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Related Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
Regulation of Heart Rates01:31

Regulation of Heart Rates

The regulation of heart rate is a complex process controlled by the autonomic nervous system (ANS), hormonal influences, and intrinsic cardiac mechanisms. The ANS has two main components: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).
The SNS increases heart rate through the release of norepinephrine and epinephrine, which act on beta-1 adrenergic receptors in the heart. This action increases the rate of depolarization in the sinoatrial (SA) node, the heart's...

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Related Experiment Video

Updated: May 8, 2026

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
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Modeling heart rate (HR) dynamics to reconstruct missing HR data in trail running.

Tjorven Schnack1,2,3, Raimundo Sanchez4, Arnold Baca5

  • 1Department of Sport and Human Movement Science, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, Vienna, 1150, Austria. tjorven.josef.schnack@univie.ac.at.

BMC Sports Science, Medicine & Rehabilitation
|May 7, 2026
PubMed
Summary

Accurate heart rate (HR) reconstruction is crucial for trail running. A new method using GNSS data and HR dynamics (HRMD) significantly reduces errors and bias in long, non-steady gaps compared to linear interpolation.

Keywords:
HR modelHeart rateImputationMissing dataReconstructionRunningTrail

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Area of Science:

  • Exercise Physiology
  • Biomedical Engineering
  • Wearable Technology

Background:

  • Heart rate (HR) is a key metric for exercise intensity, but outdoor recordings suffer from artifacts and gaps.
  • Existing reconstruction methods like linear interpolation (LI) are inadequate for trail running due to frequent intensity changes and ignored HR dynamics.
  • A novel approach leveraging Global Navigation Satellite System (GNSS) data and HR dynamics is proposed for improved reconstruction.

Purpose of the Study:

  • To develop and evaluate a new heart rate reconstruction method (HRMD) that integrates GNSS-derived energy expenditure and HR dynamics.
  • To compare the accuracy and bias of HRMD against traditional linear interpolation (LI) during simulated trail running conditions.

Main Methods:

  • 12 recreational trail runners performed trail running bouts, with simulated HR gaps (1-800 beats) at different running phases.
  • GNSS data estimated energy expenditure, modeled HR dynamics using a first-order differential equation with individualized parameters.
  • HRMD, including drift correction, was compared to LI and a basic HR model (HRM) using RMSE, mean error (ME), and limits of agreement (LoA).

Main Results:

  • HRMD and LI showed similar low median RMSE (approx. 2 bpm), outperforming HRM (approx. 3 bpm).
  • HRMD significantly outperformed LI in non-steady conditions (onset, switch) for gaps of 200+ beats, reducing mean error by up to 14 bpm.
  • LI exhibited length-dependent bias (ME up to 14.6 bpm, LoA up to 20.3 bpm) at onset, while HRMD maintained ME near zero with LoA < 10 bpm.

Conclusions:

  • Linear interpolation is insufficient for long, non-steady HR gaps common in trail running.
  • The proposed HRMD method effectively reduces error and bias in challenging trail running scenarios by integrating GNSS data and HR dynamics.
  • HRMD offers a practical solution for accurate HR reconstruction using readily available wearable technology, though generalizability requires further study.