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Objectives
To assess the impact of HIIT performed at school, i.e. both in connection with physical education (intra-PE) and extracurricular sports activities (extra-PE), on the physical fitness and health of children and adolescents.
Methods
PubMed and SPORTDiscus were searched systematically utilizing the following criteria for inclusion: (1) healthy children and adolescents (5–18 years old) of normal weight; (2) HIIT performed intra- and/or extra-PE for at least 5 days at an intensity ≥ 80% of maximal heart rate (HR\(_{max}\)) or peak oxygen uptake (VO\(_{2peak}\)) or as Functional HIIT; (3) comparison with a control (HIIT versus alternative interventions); and (4) pre- and post-analysis of parameters related to physical fitness and health. The outcomes with HIIT and the control interventions were compared utilizing Hedges’ g effect size (ES) and associated 95% confidence intervals.
Results
Eleven studies involving 707 participants who performed intra-PE and 388 participants extra-PE HIIT were included. In comparison with the control interventions, intra-PE HIIT improved mean ES for neuromuscular and anaerobic performance (ES jump performance: 5.89 ± 5.67 (range 1.88–9.90); ES number of push-ups: 6.22 (range n.a.); ES number of sit-ups: 2.66 ± 2.02 (range 1.24–4.09)), as well as ES fasting glucose levels (− 2.68 (range n.a.)) more effectively, with large effect sizes. Extra-PE HIIT improved mean ES for neuromuscular and anaerobic performance (ES jump performance: 1.81 (range n.a.); ES number of sit-ups: 2.60 (range n.a.)) to an even greater extent, again with large effect sizes. Neither form of HIIT was more beneficial for parameters related to cardiorespiratory fitness than the control interventions.
Conclusion
Compared to other forms of exercise (e.g. low-to-moderate-intensity running or walking), both intra- and extra-PE HIIT result in greater improvements in neuromuscular and anaerobic performance, as well as in fasting levels of glucose in school children.
The aim of this study was to investigate inter-day and -week as well as intra- and inter-individual variation of selected biomarkers in high-performance youth soccer players to assist practitioners interpreting player’s internal load to counteract underperformance and unwanted health risks. Eleven male youth soccer players were tested multiple times during two 3-week periods at midpoint (3-wkmid) and at the end (3-wkend) of the first half of a German under-19 1. Bundesliga season. The levels of creatine kinase (CK), urea, and C-reactive protein (CRP) were measured during 3-wkmid and 3-wkend each Monday, Wednesday, and Friday. In 3-wkmid the CK median was 14% higher (241 vs. 212 U/L) compared to 3-wkend (P = 0.26, ES = 0.16). Overall, the medians of CK, urea (P = 0.59, ES = 0.08), and CRP (P = 0.56, ES = 0.10) during 3-wkmid did not differ to the values of 3-wkend. Daily coefficient of variations (CVs) ranged from 22 to 71% (CK), 17 to 37% (urea), and 9 to 164% (CRP). Individual medians ranged from 101 to 350 U/L (CK), 23 to 50 mg/dL (urea), and 0.6 to 1.1 mg/L (CRP). High intra-individual variability was demonstrated by large intra-individual CVs (medians: CK 50%, urea 18%, and CRP 45%). Our data show (i) large inter-day and inter-week variability of all biomarkers, depending on the external load and (ii) considerable inter- and intra-individual parameter variations. Creatine kinase concentrations could sensitively reflect soccer-specific loads during the season.
Background
Repeated sprint performance is determined by explosive production of power, as well as rapid recovery between successive sprints, and there is evidence that compression garments and sports taping can improve both of these factors.
Methods
In each of two sub-studies, female athletes performed two sets of 30 30-m sprints (one sprint per minute), one set wearing compression garment with adhesive silicone stripes (CGSS) intended to mimic taping and the other with normal clothing, in randomized order. Sub-study 1 (n = 12) focused on cardio-respiratory, metabolic, hemodynamic and perceptual responses, while neuronal and biomechanical parameters were examined in sub-study 2 (n = 12).
Results
In both sub-studies the CGSS improved repeated sprint performance during the final 10 sprints (best P < 0.01, d = 0.61). None of the cardio-respiratory or metabolic variables monitored were altered by wearing this garment (best P = 0.06, d = 0.71). Also during the final 10 sprints, rating of perceived exertion by the upper leg muscles was reduced (P = 0.01, d = 1.1), step length increased (P = 0.01, d = 0.91) and activation of the m. rectus femoris elevated (P = 0.01, d = 1.24), while the hip flexion angle was lowered throughout the protocol (best P < 0.01, d = 2.28) and step frequency (best P = 0.34, d = 0.2) remained unaltered.
Conclusion
Although the physiological parameters monitored were unchanged, the CGSS appears to improve performance during 30 30-m repeated sprints by reducing perceived exertion and altering running technique.
Background
Repeated sprint performance is determined by explosive production of power, as well as rapid recovery between successive sprints, and there is evidence that compression garments and sports taping can improve both of these factors.
Methods
In each of two sub-studies, female athletes performed two sets of 30 30-m sprints (one sprint per minute), one set wearing compression garment with adhesive silicone stripes (CGSS) intended to mimic taping and the other with normal clothing, in randomized order. Sub-study 1 (n = 12) focused on cardio-respiratory, metabolic, hemodynamic and perceptual responses, while neuronal and biomechanical parameters were examined in sub-study 2 (n = 12).
Results
In both sub-studies the CGSS improved repeated sprint performance during the final 10 sprints (best P < 0.01, d = 0.61). None of the cardio-respiratory or metabolic variables monitored were altered by wearing this garment (best P = 0.06, d = 0.71). Also during the final 10 sprints, rating of perceived exertion by the upper leg muscles was reduced (P = 0.01, d = 1.1), step length increased (P = 0.01, d = 0.91) and activation of the m. rectus femoris elevated (P = 0.01, d = 1.24), while the hip flexion angle was lowered throughout the protocol (best P < 0.01, d = 2.28) and step frequency (best P = 0.34, d = 0.2) remained unaltered.
Conclusion
Although the physiological parameters monitored were unchanged, the CGSS appears to improve performance during 30 30-m repeated sprints by reducing perceived exertion and altering running technique.
The aim of this study was to evaluate the effect of a repeated sprint training with multi-directional change-of-direction (COD) movements (RSmulti) compared to repeated shuttle sprints (RSS) on variables related to COD speed and reactive agility. Nineteen highly-trained male U15 soccer players were assigned into two groups performing either RSmulti or RSS. For both groups, each training session involved 20 repeated 15 s sprints interspersed with 30 s recovery. With RSmulti the COD movements were randomized and performed in response to a visual stimulus, while the RSS involved predefined 180° COD movements. Before and following the six training sessions, performance in the Illinois agility test (IAT), COD speed in response to a visual stimulus, 20 m linear sprint time and vertical jumping height were assessed. Both groups improved their performance in the IAT (p < 0.01, ES = 1.13; p = 0.01, ES = 0.55). The COD speed in response to a visual stimulus improved with the RSmulti (p < 0.01, ES = 1.03), but not the RSS (p = 0.46, ES = 0.28). No differences were found for 20 m sprint time (P=0.73, ES = 0.07; p = 0.14, ES = 0.28) or vertical jumping height (p = 0.46, ES = 0.11; p = 0.29, ES = 0.12) for the RSmulti and RSS, respectively. In conclusion, performance in the IAT improved with the RSmulti as well as RSS. With the RSmulti however, the COD movements are performed in response to a visual stimulus, which may result in specific adaptations that improve COD speed and reactive agility in young highly trained soccer players.
Purpose:
The aim of the study was to evaluate the mucosal immune function and circadian variation of salivary cortisol, Immunoglobin-A (sIgA) secretion rate and mood during a period of high-intensity interval training (HIIT) compared to long-slow distance training (LSD).
Methods:
Recreational male runners (n = 28) completed nine sessions of either HIIT or LSD within 3 weeks. The HIIT involved 4 × 4 min of running at 90–95% of maximum heart rate interspersed with 3 min of active recovery while the LSD comprised of continuous running at 70–75% of maximum heart rate for 60–80 min. The psycho-immunological stress-response was investigated with a full daily profile of salivary cortisol and immunoglobin-A (sIgA) secretion rate along with the mood state on a baseline day, the first and last day of training and at follow-up 4 days after the last day of training. Before and after the training period, each athlete's running performance and peak oxygen uptake (V·O\(_{2peak}\)) was determined with an incremental exercise test.
Results:
The HIIT resulted in a longer time-to-exhaustion (P = 0.02) and increased V·O\(_{2peak}\) compared to LSD (P = 0.01). The circadian variation of sIgA secretion rate showed highest values in the morning immediately after waking up followed by a decrease throughout the day in both groups (P < 0.05). With HIIT, the wake-up response of sIgA secretion rate was higher on the last day of training (P < 0.01) as well as the area under the curve (AUC\(_{G}\)) higher on the first and last day of training and follow-up compared to the LSD (P = 0.01). Also the AUC\(_{G}\) for the sIgA secretion rate correlated with the increase in V·O\(_{2peak}\) and running performance. The AUC\(_{G}\) for cortisol remained unaffected on the first and last day of training but increased on the follow-up day with both, HIIT and LSD (P < 0.01).
Conclusion:
The increased sIgA secretion rate with the HIIT indicates no compromised mucosal immune function compared to LSD and shows the functional adaptation of the mucosal immune system in response to the increased stress and training load of nine sessions of HIIT.
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.
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 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.
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.
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.
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.
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.
Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis
(2020)
Background:
Decreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA.
Objective:
The aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users’ PA behavior.
Methods:
The BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin Vívoactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature.
Results:
The most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin Vívoactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA.
Conclusions:
Wearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations.
Monitoring variations in the functioning of the autonomic nervous system may help personalize training of runners and provide more pronounced physiological adaptations and performance improvements. We systematically reviewed the scientific literature comparing physiological adaptations and/or improvements in performance following training based on responses of the autonomic nervous system (ie, changes in heart rate variability) and predefined training. PubMed, SPORTDiscus, and Web of Science were searched systematically in July 2019. Keywords related to endurance, running, autonomic nervous system, and training. Studies were included if they (a) involved interventions consisting predominantly of running training; (b) lasted at least 3 weeks; (c) reported pre‐ and post‐intervention assessment of running performance and/or physiological parameters; (d) included an experimental group performing training adjusted continuously on the basis of alterations in HRV and a control group; and (e) involved healthy runners. Five studies involving six interventions and 166 participants fulfilled our inclusion criteria. Four HRV‐based interventions reduced the amount of moderate‐ and/or high‐intensity training significantly. In five interventions, improvements in performance parameters (3000 m, 5000 m, Loadmax, Tlim) were more pronounced following HRV‐based training. Peak oxygen uptake (VO\(_{2peak}\)) and submaximal running parameters (eg, LT1, LT2) improved following both HRV‐based and predefined training, with no clear difference in the extent of improvement in VO\(_{2peak}\). Submaximal running parameters tended to improve more following HRV‐based training. Research findings to date have been limited and inconsistent. Both HRV‐based and predefined training improve running performance and certain submaximal physiological adaptations, with effects of the former training tending to be greater.
The present study assessed the short-term effect of 6 min classroom-based micro-sessions of multi-joint functional high-intensity circuit training (FunctionalHIIT) performed by students during regular classes on parameters related to functional strength and cardiorespiratory fitness. In this randomized controlled 4-week study, 17 students (11 male; 6 female; age: 11.6 ± 0.2 years) performed 6 min of FunctionalHIIT (targeting >17 on the Borg scale) 4 days per week during regular school classes and 18 students (11 male; 7 female; age: 11.7 ± 0.3 years) served as control group (CG) without any additional in-class physical activity. The FunctionalHIIT group completed 86% of all planned sessions (mean duration: 6.0 ± 1.5 min) with a mean RPE of 17.3 ± 2.1. Body height, mass and BMI did not differ between the groups at baseline or between pre- and post-testing (p > 0.05; eta2 ≤ 0.218). The performances in lateral jumping (p < 0.000; part eta2 = 0.382; Δ% 4.6 ± 8.6), sit-ups (p < 0.000; part eta2 = 0.485; Δ% 3.1 ± 8.6) and 20-m sprints (p < 0.000; part eta2 = 0.691; Δ% 15.8 ± 5.4) improved in both groups with greater increase following FunctionalHIIT. No baseline differences and no interaction effects occurred in performance of 6 min run, flexibility, push-ups, balance, and long jump. Classroom-based FunctionalHIIT sessions, performed 4 days per week during 4 weeks did not improve variables related to aerobic endurance performance but enhanced certain parameters of functional strength in schoolchildren. As time is limited in the educational system of schools, FunctionalHIIT during regular school classes could offer a new perspective for increasing functional strength in schoolchildren.
High-Intensity Interval Training Performed by Young Athletes: A Systematic Review and Meta-Analysis
(2018)
Background: High-intensity interval training (HIIT) is as a time-efficient alternative to moderate- or low-intensity continuous exercise for improving variables related to endurance and anaerobic performance in young and adolescent athletes.
Objectives: To assess original research about enhancement of endurance and anaerobic exercise performance in young and adolescent athletes performing HIIT.
Method: Relevant articles published in peer-reviewed journals were retrieved from the electronic databases PubMed and SPORTDiscus in December 2017. Inclusion criteria were: (i) controlled trials (HIIT vs. alternative training protocol) with pre-post design; (ii) healthy young athletes (≤18 years); (iii) assessing variables related to endurance and exercise performance. Hedges' g effect size (ES), and associated 95% confidence intervals were calculated for comparison of any outcome between experimental (HIIT) and alternative training protocol.
Results: Twenty four studies, involving 577 athletes (mean age: 15.5 ± 2.2 years), were included in this review. HIIT exerted no or small positive mean ES on peak oxygen uptake (VO2peak), running performance, repeated sprint ability, jumping performance and submaximal heart rate. Although the mean ES for changes in VO2peak with HIIT is small (mean g = 0.10±0.28), the average increase in VO2peak from pre to post HIIT-interventions were 7.2 ± 6.9% vs. 4.3 ± 6.9% with any other alternative intervention. HIIT largely and positively affected running speed and oxygen consumption at various lactate- or ventilatory-based thresholds, as well as for sprint running performance. Calculations showed negative mean ES for change-of-direction ability (large), and peak blood lactate concentrations (small). Mean duration per training session for HIIT was shorter than for control interventions (28 ± 15 min vs. 38 ± 24 min).
Conclusion: The present findings suggest that young athletes performing HIIT may improve certain important variables related to aerobic, as well as anaerobic, performance. With HIIT, most variables related to endurance improved to a higher extent, compared to alternative training protocols. However, based on ES, HIIT did not show clear superiority to the alternative training protocols. Nevertheless, young athletes may benefit from HIIT as it requires less time per training session leaving more time for training sport specific skills.