Influence of the Game Context and Levels of Physical Activity on the Behavior of Basketball Coaches / Influencia del Contexto de Juego y Niveles de Actividad Física en el Comportamiento de Entrenadores de Baloncesto

Coaches can exert a significant influence on players and games. The aim of this study is to assess behavior in the form of heart rate (HR) responses and distance traveled of basketball head coaches during matches considering the different match contexts faced as a score differences and time outs during the game direction and levels of physical activity and coaches experience. Four basketball head coaches are recruited in this case report. Mean and maximal absolute and relative HR are determined across matches. Scoring streaks (±6 consecutive points) and time outs are considered as influence value. The in-match distance traveled measured using microsensors, coaching experience, and daily activity on HR responses is also determined. Higher HR are evident across matches compared to periods prior to and following matches. HR is significantly (P <.05) elevated during later time-outs compared to earlier time-outs, with positive scoring streaks yielding a tendency towards higher HR compared to negative and no scoring streaks. Experience (rs = -0.91, P <.001), daily activity levels (rs = -0.83, P <.001), and weekly METS (rs = -0.78, P =.002) are negatively associated with match HR, while in-match distance (rs = 0.69, P =.013) is positively associated with match HR. These data indicate that matches elevate the cardiovascular stress imposed on basketball head coaches, with later time-outs and positive scoring streaks promoting heightened HR. Moreover, in-match activity and personal characteristics (less experience and lower physical activity) further augment match HR in coaches.(AU)

Moderate-intensity exercise performed in the evening does not impair sleep in healthy males.

The aim of this study was to examine the effect of single bouts of moderate-intensity aerobic exercise and moderate-intensity resistance exercise performed in the evening on the sleep of healthy young males. The study employed a repeated-measures, counterbalanced, crossover design with three conditions (control, evening aerobic exercise, evening resistance exercise). Twelve male participants (mean ± SD; age: 21.9 ± 2.7 yr) attended the laboratory on three occasions separated by one day between each visit. Between 20:45 h and 21:30 h, participants completed either no exercise, 30 min of aerobic exercise at 75%HRmax, or 30 min of resistance exercise corresponding to 75% of 10-repetition maximum. A 9-h sleep opportunity was provided between 23:00 h and 08:00 h. Core body temperature was measured using ingestible temperature capsules and sleep was measured using polysomnography. Core body temperature was higher during the aerobic exercise and resistance exercise compared to control (p = 0.001). There was no difference in core body temperature at bedtime between the conditions. Sleep onset latency, total sleep time, slow-wave sleep duration, REM sleep duration, wake after sleep onset and sleep efficiency were similar in each condition (p > 0.05). Single bouts of moderate-intensity aerobic exercise or moderate-intensity resistance exercise performed in the evening did not impact subsequent night-time sleep. Core body temperature increased during both forms of exercise, but returned to pre-exercise levels in the 90 min prior to bedtime. Healthy young males can engage in a single bout of moderate-intensity aerobic exercise or moderate-intensity resistance exercise ceasing 90 min before bed without compromising their subsequent sleep.

Exercise training in overweight and obese children: Recreational football and high-intensity interval training provide similar benefits to physical fitness.

This study compared the effects of recreational football and high-intensity interval training (HIIT) on body composition, muscular fitness, and cardiorespiratory fitness in overweight and obese children. Forty-two overweight/obese males aged 11-13 years [body mass index (BMI) >20.5 kg/m2 ] were randomly assigned to a recreational football training group (n = 14; 157.9 ± 5.8 cm; 63.7 ± 12.6 kg), HIIT group (n = 14; 163.8 ± 9.4 cm; 71.5 ± 10.5 kg), or nontraining control group (n = 14; 162.7 ± 9.3 cm; 67.4 ± 16.1 kg). Physical fitness components were measured at baseline and after 12 weeks of training at the same time of the day and under similar conditions, including body composition, muscular fitness (lower-body power, change-of-direction speed, and flexibility), and cardiovascular fitness (Yo-Yo Intermittent Endurance test distance, resting heart rate, and blood pressure). Lean body mass (4.3%, ES = 0.40; 95% CI: -0.48, 1.29; P = .382) and muscle mass 4.4% (ES = 0.40; 95% CI: -0.48, 1.29; P = .378) very likely increased in the recreational football group, while possible improvements were observed in the HIIT group (lean body mass: 2.5%, ES = 0.22; 95% CI: -0.62, 1.06; P = .607, muscle mass: 2.8%, ES = 0.23; 95% CI: -0.61, 1.07; P = .594). Only trivial increases were observed in the control group for lean body mass (0.5%, ES = 0.05; 95% CI: -0.70, 0.79; P = .906) and muscle mass (1.1%, ES = 0.09; 95% CI: -0.65, 0.83; P = .814). Significant differences were found between the recreational football and control groups in post-training body mass (P = .034) and body mass index (P = .017). Body fat very likely decreased in the recreational football group (-7.7%, ES = -0.41; 95% CI: -1.29, 0.48; P = .376) and possibly decreased in the HIIT group (-5.2%, ES = -0.22; 95% CI: -1.05, 0.62; P = .607), with a trivial reduction in the control group (-1.1%, ES = -0.04; 95% CI: -0.78, 0.70; P = .914). Very likely increases in lower-body power were evident in the recreational football (17.0%, ES = 0.76; 95% CI: -0.15, 1.66; P = .107) and control groups (16.1%, ES = 0.55; 95% CI: -0.20, 1.31; P = .156), while small improvements were observed in the HIIT group (6.0%, ES = 0.24; 95% CI: -0.60, 1.08; P = .580, possible). Likely to most likely improvements in Yo-Yo Intermittent Endurance test performance and change-of-direction speed were noted in the recreational football group (Yo-Yo: 79.8%, ES = 1.09; 95% CI: 0.16, 2.03; P = .025, change-of-direction speed: -10.6%, ES = -1.05; 95% CI: -1.98, -0.12; P = .031) and the HIIT group (Yo-Yo: 81.2%, ES = 1.03; 95% CI: 0.15, 1.92; P = .025, change-of-direction speed: -5.4%, ES = -0.91; 95% CI: -1.79, -0.04; P = .045). Diastolic blood pressure likely decreased in the recreational football (-8.6%, ES = -0.74; 95% CI: -1.64, 0.17; P = .116) and HIIT groups (-9.8%, ES = -0.57; 95% CI: -1.40, 0.30; P = .195), with a possible increase in the control group (1.2%, ES = 0.21; 95% CI: -0.53, 0.96; P = .068). Recreational football and HIIT elicited improvements in all muscular and cardiorespiratory fitness measures. In contrast, the control group, which performed only physical education classes, increased body mass, BMI, and fat mass. Therefore, additional activities such as recreational football or HIIT might counter the prevalence of overweight and obesity in children.

A gut reaction: the combined influence of exercise and diet on gastrointestinal microbiota in rats.

AIMS: Intestinal microbiota modulates the development of clinical conditions, including metabolic syndrome and obesity. Many of these conditions are influenced by nutritional and exercise behaviours. This study aimed to investigate the ability of exercise to re-shape the intestinal microbiota and the influence of the diet on the process. METHODS AND RESULTS: A rat model was used to examine the intestinal microbiota responses to four activity conditions, including: high-intensity interval training (HIIT), light-intensity training (LIT), sedentary and normal control, each containing two nutritional conditions: high-fat high-fructose diet (HF) and standard chow (SC) diet. No significant differences in microbiota were apparent between activity conditions in rats fed a HF diet but changes in the presence/absence of phylotypes were observed in the LIT and HIIT groups. In rats fed SC, significant differences in intestinal microbiota were evident between exercised and nonexercised rats. Both LIT and HIIT induced significant differences in intestinal microbiota in SC-fed rats compared to their respective SC-fed controls. Characterization of the exercise-induced bacterial phylotypes indicated an increase in bacteria likely capable of degrading resistant polysaccharides and an increase in short chain fatty acid producers. CONCLUSIONS: While a significant effect of exercise on microbiota composition occurred in SC-fed rats, the HF-fed rats microbiota showed little response. These data suggest that a HF diet prevented microbiota differentiation in response to exercise. SIGNIFICANCE AND IMPACT OF THE STUDY: The importance of diet-exercise interaction is extended to the level of intestinal bacteria and gut health.

The importance of open- and closed-skill agility for team selection of adult male basketball players.

AIM: Open-skill agility qualities have yet to be described in adult male basketball players. Further, the importance of open- and closed-skill agility for team selection remains unknown. Thus, this study aimed to: 1) describe the open- and closed-skill agility of adult male basketball players; and 2) compare these properties between starting and non-starting players. METHODS: A cross-sectional between-group design was used. Six starting (playing time: 30.1 ± 8.8 min; age: 30.5 ± 4.8 years; height: 192.1 ± 7.7 cm; body mass: 100.5 ± 15.0 kg; VO(2max): 48.4 ± 6.6 mL∙kg⁻¹âˆ™min⁻¹) and six non-starting (4.3 ± 3.6 min; 21.3 ± 5 years; 185.7 ± 7.4 cm; 94.4 ± 17.9 kg; 50.6 ± 3.9 mL∙kg⁻¹âˆ™min⁻¹) state-level basketball players completed multiple trials for the Change of Direction Speed Test (CODST) and Reactive Agility Test (RAT). RESULTS: No statistically significant between-group differences were evident for CODST movement time (starters: 1.652 ± 0.047 s; non-starters: 1.626 ± 0.040 s, P=0.68), RAT response time (starters: 307.5 ± 100.5 ms; non-starters: 426.5 ± 140.7 ms, P=0.12), and RAT decision-making time (starters: 110.7 ± 11.0 ms; non-starters: 147.3 ± 14.2 ms, P=0.08). However, starters (2.001 ± 0.051 s) possessed significantly (P=0.02) faster RAT total movement times than non-starters (2.182 ± 0.040 s). CONCLUSION: These data support the utility of perceptual and cognitive components of agility performance in distinguishing starting from non-starting players in basketball. Consequently, basketball coaching and conditioning staff should incorporate sport-specific reactive training drills for all players during the annual conditioning plan.