Sensor-Based Monitoring of Heart-Rate Responses to Ball Type in 9–11-Year-Old Tennis Players: A Preliminary Bayesian Repeated-Measures Study

Understanding internal load in youth tennis is essential for optimizing training strategies and ensuring healthy development. This study analyzed the influence of ball type (green vs. standard) on heart rate (HR) responses in 19 players (10.17 ± 1.1 years). Participants competed in 72 matches under a cross-over design while monitored with Wimu Pro™ Electronic Performance and Tracking Systems and Garmin HR bands. Data were analyzed using a Bayesian repeated-measures ANOVA, incorporating biological maturation and physical activity levels as covariates. Results indicated anecdotal evidence in favor of the null model for Max HR and moderate evidence in favor of the null model for Avg HR. These findings suggest no clear evidence of a meaningful ball-type effect on acute cardiovascular responses under the short-format match-play conditions studied. Individual characteristics, particularly maturity-related indicators and habitual physical activity, appeared more informative than ball type, although their evidential support differed across heart-rate outcomes. Therefore, the acute physiological response appeared to be similar between ball conditions in this specific pilot setting, but these findings should be interpreted as preliminary rather than definitive. Overall, these findings highlight the value of sensor-based monitoring for contextualizing internal load in youth tennis and suggest that equipment transitions should be guided by individual developmental and activity-related characteristics rather than ball type alone.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2026-07-16
DOI
https://doi.org/10.3390/s26144521
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sensor-Based Monitoring of Heart-Rate Responses to Ball Type in 9–11-Year-Old Tennis Players: A Preliminary Bayesian Repeated-Measures Study

José Francisco Guzmán Luján, Miguel Crespo, José María Giménez‐Egido, Rafael Martí­nez-Gallego et al.
Sensors
Sports Performance and Training
article

Sensor-Based Monitoring of Heart-Rate Responses to Ball Type in 9–11-Year-Old Tennis Players: A Preliminary Bayesian Repeated-Measures Study

José Francisco Guzmán Luján, Miguel Crespo, José María Giménez‐Egido, Rafael Martí­nez-Gallego, Ana Piquer-Piquer, Manrique Rodríguez-Campos
article en

Abstract

Understanding internal load in youth tennis is essential for optimizing training strategies and ensuring healthy development. This study analyzed the influence of ball type (green vs. standard) on heart rate (HR) responses in 19 players (10.17 ± 1.1 years). Participants competed in 72 matches under a cross-over design while monitored with Wimu Pro™ Electronic Performance and Tracking Systems and Garmin HR bands. Data were analyzed using a Bayesian repeated-measures ANOVA, incorporating biological maturation and physical activity levels as covariates. Results indicated anecdotal evidence in favor of the null model for Max HR and moderate evidence in favor of the null model for Avg HR. These findings suggest no clear evidence of a meaningful ball-type effect on acute cardiovascular responses under the short-format match-play conditions studied. Individual characteristics, particularly maturity-related indicators and habitual physical activity, appeared more informative than ball type, although their evidential support differed across heart-rate outcomes. Therefore, the acute physiological response appeared to be similar between ball conditions in this specific pilot setting, but these findings should be interpreted as preliminary rather than definitive. Overall, these findings highlight the value of sensor-based monitoring for contextualizing internal load in youth tennis and suggest that equipment transitions should be guided by individual developmental and activity-related characteristics rather than ball type alone.

SensorsVol. 26(14)
Universitat de València (ES), Lawn Tennis Association (GB), Universidad de Murcia (ES)
Openalex Percentile: Top 7%
Sports Performance and Training
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.