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Comparing cadence-based and machine learning based estimates for physical activity intensity classification: The UK

Le Wei1, Matthew N Ahmadi1, Mark Hamer2

  • 1Mackenzie 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.

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Summary

Cadence thresholds offer acceptable group-level physical activity intensity estimates, but may not be suitable for individual assessments. One-level cadence works for moderate-to-vigorous activity, while two-level cadence is better for vigorous and light activity.

Keywords:
AccelerometerAlgorithmStepsThresholdWearablesWrist-worn

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Area of Science:

  • Physical activity research
  • Wearable sensor technology
  • Health and fitness monitoring

Background:

  • Cadence thresholds are commonly used to classify physical activity intensity in health research.
  • Validating these methods against advanced algorithms is crucial for accurate data interpretation.
  • Wrist-worn accelerometers provide objective measures of physical activity.

Purpose of the Study:

  • To evaluate the convergent validity of two cadence-based physical activity intensity classification methods.
  • To compare cadence-based approaches against a machine-learning-based intensity schema.
  • To assess the accuracy of cadence thresholds for estimating time spent at different physical activity intensities.

Main Methods:

  • A validity study involving 84,315 participants aged 40 years and older.
  • Comparison of one-level and two-level cadence methods with a machine-learning intensity schema.
  • Analysis using overlapping plots, mean absolute error, and Spearman's correlation coefficient.
  • Evaluation of agreement based on practically-important-difference thresholds for various intensities.

Main Results:

  • One-level cadence provided acceptable group-level estimates for moderate-to-vigorous and moderate physical activity.
  • Two-level cadence demonstrated acceptable group-level estimates for vigorous and light physical activity.
  • Individual-level estimates showed high differences between cadence-based and machine-learning methods, with low proportions of participants within practically-important-difference ranges.

Conclusions:

  • Cadence-based methods show potential for group-level physical activity intensity estimation.
  • One-level cadence is suitable for moderate-to-vigorous and moderate intensity assessment at the group level.
  • Two-level cadence is better for vigorous and light intensity assessment at the group level.
  • Cadence-based methods are likely inappropriate for individual-level intensity-specific time estimation.