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Purpose: To prescribe training loads to improve performance, one must know how an athlete is responding to loading. The maximal rate of heart-rate increase (rHRI) during the transition from rest to exercise is linearly related to changes in endurance exercise performance and can be used to infer how athletes are responding to changes in training load. Relationships between rHRI and anaerobic exercise performance have not been evaluated. The objective of this study was to evaluate relationships between rHRI and anaerobic exercise performance. Methods: Eighteen recreational strength and power athletes (13 male and 5 female) were tested on a cycle ergometer for rHRI, 6-second peak power output, anaerobic capacity (30-s average power), and blood lactate concentration prior to (PRE), and 1 (POST1) and 3 (POST3) hours after fatiguing high-intensity interval cycling. Results: Compared with PRE, rHRI was slower at POST1 (effect size [ES] = −0.38, P = .045) but not POST3 (ES = −0.36, P = .11). PPO was not changed at POST1 (ES = −0.12, P = .19) but reduced at POST3 (ES = −0.52, P = .01). Anaerobic capacity was reduced at POST1 (ES = −1.24, P P P P = .11). rHRI was positively related to PPO (B = 0.19, P = .03) and anaerobic capacity (B = 0.14, P = .005) and inversely related to blood lactate concentration (B = −0.22, P = .04). Conclusions: rHRI is linearly related to acute changes in anaerobic exercise performance and may indicate how athletes are responding to training to guide the application of training loads.

Refereed/Peer-reviewed Objectives: Maximal aerobic speed (MAS) may be predicted from 2.0 km time trial (TT) running speed in male Australian Rules football (AF) players. Given the between-sex differences in physiological variables precursory to endurance performance, and the impact of this on MAS distance limit, this study determined running speed across various TT distances best approximating MAS in female AF players. Method: 33 female AF players completed assessments of MAS and TT performance separated by at least 48 h. In a randomised order, half of the athletes completed TT distances of 1.2, 1.6 and 2.0 km, and the other half completed distances of 1.4, 1.8 and 2.2 km. Bias, limits of agreement (LOA) and linear mixed modelling determined agreement between TT-derived running speed and MAS. Results: Average speed for all TT distances were different to MAS (bias≥0.59 ± 0.45 km·h−1; p ≤ 0.015) with the exception of the 1.4 km TT (bias = −0.12 ± 0.26 km·h−1; p = 0.34). LOA was narrowest for the 1.4 km TT(±0.76 km·h−1; ±6.1%) compared to other TT distances (±0.82–1.67 km·h−1; ±6.7–12.9%). A significant linear association between bias and TT distance (r = −0.73; p < 0.001) indicated TT speed would be equal to MAS when a 1.4–1.5 km TT was completed, and that MAS may be predicted from any distance between 1.2 and 2.2 km. Conclusions: MAS was best approximated by a 1.4–1.5 km TT in female AF players, but may be predicted from TT speed for any distance between 1.2 and 2.2 km. TT-derived MAS provides a time and resource efficient method for the quantification of aerobic fitness and prescription of future training intensities.

Salagaras, BS, Mackenzie-Shalders, KL, Nelson, MJ, Fraysse, F, Wycherley, TP, Slater, GJ, McLellan, C, Kumar, K, and Coffey, VG. Comparisons of daily energy intake vs. expenditure using the GeneActiv accelerometer in elite Australian Football athletes. J Strength Cond Res 35(5): 1273–1278, 2021—To assess validity of the GeneActiv accelerometer for use within an athlete population and compare energy expenditure (EE) with energy and macronutrient intake of elite Australian Football athletes during a competition week. The GeneActiv was first assessed for utility during high-intensity exercise with indirect calorimetry. Thereafter, 14 professional Australian Football athletes (age, 24 ± 4 [SD] y; height, 1.87 ± 0.08 m; body mass, 86 ± 10 kg) wore the accelerometer and had dietary intake assessed via dietitian-led 24-hour recalls throughout a continuous 7 days of competition period (including match day). There was a significant relationship between metabolic equivalents and GeneActiv g·min−1 (SEE 1.77 METs; r2 = 0.64; p < 0.0001). Across the in-season week a significant difference only occurred on days 3 and 4 (day 3: energy intake [EI] EI 137 ± 31 kJ·kg−1·d−1; 11,763 ± 2,646 kJ·d−1 and EE: 186 ± 14 kJ·kg−1·d−1; 16,018 ± 1973 kJ·d−1; p < 0.05; d = −1.4; day 4: EI: 179 ± 44 kJ·kg−1·d−1, 15,413 ± 3,960 kJ·d−1 and EE: 225 ± 42 kJ·kg−1·d−1; 19,313 ± 3,072 kJ·d−1; d = −0.7). Carbohydrate intake (CI) was substantially below current sports nutrition recommendations on 6 of 7 days with deficits ranging from −1 to −7.2 g·kg−1·d−1 (p < 0.05), whereas daily protein and fat intake was adequate. In conclusion, the GeneActiv provides effective estimation of EE during weekly preparation for a professional team sport competition. Australian Footballers attempt to periodize dietary EI to varying daily training loads but fail to match expenditure on higher-training load days. Specific dietary strategies to increase CI may be beneficial to achieve appropriate energy balance and macronutrient distribution, particularly on days where athletes undertake multiple training sessions. Refereed/Peer-reviewed

Golf swing analysis is common in both recreational and professional levels where players are searching for improvements in shot accuracy and distance. The use of motion analysis systems such as the portable Polhemus Liberty system is gaining interest by coaches and players, however, to date, no research has examined the usefulness of the Polhemus Liberty system for golf swing analysis. Therefore, the purpose of this study was to determine the reliability of the Polhemus Liberty system and validity compared to the VICON Nexus motion analysis system when assessing segment (pelvis and thorax) and joint (shoulder, elbow and wrist) angular kinematics during a golf swing at key events (address, top of backswing and impact). Fifteen elite amateur/professional golfers performed ten golf swing trials within specified bounds using their 5-iron club. Reliability was assessed using interclass coefficient, effect size and t-test statistics by all participants completing two separate testing sessions on separate days following the same experimental protocol. Validity was assessed using effect size, Pearson correlation and t-test statistics by comparing swings captured using both Polhemus Liberty and VICON Nexus concurrently. Results demonstrated no difference in ball outcome results using the Trackman launch monitor (P &gt, 0.05) and that the Polhemus Liberty system was reliable across the two sessions for all segment (pelvis and thorax) and joint (lead shoulder (gleno-humeral joint), elbow and wrist) angular kinematics (P &gt, 0.05). Validity analysis showed that the Polhemus Liberty system for the segments (pelvis and thorax) and joints (lead shoulder and wrist) were different compared to the VICON Nexus data at key events during the golf swing. Although validity could not be confirmed against VICON Nexus modeling, the Polhemus Liberty system may still be useful for golf swing analysis across training sessions. However, caution should be applied when comparing data from the system to published research data using different motion analysis methods.

The maximal rate of heart rate (HR) increase (rHRI), a marker of HR acceleration during transition from rest to submaximal exercise, correlates with exercise performance. In this cohort study, whether rHRI tracked performance better when evaluated over shorter time-periods which include a greater proportion of HR acceleration and less steady-state HR was evaluated. rHRI and five-km treadmill running time-trial performance (5TTT) were assessed in 15 runners following one week of light training (LT), two weeks of heavy training (HT) and 10-day taper (T). rHRI was the first derivative maximum of a sigmoidal curve fit to one, two, three and four minutes of R-R data during transition from rest to running at 8 km/h (rHRI8 km/h), 10.5 km/h, 13 km/h and transition from 8 to 13 km/h (rHRI8–13km/h). 5TTT time increased from LT to HT (effect size [ES] 1.0, p p 8 km/h assessed over two (B = −5.54, p = 0.04) three (B = −5.34, p = 0.04) and four (B = −5.37, p = 0.04) minutes, and rHRI8–13km/h over one (B = −11.62, p = 0.006) and three (B = −11.44, p = 0.03) minutes. 5TTT correlated most consistently with rHRI8 km/h. rHRI8 km/h assessed over two to four minutes may be suitable for evaluating athlete responses to training.

© 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 12 month embargo from date of publication (February 2018) in accordance with the publisher’s archiving policy, Objective To determine the change in walking gait biomechanics after total hip arthroplasty (THA) for osteoarthritis (OA) compared to the pre-operative gait status, and to compare the recovery of gait following THA with healthy individuals. Methods Systematic review with meta-analysis of studies investigating changes in gait biomechanics after THA compared to (1) preoperative levels and (2) healthy individuals. Data were pooled at commonly reported time points and standardised mean differences (SMDs) were calculated in meta-analyses for spatiotemporal, kinematic and kinetic parameters. Results Seventy-four studies with a total of 2,477 patients were included. At 6 weeks postoperative, increases were evident for walking speed (SMD: 0.32, 95% confidence intervals (CI) 0.14, 0.50), stride length (SMD: 0.40, 95% CI 0.19, 0.61), step length (SMD: 0.41, 95% CI 0.23, 0.59), and transverse plane hip range of motion (ROM) (SMD: 0.36, 95% CI 0.05, 0.67) compared to pre-operative gait. Sagittal, coronal and transverse hip ROM was significantly increased at 3 months (SMDs: 0.50 to 1.07). At 12 months postoperative, patients demonstrated deficits compared with healthy individuals for walking speed (SMD: −0.59, 95% CI −1.08 to −0.11), stride length (SMD: −1.27, 95% CI -1.63, −0.91), single limb support time (SMD: −0.82, 95% CI −1.23, −0.41) and sagittal plane hip ROM (SMD: −1.16, 95% CI −1.83, −0.49). Risk of bias scores ranged from seven to 24 out of 26. Conclusions Following THA for OA, early improvements were demonstrated for spatiotemporal and kinematic gait patterns compared to the pre-operative levels. Deficits were still observed in THA patients compared to healthy individuals at 12 months.

Refereed/Peer-reviewed The effect of the menstrual cycle on athlete performance, wellbeing and perceived exertion and fatigue is not well understood. Furthermore, it has not been investigated specifically in Australian Football athletes. This pilot study aimed to explore how naturally menstruating Australian Football athletes may be affected by menstrual cycle phase. The data collected from the routine monitoring of five naturally menstruating athletes (average menstrual cycle length of 28 ± 3 [SD] days) in one team (athlete age range 18–35 years) competing in the Women’s Australian Football League during the 2019 season were retrospectively analysed to compare performance (countermovement jump parameters and adductor squeeze pressure), perceived exertion, perceived fatigue and wellbeing (perceived sleep quality, stress and soreness) outcomes between the follicular and luteal phases. Performance, perceived exertion, stress and soreness did not appear to be affected by menstrual cycle phase (p > 0.17). However, perceived fatigue appeared to be significantly greater (p = 0.042) and sleep quality worse (p = 0.005) in the luteal phase. This pilot study suggests further research focusing on the effect of menstrual cycle phase on subjective fatigue and wellbeing is warranted.

Maximal rate of heart rate (HR) increase (rHRI) as a measure of HR acceleration during the transition from rest to exercise, or during an increase in workload, tracks exercise performance. rHRI assessed at relative rather than absolute workloads may track performance better, and a field test would increase applicability. This study therefore aimed to evaluate the sensitivity of rHRI assessed at individualised relative workloads during treadmill and overground running for tracking exercise performance. Treadmill running performance (5 km time trial; 5TTT) and rHRI were assessed in 11 male runners following 1 week of light training (LT), 2 weeks of heavy training (HT) and a 10-day taper (T). rHRI was the first derivative maximum of a sigmoidal curve fit to HR data collected during 5 min of treadmill running at 65% peak HR (rHRI65%), and subsequent transition to 85% peak HR (rHRI85%). Participants ran at the same speeds overground, paced by a foot-mounted accelerometer. Time to complete 5TTT likely increased following HT (ES = 0.14 ± 0.03), and almost certainly decreased following T (ES = − 0.30 ± 0.07). Treadmill and field rHRI65% likely increased after HT in comparison to LT (ES ≤ 0.48 ± 0.32), and was unchanged at T. Treadmill and field rHRI85% was unchanged at HT in comparison to LT, and likely decreased at T in comparison to LT (ES ≤ − 0.55 ± 0.50). 5TTT was not correlated with treadmill or field rHRI65% or rHRI85%. rHRI65% was highly correlated between treadmill and field tests across LT, HT and T (r ≥ 0.63), but correlations for rHRI85% were trivial to moderate (r ≤ 0.42). rHRI assessed at relative exercise intensities does not track performance. rHRI assessed during the transition from rest to running overground and on a treadmill at the same running speed were highly correlated, suggesting that rHRI can be validly assessed under field conditions at 65% of peak HR.

Background: There are no known objective biomarkers to assist with the diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). A small number of studies have shown that ME/CFS patients exhibit an earlier onset of ventilatory threshold (VT) on the second of two cardiopulmonary exercise tests (CPET) performed on consecutive days. However, cut-off values which could be used to differentiate between ME/CFS patients have not been established. Methods: 16 ME/CFS patients and 10 healthy controls underwent CPET on a cycle-ergometer on 2-consecutive days. Heart rate (HR), ventilation, ratings of perceived exertion (RPE) and work rate (WR) were assessed on both days. Results: WR at VT decreased from day 1 to day 2 and by a greater magnitude in ME/CFS patients (p < 0.01 group × time interaction). No interaction effects were found for any other parameters. ROC curve analysis of the percentage change in WR at VT revealed decreases of - 6.3% to - 9.8% provided optimal sensitivity and specificity respectively for distinguishing between patients with ME/CFS and controls. Conclusion: The decrease in WR at VT of 6.3-9.8% on the 2nd day of consecutive-day CPET may represent an objective biomarker that can be used to assist with the diagnosis of ME/CFS. Refereed/Peer-reviewed

BACKGROUND: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex condition with no reliable diagnostic biomarkers. Studies have shown evidence of autonomic dysfunction in patients with ME/CFS, but results have been equivocal. Heart rate (HR) parameters can reflect changes in autonomic function in healthy individuals; however, this has not been thoroughly evaluated in ME/CFS. METHODS: A systematic database search for case-control literature was performed. Meta-analysis was performed to determine differences in HR parameters between ME/CFS patients and controls. RESULTS: Sixty-four articles were included in the systematic review. HR parameters assessed in ME/CFS patients and controls were grouped into ten categories: resting HR (RHR), maximal HR (HRmax), HR during submaximal exercise, HR response to head-up tilt testing (HRtilt), resting HR variability (HRVrest), HR variability during head-up tilt testing (HRVtilt), orthostatic HR response (HROR), HR during mental task(s) (HRmentaltask), daily average HR (HRdailyaverage), and HR recovery (HRR) Meta-analysis revealed RHR (MD ± 95% CI = 4.14 ± 1.38, P

Purpose: Correlations between fatigue-induced changes in exercise performance and maximal rate of heart rate (HR) increase (rHRI) may be affected by exercise intensity during assessment. This study evaluated the sensitivity of rHRI for tracking performance when assessed at varying exercise intensities. Method: Performance (time to complete a 5-km treadmill time-trial [5TTT]) and rHRI were assessed in 15 male runners following 1 week of light training, 2 weeks of heavy training (HT), and a 10-day taper (T). Maximal rate of HR increase (measured in bpm·s−¹) was the first derivative maximum of a sigmoidal curve fit to HR data recorded during 5 min of running at 8 km·h⁻¹ (rHRI⁸–¹³ᵏᵐ·ʰ−¹), and during subsequent transition to 13 km·h−¹ (rHRI⁸–¹³ᵏᵐ·ʰ−¹) for a further 5 min. Results: Time to complete a 5-km treadmill time-trial was likely slower following HT (effect size ± 90% confidence interval = 0.16 ± 0.06), and almost certainly faster following T (–0.34 ± 0.08). Maximal rate of HR increase during 5 min of running at 8 km·h−¹ and rHRI⁸–¹³ᵏᵐ·ʰ−¹ were unchanged following HT and likely increased following T (0.77 ± 0.45 and 0.66 ± 0.62, respectively). A moderate within-individual correlation was found between 5TTT and rHRI⁸–¹³ᵏᵐ·ʰ−¹ (r value ± 90% confidence interval = –.35 ± .32). However, in a subgroup of athletes (n = 7) who were almost certainly slower to complete the 5TTT (4.22 ± 0.88), larger correlations were found between the 5TTT and rHRI⁸–¹³ᵏᵐ·ʰ−¹ (r = –.84 ± .22) and rHRI⁸–¹³ᵏᵐ·ʰ−¹ (r = –.52 ± .41). Steady-state HR during rHRI assessment in this group was very likely greater than in the faster subgroup (≥ 1.34 ± 0.86). Conclusions: The 5TTT performance was tracked by both rHRI⁸–¹³ᵏᵐ·ʰ−¹ and rHRI⁸–¹³ᵏᵐ·ʰ−¹. Correlations between rHRI and performance were stronger in a subgroup of athletes who exhibited a slower 5TTT. Individualized workloads during rHRI assessment may be required to account for varying levels of physical conditioning. Refereed/Peer-reviewed

Purpose: Being able to identify how an athlete is responding to training would be useful to optimise adaptation and performance. The maximal rate of heart rate increase (rHRI), a marker of heart rate acceleration has been shown to correlate with performance changes in response to changes in training load in male athletes; however, it has not been established if it also correlates with performance changes in female athletes. Methods: rHRI and cycling performance were assessed in six female cyclists following 7 days of light training (LT), 14 days of heavy training (HT) and a 10 day taper period. rHRI was the first derivative maximum of a sigmoidal curve fit to R-R data recorded during 5 min of cycling at 100 W. Cycling performance was assessed as work done (kJ) during time-trials of 5 (5TT) and 60 (60TT) min duration. Results: 5TT was possibly decreased at HT (ES ± 90% confidence interval = − 0.16 ± 0.25; p = 0.60), while, 5TT and 60TT very likely to almost certainly increased from HT to taper (ES = 0.71 ± 0.24; p = 0.007 and ES = 0.42 ± 0.19; p = 0.02, respectively). Large within-subject correlations were found between rHRI, and 5TT (r = 0.65 ± 0.37; p = 0.02) and 60TT (r = 0.70 ± 0.31; p = 0.008). Conclusions: rHRI during the transition from rest to light exercise correlates with training induced-changes in exercise performance in females, suggesting that rHRI may be a useful monitoring tool for female athletes. Refereed/Peer-reviewed

Purpose: Knowledge of aerobic performance capacity allows for the optimisation of training programs in aerobically dominant sports. Maximal aerobic speed (MAS) is a measure of aerobic performance; however, the time and personnel demands of establishing MAS are considerable. This study aimed to determine whether time-trials (TT), which are shorter and less onerous than traditional MAS protocols, may be used to predict MAS. Methods: 28 Australian Rules football players completed a test of MAS, followed by TTs of six different distances in random order, each separated by at least 48 h. Half of the participants completed TT distances of 1200, 1600 and 2000 m, and the others completed distances of 1400, 1800 and 2200 m. Results: Average speed for the 1200 and 1400 m TTs were greater than MAS (P < 0.01). Average speed for 1600, 1800, 2000 and 2200 m TTs were not different from MAS (P > 0.08). Average speed for all TT distances correlated with MAS (r = 0.69–0.84; P < 0.02), but there was a negative association between the difference in average TT speed and MAS with increasing TT distance (r = −0.79; P < 0.01). Average TT speed over the 2000 m distance exhibited the best agreement with MAS. Conclusions: MAS may be predicted from the average speed during a TT for any distance between 1200 and 2200 m, with 2000 m being optimal. Performance of a TT may provide a simple alternative to traditional MAS testing. Refereed/Peer-reviewed

Objectives: Heart rate kinetics are faster in well-trained athletes at exercise onset, indicating sensitivity to training status, but whether they track performance changes due to changes in training load is unknown. Design: Randomised, counterbalanced, cross-over. Methods: 17 cyclists completed two weeks of light and two weeks of heavy training. The day after each training period heart rate was recorded during 5. min cycling at 100. W to determine the maximal rate of heart rate increase. Participants then performed a 5. min cycle time-trial after which heart rate recovery was determined. Results: Work during 5. min cycle time-trial decreased 3.5% (P