Institut für Sportwissenschaft
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Purpose
Pronounced differences in individual physiological adaptation may occur following various training mesocycles in runners. Here we aimed to assess the individual changes in performance and physiological adaptation of recreational runners performing mesocycles with different intensity, duration and frequency.
Methods
Employing a randomized cross-over design, the intra-individual physiological responses [i.e., peak (\(\dot{VO}_{2peak}\)) and submaximal (\(\dot{VO}_{2submax}\)) oxygen uptake, velocity at lactate thresholds (V\(_2\), V\(_4\))] and performance (time-to-exhaustion (TTE)) of 13 recreational runners who performed three 3-week sessions of high-intensity interval training (HIIT), high-volume low-intensity training (HVLIT) or more but shorter sessions of HVLIT (high-frequency training; HFT) were assessed.
Results
\(\dot{VO}_{2submax}\), V\(_2\), V\(_4\) and TTE were not altered by HIIT, HVLIT or HFT (p > 0.05). \(\dot{VO}_{2peak}\) improved to the same extent following HVLIT (p = 0.045) and HFT (p = 0.02). The number of moderately negative responders was higher following HIIT (15.4%); and HFT (15.4%) than HVLIT (7.6%). The number of very positive responders was higher following HVLIT (38.5%) than HFT (23%) or HIIT (7.7%). 46% of the runners responded positively to two mesocycles, while 23% did not respond to any.
Conclusion
On a group level, none of the interventions altered \(\dot{VO}_{2submax}\), V\(_2\), V\(_4\) or TTE, while HVLIT and HFT improved \(\dot{VO}_{2peak}\). The mean adaptation index indicated similar numbers of positive, negative and non-responders to HIIT, HVLIT and HFT, but more very positive responders to HVLIT than HFT or HIIT. 46% responded positively to two mesocycles, while 23% did not respond to any. These findings indicate that the magnitude of responses to HIIT, HVLIT and HFT is highly individual and no pattern was apparent.
Background: Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity.
Objective: Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa).
Methods: While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE.
Results: While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9%-4.3%, 2.2%-6.7%, 2.9%-9.2%, and 4.1%-19.1%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5%-27.1%, 16.3%-28.0%, 15.9%-34.5%, and 8.0%-32.3%, respectively.
Conclusions: The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested."
Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies.
Athletes schedule their training and recovery in periods, often utilizing a pre-defined strategy. To avoid underperformance and/or compromised health, the external load during training should take into account the individual’s physiological and perceptual responses. No single variable provides an adequate basis for planning, but continuous monitoring of a combination of several indicators of internal and external load during training, recovery and off-training as well may allow individual responsive adjustments of a training program in an effective manner. From a practical perspective, including that of coaches, monitoring of potential changes in health and performance should ideally be valid, reliable and sensitive, as well as time-efficient, easily applicable, non-fatiguing and as non-invasive as possible. Accordingly, smartphone applications, wearable sensors and point-of-care testing appear to offer a suitable monitoring framework allowing responsive adjustments to exercise prescription. Here, we outline 24-h monitoring of selected parameters by these technologies that (i) allows responsive adjustments of exercise programs, (ii) enhances performance and/or (iii) reduces the risk for overuse, injury and/or illness.
Posture and mobility are important aspects for spinal health. In the context of low back pain, strategies to alter postural anomalies (e.g., hyper/hypolordosis, hyper/hypokyphosis) and mobility deficits (e.g., bending restrictions) have been of interest to researchers and clinicians. Machine-based isolated lumbar extension resistance exercise (ILEX) has been used successfully for rehabilitation of patients suffering from low back pain. The aim of this study was to analyse the immediate effects of ILEX on spinal posture and mobility. In this interventional cohort study, the posture and mobility measures of 33 healthy individuals (m = 17, f = 16; mean age 30.0 years) were taken using the surface-based Spinal Mouse system (IDIAG M360©, Fehraltdorf, Switzerland). Individuals performed one exercise set to full exhaustion with an ILEX-device (Powerspine, Wuerzburg, Germany) in a standardized setup, including uniform range of motion and time under tension. Scans were made immediately before and after the exercise. There was an immediate significant decrease in standing lumbar lordosis and thoracic kyphosis. No change could be observed in standing pelvic tilt. Mobility measures showed a significant decrease in the lumbar spine and an increase in the sacrum. The results show that ILEX alters spine posture and mobility in the short-term, which may benefit certain patient groups.
The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.
Objectives: The aim of this study was to examine the effect of time of day on short-term repetitive maximal performance and psychological variables in elite judo athletes.
Methods: Fourteen Tunisian elite male judokas (age: 21 ± 1 years, height:172 ± 7 cm, body-mass: 70.0 ± 8.1 kg) performed a repeated shuttle sprint and jump ability (RSSJA) test (6 m × 2 m × 12.5 m every 25-s incorporating one countermovement jump (CMJ) between sprints) in the morning (7:00 a.m.) and afternoon (5:00 p.m.). Psychological variables (Profile of mood states (POMS-f) and Hooper questionnaires) were assessed before and ratings of perceived exertion (RPE) immediately after the RSSJA.
Results: Sprint times (p > 0.05) of the six repetition, fatigue index of sprints (p > 0.05) as well as mean (p > 0.05) jump height and fatigue index (p > 0.05) of CMJ did not differ between morning and afternoon. No differences were observed between the two times-of-day for anxiety, anger, confusion, depression, fatigue, interpersonal relationship, sleep, and muscle soreness (p > 0.05). Jump height in CMJ 3 and 4 (p < 0.05) and RPE (p < 0.05) and vigor (p < 0.01) scores were higher in the afternoon compared to the morning. Stress was higher in the morning compared to the afternoon (p < 0.01).
Conclusion: In contrast to previous research, repeated sprint running performance and mood states of the tested elite athletes showed no-strong dependency of time-of-day of testing. A possible explanation can be the habituation of the judo athletes to work out early in the morning.
The aim of the study was to evaluate the reliability and validity of cardiorespiratory and metabolic variables, that is, peak oxygen uptake (V'O\(_{2peak}\)) and heart rate (HR\(_{peak}\)), obtained from an agility‐like incremental exercise test for team sport athletes. To investigate the test–retest reliability, 25 team sport athletes (age: 22 ± 3 years, body mass: 75 ± 7 kg, height: 182 ± 6 cm) performed an agility‐like incremental exercise test on the SpeedCourt (SC) system incorporating multidirectional change‐of‐direction (COD) movements twice. For each step of the incremental SC test, the athletes covered a 40‐m distance interspersed with a 10‐sec rest period. Each 40 m distance was split into short sprints (2.25–6.36 m) separated by multidirectional COD movements (0°–180°), which were performed in response to an external visual stimulus. All performance and physiological data were validated with variables obtained from a ramp‐like treadmill and Yo‐Yo intermittent recovery level 2 test (Yo‐Yo IR2). The incremental SC test revealed high test–retest reliability for the time to exhaustion (ICC = 0.85, typical error [TE] = 0.44, and CV% = 3.88), V'O\(_{2peak}\), HR\(_{peak}\), ventilation, and breathing frequency (ICC = 0.84, 0.72, 0.89, 0.77, respectively). The time to exhaustion (r = 0.50, 0.74) of the incremental SC test as well as the peak values for V'O\(_{2}\) (r = 0.59, 0.52), HR (r = 0.75, 0.78), ventilation (r = 0.57, 0.57), and breathing frequency (r = 0.68, 0.68) were significantly correlated (P ≤ 0.01) with the ramp‐like treadmill test and the Yo‐Yo IR2, respectively. The incremental SC test represents a reliable and valid method to assess peak values for V'O\(_{2}\) and HR with respect to the specific demand of team sport match play by incorporating multidirectional COD movements, decision making, and cognitive components.