Effects of resistance training on running economy: a systematic review and meta-analysis

duction: Running economy (RE) is a key determinant of endurance performance in long- distance runners, influencing their efficiency and energy expenditure. Resistance training has been proposed as a strategy to enhance RE, yet its effectiveness remains debated. Objective: We aimed to systematically and meta-analytically review manuscripts describing the effects of resistance training on RE in long-distance runners (≥10 km). Methodology: A syste- matic search of the literature was conducted in the PubMed, Web of Science and Scopus data- bases for manuscripts with no date restriction other than being published until March 2024. Results: Out of 895 studies, 8 met the inclusion criteria. Risk of bias was calculated, and stan- dardized mean differences (SMDs) were weighted by inverse variance to calculate the overall effect and its 95% confidence interval (CI) of resistance training on RE. A total of 80 subjects from the included studies were analyzed (age: 36.2 ± 6 yrs; VO2max: 49.2 ± 4 ml.kg.min-1). The mean training intensity was 83% 1RM ± 7.5%. Resistance training improved RE in long-dis- tance runners (p= 0.003, d= 0.47) while training intensity, race specialty and duration of the training protocol positively modulated such adaptations. Discussion: These results provide a framework that resistance training evokes positive effects in RE in long-distance runners corroborating with previous studies. Conclusions: The present study showed that resistance training improves RE. Training struc- ture appears to be the key point, and further studies are required to provide information on the effects of resistance training on RE in long-distance runners. Keywords Distance running; oxygen cost; running; strength training. Resumen

training evokes positive effects in RE in long-distance runners corroborating with previous studies. Conclusions: The present study showed that resistance training improves RE. Training struc- ture appears to be the key point, and further studies are required to provide information on the effects of resistance training on RE in long-distance runners. Keywords Distance running; oxygen cost; running; strength training. Resumen Introducción: La economía de carrera (RE) es un factor determinante en el rendimiento de co- rredores de larga distancia, al influir en la eficiencia y el gasto energético. El entrenamiento de resistencia ha sido propuesto como estrategia para mejorar la RE, aunque su efectividad aún se debate. Objetivo: Revisar sistemática y metaanalíticamente estudios que evaluaron los efectos del en- trenamiento de resistencia sobre la RE en corredores de larga distancia (≥10 km). Metodología: Se realizó una búsqueda sistemática en PubMed, Web of Science y Scopus, sin res- tricción de fecha, hasta marzo de 2024. Se analizaron los manuscritos que cumplieron los crite- rios de inclusión. Resultados: De 895 estudios encontrados, 8 fueron incluidos. Se evaluó el riesgo de sesgo y se calcularon las diferencias medias estandarizadas (SMDs), ponderadas por varianza inversa, para estimar el efecto general del entrenamiento de resistencia sobre la RE, con IC del 95%. En total, se analizaron 80 sujetos (edad: 36.2 ± 6 años; VO₂max: 49.2 ± 4 ml·kg⁻¹·min⁻¹). La inten- sidad promedio del entrenamiento fue del 83% de 1RM (±7.5%). El entrenamiento de resisten- cia mejoró significativamente la RE (p = 0.003, d = 0.47). La intensidad, la duración del proto- colo y la especialidad en la carrera influyeron positivamente en los resultados. Discusión: Los hallazgos respaldan que el entrenamiento de resistencia mejora la RE en corre- dores de larga distancia. Conclusiones: La estructura del entrenamiento influye en los resultados, y se requieren más estudios para profundizar en sus efectos. Palabras clave Carrera de larga distancia; costo de oxígeno; correr; entrenamiento de fuerza. Effects of resistance training on running economy: a systematic review and meta-analysis Efectos del entrenamiento de resistencia en la economía de carrera: una revisión sistemática y un metaanálisis

entrenamiento influye en los resultados, y se requieren más estudios para profundizar en sus efectos. Palabras clave Carrera de larga distancia; costo de oxígeno; correr; entrenamiento de fuerza. Effects of resistance training on running economy: a systematic review and meta-analysis Efectos del entrenamiento de resistencia en la economía de carrera: una revisión sistemática y un metaanálisis

2025 (Octubre), Retos, 71, 275-287 ISSN: 1579-1726, eISSN: 1988-2041 https://recyt.fecyt.es/index.php/retos/index 276 Introduction Running economy (RE) is commonly defined as the steady-state oxygen uptake (VO2) required to sus- tain a given submaximal speed and has been widely studied over the past decades (Balsalobre-Fernan- dez et al., 2016). It has been proposed that athletes with the same VO2max may differ in their fractional VO2 at a given submaximal intensity. RE is the result of a myriad of factors that influence endurance performance (Van Hooren et al., 2024). Although multiple factors might influence RE and, consequently, performance, neuromuscular efficiency and cardiorespiratory capacity have been identified as two of its most important modulators (Berryman et al., 2018; Blagrove et al., 2018). Previous research has been dedicated to describing the efficacy of resistance training in improving mus- cle strength and influencing middle- and long-distance running performance (Llanos-Lagos C, 2024). Recently, Eihara et al. (Eihara et al., 2022) explored different types of resistance training protocols aim- ing to improve RE (without distance distinction). They confirmed and described the efficacy of a combi- nation of plyometrics and isometric training in improving running economy. Additionally, different mechanisms have been suggested to explain the relationship between neuromuscular activation and running performance. It has been proposed that resistance training improves neural function, favoring the utilization of type IIa and IIx muscle fibers. This mechanism allows the optimization of the stretch- shortening cycle (SSC), which has been reported to contribute to RE in middle- and long-distance run- ning (Aagaard et al., 2011; Ramirez-Campillo et al., 2021; Rønnestad & Mujika, 2014). Despite these claims, the findings in the literature remain controversial regarding the positive effects of resistance training on RE. In previous studies, decrements in aerobic and muscular adaptations were reported when resistance training and endurance training were combined (Balsalobre-Fernandez et al., 2016; Hickson, 1980). This training approach may be counterintuitive, as there is evidence suggesting that muscle hypertrophy is associated with a reduction in mitochondrial density and distribution in muscle fibers (Wilson et al., 2012). In this sense, the configuration of resistance training programs seems to influence RE (Eihara et al.,

resistance training and endurance training were combined (Balsalobre-Fernandez et al., 2016; Hickson, 1980). This training approach may be counterintuitive, as there is evidence suggesting that muscle hypertrophy is associated with a reduction in mitochondrial density and distribution in muscle fibers (Wilson et al., 2012). In this sense, the configuration of resistance training programs seems to influence RE (Eihara et al., 2022). Previously, Palmer et al. (Palmer & Sleivert, 2001) determined the acute effects of a single high-intensity resistance training session (80% 1RM) on RE in well-trained dis- tance runners (VO2max: 66.6 ± 10.2 ml.kg-1.min-1). RE was impaired up to eight hours after the session and remained so 24 hours thereafter. Contrastingly, Taipale et al. (Taipale et al., 2010) showed that 28 weeks of resistance training improved RE in recreational runners. More recently, a meta-analysis by Berryman et al. (Berryman et al., 2018) described that chronic resistance training positively modulates RE in runners of different performance levels. Despite growing interest from the scientific community, no meta-analysis to date has specifically fo- cused on the effects of resistance training on running economy in long-distance runners (≥10 km), who present distinct physiological demands compared to middle-distance athletes. In contrast to previous meta-analyses that grouped middle- and long-distance runners together, the present study focuses ex- clusively on long-distance runners (≥10 km), a subgroup with unique physiological and biomechanical demands often overlooked in prior syntheses. Variables such as intensity, running specialization, and the type of exercises used represent existing gaps in the literature (Berryman et al., 2018; Denadai et al., 2017; Llanos-Lagos C, 2024). Furthermore, earlier reviews often failed to analyze specific resistance training protocols (e.g., plyometric vs. isometric) or account for training status, limiting their applicabil- ity to specialized athlete populations. Recent systematic reviews (e.g., Eihara et al., 2022 (Eihara et al., 2022); Llanos-Lagos et al., 2024 (Llanos-Lagos C, 2024)) have started to differentiate between heavy- load, plyometric, submaximal, and combined methods, and to evaluate their effectiveness across train- ing-status subgroups, reinforcing the need for our more targeted meta-analysis. Thus, the present study aimed to describe the effects of resistance training on RE in

(e.g., Eihara et al., 2022 (Eihara et al., 2022); Llanos-Lagos et al., 2024 (Llanos-Lagos C, 2024)) have started to differentiate between heavy- load, plyometric, submaximal, and combined methods, and to evaluate their effectiveness across train- ing-status subgroups, reinforcing the need for our more targeted meta-analysis. Thus, the present study aimed to describe the effects of resistance training on RE in long-distance run- ners (i.e., those who compete in events ≥ 10 km). In particular, we evaluated how different training pro- tocols—such as heavy-load strength training (≥80% 1RM), plyometric exercises, and combined meth- ods—affect running economy based on intervention duration and training background. Based on previ- ous studies (Berryman et al., 2018; Denadai et al., 2017; Van Hooren et al., 2024), we hypothesized that high-intensity resistance training (e.g., heavy or combined plyometric/strength protocols) performed over a minimum of eight weeks would yield greater improvements in running economy than low-load or short-term interventions.

2025 (Octubre), Retos, 71, 275-287 ISSN: 1579-1726, eISSN: 1988-2041 https://recyt.fecyt.es/index.php/retos/index 277 Method Experimental Approach to the problem The current systematic review and meta-analysis comprised a comprehensive synthesis quantifying ef- fectiveness of resistance training on running economy in long-distance runners (≥10 km). Primary anal- yses investigated the athlete demographic (e.g., running specialty, training level, training type) and training dose (e.g., frequency, volume and intensity). The secondary analyses investigated relationships between changes in running economy and the application of resistance training. The studies were clas- sified by The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009). The selected studies were analyzed to assess the effects of resistance training on RE, and the risk of bias was calculated. Egger’s regression intercept test and visual inspection of the funnel plot were applied to detect the potential of publication bias. The methodological quality of the included studies was assessed using the Physiotherapy Evidence Database (PEDro) scale (de Morton, 2009). Registration of systematic review protocol A systematic search of randomized controlled trials investigating the effects of resistance training on RE was conducted. Before the research began, the study project was registered at the International pro- spective register of systematic reviews (PROSPERO nº: CRD42022339621). Search strategy After PROSPERO approval, searches were conducted in the PubMed, Scopus and Web of Science data- bases. Included studies were published with no date restriction up to March 2024. The keywords used to identify the studies were similar to those used in a study on the use of different training methods to investigate RE in endurance athletes (Denadai et al., 2017). The search terms were: “running economy” and “weight training” OR “resistance training” OR “strength training”. The systematic search was carried out by two independent researchers. In case of disagreement between the two independent researchers, a third researcher was consulted. Disagreements were resolved using the eligibility criteria. If there was any remaining doubt, the corresponding author was contacted to clarify discrepancies. If the corre- sponding author did not return the contact, the paper was excluded to avoid the risk of bias. Papers were initially screened based

In case of disagreement between the two independent researchers, a third researcher was consulted. Disagreements were resolved using the eligibility criteria. If there was any remaining doubt, the corresponding author was contacted to clarify discrepancies. If the corre- sponding author did not return the contact, the paper was excluded to avoid the risk of bias. Papers were initially screened based on their title, abstract or key-words, and only articles in the English lan- guage were included for further analysis. All analyses were performed in the Rayyan® website. When data were missing or incomplete, study authors were contacted for clarification. If data remained una- vailable, the respective outcome was excluded from the quantitative synthesis. Eligibility criteria The PICOS (problem, intervention, comparison, outcome and study design) approach was used to rate studies for eligibility (Liberati et al., 2009). Only runners who ran track or road modalities were in- cluded. Trail and cross-country running were excluded because these modalities involve uneven terrain, variable environmental conditions, and elevation changes that significantly affect running economy (RE) independently of physiological adaptations. Including such studies would introduce considerable heterogeneity, limiting the ability to compare results across standardized resistance training interven- tions. Therefore, to ensure methodological consistency and comparability, only studies involving road or track running on relatively flat and controlled surfaces were included (Tjelta & Enoksen, 2010). We utilized the resistance training definition by Faigenbaum et al. (Faigenbaum & Myer, 2010). “The term resistance training refers to a specialized method of physical conditioning that involves the progressive use of a wide range of resistive loads, different movement velocities and a variety of training modalities including weight machines, free weights (barbells and dumbbells), elastic bands, medicine balls and plyometrics”. Table 1a shows the inclusion eligibility criteria.

2025 (Octubre), Retos, 71, 275-287 ISSN: 1579-1726, eISSN: 1988-2041 https://recyt.fecyt.es/index.php/retos/index 278 Table 1a. Inclusion criteria following PICOS recommendation. Inclusion P Participants: males and females performing road or track long-distance running (10 km or more); at least three years of endurance training experience. I Intervention: Resistance training (weight machines, free weight, barbells and dumbbells, plyometrics, isoinertial, isometric, body weight, elastic bands, medicine ball). C Comparison: Healthy control group (sedentary and/or no resistance and/or endurance training intervention). O Outcome: Running economy, pre- and post-analysis (VO2). S Study design: Parallel and RCTs. Table 1b shows the exclusion eligibility criteria: Table 1b. Exclusion criteria following PICOS recommendation. Exclusion P Participants: Running any distance up to 9.99km. Trail or cross country running. I Intervention: Whole body vibration, electrostimulation. C Comparison: Other than running economy (i.e., max. vel.); analysis through resistance training. O Outcome: Utilization of any variable other than VO2max (lactate, velocity, neuromuscular efficiency, heart rate, substrate utilization, core temperature, fatigue, biomechanics) as a surrogate of running economy. S Study design: Cross-sectional, review of literature, case reports, special communication, letters to the editor, invited commentaries, errata. This systematic review and meta-analysis was performed in accordance with The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (figure 1) (Moher et al., 2009). Figure 1. Flowchart of the study selection. Data extraction Participant characteristics, sample size, study design, intervention characteristics (i.e., level of training, race distance, number of exercises per session, weeks of intervention, exercises included in interven- tion, outcomes), and characteristic of the tests to determine RE were extracted from the included papers. Risk of bias assessment Egger’s regression intercept test and visual inspection of the funnel plot were applied to detect the po- tential of publication bias. The methodological quality of the included studies was assessed by at least two independent researchers using the Physiotherapy Evidence Database (PEDro) scale (de Morton,

2025 (Octubre), Retos, 71, 275-287 ISSN: 1579-1726, eISSN: 1988-2041 https://recyt.fecyt.es/index.php/retos/index 279 2009). The PEDro scale consists of 11 items related to methodological quality. Items 2–11 are scored as either “0” (criterion not met) or “1” (criterion met), resulting in a total score ranging from 0 to 10, with higher scores indicating better quality (Vorup et al., 2016). In the present review, two independent re- searchers evaluated the methodological quality of each study using the PEDro scale. In cases of disa- greement, a third researcher was consulted, and consensus was reached through discussion. Systematic differences (heterogeneity) were assessed utilizing I 2 statistics, which represent the percentage of het- erogeneity of the selected studies. I 2 values of 25%, 50%, and 75% indicate low, moderate and high heterogeneity (Higgins et al., 2003). Statistical analysis Statistical analyses were performed using OpenMetaAnalyst® (AHRQ, Maryland, USA) with the level of significance set at p < 0.05. Standardized mean differences (SMDs) for each study were calculated using the DerSimonian-Laird random-effects model, which is widely employed in meta-analyses when heter- ogeneity is expected among studies. This model accounts for both within-study and between-study var- iance, making it appropriate for the present analysis, which included studies with varying sample sizes, interventions, and outcome measures. SMDs were weighted by the inverse variance to estimate the overall effect size. All the data are reported as mean ± 95% confidence interval (CI). The meta-analysis was performed based on RE improvement, represented by a reduction in oxygen cost at the same abso- lute intensity, and thus presents a negative SMDs change. The magnitude of difference in RE was calcu- lated using Cohen’s d to interpret the magnitude of SMDs (DerSimonian & Laird, 1986). The ES classifi- cation was <0.20, trivial; 0.20 to 0.49, small; 0.50 to 0.79, moderate; and >0.80, large (Cohen, 1988). Results From 895 studies obtained in three databases, eight studies met the inclusion criteria for quantitative synthesis. The PEDro scores for the eight studies were good and similar (range: 6-7) (table 2). Table 2. PEDro ratings of evidence levels of the included studies. Study Q2 Q3 Q4 Q5 Q6

0.49, small; 0.50 to 0.79, moderate; and >0.80, large (Cohen, 1988). Results From 895 studies obtained in three databases, eight studies met the inclusion criteria for quantitative synthesis. The PEDro scores for the eight studies were good and similar (range: 6-7) (table 2). Table 2. PEDro ratings of evidence levels of the included studies. Study Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Total Spurrs et al. (Spurrs et al., 2003) 1 0 1 0 0 0 1 1 1 1 6 Ferrauti et al. (Ferrauti et al., 2010) 1 1 1 0 0 0 0 1 1 1 6 Albracht et al. (Albracht & Arampatzis, 2013) 0 1 1 0 0 0 1 1 1 1 6 Piacentini et al. (Piacentini et al., 2013) 1 1 1 0 0 0 1 1 1 1 7 Damasceno et al. (Damasceno et al., 2015) 1 1 1 1 0 0 0 1 1 1 7 Vorup et al. (Vorup et al., 2016) 1 0 1 0 0 0 1 1 1 1 6 Giovanelli et al. (Giovanelli et al., 2017) 0 1 1 0 0 0 1 1 1 1 6 Festa et al. (Festa et al., 2018) 1 1 1 1 0 0 0 1 1 1 7 Median score 6 The subjects’ characteristics and the training design are shown in table 3: Table 3. Main findings of the included studies from the endurance training group added resistance training. Study N (sex) Study population Racing specialty Exercises per session (n) Sessions per week (n) Training duration (weeks) Main activity Results Spurrs et al. (Spurrs et al., 2003) 8 M Recreational runners 10 km 4 2 6 Plyometric exercises Improved RE Ferrauti et al. (Ferrauti et al., 2010) 9 M 2 FM Recreational runners 42 km 6 2 8 Gym lower and upper limbs machines Improved RE Albracht et al. (Albracht & Arampatzis, 2013) 13 NIG Recreational runners 10 km 1 4 14 Dynamometer Improved RE

M 2 FM Recreational runners 42 km 6 2 8 Gym lower and upper limbs machines Improved RE Albracht et al. (Albracht & Arampatzis, 2013) 13 NIG Recreational runners 10 km 1 4 14 Dynamometer Improved RE

This study reviews the effects of resistance training on running economy in long-distance runners.