Loading recommendations for resistance training are typically prescribed along what has come to be known as the “repetition continuum”, which proposes that the number of repetitions performed at a given magnitude of load will result in specific adaptations. Specifically, the theory postulates that heavy load training optimizes increases maximal strength, moderate load training optimizes increases muscle hypertrophy, and low-load training optimizes increases local muscular endurance. However, despite the widespread acceptance of this theory, current research fails to support some of its underlying presumptions. Based on the emerging evidence, we propose a new paradigm whereby muscular adaptations can be obtained, and in some cases optimized, across a wide spectrum of loading zones. The nuances and implications of this paradigm are discussed herein.

Human muscle fibers are generally classified by myosin heavy chain (MHC) isoforms characterized by slow to fast contractile speeds. Type I, or slow-twitch fibers, are seen in high abundance in elite endurance athletes, such as long-distance runners and cyclists. Alternatively, fast-twitch IIa and IIx fibers are abundant in elite power athletes, such as weightlifters and sprinters. While cross-sectional comparisons have shown marked differences between athletes, longitudinal data have not clearly converged on patterns in fiber type shifts over time, particularly between slow and fast fibers. However, not all fiber type identification techniques are created equal and, thus, may limit interpretation. Hybrid fibers, which express more than one MHC type (I/IIa, IIa/IIx, I/IIa/IIx), may make up a significant proportion of fibers. The measurement of the distribution of fibers would necessitate the ability to identify hybrid fibers, which is best done through single fiber analysis. Current evidence using the most appropriate techniques suggests a clear ability of fibers to shift between hybrid and pure fibers as well as between slow and fast fiber types. The context and extent to which this occurs, along with the limitations of current evidence, are discussed herein.

Purpose: To conduct a systematic review and multilevel meta-analysis of the current literature as to the effects of interval training (IT) vs moderate intensity continuous training (MICT) on measures of body composition, both on a whole-body and regional level. Methods: We searched English-language papers on PubMed/MEDLINE, Scopus, CINAHL, and sportrxiv for the following inclusion criteria: (a) randomized controlled trials that directly compared IT vs MICT body composition using a validated measure in healthy children and adults; (b) training was carried out a minimum of once per week for at least four weeks; (c) published in a peer-reviewed English language journal or on a pre-print server. Results: The main model for fat mass effects revealed a trivial standardized point estimate with high precision for the interval estimate, with moderate heterogeneity (−0.016 (95%CI −0.07 to 0.04); I² = 36%). The main model for fat-free mass (FFM) effects revealed a trivial standardized point estimate with high precision for the interval estimate, with negligible heterogeneity (−0.0004 (95%CI −0.05 to 0.05); I² = 16%). The GRADE summary of findings suggested high certainty for both main model effects. Conclusions: Our findings provide compelling evidence that the pattern of intensity of effort and volume during endurance exercise (i.e., IT vs MICT) has minimal influence on longitudinal changes in fat mass and FFM, which are likely to minimal anyway. Trial registration number: This study was preregistered on the Open Science Framework.

We examined how set-volume equated resistance training using either the back squat (SQ) or hip thrust (HT) affected hypertrophy and various strength outcomes. Untrained college-aged participants were randomized into HT (n = 18) or SQ (n = 16) groups. Surface electromyograms (sEMG) from the right gluteus maximus and medius muscles were obtained during the first training session. Participants completed 9 weeks of supervised training (15–17 sessions), before and after which gluteus and leg muscle cross-sectional area (mCSA) was assessed via magnetic resonance imaging. Strength was also assessed prior to and after the training intervention via three-repetition maximum (3RM) testing and an isometric wall push test. Gluteus mCSA increases were similar across both groups. Specifically, estimates [(−) favors HT (+) favors SQ] modestly favored the HT versus SQ for lower [effect ±SE, −1.6 ± 2.1 cm²; CI_95% (−6.1, 2.0)], mid [−0.5 ± 1.7 cm²; CI_95% (−4.0, 2.6)], and upper [−0.5 ± 2.6 cm²; CI_95% (−5.8, 4.1)] gluteal mCSAs but with appreciable variance. Gluteus medius + minimus [−1.8 ± 1.5 cm²; CI_95% (−4.6, 1.4)] and hamstrings [0.1 ± 0.6 cm²; CI_95% (−0.9, 1.4)] mCSA demonstrated little to no growth with small differences between groups. mCSA changes were greater in SQ for the quadriceps [3.6 ± 1.5 cm²; CI_95% (0.7, 6.4)] and adductors [2.5 ± 0.7 cm²; CI_95% (1.2, 3.9)]. Squat 3RM increases favored SQ [14 ± 2 kg; CI_95% (9, 18),] and hip thrust 3RM favored HT [−26 ± 5 kg; CI_95% (−34, −16)]. 3RM deadlift [0 ± 2 kg; CI_95% (−4, 3)] and wall push strength [−7 ± 12N; CI_95% (−32, 17)] similarly improved. All measured gluteal sites showed greater mean sEMG amplitudes during the first bout hip thrust versus squat set, but this did not consistently predict gluteal hypertrophy outcomes. Squat and hip thrust training elicited similar gluteal hypertrophy, greater thigh hypertrophy in SQ, strength increases that favored exercise allocation, and similar deadlift and wall push strength increases.

The inconsistency and disparities in existing functional training definitions have led to confusion when explaining the concept and its potential. The wide range of interpretations suggests that any training approach could be deemed functional, thereby diminishing the significance of the term and significantly limiting its understanding and application. Thus, this paper aimed to develop the first consensual definition of functional training using an international e-Delphi method. From a panel of 31 experts initially selected, 13 participated in the consensus. The panel presented the following definition: 'Functional training is a physical interventional approach that contributes to the enhancement of human performance, according to individual goals, in sports, daily life, rehabilitation, or fitness, and takes into consideration the specificity of the task and the unique responsiveness of each individual'. However, redundancy of the functional training definition emerged as a relevant consideration for this conceptual and methodological advancement, and a proposal to avoid the distinction between functional training and the general concept of training was presented (i.e., no real use of functional training as a concept). It was proposed that a training program or regimen could be analyzed based on a continuum of functionality, which could support further developments in this topic.

The purpose of this study was to improve our understanding of the relative contributions of biomechanical, anthropometric, and psychological factors in explaining maximal bench press (BP) strength in a heterogeneous, resistance-trained sample. Eighteen college-aged participants reported to the laboratory for three visits. The first visit consisted of psychometric testing. The second visit assessed participants’ anthropometrics, additional psychometric outcomes, and bench press one repetition maximum (1RM). Participants performed isometric dynamometry testing for horizontal shoulder adduction and elbow extension at a predicted sticking point joint position. Multiple linear regression was used to examine the relationships between the biomechanical, anthropometric, and psychological variables and BP 1RM. Our primary multiple linear regression accounted for 43% of the variance in BP strength (F(3,14) = 5.34, p = 0.01; R² = 0.53; adjusted R² = 0.43). The sum of peak isometric net joint moments from the shoulder and elbow had the greatest standardized effect (0.59), followed by lean body mass (0.27) and self-efficacy (0.17). The variance in BP 1RM can be similarly captured (R² = 0.48) by a single principal component containing anthropometric, biomechanics, and psychological variables. Pearson correlations with BP strength were generally greater among anthropometric and biomechanical variables as compared to psychological variables. These data suggest that BP strength among a heterogeneous, resistance-trained population is explained by multiple factors and is more strongly associated with physical than psychological variables.