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Coppus, TA, Anderson, T, Hurley, E, Gill, DL, and Brown, PK. The practical utility of objective training load indices in Division I college soccer players. J Strength Cond Res 36(4): 1026-1030, 2022-The aim of this study was to investigate the association of subjective session rating of perceived exertion with objective training load measures and explored data reduction approaches for 26 commonly measured objective load metrics. One National Collegiate Athletic Association Division I male soccer team (N = 28) provided 26 objective measures via a global positioning system and heart rate monitor system and self-report rating of perceived exertion measures for all team activities through the 12-week 2019 fall championship season. The singular associations between session rating of perceived exertion (sRPE) and objective load metrics were assessed via repeated-measures correlation and collectively assessed via elastic net regression. Principal components analysis was then used to reduce the dimensionality of objective load variables. Level of significance for the study was [alpha] <= 0.05. The training load variable had the highest correlation with sRPE (r = 0.85) among the 26 metrics. Twenty variables were retained in the elastic-net regression and demonstrated an R 2 of 0.76. The principal components analysis demonstrated a 4-component solution. The components were qualitatively described as "moderate- or high-intensity external load," "low-intensity load," "high-intensity internal load," and "average internal load" and accounted for 46, 32, 16, and 6% of the variance, respectively. The sRPE metric had strong correlations with multiple objective measures, demonstrating its utility for practitioners monitoring training load in collegiate soccer with limited resources. Furthermore, the consolidation of 26 metrics into 4 components while retaining data variability allows coaches to focus on a small number of metrics and employ a data-driven approach to training. /p> [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate relationships between perceived wellness, sleep, and acute: chronic workload ratio (ACWR) throughout a collegiate men's soccer season. Sixty male collegiate soccer players (mean[M] ± SD; age, 2162 year; body mass, 77.6 ± 6.5 kg; height, 180.1 ± 6.4 cm; body fat%, 9.9 ± 3.9%; and V̇O2max, 53.1 ± 5.0 ml·kg-1·min-1) participated in this study. During each session, players used a heart rate and global positioning satellite-enabled chest strap to measure training impulse and ACWR. The ACWR values were trichotomized at the individual level giving an equal number of observations within each ACWR category of low, moderate, and high ACWR (M ± SD; low, 0.658 ± 0.23; moderate, 0.92 ± 0.15; and high, 1.17 ± 0.16). Stress, fatigue, and soreness levels were collected using 1-10 Likert scales and sleep duration, and sleep quality were measured by the Karolinska Sleep Diary. Stress, fatigue, soreness levels, and sleep quality were transformed to corresponding z-scores at the individual level. Fatigue levels were significantly higher when ACWR was high compared with low (mean difference [95% confidence intervals], effect size, p-value; 0.31 [0.-1, 0.42], 0.29, p,0.001) and moderate (0.14 [0.03, 0.24], 0.13, p = 0.01). Fatigue levels were also significantly higher when the ACWR was moderate compared with low (0.18 [0.07, 0.28], 0.16, p = 0.001). Soreness levels were significantly higher when the ACWR was high compared with low (0.25 [0.14, 0.36], 0.23, p< 0.001). Stress levels were significantly greater when the ACWR was high compared with low (0.19, [0.08, 0.29], 0.18, p< 0.001) and compared with moderate (0.15, [0.05, 0.25], 0.14, p50.004). There were no differences in sleep duration or sleep quality in different ACWR. The ACWR may be a useful tool to achieve an appropriate balance between training and recovery to manage daily fatigue and soreness levels in athletes. [ABSTRACT FROM AUTHOR]

The free testosterone to cortisol ratio (fTC) and heart rate variability (HRV) indices have been independently proposed as objective biomarkers of physiological resilience and readiness, respectively. It is hypothesized that individuals that enter a training period with high resilience and readiness would have a greater positive response to the training. PURPOSE: Therefore, the purpose of this study was to investigate the association between fTC and HRV with fitness changes in division I male college soccer players. METHODS: Two division I teams were recruited to participate in this study. Participants completed an orthostatic HRV challenge (supine, sitting, standing) and provided blood samples prior to the 6-week Spring training period. The HRV indices of root mean square of successive differences (RMSSD) and high frequency power (autoregressive, normalized; HF) were calculated in all postural conditions. Blood samples were assayed for free testosterone and cortisol using competitive binding ELISAs. Participants completed the yo-yo intermittent recovery test (YYIR) before and after the 6-week training period and the absolute increase in distance (m) achieved during the YYIR represented the change in fitness (YYIRΔ). Separate linear regression models tested the association of fTC and all HRV indices and their interactions with YYIRΔ. All predictors were first centered and scaled and alpha level was set at p < 0.05 for all models. RESULTS: Participants (n = 21, age = 19.5±1.0 years, height = 179.0±6.4 cm, weight = 76.9±9.4 kg, body fat = 12.6±4.3 %) from the two teams did not differ in YYIRΔ (mean±SD = 438±212, p = 0.64). A significant association with YYIRΔ was observed for the interaction between fTC and HFsitting (F(3,17) = 4.31, β = 4.92,p = 0.02, R² = 0.33). No other indices or interactions were significantly associated with YYIRΔ. CONCLUSIONS: These results suggest that HRV and fTC represent potential objective pre-training biomarkers capable of identifying athletes that will be most responsive to individualized fitness and conditioning programs. Further research is necessary to confirm the utility of these specific objective biomarkers in larger cohorts and across multiple training situations. [ABSTRACT FROM AUTHOR]

PURPOSE: Animal research suggests that variations in daily dietary macronutrient distribution may impact fluid consumption. However, this has not been fully investigated in humans. Thus, the purpose of this study was to determine the impact of dietary intake on fluid intake, thirst, and urinary hydration biomarkers. METHODS: Thirty-four participants (44% female; age: 23±4 years; height: 172.9±10.3cm; body mass: 77.2±18.1kg; body fat: 18.4± 8.4%) recorded their food and fluid intake, and provided a 24h urine sample over seven consecutive days. Urinary hydration biomarkers included urine volume (UVOL), urine osmolality (UOSM), urine specific gravity (USG), and urine color (UCOL). Each morning, participants completed two subjective ratings of perceived thirst; a 9-point Likert scale, and 100mm visual analog scales comprised of six indices (thirstiness, pleasantness, dryness, taste, fullness, sickness). RESULTS: Mean weekly macronutrient percentages and total calorie intake were used in multiple regression models as predictors of average total fluid intake, thirst, and urinary hydration markers. Throughout the week, participants reported consuming, on average, 1945±429 calories (36.5±4.4% fat, 44.3±6.3% carbohydrate, 18.5±4.5% protein) and 2508±1122mL fluid per day. Mean 7-day 24h urinary hydration markers were UVOL:1724±850mL, USG :1.017±0.005, UOSM: 565±212, UCOL: 3.89±0.99. Dietary intake that was higher in percentage of fat consumed was associated with higher ratings of "fullness" on morning thirst scale sub ratings (adj R² = 0.1181, p =0.0167). Consuming a greater proportion of calories from protein was associated with increased UVOL (adj R² = 0.1370, p = 0.0340). However, protein intake was not significantly associated with fluid intake (adj R2 = 0.0569, p = 0.2516), regardless of the type of fluid consumed (p > 0.05). UOSM, USG, UCOL and the other indices of thirst were not associated with macronutrient consumption (p>0.05). CONCLUSION: These results suggest increased consumption of dietary protein intake is associated with improved hydration status based on increased 24h urine volume, with the maintenance of other urinary hydration indices. Grant or Funding Information: This study was funded by a University of North Carolina at Greensboro Office of Research Engagement New Faculty Grant. [ABSTRACT FROM AUTHOR]

Objective: The primary aim of this study was to assess the efficacy of the weight, urine, thirst (WUT) framework in predicting dehydration after a body water manipulation protocol, while concurrently determining the individual and interactive contributions of the model components., Methods: The total study sample was 93 participants (female, n = 47), recruited from two institutions. Phase 1 involved collecting daily hydration measures from free-living participants (Study 1, 58 participants for 3 days; Study 2, 35 participants for 7 days). Phase 2 entailed a two-hour passive heating protocol, where participants from Study 2 were randomly assigned to one of three groups that manipulated total body water over 24-hours using passive heating and fluid restriction. During each Phase, participants provided urine samples, underwent body mass measurements, and completed questionnaires pertaining to thirst perception. Morning and 24-hour urine samples were assessed for color, osmolality, and specific gravity. Differences between intervention groups, based on the probability of hydration status, were examined (ANOVA) and ridge regression analysis assessed the relative importance of variables within the WUT model., Results: The study revealed significant differences among the intervention groups for predicted probability of dehydration, as determined by changes in body mass (p = 0.001), urine color (p = 0.044), and thirst perception (p < 0.001). Binomial ridge regression indicated that change in body mass (58%) and thirst perception (26%) were the most influential predictors of dehydration., Conclusions: These data support use of an enhanced version of the WUT model, underscoring the significance of changes in body mass and thirst perception in the assessment of hydration status., Competing Interests: Conflict of Interest and Funding Source: This study was funded in part by a Faculty Grant awarded by the School of Health and Human Sciences as the University of North Carolina at Greensboro and the Biotechnology High Performance Computing Software Applications Institute (BHSAI). WMA declares the following conflicts of interest: he serves on the advisory boards for My Normative and Emerja Corporation, the Medical and Science Advisory Board for the Korey Stringer Institute, and the Sport Advisory Council for the Wu Tsai Human Performance Alliance where he has received compensation either as part of his role on the respective boards, or as a consultant. WMA has previously received consulting fees/honoraria/compensation/grant funding/travel support from the following entities that is related to the submitted work: Gatorade, Samsung, BSX, QKC LLC, Clif Bar & Company, Danone Research Nutricia, Nobo Inc, and Onda Vision. WMA is also the owner of Adams Sports Medicine Consulting LLC with clients on topics related to the submitted work., (Copyright © 2024 by the American College of Sports Medicine.)

Objectives: Over the past few years, the United States has seen the rapid growth of dedicated musculoskeletal urgent care centers owned and operated by individual orthopaedic practices. In June of 2014, our practice opened the first dedicated orthopaedic urgent care in the region staffed by physician assistants and supervised by orthopaedic surgeons. Our hypothesis is that such centers can safely improve orthopaedic care for ambulatory orthopaedic injuries, decrease volume for overburdened emergency departments (EDs), reduce wait times and significantly decrease the cost of care while improving access to orthopaedic specialists., Design: Retrospective review., Setting: Level 2 trauma center and physician-owned orthopaedic urgent care., Patients: Consecutive series of patients seen in the hospital ED (n = 87,629) and orthopaedic urgent care (n = 12,722)., Intervention: None., Outcomes: ED wait time, total visit time, time until being seen by provider, time until consultation with orthopaedic surgeon, total visit charges, and effect on orthopaedic practice revenue., Results: During the 12 months of study, 12,722 patients were treated in our urgent care. The average urgent care wait time until being seen by a provider was 17 minutes compared with 45 minutes in hospital ED. Total visit time was 43 minutes in the urgent care and 156 minutes in the hospital ED. Time to being seen by an orthopaedic specialist was 1.2 days for urgent care patients compared with 3.4 days for ED patients. The average charge for an urgent care visit was $461 compared with $8150 in hospital ED. During the course of study, urgent care treatment reduced charges to health care system by $97,819,458. Hospital ED orthopaedic volume did decrease as expected but total ED patient volume remained the same. There was no measureable effect on hospital ED wait times. Hospital surgical case volume did not change over the period of study and the orthopaedic census remained stable. Urgent care construction, marketing, administration, imaging, and labor costs totaled $1,664,445. Urgent care revenue from evaluation and management, imaging, durable medical equipment, and casting totaled $2,577,707. Practice revenue from follow-up care of patients who entered practice through the urgent care totaled $7,657,998., Conclusion: Dedicated musculoskeletal urgent care clinics operated by orthopaedic surgery practices can be extremely beneficial to patients, physicians, and the health care system. They clearly improve access to care, whereas significantly decreasing overall health care costs for patients with ambulatory orthopaedic conditions and injuries. In addition, they can be financially beneficial to both patients and orthopaedic surgeons alike without cannibalizing local hospital surgical volumes., Level of Evidence: Therapeutic Level III.