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Related Concept Videos

Exercise Stress Test01:26

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Introduction
Exercise stress testing, commonly known as a treadmill test, is a noninvasive procedure used to evaluate cardiovascular function and diagnose heart conditions.
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Exercise and Cardiovascular Response01:20

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Exploring Regular Exercisers' Experiences with Readiness/Recovery Scores Produced by Wearable Devices: A Descriptive

Adam H Ibrahim1, Cory T Beaumont2, Kelley Strohacker3

  • 1Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, 1914 Andy Holt Avenue Health and Physical Education Recreation Building 314, Knoxville, TN, 37996-2700, USA. aibrah11@vols.utk.edu.

Applied Psychophysiology and Biofeedback
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PubMed
Summary
This summary is machine-generated.

Wearable readiness scores guide training, but users also adjust non-exercise behaviors. Despite device data, exercisers prioritize self-awareness and personal judgment for optimizing adaptive exercise programming.

Keywords:
AutoregulationExerciseHealth behaviorHeart rate variabilityPerson-adaptiveSelf-monitoring

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Area of Science:

  • Exercise Physiology
  • Human-Computer Interaction
  • Behavioral Science

Background:

  • Meta-session autoregulation adapts exercise to individual readiness.
  • Consumer wearables offer readiness/recovery scores, often based on heart rate variability.
  • Limited research explores user interaction with wearable readiness data.

Purpose of the Study:

  • To explore exercisers' experiences with wearable readiness/recovery scores.
  • To understand how users interpret and utilize data from devices like Whoop™ and Oura™.
  • To investigate the psycho-behavioral aspects of using wearables for adaptive exercise programming.

Main Methods:

  • Qualitative study involving 17 regular exercisers using Whoop™ or Oura™ for ≥3 months.
  • Virtual semi-structured interviews to gather user experiences.
  • Reflexive thematic analysis of interview data, supported by 'in-vivo' quotes.

Main Results:

  • Users leverage wearable data to optimize training programming.
  • Users modify non-exercise behaviors (e.g., sleep, nutrition) to influence readiness scores.
  • Users recognize device limitations, emphasizing self-reliance and personal judgment for exercise decisions.

Conclusions:

  • Wearable devices offer a quantitative approach to readiness, but users integrate this with qualitative self-awareness.
  • Effective adaptive exercise programming relies on a combination of device data, behavioral adjustments, and personal judgment.
  • Understanding user experiences is crucial for refining person-adaptive exercise strategies and wearable technology.