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Sports
Special Issues
Exercise Physiological Responses and Performance Analysis
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Exercise Physiological Responses and Performance Analysis

A special issue of Sports (ISSN 2075-4663).

Deadline for manuscript submissions: 25 July 2026 | Viewed by 3675

Special Issue Editors

Prof. Dr. César Cavinato Cal Abad

E-Mail Website
Guest Editor
Reference Centre of Sport Science of Social Service of Industry, CRCE-SESI, São Paulo 05574-001, Brazil
Interests: sports science; athlete physiology; performance analysis; training load
Dr. Ali Boolani

E-Mail Website
Guest Editor
Human Performance and Nutrition Research Institute, Oklahoma State University, Stillwater, OK 74078, USA
Interests: energy and fatigue; moods; sedentary; mental health; physical activity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Exercise physiology and performance analysis represent dynamic and complementary fields that provide crucial insights into the mechanisms underlying human adaptation to physical training and competition. Advances in physiological monitoring, biomechanical modeling, and data analytics have allowed researchers and practitioners to better understand the complex interactions between internal load, external stimuli, and performance outcomes.

This Special Issue aims to gather high-quality research that explores acute and chronic physiological responses to exercise, innovative methods of performance assessment, and practical applications for optimizing training strategies across different populations and sport contexts. By integrating multidisciplinary perspectives—from molecular physiology to applied performance analytics—this Special Issue seeks to bridge the gap between science and practice, offering novel evidence to enhance health, performance, and recovery in athletes and physically active individuals.

Prof. Dr. César Cavinato Cal Abad
Dr. Ali Boolani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sports is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • exercise physiology
  • performance analysis
  • training adaptation
  • biomechanics
  • sports science
  • monitoring systems
  • athlete performance
  • recovery strategies

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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16 pages, 1583 KB  
Article
The Influence of Ginger Supplementation on Cycling Performance
by Jennifer A. Kurtz, Mabry Watson, Briana Robinson, Casey Edmondson and Laurel Wentz
Sports 2026, 14(4), 126; https://doi.org/10.3390/sports14040126 - 24 Mar 2026
Viewed by 1046
Abstract
Ginger supplementation is proposed as a natural ergogenic aid due to its anti-inflammatory and antioxidant properties, but its effects on endurance performance remain unclear. Methods: In this randomized, double-blind, placebo-controlled crossover trial, 30 trained cyclists (27 male, 3 females, aged 36 ± 11 [...] Read more.
Ginger supplementation is proposed as a natural ergogenic aid due to its anti-inflammatory and antioxidant properties, but its effects on endurance performance remain unclear. Methods: In this randomized, double-blind, placebo-controlled crossover trial, 30 trained cyclists (27 male, 3 females, aged 36 ± 11 yr) completed three visits: a baseline 75 km time trial, a post-supplementation time trial, and a second post-supplementation trial under the alternate condition. Participants consumed either ginger or placebo for four weeks. Statistics: Performance outcomes (i.e., 75 km time, VO2, power output, heart rate, and RPE) were analyzed using repeated-measures ANOVA, with repeated-measures ANCOVA to assess dietary and age influences (p < 0.05). Results: Energy and carbohydrate intake were consistent across trials and unrelated to performance. Protein intake per kg body mass predicted performance time in the placebo trial and average VO2 in the ginger trial; other macronutrients were not associated with outcomes. No significant differences were observed between ginger and placebo conditions for time to completion, VO2, power output, heart rate, or perceived muscle soreness. Conclusions: Four weeks of ginger supplementation does not improve prolonged cycling performance, recovery, or muscle soreness in trained cyclists when dietary intake is controlled. Future research should explore cellular mechanisms to determine whether ginger supplementation could influence performance or recovery in endurance athletes. Full article
(This article belongs to the Special Issue Exercise Physiological Responses and Performance Analysis)
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Review

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17 pages, 1366 KB  
Review
Mapping Handgrip Strength Research in Sports Performance: A Bibliometric Review of Applications, Trends, and Future Directions
by Exal Garcia-Carrillo, Diana Salas-Gómez, Antonio Castillo-Paredes, Boryi A. Becerra-Patiño, Claudio Farías-Valenzuela, Guillermo Cortés-Roco, Miguel Alarcón-Rivera, Héctor Fuentes-Barría and Rodrigo Yáñez-Sepúlveda
Sports 2026, 14(3), 101; https://doi.org/10.3390/sports14030101 - 4 Mar 2026
Cited by 1 | Viewed by 938
Abstract
Handgrip strength (HGS) has been considered as an indicator of muscle strength and overall physical fitness, with increasing relevance in sports science for talent identification and performance monitoring. However, no bibliometric study has been conducted to map the HGS research landscape in athletic [...] Read more.
Handgrip strength (HGS) has been considered as an indicator of muscle strength and overall physical fitness, with increasing relevance in sports science for talent identification and performance monitoring. However, no bibliometric study has been conducted to map the HGS research landscape in athletic contexts. A bibliometric analysis was conducted in the Web of Science Core Collection database, retrieving 229 publications. Typical bibliometric laws (i.e., Price’s, Bradford’s, Lotka’s, and Zipf’s) were employed to analyze publication trends, core journals, influential authors, country contributions, and keyword co-occurrences. Annual publications increased exponentially, especially after 2019, reaching 37 documents in 2024. The Journal of Strength and Conditioning Research and Journal of Sports Medicine and Physical Fitness were the most prominent journals. The United States and Spain led in productivity and impact. Key research themes included strength, performance, body composition, and physical fitness, with HGS demonstrating significant associations with sport tasks such as throwing, racquet sports, and weightlifting. HGS constitutes an accessible and valuable tool for assessing and predicting athletic performance, especially in sports requiring upper body strength and coordination. Future research should aim to expand database inclusion and address identified gaps, such as the relationship between HGS training and sport-specific outcomes. Full article
(This article belongs to the Special Issue Exercise Physiological Responses and Performance Analysis)
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20 pages, 656 KB  
Review
Machine Learning and Non-Invasive Monitoring Technologies for Training Load Management in Women’s Volleyball: A Scoping Review
by Héctor Gabriel Sanhueza Tapia, Frano Giakoni-Ramírez, Josivaldo de Souza-Lima and Arturo Diaz Suarez
Sports 2026, 14(2), 74; https://doi.org/10.3390/sports14020074 - 7 Feb 2026
Viewed by 1094
Abstract
Training load monitoring in women’s volleyball is a challenge for optimizing performance and mitigating injury risk. Non-invasive monitoring technologies and machine learning (ML) can support decision-making, but the evidence remains heterogeneous. This scoping review mapped and integrated the evidence on training load management, [...] Read more.
Training load monitoring in women’s volleyball is a challenge for optimizing performance and mitigating injury risk. Non-invasive monitoring technologies and machine learning (ML) can support decision-making, but the evidence remains heterogeneous. This scoping review mapped and integrated the evidence on training load management, fatigue, and performance in women’s volleyball and identified gaps. The PRISMA Extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) framework were followed. A systematic search was conducted in Scopus, Web of Science, and PubMed, covering January 2020 to September 2025. We included studies in female players at any competitive level, including mixed-sex studies meeting a minimum threshold of female participation, that evaluated external and/or internal load, neuromuscular or perceptual fatigue, and/or performance, using standardized data extraction and narrative/thematic synthesis. Fifty-three studies were included. Inertial measurement units (IMUs), force platforms, heart rate (HR) and heart rate variability (HRV), wellness questionnaires, and global/local positioning systems (GPSs/LPSs) were most prevalent. External-load intensity indicators (e.g., high-intensity jumps and accelerations) were reported as more sensitive to fatigue-related changes than accumulated volume. Machine learning models were less frequent and were mainly applied to multi-source integration and fatigue/readiness prediction, with recurring limitations in external validation and interpretability. Women-specific biological moderators, such as the menstrual cycle, were rarely addressed. Full article
(This article belongs to the Special Issue Exercise Physiological Responses and Performance Analysis)
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