Cailbhe Doherty1, Alison Keogh2, James Davenport2, Aonghus Lawlor3, Barry Smyth3, Brian Caulfield2. 1. Insight Centre for Data Analytics, University College Dublin, Ireland; School of Public Health, Physiotherapy and Population Science, University College Dublin, Dublin, Ireland. Electronic address: Cailbhe.doherty@insight-centre.org. 2. Insight Centre for Data Analytics, University College Dublin, Ireland; School of Public Health, Physiotherapy and Population Science, University College Dublin, Dublin, Ireland. 3. Insight Centre for Data Analytics, University College Dublin, Ireland.
Abstract
OBJECTIVES: Marathoners rely on expert-opinion and the anecdotal advice of their peers when devising their training plans for an upcoming race. The accumulation of results from multiple scientific studies has the potential to clarify the precise training requirements for the marathon. The purpose of the present study was to perform a systematic review, meta-analysis and meta-regression of available literature to determine if a dose-response relationship exists between a series of training behaviours and marathon performance. DESIGN: Systematic review, meta-analysis and meta-regression. METHODS: A systematic search of multiple literature sources was undertaken to identify observational and interventional studies of elite and recreational marathon (42.2km) runners. RESULTS: Eighty-five studies which included 137 cohorts of runners (25% female) were included in the meta-regression, with average weekly running distance, number of weekly runs, maximum running distance completed in a single week, number of runs ≥32km completed in the pre-marathon training block, average running pace during training, distance of the longest run and hours of running per week used as covariates. Separately conducted univariate random effects meta-regression models identified a negative statistical association between each of the above listed training behaviours and marathon performance (R2 0.38-0.81, p<0.001), whereby increases in a given training parameter coincided with faster marathon finish times. Meta-analysis revealed the rate of non-finishers in the marathon was 7.27% (95% CI 6.09%-8.65%). CONCLUSIONS: These data can be used by athletes and coaches to inform the development of marathon training regimes that are specific to a given target finish time.
OBJECTIVES: Marathoners rely on expert-opinion and the anecdotal advice of their peers when devising their training plans for an upcoming race. The accumulation of results from multiple scientific studies has the potential to clarify the precise training requirements for the marathon. The purpose of the present study was to perform a systematic review, meta-analysis and meta-regression of available literature to determine if a dose-response relationship exists between a series of training behaviours and marathon performance. DESIGN: Systematic review, meta-analysis and meta-regression. METHODS: A systematic search of multiple literature sources was undertaken to identify observational and interventional studies of elite and recreational marathon (42.2km) runners. RESULTS: Eighty-five studies which included 137 cohorts of runners (25% female) were included in the meta-regression, with average weekly running distance, number of weekly runs, maximum running distance completed in a single week, number of runs ≥32km completed in the pre-marathon training block, average running pace during training, distance of the longest run and hours of running per week used as covariates. Separately conducted univariate random effects meta-regression models identified a negative statistical association between each of the above listed training behaviours and marathon performance (R2 0.38-0.81, p<0.001), whereby increases in a given training parameter coincided with faster marathon finish times. Meta-analysis revealed the rate of non-finishers in the marathon was 7.27% (95% CI 6.09%-8.65%). CONCLUSIONS: These data can be used by athletes and coaches to inform the development of marathon training regimes that are specific to a given target finish time.
Authors: Véronique Billat; Luc Poinsard; Florent Palacin; Jean Renaud Pycke; Michael Maron Journal: Int J Environ Res Public Health Date: 2022-05-09 Impact factor: 4.614
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Authors: Mabliny Thuany; Thayse Natacha Gomes; Lee Hill; Thomas Rosemann; Beat Knechtle; Marcos B Almeida Journal: Int J Environ Res Public Health Date: 2021-04-05 Impact factor: 3.390