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Determining Cluster-Specific Differences in the Number of Days Required to Reliably Predict Habitual Physical

Conor Jordan Murphy1,2, Gabriel M Jouan1,3, Katrin Y Friðgeirsdóttir1,2

  • 1School of Technology, Reykjavik University Sleep Institute, Reykjavík University, Reykjavik, Capital Region, Iceland.

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Summary

The number of days needed to accurately measure physical activity varies by individual patterns. Researchers should consider these unique patterns to avoid unreliable activity estimates.

Keywords:
accelerometrydataexercisehabitualhuman behaviorintraclass correlation coefficientphysical activityreliabilityself-reportingstep countsstepswalkingwearable deviceswearables

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Area of Science:

  • Physical Activity Measurement
  • Wearable Technology
  • Data Analysis

Background:

  • Previous studies aimed to find a universal minimum for accelerometry data collection.
  • Individual physical activity is highly variable, making generic recommendations potentially unreliable.
  • Understanding individual variability is key to accurate habitual physical activity assessment.

Purpose of the Study:

  • To identify distinct physical activity patterns using clustering.
  • To determine if accelerometry duration for reliable estimation differs across these patterns.
  • To compare requirements for short-term (7-day) and medium-term (28-day) activity estimation.

Main Methods:

  • Utilized accelerometry data from two independent studies.
  • Employed agglomerative hierarchical clustering based on step count mean, SD, skewness, and kurtosis.
  • Calculated intraclass correlation coefficients (ICCs) comparing full periods to subsamples to determine reliability (ICC ≥ 0.80).

Main Results:

  • Identified 4 clusters for short-term (149 participants) and 3 for medium-term (64 participants) analyses.
  • Short-term analysis required 2-6 days, while medium-term analysis needed 6-11 days, varying by cluster.
  • Short-term physical activity clusters showed greater pattern diversity.

Conclusions:

  • Individual physical activity patterns significantly impact the required duration of accelerometry data.
  • Generic recommendations for accelerometry duration may yield unreliable physical activity estimates.
  • Researchers must consider sample-specific activity patterns to ensure accurate data interpretation.