Physical demands and physiological strain of American football referees while officiating.

OBJECTIVES: Certain occupations are associated with greater risk of triggering a sudden cardiac event because of high levels of physical exertion and extreme thermal environments in which they occur. The extent to which sports officials--particularly high school (HS) American football referees--experience these conditions is unknown. Forty-six male HS officials (72% White/Caucasian; age = 48 ± 12 years, body mass index = 31.7 ± 6.6 kg·m-2) were studied to quantify the physiological strain and physical demands of officiating. METHODS: Referee demographics (e.g., experience, habitual exercise), pre-game urine specific gravity (USG), thermal (peak core temperature [Tcore]) and cardiovascular (average heart rate [HR]) strain, kinematic activity (e.g., total distance, speed, mechanical intensity), and environmental conditions were measured during 10 regular season varsity HS football games (≈2.5 h each) in the Southeastern United States (average wet bulb globe temperature and relative humidity: 18.9 ± 6.0 °C and 78.2% ± 12.1%). Analyses included descriptive statistics, bivariate correlations, and linear regression. RESULTS: Referees covered 5.2 ± 1.2 km per game, eliciting average HR and peak Tcore of 71.5% ± 8.0% HRmax and 38.3 ± 0.5 °C, respectively; 38% began games dehydrated (USG = 1.026 ± 0.004). Multiple regression analyses revealed that obesity (ß = 0.34), not participating in regular exercise (ß = -0.36), and officiating at lower mechanical intensity (ß = -0.33) predicted greater cardiovascular strain (all p ≤ 0.03). White/Caucasian race/ethnicity (ß = 0.59), younger age (ß = -0.46), and obesity (ß = 0.28) predicted greater thermal strain (all p ≤ 0.01). CONCLUSION: HS football referees experienced elevated levels of physiological strain while officiating, with individual factors modulating the magnitude of strain. Strategies aimed at reducing obesity, increasing exercise participation, and improving cardiovascular health should be emphasized to mitigate strain and prevent cardiac events.

Association of daily sitting time and leisure-time physical activity with body fat among U.S. adults.

BACKGROUND: Prolonged sitting and reduced physical activity lead to low energy expenditures. However, little is known about the joint impact of daily sitting time and physical activity on body fat distribution. We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults. METHODS: This was a cross-sectional analysis of U.S. nationally representative data from the National Health and Nutrition Examination Survey 2011-2018 among adults aged 20 years or older. Daily sitting time and leisure-time physical activity (LTPA) were self-reported using the Global Physical Activity Questionnaire. Body fat (total and trunk fat percentage) was determined via dual X-ray absorptiometry. RESULTS: Among 10,808 adults, about 54.6% spent 6 h/day or more sitting; more than one-half reported no LTPA (inactive) or less than 150 min/week LTPA (insufficiently active) with only 43.3% reported 150 min/week or more LTPA (active) in the past week. After fully adjusting for sociodemographic data, lifestyle behaviors, and chronic conditions, prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes. When stratifying by LTPA, the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insufficiently active. In the joint analyses, inactive/insufficiently active adults who reported sitting more than 8 h/day had the highest total (female: 3.99% (95% confidence interval (95%CI):3.09%-4.88%); male: 3.79% (95%CI: 2.75%-4.82%)) and trunk body fat percentages (female: 4.21% (95%CI: 3.09%-5.32%); male: 4.07% (95%CI: 2.95%-5.19%)) when compared with those who were active and sitting less than 4 h/day. CONCLUSION: Prolonged daily sitting time was associated with increased body fat among U.S. adults. The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Resistance Training Recommendations for Children and Adolescents With Cystic Fibrosis-Related Diabetes.

Cystic fibrosis (CF) is the most prevalent hereditary life-threatening disease in the Caucasian population. With the improvement in clinical care, individuals with CF are living longer, and CF-related diabetes (CFRD) has emerged as a major complication. The diagnosis of CFRD is associated with shortening survival, increasing morbidity, worsening physical capacity, and body composition. Engagement in exercise training has become a prominent nonpharmacologic intervention that aims to improve fitness and clinical outcomes in individuals with CF and CFRD. This column will specifically focus on the potential benefits of resistance training and provide recommendations for children and adolescents with CF and CFRD.

Comparison of Bioelectrical Impedance Analysis with DXA in Adolescents with Cystic Fibrosis before and after a Resistance Training Intervention.

BACKGROUND: The purpose of this pilot study was to compare body composition metrics obtained by two portable bioelectrical impedance analysis (BIA) devices with dual-energy X-ray absorptiometry (DXA) among adolescents with cystic fibrosis (CF) before and after a resistance exercise training program. METHODS: Participants with CF were assessed using DXA, single-frequency BIA (SFBIA), and multiple-frequency BIA (MFBIA) to quantify percent body fat (%Fat), fat mass (FM), and fat-free mass (FFM) at baseline and after a home-based resistance training intervention comprised of 36, 1 h sessions completed in 12-14 weeks. Repeated measures analysis of variance, paired samples t-tests, Cohen's d effect sizes, and Pearson's correlations were used to compare differences between and within methods at baseline and post-intervention. RESULTS: Ten participants (15.8 ± 2.2 yr, 60.1 ± 15.1 kg) completed the assessments. At baseline, both SFBIA and MFBIA scales significantly underestimated %Fat and FM and overestimated FFM, with small to moderate effect sizes. Post-intervention, small, non-significant differences were found between DXA and both BIA scales for all body composition metrics. Significant changes in %Fat and FFM were observed with DXA. MFBIA displayed less constant error than SFBIA when compared to DXA for pre- and post-intervention assessments for %Fat (MFBIA: pre and post -2.8 and -0.8 vs. SFBIA: -4.6 and -2.0), FM (-0.4 and -0.4 vs. -3.0 and -1.1), and FFM (+0.8 and +0.6 vs. +3.1 and +1.3). Near-perfect correlations were observed at both time points between DXA and each BIA scale. Conclusions: Portable BIA results should be interpreted with caution, and further validation studies in CF patients are needed prior to clinical use.

Feasibility and Efficacy of Telehealth-Based Resistance Exercise Training in Adolescents with Cystic Fibrosis and Glucose Intolerance.

The aims of this study were to (1) determine the feasibility of a home-based resistance exercise training (RET) program in patients with cystic fibrosis and impaired glucose tolerance using virtual personal training and (2) observe the effects completion of the RET program had on glucose metabolism, pulmonary function, body composition, and physical fitness. The feasibility of the program was defined as 80% compliance. Ten participants (15.80 ± 2.20 yr, 25.1 ± 7.4 kg/m2) began a home-based resistance training program consisting of 36 sessions supervised via online videoconferencing. Compliance scores of 78.9% (all participants) and 81.8% (without one outlier) were observed. A significant increase was observed in 2-h C-peptide levels (2.1 ng/mL; p = 0.04), with a moderate decrease in fasting glucose (-5.2 mg/dL; p = 0.11) and a moderate increase in 2-h insulin (35.0 U/mL; p = 0.10). A small decrease in the fat percentage (-1.3%; p = 0.03) was observed in addition to increases in fat-free mass (1.5 kg; p = 0.01) and the fat-free mass index (0.4; p = 0.01). Small, yet statistically significant increases were observed in V̇O2peak (0.1 L/min p = 0.01), V̇CO2peak (0.1 L/min; p = 0.01), and ventilation (5.3 L/min; p = 0.04). Telehealth-based RET is feasible in adolescents with CF and impaired glucose tolerance and elicits small yet favorable changes in insulin secretion, body composition, and exercise capacity.

Validity of a 3-compartment body composition model using body volume derived from a novel 2-dimensional image analysis program.

BACKGROUND/OBJECTIVES: The purpose of this study was: (1) to compare body volume (BV) estimated from a 2-dimensional (2D) image analysis program (BVIMAGE), and a dual-energy x-ray absorptiometry (DXA) equation (BVDXA-Smith-Ryan) to an underwater weighing (UWW) criterion (BVUWW); (2) to compare relative adiposity (%Fat) derived from a 3-compartment (3C) model using BVIMAGE (%Fat3C-IMAGE), and a 4-compartment (4C) model using BVDXA-Smith-Ryan (%Fat4C-DXA-Smith-Ryan) to a 4C criterion model using BVUWW (%Fat4C-UWW). SUBJECT/METHODS: Forty-eight participants were included (60% male, 22.9 ± 5.0 years, 24.2 ± 2.6 kg/m2). BVIMAGE was derived using a single digital image of each participant taken from the rear/posterior view. DXA-derived BV was calculated according to Smith-Ryan et al. Bioimpedance spectroscopy and DXA were used to measure total body water and bone mineral content, respectively, in the 3C and 4C models. A standardized mean effect size (ES) assessed the magnitude of differences between models with values of 0.2, 0.5, and 0.8 for small, moderate, and large differences, respectively. Data are presented as mean ± standard deviation. RESULTS: Near-perfect correlation (r = 0.998, p < 0.001) and no mean differences (p = 0.267) were observed between BVIMAGE (69.6 ± 11.5 L) and BVUWW (69.5 ± 11.4 L). No mean differences were observed between %Fat4C-DXA-Smith-Ryan and the %Fat4C-UWW criterion (p = 0.988). Small mean differences were observed between %Fat3C-IMAGE and %Fat4C-UWW (ES = 0.2, p < 0.001). %Fat3C-IMAGE exhibited smaller SEE and TE, and tighter limits of agreement than %Fat4C-DXA-Smith-Ryan. CONCLUSIONS: The 2D image analysis program provided an accurate and non-invasive estimate of BV, and subsequently %Fat within a 3C model in generally healthy, young adults.

Vagally Derived Heart Rate Variability and Training Perturbations With Menses in Female Collegiate Rowers.

INTRODUCTION: The parasympathetically derived marker of heart rate variability, root mean square of successive R-R differences (RMSSD), and the daily fluctuations as measured by the coefficient of variation (RMSSDCV) may be useful for tracking training adaptations in athletic populations. These vagally derived markers of heart rate variability may be especially pertinent when simultaneously considering a female athlete's menstrual cycle. PURPOSE: The purpose of this study was to observe the perturbations in RMSSDCV, while considering RMSSD, across a season in the presence and absence of menses with training load in female collegiate rowers. METHODS: Thirty-six (20 [1] y, 25.6 [3.4] kg·m-2) National Collegiate Athletic Association Division I female rowers were monitored for 18 consecutive weeks across a full season. Seated, ultrashortened RMSSD measurements were obtained by the rowers on at least 3 mornings per week using a smartphone photoplethysmography device. Following the RMSSD measurement, athletes indicated the presence or absence of menstruation within the application. Individual meters rowed that week and sessions rate of perceived exertion were obtained to quantify training load. RESULTS: Longitudinal mixed-effects modeling demonstrated a significant effect of menses and time, while also considering RMSSD, such that those who were on their period had a significantly greater RMSSDCV than those who were not (11.2% vs 7.5%, respectively; P < .001). These changes were independent of meters rowed, sessions rate of perceived exertion, body mass index, birth-control use, and years of rowing experience, which were all nonsignificant predictors of RMSSDCgV (P > .05). CONCLUSION: The presence of menses appears to significantly impact RMSSDCV when also considering RMSSD, which may allow coaches to consider individualized training plans accordingly.

Agreement Between A 2-Dimensional Digital Image-Based 3-Compartment Body Composition Model and Dual Energy X-Ray Absorptiometry for The Estimation of Relative Adiposity.

The purpose of this study was to compare relative adiposity (%Fat) derived from a 2-dimensional image-based 3-component (3C) model (%Fat3C-IMAGE) and dual-energy X-ray absorptiometry (DXA) (%FatDXA) against a 5-component (5C) laboratory criterion (%Fat5C). 57 participants were included (63.2% male, 84.2% White/Caucasian, 22.5±4.7 yrs., 23.9±2.8 kg/m2). For each participant, body mass and standing height were measured to the nearest 0.1 kg and 0.1 cm, respectively. A digital image of each participant was taken using a 9.7 inch, 16g iPad Air 2 and analyzed using a commercially available application (version 1.1.2, made Health and Fitness, USA) for the estimation of body volume (BV) and inclusion in %Fat3C-IMAGE . %Fat3C-IMAGE and %Fat5C included measures of total body water derived from bioimpedance spectroscopy. The criterion %Fat5C included BV estimates derived from underwater weighing and bone mineral content measures via DXA. %FatDXA estimates were calculated from a whole-body DXA scan. A standardized mean effect size (ES) assessed the magnitude of differences between models with values of 0.2, 0.5, and 0.8 for small, moderate, and large differences, respectively. Data are presented as mean ± standard deviation. A strong correlation (r = 0.94, p <.001) and small mean difference (ES = 0.24, p <.001) was observed between %Fat3C-IMAGE (19.20±5.80) and %Fat5C (17.69±6.20) whereas a strong correlation (r = 0.87, p <.001) and moderate-large mean difference (ES = 0.70, p <.001) was observed between %FatDXA (22.01±6.81) and %Fat5C. Furthermore, %Fat3C-IMAGE (SEE = 2.20 %Fat, TE= 2.6) exhibited smaller SEE and TE than %FatDXA (SEE = 3.14 %Fat, TE = 5.5). The 3C image-based model performed slightly better in our sample of young adults than the DXA 3C model. Thus, the 2D image analysis program provides an accurate and non-invasive estimate of %Fat within a 3C model in young adults. Compared to DXA, the 3C image-based model allows for a more cost-effective and portable method of body composition assessment, potentially increasing accessibility to multi-component methods.

Comparison of Heart Rate Variability Responses to Varying Resistance Exercise Volume-Loads.

Purpose: The aim of this study was to compare the effects of low ([LV]; 4 total sets), moderate ([MV]; 8 total sets), and high set volumes ([HV]; 12 total sets) in acute full-body resistance exercise sessions on post-exercise parasympathetic reactivation measured using RMSSD. Methods: Ten resistance-trained participants (25.8 ± 6.8 yr., 173.4 ± 10.6 cm, 75.4 ± 9.9 kg) performed three resistance exercise sessions. During each session, heart rate variability (HRV) was measured pre- and for 30 min post-exercise, divided into 5-min segments stabilization, Post5-10, Post10-15, Post15-20, Post20-25, and Post25-30. Repeated-measures ANOVA was used to assess differences within and between pre-post exercise natural logarithm RMSSD (LnRMSSD) values. To assess the initial change in LnRMSSD, the delta percent change (ΔLnRMSSD) from pre-exercise to Post5-10 (ΔLnRMSSDpre-post) was calculated for each session. The ΔLnRMSSD was also calculated between Post5-10 and Post25-30 (ΔLnRMSSDpost5-30) to assess recovery. Results: Significant differences were observed between sessions and when comparing pre-exercise values to all post-exercise times across sessions (p ≤ .05). The LV session resulted in significantly higher mean LnRMSSD value (3.62) post-exercise compared to both the MV (3.11, effect size [ES] =  3.77) and HV (3.02, ES =  3.92) sessions while the MV and HV sessions produced similar responses. Across sessions no return to baseline occurred and when comparing sessions, no significant differences were found in ΔLnRMSSDpre-post or ΔLnRMSSDpost5-30. Conclusion: Acute bouts of full-body resistance exercise can cause similar reductions in LnRMSSD from pre-exercise levels and can delay parasympathetic reactivation back to baseline values during the same 30-min recovery period despite differences in set volume.

The Application of Exercise Training for Diabetic Peripheral Neuropathy.

Diabetic peripheral neuropathy (DPN) is the presence of symptoms and/or signs of peripheral nerve dysfunction in people with diabetes after the exclusion of other causes. It is associated with pain, paresthesia, sensory loss, muscle atrophy with fat infiltration, and muscular dysfunction typically starting distally in the feet and progressing proximally. Muscle deterioration within the leg and foot can lead to muscle dysfunction, reduced mobility, and increases the risk of disability, ulceration, and amputation. Exercise training is an established method for increasing the different components of physical fitness, including enhancing body composition and improving neuromuscular strength. A number of experimental studies have utilized exercise training to treat various impairments associated with DPN, such as nerve conduction velocity, pain tolerance, and balance. However, the broad spectrum of exercise training modalities implemented and differences in target outcome measurements have made it difficult to understand the efficacy of exercise training interventions or provide appropriate exercise prescription recommendations. Therefore, the aims of this review were to (1) briefly describe the pathophysiology of DPN and (2) discuss the effects of exercise training interventions on sensorimotor, metabolic, and physical functions in people with DPN.

The Utility of Body Composition Assessment in Nutrition and Clinical Practice: An Overview of Current Methodology.

Body composition is a key component for maintaining good general health and longevity. It can be influenced by a variety of factors, including genetics, environment, and lifestyle choices. The assessment of body composition is an essential tool for nutrition specialists to effectively evaluate nutritional status and monitor progression during dietary interventions. As humans age, there is a natural increase in fat mass coupled with a gradual decline in lean mass, specifically in bone and muscle mass. Individuals with a high body fat percentage are at a greater risk of cardiovascular diseases, type 2 diabetes, several types of cancer, and early mortality. Significant decreases in bone mineral density signify osteopenia and osteoporosis, while reductions in skeletal muscle mass increase the risk of developing sarcopenia. Moreover, undernutrition exacerbates the effects of many medical conditions and is important to address. Though weight tracking and calculation of BMI are used commonly by clinicians and dietitians, these measures do not provide insight on the relative contributions of fat mass and fat-free mass or the changes in these compartments that may reflect disease risk. Therefore, it is important that healthcare professionals have a critical understanding of body composition assessment and the strengths and limitations of the methods available.

Generalized Equations for Predicting Percent Body Fat from Anthropometric Measures Using a Criterion Five-Compartment Model.

INTRODUCTION: Anthropometric-based equations are used to estimate percent body fat (%BF) when laboratory methods are impractical or not available. However, because these equations are often derived from two-compartment models, they are prone to error because of the assumptions regarding fat-free mass composition. The purpose of this study was to develop a new anthropometric-based equation for the prediction of %BF, using a five-compartment (5C) model as the criterion measure. METHODS: A sample of healthy adults (52.2% female; age, 18 to 69 yr; body mass index, 15.7 to 49.5 kg·m-2) completed hydrostatic weighing, dual-energy x-ray absorptiometry, and bioimpedance spectroscopy measurements for calculation of 5C %BF (%BF5C), as well as skinfolds and circumferences. %BF5C was regressed on anthropometric measures using hierarchical variable selection in a random sample of subjects (n = 279). The resulting equation was cross-validated in the remaining participants (n = 78). New model performance was also compared with several common anthropometric-based equations. RESULTS: The new equation [%BFNew = 6.083 + (0.143 × SSnew) - (12.058 × sex) - (0.150 × age) - (0.233 × body mass index) + (0.256 × waist) + (0.162 × sex × age)] explained a significant proportion of variance in %BF5C (R2 = 0.775, SEE = 4.0%). Predictors included sum of skinfolds (SSnew, midaxillary, triceps, and thigh) and waist circumference. The new equation cross-validated well against %BF5C when compared with other existing equations, producing a large intraclass correlation coefficient (0.90), small mean bias and limits of agreement (0.4% ± 8.6%), and small measures of error (SEE = 2.5%). CONCLUSIONS: %BFNew improved on previous anthropometric-based equations, providing better overall agreement and less error in %BF estimation. The equation described in this study may provide an accurate estimate of %BF5C in healthy adults when measurement is not practical.

Utilizing a Novel 2D Image Processing System for Relating Body Composition Metrics to Performance in Collegiate Female Rowers.

The purpose of this study was to determine if rowing performance was associated with fat mass (FM) or fat-free mass (FFM) measured using a novel 2D digital image analysis system. Nineteen female rowers (ages = 20.3 ± 1.0 years, weight = 73.8 ± 8.3 kg, height = 172.7 ± 4.7 cm) participated in this study. FM and FFM were estimated with a smartphone application that uses an automated 2D image analysis program. Rowing performance was measured using a 2 km (2k) timed trial on an indoor ergometer. The average speed of the timed trial was recorded in raw units (m·s-1) and adjusted for body weight (m·s-1·kg-1). FFM was significantly correlated to unadjusted 2k speed (r = 0.67, p < 0.05), but not for FM (r = 0.44, p > 0.05). When 2k speed was adjusted to account for body weight, significant correlations were found with FM (r = -0.56, p < 0.05), but not FFM (r = -0.34, p > 0.05). These data indicate that both FM and FFM are related to rowing performance in female athletes, but the significance of the relationships is dependent on overall body mass. In addition, the novel 2D imaging system appears to be a suitable field technique when relating body composition to rowing performance.

Heart-Rate Variability Recording Time and Performance in Collegiate Female Rowers.

PURPOSE: To assess the agreement of the root mean square of successive R-R interval (RMSSD) values when recorded immediately upon waking to values recorded later in the morning prior to practice, and to determine the associations of the RMSSD recordings with performance outcomes in female rowers. METHODS: A total of 31 National Collegiate Athletic Association Division I rowers were monitored for 6 consecutive days. Two seated RMSSD measurements were obtained on at least 3 mornings using a smartphone-based photoplethysmography application. Each 1-minute RMSSD measure was recorded following a 1-minute stabilization period. The first (T1) measurement occurred at the athlete's home following waking, while the second (T2) transpired upon arrival at the team's boathouse immediately before practice. From the measures, the RMSSD mean and coefficient of variation were calculated. Two objective performance assessments were conducted on an indoor rowing ergometer on separate days: 2000-m time trial and distance covered in 30 minutes. Interteam rank was determined by the coaches, based on subjective and objective performance markers. RESULTS: The RMSSD mean (intraclass correlation coefficient = .82; 95% CI, .63 to .92) and RMSSD coefficient of variation (intraclass correlation coefficient = .75; 95% CI, .48 to .88) were strongly correlated at T1 and T2, P < .001. The RMSSD mean at T1 and T2 was moderately associated with athlete rank (r = -.55 and r = -.46, respectively), 30-minute distance (r = .40 and r = .41, respectively), and 2000 m at T1 (r = -.37), P < .05. No significant correlations were observed for the RMSSD coefficient of variation. CONCLUSION: Ultrashort RMSSD measurements taken immediately upon waking show very strong agreement with those taken later in the morning, at the practice facility. Future research should more thoroughly investigate the relationship between specific performance indices and the RMSSD mean and coefficient of variation for female collegiate rowers.

Accuracy of a Mobile 2D Imaging System for Body Volume and Subsequent Composition Estimates in a Three-Compartment Model.

PURPOSE: The purpose of the study was to compare a single two-dimensional image processing system (IMAGE) to underwater weighing (UWW) for measuring body volume (BV) and subsequently estimating body fat percentage (%Fat), fat mass (FM), and fat-free mass (FFM) via a 3-compartment (3C) model. METHODS: A sample of participants age 18-39 yr was recruited for this study (n = 67, 47.8% female). BV was measured with UWW and predicted via the IMAGE software. The BV estimates from UWW (3CUWW) and IMAGE (3CIMAGE) were separately combined with constant total body water and body mass values for 3C model calculation of %Fat, FM, and FFM. RESULTS: BV obtained from the IMAGE was 67.76 ± 12.19 and 67.72 ± 12.04 L from UWW, which was not significantly different (P = 0.578) and very largely correlated (r = 0.99, P < 0.001). When converted to %Fat (3CUWW = 21.01% ± 7.30%, 3CIMAGE = 21.08% ± 7.04%, P = 0.775), FM (3CUWW = 14.68 ± 5.15 kg, 3CIMAGE = 14.78 ± 5.08 kg, P = 0.578), and FFM (3CUWW = 57.00 ± 13.20 kg, 3CIMAGE = 56.90 ± 12.84 kg, P = 0.578) with the 3C model, no significant mean differences and very large correlations (r values ranged from 0.96 to 0.99) were observed. In addition, the standard error of estimate, total error, and 95% limits of agreement for all three metrics were small and considered acceptable. CONCLUSIONS: An IMAGE system provides valid estimates of BV that accurately estimates body composition in a 3C model.

Associations between Multimodal Fitness Assessments and Rowing Ergometer Performance in Collegiate Female Athletes.

The purpose was to examine the association of critical power from a three-minute all-out row (CP3-min) and peak power from a one-stroke maximum test (1-Stroke) with laboratory-based fitness assessments (peak oxygen consumption [V.O2peak] and Wingate anaerobic test [WAnT]) and 6000 m (6K) and 2000 m (2K) rowing ergometer performance. Thirty-one female collegiate rowers (20.2 ± 1.1 years, 70.9 ± 6.9 kg, and 172.2 ± 4.8 cm) participated in fitness and rowing performance testing. Pearson's correlations, linear regression, and Cohen's q were used to determine statistical relationships. Absolute V.O2peak values displayed significant correlations with 6Ktotal (-0.68), 6Ksplit (-0.68), 2Ktotal (-0.64), and 2Ksplit (-0.43). Relative V.O2peak displayed significant correlations with 6Ktotal (-0.36), and 6Ksplit (-0.37). CP3-min demonstrated significant correlations with 6Ktotal (-0.62), 6Ksplit (-0.62), 2Ktotal (-0.61), and 2Ksplit (-0.99). For 2Ksplit, a significant difference was observed between relative V.O2peak and CP3-min correlations with a "large" effect size (q = 2.367). Furthermore, 1-Stroke showed significant associations with 6Ktotal (-0.63), 6Ksplit (-0.63), 2Ktotal (-0.62), and 2Ksplit (-0.44), while WAnT produced non-significant correlations. Absolute V.O2peak CP3-min accounted for significant proportions of variance observed with performance measures (p < 0.05). Practitioners should consider incorporating CP3-min and 1-Stroke as additional tests for gauging rowing performance.

Validity of Smartphone Heart Rate Variability Pre- and Post-Resistance Exercise.

The aim was to examine the validity of heart rate variability (HRV) measurements from photoplethysmography (PPG) via a smartphone application pre- and post-resistance exercise (RE) and to examine the intraday and interday reliability of the smartphone PPG method. Thirty-one adults underwent two simultaneous ultrashort-term electrocardiograph (ECG) and PPG measurements followed by 1-repetition maximum testing for back squats, bench presses, and bent-over rows. The participants then performed RE, where simultaneous ultrashort-term ECG and PPG measurements were taken: two pre- and one post-exercise. The natural logarithm of the root mean square of successive normal-to-normal (R-R) differences (LnRMSSD) values were compared with paired-sample t-tests, Pearson product correlations, Cohen's d effect sizes (ESs), and Bland-Altman analysis. Intra-class correlations (ICC) were determined between PPG LnRMSSDs. Significant, small-moderate differences were found for all measurements between ECG and PPG: BasePre1 (ES = 0.42), BasePre2 (0.30), REPre1 (0.26), REPre2 (0.36), and REPost (1.14). The correlations ranged from moderate to very large: BasePre1 (r = 0.59), BasePre2 (r = 0.63), REPre1 (r = 0.63), REPre2 (r = 0.76), and REPost (r = 0.41)-all p < 0.05. The agreement for all the measurements was "moderate" (0.10-0.16). The PPG LnRMSSD exhibited "nearly-perfect" intraday reliability (ICC = 0.91) and "very large" interday reliability (0.88). The smartphone PPG was comparable to the ECG for measuring HRV at rest, but with larger error after resistance exercise.

Changes in Heart Rate Variability and Fatigue Measures Following Moderate Load Resistance Exercise.

The purpose of this study was to determine the relationship between changes in heart rate variability (HRV), neuromuscular performance, and fatigue biomarkers in response to a resistance exercise bout. The root mean square of successive RR interval differences (RMSSD), neuromuscular performance - isometric handgrip (IHG), countermovement jump (CMJ), mean propulsive velocity (MPV) - metabolic stress (lactate [Lac]) and inflammation (interleukin-6 [IL-6]) were measured in 30 subjects who performed 6×10 back squat (BS), 3×10 bench press (BP), and 3×10 bent-over rows (BR) at 70% of 1-repetition maximum (1RM). The RMSSD, neuromuscular performance, and biomarkers were measured 10 min pre-exercise and 30 min post-exercise (Post30); HRV and Lac were also measured immediately post-exercise (Post0). Pre- versus post-exercise differences were evaluated using paired-samples t-tests. Pearson's correlations were used to determine the association between changes. With the exception of IL-6 (P=0.296) and MPVBP (P=0.678), LnRMSSD, neuromuscular performance, and metabolic stress were different post- compared to pre-exercise. We observed moderate associations between ΔLnRMSSD Post0 and ΔLac Post0 (r = -0.44) and ΔLac Post30 (r = -0.55), respectively. Practitioners should use multiple training load indicators to gain an accurate depiction of recovery.