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The Effects of Visual Feedback on Performance in Heart Rate- and Power-Based-Tasks during a Constant Load Cycling Test
Author(s) -
Martin Dobiasch,
Björn Krenn,
Robert P. Lamberts,
Arnold Baca
Publication year - 2021
Publication title -
journal of sports science and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 65
ISSN - 1303-2968
DOI - 10.52082/jssm.2022.49
Subject(s) - computer science , task (project management) , athletes , visual feedback , protocol (science) , heart rate , test (biology) , cycling , simulation , power (physics) , artificial intelligence , medicine , physical therapy , history , paleontology , physics , alternative medicine , management , archaeology , pathology , quantum mechanics , biology , blood pressure , economics , radiology
Performance feedback can be essential for cyclists to help with pacing their efforts during competitions and also during standardized performance tests. However, the choice of feedback options on modern bike computers is limited. Moreover, little research on the effectiveness of the currently used feedback methods is available. In this study, two novel feedback variants using a bar or a tacho to visualize targets and deviation from targets were compared to a classic design using only numbers. Participants (6 female and 25 male trained to well-trained athletes) completed a protocol consisting of three heart rate-based tasks and one power-based task. The displays were compared with respect to their ability to guide athletes during their trials. Results showed lower root mean square error (RMSE) of the novel variants, but no significant effect of feedback variant on RMSE was found for both tasks (p > 0.05). However, when comparing the feedback variants on a person to person basis, significant differences were found for all investigated scenarios (p < 0.001). This leads to the conclusion that novel feedback variants can improve athletes’ ability to follow heart rate-based and power-based protocols, but even better results might be achieved by individualizing the feedback.

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