How do sports injury epidemiological outcomes vary depending on athletes' response rates to a weekly online questionnaire? An analysis of 39-week follow-up from 391 athletics (track and field) athletes.

OBJECTIVE: To explore how sports injury epidemiological outcomes (i.e., prevalence, average prevalence, incidence, burden, and time to first injury) vary depending on the response rates to a weekly online self-reported questionnaire for athletes. METHODS: Weekly information on athletics injuries and exposure from 391 athletics (track and field) athletes was prospectively collected over 39 weeks (control group of the PREVATHLE randomized controlled trial) using an online self-reported questionnaire. The data were used to calculate sports injury epidemiological outcomes (i.e., prevalence, average prevalence, incidence, burden, and time to first injury) for sub-groups with different minimum individual athletes' response rates (i.e., from at least 100%, at least 97%, at least 95%, … to at least 0% response rate). We then calculated the relative variation between each sub-group and the sub-group with a 100% response rate as a reference. A substantial variation was considered when the relative variation was greater than one SD or 95% CI of the respective epidemiological outcome calculated in the sub-group with a 100% response rate. RESULTS: Of 15 249 expected weekly questionnaires, 7209 were completed and returned, resulting in an overall response rate of 47.3%. The individual athletes' response rates ranged from 0% (n = 51) to 100% (n = 100). The prevalence, average weekly prevalence, and time to first injury only varied substantially for the sub-groups below a 5%, 10% and 18% minimum individual response rate, respectively. The incidence and injury burden showed substantial variations for all sub-groups with a response rate below 100%. CONCLUSIONS: Epidemiological outcomes varied depending on the minimum individual athletes' response rate, with injury prevalence, average weekly prevalence, and time to first injury varying less than injury incidence and injury burden. This highlights the need to take into account the individual response rate when calculating epidemiological outcomes, and determining the optimal study-specific cut-offs of the minimum individual response rate needed.

Perceptions and beliefs on sports injury prediction as an injury risk reduction strategy: An online survey on elite athletics (track and field) athletes, coaches, and health professionals.

OBJECTIVES: To explore perceptions and beliefs of elite athletics (track and field) athletes, coaches, and health professionals, towards the use of injury prediction as an injury risk reduction strategy. DESIGN: Cross-sectional study. METHOD: During the 2022 European Athletics Championships in Munich, registered athletes, coaches, and health professionals were asked to complete an online questionnaire on their perceptions and beliefs of injury prediction use as an injury risk reduction strategy. The perceived level of interest, intent to use, help, potential stress (psychological impact) and dissemination were assessed by a score from 0 to 100. RESULTS: We collected 54 responses from 17 countries. Elite athletics stakeholders expressed a perceived level of interest, intent to use, and help of injury prediction of (mean ± SD) 85 ± 16, 84 ± 16, and 85 ± 15, respectively. The perceived level of potential stress was 41 ± 33 (range from 0 to 100), with an important inter-individual variability in each elite athletics stakeholder's category. CONCLUSIONS: This was the first study investigating the perceptions and beliefs of elite athletics stakeholders regarding the use of injury prediction as an injury risk reduction strategy. Regardless of the stakeholders, there was a high perceived level of interest, intent to use and help reported in this potential strategy.

Risk factors for injury complaints leading to restricted participation in Athletics (Track and Field): a secondary analysis of data from 320 athletes over one season.

Objective: To investigate if several potential risk factors were associated with time to injury complaints leading to participation restriction in Athletics (ICPR). Methods: We performed a secondary analysis of data collected during 39 weeks of the 2017-2018 Athletics season in a cluster-randomised controlled trial ('PREVATHLE'). Univariate and multivariable analyses using Cox regression models were performed to analyse the association between the time to first ICPR and potential risk factors collected (1) at baseline: sex, age, height, body mass, discipline, the usual duration of Athletics training and non-specific sports training, ICPR in the preceding season (yes/no), ICPR at baseline (yes/no); (2) weekly during the season: duration and intensity of Athletics training and competition, and non-specific sports training, fitness subjective state, sleep duration and illness (yes/no); and (3) combined. Results: Data from 320 athletes were included; 138 (43.1%) athletes reported at least one ICPR during the study follow-up. The combined multivariable analyses revealed that the risk of ICPR at any given time was significantly higher in athletes with a pre-existing ICPR (hazard rate ratio, HRR 1.90, 95% CI 1.15 to 3.15; p=0.012) and lower in athletes with a higher fitness subjective state (HRR 0.63, 95% CI 0.55 to 0.73; p<0.001) and who had had at least one illness during the season (HRR 0.42, 95% CI 0.29 to 0.62; p<0.001). Conclusions: Our results provide new insights into injury risk factors in Athletics that could help with potential injury risk reduction strategies. These could be to explore the pre-existing injury presence at the season's beginning and to monitor the fitness subjective state and illnesses occurrence during the season. Trial registration: ClinicalTrials.gov Identifier: NCT03307434.