Jones, Margaret T.

Objectives The purpose of the current study was to evaluate differences in total sleep time and heart rate variability (HRV) in active-duty firefighters between on-shift and off-shift days. Methods 59 structural firefighters (age: 37.3±7.3 years; height: 1.80±0.08 m; weight: 88.9±14.1 kg and body mass index (BMI): 27.5±4.1 kg/m2) participated in this longitudinal observation study. Each morning for 15 weeks, firefighters were asked to sync a smart ring with a smartphone application to download night-time data, which were then extracted to a cloud-based software application for later analysis. The software also computed a readiness score each morning. All day types were coded as on-shift or off-shift. Results The average nightly sleep time for all firefighters was 6.95±1.24 hours. When off-shift, firefighters recorded more sleep compared with on-shift nights (off-shift: 6.97±0.50 hours vs on-shift: 6.68±0.52 hours; p<0.0001). HRV (p<0.0001) and readiness scores (p=0.02) were lower off-shift compared with on-shift. For the lagged correlation analysis, total sleep time was moderately positively correlated with the following day’s readiness score. Conclusions Firefighters recorded more total sleep time when off-shift compared with on-shift; however, HRV and readiness scores were higher on-shift. The lag-time correlations indicate an association between sleep time and HRV or readiness scores, with approximately half of the variability in readiness being attributable to changes in total sleep time, which highlights the complexity of the readiness and HRV signals/systems.

Abstract Jagim, AR, Horswill, CA, Dobbs, WC, Twohey, EE, Tinsley, GM, Fields, JB, and Jones, MT. Minimum wrestling weight for high school girls wrestling: Time to revisit minimal body fat percent. J Strength Cond Res 39(3): 332–339, 2025—The purpose of this study was to determine whether an alternative body fat percentage (BF%) could be established as a safer margin for minimal wrestling weight (MWW) determination by evaluating the distribution of BF% and MWW values naturally occurring for high school girls wrestling. Data from the 2022–2023 high school season were retrospectively analyzed. In total, 33,321 female wrestlers completed skinfold (SKF) assessments or bioelectrical impedance analysis (BIA) in accordance with sport governing body mandates for weight certification. Frequency statistics and descriptive analysis were performed to compute normative MWW and BF% profiles. Body fat percentage thresholds of 12% (12MWW) and the BF% value defining the lowest fifth percentile were used to determine the resulting MWW and minimum weight class for each method. The median ± interquartile range for BF% was 28.3 ± 9.2%. There was a difference ( p < 0.001) in BF% values between BIA (28.4 ± 6.8%) and SKF (29.4 ± 7.6%), but a trivial effect size (ES = 0.14). The fifth percentile for BF% was 19%, which was used for alternative MWW determination (19MWW). 12MWW and 19MWW were 11.2 ± 6.4 kg ( p < 0.001; ES = 1.8) and 8.6 ± 7.7 kg ( p < 0.001; ES = 1.1) lower than the body mass of the wrestlers at the time of weight certification. Nearly all BF% values were above the 12% threshold currently used to determine MWW. Current body weights were more than 9.1 kg above MWW values for both 12MWW and 19MWW. Increasing the minimum BF% threshold from 12 to 19% would affect a small percentage of wrestlers and improve the margin of safety for those intent on achieving a lower weight class.

Abstract Magee, MK, Fields, JB, Jagim, A, Lockard, B, Miller, A, Stroiney, D, and Jones, MT. Estimation of whole-body bone mineral density through air displacement plethysmography in a large sample of elite athletes. J Strength Cond Res 39(4): 427–432, 2025—Dual-energy x-ray absorptiometry (DXA) is used to determine bone mineral density (BMD) and body composition. Regulations and cost may limit DXA's utility in some athletic settings. Air displacement plethysmography (ADP), while able to measure body composition, does not measure BMD. The purpose was to evaluate the influence of body composition parameters obtained from ADP on BMD. Collegiate athletes (males, n = 153; females, n = 208) underwent ADP and DXA assessments. For ADP testing, the Brozek equation was used to estimate body fat percent (BF%) and fat-free mass (FFM) from body density. Dual-energy x-ray absorptiometry tests were completed to obtain BMD using the whole-body scan mode. Stepwise regression was conducted using age, body mass, height, sex, race, FFM (ADP), BF% (ADP), and sport type as independent variables. Total BMD (DXA) was the dependent variable. Accuracy and precision of the final equation were evaluated through Bland–Altman and linear regression. The final regression equation yielded an R 2 of 0.691 with race, mass, BF%, age, and sport type as significant predictors ( p < 0.05) of BMD. Bland–Altman and linear regression showed systematic {95% confidence interval [CI] (−0.619 to −0.360)} and proportional bias (95% CI [0.173–0.374]). Although an ADP-derived regression equation may not provide a precise prediction of BMD, it may be useful to establish risk for low BMD if DXA is unavailable. Dual-energy x-ray absorptiometry may not be feasible for some athletic programs; therefore, an estimation equation for BMD derived from ADP values is advantageous for practitioners. This equation may assist in screening athletes for risk of low BMD.

Abstract Kuhlman, NM, Jagim, AR, Jones, MT, Feit, MK, and Fields, JB. A comparison of match external load demands across women’s collegiate field sports. J Strength Cond Res 39(2): 234–241, 2025—Historically, soccer (SOC), lacrosse (LAX), and field hockey (FH) have been grouped together as “field sports” due to their intermittent nature of match play and similar physiological demands. However, questions have emerged regarding potential variations in demands between sports. The purpose of this study was to compare external match workloads between collegiate women's SOC, LAX, and FH, and examine within-sport positional differences. Athletes (SOC: n = 15; LAX: n = 15; FH: n = 14) wore a global positioning system device during all matches throughout a competitive season. External load metrics included in the analysis were total distance (TD), sprint distance (SD), accelerations (>3 m·s −2 ), sprint efforts (SE), player load (PL), PL per minute (PL/min), top speed (m·s −1 ), and distances covered in select speed zones. Soccer performed higher external load volumes (TD, PL) ( p ≤ 0.025) but incurred lower intensities (SD, SE, accelerations) ( p < 0.001) compared with LAX and FH. Within-sport positional differences were apparent in SOC ( p < 0.001–0.018), LAX ( p < 0.001–0.045), and FH ( p < 0.001–0.004). However, FH exhibited more positional variance in external load metrics compared with SOC or LAX. Field hockey midfielders covered greater volumes (TD, PL) compared with attackers and defenders ( p < 0.001), while attackers sustained greater intensities (SD, SE, top speed) compared with midfielders and defenders ( p < 0.004). Results indicate that there are significant differences in external workload parameters between sports and across positions. Practitioners should tailor sport-specific and position-specific programs to match demands, and future research should explore the proposed categorizations of LAX as a “fast collision invasion sport,” FH as a “fast contact invasion sport,” and SOC as a “slow contact invasion sport.”

Sleep is critical to athlete health and sport performance. Purpose To evaluate the relationship between objective sleep data and next-day basketball game performance. Methods Collegiate men basketball athletes (n = 4) wore smart ring sleep trackers the night before games (n = 22). Sleep variables included hours of total sleep (TS), rapid eye movement sleep duration (RM), deep sleep duration (DS), and heart rate variability (HRV). Game performance was evaluated using field goal percentage (FG%) and player efficiency (EFF). Results TS, RM, DS, and HRV demonstrated very weak relationships with FG% ( p > 0.05) and EFF ( p > 0.05). Individual player analysis revealed one athlete exhibited strong to moderate correlations for TS-EFF (r = 0.67), RS-EFF (r = 0.41), TS-FG% (r = 0.51), and RM-FG% (r = 0.67) ( p < 0.05). Sleep metrics had no effect on FG% (R 2 = 0.025) and EFF (R 2 = 0.053) ( p > 0.05). Conclusions Results suggest a lack of group-level associations between smart ring-measured sleep variables and next-day game performance. This highlights the need for individualized sleep monitoring rather than relying solely on objective metrics.

Background/Objectives: Adherence to sports-specific nutritional guidelines can help optimize athlete performance and health. However, adolescent athletes may not have adequate nutrition knowledge and understanding of specific nutritional requirements. The objective of the current study was to examine the nutrition knowledge and perceived dietary requirements of adolescent athletes. Methods: Male (n = 29, age: 15.7 ± 1.3 yrs.; height: 178.9 ± 8.3 cm; body mass: 74.7 ± 17.2 kg; body fat %: 13.9 ± 7.9%) and female (n = 15, age: 16.5 ± 1.4 yrs.; height: 169.9 ± 6.5 cm; body mass: 63.3 ± 4.5 kg; body fat %: 23.7 ± 3.8%) secondary school student-athletes completed body composition testing (hydrostatic weighing) and electronic surveys (Abridged Sports Nutrition Knowledge Questionnaire (ASNKQ); self-perception of dietary energy and macronutrient requirements). Results: Athletes demonstrated poor sports nutrition knowledge, with no differences observed between sexes for the number of questions answered correctly (males: 45.1 ± 9.8% vs. females: 43.1 ± 12.7%; p = 0.57). No relationships were observed between ASNKQ scores and body composition parameters or between ASNKQ scores and self-reported perception of dietary energy and macronutrient requirements (p > 0.05). Athletes self-reported a lower perceived energy (−560 ± 1272 kcal/d; p = 0.014) and carbohydrate intake (−73 ± 376 g/d; p = 0.014) requirement compared to calculated nutritional recommendations. Athletes self-reported a higher perceived protein intake (263 ± 586 g/d; p = 0.026) requirement compared to calculated nutritional requirements. Conclusions: The current sample of adolescent athletes from the secondary school level appears to have a poor level of sports nutrition knowledge and understanding of energy and macronutrient requirements.