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Background: Studies investigating the effects of common recovery modalities following acute strenuous exercise have reported mixed results. Objectives: This systematic review with meta-analysis and meta-regression compared the effects of cold-water immersion (CWI) against other common recovery modalities on recovery of athletic performance, perceptual outcomes, and creatine kinase (CK) following acute strenuous exercise in physically active populations. Study Design: Systematic review, meta-analysis, and meta-regression. Methods: The MEDLINE, SPORTDiscus, Scopus, Web of Science, Cochrane Library, EmCare, and Embase databases were searched up until September 2022. Studies were included if they were peer reviewed, published in English, included participants who were involved in sport or deemed physically active, compared CWI with other recovery modalities following an acute bout of strenuous exercise, and included measures of performance, perceptual measures of recovery, or CK. Results: Twenty-eight studies were meta-analysed. CWI was superior to other recovery methods for recovering from muscle soreness, and similar to other methods for recovery of muscular power and flexibility. CWI was more effective than active recovery, contrast water therapy and warm-water immersion for most recovery outcomes. Air cryotherapy was significantly more effective than CWI for the promotion of recovery of muscular strength and the immediate recovery of muscular power (1-h post-exercise). Meta-regression revealed that water temperature and exposure duration were rarely exposure moderators. Conclusion: CWI is effective for promoting recovery from acute strenuous exercise in physically active populations compared with other common recovery methods. Protocol Registration: Open Science Framework: https://doi.org/10.17605/OSF.IO/NGP7C Refereed/Peer-reviewed

Background Studies investigating the effects of cold-water immersion (CWI) on the recovery of athletic performance, perceptual measures and creatine kinase (CK) have reported mixed results in physically active populations. Objectives The purpose of this systematic review was to investigate the effects of CWI on recovery of athletic performance, perceptual measures and CK following an acute bout of exercise in physically active populations. Study Design Systematic review with meta-analysis and meta-regression. Methods A systematic search was conducted in September 2021 using Medline, SPORTDiscus, Scopus, Web of Science, Cochrane Library, EmCare and Embase databases. Studies were included if they were peer reviewed and published in English, included participants who were involved in sport or deemed physically active, compared CWI with passive recovery methods following an acute bout of strenuous exercise and included athletic performance, athlete perception and CK outcome measures. Studies were divided into two strenuous exercise subgroups: eccentric exercise and high-intensity exercise. Random effects meta-analyses were used to determine standardised mean differences (SMD) with 95% confidence intervals. Meta-regression analyses were completed with water temperature and exposure durations as continuous moderator variables. Results Fifty-two studies were included in the meta-analyses. CWI improved the recovery of muscular power 24 h after eccentric exercise (SMD 0.34 [95% CI 0.06–0.62]) and after high-intensity exercise (SMD 0.22 [95% CI 0.004–0.43]), and reduced serum CK (SMD − 0.85 [95% CI − 1.61 to − 0.08]) 24 h after high-intensity exercise. CWI also improved muscle soreness (SMD − 0.89 [95% CI − 1.48 to − 0.29]) and perceived feelings of recovery (SMD 0.66 [95% CI 0.29–1.03]) 24 h after high-intensity exercise. There was no significant influence on the recovery of strength performance following either eccentric or high-intensity exercise. Meta-regression indicated that shorter time and lower temperatures were related to the largest beneficial effects on serum CK (duration and temperature dose effects) and endurance performance (duration dose effects only) after high-intensity exercise. Conclusion CWI was an effective recovery tool after high-intensity exercise, with positive outcomes occurring for muscular power, muscle soreness, CK, and perceived recovery 24 h after exercise. However, after eccentric exercise, CWI was only effective for positively influencing muscular power 24 h after exercise. Dose–response relationships emerged for positively influencing endurance performance and reducing serum CK, indicating that shorter durations and lower temperatures may improve the efficacy of CWI if used after high-intensity exercise. Funding Emma Moore is supported by a Research Training Program (Domestic) Scholarship from the Australian Commonwealth Department of Education and Training. Protocol registration Open Science Framework: 10.17605/OSF.IO/SRB9D.

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.

Objectives: To identify and summarise the contextual factors associated with running demands in elite male Australian football (AF) gameplay that have been reported in the literature. Design: Scoping review Methods: A contextual factor in sporting gameplay is a variable associated with the interpretation of results, yet is not the primary objective of gameplay. Systematic literature searches were performed in four databases to identify what contextual factors associated with running demands in elite male AF have been reported: Scopus, SPORTDiscus, Ovid Medline and CINAHL, for terms constructed around Australian football AND running demands AND contextual factors. The present scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), and narrative synthesis was conducted. Results and Conclusion: A total of 36 unique articles were identified by the systematic literature search, which included 20 unique contextual factors. The most studied contextual factors were position (n = 13), time in game (n = 9), phases of play (n = 8), rotations (n = 7) and player rank (n = 6). Multiple contextual factors, such as playing position, aerobic fitness, rotations, time within a game, stoppages, and season phase appear to correlate with running demands in elite male AF. Many identified contextual factors have very limited published evidence and thus additional studies would help draw stronger conclusions Refereed/Peer-reviewed

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.

The aims of this study were to: (1) compare actigraphy (ACTICAL) and a commercially available sleep wearable (i.e., WHOOP) under two functionalities (i.e., sleep auto-detection (WHOOP-AUTO) and manual adjustment of sleep (WHOOP-MANUAL)) for two-stage categorisation of sleep (sleep or wake) against polysomnography, and, (2) compare WHOOP-AUTO and WHOOP-MANUAL for four-stage categorisation of sleep (wake, light sleep, slow wave sleep (SWS), or rapid eye movement sleep (REM)) against polysomnography. Six healthy adults (male: n = 3, female: n = 3, age: 23.0 ± 2.2 yr) participated in the nine-night protocol. Fifty-four sleeps assessed by ACTICAL, WHOOP-AUTO and WHOOP-MANUAL were compared to polysomnography using difference testing, Bland–Altman comparisons, and 30-s epoch-by-epoch comparisons. Compared to polysomnography, ACTICAL overestimated total sleep time (37.6 min) and underestimated wake (−37.6 min), WHOOP-AUTO underestimated SWS (−15.5 min), and WHOOP-MANUAL underestimated wake (−16.7 min). For ACTICAL, sensitivity for sleep, specificity for wake and overall agreement were 98%, 60% and 89%, respectively. For WHOOP-AUTO, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 90%, 60%, 86% and 63%, respectively. For WHOOP-MANUAL, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 97%, 45%, 90% and 62%, respectively. WHOOP-AUTO and WHOOP-MANUAL have a similar sensitivity and specificity to actigraphy for two-stage categorisation of sleep and can be used as a practical alternative to polysomnography for two-stage categorisation of sleep and four-stage categorisation of sleep.

Background There is extensive research investigating the match demands of players in the Australian Football League (AFL). Objective This systematic literature review and meta-regression sought to analyse the evolution of in-game demands in AFL matches from 2005 to 2017, focusing on the relationship between volume and intensity. Methods A systematic search of Ovid MEDLINE, Embase, Emcare, Scopus, SPORTDiscus, and Cochrane Library databases was conducted. Included studies examined the physical demands of AFL matches utilising global positioning system (GPS) technology. Meta-regression analysed the shift in reported volume (total distance and total match time) and intensity (metres per minute [m.min−1], sprint duration and acceleration) metrics for overall changes, across quarters and positional groups (forwards, nomadics and defenders) from 2005 to 2017 inclusive and for each year between 2005 and 2007, 2007 and 2010, 2010 and 2012, and 2012 and 2015/2017 breakpoints. Results Distance (p = 0.094), m.min−1 (p = 0.494), match time (p = 0.591), time over 18 km·h−1 (p = 0.271), and number of accelerations greater than 4 km·h−1 (p = 0.498) and 10 km·h−1 (p = 0.335) in 1 s did not change from 2005 to 2017. From 2005 to 2007 volume decreased (− 6.10 min of match time; p = 0.010) and intensity increased (6.8 m.min−1 increase; p = 0.023). Volume and intensity increased from 2007 to 2010, evidenced by increases in total distance (302 m; p = 0.039), time over 18 km·h−1 (0.31 min; p = 0.005), and number of accelerations greater than 4 km·h−1 (41.1; p = 0.004) and 10 km·h−1 (3.6; p = 0.005) in 1 s. From 2010 to 2012, intensity decreased, evidenced by reductions in metres per minute (− 4.3; p = 0.022), time over 18 km·h−1 (− 0.93 min; p < 0.001), and number of accelerations greater than 4 km·h−1 (− 104.4; p < 0.001) and 10 km·h−1 (− 8.3; p < 0.001) in 1 s, whilst volume stabilised with no changes in distance (p = 0.068) and match time (p = 0.443). From 2012 to 2015/2017 volume remained stable and intensity increased with time over 18 km·h−1 (0.27 min; p = 0.008) and number of accelerations greater than 4 km·h−1 (31.6; p = 0.016) in 1 s increasing. Conclusions Changes in volume and intensity of AFL match demands are defined by discrete periods from 2007 to 2010 and 2010 to 2012. The interaction of rule and interpretation changes and coaching strategies play a major role in these evolutionary changes. In turn, modified game styles impact player game demands, training, and selection priorities. Standardisation and uniformity of GPS data reporting is recommended due to inconsistencies in the literature.

The purpose of this study was to determine the reliability and validity of plantar pressure and reaction force measured using the Moticon and Pedar-x sensor insoles while rowing on a Concept2 ergometer. Nineteen participants performed four 500 m trials of ergometer rowing at 22–24 strokes/min, two trials wearing Moticon insoles and two wearing Pedar-x insoles in a randomised order. Moticon and Pedar-x insoles both showed moderate to strong test–retest reliability (ICC = 0.57–0.92) for mean and peak plantar pressure and reaction force. Paired t-test demonstrated a significant difference (p &lt, 0.001) between Moticon and Pedar-x insoles, effect size showed a large bias (ES &gt, 1.13), and Pearson’s correlation (r &lt, 0.37) showed poor agreement for all plantar pressure and reaction force variables. Compared to Pedar-x, the Moticon insoles demonstrated poor validity, however, the Moticon insoles had strong reliability. Due to poor validity, caution should be used when considering Moticon insoles to assess changes in pressure and force reliably over time, across multiple trials or sessions. Moticon’s wireless and user-friendly application would be beneficial for assessing and monitoring biomechanical parameters in rowing if validity between measures of interest and Moticon’s results can be established.

The purpose of this study was to describe the differences in training and competition loads at three distinct development levels in a junior-elite tennis academy. A total of 39 junior-elite tennis players were recruited from three squads; U/12, U/15 and U/18. Metrics of global positioning systems, accelerometer microtechnology and hitting demands were quantified to determine the external load. Ratings of perceived exertion (RPE) were used to quantify internal training and competition loads. Two training sessions and one competition match were quantified. The results showed an increase in training hitting demands as age increased from the U/15-U/18 squad (p

Purpose:Stride-to-stride fluctuations in running-stride interval display long-range correlations that break down in the presence of fatigue accumulated during an exhaustive run. The purpose of the study was to investigate whether long-range correlations in running-stride interval were reduced by fatigue accumulated during prolonged exposure to a high training load (functional overreaching) and were associated with decrements in performance caused by functional overreaching.Methods:Ten trained male runners completed 7 d of light training (LT7), 14 d of heavy training (HT14) designed to induce a state of functional overreaching, and 10 d of light training (LT10) in a fixed order. Running-stride intervals and 5-km time-trial (5TT) performance were assessed after each training phase. The strength of long-range correlations in running-stride interval was assessed at 3 speeds (8, 10.5, and 13 km/h) using detrended fluctuation analysis.Results:Relative to performance post-LT7, time to complete the 5TT was increased after HT14 (+18 s; P < .05) and decreased after LT10 (–20 s; P = .03), but stride-interval long-range correlations remained unchanged at HT14 and LT10 (P > .50). Changes in stride-interval long-range correlations measured at a 10.5-km/h running speed were negatively associated with changes in 5TT performance (r –.46; P = .03).Conclusions:Runners who were most affected by the prolonged exposure to high training load (as evidenced by greater reductions in 5TT performance) experienced the greatest reductions in stride-interval long-range correlations. Measurement of stride-interval long-range correlations may be useful for monitoring the effect of high training loads on athlete performance.

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.

The aim of the study was to compare the WHOOP strap–a wearable device that estimates sleep based on measures of movement and heart rate derived from actigraphy and photoplethysmography, respectively. Twelve healthy adults (6 females, 6 males, aged 22.9 ± 3.4 years) participated in a 10-day, laboratory-based protocol. A total of 86 sleeps were independently assessed in 30-s epochs using polysomnography and WHOOP. For WHOOP, bed times were entered by researchers and sleeps were scored by the company based on proprietary algorithms. WHOOP overestimated total sleep time by 8.2 ± 32.9 minutes compared to polysomnography, but this difference was non-significant. WHOOP was compared to polysomnography for 2-stage (i.e., wake, sleep) and 4-stage categorisation (i.e., wake, light sleep [N1 or N2], slow-wave sleep [N3], REM) of sleep periods. For 2-stage categorisation, the agreement, sensitivity to sleep, specificity for wake, and Cohen’s kappa were 89%, 95%, 51%, and 0.49, respectively. For 4-stage categorisation, the agreement, sensitivity to light sleep, SWS, REM, and wake, and Cohen’s kappa were 64%, 62%, 68%, 70%, 51%, and 0.47, respectively. In situations where polysomnography is impractical (e.g., field settings), WHOOP is a reasonable method for estimating sleep, particularly for 2-stage categorisation, if accurate bedtimes are manually entered. Refereed/Peer-reviewed

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.

Purpose: To investigate the effect of a 5-day short-term heat acclimation (STHA) protocol in dry (43°C and 20% relative humidity) or humid (32°C and 80% relative humidity) environmental conditions on endurance cycling performance in temperate conditions (21°C). Methods: In a randomized, cross-over design, 11 cyclists completed each of the two 5-day blocks of STHA matched for heat index (44°C) and total exposure time (480 min), separated by 30 days. Pre- and post-STHA temperate endurance performance (4-min mean maximal power, lactate threshold 1 and 2) was assessed; in addition, a heat stress test was used to assess individual levels of heat adaptation. Results: Differences in endurance performance were unclear. Following dry STHA, gross mechanical efficiency was likely reduced (between-condition effect size dry vs humid −0.59; 90% confidence interval, −1.05 to −0.15), oxygen uptake was likely increased for a given workload (0.64 [0.14 to 1.07]), and energy expenditure likely increased (0.59 [0.17 to 1.03]). Plasma volume expansion at day 5 of acclimation was similar (within-condition outcome 4.6% [6.3%] and 5.3% [5.1%] dry and humid, respectively) but was retained for 3 to 4 days longer after the final humid STHA exposure (−0.2% [8.1%] and 4.5% [4.2%] dry and humid, respectively). Sweat rate was very likely increased during dry STHA (0.57 [0.25 to 0.89]) and possibly increased (0.18 [−0.15 to 0.50]) during humid STHA. Conclusion: STHA induced divergent adaptations between dry and humid conditions, but did not result in differences in temperate endurance performance.

Objectives Reductions in maximal rate of heart rate increase (rHRI) correlate with performance reductions when training load is increased. This study evaluated whether rHRI tracked performance changes across a range of training states. Design Prospective intervention. Methods rHRI was assessed during five min of cycling at 100 W (rHRIcyc) and running at 8 km/h (rHRIrun) in 13 male triathletes following two weeks of light-training (LT), two weeks of heavy-training (HT) and a two-day recovery period (RP). A five min cycling time-trial assessed performance and peak oxygen consumption ( V ˙ O 2 peak ) . Results Performance likely decreased following HT (Effect size ± 90% confidence interval = −0.18 ± 0.09), then very likely increased following RP (0.32 ± 0.14). rHRIcyc very likely decreased (−0.48 ± 0.24), and rHRIrun possibly decreased (−0.33 ± 0.48), following HT. Changes in both measures were unclear following RP. Steady-state HR was almost certainly lower (−0.81 ± 0.31) during rHRIcyc than rHRIrun. A large correlation was found between reductions in performance and rHRIrun (r ± 90%; CI = 0.65 ± 0.34) from LT to HT, but was unclear for rHRIcyc. Trivial within-subject correlations were found between rHRI and performance, but the strength of relationship between rHRIrun and performance was largely associated with V ˙ O 2 peak following LT (r = −0.58 ± 0.38). Conclusions Performance reductions were most sensitively tracked by rHRIrun following HT. This may be due to rHRIrun being assessed at a higher intensity than rHRIcyc, inferred from a higher steady-state HR and supported by a stronger within-subject relationship between rHRIrun and performance in individuals with a lower V ˙ O 2 peak , in whom the same exercise intensity would represent a greater physiological stress. rHRI assessed at relatively high exercise intensities may better track performance changes.

Objectives The recovery of heart rate (HRR) after exercise is a potential indicator of fitness which has been shown to respond to changes in training. This study investigated the within-individual association between HRR and exercise performance following three different training loads. Design 11 male cyclists/triathletes were tested after two weeks of light training, two weeks of heavy training and two days of rest. Methods Exercise performance was measured using a 5-min maximal cycling time-trial. HRR was measured over 60 s during supine recovery. Results Exercise performance decreased 2.2 ± 2.5% following heavy training compared with post-light training (p = 0.01), and then increased 4.0 ± 4.2% following rest (p = 0.004). Most HRR indices indicated a more rapid recovery of heart rate (HR) following heavy training, and reverted to post light training levels following two days of rest. HRR indices did not differ between post-light training and after the rest period (p > 0.6). There were inverse within-subject relationships between indices of HRR and performance (r = −0.6, p ≤ 0.004). Peak HR decreased 3.2 ± 5.1 bpm following heavy training (p = 0.06) and significantly increased 4.9 ± 4.3 bpm following recovery (p = 0.004). There was a moderate within-subject relationship between peak HR and exercise performance (r = 0.7, p ≤ 0.001). Controlling for peak HR reduced the relationships between HRR and performance (r = −0.4–0.5, p Conclusions This study demonstrated that HRR tracks short-term changes in exercise performance within-individuals, such that increases in HRR are associated with poorer exercise performance following heavy training. Peak HR can be compromised under conditions of fatigue, and needs to be taken into account in HRR analyses.

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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

Autonomic regulation of heart rate (HR) as an indicator of the body’s ability to adapt to an exercise stimulus has been evaluated in many studies through HR variability (HRV) and post-exercise HR recovery (HRR). Recently, HR acceleration has also been investigated. Objective: The aim of this systematic literature review and meta-analysis was to evaluate the effect of negative adaptations to endurance training (i.e., a period of overreaching leading to attenuated performance) and positive adaptations (i.e., training leading to improved performance) on autonomic HR regulation in endurance-trained athletes. Methods: We searched Ovid MEDLINE, Embase, CINAHL, SPORTDiscus, PubMed, and Academic Search Premier databases from inception until April 2015. Included articles examined the effects of endurance training leading to increased or decreased exercise performance on four measures of autonomic HR regulation: resting and post-exercise HRV [vagal-related indices of the root-mean-square difference of successive normal R–R intervals (RMSSD), high frequency power (HFP) and the standard deviation of instantaneous beat-to-beat R–R interval variability (SD1) only], and post-exercise HRR and HR acceleration. Results: Of the 5377 records retrieved, 27 studies were included in the systematic review and 24 studies were included in the meta-analysis. Studies inducing increases in performance showed small increases in resting RMSSD [standardised mean difference (SMD) = 0.58; P < 0.001], HFP (SMD = 0.55; P < 0.001) and SD1 (SMD = 0.23; P = 0.16), and moderate increases in post-exercise RMSSD (SMD = 0.60; P < 0.001), HFP (SMD = 0.90; P < 0.04), SD1 (SMD = 1.20; P = 0.04), and post-exercise HRR (SMD = 0.63; P = 0.002). A large increase in HR acceleration (SMD = 1.34) was found in the single study assessing this parameter. Studies inducing decreases in performance showed a small increase in resting RMSSD (SMD = 0.26; P = 0.01), but trivial changes in resting HFP (SMD = 0.04; P = 0.77) and SD1 (SMD = 0.04; P = 0.82). Post-exercise RMSSD (SMD = 0.64; P = 0.04) and HFP (SMD = 0.49; P = 0.18) were increased, as was HRR (SMD = 0.46; P < 0.001), while HR acceleration was decreased (SMD = −0.48; P < 0.001). Conclusions: Increases in vagal-related indices of resting and post-exercise HRV, post-exercise HRR, and HR acceleration are evident when positive adaptation to training has occurred, allowing for increases in performance. However, increases in post-exercise HRV and HRR also occur in response to overreaching, demonstrating that additional measures of training tolerance may be required to determine whether training-induced changes in these parameters are related to positive or negative adaptations. Resting HRV is largely unaffected by overreaching, although this may be the result of methodological issues that warrant further investigation. HR acceleration appears to decrease in response to overreaching training, and thus may be a potential indicator of training-induced fatigue. Refereed/Peer-reviewed

Purpose:Heart-rate variability (HRV) as a measure of autonomic function may increase in response to training interventions leading to increases or decreases in performance, making HRV interpretation difficult in isolation. This study aimed to contextualize changes in HRV with subjective measures of training tolerance.Methods:Supine and standing measures of vagally mediated HRV (root-mean-square difference of successive normal RR intervals [RMSSD]) and measures of training tolerance (Daily Analysis of Life Demands for Athletes questionnaire, perception of energy levels, fatigue, and muscle soreness) were recorded daily during 1 wk of light training (LT), 2 wk of heavy training (HT), and 10 d of tapering (T) in 15 male runners/triathletes. HRV and training tolerance were analyzed as rolling 7-d averages at LT, HT, and T. Performance was assessed after LT, HT, and T with a 5-km treadmill time trial (5TTT).Results:Time to complete the 5TTT likely increased after HT (effect size [ES] ± 90% confidence interval = 0.16 ± 0.06) and then almost certainly decreased after T (ES = −0.34 ± 0.08). Training tolerance worsened after HT (ES ≥ 1.30 ± 0.41) and improved after T (ES ≥ 1.27 ± 0.49). Standing RMSSD very likely increased after HT (ES = 0.62 ± 0.26) and likely remained higher than LT at the completion of T (ES = 0.38 ± 0.21). Changes in supine RMSSD were possible or likely trivial.Conclusion:Vagally mediated HRV during standing increased in response to functional overreaching (indicating potential parasympathetic hyperactivity) and also to improvements in performance. Thus, additional measures such as training tolerance are required to interpret changes in vagally mediated HRV.

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

Background: The effect of footwear on running economy has been investigated in numerous studies. However, no systematic review and meta-analysis has synthesised the available literature and the effect of footwear on running performance is not known. Conclusions: Certain models of footwear and footwear characteristics can improve running economy. Future research in footwear performance should include measures of running performance. Methods: The Web of Science, Scopus, MEDLINE, CENTRAL (Cochrane Central Register of Controlled Trials), EMBASE, AMED (Allied and Complementary Medicine), CINAHL and SPORTDiscus databases were searched from inception up until April 2014. Included articles reported on controlled trials that examined the effects of footwear or footwear characteristics (including shoe mass, cushioning, motion control, longitudinal bending stiffness, midsole viscoelasticity, drop height and comfort) on running performance or running economy and were published in a peer-reviewed journal. Objective: The aim of this systematic review and meta-analysis was to investigate the effect of footwear on running performance and running economy in distance runners, by reviewing controlled trials that compare different footwear conditions or compare footwear with barefoot. Results: Of the 1,044 records retrieved, 19 studies were included in the systematic review and 14 studies were included in the meta-analysis. No studies were identified that reported effects on running performance. Individual studies reported significant, but trivial, beneficial effects on running economy for comfortable and stiff-soled shoes [standardised mean difference (SMD)