State dependence: Does a prior injury predict a future injury?

Demographic Factors and Instantaneous Lower Extremity Injury Occurrence in a National Collegiate Athletic Association Division I Population

CONTEXT

Temporal prediction of the lower extremity (LE) injury risk will benefit clinicians by allowing them to better leverage limited resources and target those athletes most at risk.

OBJECTIVE

To characterize the instantaneous risk of LE injury by demographic factors of sex, sport, body mass index (BMI), and injury history.

DESIGN

Descriptive epidemiologic study.

SETTING

National Collegiate Athletic Association Division I athletic program.

PATIENTS OR OTHER PARTICIPANTS

A total of 278 National Collegiate Athletic Association Division I varsity student-athletes (119 males, 159 females; age = 19.07 ± 1.21 years, height = 175.48 ± 11.06 cm, mass = 72.24 ± 12.87 kg).

MAIN OUTCOME MEASURE(S)

Injuries to the LE were tracked for 237 ± 235 consecutive days. Sex-stratified univariate Cox regression models were used to investigate the association between time to first LE injury and sport, BMI, and LE injury history. The instantaneous LE injury risk was defined as the injury risk at any given point in time after the baseline measurement. Relative risk ratios and Kaplan-Meier curves were generated. Variables identified in the univariate analysis were included in a multivariate Cox regression model.

RESULTS

Female athletes displayed similar instantaneous LE injury risk to male athletes (hazard ratio [HR] = 1.29; 95% CI= 0.91, 1.83; P = .16). Overweight athletes (BMI >25 kg/m2) had similar instantaneous LE injury risk compared with athletes with a BMI of <25 kg/m2 (HR = 1.23; 95% CI = 0.84, 1.82; P = .29). Athletes with previous LE injuries were not more likely to sustain subsequent LE injury than athletes with no previous injury (HR = 1.09; 95% CI = 0.76, 1.54; P = .64). Basketball (HR = 3.12; 95% CI = 1.51, 6.44; P = .002) and soccer (HR = 2.78; 95% CI = 1.46, 5.31; P = .002) athletes had a higher risk of LE injury than cross-country athletes. In the multivariate model, instantaneous LE injury risk was greater in female than in male athletes (HR = 1.55; 95% CI = 1.00, 2.39; P = .05), and it was greater in male athletes with a BMI of >25 kg/m2 than that in all other athletes (HR = 0.44; 95% CI = 0.19, 1.00; P = .05), but these findings were not significantly different.

CONCLUSIONS

In a collegiate athlete population, previous LE injury was not a contributor to the risk of future LE injury, whereas being female or being male with a BMI of >25 kg/m2 resulted in an increased risk of LE injury. Clinicians can use these data to extrapolate the LE injury risk occurrence to specific populations.

Assessing the cumulative effect of long-term training load on the risk of injury in team sports

Objectives

Determine how to assess the cumulative effect of training load on the risk of injury or health problems in team sports.

Methods

First, we performed a simulation based on a Norwegian Premier League male football dataset (n players=36). Training load was sampled from daily session rating of perceived exertion (sRPE). Different scenarios of the effect of sRPE on injury risk and the effect of relative sRPE on injury risk were simulated. These scenarios assumed that the probability of injury was the result of training load exposures over the previous 4 weeks. We compared seven different methods of modelling training load in their ability to model the simulated relationship. We then used the most accurate method, the distributed lag non-linear model (DLNM), to analyse data from Norwegian youth elite handball players (no. of players=205, no. of health problems=471) to illustrate how assessing the cumulative effect of training load can be done in practice.

Results

DLNM was the only method that accurately modelled the simulated relationships between training load and injury risk. In the handball example, DLNM could show the cumulative effect of training load and how much training load affected health problem risk depending on the distance in time since the training load exposure.

Conclusion

DLNM can be used to assess the cumulative effect of training load on injury risk.

Sex Moderates the Relationship between Perceptual-Motor Function and Single-Leg Squatting Mechanics

To examine the isolated and combined effects of sex and perceptual-motor function on single-leg squatting mechanics in males and females. We employed a cross-sectional design in a research laboratory. Fifty-eight females (22.2 ± 3.5 yrs, 1.60 ± .07 m, 64.1 ± 13.0 kg) and 35 males (23.5 ± 5.0 yrs, 1.80 ± .06m, 84.7 ± 15.3 kg) free from time-loss injury in the six months prior, vertigo, and vestibular conditions participated in this study. Independent variables were sex, perceptual-motor metrics (reaction time, efficiency index, conflict discrepancy), and interaction effects. Dependent variables were peak frontal plane angles of knee projection, ipsilateral trunk flexion, and contralateral pelvic drop during single-leg squatting. After accounting for the sex-specific variance and perceptual-motor function effects on frontal plane squatting kinematics, female sex amplified the associations of: higher reaction time, lower efficiency index, and higher conflict discrepancy with greater right ipsilateral peak trunk lean (R² = .13; p = .05); higher reaction time, lower efficiency index, and higher conflict discrepancy with decreased right contralateral pelvic drop (R² = .22; p < .001); higher reaction time and lower conflict discrepancy with greater right frontal plane knee projection angle (R² = .12; p = .03); and higher reaction time with greater left frontal plane knee projection angle (R² = .22; p < .001). Female sex amplified the relationship between perceptual-motor function and two-dimensional frontal plane squatting kinematics. Future work should determine the extent to which perceptual-motor improvements translate to safer movement strategies.

Not straightforward: modelling non-linearity in training load and injury research

OBJECTIVES

To determine whether the relationship between training load and injury risk is non-linear and investigate ways of handling non-linearity.

METHODS

We analysed daily training load and injury data from three cohorts: Norwegian elite U-19 football (n=81, 55% male, mean age 17 years (SD 1)), Norwegian Premier League football (n=36, 100% male, mean age 26 years (SD 4)) and elite youth handball (n=205, 36% male, mean age 17 years (SD 1)). The relationship between session rating of perceived exertion (sRPE) and probability of injury was estimated with restricted cubic splines in mixed-effects logistic regression models. Simulations were carried out to compare the ability of seven methods to model non-linear relationships, using visualisations, root-mean-squared error and coverage of prediction intervals as performance metrics.

RESULTS

No relationships were identified in the football cohorts; however, a J-shaped relationship was found between sRPE and the probability of injury on the same day for elite youth handball players (p<0.001). In the simulations, the only methods capable of non-linear modelling relationships were the quadratic model, fractional polynomials and restricted cubic splines.

CONCLUSION

The relationship between training load and injury risk should be assumed to be non-linear. Future research should apply appropriate methods to account for non-linearity, such as fractional polynomials or restricted cubic splines. We propose a guide for which method(s) to use in a range of different situations.