A literature search was performed separately and independently by two of the coauthors (AF and CH) in PubMed, SPORTDiscus, Web of Science, and Cochrane Library databases up to September 2021. The search was conducted using a Boolean search strategy with the operators “AND” and “OR” and a combination of the following keywords: (“plyometric” OR “stretch-shortening cycle” OR “stretch shortening cycle*” OR “jump training” OR jump) AND (“hypertrophy” OR “muscle size” OR “muscle mass” OR “muscle fiber” OR “muscle fibre” OR “lean body mass” OR “fat-free mass” OR “cross-sectional area” OR “quadriceps size” OR “circumference”). Keywords were determined through expert opinion, literature review, and controlled vocabulary (e.g., Medical Subject Headings). In addition, corresponding meta-analyses, as well as studies that were eligible for inclusion, were searched for additional publications in so-called “snowball” searches (Greenhalgh and Peacock, 2005). Only peer-reviewed studies written in English were considered for inclusion.

We used the PICOS (Population, Intervention, Comparison, Outcome, Study Design) approach to identify eligible studies (Moher et al., 2009). The following inclusion criteria were defined a priori: 1) Population: a cohort of healthy participants with no restriction related to age, sex, or training experience, 2) Intervention: plyometric jump training (i.e., jump exercises soliciting the stretch-shortening cycle), with a minimum duration of 4 weeks (Monti et al., 2020), 3) Comparison: active/passive control group, 4) Outcome: at least one measure of skeletal muscle hypertrophy (e.g., muscle/single fiber cross-sectional area, muscle thickness, lean body mass, muscle circumference), and 5) Study design: (randomized) controlled trials with baseline and follow up measures. We excluded studies involving individuals with pre-existing health problems (e.g., diabetes, hypertension, and asthma), an absence of a passive/active control group, plyometric jump training interventions in combination with additional interventions (e.g., nutrition), and/or lack of baseline and follow-up data.

The first author (AF) extracted the data from the included studies in a standardised template created with Microsoft Excel. A second author (MA) cross-verified the extracted data. In case of disagreement pertaining data extraction or study eligibility, co-author CH was consulted for clarification.

Of note, we included data for all skeletal muscle hypertrophy measures reported in the studies and for all time points at which they were measured. More specifically, if a study reported multiple skeletal muscle hypertrophy measures, they were all included, and if a study reported measurements during and after the training period, they were all included as well (dependence between effect sizes was handled using a multilevel robust variance estimation approach—see statistical analyses). If data were not reported in a way that was conducive to extraction for our analysis, we contacted the respective authors to request appropriate data [i.e., mean ± standard deviation (SD), raw data]. When multiple studies were published using the same data set (Skurvydas and Brazaitis, 2010; Skurvydas et al., 2010), only one study was considered (Skurvydas and Brazaitis, 2010). In cases where the authors did not respond to our request for raw data, we used WebPlotDigitizer (v4.3, Ankit Rohatgi; https://apps.automeris.io/wpd/) to extract relevant data in studies that only reported graphical data (Drevon et al., 2017).

From all included studies, we extracted 1) author and year of publication; 2) mean age of participants; 3) percentage of females in the sample; 4) training experience [i.e., athlete vs. non-athlete (see footnote ¹ )], 5) muscle group investigated [i.e., knee extensors (e.g., vastus medialis and rectus femoris) vs., plantar flexors (e.g., gastrocnemius)], 6) assessment method (i.e., ultrasound and prediction equation), 7) total training period (weeks), 8) weekly session frequency, 9) single session duration, 10) total number of jumps per week, and 11) type of jumping. The characteristics of the included studies are displayed in Tables 1, 2.

The Physiotherapy Evidence Database (PEDro) scale was used to evaluate the methodological quality of the included studies. The validity and reliability of the PEDro scale have been established in previous studies (Maher et al., 2003; de Morton, 2009). Additionally, its agreement with other assessment tools such as the Cochrane risk of bias tool has been established (Moseley et al., 2019). As blinding of participants and investigators is not feasible in exercise interventions and blinding of assessors is rarely implemented, items 5–7 were removed from the scale consistent with previous systematic reviews (Grgic et al., 2017; Schoenfeld et al., 2017). Hence the methodological quality of the included studies was rated on a scale from 0 to 7. In accordance with previous systematic reviews (Kümmel et al., 2016; Schoenfeld et al., 2017), the quality of the included studies was categorized as “poor” = 0–3, “moderate” = 4, “good” = 5, and “excellent” = 6–7 (Table 3). Additionally, to visually estimate publication bias, a contour-enhanced funnel plot was used (Harrer et al., 2019).

The meta-analysis was performed using the “metafor” (Viechtbauer, 2010) and “tidyverse” (Wickham et al., 2019) packages in R (v 4.0.2; R Core Team, https://www.r-project.org/). All analyses are available in the supplementary documentation (https://osf.io/bf478/). The standardised mean difference (SMD) was calculated by subtracting the standardised mean change of the intervention group minus the standardised mean change of the control group. The respective variance was calculated by pooling the pre-test standard deviations of both groups, an approach deemed appropriate to provide a comparatively unbiased estimate of the population effect size (Morris, 2008). The magnitude of standardised effect sizes was interpreted in accordance with Cohen’s thresholds (Cohen, 1988): trivial (< 0.2), small (0.2 to 0.5), moderate (0.5 to 0.8), and large (≥ 0.8).

Due to the nested structure of the calculated effect sizes (i.e., effects nested within groups nested within studies), multilevel mixed-effects meta-analyses with study and intra-study groups as random effects were calculated to examine the effect of plyometric jump training on skeletal muscle hypertrophy. Further, cluster robust point estimates using 95% compatibility (confidence) intervals (CIs) were calculated (Hedges et al., 2010) and weighted by inverse sampling variance to account for the within- and between-study variance (tau-squared). Restricted maximal likelihood estimation was applied in all models. A main model was created containing all effect sizes. Additional exploratory subgroup comparisons and meta-regression analyses of moderator variables were performed, including mean age, proportion of females per group, training experience, and muscle group studied, as well as training characteristics such as total training period, training frequency, session duration, and number of jumps per week. For training experience and muscle group examined, multilevel models with subgroups were calculated and robust estimates were produced. Meta-regressions were calculated for mean age, proportion of females per study, and the aforementioned training characteristics.

To avoid dichotomizing the existence of an effect in our models, we reported absolute p-values but did not employ traditional null hypothesis significance testing (Amrhein et al., 2019a; Amrhein et al., 2019b; McShane et al., 2019). We also focused on the point estimate in the interpretation with the greatest emphasis on the effects from the lower to the upper limit of the interval estimates (Nakagawa and Cuthill, 2007; Lee, 2016; Van Calster et al., 2018). The risk of small study bias was visualised through contour-enhanced funnel plots. Further, Q and I² statistics were produced and reported (Higgins et al., 2003). A significant Q statistic is usually taken as an indicator that the effects are unlikely to come from a common population. I² values indicate the degree of heterogeneity of effects as follows: 0%–40% indicates no heterogeneity, 30%–60% moderate heterogeneity, 50%–90% substantial heterogeneity, and 75%–100% considerable heterogeneity (Higgins et al., 2019). For within-participant effects, pre-post correlations for measures have rarely been reported; therefore, we adopted a range of values for correlation coefficients (r = 0.5, 0.7, and 0.9) and examined the sensitivity of the results to each of these values. Since the overall results were relatively insensitive to this range, we reported the results for r = 0.7 here and included the results for the other assumed correlation coefficients in the Supplementary Material is available on the following link: (https://osf.io/bf478/).

After initial searches and screening, nine studies were identified that met inclusion criteria. Supplementary search approaches identified six additional eligible studies. Thus, a total of 15 studies were ultimately included for analysis. All studies included active control groups. Details of the search and inclusion process are shown in the flow chart (Figure 1; https://osf.io/3u567/). The total number of participants across the included studies is 478 (range = 18–101; median = 23), comprising 245 INT (range = 9–50, median = 13) and 233 CON (range = 5–51, median = 11). The majority of the studies used different types of jumps in their training programmes (e.g., bilateral/unilateral vertical and horizontal jumps) (Kyröläinen et al., 2005; Marković et al., 2005; Herrero et al., 2006; Chelly et al., 2010; Fouré et al., 2011; Fouré et al., 2012; Chelly et al., 2014; Chelly et al., 2015; McKinlay et al., 2018; Fathi et al., 2019; Cherni et al., 2020). Four studies used one single type of jump such as bilateral countermovemt jump or lateral box jump (Skurvydas and Brazaitis, 2010; Correa et al., 2012; Earp et al., 2015; Allison et al., 2018). The mean age across studies ranged from 10.3 to 72.5 years with a median of 20.1 years. Two studies examined the effects of plyometric jump training on hypertrophy in female participants (Correa et al., 2012; Cherni et al., 2020), while one study included a mixed intervention group but used a male-only control group (Skurvydas and Brazaitis, 2010). The remaining twelve studies included male participants. Two studies included older adults (Correa et al., 2012; Allison et al., 2018). As to training experience, five studies recruited athletes (Chelly et al., 2010; Chelly et al., 2014; Chelly et al., 2015; Fathi et al., 2019; Cherni et al., 2020), while ten studies investigated the effects of plyometric jump training on skeletal muscle hypertrophy in non-athletes (Kyröläinen et al., 2005; Marković et al., 2005; Herrero et al., 2006; Skurvydas and Brazaitis, 2010; Fouré et al., 2011; Fouré et al., 2012; Correa et al., 2012; Earp et al., 2015; Allison et al., 2018; McKinlay et al., 2018). Regarding the muscle group investigated, four studies examined hypertrophy in the knee extensors (Skurvydas and Brazaitis, 2010; Correa et al., 2012; Earp et al., 2015; McKinlay et al., 2018) and four in the plantar flexor (Kyröläinen et al., 2005; Fouré et al., 2011; Fouré et al., 2012; Allison et al., 2018). In the seven remaining studies, the muscle group investigated was not specified (e.g., assessment of thigh muscle volume or thigh/calf girth) (Marković et al., 2005; Herrero et al., 2006; Chelly et al., 2010; Chelly et al., 2014; Chelly et al., 2015; Fathi et al., 2019; Cherni et al., 2020). Seven studies used ultrasound imaging technique (Skurvydas and Brazaitis, 2010; Fouré et al., 2011; Fouré et al., 2012; Correa et al., 2012; Earp et al., 2015; Allison et al., 2018; McKinlay et al., 2018), while six studies used a prediction equation to assess muscle hypertrophy (Herrero et al., 2006; Chelly et al., 2010; Chelly et al., 2014; Chelly et al., 2015; Fathi et al., 2019; Cherni et al., 2020). One study assessed muscle hypertrophy using muscle biopsy (Kyröläinen et al., 2005) and one study used tape (Marković et al., 2005). The median duration of plyometric jump training was 8 weeks (range from 4–26) and the median weekly session frequency was two (range from 2–7). The median session duration was 30 min and ranged from 5 to 60 min. However, session duration was not reported in two studies (Kyröläinen et al., 2005; Correa et al., 2012). The median of the number of jumps performed per week was 230 (range = 60–520). Full details of all included studies can be seen in Tables 1, 2. Regarding the methodological quality of the included studies, the PEDro scores ranged from 4 to 6 with a median score of 6 (Table 3).

The main model (44 effect sizes across 15 clusters median = 2, range = 1–15 effects per cluster) yielded small to moderate effects with a small point estimate [SMD = 0.47 (95% CIs = 0.23–0.71); p < 0.001] and moderate to substantial heterogeneity (I² = 57.53%). All effect sizes and interval estimates are presented in an ordered caterpillar plot (Figure 2; https://osf.io/csnrq/). The visual inspection of the funnel plot indicated a seemingly symmetrical distribution pattern of the effects that might be reflective of an apparently absence of publication bias (Figure 3; https://osf.io/gv7xq/).

The main findings of the present meta-analysis indicated small to moderate effects of plyometric jump training on skeletal muscle hypertrophy [SMD = 0.47 (95% CIs = 0.23–0.71)], regardless of sex, age, and training experience. The current outcomes corroborate the results of a recently published systematic review with meta-analysis where authors reported a moderate effect of plyometric jump training on muscle thickness (SMD = 0.59) and fascicle length (SMD = 0.51) in healthy adults (Ramírez-delaCruz et al., 2022). However, our results are relatively more conservative, which we attribute to our more stringent inclusion criteria for plyometric jump training as well as the comparison of the plyometric jump training to an active/passive control group. In sum, the findings of the current study as well as those of recent ones (Grgic et al., 2020; Ramírez-delaCruz et al., 2022) question the common belief, indicating that plyometric jump training can indeed increase not only the motor drive to the active muscles but also skeletal muscle hypertrophy.

The previously claimed limited potential for plyometric jump training-related hypertrophic adaptations has been attributed to the relatively short time under tension during the jumps and, therefore, a reduced mechanical stimulus for muscle protein synthesis (Schoenfeld, 2010; Wackerhage et al., 2019). In addition, some researchers have proposed that the inability to continually provide an overload stimulus during plyometric jump training is another potential limitation from a hypertrophy standpoint (Suchomel et al., 2018). In this context, while extra loads additional to body mass may be used with plyometric jump exercises (e.g., weighted vests) (Negra et al., 2020), caution must be taken given that heavier loads may result in greater impact forces and delay the transition time between eccentric and concentric muscle actions, which could harm the overall training stimulus (Suchomel et al., 2018). However, our findings seem to refute these claims. Indeed, the small to moderate effects of plyometric jump training on skeletal muscle hypertrophy observed in this study indicate that the high contraction velocity seems to contribute to skeletal muscle hypertrophy.

Earlier studies showed that high-velocity lengthening actions (i.e., rapid eccentric phase during movements under the stretch-shortening cycle) tend to hypertrophy type II compared with type I muscle fibers (Potteiger et al., 1999; Shepstone et al., 2005; Malisoux et al., 2006b). For example, Shepstone et al. (2005) studied the effects of two modes of resistance training programmes, fast vs. slow isokinetic lengthening action of the elbow flexors, on muscle fiber hypertrophy in healthy untrained individuals aged 24 years. The results of the study demonstrated greater hypertrophy in type IIa muscle fibers following fast (+13%) compared with slow muscle lengthening (+3%). Authors further demonstrated greater (+185%) Z-line disruption following fast compared with slow muscle lengthening. It should be noted that Z-line disruption is considered a prominent marker of muscle protein remodeling (Yu and Thornell, 2002; Yu et al., 2004). Additionally, there is direct evidence based on muscle biopsy that a single bout of plyometric exercise induces preferential damage (e.g., loss in dystrophin staining, Z-line disruption) to type II muscle fibers (Macaluso et al., 2012). Of note, exercise-induced damage to muscle tissues is discussed as a potential mechanism for skeletal muscle hypertrophy, perhaps mediated by stimulating satellite cell activity (Vierck et al., 2000; Schoenfeld, 2010; Schoenfeld, 2012). In fact, satellite cells represent the resident stem cells of skeletal muscle (Scharner and Zammit, 2011) and lead to increased muscle regeneration (Barton-Davis et al., 1999). Existing evidence indicated larger satellite cell activation and proliferation after exercise that induced muscle damage (Crameri et al., 2007).

Exercise training needs to cause positive net protein balance to induce skeletal muscle hypertrophy. To our knowledge, muscle protein synthetic responses to plyometric jump training in humans have never been examined in the literature (Grgic et al., 2020). Relevant outcomes from an animal study indicated that rats exposed to plyometric jump training showed a positive net protein balance compared with a control condition (Watt et al., 1982). Lim et al. (2017) examined the effects of one bout of single-mode traditional resistance training vs. one bout of combined traditional resistance training and plyometric jump training on satellite cell activity and anabolic signaling in elite male weightlifters. Their results revealed an increase in satellite cell activation and myofibrillar protein synthesis following both exercise modes. However, the same authors reported that single-mode traditional resistance training resulted in higher satellite cell activity with a tendency for higher expression of mTOR (mammalian target of rapamycin) and p70S6K (ribosomal protein S6 kinase) compared with combined traditional resistance training and plyometric jump training (Lim et al., 2017). Despite these intriguing results, this study does not provide insights into the effects of single-mode plyometric jump training on the anabolic signaling pathway. Therefore, researchers should seek to fill this gap in the literature. Overall, contrary to the previous speculation, plyometric jump training appears to contribute to skeletal muscle hypertrophy, regardless of sex, age, and training experience. These findings have both important scientific and practical implications.

Our findings indicate moderate effects of plyometric jump training on knee extensor hypertrophy [SMD = 0.72 (95% CIs = 0.66–0.78)]. However, the heterogeneity of the outcomes across studies yielded an equivocal effect of plyometric jump training on plantar flexor hypertrophy (SMD = 0.64; 95% CIs = −0.25 to 1.55). It has been shown that jumping exercises principally solicit activation of the knee extensors (i.e., quadriceps) and plantar flexors (e.g., gastrocnemius) but not hamstrings (Ebben et al., 2008). As such, we would expect larger hypertrophic adaptations in these muscles. Monti et al. (2020) investigated the effects of 6 weeks of plyometric jump training on knee extensor muscle mass in healthy males aged 25 years. They demonstrated increased knee extensor power (+19.7%), which was accompanied by increases in quadriceps femoris (+5.8%) and vastus lateralis (+9.6%) volume as well as mean CSA of the quadriceps femoris (+5.8%) after plyometric jump training. The same authors reported significant positive correlations between mean CSA and volume of quadriceps femoris and muscle power (R ² = 0.46 and 0.44, respectively). Additionally, McKinlay et al. (2018) examined the effects of 8 weeks of plyometric jump training on knee extensor hypertrophy and found an 8.1% post-study increase in vastus lateralis muscle thickness in adolescent soccer players aged 11–13 years. Furthermore, Váczi et al. (2014) indicated a 20.5% increase in quadriceps CSA measured via MRI in older adults following 10 weeks of plyometric jump training. In sum, plyometric jump training appears to be an effective means to improve knee extensor hypertrophy. Conversely, our findings do not support consistent hypertrophic effects in the plantar flexors. This could be due to the different mechanical properties between the patellar tendon and Achilles tendon, resulting in different hypertrophic effects on the quadriceps (knee extensor) and gastrocnemius (plantar flexor). In fact, the patellar tendon has been shown to be stiffer and, therefore, mechanically better suited to effectively transmit muscle force compared with the Achilles tendon (Wiesinger et al., 2016). Nevertheless, given the relative paucity of research, future studies should be further redirected towards the assessment of plyometric jump training on plantar flexor hypertrophy to achieve more conclusive inferences.

In regard to training experience, results showed a relatively larger effects for non-athletes [0.55 (95% CIs = 0.18–0.93)] compared with athletes [0.33 (95% CIs = 0.16–0.51)], with a substantial degree of heterogeneity observed across studies. It is well-established that previous training history moderates adaptations to further training interventions (Faigenbaum, 2000; Rhea et al., 2003; Harries et al., 2015; Figueiredo et al., 2018; Suchomel et al., 2018). Indeed, a larger magnitude of adaptation to training would be expected in individuals with less, compared with more, training experience (Faigenbaum, 2000; Suchomel et al., 2018). In this context, earlier studies (Rhea et al., 2003; Figueiredo et al., 2018) demonstrated that adaptations to traditional resistance training are moderated by the magnitude of adaptation that has already been achieved by the individual, implying that the so-called “ceiling effect” attenuates continued adaptations. To the authors’ knowledge, none of the available studies have contrasted the effects of plyometric jump training on skeletal muscle hypertrophy of athletes vs. non-athletes, highlighting a void in the current literature. Nevertheless, results from separate studies indicate large effects of plyometric jump training on skeletal muscle hypertrophy (11.5%–18.8% increase of quadriceps femoris CSA) in non-athletes (Earp et al., 2015) but only a relatively small effect in athletes (9.9% increase in thigh CSA) (Cherni et al., 2020). This is in agreement with the findings of the present study, given that trivial to large effects were observed for non-athletes and trivial to moderate effects were noted for athletes. This suggests that to achieve comparable or larger gains, individuals with greater training experience may need to increase their training volume/intensity to a level that exceeds those who are less experienced and/or fit (Suchomel et al., 2018; Chaabene et al., 2020). In summary, plyometric jump training appears to be more effective to improve skeletal muscle hypertrophy in non-athletes compared with athletes. Future studies should seek to explore the specific mechanisms that facilitate larger gains in non-athletes compared with athletes.

For the assessment methods of skeletal muscle hypertrophy, results show larger effects for ultrasound imaging [SMD = 0.74 (95% CIs = 0.59–0.89)] compared with prediction equation [SMD = 0.29 (95% CIs = 0.16–0.42)] with a clear difference between subgroups. Ultrasound is an easy, non-invasive, and rapid tool to assess muscle thickness, which in turn informs about skeletal muscle hypertrophy (Haun et al., 2019). Muscle thickness evaluated using ultrasound is highly reliable in a range of muscles (Thoirs and English, 2009). However, the prediction equation represents a valid tool that affords a crude estimate of skeletal muscle hypertrophy (Chelly et al., 2006). The major drawback of the prediction equation though is that it does not allow for the differentiation between muscle tissue, fat tissue, and bone (Chelly et al., 2006; Grgic et al., 2019). For the reasons above, it is advisable to favor using ultrasound over the prediction equation to provide more accurate insights about skeletal muscle hypertrophy. Nevertheless, the affordability of the prediction equation could make it further useful, when equipment such as ultrasound is not available. Furthermore, the hypertrophic improvement following plyometric jump training seems to be stable across other assessment methods such as biopsies or MRI. For example, Malisoux et al. (2006a) studied the effects of plyometric jump training on single muscle fiber diameters of the vastus lateralis measured using biopsies in healthy active males aged 23 years. Participants demonstrated a significant increase in type I (+11%), type IIa (+10%), and type IIa/IIx (+15%) fibers following training. The same authors reported that fiber force increased for all fiber types, in part due to increased fiber diameter (Malisoux et al., 2006a). Furthermore, Vissing et al. (2008) compared the effects of plyometric jump training vs. traditional resistance training on skeletal muscle hypertrophy via MRI in untrained males aged 25 years and revealed an increase in the CSA of the quadriceps, hamstrings, and adductor muscles (+7%–10%).

Regarding training frequency, our findings indicate that the effects of plyometric jump training on skeletal muscle hypertrophy were moderated by the weekly session frequency [β = 0.3233 (95% CIs = 0.2040–0.4425)] with a higher weekly session frequency inducing larger hypertrophic gains. Of note, most of the included studies used biweekly or triweekly plyometric jump training sessions. Specifically, three sessions of plyometric jump training per week showed large increases in skeletal muscle hypertrophy (12.8%–25.8% increase in vastus lateralis CSA) (Earp et al., 2015) compared with smaller gains following two weekly sessions (14% increase in thigh muscle volume) (Fathi et al., 2019). This is in agreement with the literature about traditional resistance training (Wernbom et al., 2007; Schoenfeld et al., 2019; Schoenfeld et al., 2021). More specifically, in a systematic review with meta-analysis of the effects of traditional resistance training frequency on skeletal muscle hypertrophy in healthy individuals, Schoenfeld et al. (2019) reported a slightly larger effect of higher compared with lower frequencies of training on hypertrophic outcomes when training volume was not equated between conditions. However, under equated-volume conditions, no additional effects of higher compared with lower frequencies of training were reported (Schoenfeld et al., 2019). The same conclusion was made in a recent consensus review in that a higher number of traditional resistance training sessions per week (e.g., 3 sessions vs. 1 session) is recommended to gain more muscle mass (Schoenfeld et al., 2021). The same authors attributed the larger benefits of manipulating training frequency to its potential effect on the distribution of the weekly training volume (Schoenfeld et al., 2021). Future studies should endeavor to better understand the interaction between plyometric jump training frequency and hypertrophic adaptations, particularly in context with alterations in volume and intensity.

With respect to age, the positive point estimate suggests that older participants tend to achieve larger hypertrophic adaptations (Figure 5; https://osf.io/g8aet/). The same observation was noted for total training period (Figure 5; https://osf.io/g8aet/), with longer exposure to plyometric jump training appearing to induce larger hypertrophic gains. However, for a single session duration (Figure 5; https://osf.io/g8aet/), there is a tendency for shorter sessions to induce larger hypertrophic gains. It should be noted though that these observations are not conclusive and hence need to be confirmed in future studies.

Some limitations of this meta-analysis need to be acknowledged. The lack of a standardised method to assess skeletal muscle hypertrophy can constitute a limitation given that different assessment techniques may disagree with one another (e.g., macroscopic vs. microscopic) (Haun et al., 2019). Indeed, studies included in this analysis have used a wide range of methods to assess skeletal muscle hypertrophy. More specifically, the included studies relied upon macroscopic assessment methods (e.g., B-mode ultrasound) (Skurvydas and Brazaitis, 2010; Fouré et al., 2011; Fouré et al., 2012; Correa et al., 2012; Earp et al., 2015; Allison et al., 2018; McKinlay et al., 2018), microscopic methods (e.g., biopsy) (Kyröläinen et al., 2005), and a prediction equation to assess muscle volume and CSA (Chelly et al., 2010; Chelly et al., 2014; Chelly et al., 2015; Fathi et al., 2019). In fact, the prediction equation represents an indirect tool that provides a crude estimate of skeletal muscle hypertrophy and does not allow for the differentiation between muscle tissue, fat tissue, and bone (Chelly et al., 2006; Grgic et al., 2019). Additionally, although skeletal muscle hypertrophy was a primary outcome in most of the included studies (Kyröläinen et al., 2005; Marković et al., 2005; Herrero et al., 2006; Correa et al., 2012; Chelly et al., 2014; Chelly et al., 2015; Earp et al., 2015; Allison et al., 2018), it was in some other studies (Chelly et al., 2010; Skurvydas and Brazaitis, 2010; Fouré et al., 2011; Fouré et al., 2012; McKinlay et al., 2018; Fathi et al., 2019; Cherni et al., 2020) a secondary outcome. As such, caution must be taken when interpreting the present findings. Furthermore, moderator analyses were computed independently, ignoring any potential interdependency (interaction) between variables. Therefore, the results of univariate analyses must be interpreted with caution. Finally, although we have included studies that used athletic samples, the resistance training expertise of these participants is generally not clear. Thus, results cannot necessarily be generalized to well-trained individuals. Further studies are warranted to determine the effects of plyometric jump training on skeletal muscle hypertrophy in those with significant resistance training experience.