UNASSIGNED: An increase in training intensity could create changes in psychological and physiological variables in competitive athletes. For this reason, it is very relevant to know how an intensive training block could influence psychological variables in competitive swimmers. This study examined the effect of an intensive training block (HIT) for 2 weeks on the anxiety state and swimming performance compared to standard aerobic training.
UNASSIGNED: Twenty-two male competition swimmers were randomly assigned to two groups: HIT group (n = 11; age = 16.5 ± 0.29 years) and control group following the standard training program (n = 11; age = 16.1 ± 0.33 years). Psychological status variables (cognitive anxiety, somatic anxiety and self-confidence) and swimming performance (100-m front crawl) were measured pre-and post-test.
UNASSIGNED: A significant effect of time was found for all psychological variables and swimming performance (F ≥ 17.6; p < 0.001; d ≥ 0.97). Furthermore, a significant group × time interaction effect was found in cognitive (F = 14.9; p < 0.001; d = 0.62) and somatic anxiety (F = 5.37; p = 0.031; d = 0.55) were found. Only a significant group effect was found in somatic anxiety (F = 27.1; p < 0.001; d = 1.2). Post hoc comparison revealed that both groups increased their cognitive anxiety and swimming performance, and decreased their self-confidence from pre to post test. However, cognitive anxiety increase significantly more in the HIT group compared to the control group. Furthermore, only the HIT training group significantly increased somatic anxiety over time, while somatic anxiety did not change significantly over time in the control group.
UNASSIGNED: Our findings indicated that a sudden increase in training intensity increased state anxiety more than standard training, but both conditions similarly enhanced swimming performance. Although the current level of psychological state is not affecting swimming performance negatively over this period, it should be regularly monitored by psychologists as it over a longer training period perhaps could have a negative influence on swimming performance.

The purpose of this study was firstly to examine the sensitivity of heart rate (HR)-based and subjective monitoring markers to intensified endurance training; and secondly, to investigate the validity of these markers to distinguish individuals in different fatigue states. A total of 24 recreational runners performed a 3-week baseline period, a 2-week overload period, and a 1-week recovery period. Performance was assessed before and after each period with a 3000m running test. Recovery was monitored with daily orthostatic tests, nocturnal HR recordings, questionnaires, and exercise data. The participants were divided into subgroups (overreached/OR, n = 8; responders/RESP, n = 12) based on the changes in performance and subjective recovery. The responses to the second week of the overload period were compared between the subgroups. RESP improved their baseline 3000 m time (p < 0.001) after the overload period (-2.5 ± 1.0%), and the change differed (p < 0.001) from OR (0.6 ± 1.2%). The changes in nocturnal HR (OR 3.2 ± 3.1%; RESP -2.8 ± 3.7%, p = 0.002) and HR variability (OR -0.7 ± 1.8%; RESP 2.1 ± 1.6%, p = 0.011) differed between the subgroups. In addition, the decrease in subjective readiness to train (p = 0.009) and increase in soreness of the legs (p = 0.04) were greater in OR compared to RESP. Nocturnal HR, readiness to train, and exercise-derived HR-running power index had ≥85% positive and negative predictive values in the discrimination between OR and RESP individuals. In conclusion, exercise tolerance can vary substantially in recreational runners. The results supported the usefulness of nocturnal HR and subjective recovery assessments in recognizing fatigue states.

Heart rate variability (HRV) is defined as the fluctuation of time intervals between adjacent heartbeats and is commonly used as a surrogate measure of autonomic function. HRV has become an increasingly measured variable by wearable technology for use in fitness and sport applications. However, with its increased use, a gap has arisen between the research and the application of this technology in strength and conditioning. The goal of this narrative literature review is to discuss current evidence and propose preliminary guidelines regarding the application of HRV in strength and conditioning. A literature review was conducted searching for HRV and strength and conditioning, aiming to focus on studies with time-domain measurements. Studies suggest that HRV is a helpful metric to assess training status, adaptability, and recovery after a training program. Although reduced HRV may be a sign of overreaching and/or overtraining syndrome, it may not be a sensitive marker in aerobic-trained athletes and therefore has different utilities for different athletic populations. There is likely utility to HRV-guided programming compared to predefined programming in several types of training. Evidence-based preliminary guidelines for the application of HRV in strength and conditioning are discussed. This is an evolving area of research, and more data are needed to evaluate the best practices for applying HRV in strength and conditioning.

Sport coaches increasingly rely on external load metrics for designing effective training programs. However, their accuracy in estimating internal load is inconsistent, and their ability to predict autonomic nervous system (ANS) deterioration is unknown. This study aimed to evaluate the relationships between internal and external training load metrics and ANS recovery and function in college football players. Football athletes were recruited from a D1 college in the southeastern US and prospectively followed for 27 weeks. Internal load was estimated via exercise cardiac load (ECL; average training heartrate (HR) × session duration) and measured with an armband monitor equipped with electrocardiographic capabilities (Warfighter MonitorTM (WFM), Tiger Tech Solutions, Miami, FL, USA). External load was estimated via the summation and rate of acceleration and decelerations as measured by a triaxial accelerometer using the WFM and an accelerometer-based (ACCEL) device (Catapult Player Load, Catapult Sports, Melbourne, Australia) worn on the mid-upper back. Baseline HR, HR variability (HRV) and HR recovery served as the indicators for ANS recovery and function, respectively. For HRV, two, time-domain metrics were measured: the standard deviation of the NN interval (SDNN) and root mean square of the standard deviation of the NN interval (rMSSD). Linear regression models evaluated the associations between ECL, ACCEL, and the indicators of ANS recovery and function acutely (24 h) and cumulatively (one- and two-week). Athletes (n = 71) were male and, on average, 21.3 ± 1.4 years of age. Acute ECL elicited stronger associations for 24 h baseline HR (R2 0.19 vs. 0.03), HR recovery (R2 0.38 vs. 0.07), SDNN (R2 0.19 vs. 0.02) and rMSSD (R2 0.19 vs. 0.02) compared to ACCEL. Similar results were found for one-week: 24 h baseline HR (R2 0.48 vs. 0.05), HR recovery (R2 0.55 vs. 0.05), SDNN (R2 0.47 vs. 0.05) and rMSSD (R2 0.47 vs. 0.05) and two-week cumulative exposures: 24 h baseline HR (R2 0.52 vs. 0.003), HR recovery (R2 0.57 vs. 0.05), SDNN (R2 0.52 vs. 0.003) and rMSSD (R2 0.52 vs. 0.002). Lastly, the ACCEL devices weakly correlated with ECL (rho = 0.47 and 0.43, p < 0.005). Our findings demonstrate that ACCEL poorly predicted ANS deterioration and underestimated internal training load. ACCEL devices may \"miss\" the finite window for preventing ANS deterioration by potentially misestimating training loads acutely and cumulatively.

UNASSIGNED: In swimming, performance gains after tapering could be influenced by the pre-taper level of fatigue. Moreover, this level of fatigue could be associated with sleep. This study aimed to assess (1) the effect of tapering on performance according to the pre-taper level of fatigue in swimmers and (2) the association between sleep and pre-taper level of fatigue.
UNASSIGNED: Physiological, psychological and biomechanical profiles were evaluated in 26 elite swimmers on 2 occasions to estimate the pre-taper level of fatigue: at T0 and T1, scheduled respectively 10 and 3 weeks before the main competition. Sleep quantity and quality were also evaluated at T0 and T1. Race time was officially assessed at T0, T1 and during the main competition. The level of significance was set at p ≤ .05.
UNASSIGNED: Fourteen swimmers (17 ± 2 years) were allocated to acute fatigue group (AF) and 12 swimmers (18 ± 2 years) to functional overreaching group (F-OR). From T1 to the main competition, performance was improved in AF (+1.80 ± 1.36%), while it was impaired in F-OR (-0.49 ± 1.58%, p < 0.05 vs. AF). Before taper period, total sleep time was lower in F-OR, as compared to AF. Conversely, the fragmentation index was higher in F-OR (p = .06). From wakefulness to sleep, body core temperature decreased in AF but not in F-OR.
UNASSIGNED: Performance gain after tapering was higher in AF swimmers than in overreached. Moreover, pre-taper sleep was poorer in overreached swimmers, which could contribute to their different response to the same training load. This poorer sleep could be linked to a lower regulation of internal temperature.

OBJECTIVE: This study aimed to provide an overview of some of the medical concerns surrounding the care of the pediatric endurance athletes and add to the limited literature specific to the pediatric endurance athlete.
RESULTS: Endurance athletes are at risk for overtraining, relative energy deficiency in sport (RED-S), overuse injuries, nutritional deficiencies, and sleep dysfunction. Youth runners and female endurance athletes are particularly high-risk populations for RED-S; nutritional deficiencies and their care should involve thoughtful mitigation of modifiable risk factors. The growing endurance athlete may experience slightly different cardiac adaptations than the adult endurance athlete with the long-term implications of these changes still unclear. Endurance sports are common among youth athletes. Multidisciplinary care that includes screening and early intervention for high-risk areas is critical to optimize their care and promote, safe lifelong sport participation.

OBJECTIVE: The purpose of the study was to investigate whether carbohydrate utilization is altered during exercise in overreached endurance athletes and examine the utility of continuous glucose monitors (CGM) to detect overreaching status.
METHODS: Eleven endurance athletes (M:8, F:3) completed a 5-week training block consisting of 1 week of reduced training (PRE), 3 weeks of high-intensity overload training (POST), and 1 week of recovery training (REC). Participants completed a Lamberts and Lambert Submaximal Cycling Test (LSCT) and 5 km time-trial at PRE, POST, and REC time points, 15 min following the ingestion of a 50 g glucose beverage with glucose recorded each minute via CGM.
RESULTS: Performance in the 5 km time-trial was reduced at POST (∆-7 ± 10 W, p = 0.04, η p 2  = 0.35) and improved at REC (∆12 ± 9 W from PRE, p = 0.01, η p 2  = 0.66), with reductions in peak lactate (∆-3.0 ± 2.0 mmol/L, p = 0.001, η p 2  = 0.71), peak HR (∆-6 ± 3 bpm, p < 0.001, η p 2  = 0.86), and Hooper-Mackinnon well-being scores (∆10 ± 5 a.u., p < 0.001, η p 2  = 0.79), indicating athletes were functionally overreached. The respiratory exchange ratio was suppressed at POST relative to REC during the 60% (POST: 0.80 ± 0.05, REC: 0.87 ± 0.05, p < 0.001, η p 2  = 0.74), and 80% (POST: 0.93 ± 0.05, REC: 1.00 ± 0.05, p = 0.003, η p 2  = 0.68) of HR-matched submaximal stages of the LSCT. CGM glucose was reduced during HR-matched submaximal exercise in the LSCT at POST (p = 0.047, η p 2  = 0.36), but not the 5 km time-trial (p = 0.07, η p 2  = 0.28) in overreached athletes.
CONCLUSIONS: This preliminary investigation demonstrates a reduction in CGM-derived glucose and carbohydrate oxidation during submaximal exercise in overreached athletes. The use of CGM during submaximal exercise following standardized nutrition could be employed as a monitoring tool to detect overreaching in endurance athletes.

OBJECTIVE: There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices\' sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables.
METHODS: Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100-2000 Hz) and signal length (100-300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable.
RESULTS: Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: -.02 to -.08; P ≤ .045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: -.05 to -.07; P < .010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤ .043) but not 300 Hz (P = .069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change: .13 and .22, respectively).
CONCLUSIONS: Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables.

Current metrics like baseline heart rate (HR) and HR recovery fail in predicting overtraining (OT), a syndrome manifesting from a deteriorating autonomic nervous system (ANS). Preventing OT requires tracking the influence of internal physiological loads induced by exercise training programs on the ANS. Therefore, this study evaluated the predictability of a novel, exercise cardiac load metric on the deterioration of the ANS. Twenty male American football players, with an average age of 21.3 years and body mass indices ranging from 23.7 to 39.2 kg/m2 were included in this study. Subjects participated in 40 strength- and power-focused exercise sessions over 8 weeks and wore armband monitors (Warfighter Monitor, Tiger Tech Solutions) equipped with electrocardiography capabilities. Exercise cardiac load was the product of average training HR and duration. Baseline HR, HR variability (HRV), average HR, and peak HR were also measured. HR recovery was measured on the following day. HRV indices assessed included the standard deviation of NN intervals (SDNN) and root mean square of successive RR interval differences (rMSSD) Linear regression models assessed the relationships between each cardiac metric and HR recovery, with statistical significance set at α < 0.05. Subjects were predominantly non-Hispanic black (70%) and aged 21.3 (±1.4) years. Adjusted models showed that exercise cardiac load elicited the strongest negative association with HR recovery for previous day (β = -0.18 ± 0.03; p < 0.0000), one-week (β = -0.20 ± 0.03; p < 0.0000) and two-week (β = -0.26 ± 0.03; p < 0.0000) training periods compared to average HR (βetas: -0.09 to -0.02; p < 0.0000) and peak HR (βetas: -0.13 to -0.23; p < 0.0000). Statistically significant relationships were also found for baseline HR (p < 0.0000), SDNN (p < 0.0000) and rMSSD (p < 0.0000). Exercise cardiac load appears to best predict ANS deterioration across one- to two-week training periods, showing a capability for tracking an athlete\'s physiological tolerance and ANS response. Importantly, this information may increase the effectiveness of exercise training programs, enhance performance, and prevent OT.

Muscle overload injuries in strength training might be prevented by providing personalized feedback about muscle load during a workout. In the present study, a new muscle load feedback application, which monitors and visualizes the loading of specific muscle groups, was developed in collaboration with the fitness company Gymstory. The aim of the present study was to examine the effectiveness of this feedback application in managing muscle load balance, muscle load level, and muscle soreness, and to evaluate how its actual use was experienced. Thirty participants were randomly distributed into \'control\', \'partial feedback\', and \'complete feedback\' groups and monitored for eight workouts using the automatic exercise tracking system of Gymstory. The control group received no feedback, while the partial feedback group received a visualization of their estimated cumulative muscle load after each exercise, and the participants in the complete feedback group received this visualization together with suggestions for the next exercise to target muscle groups that had not been loaded yet. Generalized estimation equations (GEEs) were used to compare muscle load balance and soreness, and a one-way ANOVA was used to compare user experience scores between groups. The complete feedback group showed a significantly better muscle load balance (β = -18.9; 95% CI [-29.3, -8.6]), adhered better to the load suggestion provided by the application (significant interactions), and had higher user experience scores for Attractiveness (p = 0.036), Stimulation (p = 0.031), and Novelty (p = 0.019) than the control group. No significant group differences were found for muscle soreness. Based on these results, it was concluded that personal feedback about muscle load in the form of a muscle body map in combination with exercise suggestions can effectively guide strength training practitioners towards certain load levels and more balanced cumulative muscle loads. This application has potential to be applied in strength training practice as a training tool and may help in preventing muscle overload.