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Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies.

Publication ,  Journal Article
Windt, J; Ardern, CL; Gabbett, TJ; Khan, KM; Cook, CE; Sporer, BC; Zumbo, BD
Published in: BMJ Open
October 2, 2018

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.

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Published In

BMJ Open

DOI

EISSN

2044-6055

Publication Date

October 2, 2018

Volume

8

Issue

10

Start / End Page

e022626

Location

England

Related Subject Headings

  • Workload
  • Risk Factors
  • Research Design
  • Prospective Studies
  • Physical Conditioning, Human
  • Models, Theoretical
  • Longitudinal Studies
  • Humans
  • Athletic Injuries
  • Athletes
 

Citation

APA
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ICMJE
MLA
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Windt, J., Ardern, C. L., Gabbett, T. J., Khan, K. M., Cook, C. E., Sporer, B. C., & Zumbo, B. D. (2018). Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies. BMJ Open, 8(10), e022626. https://doi.org/10.1136/bmjopen-2018-022626
Windt, Johann, Clare L. Ardern, Tim J. Gabbett, Karim M. Khan, Chad E. Cook, Ben C. Sporer, and Bruno D. Zumbo. “Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies.BMJ Open 8, no. 10 (October 2, 2018): e022626. https://doi.org/10.1136/bmjopen-2018-022626.
Windt J, Ardern CL, Gabbett TJ, Khan KM, Cook CE, Sporer BC, et al. Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies. BMJ Open. 2018 Oct 2;8(10):e022626.
Windt, Johann, et al. “Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies.BMJ Open, vol. 8, no. 10, Oct. 2018, p. e022626. Pubmed, doi:10.1136/bmjopen-2018-022626.
Windt J, Ardern CL, Gabbett TJ, Khan KM, Cook CE, Sporer BC, Zumbo BD. Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies. BMJ Open. 2018 Oct 2;8(10):e022626.

Published In

BMJ Open

DOI

EISSN

2044-6055

Publication Date

October 2, 2018

Volume

8

Issue

10

Start / End Page

e022626

Location

England

Related Subject Headings

  • Workload
  • Risk Factors
  • Research Design
  • Prospective Studies
  • Physical Conditioning, Human
  • Models, Theoretical
  • Longitudinal Studies
  • Humans
  • Athletic Injuries
  • Athletes