Handling and reporting missing data in training load and injury risk research

Bache-Mathiesen, Lena Kristin; Andersen, Thor Einar; Clarsen, Benjamin; Fagerland, Morten Wang

doi:10.1080/24733938.2021.1998587

Cited by 10 publications

(16 citation statements)

References 61 publications

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“…REDI has previously been compared on observed training load values where the true relationship between training load and injury was unknown, 35 and it was recommended for its ability to handle missing data. 16 We believe that using imputation methods is more suitable for longitudinal data, 33 and in such cases, the advantage of specifying weights on missing observations is no longer applicable. REDI was among the methods that do not require a full time period (ie, 28 days) before the first calculation, but for comparability, we had to run it with the same limitation as the other methods.…”

Section: Discussionmentioning

confidence: 99%

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Assessing the cumulative effect of long-term training load on the risk of injury in team sports

Bache-Mathiesen

Andersen

Dalen-Lorentsen

et al. 2022

BMJ Open Sport Exerc Med

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ObjectivesDetermine how to assess the cumulative effect of training load on the risk of injury or health problems in team sports.MethodsFirst, we performed a simulation based on a Norwegian Premier League male football dataset (n players=36). Training load was sampled from daily session rating of perceived exertion (sRPE). Different scenarios of the effect of sRPE on injury risk and the effect of relative sRPE on injury risk were simulated. These scenarios assumed that the probability of injury was the result of training load exposures over the previous 4 weeks. We compared seven different methods of modelling training load in their ability to model the simulated relationship. We then used the most accurate method, the distributed lag non-linear model (DLNM), to analyse data from Norwegian youth elite handball players (no. of players=205, no. of health problems=471) to illustrate how assessing the cumulative effect of training load can be done in practice.ResultsDLNM was the only method that accurately modelled the simulated relationships between training load and injury risk. In the handball example, DLNM could show the cumulative effect of training load and how much training load affected health problem risk depending on the distance in time since the training load exposure.ConclusionDLNM can be used to assess the cumulative effect of training load on injury risk.

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Section: Discussionmentioning

confidence: 99%

“…Missing sRPE data were imputed with multiple imputation. 33 Cox regression was run with health problem (yes/no) as the outcome and the DLNM of sRPE as the exposure. 9 We adjusted for sex and age as potential confounders and included a frailty term to account for recurrent events.…”

Section: Methodsmentioning

confidence: 99%

Assessing the cumulative effect of long-term training load on the risk of injury in team sports

Bache-Mathiesen

Andersen

Dalen-Lorentsen

et al. 2022

BMJ Open Sport Exerc Med

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View full text Add to dashboard Cite

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“… 14 The data were imputed weekly with multiple imputations using linear regression with chained equations. 3 The variables used in the imputation model were age, player position, week, number of training sessions and games in the 2 weeks before imputation, and the latest number of training sessions and games (the variables imputed). After each round of imputation, we calculated the 2-week difference in training/game loads as well as the cumulative training/game loads of the previous 2, 3, 4, and 6 weeks.…”

Section: Methodsmentioning

confidence: 99%

Association of Training and Game Loads to Injury Risk in Junior Male Elite Ice Hockey Players: A Prospective Cohort Study

Nordstrøm

Bahr

Bache-Mathiesen

et al. 2022

Orthopaedic Journal of Sports Medicine

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Background: Training and game loads are potential risk factors of injury in junior elite ice hockey, but the association of training and game loads to injuries is unknown. Purpose: To investigate the association of chronic training and game loads to injury risk in junior male elite ice hockey players. Study Design: Cohort study; Level of evidence, 2. Methods: In this prospective cohort study, we monitored all health problems among 159 male junior ice hockey players (mean age, 16 years; range, 15-19 years) at sports-specific high schools during the 2018-2019 school year. Players reported their health problems every week using the Oslo Sports Trauma Research Center Overuse Questionnaire on Health Problems (OSTRC-H2). The number of training sessions and games was reported for 33 weeks. We calculated the previous 2-week difference in training/game loads as well as the cumulative training/game loads of the previous 2, 3, 4, and 6 weeks and explored potential associations between training/game loads and injury risk using mixed-effects logistic regression. Results: The players reported 133 acute injuries, 75 overuse injuries, and 162 illnesses in total, and an average of 8.8 (SD ±3.9) training sessions and 0.9 (SD ± 1.1) games per week. We found no association between the difference of the two previous weeks or the previous 2- 3- and 4-week cumulative, training or game load and acute injuries, nor the difference of the two previous weeks, or the previous 4- and 6-week cumulative, training or game load and overuse injuries (OR, ∼1.0; P > .05 in all models). Conclusion: In the current study of junior elite ice hockey players, there was no evidence of an association between cumulative exposure to training/game loads and injury risk.

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“…I first conducted a brief review of the training load and injury risk field to obtain a rough estimate of how missing data were currently handled 4. This information would guide my methodological choices for comparing methods of dealing with missing data.…”

Section: How Did I Do It?mentioning

confidence: 99%

“…In three studies,4–6 I simulated hypothetical relationships between training load and the probability of injury, using 1-season cohort data from Norwegian Men’s Premier League football (n=39 players) and Norwegian academy U-19 football (n=81 players). Ideally, a study uses multiple measures to capture different aspects of training load.…”

Section: How Did I Do It?mentioning

confidence: 99%

Handling and reporting missing data in training load and injury risk research

Cited by 10 publications

References 61 publications

Assessing the cumulative effect of long-term training load on the risk of injury in team sports

Assessing the cumulative effect of long-term training load on the risk of injury in team sports

Association of Training and Game Loads to Injury Risk in Junior Male Elite Ice Hockey Players: A Prospective Cohort Study

Improving statistical methodology in training load and injury risk research (PhD Academy Award)

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