Estimating the cost of training disruptions on marathon performance

Completing a marathon usually requires at least 12–16 weeks of consistent training, but busy lifestyles, illness or injury, and motivational issues can all conspire to disrupt training. This study aims to investigate the frequency and performance cost of training disruptions, especially among recreational runners. Using more than 15 million activities, from 300,000 recreational runners who completed marathons during 2014–2017, we identified periods of varying durations up to 16 weeks before the marathon where runners experienced a complete cessation of training (so-called training disruptions). We identified runners who had completed multiple marathons including: (i) at least one disrupted marathon with a long training disruption of ≥7 days; and (ii) at least one undisrupted marathon with no training disruptions. Next, we calculated the performance cost of long training disruptions as the percentage difference between these disrupted and undisrupted marathon times, comparing the frequency and cost of training disruptions according to the sex, age, and ability of runner, and whether the disruptions occurred early or late in training. Over 50% of runners experienced short training disruptions up to and including 6 days, but longer disruptions were found to be increasingly less frequent among those who made it to race-day. Runners who experience longer training disruptions (≥7 days) suffer a finish-time cost of 5–8% compared to when the same runners experienced only short training disruptions (<7 days). While we found little difference (<5%) in the likelihood of disruptions—when comparing runners based on sex, age, ability, and the timing of a disruption—we did find significant differences in the the cost of disruptions (10–15%) among these groups. Two sample t-tests indicate that long training disruptions lead to a greater finish-time cost for males (5%) than females (3.5%). Faster runners also experience a greater finish-time cost (5.4%) than slower runners (2.6%). And, when disruptions occur late in training (close to race-day), they are associated with a greater finish-time cost (5.2%) than similar disruptions occurring earlier in training (4.4%). By parameterising and quantifying the cost of training disruptions, this work can help runners and coaches to better understand the relationship between training consistency and marathon performance. This has the potential to help them to better evaluate disruption risk during training and to plan for race-day more appropriately when disruptions do occur.

[1]  Barry Smyth,et al.  Longer Disciplined Tapers Improve Marathon Performance for Recreational Runners , 2021, Frontiers in Sports and Active Living.

[2]  J. Ho,et al.  Two weeks of detraining reduces cardiopulmonary function and muscular fitness in endurance athletes , 2021, European journal of sport science.

[3]  Thorsten Emig,et al.  Human running performance from real-world big data , 2020, Nature Communications.

[4]  Barry Smyth,et al.  Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners , 2020, RecSys.

[5]  P. Nikolaidis,et al.  The “New York City Marathon”: participation and performance trends of 1.2M runners during half-century , 2020, Research in sports medicine.

[6]  Brian Caulfield,et al.  An evaluation of the training determinants of marathon performance: A meta-analysis with meta-regression. , 2019, Journal of science and medicine in sport.

[7]  R. Deaner,et al.  Risk Taking Runners Slow More in the Marathon , 2019, Front. Psychol..

[8]  Barry Smyth,et al.  Fast starters and slow finishers: A large-scale data analysis of pacing at the beginning and end of the marathon for recreational runners , 2018, Journal of Sports Analytics.

[9]  Arturo Casado,et al.  The Effect of Periodization and Training Intensity Distribution on Middle- and Long-Distance Running Performance: A Systematic Review. , 2017, International journal of sports physiology and performance.

[10]  Calvin Hubble,et al.  Gender Differences in Marathon Pacing and Performance Prediction , 2015 .

[11]  Bas Kluitenberg,et al.  What are the Differences in Injury Proportions Between Different Populations of Runners? A Systematic Review and Meta-Analysis , 2015, Sports Medicine.

[12]  Devin G. Pope,et al.  Reference-Dependent Preferences: Evidence from Marathon Runners , 2014, Manag. Sci..

[13]  E. Mohammadi,et al.  Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[14]  Michael Kellmann,et al.  Enhancing Recovery: Preventing UnderPerformance in Athletes , 2002 .

[15]  I Mujika,et al.  The Influence of Training Characteristics and Tapering on the Adaptation in Highly Trained Individuals: A Review , 1998, International journal of sports medicine.

[16]  J. Houmard Impact of Reduced Training on Performance in Endurance Athletes , 1991, Sports medicine.

[17]  John W. Shepherd,et al.  Motives for participation in recreational running. , 1989 .

[18]  J. Shepherd,et al.  Pre-race drop-out in marathon runners: reasons for withdrawal and future plans. , 1987, British journal of sports medicine.

[19]  Barry Smyth,et al.  A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries , 2021, International Conference on Case-Based Reasoning.

[20]  Barry Smyth,et al.  Exploring the wall in marathon running , 2020 .

[21]  Barry Smyth,et al.  Running with Recommendation , 2017, HealthRecSys@RecSys.

[22]  Robert O. Deaner,et al.  More males run fast A stable sex difference in competitiveness in U.S. distance runners , 2006 .

[23]  R. Burnham,et al.  The effects of a reduced exercise duration taper programme on performance and muscle enzymes of endurance cyclists , 2005, European Journal of Applied Physiology and Occupational Physiology.

[24]  Mirjam Minor,et al.  International Conference on Case-Based Reasoning (ICCBR-99) , 1999, Künstliche Intell..

[25]  Agnar Aamodt,et al.  Case-Based Reasoning Research and Development , 1995, Lecture Notes in Computer Science.

[26]  Robert C. Wolpert,et al.  A Review of the , 1985 .