Biology of Sport
eISSN: 2083-1862
ISSN: 0860-021X
Biology of Sport
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Open access

without publication fees
1/2024
vol. 41
 
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abstract:
Original paper

A new statistical approach to training load and injury risk: separating the acute from the chronic load

Lena Kristin Bache-Mathiesen
1
,
Thor Einar Andersen
1
,
Torstein Dalen-Lorentsen
1, 2
,
Montassar Tabben
3
,
Karim Chamari
3
,
Benjamin Clarsen
1, 4, 5
,
Morten Wang Fagerland
1, 6

  1. Oslo Sports Trauma Research Centre, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway
  2. Department of Smart Sensors and Microsystems, SINTEF Digital, Oslo, Norway
  3. Aspetar, Orthopaedic and Sports Medicine Hospital, FIFA Medical Centre of Excellence, Doha, Qatar
  4. Centre for Disease Burden, Norwegian Institute of Public Health, Bergen, Norway
  5. Department of Health and Functioning, Faculty of Health and Social Sciences, Western Norway University of Applied Sciences, Bergen, Norway
  6. Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
Biol Sport. 2024;41(1):119–134
DOI: https://doi.org/10.5114/biolsport.2024.127388
Online publish date: 2023/07/19
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The relationship between recent (acute) training load relative to long-term (chronic) training load may be associated with sports injury risk. We explored the potential for modelling acute and chronic loads separately to address current statistical methodology limitations. We also determined whether there was any evidence of an interaction in the association between acute and chronic training loads and injury risk in football. A men’s Qatar Stars League football cohort (1 465 players, 1 977 injuries), where training load was defined as the number of minutes of activity, and a Norwegian elite U-19 football cohort (81 players, 60 injuries), where training load was defined as the session rating of perceived exertion (sRPE). Mixed logistic regression was run with training load on the current day (acute load) and cumulative past training load estimated by distributed lag non-linear models (chronic load) as independent variables. Injury was the outcome. An interaction between acute and chronic training load was modelled. In both football populations, we observed that the risk of injury on the current day for different values of acute training load was highest for players with low chronic load, followed by high and then medium chronic load. The slopes varied substantially between different levels of chronic training load, indicating an interaction. Modelling acute and chronic loads separately in regression models is a suitable statistical approach for analysing the association between relative training load and injury risk in injury prevention research. Sports scientists should also consider the potential for interactions between acute and chronic load.
keywords:

training monitoring, load monitoring, soccer, ACWR, cute:chronic

  
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