The association between maximal muscle strength, disease severity and psychopharmacotherapy among young to middle-aged inpatients with affective disorders - a prospective pilot study.

BACKGROUND: Motor alterations and lowered physical activity are common in affective disorders. Previous research has indicated a link between depressive symptoms and declining muscle strength primarily focusing on the elderly but not younger individuals. Thus, we aimed to evaluate the relationship between mood and muscle strength in a sample of N = 73 young to middle-aged hospitalized patients (18-49 years, mean age 30.7 years) diagnosed with major depressive, bipolar and schizoaffective disorder, with a focus on moderating effects of psychopharmacotherapy. The study was carried out as a prospective observational study at a German psychiatric university hospital between September 2021 and March 2022. METHODS: Employing a standardized strength circuit consisting of computerized strength training devices, we measured the maximal muscle strength (Fmax) using three repetitions maximum across four muscle regions (abdomen, arm, back, leg) at three time points (t1-t3) over four weeks accompanied by psychometric testing (MADRS, BPRS, YRMS) and blood lipid profiling in a clinical setting. For analysis of psychopharmacotherapy, medication was split into activating (AM) and inhibiting (IM) medication and dosages were normalized by the respective WHO defined daily dose. RESULTS: While we observed a significant decrease of the MADRS score and increase of the relative total Fmax (rTFmax) in the first two weeks (t1-t2) but not later (both p < .001), we did not reveal a significant bivariate correlation between disease severity (MADRS) and muscle strength (rTFmax) at any of the timepoints. Individuals with longer disease history displayed reduced rTFmax (p = .048). IM was significantly associated with decreased rTFmax (p = .032). Regression models provide a more substantial effect of gender, age, and IM on muscle strength than the depressive episode itself (p < .001). CONCLUSIONS: The results of the study indicate that disease severity and muscle strength are not associated in young to middle-aged inpatients with affective disorders using a strength circuit as observational measurement. Future research will be needed to differentiate the effect of medication, gender, and age on muscle strength and to develop interventions for prevention of muscle weakness, especially in younger patients with chronic affective illnesses.

Heart Rate Variability-Derived Thresholds for Exercise Intensity Prescription in Endurance Sports: A Systematic Review of Interrelations and Agreement with Different Ventilatory and Blood Lactate Thresholds.

BACKGROUND: Exercise intensities are prescribed using specific intensity zones (moderate, heavy, and severe) determined by a 'lower' and a 'higher' threshold. Typically, ventilatory (VT) or blood lactate thresholds (LT), and critical power/speed concepts (CP/CS) are used. Various heart rate variability-derived thresholds (HRVTs) using different HRV indices may constitute applicable alternatives, but a systematic review of the proximity of HRVTs to established threshold concepts is lacking. OBJECTIVE: This systematic review aims to provide an overview of studies that determined HRVTs during endurance exercise in healthy adults in comparison with a reference VT and/or LT concept. METHODS: A systematic literature search for studies determining HRVTs in healthy individuals during endurance exercise and comparing them with VTs or LTs was conducted in Scopus, PubMed and Web of Science (until January 2022). Studies claiming to describe similar physiological boundaries to delineate moderate from heavy (HRVTlow vs. VTlow and/or LTlow), and heavy from severe intensity zone (HRVThigh vs. VThigh and/or LThigh) were grouped and their results synthesized. RESULTS: Twenty-seven included studies (461 participants) showed a mean difference in relative HR between HRVTlow and VTlow of - 0.6%bpm in weighted means and 0.02%bpm between HRVTlow and LTlow. Bias between HR at HRVTlow and VTlow was 1 bpm (limits of agreement (LoA): - 10.9 to 12.8 bpm) and 2.7 bpm (LoA: - 20.4 to 25.8 bpm) between HRVTlow and LTlow. Mean difference in HR between HRVThigh and VThigh was 0.3%bpm in weighted means and 2.9%bpm between HRVThigh and LThigh while bias between HR at HRVThigh and VThigh was - 4 bpm (LoA: - 17.9 to 9.9 bpm) and 2.5 bpm (LoA: - 12.1 to 17.1 bpm) between HRVThigh and LThigh. CONCLUSION: HRVTlow seems to be a promising approach for the determination of a 'lower' threshold comparable to VTlow and potentially for HRVThigh compared to VThigh, although the latter needs further empirical evaluation. LoA for both intensity zone boundaries indicates bias of HRVTs on an individual level. Taken together, HRVTs can be a promising alternative for prescribing exercise intensity in healthy, male athletes undertaking endurance activities but due to the heterogeneity of study design, threshold concepts, standardization, and lack of female participants, further research is necessary to draw more robust and nuanced conclusions.

Reliability of the 3-Component Model of Aerobic, Anaerobic Lactic, and Anaerobic Alactic Energy Distribution (PCr-LA-O2) for Energetic Profiling of Continuous and Intermittent Exercise.

PURPOSE: To assess the test-retest reliability of the continuous (PCr-LA-O2) and intermittent (PCr-LA-O2int) version of the 3-component model of energy distribution in an applied setting. METHODS: Sixteen male handball players (age 23 [3] y, height 185 [7] cm, weight 85 [14] kg) completed the 30-15 Intermittent Fitness Test (30-15IFT) twice. Performance was assessed by peak speed (speed of the last successfully completed stage of the 30-15IFT [VIFT], in kilometers per hour) and time to exhaustion (in seconds). Oxygen uptake (in milliliters per kilogram per minute) and blood lactate concentrations (in millimoles per liter) were obtained before, during, and until 15 minutes after exercise. Total metabolic energy (in joules per kilogram), total metabolic power (in watts per kilogram), and energy shares (in joules per kilogram and percentage) of the aerobic (energy contribution of the aerobic system [WAERint]), anaerobic lactic, and anaerobic alactic (anaerobic alactic energy [WPCrint]) systems were calculated using both model versions, respectively. RESULTS: Test-retest reliability was very good for VIFT (limits of agreement [LoA]: -1.13 to 0.63 km·h-1, coefficient of variation [CV%] 1.68), time to exhaustion (LoA: -101 to 38 s, CV% 2.92), peak oxygen uptake (LoA: -2.68 to 4.04 mL·min-1·kg-1, CV% 1.48), and peak heart rate (-6.9 to 7.7 beats·min-1, CV% 1.1), but moderate for change in blood lactate concentration (LoA: -3.84 to 4.07 mmol·L-1, CV% 11.43). Reliability of the modeled total energy and its fractions were high for total metabolic energy (LoA: -1489 to 1177 J·kg-1, CV% 2.88), total metabolic power (LoA: -2.0 to 1.9 W·kg-1, CV% 3.58), contribution of aerobic (LoA: -1673 to 1283 J·kg-1, CV% 3.62), WAERint (LoA: -1760 to 2160 J·kg-1, CV% 6.04), and moderate for anaerobic alactic (LoA: -368 to 439 J·kg-1, CV% 14.85), WPCrint (LoA: -1707 to 988 J·kg-1, CV% 9.98), and energy share of anaerobic lactic concentration (LoA: -229 to 235 J·kg-1, CV% 11.43). CONCLUSION: Considering the inherent fluctuations of the underlying energetics, the reliabilities of both versions of the 3-component model of energy distribution are acceptable for applied settings.

Energetics of Floor Gymnastics: Aerobic and Anaerobic Share in Male and Female Sub-elite Gymnasts.

BACKGROUND: Artistic gymnastics is a popular Olympic discipline where female athletes compete in four and male athletes in six events with floor exercise having the longest competition duration in Women's and Men's artistic gymnastics (WAG, MAG). To date no valid information on the energetics of floor gymnastics is available although this may be important for specific conditioning programming. This study evaluated the metabolic profile of a simulated floor competition in sub-elite gymnasts. METHODS: 17 (9 male, 8 female) sub-elite gymnasts aged 22.5 ± 2.6y took part in a floor-training-competition where oxygen uptake was measured during and until 15 min post-exercise. Additionally, resting and peak blood lactate concentration after exercise were obtained. The PCr-LA-O2 method was used to calculate the metabolic energy and the relative aerobic (WAER), anaerobic alactic (WPCr) and anaerobic lactic (WBLC) energy contribution. Further, the athletes completed a 30 s Bosco-jumping test, a countermovement jump and a drop jump. RESULTS: The competition scores were 9.2 (CI:8.9-9.3) in WAG and 10.6 (CI:10.4-10.9) in MAG. The metabolic profile of the floor routine was mainly aerobic (58.9%, CI: 56.0-61.8%) followed by the anaerobic alactic (24.2%, CI: 21.3-27.1%) and anaerobic lactic shares (16.9%, CI:14.9-18.8%). While sex had a significant (p = .010, d = 1.207) large effect on energy contribution, this was not the case for competition duration (p = .728, d = 0.061). Relative energy contribution of WAG and MAG differed in WAER (64.0 ± 4.7% vs. 54.4 ± 6.8%, p = .004, d = 1.739) but not in WPCr (21.3 ± 6.1% vs. 26.7 ± 8.0%, p = .144, d = 0.801) and WBLC (14.7 ± 5.4% vs. 18.9 ± 4.2%, p = .085, d = 0.954). Further no correlation between any energy share and performance was found but between WPCr and training experience (r = .680, p = .044) and WBLC and competition level (r = .668, p = .049). CONCLUSION: The results show a predominant aerobic energy contribution and a considerable anaerobic contribution with no significant difference between anaerobic shares. Consequently, gymnastic specific aerobic training should not be neglected, while a different aerobic share in WAG and MAG strengthens sex-specific conditioning. All in all, the specific metabolic share must secure adequate energy provision, while relative proportions of the two anaerobic pathways seem to depend on training and competition history.

Real-Time Estimation of Aerobic Threshold and Exercise Intensity Distribution Using Fractal Correlation Properties of Heart Rate Variability: A Single-Case Field Application in a Former Olympic Triathlete.

A non-linear heart rate variability (HRV) index based on fractal correlation properties called alpha1 of Detrended Fluctuation Analysis (DFA-alpha1), has been shown to change with endurance exercise intensity. Its unique advantage is that it provides information about current absolute exercise intensity without prior lactate or gas exchange testing. Therefore, real-time assessment of this metric during field conditions using a wearable monitoring device could directly provide a valuable exercise intensity distribution without prior laboratory testing for different applied field settings in endurance sports. Until of late no mobile based product could display DFA-alpha1 in real-time using off the shelf consumer products. Recently an app designed for iOS and Android devices, HRV Logger, was updated to assess DFA-alpha1 in real-time. This brief research report illustrates the potential merits of real-time monitoring of this metric for the purposes of aerobic threshold (AT) estimation and exercise intensity demarcation between low (zone 1) and moderate (zone 2) in a former Olympic triathlete. In a single-case feasibility study, three practically relevant scenarios were successfully evaluated in cycling, (1) estimation of a HRV threshold (HRVT) as an adequate proxy for AT using Kubios HRV software via a typical cycling stage test, (2) estimation of the HRVT during real-time monitoring using a cycling 6 min stage test, (3) a simulated 1 h training ride with enforcement of low intensity boundaries and real-time HRVT confirmation. This single-case field evaluation illustrates the potential of an easy-to-use and low cost real-time estimation of the aerobic threshold and exercise intensity distribution using fractal correlation properties of HRV. Furthermore, this approach may enhance the translation of science into endurance sports practice for future real-world settings.

[Extended medical preparticipation screening as a useful option for injury prevention in professional sports]. / Erweiterte medizinische Eingangsuntersuchungen als sinnvolle Instrumentarien zur Verletzungsprävention im Hochleistungssport am Beispiel eines Basketball-Bundesligisten.

BACKGROUND: Preparticipation screening is important in order to make a statement about an athlete's health. The evaluation includes both an internal medicine/cardiology and an orthopaedic section. In professional team sports, players have to undergo medical screening on an annual basis to obtain their license and be cleared for play. Screening delivers information about the acute health condition of the athlete but only gives an indirect statement on his/her functional status and performance. This gap has been tried to be closed with functional, sports-specific performance testing in the past few years. In the event of future injury, the collected data can be used as a baseline level to monitor the progress in an athlete's rehabilitation process. This provides a huge advantage in the return-to-play diagnosis. MATERIAL & METHODS: Based on the assumption that only a healthy player can perform to the best of his or her ability, we have extended our medical screening for a professional basketball team. Since the 2012/2013 season, a test battery was added with a view to basketball-specific conditioning. The collected data was prospectively correlated to injury occurrence. RESULTS: Seventy-one players were tested in 5 different categories. We have documented 142 injuries which lead to a downtime of 23 days (range 1-347 days). The injuries mainly involved the lower extremity, in particular the ankle, the knee and the thigh muscles. There was a clear trend indicating that players performing weaker in the agility tests sustained more injuries (r = 0.34, p = 0.029). Athletes who performed worse in the Yo-Yo test suffered from significantly more thigh muscle injuries (r = 0.266, p = 0.012). CONCLUSION: Pre-participation screening is a useful tool in injury prevention, which helps to detect injuries or chronic stress complaints, especially in, but not limited to professional sports. Adding sports-specific performance testing may reveal potential deficits in agility and endurance which could lead to an increased injury risk. In addition, it allows to obtain baseline data which could be used to show the progress in rehabilitation in the event of an injury.

Metabolic Profiles of the 30-15 Intermittent Fitness Test and the Corresponding Continuous Version in Team-Sport Athletes-Elucidating the Role of Inter-Effort Recovery.

PURPOSE: To elucidate the role of inter-effort recovery in shuttle running by comparing the metabolic profiles of the 30-15 Intermittent Fitness Test (30-15IFT) and the corresponding continuous version (30-15IFT-CONT). METHODS: Sixteen state-level handball players (age = 23 [3] y, height = 185 [7] cm, weight = 85 [14] kg) completed the 30-15IFT and 30-15IFT-CONT, and speed at the last completed stage (in kilometers per hour) and time to exhaustion (in seconds) were assessed. Furthermore, oxygen uptake (in milliliters per kilogram per minute) and blood lactate were obtained preexercise, during exercise, and until 15 minutes postexercise. Metabolic energy (in kilojoules), metabolic power (in Watts per kilogram), and relative (in percentage) energy contribution of the aerobic (WAER, WAERint), anaerobic lactic (WBLC, WBLCint), and anaerobic alactic (WPCr, WPCrint) systems were calculated by PCr-La-O2 method for 30-15IFT-CONT and 30-15IFT. RESULTS: No difference in peak oxygen uptake was found between 30-15IFT and 30-15IFT-CONT (60.6 [6.6] vs 60.5 [5.1] mL·kg-1·min-1, P = .165, d = 0.20), whereas speed at the last completed stage was higher in 30-15IFT (18.3 [1.4] vs 16.1 [1.0] km·h-1, P < .001, d = 1.17). Metabolic energy was also higher in 30-15IFT (1224.2 [269.6] vs 772.8 [63.1] kJ, P < .001, d = 5.60), and metabolic profiles differed substantially for aerobic (30-15IFT = 67.2 [5.2] vs 30-15IFT-CONT = 85.2% [2.5%], P < .001, d = -4.01), anaerobic lactic (30-15IFT = 4.4 [1.4] vs 30-15IFT-CONT = 6.2% [1.8%], P < .001, d = -1.04), and anaerobic alactic (30-15IFT = 28.4 [4.7] vs 30-15IFT-CONT = 8.6% [2.1%], P < .001, d = 5.43) components. CONCLUSIONS: Both 30-15IFT and 30-15IFT-CONT are mainly fueled by aerobic energy, but their metabolic profiles differ substantially in both aerobic and anaerobic alactic energy contribution. Due to the presence of inter-effort recovery, intermittent shuttle runs rely to a higher extent on anaerobic alactic energy and a fast, aerobic replenishment of PCr during the short breaks between shuttles.

The Metabolic Relevance of Type of Locomotion in Anaerobic Testing: Bosco Continuous Jumping Test Versus Wingate Anaerobic Test of the Same Duration.

PURPOSE: To evaluate the metabolic relevance of type of locomotion in anaerobic testing by analyzing and comparing the metabolic profile of the Bosco Continuous Jumping Test (CJ30) with the corresponding profile of the Wingate Anaerobic Test (WAnT). METHODS: A total of 11 well-trained, male team-sport athletes (age = 23.7 [2.2] y, height = 184.1 [2.8] cm, weight = 82.4 [6.4] kg) completed a CJ30 and WAnT each. During the WAnT, power data and revolutions per minute were recorded, and during the CJ30, jump height and jumping frequency were recorded. In addition, oxygen uptake and blood lactate concentration were assessed, and metabolic profiles were determined via the PCr-LA-O2 method. RESULTS: In the CJ30, metabolic energy was lower (109.3 [18.0] vs 143.0 [13.1] kJ, P < .001, d = -2.302), while peak power (24.8 [4.4] vs 11.8 [0.5] W·kg-1, P < .001, d = 3.59) and mean power (20.8 [3.6] vs 9.1 [0.5] W·kg-1, P < .001, d = 4.14) were higher than in the WAnT. The metabolic profiles of the CJ30 (aerobic energy = 20.00% [4.7%], anaerobic alactic energy [WPCr] = 45.6% [4.5%], anaerobic lactic energy = 34.4% [5.2%]) and the WAnT (aerobic energy = 16.0% [3.0%], anaerobic alactic WPCr = 34.5% [5.0%], anaerobic lactic energy = 49.5% [3.3%]) are highly anaerobic. Absolute energy contribution for the CJ30 and WAnT was equal in WPCr (49.9 [11.1] vs 50.2 [11.2] kJ), but anaerobic lactic energy (37.7 [7.7] vs 69.9 [5.3] kJ) and aerobic energy (20.6 [5.7] vs 23.0 [4.0] kJ) were higher in the WAnT. Mechanical efficiency was substantially higher in the CJ30 (37.9% [4.5%] vs 15.6% [1.0%], P < .001, d = 6.86), while the fatigue index was lower (18.5% [3.8%] vs 23.2% [3.1%], P < .001, d = -1.38) than in the WAnT. CONCLUSIONS: Although the anaerobic share in both tests is similar and predominant, the CJ30 primarily taxes the WPCr system, while the WAnT more strongly relies on the glycolytic pathway. Thus, the 2 tests should not be used interchangeably, and the type of locomotion seems crucial when choosing an anaerobic test for a specific sport.

Correlation Properties of Heart Rate Variability during a Marathon Race in Recreational Runners: Potential Biomarker of Complex Regulation during Endurance Exercise.

There is only very limited data examining cardiovascular responses in real-world endurance training/competition. The present study examines the influence of a marathon race on non-linear dynamics of heart rate (HR) variability (HRV). Eleven male recreational runners performed a self-paced marathon road race on an almost flat profile. During the race, heart rate and beat-to-beat (RR) intervals were recorded continuously. Besides HRV time-domain measurements, fractal correlation properties using short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA-alpha1) were calculated. The mean finishing time was 3:10:22 ± 0:17:56 h:min:s with a blood lactate concentration of 4.04 ± 1.12 mmol/L at the end of the race. Comparing the beginning to the end segment of the marathon race (Begin vs. End) significant increases could be found for km split time (p < .001, d = .934) and for HR (p = .010, d = .804). Significant decreases could be found for meanRR (p = .013, d = .798) and DFA-alpha1 (p = .003, d = 1.132). DFA-alpha1 showed an appropriate dynamic range throughout the race consisting of both uncorrelated and anti-correlated values. Lactate was consistent with sustained high intensity exercise when measured at the end of the event. Despite the runners slowing after halfway, DFA-alpha1 continued to fall to values seen in the highest intensity domain during incremental exercise testing in agreement with lactate assessment. Therefore, the discrepancy between the reduced running pace with that of the decline of DFA-alpha1, demonstrate the benefit of using this dimensionless HRV index as a biomarker of internal load during exercise over the course of a marathon race.

Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription?

Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g., blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non-linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behavior and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport-specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a "global" system parameter and proxy for organismic demands.

Energetic Profiles of the Yo-Yo Intermittent Recovery Tests 1 and 2.

PURPOSE: To analyze the energetic profiles of the Yo-Yo Intermittent Recovery Tests 1 and 2 (YYIR1 and YYIR2). METHODS: Intermittent running distance (IR1D and IR2D), time to exhaustion (IR1T and IR2T), and total recovery time between shuttles (IR1R and IR2R) were measured in 10 well-trained male athletes (age 24.4 [2.0] y, height 182 [1] cm, weight 75.8 [7.9] kg). Respiratory gases and blood lactate (BLC) were obtained preexercise, during exercise, and until 15 min postexercise. Metabolic energy, average metabolic power , and energy share (percentage of aerobic [WAER], anaerobic lactic [WBLC], and anaerobic alactic energy system [WPCr]) were calculated using the PCr-La-O2 method. RESULTS: Peak oxygen consumption was possibly higher in YYIR2 (60.3 [5.1] mL·kg-1·min-1) than in YYIR1 (P = .116, 57.7 [4.5] mL·kg-1·min-1, d = -0.58). IR1D, IR1T, and IR1R were very likely higher than IR2D, IR2T, and IR2R, respectively (P < .001, 1876 [391] vs 672 [132] m, d = -2.83; P < .001, 916 [175] vs 304 [57] s, d = -3.03; and P < .001, 460 [100] vs 150 [40] s, d = -2.83). Metabolic energy was most likely lower in YYIR2 than in YYIR1 (P < .001, 493.5 [118.1] vs 984.8 [171.7] kJ, d = 3.24). Average metabolic power was most likely higher in YYIR2 than in YYIR1 (P < .001, 21.5 [1.7] vs 14.5 [2.2] W·kg-1, d = 3.54). When considering aerobic phosphocreatine restoration during breaks between shuttles, WAER (P = .693, 49% [10%] vs 48% [5%], d = -0.16) was similar, WPCr (P = .165, 47% [11%] vs 42% [6%], d = -0.54) possibly higher, and WBLC (P < .001, 4% [1%] vs 10% [3%], d = 1.95) almost certainly lower in YYIR1 than in YYIR2. CONCLUSIONS: WAER and WPCr are predominant in YYIR1 and YYIR2 with almost identical WAER. Higher IR1D and IR1T in YYIR1 result in higher metabolic energy but lower average metabolic power and slightly lower peak oxygen consumption. Higher IR1R allows for higher reliance on WPCr in YYIR1, while YYIR2 requires a higher fraction of WBLC.

A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability.

The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA a1), a nonlinear index of heart rate variability (HRV) based on fractal correlation properties, has been shown to steadily change with increasing exercise intensity. To date, no study has specifically examined using the behavior of this index as a method for defining a low intensity exercise zone. The aim of this report is to compare both oxygen intake (VO2) and heart rate (HR) reached at the first ventilatory threshold (VT1), a well-established delimiter of low intensity exercise, to those derived from a predefined DFA a1 transitional value. Gas exchange and HRV data were obtained from 15 participants during an incremental treadmill run. Comparison of both VO2 and HR reached at VT1 defined by gas exchange (VT1 GAS) was made to those parameters derived from analysis of DFA a1 reaching a value of 0.75 (HRVT). Based on Bland Altman analysis, linear regression, intraclass correlation (ICC) and t testing, there was strong agreement between VT1 GAS and HRVT as measured by both HR and VO2. Mean VT1 GAS was reached at 39.8 ml/kg/min with a HR of 152 bpm compared to mean HRVT which was reached at 40.1 ml/kg/min with a HR of 154 bpm. Strong linear relationships were seen between test modalities, with Pearson's r values of 0.99 (p < 0.001) and.97 (p < 0.001) for VO2 and HR comparisons, respectively. Intraclass correlation between VT1 GAS and HRVT was 0.99 for VO2 and 0.96 for HR. In addition, comparison of VT1 GAS and HRVT showed no differences by t testing, also supporting the method validity. In conclusion, it appears that reaching a DFA a1 value of 0.75 on an incremental treadmill test is closely associated with crossing the first ventilatory threshold. As training intensity below the first ventilatory threshold is felt to have great importance for endurance sport, utilization of DFA a1 activity may provide guidance for a valid low training zone.

Correlation properties of heart rate variability during endurance exercise: A systematic review.

BACKGROUND: Non-linear measures of heart rate variability (HRV) may provide new opportunities to monitor cardiac autonomic regulation during exercise. In healthy individuals, the HRV signal is mainly composed of quasi-periodic oscillations, but it also possesses random fluctuations and so-called fractal structures. One widely applied approach to investigate fractal correlation properties of heart rate (HR) time series is the detrended fluctuation analysis (DFA). DFA is a non-linear method to quantify the fractal scale and the degree of correlation of a time series. Regarding the HRV analysis, it should be noted that the short-term scaling exponent alpha1 of DFA has been used not only to assess cardiovascular risk but also to assess prognosis and predict mortality in clinical settings. It has also been proven to be useful for application in exercise settings including higher exercise intensities, non-stationary data segments, and relatively short recording times. METHOD: Therefore, the purpose of this systematic review was to analyze studies that investigated the effects of acute dynamic endurance exercise on DFA-alpha1 as a proxy of correlation properties in the HR time series. RESULTS: The initial search identified 442 articles (351 in PubMed, 91 in Scopus), of which 11 met all inclusion criteria. CONCLUSIONS: The included studies show that DFA-alpha1 of HRV is suitable for distinguishing between different organismic demands during endurance exercise and may prove helpful to monitor responses to different exercise intensities, movement frequencies, and exercise durations. Additionally, non-linear DFA of HRV is a suitable analytical approach, providing a differentiated and qualitative view of exercise physiology.

Effects of Acute Normobaric Hypoxia on Non-linear Dynamics of Cardiac Autonomic Activity During Constant Workload Cycling Exercise.

AIM: Measurements of Non-linear dynamics of heart rate variability (HRV) provide new possibilities to monitor cardiac autonomic activity during exercise under different environmental conditions. Using detrended fluctuation analysis (DFA) technique to assess correlation properties of heart rate (HR) dynamics, the present study examines the influence of normobaric hypoxic conditions (HC) in comparison to normoxic conditions (NC) during a constant workload exercise. MATERIALS AND METHODS: Nine well trained cyclists performed a continuous workload exercise on a cycle ergometer with an intensity corresponding to the individual anaerobic threshold until voluntary exhaustion under both NC and HC (15% O2). The individual exercise duration was normalized to 10% sections (10-100%). During exercise HR and RR-intervals were continuously-recorded. Besides HRV time-domain measurements (meanRR, SDNN), fractal correlation properties using short-term scaling exponent alpha1 of DFA were calculated. Additionally, blood lactate (La), oxygen saturation of the blood (SpO2), and rating of perceived exertion (RPE) were recorded in regular time intervals. RESULTS: We observed significant changes under NC and HC for all parameters from the beginning to the end of the exercise (10% vs. 100%) except for SpO2 and SDNN during NC: increases for HR, La, and RPE in both conditions; decreases for SpO2 and SDNN during HC, meanRR and DFA-alpha1 during both conditions. Under HC HR (40-70%), La (10-90%), and RPE (50-90%) were significantly-higher, SpO2 (10-100%), meanRR (40-70%), and DFA-alpha1 (20-60%) were significantly-lower than under NC. CONCLUSION: Under both conditions, prolonged exercise until voluntary exhaustion provokes a lower total variability combined with a reduction in the amplitude and correlation properties of RR fluctuations which may be attributed to increased organismic demands. Additionally, HC provoked higher demands and loss of correlation properties at an earlier stage during the exercise regime, implying an accelerated alteration of cardiac autonomic regulation.

Heart Rate Variability Monitoring During Strength and High-Intensity Interval Training Overload Microcycles.

Objective: In two independent study arms, we determine the effects of strength training (ST) and high-intensity interval training (HIIT) overload on cardiac autonomic modulation by measuring heart rate (HR) and vagal heart rate variability (HRV). Methods: In the study, 37 well-trained athletes (ST: 7 female, 12 male; HIIT: 9 female, 9 male) were subjected to orthostatic tests (HR and HRV recordings) each day during a 4-day baseline period, a 6-day overload microcycle, and a 4-day recovery period. Discipline-specific performance was assessed before and 1 and 4 days after training. Results: Following ST overload, supine HR, and vagal HRV (Ln RMSSD) were clearly increased and decreased (small effects), respectively, and the standing recordings remained unchanged. In contrast, HIIT overload resulted in decreased HR and increased Ln RMSSD in the standing position (small effects), whereas supine recordings remained unaltered. During the recovery period, these responses were reversed (ST: small effects, HIIT: trivial to small effects). The correlations between changes in HR, vagal HRV measures, and performance were weak or inconsistent. At the group and individual levels, moderate to strong negative correlations were found between HR and Ln RMSSD when analyzing changes between testing days (ST: supine and standing position, HIIT: standing position) and individual time series, respectively. Use of rolling 2-4-day averages enabled more precise estimation of mean changes with smaller confidence intervals compared to single-day values of HR or Ln RMSSD. However, the use of averaged values displayed unclear effects for evaluating associations between HR, vagal HRV measures, and performance changes, and have the potential to be detrimental for classification of individual short-term responses. Conclusion: Measures of HR and Ln RMSSD during an orthostatic test could reveal different autonomic responses following ST or HIIT which may not be discovered by supine or standing measures alone. However, these autonomic changes were not consistently related to short-term changes in performance and the use of rolling averages may alter these relationships differently on group and individual level.

Effects of a Short-Term Cycling Interval Session and Active Recovery on Non-Linear Dynamics of Cardiac Autonomic Activity in Endurance Trained Cyclists.

Measurement of the non-linear dynamics of physiologic variability in a heart rate time series (HRV) provides new opportunities to monitor cardiac autonomic activity during exercise and recovery periods. Using the Detrended Fluctuation Analysis (DFA) technique to assess correlation properties, the present study examines the influence of exercise intensity and recovery on total variability and complexity in the non-linear dynamics of HRV. Sixteen well-trained cyclists performed interval sessions with active recovery periods. During exercise, heart rate (HR) and beat-to-beat (RR)-intervals were recorded continuously. HRV time domain measurements and fractal correlation properties were analyzed using the short-term scaling exponent alpha1 of DFA. Lactate (La) levels and the rate of perceived exertion (RPE) were also recorded at regular time intervals. HR, La, and RPE showed increased values during the interval blocks (p < 0.05). In contrast, meanRR and DFA-alpha1 showed decreased values during the interval blocks (p < 0.05). Also, DFA-alpha1 increased to the level in the warm-up periods during active recovery (p < 0.05) and remained unchanged until the end of active recovery (p = 1.000). The present data verify a decrease in the overall variability, as well as a reduction in the complexity of the RR-interval-fluctuations, owing to increased organismic demands. The acute increase in DFA-alpha1 following intensity-based training stimuli in active recovery may be interpreted as a systematic reorganization of the organism with increased correlation properties in cardiac autonomic activity in endurance trained cyclists.

Non-linear dynamics of heart rate variability during incremental cycling exercise.

Within the last years complex models of cardiovascular regulation and exercise fatigue have implemented heart rate variability (HRV) as a measure of autonomic nervous system. Using detrended fluctuation analysis (DFA) to assess heart rate correlation properties, the present study examines the influence of exercise intensity on total variability and complexity in non-linear dynamics of HRV. Sixteen cyclists performed a graded exercise test on a bicycle ergometer. HRV time domain measures and fractal correlation properties were analyzed using short-term scaling exponent alpha1 of DFA. Amplitude and complexity of HRV parameters decreased significantly. DFA-alpha1 increased from rest to low exercise intensity and showed an almost linear decrease from higher intensities until exhaustion. These findings support a qualitative change in self-organized heart rate regulation from a complex autonomic control at rest and low intensities towards a breakdown of the interaction in control mechanisms with non-autonomic heart rate control dominating at high intensities.

Non-linear dynamics of cardiac autonomic activity during cycling exercise with varied cadence.

In recent years, complex models of cardiac regulation have integrated heart rate variability (HRV) as a measure of the cardiac autonomic activity during exercise. Using detrended fluctuation analysis (DFA) technique, the present study examines the influence of cycling cadence and exercise duration on non-linear dynamics of HRV. Sixteen trained cyclists performed a 60-minute exercise bout at 90% of the individual anaerobic threshold on a bicycle ergometer. Cadence was changed every 10 min (90-120-60-120-60-90 rpm). Heart rate (HR) and RR-intervals were recorded continuously during exercise. HRV time domain measures (meanRR, SDNN) and correlation properties were analyzed using short-term scaling exponent alpha1 of DFA. Moreover, blood lactate (La) and rating of perceived exertion (RPE) were recorded at regular intervals at the end of condition. HR, La and RPE increased significantly at 120 rpm compared to 60 rpm. In contrast, all analyzed HRV parameters (meanRR, SDNN, DFA-alpha1) showed a significant decrease during cycling at 120 rpm compared to 60 rpm. The comparison of the first and last 10 min with the same cadence indicates a significant increase in HR and RPE, but also a significant decrease in all analyzed HRV measures. The decrease of HRV values over time and in relation to the increase in cadence indicates a decrease in the overall variability as well as a reduction in complexity of the RR-interval-fluctuations due to the increased organismic demands. Therefore, the decrease of DFA-alpha1 might be associated with a withdrawal of the organismic system aiming at the maintenance of the homeostasis under the control of the central nervous system. In this context, non-linear HRV analyses provide a more systemic view of cardiac regulation during exercise.

Energetic Profile of the Basketball Exercise Simulation Test in Junior Elite Players.

PURPOSE: To analyze the energetic profile of the Basketball Exercise Simulation Test (BEST). METHODS: Ten male elite junior basketball players (age 15.5 [0.6] y, height 180 [9] cm, and body mass 66.1 [11.2] kg) performed a modified BEST (20 circuits consisting of jumping, sprinting, jogging, shuffling, and short breaks) simulating professional basketball game play. Circuit time, sprint time, sprint decrement, oxygen uptake (VO2), heart rate, and blood lactate concentration (blc) were obtained. Metabolic energy and metabolic power above rest (Wtot and Ptot), as well as energy share in terms of aerobic (Waer), glycolytic (Wblc), and high-energy phosphates (WPCr), were calculated from VO2 during exercise, net lactate production, and the fast component of postexercise VO2 kinetics, respectively. RESULTS: Waer, Wblc, and WPCr reflect 89% (2%), 5% (1%), and 6% (1%) of total energy needed, respectively. Assuming an aerobic replenishment of PCr energy stores during short breaks, the adjusted energy share yielded Waer 66% (4%), Wblc 5% (1%), and WPCr 29% (1%). Waer and WPCr were negatively correlated (-0.72 and -0.59) with sprint time, which was not the case for Wblc. CONCLUSIONS: Consistent with general findings on energy system interaction during repeated high-intensity exercise bouts, the intermittent profile of the BEST relies primarily on aerobic energy combined with repetitive supplementation by anaerobic utilization of high-energy phosphates.

Self-selected intensity exercise in the treatment of major depression: A pragmatic RCT.

OBJECTIVE: The purpose of the study was to determine the therapeutic effect of physical exercise for patients with unipolar depression. Participants took part in an 8-week walking/running aerobic exercise program at a local sports club. METHODS: Forty-six outpatients aged 18-65 years and diagnosed with mild to severe depression (ICD-10 criteria) were randomly assigned to an intervention group or wait list. Treatment as usual was continued. The Hamilton Rating Scale for Depression (HRSD-17) served as the main outcome measure. Secondary outcome measures were Beck Depression Inventory (BDI-II), Fitness Index (FI), and VO(2) max as estimated by Urho Kaleka Kekkonen or UKK 2-km Walk Test. RESULTS: Out of forty-six participants, 24% dropped out. Participants attended 58% of exercise sessions. All randomized participants were included in intention-to-treat (ITT) analysis. Analysis of covariance or ANCOVA showed a large reduction of depressive symptoms in HRSD-17 scores (Cohen's d: 1.8; mean change 8.2, p < .0001). BDI-II (Cohen's d: 0.50; mean change: 4.7, p = 0.09), FI scores (Cohen's d: 0.27; mean change: 5.3, p = 0.08), and VO2 max did not change significantly. CONCLUSIONS: We observed a large and clinically significant change in HRSD-17 scores. Moderate changes in BDI-II scores without clinical significance and small changes in physical fitness assessments were observed.