Sex differences in absolute and relative changes in muscle size following resistance training in healthy adults: a systematic review with Bayesian meta-analysis

Research question(s)

The research question(s) were defined using the participants, interventions, comparisons, outcomes, and study design (PICOS) framework, as follows. The primary research question was: “What is the estimated difference in muscle hypertrophy following RT between young to middle-aged males and females, in both absolute and relative (%) terms?”. To facilitate the interpretation of this research question, we also investigated whether the assessment of muscle size (i.e., body region assessed, type of measurement used, muscle fibre type) or participant RT experience (years) had a moderating effect on the overall outcome of the meta-analysis.

Literature search strategy

The literature search followed the PRISMA guidelines (Page et al., 2021). Original literature searches of the PubMed, SCOPUS and SPORTDiscus databases were started in May 2023 and completed in June 2023. However, an updated systematic search was conducted in August 2024 returning two studies (Grandperrin et al., 2024; Sterczala et al., 2024). The following search terms were used and adapted for each individual database: “resistance training” OR “resistance exercise” OR “strength training” AND “gender” OR “women” OR “woman” OR “female” OR “sex” OR “sex difference” AND “muscle hypertrophy” OR “muscle size” OR “muscle growth” OR “muscle mass” OR “muscle thickness” OR “cross-sectional area”. Search terms were added using the NOT term to reduce the number of irrelevant studies according to exclusion criteria (e.g., older, elderly, sarcopenia, cancer). The reference list of previous meta-analyses (Jones et al., 2021; Roberts, Nuckols & Krieger, 2020) and the retrieved articles were manually searched, and six additional studies (Hakkinen et al., 1998; Hakkinen et al., 2001; Hurlbut et al., 2002; Ivey et al., 2000; Kojic, Mandic & Ilic, 2021; Kosek et al., 2006) that met the inclusion criteria were identified and subject to the screening process, with full-text review confirming eligibility for inclusion (Fig. 1). Only studies conducted in humans were included.

Study selection

Covidence software (Veritas Health Innovations, Melbourne, Australia) was used to manage and conduct the systematic study selection process, including the removal of duplicates and the exclusion of ineligible studies at each stage of the screening process. An overview of the article identification process is shown in Fig. 1. The article identification process was completed independently (to reduce any bias during this process) by two authors (MR and JF) with any disagreement resolved through discussion with LH, who acted as a referee to make the final decision. Finally, the authors (MR and JF) reviewed the full text to determine eligibility for inclusion based on the inclusion criteria. If any studies were added through reference checking or manual searching, they were subjected to the same screening process as if they were found in the initial database search.

Inclusion criteria

Studies were included if: (1) participants were apparently healthy young to middle-aged (18–45 years old) adults of any RT experience, (2) the experimental comparison involved male and female participants completing the same RT intervention (e.g., set volume, load, frequency, exercises, proximity-to-failure), and (3) one of the following measures of pre- to post-intervention changes in muscle size were included; (a) muscle thickness, (b) whole-limb or muscle CSA or volume, (c) muscle fCSA, or (d) lean body/fat free mass via dual X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA). It is important to note that indirect measures of muscle size (BIA and DXA) may not directly equate to changes in skeletal muscle mass and may be affected by fluid alterations (Bone et al., 2017; Rodriguez et al., 2024). Nonetheless, many studies in the relevant literature assess muscle size with BIA and DXA to infer whole-body changes, which we considered relevant to our target population and research question. Only original research articles (English language) in peer reviewed journals were included. Articles that did not meet these criteria were excluded.

Data extraction

Data extraction was carried out by the principal investigators (MR and JF) to capture key information in a table format (Table 1). The following participant characteristics were extracted: (1) RT status (i.e., untrained, or resistance-trained), (2) age, and (3) sex. The following article characteristics were also extracted: (1) first author, (2) sample size, (3) publication date, (4) intervention groups/protocol outlines and duration, and (5) key findings (i.e., percentage increase in muscle size from pre- to post-intervention and an indication of whether any muscle hypertrophy was statistically different between sexes). Raw data from pre- and post-intervention for muscle hypertrophy outcomes were extracted for meta-analysis (if figures were used instead of numerical data, those data were extracted using Web Plot Digitizer (Version 4.6, CA, USA)). Studies were classified as recruiting ‘resistance-trained’ participants if the participants had any level of RT experience immediately prior to study commencement, whereas studies that involved a RT prohibitory period (e.g., “no RT 6-months prior to study commencement”) were classified as recruiting untrained participants. Considering the absence of detail regarding training status in some studies further classification of training status (e.g., “beginner”, “intermediate”, “advanced”, “highly advanced”) with multiple criteria (Santos Junior et al., 2021) is difficult. Several studies prescribed loads based on repetition maximum (RM) rather than as a percentage of 1-RM, with adjustments made throughout the RT intervention. This variation made it challenging to determine an accurate load for data analysis. Similarly, in some studies, the number of sets per muscle group per week was adjusted across the RT intervention, and due to vague reporting, accurately extracting these values was difficult. Furthermore, set termination methods were often ambiguously described, and given the inconsistencies in the definitions of set failure within the RT literature (Refalo et al., 2022), we refrained from classifying studies based on proximity-to-failure. Consequently, and despite the methods stated in our pre-registration (https://osf.io/trz3y/), we decided to omit RT characteristics (i.e., load, set volume, proximity-to-failure) from our sub-group analyses to ensure our results were not confounded by inaccurate data extraction.

Methodological quality assessment

Evaluation of methodological study quality (including risk of bias) was conducted (by MR) using the tool for the assessment of study quality and reporting in exercise (TESTEX) scale (Smart et al., 2015). Any ambiguities in methodological quality assessment were resolved by discussion between MR and JF. The TESTEX scale is an exercise science-specific scale used to assess the quality and reporting of exercise training trials. The scale contains 12 criteria that can either be scored a ‘one’ or not scored at all; 1, eligibility; 2, randomisation; 3, allocation concealment; 4, groups similar at baseline; 5, assessor blinding; 6, outcome measures assessed in 85% of patients (three possible points); 7, intention-to-treat; 8, between-group statistical comparisons (two possible points); 9, point-estimates of all measures included; 10, activity monitoring in control groups; 11, relative exercise intensity remained constant; 12, exercise parameters recorded. The best possible total score is 15 points.

Statistical analysis

To provide a more flexible modelling approach and enable results to be interpreted intuitively through reporting of probabilities (Kruschke & Liddell, 2018), we carried out a Bayesian meta-analysis using the “brms” (Bürkner, 2017) package in R (v 4.0.2; R Core Team, 2020). Detailed statistical analysis procedures can be found on the Open Science Framework (https://osf.io/trz3y/). Posterior draws were extracted using “tidybayes” (Kay, 2024) and effect estimates calculated using “emmeans” (Lenth, 2025). The absolute (mean and standard deviation) changes in muscle size from pre- to post-intervention for both male and female participants were extracted from each study. Standardised mean differences were calculated using the pooled standard deviation of baseline values as the denominator (via the “escalc” function in the “metafor” (Viechtbauer, 2010) package) to provide a more balanced estimate of variability between males and females (Morris, 2008). Pooling the standard deviations accounts for variability in both groups and avoids bias from using group-specific standard deviations. This method ensures a more robust comparison, especially when baseline variability differs between groups. Converting absolute values to relative changes for SMD calculation may not be statistically efficient (Vickers, 2001) so we therefore calculated the log response ratio (lnRR) for an interaction effect of group × time factorial design (Lajeunesse, 2015). To enhance practical interpretation, we exponentiated the lnRR values with a correction factor for transformation bias (Spake et al., 2023), thereby converting them to percentage change scores (Exp. % Change of lnRR). Positive values indicate greater muscle size increases in males, and negative values indicate greater increases in females. The Bayesian hierarchical analysis accounted for nested observations and used “shrinkage” to adjust study-level effects towards the overall mean (Kruschke & Liddell, 2018). Shrinkage-adjusted effect estimates are presented (see raw estimates in Fig. S1, Supplemental Digital Content 4). Due to the lack of reported correlations between pre- and post-test measures, we assumed a correlation coefficient of r = 0.87 from a recent meta-analysis (Jones et al., 2021) and conducted sensitivity analyses using r values from 0.7 to 0.99 (see Table S1, Supplemental Digital Content 1). Non-informative priors were used, and inferences were drawn from posterior distributions via Hamiltonian MCMC and highest density intervals (HDI). Interpretations were based on the size of the mean effect estimate (Swinton & Murphy, 2022), HDI limits (Swinton & Murphy, 2022), and the posterior probability (ranging from 50% to 100%) that an effect estimate goes in a particular direction (pd) (Kelter, 2023). Publication bias was visually assessed using funnel plots.

Search results and study characteristics

A total of 30 studies met the inclusion criteria. A PRISMA diagram of the systematic literature search and study selection process is displayed in Fig. 1. Data from one study (Hammarstrom et al., 2020) could not be retrieved; the remaining 29 studies were systematically reviewed and meta-analysed. Visual inspection of funnel plots (see Fig. S1, Supplemental Digital Content 2) identified no publication bias. A total of 1,278 male and 1,537 female data points were included in the meta-analysis, with the mean age of males being 26 ± 4 (range: 20 to 42) and females also 26 ± 4 (range: 19 to 41) years. Six (Alway et al., 1992; Grandperrin et al., 2024; Hammarstrom et al., 2020; Rissanen et al., 2022; Schwanbeck et al., 2020; Sterczala et al., 2024) out of the 29 studies involved participants with some RT experience, with the remainder of the studies involving participants with either (i) no RT experience (n = 4), or (ii) no RT experience 5-years (n = 1), 1-year (n = 7), 8-months (n = 1), 6-months (n = 7), and 3-months (n = 2) prior to study commencement. However, in some cases the exact RT experience (years) of the ‘resistance-trained’ participants was vaguely described and therefore unclear (Table 1). In total, 68 muscle hypertrophy outcomes were extracted, with some studies reporting numerous direct outcomes: (i) muscle CSA using magnetic resonance imaging (MRI) (Hammarstrom et al., 2020; Hubal et al., 2005; Lundberg et al., 2019; Walsh et al., 2009), ultrasound (Hakkinen et al., 1998; Kojic, Mandic & Ilic, 2021; Reece et al., 2023; Rissanen et al., 2022), or computed tomography (CT) (Alway et al., 1992; Cureton et al., 1988; O’Hagan et al., 1995), (ii) muscle fCSA using biopsy samples (Abou Sawan et al., 2021; Hakkinen et al., 2001; Kosek et al., 2006; Moesgaard et al., 2022; Psilander et al., 2019; Reece et al., 2023; Sterczala et al., 2024), (iii) muscle physiological CSA using ultrasound (McMahon et al., 2018), (iv) muscle volume using MRI (Ivey et al., 2000; Lundberg et al., 2019; Peterson et al., 2011), and (v) muscle thickness using ultrasound (Abe et al., 2000; Abou Sawan et al., 2022; Coratella et al., 2018; Kojic, Mandic & Ilic, 2021; Nunes et al., 2020; Psilander et al., 2019; Schwanbeck et al., 2020), and other studies using indirect outcomes: (i) lean mass using DXA (Abe et al., 2000; Fernandez-Gonzalo et al., 2014; Grandperrin et al., 2024; Hurlbut et al., 2002; Kosek et al., 2006; Sterczala et al., 2024), and (ii) estimated skeletal muscle mass using bioelectrical impedance analysis (BIA) (Ribeiro et al., 2014). Most of the muscle hypertrophy outcomes were assessed in the lower body (69% of outcomes (Abe et al., 2000; Abou Sawan et al., 2021; Abou Sawan et al., 2022; Coratella et al., 2018; Cureton et al., 1988; Fernandez-Gonzalo et al., 2014; Hakkinen et al., 1998; Hakkinen et al., 2001; Hammarstrom et al., 2020; Ivey et al., 2000; Kojic, Mandic & Ilic, 2021; Kosek et al., 2006; Lundberg et al., 2019; McMahon et al., 2018; Moesgaard et al., 2022; Psilander et al., 2019; Reece et al., 2023; Rissanen et al., 2022; Schwanbeck et al., 2020; Sterczala et al., 2024); quadriceps and hamstrings) versus the upper-body (22% of outcomes (Abe et al., 2000; Alway et al., 1992; Cureton et al., 1988; Hubal et al., 2005; Kojic, Mandic & Ilic, 2021; Nunes et al., 2020; O’Hagan et al., 1995; Peterson et al., 2011; Schwanbeck et al., 2020; Walsh et al., 2009); biceps, triceps, and chest), with 9% of outcomes (Abe et al., 2000; Grandperrin et al., 2024; Hurlbut et al., 2002; Kosek et al., 2006; Ribeiro et al., 2014; Sterczala et al., 2024) assessing lean mass of the upper- and lower-body combined (i.e., total body lean mass). In some instances, studies were excluded from sub-group analyses because (i) outcome measures were only employed in one study (e.g., pCSA (McMahon et al., 2018) and skeletal muscle mass via BIA (Ribeiro et al., 2014)), and (ii) measures of lean mass were not separated into upper- or lower-body (Grandperrin et al., 2024; Hurlbut et al., 2002; Kosek et al., 2006; Ribeiro et al., 2014; Sterczala et al., 2024). The duration of the RT interventions ranged from six to 24 weeks, with a mean of 11 weeks. For a comprehensive summary of other RT characteristics, see Table 1.

Methodological quality

A detailed overview of the methodological quality of included studies was conducted using the TESTEX scale (see Table S1, Supplemental Digital Content 3). Study quality scores ranged from 9 to 12 (out of a possible 15), with mean and median scores of 10. Although each study had some risk of bias, many studies lost points due to (i) no activity monitoring, (ii) no ‘intention-to-treat’ analysis of participants who had withdrawn, and (iii) no reporting of adverse incidents or compliance rate of participants. Overall, a total of 19 out of 29 (66%) studies scored highly (>10) on the TESTEX scale and visual inspection of methodological quality results revealed no impact of study quality on the effect size estimates generated. Considering that all included studies involved a comparison between males and females, no randomisation procedures were required, allocation concealment was not possible, and muscle size differed at baseline, thus, criterion ‘2’ (i.e., “randomisation specified”), criterion ‘3’ (i.e., “allocation concealment”), and criterion ‘4’ (i.e., “groups similar at baseline”) were given one point for every study. Although randomisation of participants into groups was not necessary in the studies included in this systematic review with meta-analysis, studies that involved different RT groups for each sex, and/or a control group, did employ appropriate randomisation procedures (Abe et al., 2000; Lundberg et al., 2019; McMahon et al., 2018; Nunes et al., 2020; O’Hagan et al., 1995; Psilander et al., 2019; Reece et al., 2023; Rissanen et al., 2022; Schwanbeck et al., 2020).

Meta-analysis results

Meta-analysis (including all 68 outcomes) of absolute changes in muscle size from pre- to post-intervention (Fig. 2) estimated a 100% probability of superior absolute muscle hypertrophy in males compared to females (SMD = 0.19 (95% HDI: 0.11 to 0.28)). The HDI covers ESs that suggest a negligible to small effect (favouring males), with low to moderate between-study variance identified (τ = 0.09 (95% HDI: 0.01 to 0.20)). This between-study variance represents 45% of the total variance, with additional contributions from the group (I²_group = 27%) and observation (I²_observation = 13%) levels. Additionally, meta-analysis (including all 68 outcomes) of lnRR to assess relative changes in muscle size from pre- to post-intervention (Fig. 3) estimated similar muscle hypertrophy in males and females (lnRR = 0.01 (95% HDI: −0.01 to 0.03); pd = 74%). The HDI covers ESs that suggest a negligible effect, with negligible between-study variance identified (τ = 0.01 (95% HDI: 0.00 to 0.03)). Exponentiated percentage changes calculated from lnRR also showed similar muscle hypertrophy between males and females (Exp. % Change of lnRR = 0.69% (95% HDI: −1.50% to 2.88%)). Results of secondary sub-group analyses are displayed in Table 2 (and see Fig. S1 Supplemental Digital Content 5). Raw SMD and Exp. % Change of lnRR of meta-analysed studies are displayed in Supplemental Digital Content (see Fig. S1, Supplemental Digital Content 4).

Sensitivity analysis

Sensitivity analysis of r values from 0.7 to 0.99 found SMDs between 0.17 and 0.22 (meta-analysis result = 0.19). The primary analysis was conducted with an a priori assumption that the correlation coefficient between pre-test and post-test measures was r = 0.87; this is a reasonable assumption that was obtained from previous literature (Jones et al., 2021), with sensitivity analyses indicating little impact of different correlation coefficient values on the pooled SMD. As such the results of our meta-analysis may be interpreted with increased confidence. Results of sensitivity analysis are displayed in Supplemental Digital Content (see Table S1, Supplemental Digital Content 1).

Absolute and relative changes in muscle size

This systematic review with meta-analysis extends previous findings with a total of 29 included studies (versus 10 in a previous meta-analysis (Roberts, Nuckols & Krieger, 2020)), providing an up to date synthesis of the current literature investigating biological sex differences in both absolute and relative muscle hypertrophy following RT. We found absolute increases in muscle size following RT slightly favoured males compared to females (SMD = 0.19), however, the relative increase in muscle size (percentage increase from baseline) following RT was similar between sexes (Exp. % Change of lnRR = 0.69%). Inherent differences in testosterone levels between sexes (Nuzzo, 2023a) are known to be responsible for larger baseline muscle size in males compared to females on average (e.g., out of 68 observations extracted from reviewed studies, only two showed larger baseline muscle size in females). Therefore, differences in absolute muscle hypertrophy that are observed between sexes are likely due to differences in baseline muscle size, whereas relative muscle hypertrophy is based on the proportional increase from baseline size. For example, since females start with less muscle mass on average, the absolute increase will be smaller even if the proportional change (i.e., relative muscle hypertrophy) is similar to that of males. Considering the similar relative increases in muscle size that are observed between sexes, physiological signals (e.g., mechanical tension mediated anabolic signalling, metabolic stress (Wackerhage et al., 2019)) other than sex-specific hormonal balance may play the primary role in promoting muscle hypertrophy following RT (Wackerhage et al., 2019). Supportive of our findings is research highlighting (i) the anabolic properties of estradiol that may contribute to muscle hypertrophy (Chidi-Ogbolu & Baar, 2018; Haizlip, Harrison & Leinwand, 2015; Hansen & Kjaer, 2014), (ii) the positive association between androgen receptor content with muscle hypertrophy (Morton et al., 2018), (iii) similarities in post-exercise protein synthesis and molecular signalling between sexes that triggers muscle hypertrophy (Dreyer et al., 2010; West et al., 2012), and (iv) the acute post-exercise elevation in anabolic hormones does not play a major role in stimulating muscle protein synthesis (Van Every, D’Souza & Phillips, 2024). Taken as a whole, our data suggest RT is likely to induce slightly greater absolute increases in muscle size in males compared to females, but similar relative increases in muscle size from baseline. These findings therefore suggest comparable muscle hypertrophic potential between males and females following RT.

Moderators of absolute and relative changes in muscle size

Sub-group analyses were conducted to assess possible variability in muscle hypertrophy outcomes across measures and body regions. Absolute differences in muscle hypertrophy between sexes were more evident with direct measures (i.e., muscle volume, muscle thickness, and muscle CSA and fCSA) versus indirect measures (i.e., lean mass). However, measures of lean mass should be interpreted with caution as they can be influenced by fluid alterations and may be less accurate versus other direct measures used (Rodriguez et al., 2024). Nonetheless, relative changes in muscle size between sexes were similar across all measures employed. Although sex differences in relative hypertrophy slightly favoured muscle fCSA versus other measures, the very wide HDIs suggests high variability in the response. We categorised body regions measured into either upper- or lower-body and found relative changes in muscle size between sexes were similar independent of the body region assessed. However, absolute changes in muscle size of the upper-body slightly favoured males (SMD = 0.30 versus 0.17), may be attributed to larger baseline differences in muscle size between sexes in the upper- versus lower-body (Nuzzo, 2023a). Overall, these data suggest (i) sex differences in absolute and relative muscle hypertrophy do not appear to depend on the measurement of muscle size, and (ii) males experience slightly greater absolute muscle hypertrophy versus females in body regions where larger baseline differences are evident (i.e., in upper-body versus lower-body muscles).

A total of seven studies (n = 170) using histochemical analysis of skeletal muscle biopsies to determine fCSA (Abou Sawan et al., 2021; Hakkinen et al., 2001; Kosek et al., 2006; Moesgaard et al., 2022; Psilander et al., 2019; Reece et al., 2023; Sterczala et al., 2024) were meta-analysed. Similar to previous findings (Abou Sawan et al., 2021; Moesgaard et al., 2022), we observed a > 90% probability of absolute (SMD = 0.39) and relative (Exp. % Change of lnRR = 12.7%) type I muscle fibre hypertrophy favouring males compared to females, providing further support males have a greater capacity to hypertrophy type I muscle fibres than females. However, the 95% HDIs covered wide effect estimates for both absolute and relative type I muscle fibre hypertrophy, suggesting considerable uncertainty in outcomes. Conversely, we estimated a negligible difference in (i) relative hypertrophy of type II muscle fibres favouring females versus males (Exp. % Change of lnRR = −2.29%; pd = 62%), and (ii) absolute hypertrophy of type II muscle fibres between sexes, despite larger baseline mean muscle fCSA for males (4,616 ± 713 µm²) versus females (3,652 ± 621 µm²) across all studies included in our meta-analysis. These findings support the possibility for sex-specific differences in muscle fibre type hypertrophy. Nonetheless, despite all studies assessing muscle fCSA with histochemical analysis of skeletal muscle biopsies, variability in the number of muscle fibres chosen and subsequently analysed per participant (range = 37 to 374), and how studies reported type II muscle fCSA based on the combination of type IIa and IIx values (which differ in size at baseline and in their physiological response to chronic exercise (Reece et al., 2023)), may have influenced our findings. As such, due to the intricate nature of measuring muscle fCSA in research and the uncertainty and variability in responses observed (see Fig. S1, Supplemental Digital Content 4), our findings should be interpreted with caution and used to inform future research that compares muscle fibre type-specific hypertrophy between males and females.

The RT experience of participants did not seem to influence sex differences in absolute and relative muscle hypertrophy following RT. Previous research has indicated that long-term RT experience alters the physiological response to RT (Damas et al., 2015) and may also cause muscle fibre type transitions that could influence sex-specific muscle hypertrophy (Plotkin et al., 2021). For example, a study in high-level competitive weightlifters (i.e., World/Olympic and National level) found years competing in weightlifting influences the proportion of type II muscle fibres more than biological sex per se, with females having a higher abundance of type II muscle fibres than males (Serrano et al., 2019). Whether a higher proportion of type II muscle fibres in highly trained females would influence sex differences in whole muscle and muscle fibre type-specific hypertrophy remains to be explored. Given only six of the 29 studies included in the meta-analysis involved resistance-trained participants, further research investigating sex differences in muscle hypertrophy within resistance-trained samples is encouraged.

Limitations & future research directions

Although most (66%) of the included studies were of ‘high’ quality, a brief overview of key findings in Table 1 suggests that results are consistent across both low and high quality studies. Our sub-group analysis investigating hypertrophy of type I and type II fibres only involved seven studies with a total of 170 participants, and the wide HDIs highlight the variability in outcomes. Although interpretations about muscle fibre type-specific hypertrophy were based on data from 170 participants, it is possible that a larger pool of evidence may strengthen or weaken the findings. Only six out of 29 studies were conducted in resistance-trained participants, however, in some cases the RT experience (years) of the ‘resistance-trained’ participants was vaguely described and therefore unclear (Table 1). Differences in program design (e.g., volume, intensity, frequency, and exercise selection) and duration across studies also introduces heterogeneity into the analysis, potentially affecting the results. However, results indicate minimal between-study variance, suggesting consistent sex-related effects on hypertrophy across studies. Nonetheless, sources of heterogeneity like RT protocols, exact participant training status, and individual factors (e.g., nutrition, hormonal responses) also produce variance at the group and observation levels. Finally, considering exact correlation coefficient values (between pre-test and post-test measures) could not be retrieved from individual studies, we used an a priori assumption of r = 0.87 to calculate SMDs. However, sensitivity analyses showed minimal impact of varying correlation coefficients on the pooled SMD, supporting the robustness of our meta-analysis results. Future research should investigate sex differences in muscle hypertrophy amongst resistance-trained samples and clearly report RT status of participants (e.g., years of experience). Moreover, the influence of muscle fibre type composition on sex-specific hypertrophy warrants exploration, particularly in highly trained individuals where fibre type distribution may differ from untrained individuals. Future research should also seek to minimise variability in histochemical analyses by standardising methods for measuring muscle fCSA and addressing factors such as the number of fibres analysed per participant. These efforts will enhance the understanding and application of resistance training across populations.

Practical applications

Our findings suggest healthy adult males and females can experience similar muscle hypertrophy following RT, and thus, may experience similar benefits from increases in muscle size. For example, (i) low skeletal muscle mass index is associated with an increased risk of all-cause mortality (Wang et al., 2023), and (ii) some physiological characteristics important for athletic performance (e.g., force production, rate of force development, fatigue resistance) may be influenced by muscle size (Kavvoura et al., 2018; Taber et al., 2019). Considering RT experience may not influence sex differences in muscle hypertrophy, RT programs can follow similar structures for both untrained and resistance-trained males and females, with primary differences in RT prescription based on long-term goals (e.g., aesthetics or performance-based goals) and individual characteristics (e.g., enjoyment, perceptions of discomfort, preferences, stress tolerance, etc). However, potential sex differences in short-term responses to RT, such as neuromuscular fatigue and muscle damage, may be greater in males compared to females (Enns & Tiidus, 2010; Hunter, 2014; Hunter, 2016) and should be considered in RT prescription.