1. Introduction
Handball has traditionally been described as a discipline of high physiological demand, characterized by the need to perform intermittent high-intensity actions such as sprints, decelerations, re-accelerations, jumps, throws, physical contacts, stops, and frequent changes in directions [1]. These specific demands of the game are particularly evident during competition periods [2], in which athletes must demonstrate a high capacity to repeat power effort in short recovery intervals [3]; to achieve this efficiently and sustainably, a solid foundation of physical conditioning is required, involving key capacities such as strength, speed, and endurance [4].
In this context, the literature has reported numerous studies focused on optimizing the physical preparation of adult players through various methodologies and intervention strategies; for example, the study by Hermassi et al. [5] demonstrated that a 12-week circuit-based training protocol can significantly improve jumping ability and aerobic endurance in handball players. Similarly, Aloui et al. [6] highlighted the benefits of resistance band training applied over eight weeks in combination with regular planning, achieving significant improvements in throwing power and upper body musculature without increasing muscle volume, which helps maintain the athlete’s optimal weight. The most recent systematic reviews have identified high-intensity interval training (HIIT) as one of the most effective methods to improve VO2max in sports with intermittent profiles. For instance, a study published in 2024 evaluated the effects of HIIT on university athletes practicing basketball, ecuavoley, and taekwondo, finding significant improvements in cardiovascular endurance, measured through VO2max and duration in the shuttle run test [7]. Moreover, a 2022 a study analyzed the effectiveness of HIIT in improving general fitness in school-aged students, concluding that this method is effective in enhancing VO2max [8]. Although these studies are not focused exclusively on handball, their findings suggest that HIIT may be an effective strategy for improving VO2max in intermittent sports [9]. In this regard, Henrique et al. highlights the predominance of this type of intervention in both research and professional training practice, due to its effectiveness in improving both aerobic and anaerobic endurance in an integrated way [10].
However, while the focus on performance during adulthood has been thoroughly documented, there is growing consensus on the need to understand how these capacities are built and developed from early ages [11,12,13]. The performance observed in adulthood is largely the result of a formative process that begins in childhood and continues through adolescence [14], where the development of physical capacities must be integrated with the acquisition of handball-specific technical and tactical skills [15]. This formative view demands long-term planning that considers the particularities of children’s and adolescents’ motor and physiological development [16]. Various authors have proposed the existence of developmental stages in athlete formation, structured according to chronological age and degree of biological maturity. According to the model proposed by Balyi and Hamilton, for example, children between the ages of 6 and 9 go through the so-called “train to move” stage, where the main objective is the development of fundamental motor skills and general movement patterns [17]. At this stage, physical conditioning should focus on developing general strength, contraction speed, and motor coordination [18,19], prioritizing a variety of stimuli and movement quality over early specialization [20]. It is a critical period for stimulating fast and mixed muscle fibers through sports tasks that also promote an initial understanding of the game [21].
With the onset of puberty, young athletes enter the “train to train” stage, during which more specific handball skills are consolidated and basic tactical elements such as off-the-ball movements, one-on-one actions, contact defense, game reading, and switching marks are introduced [22]. These skills must be executed at maximum speed over sustained periods [23]. Coincidentally, this stage often overlaps with peak height velocity (PHV), implying significant morphological changes, such as the accelerated lengthening of body segments and a neuromuscular restructuring that requires the functional re-coordination of the body [24]. Subsequently, upon reaching higher-level competitive categories, players enter the “train to compete” stage, where the focus is no longer solely on development, but also on achieving results. Here, the complexity of technical–tactical tasks increases significantly, and a greater integration between the physical component and situational performance is required [25]. In this phase, physical training becomes more specific, including content such as maximum strength development (even with hypertrophy goals in certain cases) and improvement in the aerobic-anaerobic threshold, running speed, reaction time, and changes in direction [26,27]. Prioritizing these components not only impacts immediate performance but also shapes the athlete’s profile in the medium and long term [28].
Despite the growing body of scientific evidence on performance at advanced stages, there is still a gap in the literature regarding methodologies and intervention models for physical conditioning specifically designed for formative stages [29]. Most studies have focused on tactical proposals, an analysis of offensive and defensive systems, or physical interventions aimed at adults [30,31]. Of course, there are relevant works on injury prevention, physical performance improvement, and power development, but these mainly focus on athletes who have already reached biological maturity and a competitive level [32]. This mismatch highlights the need to broaden the research focus toward formative contexts, considering the specificity of handball and the characteristics of children’s and adolescents’ motor development. The absence of systematized and validated models for physical training at early stages represents a significant limitation for both coaches and curriculum designers in school and federative sports contexts [33]. As Manna et al. point out, training at these ages should adhere to the principle of developmental specificity, avoiding the premature application of high-performance methodologies [34].
Therefore, it is essential to establish a line of research that explores, validates, and disseminates physical training programs adapted to the developmental stages of children and young handball players. This line should consider variables such as relative load, stimulus density, difficulty progression, and integration of playfulness, without neglecting the technical and tactical aspects of the game. In this regard, a systematic review emerges as a suitable methodological tool to identify, analyze, and synthesize existing proposals in the scientific literature, allowing for the identification of trends, detection of gaps, and suggestion of future lines of action. Accordingly, the main objective of this systematic review is to compile, from the most relevant scientific databases, empirical research and academic documents that describe and evaluate methodologies for the development of physical and motor capacities in children and adolescents undergoing formative processes in the context of handball. Based on this compilation, the aim is to examine both the characteristics and the effectiveness of training interventions applied to youth handball players. The systematic review identifies and classifies the types of training approaches used, while the meta-analysis quantifies their effects on physical fitness outcomes in children and adolescents. This dual strategy not only synthesizes current scientific knowledge but also provides actionable insights to design effective, developmentally appropriate training programs formative stages.
2. Materials and Methods
2.1. Study Design
The following systematic review and meta-analysis were conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [35] and were prospectively registered through the OSF registry, available at
2.2. Information Sources and Search Strategy
A systematic search was carried out in five electronic databases: Web of Science, Scopus, SPORTDiscus, PubMed, and MEDLINE, covering the period from each database’s inception until December 2024. The keyword combinations used Boolean operators OR/AND: (“handball” OR “team handball”) AND (“youth” OR “children” OR “adolescent”) AND (“training” OR “intervention”) AND (“strength” OR “plyometric” OR “resistance”) AND (“performance” OR “fitness”). Full details of the strategies are documented in the attached search matrix, (Supplementary Materials Table S1) available at
2.3. Eligibility Criteria
The inclusion criteria were defined following the PICOS framework. Specifically, for this study, the population consisted of youth handball players aged 8 to 18 years, participating in federated, school-based, or competitive programs. The interventions had to include any type of structured physical training such as plyometric, strength, resistance band, speed, agility, or endurance programs. The comparator had to be either a control group with no intervention or an alternative intervention group. Regarding outcomes, studies needed to report quantitative pre- and post-intervention data on at least one physical fitness variable, such as jumping, speed, power, strength, agility, or endurance. Accepted study designs included experimental studies, such as randomized controlled trials and quasi-experimental designs. Exclusion criteria included studies conducted on adult populations, interventions focused exclusively on technical–tactical aspects, studies without a comparator group, protocols, narrative reviews, and those lacking sufficient data for qualitative or quantitative analysis. For clarity, the PICOS criteria are also summarized in Table 1.
2.4. Study Selection and Data Extraction Process
All records were managed using the Refworks reference manager (ProQuest), and the studies were processed in three stages: (i) duplicate removal, (ii) screening by title and abstract, and (iii) full-text review. Two independent researchers (B-F, G and H-T, C) performed the selection and data extraction in parallel. Discrepancies were resolved by consensus or by the intervention of a third reviewer (E-L, S), who made the final decision based on PICOS criteria. For each study, the following data were extracted: author, year of publication, country, participant characteristics (age, gender, sample size), type of intervention and comparator, duration and frequency of the program, measured variables, evaluation methods, and results.
2.5. Risk of Publication Bias Between Studies
The assessment of a publication bias was conducted exclusively within the meta-analytic component of this review. To evaluate potential asymmetry in the distribution of effect sizes, funnel plots were first examined visually. This qualitative inspection was then complemented by Egger’s regression test [36], applied at a significance threshold of p ≤ 0.05, to statistically detect small-study effects that may indicate a bias in the published data.
2.6. Methodological Quality and Risk of Bias Assessment
The methodological quality and risk of bias of each included study were evaluated using the revised Cochrane Risk of Bias Tool (RoB 2.0) [37], which provides a structured framework for assessing potential sources of bias across five key domains in randomized trials: (i) random sequence generation, (ii) allocation concealment, (iii) blinding of participants and assessors, (iv) incomplete outcome data, and (v) selective reporting. For each domain, a “Yes” response indicated a low risk of bias, “No” indicated a high risk, and “Unclear” indicated a possible bias due to a lack of information or uncertainty. The assessment was conducted by two independent reviewers.
2.7. Synthesis of Results and Statistical Analysis
Studies were eligible for inclusion in the meta-analysis if they implemented a structured physical training intervention in an experimental group and included a control group that received no additional training. Furthermore, only trials that reported pre- and post-intervention measurements of performance outcomes were considered. Those that did not meet these methodological criteria were excluded from the quantitative synthesis. To assess quality and interpret bias risk, version 5.4 of Review Manager (Copenhagen, Denmark: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) was used. The same software was used to conduct both descriptive and inferential analyses within the meta-analysis. For each included study, effect sizes were computed by comparing outcomes between the experimental group, which received a structured physical training intervention, and the control group. Standardized mean differences (SMDs) were calculated using Hedges’ g [38], with corresponding standard errors. The overall pooled effects and 95% confidence intervals (CIs) were estimated using inverse variance weighting. In parallel, unstandardized effect sizes (ESs) were derived by subtracting post-intervention means between groups and normalizing by the pooled standard deviation. The magnitude of effects was interpreted based on Cohen’s thresholds: <0.2 (trivial), 0.2–0.5 (small), 0.5–0.8 (moderate), and >0.8 (large) [39]. Furthermore, for each pooled outcome, heterogeneity metrics were extracted and reported: Tau2, Cochran’s Q, degrees of freedom, exact p-values, and I2 percentages.
3. Results
The process of study identification and selection is described in Figure 1, following the PRISMA methodology. The systematic search in electronic databases yielded a total of 665 records. After removing 310 duplicates, 355 titles and abstracts were screened, of which 276 were excluded for not meeting the eligibility criteria. Subsequently, 79 full-text articles were assessed. Of these, 18 were excluded for various reasons, including populations outside the defined age range or the absence of a structured physical intervention. Finally, sixty-one studies were included in the qualitative synthesis (systematic review), and of these, eight met the methodological and statistical criteria to be included in the meta-analysis.
3.1. Descriptive Results of the Systematic Review
A detailed summary of the 61 studies included in this systematic review is available in Supplementary Materials Table S2 (
Assessment of Methodological Quality and Risk of Bias of Individual Studies
The methodological assessment of the 61 studies included [40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100] in this review revealed significant differences in the quality of design and in how procedures were reported. Regarding selection bias, approximately half of the studies clearly described how they generated the random sequence—among them, Hammami et al. [65] and Aloui et al. [42]—while others, such as Zakas et al. [100] and Mascarenhas et al. [81], did not provide sufficient information, raising concerns about the validity of this process. A similar issue occurred with allocation concealment, which was poorly detailed in most studies, making it difficult to accurately evaluate this criterion. Performance bias related to the blinding of participants and personnel was among the most common. In many studies, such as those by Haddad et al. [63] and Pancar et al. [88], no clear masking mechanisms were implemented, whereas only a few, such as Prieske et al. [91], reported specific procedures to control for this type of bias. Detection bias, associated with the blinding of outcome assessors, also showed variability: Gaamouri et al. [58] adequately reported this aspect, while studies such as Gorostiaga et al. [62] failed to mention it.
On the other hand, attrition bias was generally low. Most studies maintained adequate participant follow-up, and dropout rates were acceptable. With respect to reporting bias, most studies presented their main results; however, some, like Falch et al. [57], did not indicate whether they had a pre-registered protocol, which made it difficult to fully assess this aspect.
Finally, in relation to other biases, such as the disclosure of conflicts of interest, some studies were transparent—for instance, those by Hammami et al. [71]—while others, such as Jurisic et al. [79], did not provide any information in this regard. Overall, although a portion of the studies demonstrated strong methodological conduct across several domains, key weaknesses remain in areas such as blinding, allocation concealment, and transparency in conflict-of-interest declarations. These elements are described in Figure 2. Although the Cochrane Risk of Bias was used to systematically assess methodological quality, the included studies exhibited heterogeneity across several domains, particularly in randomization procedures, blinding of outcome assessors, and training load documentation. This variability limits the strength of some pooled estimates and could have contributed to residual bias in the overall synthesis. However, the use of a random-effects model, subgroup analyses by training modality, and the cautious interpretation of pooled results helped mitigate these risks. Future trials should aim to standardize key methodological elements and adhere to reporting standards to improve synthesis reliability in this field.
3.2. Results of the Meta-Analysis
Eight studies with both experimental and control groups, reporting pre- and post-intervention data, were included in the meta-analysis. These studies were grouped and analyzed across four primary outcomes: (i) jump height (CMJ), (ii) peak power, (iii) sprint time, and (iv) jump height following elastic band training.
3.2.1. Risk of Bias Among Studies
Egger’s analysis suggested that the primary variables evaluated in the studies that were part of the meta-analysis showed publication bias after plyometric training: (a) peak power: z = 5.51, p < 0.00001; (b) time sprint: z = 3.81, p = 0.0001; (c) jump height: z = 15.09, p < 0.00001; and (d) effect of elastic band training on jump height: z = 10.05, p < 0.00001 (Figure 3).
3.2.2. Effect of Plyometric Training on Peak Power
Four studies were included in the meta-analysis to evaluate the effect of plyometric training on peak power in handball players. The analysis showed a statistically significant effect in favor of the experimental group (SMD = 1.41; 95% CI: 0.91 to 1.91; p < 0.00001), indicating that the plyometric intervention led to substantial improvements in peak power output compared to the control group. The individual effect sizes of the studies are presented in Figure 4, ranging from 0.80 to 2.00. The study by Chelly et al. [52] reported the highest individual effect (SMD = 2.00; 95% CI: 0.97 to 3.04), followed by Aloui et al. [42] (SMD = 1.68; 95% CI: 0.82 to 2.54), while Pancar et al. [88] reported the lowest effect (SMD = 0.80; 95% CI: 0.03 to 1.58). All studies consistently favored the experimental group. Heterogeneity among studies was low (I2 = 25%; Chi2 = 3.99, df = 3; p = 0.26), indicating acceptable variability across individual results. The Tau2 value was 0.06, supporting the stability of the observed effect size. Overall, the results suggest that plyometric training is an effective intervention for improving peak power in handball players, demonstrating high consistency across studies and positive effects in all comparisons analyzed.
3.2.3. Effect of Plyometric Training on Time Sprint Performance
Fourteen comparisons grouped into four distances (5 m, 10 m, 20 m, and 30 m) were included to evaluate the effect of plyometric training on time sprint performance in handball players. The overall analysis presented in Figure 5 revealed a significant effect in favor of the experimental group (SMD = −1.27; 95% CI: −1.93 to −0.62; p < 0.001), indicating a consistent improvement in sprint times following the intervention. When disaggregated by distance, a statistically significant effect was observed in the 5 m test (SMD = −1.22; 95% CI: −2.29 to −0.15; p = 0.03), whereas no significant effects were detected at 10 and 20 m (SMD = −0.87; 95% CI: −2.24 to 0.51; p = 0.22 and SMD = −0.76; 95% CI: −2.00 to 0.48; p = 0.23, respectively). However, at the 30 m distance, a statistically significant and large effect was recorded (SMD = −3.15; 95% CI: −5.29 to −1.01; p = 0.004), suggesting greater sensitivity to the intervention in longer-duration sprints. Overall heterogeneity was high (I2 = 89%), and the test for subgroup differences was not significant (Chi2 = 3.91; p = 0.27), indicating that although effect magnitudes varied, the direction of the effect remained consistent across the different distances analyzed. Studies such as those by Hammami et al. [65] and Pancar et al. [88] made notable contributions to the observed results, particularly in the longer sprint segments.
3.2.4. Effect of Plyometric Training on Vertical Jump Performance
A total of eleven comparisons were included to evaluate the effect of plyometric training on vertical jump performance in handball players, specifically in two tests: countermovement jump (CMJ) and squat jump (SJ). The analysis presented in Figure 6 showed a statistically significant effect in favor of the experimental group (SMD = 3.69; 95% CI: 3.21 to 4.17; p < 0.00001), indicating a substantial improvement in jump performance following the plyometric intervention. The subgroup analysis by jump type revealed significant effects in both cases. For the SJ, an effect size of 3.41 was found (95% CI: 2.59 to 4.24; p < 0.00001) with moderate heterogeneity (I2 = 54%). Similarly, for the CMJ, the effect size was 3.67 (95% CI: 2.59 to 4.74; p < 0.00001), also with moderate heterogeneity (I2 = 66%). The test for subgroup differences was not significant (Chi2 = 0.13; p = 0.71), suggesting that both jump types respond similarly to the intervention. The studies by Chelly et al. [52], Hammami et al. [65], and Gaamouri et al. [59] consistently contributed positive effects in both jump types, strengthening the overall effect. The global heterogeneity of the analysis was moderate (I2 = 58%), supporting the consistency of the findings despite the methodological diversity among the studies.
3.2.5. Effect of Resistance Band Training Program on Jump Performance
Eight comparisons were included, distributed across two subgroups of CMJ and SJ jump tests, with the aim of evaluating the effect of a resistance band training program on jump performance in handball players. Specifically, Figure 7 showed a significant effect in favor of the experimental group (SMD = 1.56; 95% CI: 1.25 to 1.86; p < 0.00001), indicating a robust improvement in jump performance following the resistance band intervention. In the CMJ subgroup, comprising four studies, a significant positive effect was observed (SMD = 1.69; 95% CI: 1.11 to 2.27; p < 0.00001), with moderate heterogeneity (I2 = 50%). In the SJ subgroup, also consisting of four studies, a significant effect was found (SMD = 1.46; 95% CI: 1.06 to 1.85; p < 0.00001), with no evidence of heterogeneity (I2 = 0%). The test for subgroup differences was not significant (Chi2 = 0.45; p = 0.50), suggesting that both jump types respond similarly to resistance band training. Recent studies by Hammami et al. [71,72] and Gaamouri et al. [59,60] demonstrated consistent and relevant contributions in both jump types, strengthening the evidence of the positive impact of this training modality. The overall heterogeneity of the meta-analysis was low (I2 = 14%), further reinforcing the robustness of the results.
4. Discussion
The results of this systematic review and meta-analysis confirm that multiple physical training interventions can significantly improve physical performance in children and adolescents who practice handball. Among these, plyometric training and resistance band exercises stand out, having demonstrated substantial positive effects on key variables such as peak power, jump performance (CMJ and SJ), and sprint performance. Specifically, resistance band training showed robust improvements in both countermovement jump (SMD = 1.69) and squat jump (SMD = 1.46), with low heterogeneity (I2 = 14%), supporting the strength of the observed effects. These findings align with evidence reported in related sports contexts, such as basketball, where studies like Sáez de Villarreal et al. [101] documented improvements in vertical jump and anaerobic performance following plyometric interventions, and volleyball, where Agopyan et al. reported significant increases in jump power and spike speed in adolescent players after Thera-Band programs [102].
From a variable-specific perspective, plyometric training showed systematic improvements in peak power (SMD = 1.41), a central variable for executing explosive actions such as jumps, throws, and changes in direction. Regarding sprint performance, differentiated effects were observed, depending on the distance: significant improvements were noted in 5 m (SMD = −1.22) and 30 m sprints (SMD = −3.15), suggesting adaptations in both initial acceleration and sustained speed over longer distances. This pattern may be explained by progressive improvements in intermuscular coordination, the efficient recruitment of high-threshold motor units, and strengthening of the stretch-shortening cycle—physiological mechanisms widely recognized in the literature as outcomes of systematic exposure to plyometric stimuli [103,104]. These adaptations, far from being transient, represent structural and functional enhancements of the neuromuscular system, particularly relevant in developing populations where plasticity mechanisms are highly responsive to targeted stimuli.
Similarly, Aloui et al. [6] demonstrated comparable effects in adult handball players, suggesting that physiological adaptations related to strength and power can be safely and effectively induced from early developmental stages, provided that the training load design adheres to principles appropriate for children and youth. This approach contrasts with previous reviews focused on adult populations or technical–tactical interventions, without thoroughly addressing the specificity of physical training in childhood and adolescence. This distinction grants the present study added value and direct applicability for designing programs in formative categories. In terms of vertical jump performance, both CMJ and SJ showed positive responses to interventions, indicating the reinforcement of the stretch-shortening cycle, a core component in explosive movement efficiency.
In line with the emerging literature, resistance band interventions have been established as an effective and safe strategy for developing explosive physical qualities in young athletes, particularly in contexts with infrastructure limitations or low-impact requirements. Their low cost, portability, and ease of implementation make them highly accessible tools in schools, clubs, and training academies. Although previous studies, such as those by Montes-Salas et al., Agopyan et al., and Soto-García et al., have reported positive effects on vertical jump performance following programs incorporating or simulating resistance band characteristics [101,105,106], these findings should be interpreted with caution. Montes-Salas et al. [105], for instance, reported improvements in explosive strength and change in direction in youth handball players, but their systematic review design did not include a quantitative analysis of effects. Agopyan et al. [101] described significant increases in CMJ and peak power in volleyball players after eight weeks of Thera-Band training; however, applying these findings beyond volleyball presents limitations in generalizing results to handball. Similarly, Soto-García et al.’s [106] findings, focused on bodyweight and plyometric protocols without resistance bands, while relevant for their economical and functional approach, do not directly attribute benefits to this specific training modality.
In contrast, the results of this meta-analysis offer a substantially more robust and specific body of evidence. In particular, the eight comparisons on resistance band training in formative-stage handball players showed statistically significant and consistent improvements in vertical jump performance, both in CMJ (SMD = 1.69; 95% CI: 1.11 to 2.27) and SJ (SMD = 1.46; 95% CI: 1.06 to 1.85), with low to moderate heterogeneity levels (global I2 = 14%). This sustained and homogeneous effect reinforces the relevance of this intervention modality not only as a practical solution but also as an effective and scientifically validated strategy to enhance neuromuscular performance in young handball players. This underscores the need for the systematic incorporation of such interventions into physical training programs for developmental stages, especially in contexts with limited access to specialized equipment.
Despite the reported benefits, methodological limitations persist, affecting the strength of the results. A significant proportion of the studies included in this review exhibited deficiencies in essential experimental design aspects, such as the lack of participant and assessor blinding, allocation concealment, and absence of pre-established protocols. These omissions increase the risk of bias, particularly in performance and detection domains, as revealed by the Cochrane Risk of Bias Tool. Additionally, critical variables such as biological maturation or pubertal stage were not systematically considered, and most studies did not disaggregate results by gender, limiting the interpretation of subgroup-specific differences. This methodological gap hinders a precise understanding of training responses during sensitive developmental stages.
This scenario contrasts with sports like football and rugby, where studies such as Ferley et al. and Gabbett et al. have more rigorously integrated biological maturity control and standardized training load criteria [26,31]. These approaches have proven effective in reducing variability in training effects and enhancing practical transferability. Similarly, Lloyd and Oliver [24] emphasized the need to tailor interventions to the athlete’s developmental stage, highlighting the urgency of adopting these criteria in youth handball.
Furthermore, heterogeneity in protocol planning (volume, frequency, duration, and intensity) poses another challenge. While partly reflecting the principle of individualized training, there is a pressing need for greater systematization to enable valid comparisons between studies and facilitate the practical application of the evidence.
From a developmental perspective, the findings of this review and meta-analysis clearly reinforce the necessity of designing physical training programs that align with the comprehensive developmental needs of children and adolescents. Rather than applying homogeneous approaches or replicating adult-oriented models, current evidence supports that interventions during formative stages must align closely with biological timing, motor development trajectories, and psychosocial conditions of young athletes. Models such as Long-Term Athlete Development (LTAD) emphasize that chronological age alone is insufficient as an organizing criterion for training, advocating for consideration of variables like biological maturity, sociocultural environment, and specific learning needs [24,105]. However, the studies analyzed in this review demonstrate the irregular implementation of these principles. Most do not incorporate objective measures of pubertal stage nor adjust training loads to participants’ biological profiles, limiting both evidence quality and the potential for designing truly personalized and effective programs. Advancing toward greater systematization and coherence in intervention design is therefore not only desirable but essential. The effective integration of formative principles proposed by LTAD, along with progressive load criteria and contextual adaptability, is key to optimizing training effects and ensuring safe, sustained, and transferable physical development.
A relevant aspect emerging from this review is the limited integration between physical training content and the technical–tactical components of handball. This methodological separation represents a critical weakness, given that physical capacities—such as explosive strength, speed, or agility—are always expressed in situational contexts requiring decision-making, environmental adaptation, and coordinated execution under high variability. Consequently, training programs that artificially fragment these components risk generating adaptations that do not effectively transfer to real-game situations [107]. It is therefore essential for future research to advance toward integrated or contextualized approaches, where physical conditioning is developed through representative tasks aligned with handball’s internal logic and perceptual-motor demands.
From an applied perspective, the findings of this review offer valuable guidance for coaches, educators, and sports planners working with youth athletes. Plyometric and resistance band interventions, which showed positive effects on physical performance—particularly in vertical jump, power, and speed—can be progressively incorporated, provided that fundamental principles such as alignment with biological age, load management, and integration with technical content are respected. Their low cost and versatility make these strategies viable even in schools or clubs with limited infrastructure, without compromising stimulus quality.
Finally, it is imperative that future research addresses the key methodological gaps identified. Studies with greater experimental controls are recommended, incorporating longitudinal follow-ups to assess the sustainability of adaptations and considering moderating variables such as gender, bone age, pubertal stage, and competitive level. Additionally, it would be desirable to include functional indicators evaluating the transfer of physical improvements to situational performance, thereby validating training programs that are truly effective, safe, and adapted to the context of young handball players.
Comparative evidence from team sports such as football, rugby, and basketball consistently shows that targeted plyometric and resistance training enhances key physical capacities in youth athletes—namely sprint velocity, maximal strength, and change-in-direction performance [108,109,110]. These improvements are particularly notable when training programs are adapted to the athlete’s biological maturity, with plyometric methods proving especially effective prior to peak height velocity and combined modalities yielding greater benefits in post-peak heigh velocity stages [111]. In contrast, the literature on youth handball reflects a delayed and inconsistent incorporation of these principles. While developmental models such as the Long-Term Athlete Development (LTAD) framework and the Youth Physical Development Model have been formally integrated into the physical preparation systems of rugby and football federations [112,113], handball research continues to lack unified criteria for training periodization and load progression. Some recent contributions in handball have explored performance diagnostics and proposed multidimensional evaluation tools [114], yet these remain disconnected from broader developmental planning. Likewise, periodization studies in elite female players suggest potential advantages of block sequencing over traditional approaches [115], but without anchoring these findings in biological or pedagogical frameworks. This disconnect highlights a fundamental challenge: while other sports are aligning empirical research with applied models to guide training from early stages, handball remains fragmented in its developmental logic. Bridging this gap demands the systematic adoption of validated principles that consider growth, maturation, and sport-specific demands—not as peripheral variables, but as core pillars of youth performance planning.
5. Future Directions
Future studies should incorporate a broader range of moderating variables, such as biological maturation, gender, and training history, to better capture variability in training responsiveness during adolescence. It is also necessary to move toward the greater standardization of intervention protocols, explicitly detailing volume, intensity, frequency, and load progression criteria. Furthermore, future trials should prioritize longitudinal follow-up designs to assess the retention of training-induced adaptations and their transferability to real-game scenarios. Given the limitations in validity observed in several included trials, researchers are encouraged to develop and test integrated training models, where physical, technical, and cognitive demands are combined in sport-specific contexts. Studies should also explore the cost-effectiveness and scalability of different intervention models to facilitate implementations in school and community sport environments with limited resources.
6. Study Limitations
This review has several limitations that warrant consideration. Firstly, most included studies did not report on participants’ biological maturity status, which may have influenced interindividual variability in training responsiveness. Secondly, data disaggregation by gender was lacking in many cases, precluding subgroup analyses that could reveal gender-specific adaptations. Thirdly, although methodological quality was generally acceptable, blinding procedures and allocation concealment were insufficiently detailed in many studies, increasing the risk of bias. Fourthly, subgroup analyses by age were not conducted due to the inconsistent reporting of mean ages and maturational stages across studies, which limits the ability to interpret training effects in early versus late adolescence. In addition, the high heterogeneity in training protocols (duration, frequency, load types, supervision) limits the ability to isolate the effect of specific program characteristics.
7. Conclusions
The present systematic review and meta-analysis provides strong evidence that plyometric and resistance band training interventions are effective in improving key physical fitness components (e.g., jump height, sprint speed, strength) in children and adolescent handball players. These interventions are particularly relevant due to their adaptability, low cost, and feasibility in diverse training contexts. However, the quality of evidence is moderated by methodological heterogeneity and a general lack of standardization in intervention design. Future research should prioritize experimental rigor, integration with technical–tactical training, and personalization based on developmental stages. By addressing these gaps, we can move toward more evidence-based, developmentally aligned, and contextually grounded training programs for youth athletes in handball. Nonetheless, the generalizability of these findings should be approached with caution. Most included studies focused on male adolescents aged 12 to 17 years, with limited data disaggregated by gender or maturational status. Moreover, the interventions were primarily delivered in structured environments—such as club-based or school sport settings—with trained supervision and access to basic equipment. For practical implementation, it is essential to ensure qualified oversight, individualized load progression, and adherence to fundamental movement quality standards. The applicability of these protocols in less controlled environments (e.g., community programs or school curricula) requires further investigation to validate their feasibility and safety.
Conceptualization, G.B.-F., C.H.-T., S.E.-L., and R.Y.-S.; methodology, G.B.-F., C.H.-T., and S.E.-L.; software, G.B.-F., G.C.-R., and R.Y.-S. validation, J.P.Z.-C. and F.A.; formal analysis, G.B.-F., C.H.-T., and S.E.-L.; investigation, G.B.-F. and C.H.-T.; resources, J.P.Z.-C., G.C.-R., and F.A.; writing—original draft preparation, G.B.-F., C.H.-T., S.E.-L., and R.Y.-S.; writing—review and editing, J.P.Z.-C., G.C.-R., and F.A.; visualization, F.A.; supervision, R.Y.-S. and F.A.; project administration, C.H.-T. and S.E.-L.; funding acquisition, G.C.-R. and F.A. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The raw data supporting the conclusions of this article will be made available by the authors on request.
The authors declare no conflicts of interest.
Footnotes
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Figure 1 PRISMA flow diagram of articles that were selected.
Figure 2 Risk of bias graph: review authors’ judgements about each risk of bias item presented as percentages across all included studies.
Figure 3 The standard error in different modes of training for peak power, time sprint, and jump height. (a) Plyometric training for peak power; (b) plyometric training for time sprint; (c) plyometric training for jump height; and (d) elastic band training on jump height; SE: standard error; SMD: standardized mean difference.
Figure 4 Forest plot comparing the effects of plyometric training on peak power levels. SD: standard deviation [
Figure 5 Forest plot comparing the effects of plyometric training on time sprint. SD: standard deviation [
Figure 6 Forest plot comparing the effects of plyometric training on jump height. SD: standard deviation [
Figure 7 Forest plot comparing the effects of elastic band training on jump height. SD: standard deviation [
PICOS inclusion criteria.
| Component | Criteria |
|---|---|
| Population | Youth handball players (aged 8–18 years) |
| Intervention | Structured physical training programs (e.g., plyometric, resistance, strength) |
| Comparator | No intervention or alternative training |
| Outcomes | Physical performance variables (e.g., jumping, sprint, strength, agility) |
| Study design | experimental studies, such as randomized controlled trials and quasi-experimental designs |
Supplementary Materials
The following supporting information can be downloaded at:
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; Hinojosa-Torres, Claudio 2 ; Espoz-Lazo Sebastián 3
; Zavala-Crichton, Juan Pablo 2
; Cortés-Roco, Guillermo 4
; Yáñez-Sepúlveda, Rodrigo 2
; Alacid Fernando 5
1 Núcleo de Investigación en Salud, Actividad Física y Deporte ISAFYD, Universidad de Las Américas, Viña del Mar 2531098, Chile; [email protected], Escuela de Ciencias de la actividad Física, el Deporte y la Salud, Universidad de Santiago de Chile (USACH), Santiago 9170022, Chile
2 Facultad de Educación y Ciencias Sociales, Universidad Andres Bello, Viña del Mar 2520000, Chile; [email protected] (C.H.-T.); [email protected] (J.P.Z.-C.); [email protected] (R.Y.-S.)
3 Facultad de Educación, Pontificia Universidad Católica de Chile, Santiago 8520000, Chile; [email protected]
4 Faculty of Life Sciences, Universidad Viña del Mar, Viña del Mar 2520000, Chile; [email protected]
5 Department of Education, Health Research Center, University of Almeria, 04120 Almeria, Spain