Sedentary behaviour, physical activity and cardiometabolic health in highly trained athletes: A systematic review and meta-analysis

Empowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management

Artificial Intelligence (AI) is transforming the field of sports science by providing unprecedented insights and tools that enhance training, performance, and health management. This work examines how AI is advancing the role of sports scientists, particularly in team sports environments, by improving training load management, sports performance, and player well-being. It explores key dimensions such as load optimization, injury prevention and return-to-play, sports performance, talent identification and scouting, off-training behavior, sleep quality, and menstrual cycle management. Practical examples illustrate how AI applications have significantly advanced each area and how they support and enhance the effectiveness of sports scientists. This manuscript also underscores the importance of ensuring that AI technologies are context-specific and communicated transparently. Additionally, it calls for academic institutions to update their curriculums with AI-focused education, preparing future sports professionals to fully harness its potential. Finally, the manuscript addresses future challenges, such as the unpredictable nature of team sports, emphasizing the need for interdisciplinary collaboration, including clear communication and mutual understanding between sports scientists and AI experts, and the critical balance between AI-driven insights and human expertise.

Characteristics of non-exercise activity thermogenesis in male collegiate athletes under real-life conditions

Athletes experience high total energy expenditure; therefore, it is important to understand the characteristics of the components contributing to this expenditure. To date, few studies have examined particularly the volume and activity intensity of non-exercise activity thermogenesis (NEAT) in athletes compared to non-athletes under real-life conditions. This study aimed to determine the volume and intensity of NEAT in collegiate athletes. Highly trained Japanese male collegiate athletes (n = 21) and healthy sedentary male students (n = 12) participated in this study. All measurements were obtained during the athletes' regular training season under real-life conditions. NEAT was calculated using metabolic equivalent (MET) data using an accelerometer. The participants were asked to wear a validated triaxial accelerometer for 7 consecutive days. Physical activity intensity in NEAT was classified into sedentary (1.0-1.5 METs), light (1.6-2.9 METs), moderate (3.0-5.9 METs), and vigorous (≥6 METs) intensity. NEAT was significantly higher in athletes than in non-athletes (821 ± 185 kcal/day vs. 643 ± 164 kcal/day, p = 0.009). Although there was no significant difference in NEAT values relative to body weight (BW) between the groups (athletes: 10.5 ± 1.7 kcal/kg BW/day, non-athletes: 10.4 ± 2.2 kcal/kg BW/day, p = 0.939), NEAT to BW per hour was significantly higher in athletes than in non-athletes (0.81 ± 0.16 kcal/kg BW/h vs. 0.66 ± 0.12 kcal/kg BW/h, p = 0.013). Athletes spent less time in sedentary and light-intensity activities and more time in vigorous-intensity activities than non-athletes (p < 0.001, p = 0.019, and p = 0.030, respectively). Athletes expended more energy on vigorous- and moderate-intensity activities than non-athletes (p = 0.009 and p = 0.011, respectively). This study suggests that athletes' NEAT relative to BW per day is similar to that of non-athletes, but athletes spend less time on NEAT, which makes them more active in their daily lives when not exercising and sleeping.

Sedentarism in Recreational Marathon Runners

Aim

Although it has been previously observed that sedentary behavior (SB) was not related to training duration in marathon runners, little information existed about the relationship of SB with training, anthropometric and physiological characteristics in this population. This study aimed to investigate the prevalence of SB and its correlation with performance parameters (such as body fat percentage, maximal oxygen uptake and weekly training volume) as well as its variation by sex and day (ie, weekdays versus weekend) in recreational marathon runners.

Methods

A total of 151 finishers (women, n = 29; men, n = 122; age 43.1 (8.7) years, mean (standard deviation)) in the Athens marathon 2017 performed a series of anthropometric and physiological tests, and completed the Multi-context sitting time questionnaire.

Results

SB did not correlate with anthropometric and physiological characteristics and no difference in these characteristics was shown between low and high sedentary participants (p > 0.05). SB did not differ between women and men (p > 0.05), but differed between working and non-working days (p < 0.05).

Conclusion

In contrast to previous findings on the general population indicating an association of a high SB with a low cardiorespiratory and muscular fitness, our finding of no correlation between SB and physical fitness in marathon runners suggested that endurance exercise might offset the negative effects of SB.

Effectiveness of wearable technology to optimize youth soccer players' off-training behaviour and training responses: a parallel group randomized trial

BACKGROUND

Although youth soccer players meet and exceed the weekly physical activity (PA) guidelines also exhibit high off-training sedentary behaviour (SB) levels. Recent evidence indicated that low PA preceding training leads to decreased PA intensity in soccer practice and that SB impairs sports performance and recovery. Thus, a parallel group randomized trial examined the effect of activity wristbands on young players' off-training PA and SB profiles; assessed the added value of wearable wristbands with PA warnings; and investigated whether manipulating off-training PA can affect the players' training responses.

METHODS

Thirty-two adolescent soccer players (16.1 ± 0.9 years old) were monitored during weekdays for two weeks (interspersed with one week). Players were randomly assigned to a reminder to move (REM) and a non-reminder to move group (nREM). The REM wore an activity wristband (Fitbit Charge 2) with PA warnings in the last week of research, while the nREM wore identical monitors without PA feedback. Throughout the study, off-training PA was assessed using tri-axial accelerometers, and training responses were analysed using wearable inertial monitoring units. Gardner-Altman estimation plots and a Fisher's Exact Test of Independence estimated each group's off-training PA changes between the monitored weeks. Complementary, an analysis of covariance (ANCOVA) quantified the effect of Fitbit wristband configurations (with a reminder to move or without a reminder to move) on the off-training PA and soccer training external load variables. The independent variable group had two levels (REM and nREM). The off-training PA and training load values collected in the first week were introduced in the model as a covariate, the groups were designated fixed factors and the off-training PA and training load from the third week were the dependent variables. The team was also included as a covariate and the between-group differences post-intervention were adjusted for baseline and team.

RESULTS

Interestingly, results showed that different wearable wristbands did not influence the players' off-training PA profiles (p > 0.05). Concomitantly, no differences were observed in training responses (p > 0.05).

CONCLUSION

Findings emphasize the importance of health and youth sports organizations in developing newer approaches for promoting healthier lifestyles, beyond training practices, with potentially favourable implications for sports performance.

Energy constraint and compensation: Insights from endurance athletes

The Constrained Model of Total Energy Expenditure predicts that increased physical activity may not influence total energy expenditure, but instead, induces compensatory energetic savings in other processes. Much remains unknown, however, about concepts of energy expenditure, constraint and compensation in different populations, and it is unclear whether this model applies to endurance athletes, who expend very large amounts of energy during training and competition. Furthermore, it is well-established that some endurance athletes consciously or unconsciously fail to meet their energy requirements via adequate food intake, thus exacerbating the extent of energetic stress that they experience. Within this review we A) Describe unique characteristics of endurance athletes that render them a useful model to investigate energy constraints and compensations, B) Consider the factors that may combine to constrain activity and total energy expenditure, and C) Describe compensations that occur when activity energy expenditure is high and unmet by adequate energy intake. Our main conclusions are as follows: A) Higher activity levels, as observed in endurance athletes, may indeed increase total energy expenditure, albeit to a lesser degree than may be predicted by an additive model, given that some compensation is likely to occur; B) That while a range of factors may combine to constrain sustained high activity levels, the ability to ingest, digest, absorb and deliver sufficient calories from food to the working muscle is likely the primary determinant in most situations and C) That energetic compensation that occurs in the face of high activity expenditure may be primarily driven by low energy availability i.e., the amount of energy available for all biological processes after the demands of exercise have been met, and not by activity expenditure per se.

Evidence that heat acclimation training may alter sleep and incidental activity

This randomized cross-over study tested the hypothesis that heat acclimation training would detrimentally affect sleep variables and alter incidental physical activity compared to a thermoneutral training control condition. Eight recreationally trained males (V̇O2peak 49±4.9 mL.kg-1.min-1) completed two separate interventions separated by at least 31 days: 5 consecutive day training blocks of moderate-intensity cycling (60 min·day-1 at 50% peak power output) in a hot (34.9±0.7 °C and 53±4 % relative humidity) and a temperate (22.2±2.6 °C; 65±8 % relative humidity) environment. Wrist-mounted accelerometers were worn continuously for the length of the training blocks and recorded physical activity, sleep quality and quantity. Data were analysed in a Bayesian framework, with the results presented as the posterior probability that a coefficient was greater or less than zero. Compared to the temperate training environment, heat acclimation impaired sleep efficiency (Pr β<0 = .979) and wake on sleep onset (Pr β>0 = .917). Daily sedentary time was, on average, 35 min longer (Pr β>0 = .973) and light physical activity time 18 min shorter (Pr β>0 = .960) during the heat acclimation period. No differences were observed between conditions in sleep duration, subjective sleep quality, or moderate or vigorous physical activity. These findings may suggest that athletes and coaches need to be cognisant that heat acclimation training may alter sleep quality and increase sedentary behaviour.HighlightsFive consecutive days of heat training negatively affected some objective measures of sleep quality and incidental physical activity in recreationally trained athletes.Athletes and coaches need to be aware of the potential unintended consequences of using heat acclimation on sleep behaviours.