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April 12, 2026Journal of Functional Morphology and KinesiologyOpen Access

Functional Data Analysis of post-exercise recovery in cyclists revealed that blood lactate and diastolic blood pressure provided statistically significant discrimination of recovery phenotypes.

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Why the study?

Does Functional Data Analysis (FDA) effectively characterize individual physiological response dynamics following high-intensity effort in trained cyclists?

Population

21 trained cyclists (10 professionals, 11 amateurs)

Design

Other

Follow-up

20 minutes

Key result

Functional Data Analysis of post-exercise recovery in cyclists revealed that blood lactate and diastolic blood pressure provided statistically significant discrimination of recovery phenotypes.

Authors

AOAdrian OdriozolaCTCristina TîrnȃucȃAGA. Hernández González

Discussion

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Overview

Functional Data Analysis is a feasible and informative approach for classifying physiological recovery phenotypes after high-intensity effort while preserving temporal structure.

Key Points

  • This study evaluates the effectiveness of Functional Data Analysis to capture individual physiological response dynamics after high-intensity exercise.
  • Collected physiological time-series data from 21 trained cyclists after a functional threshold power (FTP) test.
  • Utilized FDataGrid for representing data without smoothing or basis expansion.
  • Employed unsupervised clustering (K-means, Fuzzy K-means) and supervised classification methods (K-nearest Neighbors, functional QDA).
  • Estimated model performance with 5-Fold Cross-Validation, analyzing accuracy and sensitivity.
  • Examined recovery profiles for lactate and other metabolic variables.
  • Lactate and diastolic blood pressure showed significant distinction across classification models.
  • Heart rate provided modest discrimination and glucose had intermediate performance.
  • Unsupervised analyses indicated diverse lactate recovery profiles that address interindividual differences.
  • Functional Data Analysis proved beneficial in capturing temporal dynamics of recovery.

Structured PICO

Does Functional Data Analysis (FDA) effectively characterize individual physiological response dynamics following high-intensity effort in trained cyclists?

P
Population
21 trained cyclists (10 professionals, 11 amateurs)
I
Intervention
Functional Data Analysis (FDA) of physiological time-series data (blood lactate, heart rate, blood pressure, and glucose levels) following a functional threshold power (FTP) test
O
Outcome
Efficacy of Functional Data Analysis (FDA) for characterizing individual response dynamics and classifying recovery phenotypes

Functional Data Analysis is a feasible and informative approach for classifying physiological recovery phenotypes after high-intensity effort while preserving temporal structure.

Limitations

  • small sample size
  • sparse time points
  • need for external validation in larger, independent cohorts

Cite This Study

Odriozola et al. (2026) studied this question. Functional Data Analysis of post-exercise recovery in cyclists revealed that blood lactate and diastolic blood pressure provided statistically significant discrimination of recovery phenotypes.

synapsesocial.com/papers/69db36c24fe01fead37c4ba0https://doi.org/10.3390/jfmk11020151
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Impact of the environment on health status of intensive care unit patients: Functional data analysis using wearable monitoring systems2025
  2. 2Functional Data Analysis of the Power–Duration Relationship in Cyclists2025
  3. 3Physiological State Recognition via HRV and Fractal Analysis Using AI and Unsupervised Clustering2025 · 4 citations
  4. 4Delayed heart rate recovery and its variability in fitness functional training compared to endurance athletes: a cross-sectional analysis2026
  5. 5Optimal Experimental Designs for Sparse Functional Data: A Review2025