- Title
- A multidimensional approach to performance prediction in Olympic distance cross-country mountain bikers
- Creator
- Novak, Andrew R.; Bennett, Kyle J. M.; Fransen, Job; Dascombe, Ben J.
- Relation
- Journal of Sports Sciences Vol. 36, Issue 1, p. 71-78
- Publisher Link
- http://dx.doi.org/10.1080/02640414.2017.1280611
- Publisher
- Routledge
- Resource Type
- journal article
- Date
- 2018
- Description
- This study adopted a multidimensional approach to performance prediction within Olympic distance cross-country mountain biking (XCO-MTB). Twelve competitive XCO-MTB cyclists (VO₂max 60.8 ± 6.7 ml · kg−1 · min−1) completed an incremental cycling test, maximal hand grip strength test, cycling power profile (maximal efforts lasting 6–600 s), decision-making test and an individual XCO-MTB time-trial (34.25 km). A hierarchical approach using multiple linear regression analyses was used to develop predictive models of performance across 10 circuit subsections and the total time-trial. The strongest model to predict overall time-trial performance achieved prediction accuracy of 127.1 s across 6246.8 ± 452.0 s (adjusted R² = 0.92; P < 0.01). This model included VO2max relative to total cycling mass, maximal mean power across 5 and 30 s, peak left hand grip strength, and response time for correct decisions in the decision-making task. A range of factors contributed to the models for each individual subsection of the circuit with varying predictive strength (adjusted R2: 0.62–0.97; P < 0.05). The high prediction accuracy for the total time-trial supports that a multidimensional approach should be taken to develop XCO-MTB performance. Additionally, individual models for circuit subsections may help guide training practices relative to the specific trail characteristics of various XCO-MTB circuits.
- Subject
- cycling; physiology; power output; decision-making
- Identifier
- http://hdl.handle.net/1959.13/1409406
- Identifier
- uon:36015
- Identifier
- ISSN:0264-0414
- Language
- eng
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