With the growth of social media as a platform to share information, veganism is becoming more visible, and could be becoming more accepted in sports and in the health and fitness industry. However, to date, there appears to be a lack of literature that discusses how to manage vegan diets for athletic purposes. This article attempted to review literature in order to provide recommendations for how to construct a vegan diet for athletes and exercisers. While little data could be found in the sports nutrition literature specifically, it was revealed elsewhere that veganism creates challenges that need to be accounted for when designing a nutritious diet. This included the sufficiency of energy and protein; the adequacy of vitamin B12, iron, zinc, calcium, iodine and vitamin D; and the lack of the long-chain n-3 fatty acids EPA and DHA in most plant-based sources. However, via the strategic management of food and appropriate supplementation, it is the contention of this article that a nutritive vegan diet can be designed to achieve the dietary needs of most athletes satisfactorily. Further, it was suggested here that creatine and β-alanine supplementation might be of particular use to vegan athletes, owing to vegetarian diets promoting lower muscle creatine and lower muscle carnosine levels in consumers. Empirical research is needed to examine the effects of vegan diets in athletic populations however, especially if this movement grows in popularity, to ensure that the health and performance of athletic vegans is optimised in accordance with developments in sports nutrition knowledge.

This study investigated the inter-day and intra-device reliability, and criterion validity of six devices for measuring barbell velocity in the free-weight back squat and power clean. In total, 10 competitive weightlifters completed an initial one repetition maximum (1RM) assessment followed by three load-velocity profiles (40–100% 1RM) in both exercises on four separate occasions. Mean and peak velocity was measured simultaneously on each device and compared to 3D motion capture for all repetitions. Reliability was assessed via coefficient of variation (CV) and typical error (TE). Least products regression (LPR) (R²) and limits of agreement (LOA) assessed the validity of the devices. The Gymaware was the most reliable for both exercises (CV < 10%; TE < 0.11 m·s⁻¹, except 100% 1RM (mean velocity) and 90‒100% 1RM (peak velocity)), with MyLift and PUSH following a similar trend. Poorer reliability was observed for Beast Sensor and Bar Sensei (CV = 5.1–119.9%; TE = 0.08–0.48 m·s⁻¹). The Gymaware was the most valid device, with small systematic bias and no proportional or fixed bias evident across both exercises (R² > 0.42–0.99 LOA = −0.03–0.03 m·s⁻¹). Comparable validity data was observed for MyLift in the back squat. Both PUSH devices produced some fixed and proportional bias, with Beast Sensor and Bar Sensei being the least valid devices across both exercises (R² > 0.00–0.96, LOA = −0.36–0.46 m·s⁻¹). Linear position transducers and smartphone applications could be used to obtain velocity-based data, with inertial measurement units demonstrating poorer reliability and validity.

Background: Optimal prescription of resistance exercise load (kg) is essential for the development of maximal strength. Two methods are commonly used in practice with no clear consensus on the most effective approach for the improvement of maximal strength.Objective: The primary aim of this review was to compare the effectiveness of percentage 1RM (% 1RM) and repetition maximum targets (RM) as load prescription methods for the development of maximal strength.Methods: Electronic database searches of MEDLINE, SPORTDiscus, Scopus, and CINAHL Complete were conducted in accordance with PRISMA guidelines. Studies were eligible for inclusion if a direct measure of maximal strength was used, a non-training control group was a comparator, the training intervention was > 4 weeks in duration and was replicable, and participants were defined as healthy and between the ages of 18–40. Methodological quality of the studies was evaluated using a modified Downs and Black checklist. Percentage change (%) and 95% confidence intervals (CI) for all strength-based training groups were calculated. Statistical significance (p < 0.05) was reported from each study.Results: Twenty-two studies comprising a total of 761 participants (585 males and 176 females) were found to meet the inclusion criteria. 12 studies were returned for % 1RM, with 10 for RM. All studies showed statistically significant improvements in maximal strength in the training groups (31.3 ± 21.9%; 95% CI 33.1–29.5%). The mean quality rating for all studies was 17.7 ± 2.3. Four studies achieved a good methodological rating, with the remainder classified as moderate.Conclusions: Both % 1RM and RM are effective tools for improving maximal strength. % 1RM appears to be a better prescriptive method than RM potentially due to a more sophisticated management of residual fatigue. However, large heterogeneity was present within this data. Lower body and multi-joint exercises appear to be more appropriate for developing maximal strength. Greater consensus is required in defining optimal training prescriptions, physiological adaptations, and training status.

Long-term weight management consists of weight-loss, weight-loss maintenance, and weight-gain stages. Qualitative insights into weight management are now appearing in the literature however research appears to be biased towards explorations of weight-loss maintenance. The qualitative understanding of weight loss, which begets weight-loss maintenance and might establish the experiences and behaviours necessary for successful long-term weight management, is comparatively under-investigated. The aim of this study was to investigate the weight-loss experiences of a sample of participants not aligned to clinical intervention research, in order to understand the weight-loss experiences of a naturalistic sample. Participants (n = 8) with weight-loss (n = 4) and weight-maintenance experiences (n = 4) were interviewed using a semi-structured interview to understand the weight-loss experience. Interview data was analysed thematically using Framework Analysis and was underpinned by realist meta-theory. Weight loss was experienced as an enduring challenge, where factors that assisted weight loss were developed and experienced dichotomously to factors that hindered it. Participants described barriers to (dichotomous thinking, environments, social pressures and weight centeredness) and facilitators of (mindfulness, knowledge, exercise, readiness to change, structure, self-monitoring and social support) their weight-loss goals in rich detail, highlighting that weight loss was a complex experience. Weight loss was a difficult task, with physical, social, behavioural and environmental elements that appeared to assist and inhibit weight-loss efforts concurrently. Health professionals might need to better understand the day-to-day challenges of dieters in order to provide more effective, tailored treatments. Future research should look to investigate the psycho-social consequences of weight-loss dieting, in particular self-imposed social exclusion and spousal sabotage and flexible approaches to treatment.

The study aim was to compare different predictive models in one repetition maximum (1RM) estimation from load-velocity profile (LVP) data. Fourteen strength-trained men underwent initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Profiles were constructed via a combined method (jump squat (0 load, 30–60% 1RM) + back squat (70–100% 1RM)) or back squat only (0 load, 30–100% 1RM) in 10% increments. Quadratic and linear regression modeling was applied to the data to estimate 80% 1RM (kg) using 80% 1RM mean velocity identified in LVP one as the reference point, with load (kg), then extrapolated to predict 1RM. The 1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (g/${\eta }_p^2$), Pearson correlation coefficients (r), paired t-tests, standard error of the estimate (SEE), and limits of agreement (LOA). p < 0.05. All models reported systematic bias < 10 kg, r > 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (p = 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (p < 0.001; ${\eta }_p^2$ = 0.90) and back squat (p = 0.004, ${\eta }_p^2$ = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001, ${\eta }_p^2$ = 0.67–0.80), but not quadratic (p = 0.632–0.929, ${\eta }_p^2$= 0.001–0.18). Quadratic modeling employing the combined method rendered the greatest predictive validity. Practitioners should therefore utilize this method when looking to predict daily 1RMs as a means of load autoregulation.

Combat sports have been practiced for millennia and today are predominant sports at the Olympic games, with international organizations that host world, continental and national championships at amateur standard. There are also an increasing number of professional combat sports with global audiences. The growing popularity of professional combat sports and their importance at the Olympic games have led to an increase in scientific studies that characterize the physical, physiological, nutritional, biomechanical and training strategies of combat sports athletes. These studies characterize combat sports as high-intensity sports which require training strategies to develop the high-intensity capabilities of athletes. Therefore, the aim of this article is to (i) summarize the physiological demands of combat sports; (ii) present the primary considerations required to program high-intensity conditioning for athletes; (iii) define and present key high-intensity conditioning methods; and (iv) provide guidance for scientists and coaches to help prepare athletes under common but differing circumstances.