Gustavo Nakaoka1, Saulo Delfino Barboza2,3,4, Evert Verhagen3,4,5, Willem van Mechelen3,4,5,6,7,8, Luiz Hespanhol9,3,4. 1. Masters and Doctoral Programs in Physical Therapy , Universidade Cidade de São Paulo (UNICID), Rua Cesário Galeno 448, Tatuapé, São Paulo, SP, 03071-100, Brazil. gustavo.nakaoka@gmail.com. 2. Master Program in Health and Education , University of Ribeirão Preto, São Paulo, Brazil. 3. Department of Public and Occupational Health (DPOH), Amsterdam Public Health Research Institute (APH), Amsterdam Universities Medical Centers, Location VU University Medical Center Amsterdam (VUmc), Amsterdam, The Netherlands. 4. Amsterdam Collaboration on Health and Safety in Sports (ACHSS), Amsterdam Movement Sciences, Amsterdam Universities Medical Centers, Location VU University Medical Center Amsterdam (VUmc), Amsterdam, The Netherlands. 5. Division of Exercise Science and Sports Medicine (ESSM), Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa. 6. Faculty of Health and Behavioural Sciences, School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, Australia. 7. School of Public Health, Physiotherapy and Population Sciences, University College Dublin, Dublin, Ireland. 8. Center of Human Movement Sciences, University Medical Center Groningen, Groningen, the Netherlands. 9. Masters and Doctoral Programs in Physical Therapy , Universidade Cidade de São Paulo (UNICID), Rua Cesário Galeno 448, Tatuapé, São Paulo, SP, 03071-100, Brazil.
Abstract
OBJECTIVE: To investigate the association between the acute:chronic workload ratio (ACWR) and running-related injuries (RRI). METHODS: This is a secondary analysis using a database composed of data from three studies conducted with the same RRI surveillance system. Longitudinal data comprising running exposure (workload) and RRI were collected biweekly during the respective cohorts' follow-up (18-65 weeks). ACWR was calculated as the most recent (i.e., acute) external workload (last 2 weeks) divided by the average external (i.e., chronic) workload of the last 4, 6, 8, 10 and 12 weeks. Three methods were used to calculate the ACWR: uncoupled, coupled and exponentially weighted moving averages (EWMA). Bayesian logistic mixed models were used to analyse the data. RESULTS: The sample was composed of 435 runners. Runners whose ACWR was under 0.70 had about 10% predicted probability of sustaining RRI (9.6%; 95% credible interval [CrI] 7.5-12.4), while those whose ACWR was higher than 1.38 had about 1% predicted probability of sustaining RRI (1.3%; 95% CrI 0.7-1.7). The association between the ACWR and RRI was significant, varying from a small to a moderate association (1-10%). The higher the ACWR, the lower the RRI risk. CONCLUSIONS: The ACWR showed an inversely proportional association with RRI risk that can be represented by a smooth L-shaped, second-order, polynomial decay curve. The ACWR using hours or kilometres yielded similar results. The coupled and uncoupled methods revealed similar associations with RRIs. The uncoupled method presented the best discrimination for ACWR strata. The EWMA method yielded sparse and non-significant results.
OBJECTIVE: To investigate the association between the acute:chronic workload ratio (ACWR) and running-related injuries (RRI). METHODS: This is a secondary analysis using a database composed of data from three studies conducted with the same RRI surveillance system. Longitudinal data comprising running exposure (workload) and RRI were collected biweekly during the respective cohorts' follow-up (18-65 weeks). ACWR was calculated as the most recent (i.e., acute) external workload (last 2 weeks) divided by the average external (i.e., chronic) workload of the last 4, 6, 8, 10 and 12 weeks. Three methods were used to calculate the ACWR: uncoupled, coupled and exponentially weighted moving averages (EWMA). Bayesian logistic mixed models were used to analyse the data. RESULTS: The sample was composed of 435 runners. Runners whose ACWR was under 0.70 had about 10% predicted probability of sustaining RRI (9.6%; 95% credible interval [CrI] 7.5-12.4), while those whose ACWR was higher than 1.38 had about 1% predicted probability of sustaining RRI (1.3%; 95% CrI 0.7-1.7). The association between the ACWR and RRI was significant, varying from a small to a moderate association (1-10%). The higher the ACWR, the lower the RRI risk. CONCLUSIONS: The ACWR showed an inversely proportional association with RRI risk that can be represented by a smooth L-shaped, second-order, polynomial decay curve. The ACWR using hours or kilometres yielded similar results. The coupled and uncoupled methods revealed similar associations with RRIs. The uncoupled method presented the best discrimination for ACWR strata. The EWMA method yielded sparse and non-significant results.
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