(28) ALLOSTATIC LOAD IN FIREFIGHTER RECRUITS: WEEKLY PHYSIOLOGICAL STRESS TRENDS AND LOAD MANAGEMENT IMPLICATIONS

Purpose: To examine the dynamics of physiological indicators of allostatic load (AL) over a 6-month fire academy (FA) and how weekly variations impact stress responses in firefighter (FF) recruits.Methods: Data from two 30-week FA classes (n=34 recruits; 17 per class) were analyzed. Class A completed EMT training before FF training, while Class B completed FF training before EMT training. FF recruits had daily morning measurements taken in a rested state: body weight (BW), resting heart rate (RHR), respiratory rate (RR), systolic and diastolic blood pressure (BP), and self-reported stress (SRS) levels. For each recruit, weekly averages were computed, and changes in measures were assessed for FIRE training, EMT training, and the entire FA using t-tests. Linear mixed-effects models (LMMs) were used to evaluate weekly changes, with week as a fixed effect and random intercepts to account for individual differences and repeated measures. The model fit was assessed using the AIC and BIC. The intra-class correlation coefficient (ICC) was computed to determine the proportion of variance due to individual differences. To assess overall trends, linear and non-linear growth models (quadratic and cubic spline) were computed. Likelihood ratio tests were conducted to determine whether nonlinear models provided a significantly better fit than linear models.Results: At baseline, the only significant difference between classes was systolic blood pressure (Class A >Class B; t(26.96)=2.283, p=0.031). Over the entire FA, Class A experienced a significant increase in diastolic BP (p=0.038, d=0.61), whereas Class B showed no significant (p>0.05) overall changes in AL markers. However, LME models revealed that week significantly influenced multiple AL measures, indicating fluctuations in stress responses throughout the academy. For Class A, the LME model was found to have a significant effect of week on BW (F=1.943, p=0.004, ICC=0.99), Urine (F=4.101, p< 0.001, ICC=0.73), RHR (F=1.755, p=0.012, ICC=0.71), RR (F=1.807, p=0.009, ICC=0.55), self-report stress (F=4.160, p< 0.001, ICC=0.56), systolic BP (F=5.130, p< 0.001, ICC=0.53) and diastolic BP (F=2.719, p< 0.001, ICC=0.56). For Class B, the LME indicated a significant effect of week on BW (F=3.235, p< 0.001, ICC=0.99), Urine (F=1.543, p=0.040, ICC=0.74), self-report stress (F=3.716, p< 0.001, ICC=0.44), systolic BP (F=2.112, p< 0.001, ICC=0.70) and diastolic BP (F=2.570, p< 0.001, ICC=0.64). Further modeling analyses demonstrated that nonlinear models provided a better fit for AL dynamics than linear models, particularly for body weight, RHR, systolic BP, and diastolic BP. Specifically, cubic spline models significantly improved model fit over linear models for RHR (p=0.033), systolic BP (p=0.045), and diastolic BP (p=0.047), suggesting complex physiological adaptation patterns rather than a simple linear declines. Conclusionss: While few overall changes in AL were observed from the start to end of the FA, weekly fluctuations highlight the impact of training demands and individual variability on physiological stress responses. The nonlinear nature of the responses highlight complex physiological adaptations occurring throughout the FA. Practical Applications: Strength and conditioning professionals should consider implementing load management strategies to balance training stress and build resilience in FF recruits. Continuous AL monitoring can help identify recruits at risk for heightened stress, enabling proactive adjustments in training and recovery. Acknowledgements: None