{'fraction_non_alphanumeric': 0.030511316010058676, 'fraction_numerical': 0.019279128248113998, 'mean_word_length': 4.895256916996048, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The dose–response relationship between resistance training volume and muscle hypertrophy: are there really still any doubts?

We read with interest the letter to the editor entitled “Is it possible to reach reliable conclusions doing meta-analyses with resistance training studies?” (Arruda et al., 2016). We thank the journal for giving us an opportunity to respond to the points raised by the authors. First, Arruda et al. (2016) stated that meta-analyses “usually combine studies that have important methodological differences” and that “the summary effect can be largely influenced by these differences across studies”. They refer Field (Field, 2015) in this statement. The authors fail to note that, in this context, Field was discussing the use of meta-analysis to aggregate studies with not only different designs but also different questions (such as pooling studies that examine at cognitive behavioural therapy, psychodynamic therapy, etc.). Our analysis was isolated to studies that manipulated the number of sets to examine the impact of set volume on hypertrophy. It should also be noted that Field discussed the use of multilevel models to improve on this limitation of metaanalyses, which is exactly the type of model we used in our analysis. In fact, the model we used is highly conservative, and is ideal for aggregating studies with marked heterogeneity. Our statistical model assumes that studies are heterogeneous and that we cannot account for all possible sources of variance between studies. Second, Arruda et al. (2016) discussed how “resistance training outcomes are largely influenced by many variables that interact with each other”, and “trying to estimate the impact of one variable when others are not controlled has a high risk of bias”. However, other variables were controlled in each individual study, as we selected studies where the only variable that was manipulated was the number of sets. The Forest plot in Figure 2 of our paper shows the effect size difference between higher and lower volumes in each individual study; each individual point is not impacted by the other studies. If there was no impact of volume, then we would expect a random distribution of these data points around 0. However, this is not what occurred; 13 of the 15 studies are to the right of the centre line favouring higher-volume protocols and only 1 is to the left favouring lower-volume protocols; the probability that this distribution occurred by random chance would be extremely unlikely. Third, Arruda et al. (2016) stated: “Although the authors made an effort to control for confounding variables by using regression models, these did not include other resistance training variables ...” This is incorrect. The duration of training and the portion of the body trained were included as variables. Other resistance training variables were not included because they were highly homogeneous across studies, and thus would not have any impact on outcomes. For example, the number of repetitions per set was not included because of the homogeneity and narrow range of repetitions across all studies. Training frequency was not included as it too was highly homogeneous across studies (2 or 3 days per week across nearly all studies). The authors went on to mention potential issues with upper versus lower body resistance training programmes by citing that of the 5 studies that included both upper and lower body training, 4 had different patterns of muscle hypertrophy, with upper body generally responding better to higher volumes than lower body. However, this is incorrect. For example, Radaelli et al. (2014) and Bottaro, Veloso, Wagner, and Gentil (2011), report greater percentage gains in the quadriceps with higher volume, but greater percentage gains in the biceps with lower volume. Ostrowski, Wilson, Weatherby, Murphy, and Little (1997) showed similar volume effects between upper and lower body; the percentage gain in triceps increased from 7 to 14 weekly sets, with no further benefit at 27 weekly sets. Similarly, quadriceps showed increased percentage gains from 6 to 12 weekly sets (volume above 12 weekly sets was not examined). While we agree that our analysis might not have been adequately powered to detect an interaction between volume and the portion of the body trained, it should be noted that 10 studies in our analysis included upper body training and 13 included lower body training. Sub-analysis of the data via meta-regression demonstrated dose–response volume effects both for lower and upper body muscles when considered independently (Schoenfeld, Ogborn, & Krieger, 2016). Thus, there is dose– response effect both for the upper and lower body. However, there is insufficient data to determine whether the effect varies between these conditions. Fourth, Arruda et al. (2016) stated: “Another possible caveat is analyzing the number of sets per exercise, instead of the number of sets in which the muscle was involved”. Our metaanalysis did in fact consider the muscle groups trained per week, not the sets per exercise. This is clearly stated in the methods section and noted throughout the manuscript. The confusion could lie in the mislabelling of Table 1 as “weekly sets per exercise” when it should have been labelled as “weekly sets per muscle group”. We regret that this error was not detected at the proof-reading stage. Arruda et al. (2016) went on to state that “upper body multi joint exercises should be included when counting the number of sets for arm muscles” and suggest that this was not accounted for in our analysis. This claim is incorrect; we did in fact include the relevant arm muscles involved in all multi-joint upper exercises as part of the meta-analysis. Fifth, Arruda et al. (2016) make a case that differences in training to failure could have influenced results. They discuss JOURNAL OF SPORTS SCIENCES, 2017 VOL. 35, NO. 20, 1985–1987 https://doi.org/10.1080/02640414.2016.1243800