensity-specific PA duration with all-cause (ACM) and CVD mortality using the cadence-based and ML methods in 68 561 UK Biobank participants wearing wrist-worn accelerometers. The two-stage-ML method categorized activity type and then intensity.
View Article and Find Full Text PDFJ Sci Med Sport
August 2024
Mackenzie Wearables Research Hub, Charles Perkins Centre, The University of Sydney, Australia; School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia. Electronic address:
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.
J Gerontol A Biol Sci Med Sci
August 2018
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.
Background: The longitudinal association between cerebral amyloid-beta (Aβ) and change in gait, and whether this association is mediated by cortical thickness, has yet to be determined.
Methods: We included 439 clinically normal (CN) participants, aged 50-69 years and enrolled in the Mayo Clinic Study of Aging with cerebral Aβ, cortical thickness, and gait measurements. Cerebral Aβ deposition was assessed by Pittsburgh Compound B (PiB)-PET in multiple regions of interest (ROIs) (ie, frontal, orbitofrontal, parietal, temporal, anterior cingulate, posterior cingulate/precuneus, and motor).
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