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Objective: To examine the effect of plyometric jump training on skeletal muscle hypertrophy in healthy individuals.
Methods: A systematic literature search was conducted in the databases PubMed, SPORTDiscus, Web of Science, and Cochrane Library up to September 2021.
Results: Fifteen studies met the inclusion criteria. The main overall finding (44 effect sizes across 15 clusters median = 2, range = 1–15 effects per cluster) indicated that plyometric jump training had small to moderate effects [standardised mean difference (SMD) = 0.47 (95% CIs = 0.23–0.71); p < 0.001] on skeletal muscle hypertrophy. Subgroup analyses for training experience revealed trivial to large effects in non-athletes [SMD = 0.55 (95% CIs = 0.18–0.93); p = 0.007] and trivial to moderate effects in athletes [SMD = 0.33 (95% CIs = 0.16–0.51); p = 0.001]. Regarding muscle groups, results showed moderate effects for the knee extensors [SMD = 0.72 (95% CIs = 0.66–0.78), p < 0.001] and equivocal effects for the plantar flexors [SMD = 0.65 (95% CIs = −0.25–1.55); p = 0.143]. As to the assessment methods of skeletal muscle hypertrophy, findings indicated trivial to small effects for prediction equations [SMD = 0.29 (95% CIs = 0.16–0.42); p < 0.001] and moderate-to-large effects for ultrasound imaging [SMD = 0.74 (95% CIs = 0.59–0.89); p < 0.001]. Meta-regression analysis indicated that the weekly session frequency moderates the effect of plyometric jump training on skeletal muscle hypertrophy, with a higher weekly session frequency inducing larger hypertrophic gains [β = 0.3233 (95% CIs = 0.2041–0.4425); p < 0.001]. We found no clear evidence that age, sex, total training period, single session duration, or the number of jumps per week moderate the effect of plyometric jump training on skeletal muscle hypertrophy [β = −0.0133 to 0.0433 (95% CIs = −0.0387 to 0.1215); p = 0.101–0.751].
Conclusion: Plyometric jump training can induce skeletal muscle hypertrophy, regardless of age and sex. There is evidence for relatively larger effects in non-athletes compared with athletes. Further, the weekly session frequency seems to moderate the effect of plyometric jump training on skeletal muscle hypertrophy, whereby more frequent weekly plyometric jump training sessions elicit larger hypertrophic adaptations.
Objective: To examine the effect of plyometric jump training on skeletal muscle hypertrophy in healthy individuals.
Methods: A systematic literature search was conducted in the databases PubMed, SPORTDiscus, Web of Science, and Cochrane Library up to September 2021.
Results: Fifteen studies met the inclusion criteria. The main overall finding (44 effect sizes across 15 clusters median = 2, range = 1–15 effects per cluster) indicated that plyometric jump training had small to moderate effects [standardised mean difference (SMD) = 0.47 (95% CIs = 0.23–0.71); p < 0.001] on skeletal muscle hypertrophy. Subgroup analyses for training experience revealed trivial to large effects in non-athletes [SMD = 0.55 (95% CIs = 0.18–0.93); p = 0.007] and trivial to moderate effects in athletes [SMD = 0.33 (95% CIs = 0.16–0.51); p = 0.001]. Regarding muscle groups, results showed moderate effects for the knee extensors [SMD = 0.72 (95% CIs = 0.66–0.78), p < 0.001] and equivocal effects for the plantar flexors [SMD = 0.65 (95% CIs = −0.25–1.55); p = 0.143]. As to the assessment methods of skeletal muscle hypertrophy, findings indicated trivial to small effects for prediction equations [SMD = 0.29 (95% CIs = 0.16–0.42); p < 0.001] and moderate-to-large effects for ultrasound imaging [SMD = 0.74 (95% CIs = 0.59–0.89); p < 0.001]. Meta-regression analysis indicated that the weekly session frequency moderates the effect of plyometric jump training on skeletal muscle hypertrophy, with a higher weekly session frequency inducing larger hypertrophic gains [β = 0.3233 (95% CIs = 0.2041–0.4425); p < 0.001]. We found no clear evidence that age, sex, total training period, single session duration, or the number of jumps per week moderate the effect of plyometric jump training on skeletal muscle hypertrophy [β = −0.0133 to 0.0433 (95% CIs = −0.0387 to 0.1215); p = 0.101–0.751].
Conclusion: Plyometric jump training can induce skeletal muscle hypertrophy, regardless of age and sex. There is evidence for relatively larger effects in non-athletes compared with athletes. Further, the weekly session frequency seems to moderate the effect of plyometric jump training on skeletal muscle hypertrophy, whereby more frequent weekly plyometric jump training sessions elicit larger hypertrophic adaptations.
Background
Static stretching (SS) can impair performance and increase range of motion of a non-exercised or non-stretched muscle, respectively. An underdeveloped research area is the effect of unilateral stretching on non-local force output.
Objective
The objective of this review was to describe the effects of unilateral SS on contralateral, non-stretched, muscle force and identify gaps in the literature.
Methods
A systematic literature search following preferred reporting items for systematic review and meta-analyses Protocols guidelines was performed according to prescribed inclusion and exclusion criteria. Weighted means and ranges highlighted the non-local force output response to unilateral stretching. The physiotherapy evidence database scale was used to assess study risk of bias and methodological quality.
Results
Unilateral stretching protocols from six studies involved 6.3 +/- 2 repetitions of 36.3 +/- 7.4 s with 19.3 +/- 5.7 s recovery between stretches. The mean stretch-induced force deficits exhibited small magnitude effect sizes for both the stretched (-6.7 +/- 7.1%, d = -0.35: 0.01 to -1.8) and contralateral, non-stretched, muscles (-4.0 +/- 4.9%, d = , 0.22: 0.08 to 1.1). Control measures exhibited trivial deficits.
Conclusion
The limited literature examining non-local effects of prolonged SS revealed that both the stretched and contralateral, non-stretched, limbs of young adults demonstrate small magnitude force deficits. However, the frequency of studies with these effects were similar with three measures demonstrating deficits, and four measures showing trivial changes. These results highlight the possible global (non-local) effects of prolonged SS. Further research should investigate effects of lower intensity stretching, upper versus lower body stretching, different age groups, incorporate full warm-ups, and identify predominant mechanisms among others.
Objective:
This study aimed to systematically review and meta-analyze the effect of flywheel resistance training (FRT) versus traditional resistance training (TRT) on change of direction (CoD) performance in male athletes.
Methods:
Five databases were screened up to December 2021.
Results:
Seven studies were included. The results indicated a significantly larger effect of FRT compared with TRT (standardized mean difference [SMD] = 0.64). A within-group comparison indicated a significant large effect of FRT on CoD performance (SMD = 1.63). For TRT, a significant moderate effect was observed (SMD = 0.62). FRT of <= 2 sessions/week resulted in a significant large effect (SMD = 1.33), whereas no significant effect was noted for >2 sessions/week. Additionally, a significant large effect of <= 12 FRT sessions (SMD = 1.83) was observed, with no effect of >12 sessions. Regarding TRT, no significant effects of any of the training factors were detected (p > 0.05).
Conclusions:
FRT appears to be more effective than TRT in improving CoD performance in male athletes. Independently computed single training factor analyses for FRT indicated that <= 2 sessions/week resulted in a larger effect on CoD performance than >2 sessions/week. Additionally, a total of <= 12 FRT sessions induced a larger effect than >12 training sessions. Practitioners in sports, in which accelerative and decelerative actions occur in quick succession to change direction, should regularly implement FRT.
This study examined the effects of an 8-week plyometric training (PT) program on components of physical fitness in young female handball players. Twenty-one female adolescent handball players were assigned to an experimental group (EG, n = 12; age = 15.9 +/- 0.2 years) or an active control group (CG, n = 9, age = 15.9 +/- 0.3 years). While EG performed plyometric exercises in replacement of some handball-specific drills, CG maintained the regular training schedule. Baseline and follow-up tests were performed for the assessment of linear speed (i.e., 5-, 10-, and 20-m time), change-of-direction (CoD) speed (i.e., T-test time), muscle power (i.e., countermovement jump [CMJ] height and reactive strength index [RSI]), and repeated sprint ability (RSA) (RSA total time [RSA(total)], RSA best time [RSA(best)], and RSA fatigue index [RSA(FI)]). Data were analyzed using magnitude-based inferences. Within-group analyses for the EG revealed moderate-to-large improvements for the 5-m (effect size [ES] = 0.81 [0.1-1.5]), 10-m sprint time (ES = 0.84 [0.1-1.5]), RSI (ES = 0.75 [0.1-1.4]), RSA(FI) (ES = 0.65 [0.0-1.3]), and T-test time (ES = 1.46 [0.7-2.2]). Trivial-to-small ES was observed for RSA(best) (ES = 0.18 [-0.5 to 0.9]), RSA(total) (ES = 0.45 [-0.2 to 1.1]), 20-m sprint time (ES = 0.56 [-0.1 to 1.2]), and CMJ height (ES = 0.57 [-0.1 to 1.3]). For the CG, within-group analyses showed a moderate performance decline for T-test time (ES = -0.71 [-1.5 to 0.1]), small decreases for 5-m sprint time (ES = -0.46 [-1.2 to 0.3]), and a trivial decline for 10-m (ES = -0.10 [-0.9 to 0.7]) and 20-m sprint times (ES = -0.16 [-0.9 to 0.6]), RSA(total) (ES = 0.0 [-0.8 to 0.8]), and RSA(best) (ES = -0.20 [-0.9 to 0.6]). The control group achieved trivial-to-small improvements for CMJ height (ES = 0.10 [-0.68 to 0.87]) and RSI (ES = 0.30 [-0.5 to 1.1]). In conclusion, a short-term in-season PT program, in replacement of handball-specific drills, is effective in improving measures of physical fitness (i.e., linear/CoD speed, jumping, and RSA) in young female handball players.
Background
Change-of-direction (CoD) speed is a physical fitness attribute in many field-based team and individual sports. To date, no systematic review with meta-analysis available has examined the effects of resistance training (RT) on CoD speed in youth and adults.
Objective
To aggregate the effects of RT on CoD speed in youth and young physically active and athletic adults, and to identify the key RT programme variables for training prescription.
Data sources
A systematic literature search was conducted with PubMed, Web of Science, and Google Scholar, with no date restrictions, up to October 2019, to identify studies related to the effects of RT on CoD speed.
Study Eligibility Criteria
Only controlled studies with baseline and follow-up measures were included if they examined the effects of RT (i.e., muscle actions against external resistances) on CoD speed in healthy youth (8-18 years) and young physically active/athletic male or female adults (19-28 years).
Study Appraisal and Synthesis Methods
A random-effects model was used to calculate weighted standardised mean differences (SMD) between intervention and control groups. In addition, an independent single training factor analysis (i.e., RT frequency, intensity, volume) was undertaken. Further, to verify if any RT variable moderated effects on CoD speed, a multivariate random-effects meta-regression was conducted. The methodological quality of the included studies was assessed using the physiotherapy evidence database (PEDro) scale.
Results
Fifteen studies, comprising 19 experimental groups, were included. The methodological quality of the studies was acceptable with a median PEDro score of 6. There was a significant large effect size of RT on CoD speed across all studies (SMD = - 0.82 [- 1.14 to - 0.49]). Subgroup analyses showed large effect sizes on CoD speed in males (SMD = - 0.95) contrasting with moderate improvements in females (SMD = - 0.60). There were large effect sizes on CoD speed in children (SMD = - 1.28) and adolescents (SMD = - 1.21) contrasting with moderate effects in adults (SMD = - 0.63). There was a moderate effect in elite athletes (SMD = - 0.69) contrasting with a large effect in subelite athletes (SMD = - 0.86). Differences between subgroups were not statistically significant. Similar improvements were observed regarding the effects of independently computed training variables. In terms of RT frequency, our results indicated that two sessions per week induced large effects on CoD speed (SMD = - 1.07) while programmes with three sessions resulted in moderate effects (SMD = - 0.53). For total training intervention duration, we observed large effects for <= 8 weeks (SMD = - 0.81) and > 8 weeks (SMD = - 0.85). For single session duration, we found large effects for <= 30 min and >= 45 min (both SMD = - 1.00). In terms of number of training sessions, we identified large effects for <= 16 sessions (SMD = - 0.83) and > 16 sessions (SMD = - 0.81). For training intensity, we found moderate effects for light-to-moderate (SMD = - 0.76) and vigorous-to-near maximal intensities (SMD = - 0.77). With regards to RT type, we observed large effects for free weights (SMD = - 0.99) and machine-based training (SMD = - 0.80). For combined free weights and machine-based training, moderate effects were identified (SMD = - 0.77). The meta-regression outcomes showed that none of the included training variables significantly predicted the effects of RT on CoD speed (R-2 = 0.00).
Conclusions
RT seems to be an effective means to improve CoD speed in youth and young physically active and athletic adults. Our findings indicate that the impact of RT on CoD speed may be more prominent in males than in females and in youth than in adults. Additionally, independently computed single factor analyses for different training variables showed that higher compared with lower RT intensities, frequencies, and volumes appear not to have an advantage on the magnitude of CoD speed improvements. In terms of RT type, similar improvements were observed following machine-based and free weights training.
This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players’ RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg’s 0–10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12–15.9 km/h; 16–19.9 km/h; 20–24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p = 0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p = 0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.
This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players’ RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg’s 0–10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12–15.9 km/h; 16–19.9 km/h; 20–24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p = 0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p = 0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.
Biological maturation has been shown to affect male youths' responses to plyometric training (PT). However, to date, no researcher has examined the effect of maturation on the effects of PT in female youth. We undertook the first controlled intervention study to examine this, focusing on adaptive responses to countermovement jump (CMJ), reactive strength index (RSI), and change of direction (COD) performance in groups of female youth divided by maturation status (years from peak height velocity [PHV]). The training program lasted 7 weeks with subjects undertaking 2 sessions of PT per week. In the mid-PHV group, there was a small increase (effect size; 90% confidence interval = 0.40; -0.23 to 1.03) in CMJ performance. No changes were observed in the post-PHV group (0.02; -0.68 to 0.72). For RSI, there was a moderate increase in the mid-PHV group (0.94; 0.29-1.59) with only a trivial increase in the post-PHV group (0.06; -0.65 to 0.76). The intervention exerted no positive effect on COD performance in any group. Plyometric training seems to enhance CMJ and RSI in female youth, although the magnitude of adaptation could be affected by maturation status. A twice-per-week program of multidirectional jumping and hopping, with bilateral and unilateral components, can be used as a preparatory precursor to physical education classes or recreational sport.
Background Small-sided games have been suggested as a viable alternative to conventional endurance training to enhance endurance performance in youth soccer players. This has important implications for long-term athlete development because it suggests that players can increase aerobic endurance through activities that closely resemble their sport of choice. Data Sources The data sources utilised were Google Scholar, PubMed and Microsoft Academic. Study Eligibility Criteria Studies were eligible for inclusion if interventions were carried out in male soccer players (aged < 18years) and compared the effects of small-sided games and conventional endurance training on aerobic endurance performance. We defined small-sided games as modified [soccer] games played on reduced pitch areas, often using adapted rules and involving a smaller number of players than traditional games. We defined conventional endurance training as continuous running or extensive interval training consisting of work durations>3min. Study Appraisal and Synthesis Methods The inverse-variance random-effects model for meta-analyses was used because it allocates a proportionate weight to trials based on the size of their individual standard errors and facilitates analysis whilst accounting for heterogeneity across studies. Effect sizes were represented by the standardised mean difference and presented alongside 95% confidence intervals. Results Seven studies were included in this meta-analysis. Both modes of training were effective in increasing endurance performance. Within-mode effect sizes were both of moderate magnitude [small-sided games: 0.82 (95% confidence interval 0.05, 1.60), Z=2.07 (p=0.04); conventional endurance training: 0.89 (95% confidence interval 0.06, 1.72), Z=2.10 (p=0.04)]. There were only trivial differences [0.04 (95% confidence interval -0.36, 0.43), Z=0.18 (p=0.86)] between the effects on aerobic endurance performance of small-sided games and conventional endurance training. Subgroup analyses showed mostly trivial differences between the training methods across key programming variables such as set duration (>= or < 4 min) and recovery period between sets (>= or< 3min). Programmes that were longer than 8 weeks favoured small-sided games [effect size=0.45 (95% confidence interval -0.12, 1.02), Z=1.54 (p=0.12)], with the opposite being true for conventional endurance training [effect size=-0.33 (95% confidence interval -0.79, 0.14), Z=1.39 (p=0.16)]. Programmes with more than 4 sets per session favoured small-sided games [effect size=0.53 (95% confidence interval -0.52, 1.58), Z=0.98 (p=0.33)] with only a trivial difference between those with 4, or fewer, sets [effect size=-0.13 (95% confidence interval -0.52, 0.26), Z=0.65 (p=0.52)]. Conclusions Small-sided games are as effective as conventional endurance training for increasing aerobic endurance performance in male youth soccer players. This is important for practitioners as it means that small-sided games can allow both endurance and skills training to be carried out simultaneously, thus providing a more efficient training stimulus. Small-sided games offer the same benefits as conventional endurance training with two sessions per week, with4 sets of 4 min of activity, interspersed with recovery periods of 3min, recommended in this population.