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Institute
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."
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.
PurposeOur aims were to examine (i) the internal load during simulated soccer match-play by elite youth players; and (ii) the time-course of subsequent recovery from perceived and performance fatigability.
MethodsEleven male youth players (16 ± 1 years, 178 ± 7 cm, 67 ± 7 kg) participated in a 2 × 40-min simulated soccer match, completing 30 rounds (160 s each) with every round including multidirectional and linear sprinting (LS20m), jumping (CMJ) and running at different intensities. During each round, LS20m, CMJ, agility, heart rate (HR), oxygen uptake (VO2), energy expenditure (EE), substrate utilization and perceived exertion RPE were assessed. In addition, the blood level of lactate (Lac) was obtained after each of the five rounds. Creatine kinase (CK) concentration, maximal voluntary isometric knee extension and flexion, CMJ, number of skippings in 30 s, and subjective ratings on the Acute Recovery and Stress Scale (ARSS) were examined before and immediately, 24 and 48 h after the simulation.
Results: During the game %HR\(_{peak}\) (p < 0.05, d = 1.08), %VO2\(_{peak}\) (p < 0.05; d = 0.68), Lac (p < 0.05, d = 2.59), RPE\(_{total}\) (p < 0.05, d = 4.59), and RPE\(_{legs}\) (p < 0.05, d = 4.45) all increased with time during both halves (all p < 0.05). Agility improved (p < 0.05, d = 0.70) over the time-course of the game, with no changes in LS20m (p ≥ 0.05, d = 0.34) or CMJ (p ≥ 0.05, d = 0.27). EE was similar during both halves (528 ± 58 vs. 514 ± 61 kcal; p = 0.60; d = 0.23), with 62% (second half: 65%) carbohydrate, 9% (9%) protein and 26% (27%) fat utilization. With respect to recovery, maximal voluntary knee extension (p ≥ 0.05, d = 0.50) and flexion force (p ≥ 0.05, d = 0.19), CMJ (p ≥ 0.05, d = 0.13), number of ground contacts (p ≥ 0.05, d = 0.57) and average contact time (p ≥ 0.05, d = 0.39) during 30-s of skipping remained unaltered 24 and 48 h after the game. Most ARSS dimensions of load (p < 0.05, d = 3.79) and recovery (p < 0.05, d = 3.22) returned to baseline levels after 24 h of recovery. Relative to baseline values, CK was elevated immediately and 24 h after (p < 0.05, d = 2.03) and normalized 48 h later.
ConclusionIn youth soccer players the simulated match evoked considerable circulatory, metabolic and perceptual load, with an EE of 1042 ± 118 kcal. Among the indicators of perceived and performance fatigability examined, the level of CK and certain subjective ratings differed considerably immediately following or 24–48 h after a 2 × 40-min simulated soccer match in comparison to baseline. Accordingly, monitoring these variables may assist coaches in assessing a U17 player’s perceived and performance fatigability in connection with scheduling training following a soccer match.
Background
High-intensity interval training (HIIT) is frequently employed to improve the endurance of various types of athletes. To determine whether youth soccer players may benefit from the intermittent load and time efficiency of HIIT, we performed a meta-analysis of the relevant scientific literature.
Objectives
Our primary objective was to compare changes in various physiological parameters related to the performance of youth soccer players in response to running-based HIIT to the effects of other common training protocols (i.e., small-sided games, technical training and soccer-specific training, or high-volume endurance training). A secondary objective was to compare specifically running-based HIIT to a soccer-specific form of HIIT known as small-sided games (SSG) in this same respect, since this latter type of training is being discussed extensively by coaches.
Method
A systematic search of the PubMed, SPORTDiscus, and Web of Science databases was performed in August of 2017 and updated during the review process in December of 2018. The criteria for inclusion of articles for analysis were as follows: (1) comparison of HIIT to SSG or some other training protocol employing a pre-post design, (2) involvement of healthy young athletes (≤ 18 years old), and (3) assessment of variables related to endurance or soccer performance. Hedges’ g effect size (dppc2) and associated 95% confidence intervals for the comparison of the responses to HIIT and other interventions were calculated.
Results
Nine studies, involving 232 young soccer players (mean age 16.2 ± 1.6 years), were examined. Endurance training in the form of HIIT or SSG produced similar positive effects on most parameters assessed, including peak oxygen uptake and maximal running performance during incremental running (expressed as Vmax or maximal aerobic speed (MAS)), shuttle runs (expressed as the distance covered or time to exhaustion), and time-trials, as well as submaximal variables such as running economy and running velocity at the lactate threshold. HIIT induced a moderate improvement in soccer-related tests involving technical exercises with the soccer ball and other game-specific parameters (i.e., total distance covered, number of sprints, and number of involvements with the ball). Neuromuscular parameters were largely unaffected by HIIT or SSG.
Conclusion
The present meta-analysis indicates that HIIT and SSG have equally beneficial impacts on variables related to the endurance and soccer-specific performance of youth soccer players, but little influence on neuromuscular performance.
The effects of circuit-like functional high-intensity training (Circuit\(_{HIIT}\)) alone or in combination with high-volume low-intensity exercise (Circuit\(_{combined}\)) on selected cardio-respiratory and metabolic parameters, body composition, functional strength and the quality of life of overweight women were compared. In this single-center, two-armed randomized, controlled study, overweight women performed 9-weeks (3 sessions·wk\(^{−1}\)) of either Circuit\(_{HIIT}\) (n = 11), or Circuit\(_{combined}\) (n = 8). Peak oxygen uptake and perception of physical pain were increased to a greater extent (p < 0.05) by Circuit\(_{HIIT}\), whereas Circuit\(_{combined}\) improved perception of general health more (p < 0.05). Both interventions lowered body mass, body-mass-index, waist-to-hip ratio, fat mass, and enhanced fat-free mass; decreased ratings of perceived exertion during submaximal treadmill running; improved the numbers of push-ups, burpees, one-legged squats, and 30-s skipping performed, as well as the height of counter-movement jumps; and improved physical and social functioning, role of physical limitations, vitality, role of emotional limitations, and mental health to a similar extent (all p < 0.05). Either forms of these multi-stimulating, circuit-like, multiple-joint training can be employed to improve body composition, selected variables of functional strength, and certain dimensions of quality of life in overweight women. However, Circuit\(_{HIIT}\) improves peak oxygen uptake to a greater extent, but with more perception of pain, whereas Circuit\(_{Combined}\) results in better perception of general health.
Virtual online training has emerged as one of the top 20 worldwide fitness trends for 2021 and continues to develop rapidly. Although this allows the cycling community to engage in virtual training and competition, critical evaluation of virtual training platforms is limited. Here, we discuss the strengths, weaknesses, opportunities and threats associated with virtual training technology and cycling in an attempt to enhance awareness of such aspects. Strengths include immersive worlds, innovative drafting mechanics, and versatility. Weaknesses include questionable data accuracy, inadequate strength and reliability of power-speed algorithms. Opportunities exist for expanding strategic partnerships with major cycling races, brands, and sponsors and improving user experience with the addition of video capture and “e-coaching.” Threats are present in the form of cheating during competition, and a lack of uptake and acceptance by a broader community.
The purpose of this study was to determine whether an individually designed incremental exercise protocol results in greater rates of oxygen uptake VO\(_{2max}\) than standardized testing. Fourteen well-trained, male runners performed five incremental protocols in randomized order to measure their VO\(_{2max}\): i) an incremental test (INC\(_{S+I}\)) with pre-defined increases in speed (2 min at 8.64 km.h\(^{-1}\), then a rise of 1.44 km.h\(^{-1}\) every 30 s up to 14.4 km.h\(^{-1}\)) and thereafter inclination (0.5.every 30 s); ii) an incremental test (INC\(_{I}\)) at constant speed (14.4 km.h\(^{-1}\)) and increasing inclination (2 degrees every 2 min from the initial 0 degrees); iii) an incremental test (INC\(_{S}\)) at constant inclination (0 degrees) and increasing speed (0.5 km.h\(^{-1}\) every 30 s from the initial 12.0 km.h\(^{-1}\)); iv) a graded exercise protocol (GXP) at a 1 degrees incline with increasing speed (initially 8.64 km.h\(^{-1}\) + 1.44 km.h\(^{-1}\) every 5 min); v) an individual exercise protocol (INDXP) in which the runner chose the inclination and speed. VO\(_{2max}\) was lowest (-4.2%) during the GXP (p = 0.01; d = 0.06 - 0.61) compared to all other tests. The highest rating of perceived exertion, heart rate, ventilation and end-exercise blood lactate concentration were similar between the different protocols (p < 0.05). The time to exhaustion ranged from 7 min 18 sec (INC\(_{S}\)) to 25 min 30 sec (GXP) (p = 0.01). The VO\(_{2max}\) attained by employing an individual treadmill protocol does not differ from the values derived from various standardized incremental protocols.
Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.
To elucidate the mechanisms underlying the differences in adaptation of arm and leg muscles to sprint training, over a period of 11 days 16 untrained men performed six sessions of 4–6 × 30-s all-out sprints (SIT) with the legs and arms, separately, with a 1-h interval of recovery. Limb-specific VO2peak, sprint performance (two 30-s Wingate tests with 4-min recovery), muscle efficiency and time-trial performance (TT, 5-min all-out) were assessed and biopsies from the m. vastus lateralis and m. triceps brachii taken before and after training. VO2peak and Wmax increased 3–11% after training, with a more pronounced change in the arms (P < 0.05). Gross efficiency improved for the arms (+8.8%, P < 0.05), but not the legs (−0.6%). Wingate peak and mean power outputs improved similarly for the arms and legs, as did TT performance. After training, VO2 during the two Wingate tests was increased by 52 and 6% for the arms and legs, respectively (P < 0.001). In the case of the arms, VO2 was higher during the first than second Wingate test (64 vs. 44%, P < 0.05). During the TT, relative exercise intensity, HR, VO2, VCO2, VE, and Vt were all lower during arm-cranking than leg-pedaling, and oxidation of fat was minimal, remaining so after training. Despite the higher relative intensity, fat oxidation was 70% greater during leg-pedaling (P = 0.017). The aerobic energy contribution in the legs was larger than for the arms during the Wingate tests, although VO2 for the arms was enhanced more by training, reducing the O2 deficit after SIT. The levels of muscle glycogen, as well as the myosin heavy chain composition were unchanged in both cases, while the activities of 3-hydroxyacyl-CoA-dehydrogenase and citrate synthase were elevated only in the legs and capillarization enhanced in both limbs. Multiple regression analysis demonstrated that the variables that predict TT performance differ for the arms and legs. The primary mechanism of adaptation to SIT by both the arms and legs is enhancement of aerobic energy production. However, with their higher proportion of fast muscle fibers, the arms exhibit greater plasticity.
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.