Training Parameters That Influence Heart Rate Recovery In Collegiate Field Hockey Players

Document Type

Conference Presentation

Department

Health, Exercise, and Sport Sciences Department

Conference Title

American College of Sports Medicine - Medicine & Science in Sports & Exercise conference

Organization

American College of Sports Medicine

Location

Boston, MA

Date of Presentation

10-1-2024

Journal Publication

Medicine & Science in Sports & Exercise

ISSN

0195-9131

DOI

10.1249/01.mss.0001059780.03415.a3

Volume

56

Issue

10S

First Page

833

Abstract

Collegiate field hockey involves vigorous aerobic and anaerobic stress throughout a 132-day season. To maintain optimal performance, appropriate training loads and recovery durations are necessary. PURPOSE: To examine the effect of different training variables on the duration of cardiovascular recovery in field hockey players. METHODS: We tracked 19 D1 field hockey players during their competitive season using Polar Team Pro monitors. The tracking period lasted 88 days and included 51 practices. Competitions were not evaluated owing to inconsistent play patterns and the possibility of psychological stress confounding physiological parameters. Data were collected on average heart rate (HRavg), maximum heart rate (HRmax), number of sprints performed, average and maximum running speeds, total distance in meters, distances covered in 5 speed zones, total energy expenditure, training load score, and duration of heart rate recovery. Linear regression tested the effect of all performance variables on duration of heart rate recovery. RESULTS: During each practice, HRavg was 138.3 ± 15.0 bpm, HRmax was 189.4 ± 13.8 bpm, players performed 48.6 ± 21.9 sprints, had an average speed of 2.9 ± 0.7 km/h, reached a maximum speed of 23.7 ± 4.1 km/h, and covered 3,870.6 ± 1,579.2 meters. Estimated energy expenditure was 645.0 ± 259.6 kcals, training load score was 117.5 ± 63.5, and duration of heart rate recovery was 27.5 ± 36.9 hours. Recovery time was positively correlated with all performance variables (p < 0.001). When all predictors were simultaneously included in a linear regression, the average variance inflation factor was 34.6. The strongest variable was training load. With all predictors included, the model was significant (R2 = 0.654; p < 0.001) and each additional point of training load predicted 0.7 additional hours of recovery duration (p < 0.001; 95% CI of β: 0.581, 0.768). In a simple linear regression, the model retained significance (R2 = 0.597; p < 0.001) and each additional point of training load predicted 0.4 additional hours of recovery duration (p < 0.001; 95% CI of β: 0.422, 0.470). CONCLUSION: Coaching and training staff may consider adjusting the frequency and intensity of practice sessions according to training loads to avoid undue cardiovascular stress and maintain peak performance throughout the season.

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