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Getting the most out of intensive longitudinal data: A methodological review of workload-injury studies
journal contributionposted on 11.08.2021, 05:14 by J Windt, Clare ArdernClare Ardern, TJ Gabbett, Karim M Khan, CE Cook, BC Sporer, BD Zumbo
Objectives To systematically identify and qualitatively review the statistical approaches used in prospective cohort studies of team sports that reported intensive longitudinal data (ILD) (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Since longitudinal research can be improved by aligning the (1) theoretical model, (2) temporal design and (3) statistical approach, we reviewed the statistical approaches used in these studies to evaluate how closely they aligned these three components. Design Methodological review. Methods After finding 6 systematic reviews and 1 consensus statement in our systematic search, we extracted 34 original prospective cohort studies of team sports that reported ILD (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Using Professor Linda Collins' three-part framework of aligning the theoretical model, temporal design and statistical approach, we qualitatively assessed how well the statistical approaches aligned with the intensive longitudinal nature of the data, and with the underlying theoretical model. Finally, we discussed the implications of each statistical approach and provide recommendations for future research. Results Statistical methods such as correlations, t-tests and simple linear/logistic regression were commonly used. However, these methods did not adequately address the (1) themes of theoretical models underlying workloads and injury, nor the (2) temporal design challenges (ILD). Although time-to-event analyses (eg, Cox proportional hazards and frailty models) and multilevel modelling are better-suited for ILD, these were used in fewer than a 10% of the studies (n=3). Conclusions Rapidly accelerating availability of ILD is the norm in many fields of healthcare delivery and thus health research. These data present an opportunity to better address research questions, especially when appropriate statistical analyses are chosen.
JW was a Vanier Scholar funded by the Canadian Institutes of Health Research.
Pagination17p. (p. 1-17)
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Science & TechnologyLife Sciences & BiomedicineMedicine, General & InternalGeneral & Internal MedicineTRAINING LOADRISK-FACTORSBOWLING WORKLOADCONSENSUS STATEMENTCAUSAL INFERENCESPORTS INJURIESRUNNING LOADSMISSING DATAPART 1ELITEHumansAthletic InjuriesRisk FactorsLongitudinal StudiesProspective StudiesModels, TheoreticalResearch DesignWorkloadAthletesPhysical Conditioning, Humanathletic injurymethodologystatisticstraining loadworkloads