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Most combat sports (CS) are structured in weight categories, and it is very common to carry out body weight adjustment strategies in order to compete in lower weight categories. For this reason, different rapid weight loss (RWL) strategies are usually performed to pass the pre-competition weigh-in test, and then a replenishment of fluids and carbohydrate-rich foods is conducted in an attempt to recover the weight and avoid a performance loss. In this context, no clear references have been found on whether these types of strategies have negative effects, impairing the athlete’s combat and/or physical performance. For this reason, the aim of this study was to review the scientific literature on the effect of rapid weight reduction strategies on the performance of CS athletes. A literature search was performed through four different databases (PubMed, SPORTDiscus, Web of Science and ScienceDirect). Four inclusion criteria were established as follows: (1) the subjects had to be competitors in the CS and carry out RWL strategies; (2) at least two measurement points, that is, normal conditions and dehydration condition; (3) measurements in a real competition or simulating the same conditions; (4) original research articles written in English or Spanish and available in full text. Finally, a total of 16 articles were finally included in this research. All subjects (n = 184) were athletes from combat disciplines, with a minimum of 3–4 years of practice, as well as with certain experience in RWL. Six of the studies reported that an RWL strategy of around 5% of body weight loss did not affect performance parameters. However, the other ten studies with RWL between 3 and 6% or even higher reported negative effects or impairments on different parameters related to performance and/or athlete’s psychophysiology, such as perceived fatigue, mood states, strength and power production, as well as changes in hormonal, blood and urine parameters, body composition, or the kinematics of the technical gesture. Although there is still no clear answer to the issue approached in this research, in general terms, it seems that in order to guarantee an acceptable athletic performance of the competitor, the weight loss should not exceed 3% to ≤5% of body weight together with ≥24 h for adequate (or at least partial) recovery and rehydration processes. In addition, it is highly recommended to lose weight progressively over several weeks, especially focusing on competitions lasting several days, as well as multiple rounds or qualifying stages.

The aim of this study was to examine the association of sedentary behaviour patterns with frailty in older people. Clinical setting. Cross-sectional, observational study. A triaxial accelerometer was used in a subsample from the Toledo Study for Healthy Aging (519 participants, 67-97 years) to assess several sedentary behaviour patterns including sedentary time per day, the number and duration (min) of breaks in sedentary time per day, and the proportion of the day spent in sedentary bouts of 10 minutes or more. Frailty was assessed using the Frailty Trait Scale (FTS). Regression analysis was used to ascertain the associations between sedentary behaviour patterns and frailty. Sedentary time per day and the proportion of the day spent in sedentary bouts of 10 minutes or more, were positively associated with frailty in the study sample. Conversely, the time spent in breaks in sedentary time was negatively associated with frailty. In summary, breaking up sedentary time and time spent in sedentary behaviour are associated with frailty in older people.

This study explores the relationships between biochemical phenotypes identified using machine learning, and key health outcomes, including body composition, physical function, and mortality risk. Data were collected from 536 physically active Spanish participants aged over 65 years (76.5% women) enrolled in the EXERNET cohort (2017–2018), with a 6-year mortality follow-up. Principal component analysis, and hierarchical and k-means clustering was used to identify distinct biochemical profiles. Associations between clusters and health outcomes were assessed using analysis of covariance and Cox proportional hazards models. Three distinct clusters emerged: ‘Healthy’, characterized by biochemical values within the normal range and used as the reference group; ‘Metabolic’, marked by dysregulated metabolic parameters; and ‘Hepatic’, which exhibited impaired liver function markers. Notably, all clusters showed subclinical levels of dysfunction. The ‘Healthy Cluster’ demonstrated the highest levels of organized physical activity (90%, p  < 0.001), whereas the ‘Metabolic Cluster’ showed poorer body composition and reduced physical performance. Both the ‘Metabolic’ and ‘Hepatic’ clusters demonstrated a higher mortality risk, as confirmed through Cox regression analyses. Adjusted hazard ratios were significantly elevated when considering physical activity and adiposity, with values of 3.45 and 3.71 for the ‘Metabolic Cluster’, and 3.01 and 3.85 for the ‘Hepatic Cluster’ ( p  < 0.05). This study underscores the strong link between metabolic health, physical activity, body composition and 6-years mortality risk in older adults. Machine learning techniques for identifying phenotypic clusters offers a promising tool for early detection and targeted interventions to improve aging outcomes.

The authors wish to thank Dr. Andrew S. Greenberg for kindly providing the anti-perilipin A antibody. Special thanks are given to José Navarro de Tuero for his excellent technical assistance. The specialized advice from Tony Webster in editing the English version of the manuscript is also acknowledged.

Background Although supervised exercise is frequently recommended for older adults, its superiority over unsupervised exercise remains uncertain. Furthermore, whether motivational techniques could help to enhance the effectiveness of the latter remains to be elucidated. The present randomized controlled trial aims to determine the role of supervision and motivational strategies on the safety, adherence, efficacy, and cost-effectiveness of different exercise programs for improving physical and mental health in older adults. Methods Participants ( n  = 120, aged 60–75 years) will be randomly allocated into five groups: 1-Control (CON), 2-Supervised exercise without motivational intervention (SUP), 3- Supervised exercise with motivational intervention (SUP +), 4- Unsupervised exercise without motivational intervention (UNSUP) and 5- Unsupervised exercise with motivational intervention (UNSUP +). Over 24 weeks, all exercise groups will participate in a multicomponent exercise program three times/week (performed in group classes at a center for SUP and SUP + , or home without supervision but with the help of a mobile app for UNSUP and UNSUP +), while the CON group will maintain their usual lifestyle. The motivational intervention (for SUP + and UNSUP + groups) will be based on the self-determination theory, including strategies such as phone calls, interactive workshops, motivational messages, informative infographics and videos. Primary outcomes will include safety, adherence, costs, and lower-body muscular function using a leg press machine. Secondary outcomes will include upper-body muscular function, physical and cardiorespiratory function, blood pressure and heart rate, body composition, health-related quality of life, cognitive performance, anxiety, depression, physical activity levels, sleep and sedentarism, biochemical markers, motivators and barriers to exercise. Assessments will be conducted at baseline, mid-intervention ( i.e., week 13), at the end of the intervention ( i.e., week 25), and 24 weeks later ( i.e., week 49). Discussion The findings of this trial might provide valuable insights into the role of supervision and motivational strategies on the effectiveness of exercise programs for older adults. Additionally, the study could contribute to developing cost-effective interventions, supporting the design of future public policies for healthy aging. Trial registration NCT05619250. Registered 16 November 2022.

Muscle size and architecture’s contribution to force and power production in young female acrobatic gymnasts (ACROs) remains unclear. This cross-sectional study examined the associations between quadriceps muscle size and architecture and strength–power performance in young elite female ACROs. Twenty base athletes (12–18 years) underwent ultrasound assessment of rectus femoris (RF) and vastus lateralis (VL) cross-sectional area (CSA), VL muscle volume, VL fascicle length, and VL pennation angle. Participants were additionally classified as pre/mid-pubertal (Tanner stages 1–3) and post-pubertal (Tanner stages 4–5) for descriptive analyses. Performance testing included one-repetition maximum (1RM) squat and hang power clean (HHPC), squat power (Pmax), and countermovement jump (CMJ). In adjusted (Tanner stage and height) linear regression models, VL CSA at 35% and 50% of femur length was positively associated with 1RM squat (β = 2.38–2.31 kg·cm−2; p = 0.031–0.011) and Pmax (β = 45.75–38.43 W·cm−2; p < 0.001). No associations were observed for CMJ, HHPC, or RF variables. Mid-thigh VL size appears to be an independent predictor of squat strength and power in ACRO.

Purpose: Although chronic resting hormonal changes were traditionally considered to modulate muscle tissue remodeling and growth, our knowledge of exercise on the acute post-exercise hormonal response is limited. Moreover, the type of exercise protocol may trigger different hormonal profiles. The aim of this study was to evaluate changes in muscle damage, as well as hormonal and inflammatory markers following the response to three different resistance training protocols. Methods: A crossover study was conducted in which 33 recreationally active men were randomly assigned to three different training groups: high-intensity interval training (HIIT), concurrent training (CT), and high-intensity resistance circuit (HRC) training. Blood biomarkers were measured by standard procedures at rest, after exercise (P0), 30 min (P1), 24 h (P24), and 48 h (P48) after exercise. Results: Regarding testosterone, the Friedman test detected a significant time × group interaction (p = 0.004), and Durbin–Conover showed higher levels in HRC compared to HIIT at P1 (p = 0.006) and P48 (p = 0.021). However, CT showed higher levels than HIIT (p = 0.008) at P1. Concerning myostatin, there was a trend in the time × group interaction (p = 0.056) with lower values in HRC compared to CT in P1 (p = 0.003), and a trend between HRC and HIIT in P1 (p = 0.056). Conclusions: HRC generates higher levels of testosterone than HIIT in the acute (P1) and late (P48) phases of recovery and produces lower levels of myostatin than CT and HIIT (P1) in the acute phase of recovery.

To determine whether androgen receptor (AR) CAG (polyglutamine) and GGN (polyglycine) polymorphisms influence bone mineral density (BMD), osteocalcin and free serum testosterone concentration in young men. Whole body, lumbar spine and femoral bone mineral content (BMC) and BMD, Dual X-ray Absorptiometry (DXA), AR repeat polymorphisms (PCR), osteocalcin and free testosterone (ELISA) were determined in 282 healthy men (28.6+/-7.6 years). Individuals were grouped as CAG short (CAG(S)) if harboring repeat lengths of < or = 21 or CAG long (CAG(L)) if CAG > 21, and GGN was considered short (GGN(S)) or long (GGN(L)) if GGN < or = 23 or > 23. There was an inverse association between logarithm of CAG and GGN length and Ward's Triangle BMC (r = -0.15 and -0.15, P<0.05, age and height adjusted). No associations between CAG or GGN repeat length and regional BMC or BMD were observed after adjusting for age. Whole body and regional BMC and BMD values were similar in men harboring CAG(S), CAG(L), GGN(S) or GGN(L) AR repeat polymorphisms. Men harboring the combination CAG(L)+GGN(L) had 6.3 and 4.4% higher lumbar spine BMC and BMD than men with the haplotype CAG(S)+GGN(S) (both P<0.05). Femoral neck BMD was 4.8% higher in the CAG(S)+GGN(S) compared with the CAG(L)+GGN(S) men (P<0.05). CAG(S), CAG(L), GGN(S), GGN(L) men had similar osteocalcin concentration as well as the four CAG-GGN haplotypes studied. AR polymorphisms have an influence on BMC and BMD in healthy adult humans, which cannot be explained through effects in osteoblastic activity.

The study was supported by Ministerio de Educación y Ciencia (DEP2006-56076-C06-04/ACTI) and FEDER (Fondo Europeo de Desarrollo Regional or European Regional Development Fund (ERDF)), Consejería de Educación, Cultura y Deportes del Gobierno de Canarias (2006/179 0001 and FEDER). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Background: Resistance training (RT) promotes muscle hypertrophy and strength gains in both men and women. However, sex differences in neuromuscular performance, muscle fiber composition, and the hormonal environment influence strength and power adaptations. While men generally exhibit greater absolute and relative strength, it remains unclear to what extent these differences persist across various load intensities. A better understanding of sex-specific strength and power profiles may help optimize training strategies. The aim of this study was to compare strength and power performance during the bench press exercise in physically active males and females, relative to body mass and fat-free mass (FFM). Methods: Twenty-nine physically active individuals (16 men: 21.3 ± 4.1 years, 13 women: 22.6 ± 4.9 years) performed a one-repetition maximum (1RM) test and an incremental velocity-based assessment at 45%, 55%, 65%, 75%, and 85% of the 1RM using a Smith machine. The barbell velocity was measured via a linear transducer, with the mean propulsive velocity (MPV) recorded for each load. Power-related variables (e.g., peak force [F0], maximal velocity [V0], and maximal power [Pmax]) were analyzed. To account for differences in body composition, data were adjusted for body mass and FFM. Results: Men exhibited significantly greater strength and power than women across most loads when adjusted for both body mass and fat-free mass (FFM) (p < 0.05). These differences were particularly pronounced when normalized to FFM (45–75%1RM; p = 0.001–0.031), with large effect sizes observed (ηp2 = 0.185–0.383). Notably, sex differences in mean propulsive velocity (MPV) disappeared at 85%1RM (p = 0.208; ηp2 = 0.06), suggesting that maximal neuromuscular recruitment may minimize sex-related disparities at higher intensities. Furthermore, men demonstrated significantly higher values in six of the seven power-related variables, with no significant differences in the %1RM required to achieve an optimal power output. Conclusions: These findings confirm that men exhibit greater strength and power than women, even after adjusting for body composition. However, at high relative loads (≥85%1RM), sex differences in movement velocity appear to diminish, likely due to similar recruitment patterns of high-threshold motor units. These results highlight the importance of sex-specific resistance training programs, particularly in relation to load prescription and the application of velocity-based training methods.