The Effect of Strength Training Methods on Middle-Distance and Long-Distance Runners’ Athletic Performance: A Systematic Review with Meta-analysis

Background The running performance of middle-distance and long-distance runners is determined by factors such as maximal oxygen uptake (VO2max), velocity at VO2max (vVO2max), maximum metabolic steady state (MMSS), running economy, and sprint capacity. Strength training is a proven strategy for improving running performance in endurance runners. However, the effects of different strength training methods on the determinants of running performance are unclear. Objective The aim of this systematic review with meta-analysis was to compare the effect of different strength training methods (e.g., high load, submaximal load, plyometric, combined) on performance (i.e., time trial and time until exhaustion) and its determinants (i.e., VO2max, vVO2max, MMSS, sprint capacity) in middle-distance and long-distance runners. Methods A systematic search was conducted across electronic databases (Web of Science, PubMed, SPORTDiscus, SCOPUS). The search included articles indexed up to November 2022, using various keywords combined with Boolean operators. The eligibility criteria were: (1) middle- and long-distance runners, without restriction on sex or training/competitive level; (2) application of a strength training method for ≥ 3 weeks, including high load training (≥ 80% of one repetition maximum), submaximal load training (40–79% of one repetition maximum), plyometric training, and combined training (i.e., two or more methods); (3) endurance running training control group under no strength training or under strength training with low loads (< 40% of one repetition maximum); (4) running performance, VO2max, vVO2max, MMSS and/or sprint capacity measured before and after a strength training intervention program; (5) randomized and non-randomized controlled studies. The certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. A random-effects meta-analysis and moderator analysis were performed using Comprehensive meta-analysis (version 3.3.0.70). Results The certainty of the evidence was very low to moderate. The studies included 324 moderately trained, 272 well trained, and 298 highly trained athletes. The strength training programs were between 6 and 40 weeks duration, with one to four intervention sessions per week. High load and combined training methods induced moderate (effect size =  − 0.469, p = 0.029) and large effect (effect size =  − 1.035, p = 0.036) on running performance, respectively. While plyometric training was not found to have a significant effect (effect size =  − 0.210, p = 0.064). None of the training methods improved VO2max, vVO2max, MMSS, or sprint capacity (all p > 0.072). Moderators related to subject (i.e., sex, age, body mass, height, VO2max, performance level, and strength training experience) and intervention (i.e., weeks, sessions per week and total sessions) characteristics had no effect on running performance variables or its determinants (all p > 0.166). Conclusions Strength training with high loads can improve performance (i.e., time trial, time to exhaustion) in middle-distance and long-distance runners. A greater improvement may be obtained when two or more strength training methods (i.e., high load training, submaximal load training and/or plyometric training) are combined, although with trivial effects on VO2max, vVO2max, MMSS, or sprint capacity. Supplementary Information The online version contains supplementary material available at 10.1007/s40279-024-02018-z.


Introduction
In middle-distance (800-3000 m) and long-distance running (5000 m to marathon) races, performance is determined by factors such as maximal oxygen uptake (VO 2 max), velocity at VO 2 max (vVO 2 max), maximum metabolic steady state (MMSS), running economy [1][2][3], and sprint capacity [4].Indeed, VO 2 max has long been used as a primary measure of an individual's cardiorespiratory fitness, and as a marker of training effect [5].The interplay between VO 2 max and running economy determines vVO 2 max [2,6], whereas the MMSS (e.g., second lactate threshold) establishes the limit of steady-state muscle metabolism [20].Running economy, defined as the amount of energy required for running at submaximal speeds [7], may differentiate running performance in athletes with similar VO 2 max levels [8], and sprint capacity can influence races that require changes of pace [9] or a final sprint [4].
The implementation of strength training (ST) can improve the performance in middle-distance and long-distance runners [10][11][12][13][14].However, previous meta-analyses have focused mainly on running economy [11][12][13] and time trial running performance [13], without exploring the effects of ST on other determinants of performance (i.e., VO 2 max, vVO 2 max, and sprint capacity).For example, it has been found that ST could induce a trivial effect on VO 2 max in endurance athletes [15].In addition, the incorporation of diverse ST methods has demonstrated improvements in running economy among endurance runners [10][11][12][13]16].Moreover, ST may improve anaerobic and neuromuscular characteristics (e.g., sprint capacity) [3].These changes may be manifested in factors influenced by these variables, such as vVO 2 max [2,6].
Strength training is a versatile method of exercise that can be customized by the manipulation of factors such as intensity, volume, inter-set rest, frequency, type and sequence of exercise, and speed of movement [17].For instance, by manipulating the load (i.e., intensity) ST may be classified as high load training (HL, i.e., ≥ 80% of 1 repetition maximum [1RM]), submaximal load training (SubL, i.e., 40-79% 1RM), or plyometric training (PL, i.e., jump-based training with light or no loads) [18,19].Each of these ST methods target a specific outcome such as maximal strength, strength at submaximal loads with higher speed of movements, or stretch-shortening cycle and muscle-tendon stiffness, respectively [18].Therefore, the effect of ST on performance and its determinants may vary depending on the specific characteristics of each ST method [14,19].For example, ST has shown improvements in fixed blood lactate after PL [20] and blood lactate concentration at 16 km/h after a combined HL and PL intervention [21].However, some studies have not shown any improvement in MMSS [22][23][24].
The concerns described above may be related to the small number of studies that have compared ST methods, with most studies simply comparing standard running training protocols to ST.A comparison of different ST methods can entail highly complex logistical planning for researchers, meaning it is not always feasible to carry out.However, a systematic review with meta-analyses may offer a viable alternative to addressing such methodological challenges by combining studies that utilise different ST methods, thus enabling their comparison.Although some systematic reviews with meta-analyses have been published involving runners [10][11][12][13][14], a more comprehensive understanding of the effects of ST methods on endurance running performance (i.e., time trial and time to exhaustion) and its other determinants (e.g., VO 2 max, vVO 2 max, MMSS, sprint capacity) is needed.
Based on the above, the aim of this systematic review with meta-analysis was to analyze the effect of different ST methods (e.g., HL, SubL, PL, combined training) on running performance (i.e., time trial and time until exhaustion) and its determinants (i.e., VO 2 max, vVO 2 max, MMSS, sprint capacity) in middle-distance and long-distance runners.

Methods
The 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [25] were followed for this systematic review and meta-analysis.
The original protocol was registered on the Open Science Framework before the data analysis (https:// osf.io/ gyeku).

Information Sources and Search Strategy
Multiple databases including PubMed, Web of Science (all databases), Scopus, and SPORTDiscus were searched using various search terms and Boolean operators (Table S1 of the Electronic Supplementary Material [ESM]).All articles indexed up to January 2022 were included for selection.The search was updated in November 2022 with notifications of new studies found in the previously searched databases.No restrictions were placed on databases regarding study design, date, language, age, or sex of the participants.Additionally, the reference lists of relevant reviews, systematic reviews, and meta-analyses were reviewed, as well as the reference lists of the articles included in the analysis.

Selection Process
Two reviewers (LL and SV) reviewed all titles and abstracts obtained from the databases.When the titles and abstracts suggested that the article might meet the inclusion criteria (Table 1), the full article was reviewed.In the case of disagreement between the two reviewers, a third reviewer (RC) was consulted.

Data Collection Process
Data were collected by an independent reviewer (LL), including subject characteristics, methodological data, endurance training, ST intervention, and main outcomes for further analysis.In those articles where only data in the form of figures were presented, the validated WebPlotDigitizer software (Version 4.5; Ankit Rohatgi, Pacifica, CA, USA) [26] was used to extract the data.The reviewers (LL, SV, and RC) discussed the extracted data collectively and discussed any disagreements or controversial data after recoding.

Eligibility Criteria
Studies were eligible for inclusion according to the PICOS criteria (Participants, Intervention, Comparator, Outcome, and Study design; Table 1).

Participants
Subjects over 16 years of age were included in the study, as puberty may influence the adaptive response to training because of hormonal changes that occur during this period [27].Strength training experience was classified as either experienced or not experienced in ST based on the information provided by each study.The initial VO 2 max level was recorded, further categorized by performance level into moderately trained (male individuals ≤ 55 mL/kg/min, female individuals ≤ 45 mL/kg/ min), well trained (male individuals 55-65 mL/kg/min, female individuals 45-55 mL/kg/min), or highly trained (≥ 65 mL/kg/min, ≥ 55 mL/kg/min) [28].When male and female performance levels were not distinguished,

Outcome Measurements
Maximal oxygen uptake, vVO 2 max, MMSS, sprint capacity, and running performance were recorded before and after the ST interventions.Maximum metabolic steady state was considered if measured as: maximal lactate steady state, second lactate threshold, onset of blood lactate accumulation, lactate turn point, critical speed, or second ventilatory threshold.Sprint capacity was measured as the speed in meters (m/s) or time to cover a distance (s), in efforts where energy resources have been released mainly from glycolysis and phosphates [29].Running performance was measured by a time trial or time to exhaustion in runs of more than 75 s, where aerobic metabolism predominates [30].If running performance was measured in more than one test (e.g., 1500 m and 10,000 m), the most similar test between studies was selected.For all outcomes, where the study reported multiple timepoints (i.e., more than two data points), the first record and the last record immediately after the intervention were recorded.

Risk of Bias, Publication Bias, and Certainty Assessment
The risk of bias of the studies was assessed using the PEDro (Physiotherapy Evidence Database) scale [31,32], with items 5-7 removed in consideration of the lack of blinding of subjects, assessors, and researchers in supervised exercise interventions [31,33].Based on previous criteria [33], the studies were categorized as low risk (≥ 6 points), moderate risk (4-5 points), and high risk (≤ 3 points).To assess the publication bias of the studies on each ST method, a funnel plot was constructed, indicating a publication bias if an asymmetry was observed.
The GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach was used to evaluate the certainty of evidence [34][35][36].High certainty of evidence was initially assumed and then downgraded based on the following criteria: risk of bias, downgraded by one or two levels if the median PEDro score was indicative of moderate risk (< 6 points) or high risk (< 4 points), respectively; inconsistency, downgraded by one level if the Cochrane Q test for heterogeneity was significant (i.e., p < 0.05); indirectness, considered low risk, as the PICOS criteria were ensured; imprecision, downgraded by one level if the number of participants in the control group with the ST group was < 800 or if the confidence interval (CI) was crossed by a small effect size)ES) [i.e., − 0.15 to 0.15]; publication bias, downgraded by one level if an asymmetry was observed in the funnel plot.

Effect Measures
A standardized mean difference between groups (i.e., control-experimental) was calculated as previously recommended [37].Effect size was calculated as Hedges' g corrected for sample size [38] to help deal with small samples [39], which are recurrent in the sport science literature [40].Where studies reported data as mean and standard error (SE), the standard deviation (SD) was calculated from the SE [41].The criteria for determining the ES magnitude were established as follows: 0.15, 0.45, and 0.80 for a small, moderate, and large effect, respectively [42].

Synthesis Methods
A meta-analysis was performed for each ST method (i.e., HL, SubL, PL, or Combined) for each of the main outcomes (i.e., VO 2 max, vVO 2 max, MMSS, sprint capacity, and running performance) when at least three studies provided an outcome measure [16].If a study had two or more comparison groups in the same analysis, the sample size of the control group was divided by the number of intervention groups [41].Because of multiple sources of variation between studies (e.g., training and participant characteristics), a randomized effect model with a restricted maximum likelihood estimation method was conducted for estimating the parameters model (τ 2 ) recommended over the traditional DerSimonian and Laird method for continuous data [43].We based the test statistic and CIs in t-distribution with a Knapp and Hartung adjustment [44].
To examine heterogeneity between studies, the Cochrane Q test was accompanied by the value of I 2 to quantify the effect of heterogeneity, with values of < 25%, 25-75%, and > 75% indicating low, moderate, and high levels of heterogeneity, respectively [41].Outliers were defined as an ES in which the upper limit of the 95% CI was lower than the lower limit of the pooled effect CI or the lower limit of the 95% CI was higher than the upper limit of the pooled effect CI [45].A sensitivity analysis was then performed with and without the outlier ES to assess their impact on the analysis [45] (i.e., p value from Q test).
A moderator analysis was performed to explore factors associated with ES (e.g., subject characteristics; ST intervention characteristics) if at least eight studies were pooled [46,47], through meta-regression (i.e., age, body mass, height, initial VO 2 max, weeks, sessions per week, and total sessions) and sub-group analysis (i.e., sex, performance level, and ST experience).Alpha was set as 0.05.A Comprehensive meta-analysis (Version 3.3.0.70) was used for the analysis and GraphPad Prism 9 (Version 9.2.0) was used to generate the plots.

Study Characteristics
The studies included in the meta-analysis are presented in Table 2 for the characteristics of the participants and the interventions, and in Table 3

Risk of Bias, Publication Bias, and Certainty Assessment
The median of risk of bias was 6 (range from 4 to 7; moderate-to-low risk of bias; Table S2 of the ESM).Publication bias was found only in the analysis of running performance in the combined group (Fig. S1 of the ESM).The results of the certainty of the evidence for each outcome are presented in Table 4.The reasons for downgrading by one or more levels of certainty were (1) risk (moderate) of bias, (2) inconsistency (i.e., significant heterogeneity was found), (3) imprecision (i.e., low number of participants and/or CI crossing the small effect size), and (4) publication bias (i.e., asymmetry in the funnel plot was found).Certainty of the evidence was moderate for eight outcomes, low in four outcomes, and very low for one outcome.

Maximum Metabolic Steady State
From the studies that measured MMSS, nine studies implemented HL [22,24,87

Running Performance
From the studies that measured running performance, eight studies implemented HL [

Discussion
The aim of this systematic review with meta-analysis was to analyze the effect of different ST methods (i.e., HL, SubL, PL, and Combined) on performance and its determinants (i.e., VO 2 max, vVO 2 max, MMSS, and sprint capacity) in middle-distance and long-distance runners.The analyses revealed that, compared with endurance training alone or with ST with very low loads, the HL produced a significant moderate effect on running performance but not PL.Furthermore, when more than two ST methods (i.e., HL, PL and/or SubL) are combined, a significant large effect on running performance is produced.In contrast, no effects on VO 2 max, vVO 2 max, MMSS, and sprint capacity were found for all ST methods analyzed.These results suggest that HL is an effective method for improving running performance without interfering with other physiological parameters (i.e., VO 2 max, vVO 2 max, and MMSS), and this effect may be enhanced when PL and HL and/or SubL are combined.

VO 2 max and vVO 2 max
Maximal oxygen uptake is defined as the highest rate at which oxygen can be taken up and utilized by the body during severe exercise [5] and is an important prerequisite for performance in middle-distance and long-distance running [115].There was no significant effect of any ST method on VO 2 max (all p > 0.544), which is consistent with previous meta-analyses in endurance athletes [10,16].An improvement in VO 2 max depends mainly on "upstream factors", which include all physiological pathways that transfer oxygen from the environment to the blood, pumping it to the periphery and distributing it to and within muscle cells [116,117].The short duration of most ST efforts (i.e., exercise duration) probably did not induce an adequate stimulus to these factors.For example, traditional ST with variable resistance elevates oxygen uptake to approximately 45% of VO 2 max [118], which is not a sufficient aerobic stimulus to improve VO 2 max [119].
Although traditional ST methods may not stimulate VO 2 max, they can induce changes in neuromuscular function, musculotendinous stiffness, and muscle fiber type [120], factors that may aid endurance velocity and, by extension, vVO 2 max [3,18].The measure of vVO 2 max is the interaction between VO 2 max and running economy [2,6], influenced by anaerobic and neuromuscular characteristics [121], and can explain differences in performance that VO 2 max and running economy alone cannot [6].Indeed, vVO 2 max has been shown to be a good predictor of performance in middle-distance [122,123] and long-distance running [124][125][126].However, we did not find a significant effect of any ST method on vVO 2 max (all p > 0.479).The lack of an effect of ST methods on vVO 2 max may be related to the test protocol used to measure this outcome, particularly the duration of the stages [127].Protocols with short duration stages and 1.00-km/h incremental changes every minute have been suggested for athletes to reach vVO 2 max, resulting in lower work and energy cost than longer duration stages [128].From a total of 15 studies included in the metaanalysis for vVO 2 max, seven studies [22,23,84,91,97,105,106] used protocols with 1.00-km/h increments every minute, or in shorter stages (i.e., 30 s), and four of these studies showed significant effects after PL [91], HL [106], SubL [97], and Combined methods [84,97].From the seven studies that applied longer duration stages (i.e., 3 min or more), three found an effect on vVO 2 max after PL [83,108] and HL [87], whereas others found no effect after Combined [86,93,102,107] and HL [107].These results are in line with the suggestion of a recent meta-analysis to use ramped protocols to elucidate the effects of plyometric jump training on vVO 2 max [16].Considering the above, future research is needed to determine which protocol is valid for detecting vVO 2 max adaptations following different ST methods.

Maximum Metabolic Steady State
Maximum metabolic steady state dictates the boundary between heavy-intensity exercise and severe-intensity exercise [129,130].Below MMSS, exercise intensity can reach a steady state of muscle metabolism, whereas above MMSS this state is altered [131], which means increasing the MMSS through training would enable an athlete to achieve a steady state at higher running speeds.Our meta-analysis found no significant effect of any ST method on MMSS (all p > 0.760).The findings suggest that the analyzed ST methods do not generate sufficient metabolic impact to improve the MMSS, which typically benefits from training at intensities around this threshold [132].Even an alternative approach [133] has been explored involving ST with low loads and high repetitions, aiming at a near-threshold intensity, showing different physiological and mechanical responses compared with aerobic training at these intensities.The absence of an effect of ST on the MMSS (and VO 2 max) implies that ST may not induce a sufficient stimulus to induce changes in metabolic factors, at least with traditional ST approaches.

Sprint Capacity
In our meta-analysis, we found no significant effect of Combined on sprint capacity (p = 0.072), but not enough studies were found to be able to perform a meta-analysis (i.e., at least three studies) for the other ST methods.Sprint capacity is an important variable because it allows runners to hold a favorable position at the start of a race and to sprint maximally towards its finish [4].This may be especially relevant in middle-distance events in which the initial and final parts of the race have a higher proportion of sprinting than longer distances.Indeed, a relationship has been found between time achieved in elite male 800-m races with maximum sprint speed in elite male 800-m runners (R 2 = 0.550) [134] and a near-significant relationship in sub-elite female 800-m runners (R 2 = 0.380, p = 0.057) [135], but this has not been correlated with changes in 5-km performance [3].As the capacity to sprint is determined by the application of skeletal muscle force (and not necessarily by anaerobic metabolism) [136], improvements may be because of improved neuromuscular capabilities [3].However, it is important to mention that all [3,86,93,111] but one [21] of the studies included sprint training in combination with ST, so these possible improvements may be due more to sprint training than to ST.Therefore, research is needed to examine the effect of ST on sprint capacity and its effect on middle-distance and long-distance races.

High Load Training
Interventions with HL improved running performance, including time trial and time to exhaustion measures (ES = − 0.469 [moderate], p = 0.029).In contrast, our metaanalyses indicated no effect of HL on VO 2 max, MMSS, and sprint capacity.Given that no improvement in VO 2 max or MMSS was found with HL training, following a model that explains performance through metabolic (VO 2 max, MMSS, and running economy) and non-metabolic (running economy and sprint capacity) factors [137,138], it is reasonable to argue that HL could improve performance through non-metabolic factors such as running economy [137].Indeed, HL can induce non-metabolic (e.g., neuromuscular) adaptations  [139] leading toward an improved rate of force development [101,139].A larger rate of force development may allow high force levels to be generated at shorter contact times (i.e., at a faster running pace) [140,141], allow a faster transition from the braking phase to the propulsive phase [140], and allows for quasi-isometric muscle conditions that favor muscle energy costs [141,142].Indeed, the rate of force development has been correlated with running economy [101,140,142].Additionally, HL can generate changes in lower limb stiffness [142][143][144], improving energy storage and release from the lower limbs during running, and this could lead to a reduction in the energy expenditure during running [145] and thus running economy [142][143][144].Additionally, increased absolute strength may reduce relative effort at submaximal running speeds, activating a lower number of higher threshold motor units, resulting in a lower energy cost during running, and thus improved running economy [141].Indeed, a secondary analysis of studies included in this systematic review revealed improved running economy after HL (ES = − 0.266, p = 0.039).
Furthermore, we included time to exhaustion as an indicator of running performance.Two studies [101,106] measured time to exhaustion at vVO 2 max, showing an improvement after HL intervention.Potentially, these improvements are due to an improvement in running economy [94,101,106] and anaerobic capacity [106].Given that the time to exhaustion at severe intensity (i.e., intensity between MMSS and VO 2 max) is constrained by a decline in force production [137] and reduced fiber recruitment [146], it is plausible that enhanced rate force development and maximal strength (i.e., 1 RM) could offset the effects of fatigue through enhanced activation of motor neurons and recruitment of muscle fibers [101].Indeed, while running at vVO 2 max, athletes with reduced decline in force production may reduce the increase in energy cost (i.e., greater muscular strength endurance) [147].Consequently, HL could contribute to the delay of muscle fatigue at this specific intensity [101].Overall, considering the myriad of factors associated with running performance [137,138,148], future studies should elucidate the underlying mechanisms (particularly non-metabolic factors) of the improvement in running performance (and fatigue resistance) following HL interventions.

Plyometric Training
Plyometric training may induce neuromuscular adaptations, such as increased motor unit recruitment and improved intermuscular coordination [149].These neuromuscular improvements have been shown to correlate with improved running economy and anaerobic capacity [137].Additionally, PL can improve stiffness and compliance (e.g., muscle, tendon, joint) [99,150].This mechanism enables greater storage and release of elastic energy within the tendon [150], resulting in reduced muscle energy expenditure [141], and thus improved running economy.Indeed, recent metaanalyses [13,16] found a significant improvement of running performance after PL.In contrast, our meta-analysis denotes no improvement of running performance after PL (ES = − 0.210, p = 0.064).One possible reason for the discrepancy is that we included a larger (more representative) number of studies in our analysis (n = 12) when compared with recent meta-analyses (n = 7-10) [13,16].However, most of the analyzed studies in previous meta-analyses (e.g., seven of ten) [16] were also included in this analysis.Further, our meta-analysis yielded a nearly significant effect for PL on running performance, with a higher ES compared with a similar meta-analysis that found a favorable running performance effect after PL (ES = − 0.210 vs − 0.170, respectively) [13].The reason for the discrepancies between published meta-analyses and our meta-analysis is currently unclear.Possible methodological differences (e.g., inclusion-exclusion criteria; statistical [meta-analytical] approach) may have played a role.

Combined
Combined involves incorporating more than one ST method into a training program.Our study revealed that Combined produced a significant large effect on running performance (ES = − 1.035, p = 0.036), producing a greater effect than the use of a single ST method alone.Interestingly, the studies that included the Combined method utilized PL in combination with HL and/or SubL, confirming that including PL with resistance training has a favorable effect on running performance [16] and a greater effect than HL alone.In addition, in a secondary analysis, we found that the Combined method has a greater effect (ES = − 0.426, p = 0.018) on running economy compared with the HL (ES = − 0.266, p = 0.039), SubL (ES = − 0.365, p = 0.131),and PL (ES = − 0.122, p = 0.167) methods used individually.Therefore, we can assume that this improvement in running economy also translates to enhanced running performance.This can be observed in the study by Li et al. [21], which found that HL alone and HL combined with PL both improved running economy and 5-km running performance.However, despite no significant differences between the two groups, the HL with PL group exhibited a higher percentage improvement than HL alone in both running economy (7.68% vs 4.89% at 14.00 km/h) and running performance (2.80% vs 2.09%) [21].
One reason for an increased effect may be that the incorporation of different ST methods can generate a variety of overloads that challenge the neuromuscular system [19] and potentially enhance running performance by eliciting diverse neuromuscular mechanisms.Additionally, the sequence of exercises corresponding to different ST methods within the same training session or in separate sessions may serve different purposes for the force-velocity profile [151].For example, contrast training (i.e., high load exercises followed by alternating plyometric exercises) could induce post-activation potentiation by improving the speed of plyometric exercises through enhancing both the force and velocity components, whereas traditional training (i.e., low load exercises followed by high load exercises) may primarily develop the force component and not be potentiated by exercises with low loads [151].However, improvements have been observed in studies where both ST methods were included in the same session [21,97,103], as well as in separate sessions [3].Of note, from the five studies that included SubL, four [3,103,110,111] instructed athletes to perform exercises with maximal velocity intention, and one [97] described the intervention as explosive training.Maximal movement velocity intention at a given load can positively influence neuromuscular adaptations [152], and therefore running performance adaptations.

Limitations and Strengths
Some limitations of this meta-analysis should be mentioned.First, because of the different composition of each of the ST methods, we decided to perform a separate analysis of each ST method on each of the performance parameters (i.e., VO 2 max, vVO 2 max, MMSS, sprint capacity, and running performance), which resulted in the SubL group not reaching the minimum number of studies (i.e., three studies) for the main analysis in any of the performance parameters, while HL and PL did not reach the minimum number of studies for sprint capacity.In addition, in some cases, the minimum number of studies (i.e., eight studies) for a moderator analysis was not achieved.Second, high heterogeneity was found for Combined in the analysis for running performance (p = 0.009, I 2 = 67.475),probably because different types of ST methods with varying effects were included in this group, and thus their effect on running performance should be interpreted with caution.Finally, in this study, we have focused mainly on aerobic parameters, but the anaerobic component is also a determinant of running performance [3], as well as durability [153].The strengths of our study are also important to note.To our knowledge, this is the first meta-analysis to analyze the effect of different ST methods on different parameters determining running performance specifically in middle-distance and long-distance runners.Furthermore, we included time to exhaustion as an indicator of running performance allowing us to increase the number of studies in the analysis and to discuss durability.

Conclusions
In summary, this systematic review with meta-analysis suggests that ST with HL improves running performance measured by a time trial and time to exhaustion.Combining the PL method with HL and/or SubL showed greater improvement in running performance compared with the ST methods alone, while the PL method alone did not enhance running performance.These improvements occurred without changes in VO 2 max, vVO 2 max, MMSS, and sprint capacity, suggesting that the adaptations are mainly due to nonmetabolic factors.These results suggest that middle-distance and long-distance coaches and athletes should consider the inclusion of more than one ST method in their training plan.Future research should aim to analyze and compare the effect of different ST methods combined and separately on running performance, as well as the underlying mechanisms related to these effects.adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/.

Fig. 2 Fig. 3 Fig. 4 Fig. 5
Fig. 2 Effect of strength training methods on maximal oxygen uptake.A positive effect size represents beneficial effects after strength training compared with control conditions.CI confidence interval, Combined high load training, plyometric training and/or submaximal load training, HL high load training, nES number of effect sizes, PL plyometric training

Fig. 6
Fig. 6 Effect of strength training methods on running performance.A negative effect size represents beneficial effects after strength training compared with control conditions.CI confidence interval, Combined high load training, plyometric training, and/or submaximal load training, HL high load training, nES number of effect sizes, PL plyometric training

Table 1
Inclusion and exclusion criteria for meta-analysis 1RM one repetition maximum, HL high load training, PL plyometric training, RM repetition maximum, SubL submaximal load training, VO 2 max maximal oxygen uptake, vVO 2 max velocity at VO 2 max PL, defined as programs aiming to improve stretch-shortening cycle functioning using exercises with light loads or body weight (e.g., jump-based training); and (4) combined training (Combined), defined as programs that included two or more ST methods.The groups that performed ST with very low loads (VL, < 40% 1RM or > 20RM) were considered as a control group.The duration of the intervention was recorded as total weeks, sessions per week, and total number of sessions.

Table 2
Participant and strength training intervention characteristics of the included studies

Table 2 (
aThe exercise was not considered relevant to be included as a strength training method in this group

Table 3
Analysis of the studies included in the meta-analysis of VO

Table 4
GRADE (Grading of Recommendations Assessment, Development and Evaluation) assessment for the certainty of evidence VO 2 max maximal oxygen uptake, vVO 2 max velocity at maximal oxygen uptake a Downgraded by one level because n < 800 and/or the 95% confidence interval crossed the small effect size b Downgraded by one level because the median PEDro scale is < 6