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Nakaoka G1, Betega P1, Barboza S2,3, Verhagen E2,3,4,5, van Mechelen W2,3,4,6,7, Hespanhol L1,2,3
1Universidade Cidade de São Paulo (UNICID), Masters and Doctoral Programs in Physical Therapy, São Paulo, Brazil, 2VU University Medical Center Amsterdam, Department of Public and Occupational Health (DPOH), Amsterdam Public Health Research Institute (APH), Amsterdam, Netherlands, 3Amsterdam Collaboration on Health and Safety in Sports (ACHSS), Amsterdam, Netherlands, 4University of Cape Town, Division of Exercise Science and Sports Medicine (ESSM), Department of Human Biology, Faculty of Health Sciences, Cape Town, South Africa, 5Federation University Australia, Australian Centre for Research into Injury in Sport and its Prevention, Ballarat, Australia, 6University of Queensland, School of Human Movement and Nutrition Sciences, Faculty of Health and Behavioural Sciences, Brisbane, Australia, 7University College Dublin, School of Public Health, Physiotherapy and Population Sciences, Dublin, Ireland
Background: Running-related injuries (RRI) may lead to drop out from running practice and may even reduce the likelihood of keeping up a physically active lifestyle. In addition, the burden of RRIs includes the utilisation of medical resources, loss of work productivity as well as direct and indirect costs. According to the scientific literature, the training workload could be either a risk or a protective factor for sports-related injuries. The acute:chronic workload ratio (ACWR) is a method that considers the current (i.e., acute workload) sport workload performed by an individual in relation to the workload this individual is prepared for (i.e., chronic workload).
Purpose: To investigate the longitudinal association between the ACWR and RRIs in Dutch runners.
Methods: This is a secondary analysis using a database composed of data gathered from three studies conducted with the same surveillance system in the Netherlands:
(1) the HealthyMilesstudy (an 18-week prospective cohort study);
(2) the HealthyTrailsstudy (a 15-month prospective cohort study); and
(3) theTrailS6study (a 6-month randomised controlled trial). The longitudinal data (running practice and RRIs) were collected every two weeks.
Bayesian logistic mixed models were used to analyse the data. A time-lag technique was applied to the RRI incidence data to ensure that the running workload (in hours of running exposure) was collected before the reporting of the RRIs. The uncoupled ACWR was calculated as the most recent workload divided by the average of the previous three biweekly periods. The ACWR data was categorised taking the 'sweet spot' (0.8≤ACWR≤1.3) described in the scientific literature as the reference group in the mixed models analysis. The model was adjusted for age, sex, body mass index, running experience and previous RRIs. Repeated measurements and cohort samples based on the three studies included in this analysis were included as random effects. The results were presented as odds ratio (OR) and the 95% credible interval (95%CrI).
Results: The sample was composed of 366 Dutch runners (54 runners training for an event and 312 trail runners). Running workloads resulting in an ACWR lower than the 'sweet spot' presented significant higher odds of sustaining RRIs (ACWR≤0.5: OR 3.22 [95%CrI 2.07-5.02]; and 0.5 ACWR 0.8: OR 1.88 [95%CrI 1.18-2.96]). Running workloads resulting in an ACWR higher than the 'sweet spot' presented non-significant higher odds of sustaining RRIs (1.3 ACWR≤1.5: OR 1.57 [95%CrI 0.90-2.73]; and ACWR>1.5: OR 1.19 [95%CrI 0.72-1.99]).
Conclusion(s): Running workloads (i.e., hours of running exposure) away from the 'sweet spot' in a given 2-week period may predict the odds of reporting RRIs in the subsequent 2-week period in Dutch runners.
Implications: The ACWR method may be useful to predict RRIs, especially when ACWR 0.8. A lower ACWR may be a marker of a recovery period following a high workload period (e.g., race) expected in running programs. After an overload period the body needs to recover in order to adapt to this load. However, if the overload was too high, runners may report an RRI after this overload period, i.e., during the recovery period.
Keywords: Athletic injuries, sports, risk management
Funding acknowledgements: CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), process number 0763/12-8, Ministry of Education of Brazil.
Purpose: To investigate the longitudinal association between the ACWR and RRIs in Dutch runners.
Methods: This is a secondary analysis using a database composed of data gathered from three studies conducted with the same surveillance system in the Netherlands:
(1) the HealthyMilesstudy (an 18-week prospective cohort study);
(2) the HealthyTrailsstudy (a 15-month prospective cohort study); and
(3) theTrailS6study (a 6-month randomised controlled trial). The longitudinal data (running practice and RRIs) were collected every two weeks.
Bayesian logistic mixed models were used to analyse the data. A time-lag technique was applied to the RRI incidence data to ensure that the running workload (in hours of running exposure) was collected before the reporting of the RRIs. The uncoupled ACWR was calculated as the most recent workload divided by the average of the previous three biweekly periods. The ACWR data was categorised taking the 'sweet spot' (0.8≤ACWR≤1.3) described in the scientific literature as the reference group in the mixed models analysis. The model was adjusted for age, sex, body mass index, running experience and previous RRIs. Repeated measurements and cohort samples based on the three studies included in this analysis were included as random effects. The results were presented as odds ratio (OR) and the 95% credible interval (95%CrI).
Results: The sample was composed of 366 Dutch runners (54 runners training for an event and 312 trail runners). Running workloads resulting in an ACWR lower than the 'sweet spot' presented significant higher odds of sustaining RRIs (ACWR≤0.5: OR 3.22 [95%CrI 2.07-5.02]; and 0.5 ACWR 0.8: OR 1.88 [95%CrI 1.18-2.96]). Running workloads resulting in an ACWR higher than the 'sweet spot' presented non-significant higher odds of sustaining RRIs (1.3 ACWR≤1.5: OR 1.57 [95%CrI 0.90-2.73]; and ACWR>1.5: OR 1.19 [95%CrI 0.72-1.99]).
Conclusion(s): Running workloads (i.e., hours of running exposure) away from the 'sweet spot' in a given 2-week period may predict the odds of reporting RRIs in the subsequent 2-week period in Dutch runners.
Implications: The ACWR method may be useful to predict RRIs, especially when ACWR 0.8. A lower ACWR may be a marker of a recovery period following a high workload period (e.g., race) expected in running programs. After an overload period the body needs to recover in order to adapt to this load. However, if the overload was too high, runners may report an RRI after this overload period, i.e., during the recovery period.
Keywords: Athletic injuries, sports, risk management
Funding acknowledgements: CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), process number 0763/12-8, Ministry of Education of Brazil.
Topic: Sport & sports injuries; Health promotion & wellbeing/healthy ageing; Non-communicable diseases (NCDs) & risk factors
Ethics approval required: Yes
Institution: VU University Medical Center Amsterdam, the Netherlands
Ethics committee: VU University Medical Center Amsterdam, the Netherlands
Ethics number: HealthyMiles: 2014.121; HealthyTrails: 2013/328; TrailS6: 2015/302
All authors, affiliations and abstracts have been published as submitted.
