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Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies
Univ British Columbia, Canada; US Olymp Comm, CO 80909 USA; US Coalit Prevent Illness and Injury Sport, CO 80907 USA.
Linköping University, Department of Medical and Health Sciences, Division of Physiotherapy. Linköping University, Faculty of Medicine and Health Sciences. La Trobe Univ, Australia.ORCID iD: 0000-0001-8102-3631
Gabbett Performance Solut, Australia; Univ Southern Queensland, Australia.
Univ British Columbia, Canada; Univ British Columbia, Canada.
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2018 (English)In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 8, no 10, article id e022626Article, review/survey (Refereed) Published
Abstract [en]

Objectives To systematically identify and qualitatively review the statistical approaches used in prospective cohort studies of team sports that reported intensive longitudinal data (ILD) (amp;gt;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 (amp;gt;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.

Place, publisher, year, edition, pages
BMJ PUBLISHING GROUP , 2018. Vol. 8, no 10, article id e022626
National Category
Sport and Fitness Sciences
Identifiers
URN: urn:nbn:se:liu:diva-153987DOI: 10.1136/bmjopen-2018-022626ISI: 000454739500107PubMedID: 30282683OAI: oai:DiVA.org:liu-153987DiVA, id: diva2:1281101
Note

Funding Agencies|Canadian Institutes of Health Research

Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-01-21

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