Running-related injury: How long does it take? Feasibility, preliminary evaluation, and German translation of the University of Wisconsin running and recovery index

Translation, cross-cultural adaptation, validation, and reliability of the University of Wisconsin running injury and recovery index to Brazilian Portuguese (UWRI-BR)

OBJECTIVE

To translate and cross-culturally adapt the University of Wisconsin Running Injury and Recovery (UWRI) to Brazilian Portuguese (UWRI-BR) and assess its psychometric properties.

METHODS

The Brazilian version of the UWRI questionnaire was based on the guideline proposed by Guillemin. The UWRI was translated, back-translated, and culturally adapted. Fifty runners responded to the pre-final version to validate the final version. Ninety-seven runners participated in the content validity of the UWRI-BR, and 58 runners in the assessment of reliability baseline 48-72 h after the first response. The Lower Extremity Functional Scale (LEFS) and 12-item Short Form Health Survey (SF-12) version 2 (SF-12_2) were applied to construct validity.

RESULTS

The final version of UWRI-BR showed a positive moderate correlation with LEFS (r = 0.65, p < 0.01) and weak with SF-12_2 (r = 0.31, p < 0.01). UWRI-BR showed excellent internal consistency (α = 0.92) and test-retest Reliability (ICC = 0.83; 95% CI (0,72-0,90).

CONCLUSION

The Brazilian version of the UWRI was a valid and reliable tool for evaluating and monitoring the progression of runners with running-related injuries.

Cross cultural adaptation, reliability and validity of the Persian version of the university of Wisconsin running injury and recovery index

BACKGROUND

The University of Wisconsin Running Injury and Recovery Index (UWRI) was developed to evaluate running ability after a running-related injury. The aim of this study was to translate and cross-culturally adapt the UWRI into Persian (UWRI-Persian) and to investigate its psychometric properties in patients with a running-related injury.

METHODS

The UWRI-Persian was translated using the Beaton guidelines. One hundred and seventy-three native Persian patients with running-related injuries were participated in the study. The exploratory factor analysis was carried out using the principal component analysis method with Varimax rotation. The construct validity of the UWRI-Persian was evaluated using the Pearson correlation with the pain self-efficacy questionnaire (PSEQ), Tampa scale for Kinesiophobia (TKS), and visual analogue scale (VAS). Test-retest reliability was tested among 64 patients who completed the form again after seven days.

RESULTS

The UWRI-Persian showed excellent internal consistency for total score (α = 0.966). An excellent internal consistency (α = 0.922) was shown for psychological response and good internal consistency (α = 0.887) for running progression. The interclass correlation coefficient for the UWRI-Persian total scores was 0.965 (95% CI, 0.942 to 0.979), indicating high intra-rater reliability. The UWRI-Persian showed a moderate correlation with the PSEQ (r = 0.425) and the TSK (r = 0.457) and a weak correlation with the VAS (r = 0.187). These findings suggest no floor or ceiling effects.

CONCLUSIONS

The UWRI is a reliable and valid tool for Persian-speaking patients with running-related injuries. The UWRI was successfully translated from English to Persian and demonstrated good to excellent internal consistency, validity and reliability with no floor or ceiling effects.

"Biofeedback-based return to sport": individualization through objective assessments

Elite athletes are regularly exposed to high and repetitive mechanical stresses and impacts, resulting in high injury rates. The consequences of injury can range from time lost from training and competition to chronic physical and psychological burden, with no guarantee that the athlete will return to preinjury levels of sport activity and performance. Prominent predictors include load management and previous injury, highlighting the importance of the postinjury period for effective return to sport (RTS). Currently, there is conflicting information on how to choose and assess the best reentry strategy. Treating RTS as a continuum, with controlled progression of training load and complexity, seems to provide benefits in this process. Furthermore, objectivity has been identified as a critical factor in improving the effectiveness of RTS. We propose that assessments derived from biomechanical measurements in functional settings can provide the objectivity needed for regular biofeedback cycles. These cycles should aim to identify weaknesses, customize the load, and inform on the status of RTS progress. This approach emphasizes individualization as the primary determinant of RTS and provides a solid foundation for achieving it.

A machine learning approach to identify risk factors for running-related injuries: study protocol for a prospective longitudinal cohort trial

BACKGROUND

Running is a very popular sport among both recreational and competitive athletes. However, participating in running is associated with a comparably high risk of sustaining an exercise-related injury. Due to the often multifactorial and individual reasons for running injuries, a shift in thinking is required to account for the dynamic process of the various risk factors. Therefore, a machine learning approach will be used to comprehensively analyze biomechanical, biological, and loading parameters in order to identify risk factors and to detect risk patterns in runners.

METHODS

The prospective longitudinal cohort study will include competitive adult athletes, running at least 20 km per week and being free of injuries three months before the start of the study. At baseline and the end of the study period, subjective questionnaires (demographics, injury history, sports participation, menstruation, medication, psychology), biomechanical measures (e.g., stride length, cadence, kinematics, kinetics, tibial shock, and tibial acceleration) and a medical examination (BMI, laboratory: blood count, creatinine, calcium, phosphate, parathyroid hormone, vitamin D, osteocalcin, bone-specific alkaline phosphatase, DPD cross-links) will be performed. During the study period (one season), continuous data collection will be performed for biomechanical parameters, injuries, internal and external load. Statistical analysis of the data is performed using machine learning (ML) methods. For this purpose, the correlation of the collected data to possible injuries is automatically learned by an ML model and from this, a ranking of the risk factors can be determined with the help of sensitivity analysis methods.

DISCUSSION

To achieve a comprehensive risk reduction of injuries in runners, a multifactorial and individual approach and analysis is necessary. Recently, the use of ML processes for the analysis of risk factors in sports was discussed and positive results have been published. This study will be the first prospective longitudinal cohort study in runners to investigate the association of biomechanical, bone health, and loading parameters as well as injuries via ML models. The results may help to predict the risk of sustaining an injury and give way for new analysis methods that may also be transferred to other sports.

TRIAL REGISTRATION

DRKS00026904 (German Clinical Trial Register DKRS), date of registration 18.10.2021.