Effects of athletic training on physical fitness and stroke velocity in healthy youth and adult tennis players: A systematic review and meta-analysis

1. Introduction

Tennis is a popular sport and is characterized by high demands on both physical fitness (e.g., strength, power, agility, speed etc.) and technical (i.e., serve/stroke technique) factors. Both factors are used to distinguish successful from less successful players, which makes them particularly relevant for training purposes. In this regard, several cross-sectional studies (1–8) and a systematic review with meta-analysis (9) showed significant differences for both stroke velocity and the underlying physical fitness components (e.g., agility, muscle power, endurance, speed) in tennis players depending on their competition level. For example, Kramer et al. (1) detected shorter 10 m sprint times, better agility scores in the Spider test, and higher jump values in elite compared to sub-elite youth male and female players. Further, Ulbricht et al. (2) determined higher stroke velocities for the tennis serve in elite vs. sub-elite youth male and female players. Therefore, the question arises about the effectiveness of athletic training to increase variables of physical fitness and stroke performance in healthy tennis players. With regard to physical fitness, intervention studies with adult players showed beneficial effects on speed, muscle strength, and endurance (10, 11). However, a recent systematic review by Xiao et al. (12) reported a differentiated picture describing (a) significant performance improvements for speed and agility, (b) conflicting evidence regarding muscle power, and (c) no evidence with respect to muscle strength, flexibility, and endurance as a result of athletic training. These discrepancies between findings are most likely due to the fact that Xiao and colleagues only included studies with 12- to 18-year-olds, in whom processes of growth, maturation, and development are still ongoing compared to adults (13). Regarding stroke velocity, positive effects of athletic training were also reported in male youth and adult players (14, 15). Consistent with these findings from original studies, a recent review also reported training-related improvements in tennis serve velocity for the majority of included studies (16).

Although the aforementioned studies have increased the knowledge about the effects of athletic training programs on variables of physical fitness and stroke velocity in healthy youth tennis players, a systematic characterization, aggregation and, most importantly, quantification of the reported intervention effects especially for adult players is still lacking. Therefore, the purpose of the present systematic review with meta-analysis was to characterize, aggregate, and quantify the effects of athletic training on measures of physical fitness and stroke velocity in healthy youth (<18 years) and adult (≥18 years) tennis players. We assumed that athletic training leads to improvements in variables of physical fitness and stroke velocity, but effectiveness will differ with respect to of players’ age (i.e., youth vs. adult tennis players).

2. Methods

2.1. Search strategy

A systematic literature search of the PubMed, Web of Science, and SportDiscus databases was performed to identify eligible articles. The following Boolean expression was used:

Tennis AND ((training OR practice OR exercise OR intervention OR program OR drill) AND (functional OR performance OR agility OR flexibility OR athletic OR strength OR power OR speed OR fitness OR physical OR stroke OR balance OR resistance)) NOT table.

The search covered the period between their inception date and August 2022. Only articles written in English with full-text access were included. In addition, the reference lists of included studies and relevant reviews were screened for relevant studies. After all duplicates were removed, both authors screened the title and abstract of all articles for eligibility according to the inclusion and exclusion criteria (Table 1). The full texts of all potentially eligible records were independently assessed by both authors and disagreements resolved through discussion and consents. The process of literature search, study selection, and exclusion criteria is presented in Figure 1 using the PRISMA flow chart (17).

FIGURE 1

Figure 1. PRISMA flow chart illustrating the different phases of literature search, study selection, and reasons for exclusion of records.

TABLE 1

Table 1. Overview of the inclusion and exclusion criteria.

2.2. Study selection criteria

The inclusion and exclusion criteria are presented in Table 1. Studies were eligible for this review if they (a) examined healthy tennis players, (b) conducted an athletic or sport-specific intervention, (c) had a control condition, (d) reported at least one parameter of physical fitness or stroke velocity, and (e) performed an intervention that lasted at least four weeks as suggested by Farrell and Turgeon (18). Studies were excluded if (a) injured tennis players or no tennis players were studied, (b) the intervention consisted of non-physical exercises (e.g., motor imagery) or electromyostimulation was performed, (c) no control condition was present, (d) results did not allow the calculation of effect size, (e) acute effects (i.e., <4 weeks) of an intervention were studied, and (f) they were not published in English language.

2.3. Study coding

Included studies were coded using the following variables: author and year of publication, number of participants, sex, age, and study group with type of intervention. Interventions were coded based on the number of weeks of training, frequency and duration of a training session, and the number of sets and repetitions. If there were increases in volume during the intervention period, ranges were reported.

The following categories of physical fitness were differentiated: speed, agility, lower-extremity muscle power, upper-extremity muscle power/strength, endurance, balance, and flexibility. Further, stroke velocity was characterized via sport-specific assessments (i.e., serve test, forehand test). Because studies reported different parameters for each category, the most frequently reported measure was used to reduce heterogeneity between studies (Table 2).

TABLE 2

Table 2. Overview of the preferred and alternative outcome by category.

2.4. Assessment of methodological quality

To assess the methodological quality of the included studies, the Physiotherapy Evidence Database (PEDro) scale was used (19). The PEDro scale rates study validity and statistical replicability of studies on a scale of 0 to 10, with ≥6 representing a cut-off score for high-quality studies (19). The predefined cut-off score of ≥6 points was not an inclusion or exclusion criterion. Quality assessment of the included studies was performed independently by both authors, and disagreements were resolved through discussion and consensus.

2.5. Statistical analyses

To quantify the effectiveness of athletic training programs on measures of physical fitness and stroke velocity, the within-subject standardized mean difference was calculated as SMD_W= (pretest mean value – posttest mean value)/pretest standard deviation and the between-subject standardized mean difference as SMD_b= (posttest mean value in the experimental group – posttest mean value in the control group)/pooled standard deviation (20) using Review Manager version 5.4.1. SMD_W and SMD_b can be positive or negative. Positive SMD_W values indicate an improvement in performance (i.e., increase in stroke velocity) from pretest to posttest, while negative SMD_W values indicate a decrease in performance (i.e., decrease in stroke velocity). Positive SMD_b values indicate an improvement in performance in favor of the experimental group (EG), while negative values indicate an improvement in favor of the control group (CG). The SMD values were reported for all players (6–42 years) as well as for youth (<18 years) and adult (≥18 years) players, separately.

SMD_W and SMD_b values can be classified and interpreted according to Cohen (21) into the following ranges: 0 ≤ 0.49 representing small effects, 0.50 ≤ 0.79 representing moderate effects, and ≥0.80 representing large effects. Further, heterogeneity (I²) was computed by using the formula provided by Deeks et al. (22): I² = (Q – df/Q) * 100% where Q is the chi-squared statistics and df represents the degrees of freedom (23). This measure describes the percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error (chance). Deeks et al. (22) postulate that heterogeneity can be interpreted as trivial (0 ≤ 40%), moderate (30 ≤ 60%), substantial (50 ≤ 90%), or considerable (75 ≤ 100%).

3. Results

3.1. Study selection

Figure 1 illustrates the different stages of the systematic literature search and the process of study selection. The search term resulted in 11,511 articles to be reviewed. In addition, 14 studies from other sources (i.e., reference lists, review articles) were added. After removing duplicates and screening titles and abstracts, 54 studies were screened for eligibility. Of these, 30 were excluded for the following reasons: eleven studies did not include a control group, one study did not examine tennis players, one study examined injured tennis players, two studies did not report relevant parameters (e.g., physical fitness, stroke velocity), five studies did not provide sufficient information on outcome measures, eight studies did not use an adequate study design, and two studies were not written in English language.

3.2. Study characteristics

Table 3 shows the characteristics of the 24 included studies. A total of 509 subjects aged between 6 and 42 years were investigated. Fifteen studies (14, 26, 28, 30, 31, 33–37, 39–43) examined youth tennis players under 18 years and nine reports (10, 11, 15, 24, 25, 27, 29, 32, 38) were conducted with adult tennis players aged between 18 and 42 years. Thirteen studies (14, 15, 24, 27–29, 31, 33, 34, 36, 39, 42, 43) investigated male tennis players, three studies (10, 11, 41) tested female players, five studies (25, 26, 30, 35, 40) examined both sexes while three studies (32, 34, 37) did not specify players' sex. Regarding performance level, four studies (10, 11, 25, 27) analyzed college players, three studies (28, 33, 34) examined national ranked players, two papers (24, 29) analyzed tournament players, one report each investigated competitive players (42), international ranked players (34), and ITN Level 3 (15). Twelve studies did not specify the players' performance level.

TABLE 3

Table 3. Studies examining the effects of athletic training programs on measures of physical fitness and stroke velocity in healthy tennis players.

3.3. Outcome measures

Fourteen studies (10, 11, 27, 30, 31, 33, 34, 36–38, 40–43) investigated the influence of athletic training on measures of physical fitness and 15 studies (10, 11, 14, 15, 24–26, 28, 29, 32, 33, 35, 37, 39, 40) on parameters of stroke velocity. In terms of physical fitness, nine studies (10, 11, 31, 33, 34, 36, 40–42) examined lower-extremity muscle power and three studies (11, 33, 40) assessed upper-extremity muscle power. Five articles (10, 11, 31, 34, 41) analyzed tennis-specific endurance, eleven articles (11, 27, 30, 33, 34, 36–38, 41–43) investigated agility, nine studies (11, 30, 31, 33, 34, 36, 40–42) evaluated speed, two papers (11, 36) explored balance, three articles (36, 40, 41) studied flexibility, and five studies (10, 11, 14, 40, 41) quantified upper-extremity muscle strength.

3.4. Intervention characteristics

In total, 33 different interventions were performed. Athletic training duration ranged from four weeks to nine months with a period of 6–8 weeks being used most frequently (n = 18 studies). The players completed two to three sessions of additional athletic training per week. Each training session lasted between 16 and 90 min, although in some studies (n = 4) session duration was not specified. On average, nine exercises were performed during each training session, although the respective number ranged from a minimum of three (34) to a maximum of 14 (10) different exercises per session. However, seven studies (15, 24, 25, 28, 29, 38, 39) did not report the number of exercises which were executed during the intervention. In eleven studies (10, 11, 14, 24–26, 28, 32, 35, 36, 42), various strength training programs (e.g., shoulder resistance training, periodized or non-periodized resistance training, single set circuit training, non-linear periodized resistance training) were conducted, six papers (14, 15, 29, 33, 35, 38) investigated the influence of plyometric training (e.g., CMJ, SJ, medicine ball chest pass), three studies (37, 40, 43) analyzed the effectiveness of core training (e.g., plank, dead bug, climbers), and two studies (36, 41) conducted functional training (e.g., burpees, jump squat, agility run, plank, squat, medicine ball throw). In addition, several other interventions were carried out: overweight racket training (15), balance training (e.g., training on unstable underground, unipedal balance exercises) (30), combined explosive strength and repeated sprint training (e.g., plyometric jumps, agility drills, CMJ, 15–20 m sprints) (31), mixed high intensity intermittent runs (34), combined training (e.g., agility, strength, endurance) (39), and agility training (27).

3.5. Methodological quality of the included trials

The included studies achieved a PEDro score between 4 and 7 points. Eighteen out of 24 studies achieved the cut-off score of ≥ 6 points, while six studies did not achieve this score. Three studies of these examined young players (36, 39, 43), and three studies explored adult players (11, 32, 38).

3.6. Effects of athletic training on measures of physical fitness

3.6.1. Speed

Figure 2 shows the effects of athletic training on measures of speed in healthy tennis players. Eight studies (30, 31, 33, 34, 36, 40–42) investigated youth players and one study (11) dealt with adult players. For all players, the weighted mean SMD_b amounted to 0.44 (9 studies, I² = 51%, Chi² = 24.2, $df=12,p=.02)$, which is indicative of a small effect favoring the EG. Further, the age-specific sub-analysis revealed a moderate effect in youth $(SM{D}_b=0.50)$ and a small effect in adult $(SM{D}_b=0.11)$ players, both in favor of the EG.

FIGURE 2

Figure 2. Effects of athletic training on measures of speed (e.g., 20 m sprint time) in healthy youth and adult tennis players. CI = confidence interval, df = degrees of freedom, SE = standard error, IV = inverse variance.

3.6.2. Agility

The effects of athletic training on variables of agility in healthy tennis players are displayed in Figure 3. Eight studies (30, 33, 34, 36, 37, 41–43) investigated youth players and three studies (11, 27, 38) analyzed adult players. When considering all players, the weighted mean SMD_b yielded 0.93 $(11\mathrm{studies},I^2=77\text{\%},Ch{i}^2=56.73,df=13,p<0.00001)$, indicating a large effect in favor of the EG. In addition, the age-specific sub-analysis showed a large effect in youth $(SM{D}_b=0.98)$ and in adult $(SM{D}_b=0.88)$ players, both in favor of the EG.

FIGURE 3

Figure 3. Effects of athletic training on measures of agility (e.g., 5-0-5 test time) in healthy youth and adult tennis players. CI = confidence interval, df = degrees of freedom, SE = standard error, IV = inverse variance.

3.6.3. Lower-extremity muscle power

Figure 4 illustrates the effects of athletic training on parameters of lower-extremity muscle power in healthy tennis players. Six studies (31, 33, 34, 36, 40, 41) examined youth players and two studies (10, 11) assessed adult players. In general, the weighted mean SMD_b was 0.88 (8 studies, $I^2=70\text{\%},Ch{i}^2=43.14,df=13,p<0.0001)$, which indicates a large effect in favor of the EG. The additionally performed age-specific sub-analysis revealed a moderate effect in youth $(SM{D}_b=0.68)$ and a large effect in adult $(SM{D}_b=1.40)$ players, both in favor of the EG.

FIGURE 4

Figure 4. Effects of athletic training on measures of lower-extremity muscle power (e.g., counter movement jump height) in healthy youth and adult tennis players. CI = confidence interval, df = degrees of freedom, SE = standard error, IV = inverse variance.

3.6.4. Upper-extremity muscle power

 Figure 5 shows the effects of athletic training on parameters of upper-extremity muscle power in healthy youth tennis players (33, 40, 42). The weighted mean SMD_b amounted to 0.72 (3 studies, I² = 0%, Chi² = 3.67, df = 4, $p=0.45)$ indicating a moderate effect in favor of the EG.

FIGURE 5

Figure 5. Effects of athletic training on measures of upper-extremity muscle power (e.g., medicine ball throw) in healthy youth tennis players. CI = confidence interval, df = degrees of freedom, SE = standard error, IV = inverse variance.

3.6.5. Upper-extremity muscle strength

 Figure 6 displays the effect of athletic training on variables of upper-extremity muscle strength in healthy tennis players. Three studies (14, 40, 41) evaluated youth players and two studies (10, 11) analyzed adult players. For all players, the weighted mean SMD_b of 0.90 (5 studies, I² = 33%, Chi² = 12.01, $df=8,p=0.15)$ indicates a large effect in favor of the EG. Further, the age-specific sub-analysis revealed a moderate effect in youth $(SM{D}_b=0.60)$ and a large effect in adult $(SM{D}_b=1.39)$ players, both in favor of the EG.

FIGURE 6

Figure 6. Effects of athletic training on measures of upper-extremity muscle strength (e.g., handgrip strength) in healthy youth and adult tennis players. CI = confidence interval, df = degrees of freedom, SE = standard error, IV = inverse variance.

3.6.6. Endurance

The effects of athletic training on measures of endurance in tennis athletes are shown in Figure 7. Three studies (31, 34, 41) examined youth players and two studies (10, 11) dealt with adult players. Overall, the weighted mean SMD_b amounted to 0.61 $(4\mathrm{studies},I^2=46\text{\%},Ch{i}^2=11.02,df=6,p=0.09)$, indicating a moderate effect favoring the EG. Moreover, a large effect was detected in youth $(SM{D}_b=0.86)$ and a small effect in adult $(SM{D}_b=0.41)$ players, both in favor of the EG.

FIGURE 7

Figure 7. Effects of athletic training on measures of endurance (e.g., VO₂max) in healthy youth and adult tennis players. CI = confidence interval, df = degrees of freedom, SE = standard error, IV = inverse variance.

3.6.7. Balance

Two studies (36, 40) investigated the effects of athletic training on measures of balance performance in healthy youth tennis players (Figure 8). Our analysis revealed a weighted mean SMD_b of 0.88 (2 studies, I² = 79%, Chi² = 14.52, $df=3,p=0.002)$ indicating a large effect in favor of the EG.

FIGURE 8

Figure 8. Effects of athletic training on measures of balance (e.g., Y balance test) in healthy youth tennis players. CI = confidence interval, df = degrees of freedom, SE = standard error, IV = inverse variance.

3.6.8. Flexibility

Three studies (36, 40, 41) examined the effects of athletic training on parameters of flexibility in healthy youth tennis players (Figure 9). The weighted mean SMD_b amounted to 0.63 (3 studies, I² = 80%, Chi² = 19.70, df = 4, $p=0.0006)$, which indicates a moderate effect in favor of the EG.

FIGURE 9

Figure 9. Effects of athletic training on measures of flexibility (e.g., sit-and-reach test) in healthy youth tennis players. CI = confidence interval, df = degrees of freedom, SE = standard error, IV = inverse variance.

3.7. Effects of athletic training on measures of stroke velocity

The effects of athletic training on parameters of stroke velocity) in healthy tennis players are shown in Figure 10. Eight studies (14, 26, 28, 33, 35, 36, 39, 40) analyzed youth players and six studies (10, 11, 15, 24, 25, 32) examined adult players. Overall, the analyses yielded a weighted mean SMD_b of 0.90 $(I^2=69\text{\%},Ch{i}^2=70.92df=22,p<0.00001)$ indicating a large effect favoring the EG. Furthermore, large effects in youth $(SM{D}_b=0.70)$ as well as in adult $(SM{D}_b=1.15)$ players were detected.

FIGURE 10

Figure 10. Effects of athletic training on measures of stroke performance (e.g., maximal stroke velocity) in healthy youth and adult tennis players. CI = confidence interval, df = degrees of freedom, SE = standard error, IV = inverse variance.

4. Discussion

To the best of our knowledge, the present systematic review with meta-analysis is the first to characterize, aggregate, and quantify the effects of athletic training programs on measures of physical fitness and stroke velocity in healthy youth and adult tennis players. Overall, the analysis of the data of 24 studies that met the criteria selection revealed for measures of physical fitness small (speed: SMD = 0.44), moderate (endurance: SMD = 0.61, upper-extremity muscle power: SMD = 0.72, flexibility: SMD = 0.63), and large (agility: SMD = 0.83, lower-extremity muscle power: SMD = 0.88, upper-extremity muscle strength: SMD = 0.90, balance: SMD = 0.88) effects, all in favor of the EG. For stroke velocity (e.g., maximal and mean stroke velocity), the analyses yielded a large effect of physical training (SMD = 0.90) also favoring the EG. Furthermore, the additionally performed sub-analyses showed differences in the effectiveness of athletic training programs on variables of physical fitness and stroke velocity when considering players' age (i.e., youth players: < 18 years vs. adult players: ≥ 18 years).

4.1. Effectiveness of athletic training on measures of physical fitness

In line with our hypothesis stating that athletic training will lead to improvements in variables of physical fitness, but the effectiveness will differ with respect to of players' age (i.e., youth vs. adult tennis players), this present systematic review with meta-analysis showed beneficial effects of athletic training on measures of physical fitness in healthy tennis players in favor of the EG, which can be classified as small to large. Specifically, large effects were detected for agility (SMD = 0.93), balance (SMD = 0.88), lower-extremity muscle power (SMD = 0.88), and upper-extremity muscle strength (SMD = 0.90), indicating a high trainability (large adaptive reserve) in these physical fitness components.

In terms of agility, the sub-analysis showed a large effect in youth (SMD = 0.98) as well as in adult (SMD = 0.88) players, suggesting a high adaptive reserve in both age groups. Precisely, agility is mainly composed of two components: (i) change of direction speed (i.e., technique, straight sprinting speed, leg muscle qualities, and anthropometry) and (ii) perceptual and decision-making factors (visual scanning, knowledge of situations, pattern recognition, and anticipation) (44). Due to their complex composition, both components require several years of training to achieve maximum performance (27), which means that training-related adaptations progress can be achieved regardless of age and with the use of different interventions. Precisely, functional training (41), plyometric training (38), and flywheel training (42) were applied and all of them resulted in positive effects on measures of agility. Furthermore, Meckel et al. (45) showed that agility accounted for almost 40% of the players’ ranking (i.e., country's youth tennis players listing). Therefore, from this and the previously reported findings of the present study, it can be deduced that the promotion of agility seems particularly important for success in tennis.

Regarding balance, the large effect (SMD = 0.88) refers exclusively to the youth tennis players, since no study was found for adult players. There seems to be a particularly high adaptive reserve due to ongoing growth, maturation, and developmental processes in children and adolescents. Particularly, the neural system is not yet fully matured and thus offers a prominent potential for the promotion of informationally determined physical fitness components like balance (46). In this regard, several original studies (47–49) as well as review articles and meta-analyses (50, 51) have shown significant improvements following balance training on measures of balance and sport-related performance in youth.

With respect to lower-extremity muscle power, the sub-analysis revealed a moderate effect (SMD = 0.68) in youth and a large effect (SMD = 1.40) in adult players. This indicates that there is a high potential for muscular adaptations at a later stage. In fact, factors favorably influencing the development and training of muscular strength, such as an increase in circulating androgens (e.g., testosterone), are reported for the transition from youth to adulthood (52, 53). In this context, Vrijens (54) showed larger improvements (i.e., isometric strength of the elbow flexors/extensors and knee flexors/extensors) in pubertal (i.e., 16-year-olds) compared to prepubertal participants (i.e., 10-year-olds) following eight weeks (3 times per week) of resistance training. Concerning upper-extremity muscle strength, the sub-analysis yielded similar results, namely a moderate effect (SMD = 0.60) in youth and a large effect (SMD = 1.39) in adult players. Thus, as for lower-extremity muscle power the same line of argumentation can be applied.

Moderate effects were obtained for upper-extremity muscle power (SMD = 0.72), flexibility (SMD = 0.63), and endurance (SMD = 0.61). Regarding upper-extremity muscle power and flexibility, the moderate effects refer solely to the youth tennis players, as no studies were found for adult players. Thus, both physical fitness components seem to be well trainable in youth tennis players. In this context, using a regression analysis Ulbricht et al. (2) showed that upper-extremity muscle power was the most correlated predictor of tennis performance (i.e., national youth ranking) in female and male elite junior tennis players. Therefore, promoting upper-extremity muscle power seems particularly worthwhile for enhancing tennis performance.

In terms of endurance, the sub-analysis showed a large effect (SMD = 0.86) in youth and a small effect (SMD = 0.41) in adult players, indicating that the former one seems to have a higher adaptive reserve. Again, it can be argued that processes such as growth, maturation, and development are not yet complete in youth compared to adult players, and the cardiovascular as well as pulmonary system offers a particular potential for the promotion of energetically determined physical fitness components such as endurance (55). In this regard, a recent systematic and meta-analysis (56) revealed beneficial effects of endurance training (i.e., high-intensity interval training) on oxygen consumption, heart rate, repeated sprint ability etc. in young athletes (mean age: 15.5 ± 2.2 years).

A small effect was found for speed (SMD = 0.44). However, the sub-analysis showed a small effect (SMD = 0.11) only in adult players but a moderate effect (SMD = 0.50) in youth players. Most likely, this is because speed is a component that is largely genetically determined (55). Thus, the potential for training-induced adaptations is relatively low. Since the neuronal system, which is responsible for speed-related processes such as the perception, processing, and transmission of information is not yet fully mature in children and adolescents compared to adults, youth players seem to have more possibilities for training-related adaptation, which may explain their moderate effect (57).

4.2. Effectiveness of athletic training on measures of stroke velocity

In accordance with our hypothesis stating that athletic training will result in enhancements in stroke velocity, but the effectiveness will differ depending on players' age (i.e., youth vs. adult tennis players), we identified large effects (SMD = 0.90) of athletic training on stroke velocity in healthy tennis players in favor of the EG. However, the sub-analysis showed a large effect (SMD = 1.15) only in adult players but a moderate effect (SMD = 0.70) in youth players. Thus, both age groups seem to have a good adaptive potential for the promotion of stroke velocity, which is even higher in adult players. In terms of adult players, the interventions used ranged from plyometric training (29) over medicine ball training (14) to periodized strength training (10, 11) (e.g., crunches, back extensions, split squats), with non-linear periodized resistance being particularly effective. In accordance to this, the German Tennis Confederation (58) recommends to improve stroke velocity by using athletic exercises such as multi-directional jumps, medicine ball throws, and core strengthening. For youth players, the German Tennis Confederation (58) recommends improving stroke velocity especially by practicing stroke techniques, as evidence exists that technical demands and the underlying motor skills and cognitive processes are acquired through several years of practice (59). In addition, athletic exercises should be performed. In the present systematic review and meta-analysis the intervention used ranged from plyometric training (14) over functional training (36) (e.g., squat, plank, dead bug) to combined training (39) (including strength, speed, agility, and endurance exercises) with combined training being particularly effective.

4.3. Limitations

This systematic review with meta-analysis has a few limitations. The used methodology varied between the included studies in terms of players' characteristics (age, sex, performance level), assessments (tests, outcomes), and interventions (modalities like duration, frequency, volume of training etc.) which is reflected in a trivial to considerable heterogeneity between studies. Thus, future studies should apply instrumented assessment methods (i.e., biomechanical tests using force plates, plantar pressure devices etc.) in addition to the frequently used field-based tests to reduce the variability in effect estimates. Further, the included studies represent healthy tennis players in the age range of 6–42 years, thus no statement can be made especially about master athletes. Moreover, of the 24 included studies, three and eleven studies examined only women and men, respectively. Therefore, no sex-specific analyses could be performed.

5. Conclusions

The systematic review and meta-analysis characterized, aggregated, and quantified the effects of athletic training programs on measures of physical fitness and stroke velocity in healthy tennis players. For measures of physical fitness, we detected small (speed), moderate (endurance, upper-extremity muscle power, flexibility), and large (agility, lower-extremity muscle power, upper-extremity muscle strength, balance) effects, all in favor of the EG. In addition, a large effect also favoring the EG was found for parameters of stroke velocity. This indicates that athletic training is effective to varying degrees and this is further influenced by players' age (i.e., youth players: < 18 years vs. adult players: ≥ 18 years). For both age groups, we therefore conclude that further research is needed to investigate optimal training regimes in order to enlarge the effectiveness especially for those fitness components that showed small- to moderate-sized changes.

6. Practical applications

The results of the present systematic review with meta-analysis reveal implications for practitioners. In terms of physical fitness outcomes, large effects and thus a high potential for training-induced adaptations were found in youth players with respect to agility, balance and endurance, and in adult players with respect to agility, lower-extremity muscle power and upper-extremity muscle strength. This age specificity in trainability should therefore be considered when designing programs for long-term athlete development. In terms of stroke velocity, large effects were detected in adult and moderate effects in youth players. This suggests similar trainability in both age categories, according to which programs to train stroke techniques should start in adolescence and continue throughout adulthood.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

Author contributions

Conceptualization: JL, TM. Data curation: JL, TM. Formal analysis: JL. Methodology: JL. Writing – original draft: JL. Writing – review & editing: JL, TM. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fspor.2022.1061087/full#supplementary-material.

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