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  1. Home
  3. Using Wear Time For The Analysis Of Consumer-grade Wearables' Data: Case Study Using Fitbit Data.
  1. Home
  3. Using Wear Time For The Analysis Of Consumer-grade Wearables' Data: Case Study Using Fitbit Data.

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Using Wear Time for the Analysis of Consumer-Grade Wearables' Data: Case Study Using Fitbit Data.

Loubna Baroudi1, Ronald Fredrick Zernicke2,3, Muneesh Tewari4,5,6,7

  • 1Department of Mechanical Engineering, University of Michigan-Ann Arbor, 2505 Hayward St, Ann Arbor, MI, 48109, United States, 1 7342626353.

JMIR Mhealth and Uhealth
|March 21, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Wearable device data analysis requires careful consideration of participant wear time compliance. Inconsistent wear time significantly impacts step count estimates, but heart rate data remains more robust, influencing research question feasibility.

Keywords:
Fitbitbehaviorcaregiverdatasetengagementmobile healthphysical activityreliabilitysmartwatchstudentsuserswalkingwear timewearable deviceswearables

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Area of Science:

  • Digital Health
  • Human Behavior Monitoring
  • Wearable Technology

Background:

  • Consumer-grade wearables offer valuable real-world human behavior data.
  • Maintaining user engagement and compliance (e.g., wear time) is a significant challenge, leading to data gaps and potential "wearables' abandonment".

Purpose of the Study:

  • To quantify the impact of wear time requirements on study results using diverse population datasets.
  • To emphasize the necessity of accounting for participant wear time in analyzing consumer-grade wearable data.
  • To determine if all research questions require the same wear time compliance.

Main Methods:

  • Analyzed 3 Fitbit datasets from 6 diverse population samples (caregivers, students, pediatric oncology patients).
  • Assessed the sensitivity of average daily step count and heart rate to varying wear time definitions (Aim 1).
  • Evaluated research question feasibility with lower compliance samples, focusing on average daily step count and average heart rate while walking (Aim 2).

    • Main Results:

    • Population average daily step count estimates varied by up to 2000 steps based on analysis methods and wear time compliance.
    • Low wear time samples (<15 hours/day) showed highest sensitivity to analysis method changes; individual step count differences exceeded 1000-3000 steps for a subset of participants.
    • Average daily heart rate estimates were robust to wear time variations; some individuals with sufficient data for heart rate while walking lacked adequate data for daily step count.

    Conclusions:

    • Demonstrated a direct relationship between parameter estimates from consumer-grade wearables and participant wear time.
    • Highlighted the critical importance of thorough wear time analysis for ensuring the relevance and reliability of wearable data findings.