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

Franssen, Wouter M. A.; Vanbrabant, Eva; Cuveele, Eline; Ivanova, Anna; Franssen, Gregor H L M; Eijnde, Bert O.

doi:10.1080/17461391.2021.1955013

Cited by 7 publications

(5 citation statements)

References 99 publications

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“…Therefore, the intensity and duration of NEAT activity were examined in detail in this study. A meta-analysis on sedentary behavior and physical activity in athletes ( 16 ) showed that athletes were significantly more inactive than the general population (time in sedentary behavior; 576 ± 136 min/day vs. 513 ± 105 min/day). Alméras et al ( 25 ) found no significant differences in daily energy expenditure or physical activity patterns during non-exercise periods between cross-country skiers and sedentary men.…”

Section: Discussionmentioning

confidence: 99%

“…An exercise training intervention study ( 15 ) found that participants tended to compensate for increased energy expenditure associated with exercise training by reducing non-training activities and spending the rest of the day on sedentary activities. A meta-analysis on sedentary behavior and physical activity in competitive and recreational athletes ( 16 ) revealed that athletes spent significantly more time engaging in sedentary behavior than the general population (time in sedentary behavior; 576 ± 136 min/day vs. 513 ± 105 min/day). Athletes have high EEE, and they may compensate for the increased energy expenditure associated with exercise training by increasing time in sedentary behavior and decreasing NEAT volume in their daily activities.…”

Section: Introductionmentioning

confidence: 99%

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

Goshozono,

Miura,

Torii

et al. 2024

Front. Sports Act. Living

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Goshozono,

Miura,

Torii

et al. 2024

Front. Sports Act. Living

View full text Add to dashboard Cite

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“…The causative factor(s) for elevated sedentariness in athletes is currently unknown. However, it has been speculated that training‐induced fatigue may attenuate off‐training physical activity behaviours (Franssen et al, 2021). The present findings support this hypothesis, with altered activity profiles, including increased sedentary time, indicating that participants may have modified their off‐training behaviours to recuperate and recover from the elevated internal training load (Izzicupo et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Evidence that heat acclimation training may alter sleep and incidental activity

Osborne

Minett

Stewart

et al. 2022

European Journal of Sport Science

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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 (V ̇O2peak 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. Highlights. Five 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.

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“…Mostly, athletes exhibit substantial amount of sedentary and light activity as well as prolonged sitting [33], surpassing the levels observed in non-athletes [34][35][36]. Physical behavior profiles are predictive to health risk factors independently on the amount of PA accumulated [37].…”

Section: Physical Activity (Pa) and Sedentary Behavior (Sb) Profilesmentioning

confidence: 99%

Precision Sports Science: What Is Next for Data Analytics for Athlete Performance and Well-Being Optimization?

Exel,

Dabnichki

2024

Applied Sciences

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In elite sports, athletic excellence demands meticulous performance preparation and a sound health status. This paper overviews the current propositions and applications of pervasive computing and data analytics and our vision on how they should be used in future frameworks to contribute to the optimal balance of athletes’ performance and health requirements. Two main areas will be discussed. The first area is Sports Performance Optimization, in which we consider interesting recent advancements in data analytics for performance improvement, equipment design, and team member recruitment and selection. We will also briefly discuss how the betting industry has been relaying and developing sports analytics. The second area is Athlete’s Wellness and Wellbeing, which will discuss how wearables and data analytics have been used to assess physical activity and sedentary behavior profiles, sleep and circadian rhythm, nutrition and eating behavior, menstrual cycles, and training/performance readiness. In the final part of this paper, we argue that a critical issue for managers to enhance their decision making is the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach. Thus, we present and discuss new theoretical and practical approaches that could potentially address this problem and identify precision medicine as a recommended methodology. This conceptualization involves the integration of pervasive computing and data analytics by employing predictive models that are constantly updated with the outcomes from monitoring tools and athletes’ feedback interventions. This framework has the potential to revolutionize how athletes’ performance and well-being are monitored, assessed, and optimized, contributing to a new era of precision in sports science and medicine.

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

Cited by 7 publications

References 99 publications

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

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

Evidence that heat acclimation training may alter sleep and incidental activity

Precision Sports Science: What Is Next for Data Analytics for Athlete Performance and Well-Being Optimization?

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