Sedentary behaviour among elite professional footballers: health and performance implications

Changes in Body Composition and Nutritional Periodization during the Training Macrocycle in Football-A Narrative Review

Nutrition periodization in football training is an important determinant of adaptation to cyclic training loads. Personalizing an athlete's diet is crucial to ensure optimal performance and body composition, depending on the phase of training. The purpose of this review is to answer the question of how the body composition of football players changes over the training macrocycle and how dietary recommendations should be tailored to specific training periods. The review of scientific evidence was conducted based on the available literature, typing in phrases related to training and nutrition periodization using the PubMed and Google Scholar database methodology tools. A literature search resulted in the selection of 346 sources directly related to the topic of the study, and then those with the highest scientific value were selected. There is a need to adjust energy and nutrient intake according to the different training phases in a football player's preparation cycle. During the preparatory phase, it is recommended to increase protein and energy intake to support anabolic processes and muscle mass development. During the competitive period, due to the intensity of matches and training, the importance of carbohydrates for glycogen replenishment and recovery is emphasized. The transition phase requires the regulation of caloric intake to prevent adverse changes in body composition. Hydration has been identified as a key element in each phase of training. Cooperation between coaches, nutritionists, and players is essential to optimize sports performance and rapid recovery, and the authors recommend continuous adaptation and nutritional optimization as an integral part of football training.

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.

Long-term function, body composition and cardiometabolic health in midlife former athletes: a scoping review

While sports medicine has traditionally focused on recovering from injury and returning athletes to sport safely after injury, there is a growing interest in the long-term health of athletes. The purpose of this scoping review was to (1) summarise the literature (methodologies and findings) on physical function, body composition and cardiometabolic health in midlife (age 40-65 years) former competitive athletes compared with non-athlete controls, (2) identify areas for future study in long-term health in athletes and (3) determine outcomes that could be evaluated in a future systematic review(s). We searched PubMed, CINAHL, Web of Science and SPORTDiscus for studies published between 2000 and 2022 evaluating former athletes and controls on physical function, body composition and/or cardiometabolic measures using MeSH terms. We identified 20 articles that met our criteria. Outcomes varied considerably across studies, most of which were cross-sectional and evaluated only males. Limited data suggest that former endurance athletes have leaner body compositions, higher aerobic capacity and better cardiometabolic indicators than controls; former athletes who maintain higher physical activity (ie, self-reported exercise) are healthier than those who do not; and former team sport athletes, who have higher injury prevalence, may have poorer functional performance than controls who were recreationally active in college. Studies rarely evaluated functional performance, did not control for prior injury or diet and seldom assessed current physical activity levels. Future research should include females and evaluate sex differences, control for prior sports-related injury(ies), quantify physical activity, use standardised outcome measures including performance-based functional assessments and incorporate longitudinal designs.

Anthropometric Profiling and Changes in Segmental Body Composition of Professional Football Players in Relation to Age over the Training Macrocycle

Body composition is an important indicator of the overall health and fitness of team sports athletes, including in football, and therefore, anthropometric profiling of elite football players is useful as part of determining their skills, strengths, and weaknesses to develop effective strength and conditioning programs. One of the tools available to coaches to track correlates of performance and health is routine body composition assessment. The purpose of this study is to describe and compare the body composition and anthropometric profiles of players using the Direct Segmental Multi-Frequency Bio-Electrical Impedance Analysis method, and to manage body composition throughout the round in the 2020-2021 season. The investigation was carried out during the Polish football league, PKO BP Ekstraklasa, spring round of the football season 2020-2021, in which male football players participated. Athletes between the ages of 18 and 25 (n = 16) made up the younger age group, while those between the ages of 26 and 31 (n = 22) made up the older age group. This manuscript is a continuation of the presentation of the results of the study, which was conducted between 7 January and 23 July 2021. At different stages of the macrocycle, participants underwent six different body composition analyses. The younger and older groups of athletes were compared, as well as measurements of time points 1-6. The dominant extremities, assisting extremities, and trunk had larger fat-free mass contents in the older age group. In the study groups, there was a difference in the fat-free mass content between measures 1-6 that was statistically significant. In the younger group, there was a statistically significant difference in the amount of fat mass content between measurements 1-6. In the older age group, no statistically significant changes were found. The study showed changes in fat-free mass and fat mass in body segments; differences were observed between age groups and between different moments of measurement. Age is an important factor in determining body composition and is also related to an athlete's experience and seniority. Anthropometric profiling and comprehensive body composition analysis are important tools used in preparing athletes for competition.

Clustering of Health Behaviors in Canadians: A Multiple Behavior Analysis of Data from the Canadian Longitudinal Study on Aging

BACKGROUND

Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease. Better understanding which behaviors tend to co-occur (i.e., cluster together) and co-vary (i.e., are correlated) may provide novel opportunities to develop more comprehensive interventions to promote multiple health behavior change. However, whether co-occurrence or co-variation-based approaches are better suited for this task remains relatively unknown.

PURPOSE

To compare the utility of co-occurrence vs. co-variation-based approaches for understanding the interconnectedness between multiple health-impacting behaviors.

METHODS

Using baseline and follow-up data (N = 40,268) from the Canadian Longitudinal Study of Aging, we examined the co-occurrence and co-variation of health behaviors. We used cluster analysis to group individuals based on their behavioral tendencies across multiple behaviors and to examine how these clusters are associated with demographic characteristics and health indicators. We compared outputs from cluster analysis to behavioral correlations and compared regression analyses of clusters and individual behaviors predicting future health outcomes.

RESULTS

Seven clusters were identified, with clusters differentiated by six of the seven health behaviors included in the analysis. Sociodemographic characteristics varied across several clusters. Correlations between behaviors were generally small. In regression analyses individual behaviors accounted for more variance in health outcomes than clusters.

CONCLUSIONS

Co-occurrence-based approaches may be more suitable for identifying sub-groups for intervention targeting while co-variation approaches are more suitable for building an understanding of the relationships between health behaviors.

Identifying the most proximal multi-level factors associated with meeting each of the 24-h movement behavior recommendations in a sample of autistic adults

BACKGROUND

Autistic adults have poorer 24-h movement behaviors, including lower levels of physical activity, more time spent being sedentary, and shorter sleep duration than neurotypical adults. Social ecological frameworks posit that 24-h movement behaviors are determined by multi-level domains; however, not known is which multi-level factors are most important to meeting each of the 24-h movement behavior guidelines among autistic adults.

OBJECTIVE

This study examined the relative importance of a range of multi-level determinants on meeting guidelines for the 24-h movement behaviors of aerobic physical activity, sedentary behavior, and sleep.

METHODS

We administered at cross-sectional electronic survey to a national self-selecting, convenience sample of autistic adults and caregivers of autistic adults residing in the USA. We used machine learning to examine the relative variable importance (VIMP) of 55 multi-level variables with meeting recommendations for physical activity, sedentary behavior, and sleep duration. VIMPs >0 indicate predictive variables/domains.

RESULTS

A greater number of group activities attended in the last 3-months, and greater independence in completing activities of daily living were most important to meeting aerobic physical activity guidelines. Group activity participation and marital status were important to meeting sedentary behavior guidelines while having a fewer number of comorbidities was most important to achieving adequate sleep.

CONCLUSIONS

These data support hypotheses about the role of family and social level interventions targeting movement behaviors in autistic adults.

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.

The integration of training and off-training activities substantially alters training volume and load analysis in elite rowers

Training studies in elite athletes traditionally focus on the relationship between scheduled training (TRAIN) and performance. Here, we added activities outside of scheduled training i.e., off-training (OFF) contributing to total training (TOTAL) to evaluate the contribution of OFF on performance. Eight elite rowers recorded OFF and TRAIN during waking hours for one season (30–45 weeks) with multisensory smartwatches. Changes in performance were assessed via rowing ergometer testing and maximum oxygen uptake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{\text{V}}}$$\end{document}V˙O_2max). Based on 1-Hz-sampling of heart rate data during TRAIN and OFF (> 60% maximum heart rate (HR_max), the volume, session count, intensity, training impulse (TRIMP), and training intensity distribution were calculated. OFF altered volume, TRIMP, and session count by 19 ± 13%, 13 ± 9%, and 41 ± 67% (p < 0.001). On an individual level, training intensity distribution changed in 3% of the valid weeks. Athletes exercised 31% of their weekly volume below 60% HR_max. Low to moderate intensities dominated during OFF with 87% (95% CI [79, 95]); however, in some weeks high-intensity activities > 89% HR_max during OFF amounted to 21 min·week⁻¹ (95% CI [4, 45]). No effect of OFF on changes of performance surrogates was found (0.072 > p > 0.604). The integration of OFF substantially altered volume, TRIMP, and session count. However, no effect on performance was found.

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

Prolonged periods of sedentary time appear to increase the risk for the development of several chronic conditions and all-cause mortality, even when moderate-to-vigorous physical activity (MVPA) is taken into consideration. However, whether the beneficial health effects of MVPA in highly active individuals remain present when leisure time is spent sedentary remains speculative. Therefore, we systematically evaluated off-training sedentary behaviour and physical activity levels in athletes. Studies were collected from four bibliographic databases (PubMed, Embase, Web of Science and The Cochrane Central Register of Controlled Trials). Studies were eligible for inclusion if they evaluated sedentary behaviour and physical activity levels among athletes. Data from athletes were compared with the general population and pooled using a random-effects model. After deduplication 3104 were identified of which 13 studies met inclusion criteria. Compared to the general population, athletes spent significantly more time in sedentary behaviour (+79 min/day; 95% confidence interval [CI]: [41, 65] min/day; p<0.001). In addition, athlete individuals spent less time in light intensity physical activity (-92 min/day; 95% CI: [-117, -66] min/day; p<0.001) and had increased levels of MVPA (+62 min/day; 95% CI: [38, 85] min/day; p<0.001) compared to the general population. Athletes exceed the average time spend sedentary per day and make them even more sedentary compared to the general population.

In-season training responses and perceived wellbeing and recovery status in professional soccer players

This study aimed to describe professional soccer players' training responses during a competitive season and to investigate the relationship between these responses with wellbeing and recovery indices. Thirteen professional soccer players from the same Spanish Second Division team were monitored during a sixteen-week in-season period. Players' external loads were analyzed using global positioning measurement units (GPS). Additionally, subjective reporting of sleep quality, sleep duration, fatigue, muscle soreness, and stress were assessed with a customized wellness questionnaire at the beginning of each training session. A two-step cluster analysis identified profiles of different training responses generally described as lower-demand sessions, intermediate-demand sessions, running-based sessions, and sprint-based sessions; which were discriminated by different total distance covered and high-intensity actions. Interestingly, no probabilistic interactions were found between these training responses with wellbeing and recovery markers (i.e., Bayes factor < 1 suggesting no evidence, for all the variables). These findings may raise concerns about using self-reporting tools, as they show that players' wellness data is probably not accounted for when coaching staff plan and optimize the training process. However, results should be interpreted with caution, due to representing a single team and coaching staff.

ARE THERE DIFFERENCES BETWEEN MALE AND FEMALE BADMINTON ATHLETES IN SLEEP, PHYSICAL ACTIVITY AND SEDENTARY TIME?

ABSTRACT Introduction: High-performance athletes often undergo periods of exhaustive training and insufficient recovery, which can lead to decreased performance, but it is not clear whether there are any differences between the sexes in the level of habitual physical activity, sedentary time or sleep quantity and quality in young highly trained badminton athletes. Objectives: The aim of this study was to compare the habitual physical activity (PA), sedentary time and sleep quantity and quality of highly trained young male and female badminton athletes and non-athletes. Methods: Twenty-seven young badminton athletes and twenty-one non-athletes (control group) were recruited. Sleep duration and quality (total time in bed, total sleep time, wake after sleep onset, sleep efficiency and latency), total counts, vigorous activity time and sedentary time were measured using a tri-axial accelerometer. Results: Male athletes spent less sedentary time (p=0.028), more time in vigorous activity (p=0.016) and had higher total counts (p<0.001) than the male control group. There were no significant differences in sedentary time (p=0.702) or vigorous activity time (p=0.205) between the female athletes and non-athletes, but the female athletes accumulated higher total counts than the female control group (p=0.003). There were no significant differences between the sexes or groups for time in bed, total sleep time, sleep efficiency, wake after sleep onset and latency (p> 0.05). Conclusion: Among males but not females, young highly trained badminton athletes had less sedentary time and more time in vigorous activity than the non-athletes, however, there were no significant differences between the sexes or groups in sleep quantity and quality. Level of evidence III; Case-control study .

Off-training physical activity and training responses as determinants of sleep quality in young soccer players

This study aimed to quantify and assess the relationship of young soccer players' off-training physical activity (PA) and training responses on sleep quality. Eleven adolescent soccer players (13 ± 0.5 years old) were monitored during weekdays for four consecutive weeks, throughout soccer practice days. Off-training PA and sleep quality were assessed using 100 Hz tri-axial accelerometers and training responses analyzed using 20 Hz global positioning measurement units. A cluster analysis classified all cases into three different dimensions, (1) off-training PA, (2) training responses and (3) sleep quality. For each dimension, the most important variables for classifying the cases into clusters were sedentary PA and moderate-to-vigorous PA; total distance covered and impacts; and sleep onset latency and sleep fragmentation index, respectively. Afterwards, a correspondence analysis was used to identify whether off-training PA and training responses affected sleep quality. Results exposed that high to medium off-training PA combined with medium to high training responses may have decreased sleep quality. Conversely, no correspondence was observed between off-training PA and training responses, with higher sleep quality. This study emphasizes the importance of sports organizations adopting a holistic approach to youth soccer players' development, that appropriately considers the inter-relationship between lifestyle, performance and health-related information.

COVID-19 pandemic impacts physical activity levels and sedentary time but not sleep quality in young badminton athletes

Purpose

Regular physical activity is a good strategy to maintain the health of athletes, and prevent pain and decreased joint flexibility during the pandemic. On the other hand, higher sedentary time during the pandemic period can have deleterious effects. The objective of this study was to compare physical activity levels, sedentary time, and sleep parameters during the pre-COVID period and the COVID-19 pandemic period in young badminton athletes.

Methods

Fifteen young badminton athletes were evaluated during a pre-COVID period (July 2019) and during the COVID-19 period (July 2020). Sleep parameters, physical activity level, and sedentary time were measured using a tri-axial accelerometer. Participants wore the accelerometer on their dominant wrist for 7 days consecutively. In addition, the average of each sleep parameter [time in bed and total sleep time in hours per day, sleep efficiency (%), wake after sleep onset (WASO, total per day), and sleep latency (minutes per day)] was reported over the 7-day period.

Results

Athletes presented increased sedentary time (pre-COVID = 7.0 ± 1.1 vs.COVID-19 = 8.9 ± 1.9 h/day, p = 0.004, d = 1.30) and significant decreases in the total PA observed in counts per day (pre-COVID = 2,967,064.4 ± 671,544.1 vs. COVID-19 = 1,868,210.2 ± 449,768.4 counts/day, p = 0.001, d = 1.99), time in vigorous PA (pre-COVID = 7.7 ± 0.9 vs. COVID-19 = 6.1 ± 1.2 h/day, p = 0.001, d = 1.56), and time in moderate-to-vigorous PA (pre-COVID = 8.1 ± 0.9 vs. COVID-19 = 6.5 ± 1.3 h/day, p = 0.001, d = 1.48). There were no significant differences for time in light and moderate PA or in sleep parameters (p > 0.05).

Conclusion

Young badminton athletes presented increased sedentary time, and decreased total physical activity, time in MVPA, and time in vigorous activities during the COVID-19 pandemic compared to the pre-COVID period, however, there were no significant differences in sleep parameters.

Physical Activity Levels of Adult Virtual Football Players

Esports, including virtual football, are a worldwide phenomenon. Yet, little is known about the physical activity levels of individuals engaged in virtual football game play. Therefore, we aimed to perform a preliminary evaluation of the levels of physical activity, sedentarism, and habits of physical training of adults engaged with virtual football in Portugal. This was a cross-sectional investigation based on a structured online survey using the International Physical Activity Questionnaire (IPAQ) and a set of questions regarding habits of physical training. The participants (n = 433) reported spending a median of 5,625 MET-min⋅week⁻¹ being physically active. Still, the participants spent 320 min/day sitting, and 150 min/day practicing virtual football. According to the IPAQ scores, high physical activity levels were reported by 84.5% of the participants, and 87.1% were considered physically active considering the WHO guidelines on physical activity and sedentary behavior. Overall, 60.0% of the participants reported planning their own physical training. Maintaining or improving overall physical health was one of the main reasons for doing physical training (66.7%), with only 6.1% responding being active to improve virtual football performance. Overall, the results showed that virtual football players accomplished the standard recommendations for physical activity, with high levels of physical activity, and encompassing regular physical training focused mostly on health promotion, rather than improved virtual football performance.

Sedentary behaviours and their relationship with body composition of athletes

During the recovery period, athletes present high sedentary behaviour (SB). In non-athletes, there is a direct relationship between SB and obesity. However, little is known about this relationship in athletes. We hypothesized that different types of SB entail different associations with body composition outcomes.We examined the associations between different types of SB and body composition outcomes in 135 athletes (70 males) aged 21.3 ± 3.9 years old. Dual-energy X-ray absorptiometry was performed to assess fat mass (FM), fat-free mass (FFM) and trunk fat mass (TFM). A validated SB questionnaire (PACE) was used to estimate total SB and specific sedentary pursuits on an average day. Multiple linear regression analyses were performed, adjusting for age, sex, weekly training time, years of sport practice, and sport type.A positive association was found for total SB and total screen time with %TFM (β = 0.220, p = 0.038 and β = 0.319, p = 0.040, respectively), while an inverse association was found for %FFM (β = -0.214, p = 0.042 and β = -0.310, p = 0.026). A higher total screen time was related with a higher %FM (β = 0.283, p = 0.035). With a much stronger effect size, cell phone screen time was positively related with %FM (β = 1.447, p = 0.001).There is still debate whether high levels of physical activity can fully counteract the harms of SB. These findings suggest that SB, particularly cell phone screen time, can compromise athletes' body composition, regardless of weekly training time. Sport federations and coaches may improve athletes' body composition by targeting specific sedentary pursuits, i.e. total screen time and cell phone screen time, during athletes' recovery time.Highlights Even in athletes, SB can compromise total and regional body composition regardless of high training time.Screen time, specifically cell phone screen time seems to be the sedentary pursuit to target in athletes.Sport federations and coaches must control SB during athletes' recovery time.

Physical Activity, Sedentary Behavior, Anxiety, and Pain Among Musicians in the United Kingdom

Context and Aims

Although some exercise-based interventions have been associated with lower levels of pain and performance-related musculoskeletal disorders (PRMDs) among musicians, the evidence is still mixed. Furthermore, little is known about musicians’ general engagement in physical activity (PA), their knowledge of PA guidelines, or the relevant training they receive on pain prevention and the sources of such training. Similarly, little is known about the relationship between PA and PRMDs and other risk factors for PRMDs.

Methods

Following a cross-sectional correlational study design, both standardized and ad hoc measurements were used to investigate self-reported PA [International Physical Activity Questionnaire – Short Form (IPAQ-SF)], knowledge of PA guidelines, and barriers to engaging in PA [Centers for Disease Control (CDC); Determinants of Physical Activity Questionnaire (DPAQ)]; sedentary behavior [Sedentary Behavior Questionnaire (SBQ)]; pain [36-Item Short Form Survey Instrument (SF-36)] and PRMDs (frequency and severity); reported physical exertion (RPE); anxiety [Hospital Anxiety and Depression Scale (HADS)]; practice behaviors (e.g., practice time; taking breaks frequency; warming up); and relevant training among conservatoire students in the United Kingdom. The entire set of questionnaires was administered both online and via hard copies between June 2017 and April 2018.

Results

Demographic information was obtained from 111 respondents, mostly undergraduate students (UGs) from seven conservatoires. They reported high levels of engagement in PA, despite poor knowledge of PA guidelines. Teachers were the most frequently mentioned source of pain prevention information (by 43% of respondents), and 62% agreed that they had received advice on why they should engage in cardio PA. Sedentary behavior was comparable to normative data. Levels of bodily pain and PRMDs were low, but 43% showed “abnormal” clinical anxiety and found playing their instruments “somewhat hard” (RPE) on average. Bodily pain interfering with practice and performance was positively correlated with frequency and severity of PRMDs, anxiety, and RPE. Frequency and severity of PRMDs were also associated with sedentary behavior at the weekend. Anxiety was associated with RPE. No association was found between PA and PRMDs.

Conclusion

The relationship between PA and PRMDs and pain remains unclear and needs further investigation. While health education needs to be improved, other pathways may need to be taken. Given the high levels of anxiety, the ideology of Western classical music itself may need to be challenged.

Can Off-Training Physical Behaviors Influence Recovery in Athletes? A Scoping Review

Recently, the attention on recovery in sport increased enormously although there is lack of scientific evidence on the role of lifestyle in terms of movement [i.e., physical behaviors (PBs)], apart from sleep. Few studies assessed physical activity (PA) and sedentary behavior (SB) in athletes. The aims of this scoping review were to answer to the following scientific questions: (1) How active/inactive are competitive athletes out of training? (2) Do off-training PBs affect recovery, performance, and health? (3) What strategies can be implemented to improve recovery using off-training PBs, apart from sleep? From 1,116 potentially relevant articles, nine were eligible for inclusion in this review. The main issues identified were related to the heterogeneity concerning the types of sports, age category, gender, competitive level, sample size, and instruments/devices adopted, the paucity of studies investigating the effects of PBs while awake on recovery, and the lack of experimental designs manipulating PBs while awake to accelerate recovery. Furthermore, PA and SB domains were rarely investigated, while no research articles focused on the combined effect of 24-h PBs. Eight out of nine studies measured PA, seven SB, and two included sleep. Three studies included training practice into PA measurement by the means of accelerometry. Overall, almost the totality of the athletes achieved recommended PA levels although they sustained prolonged SB. In conclusion, more descriptive researches are needed in different athletic populations and settings. Furthermore, experimental designs aimed at investigating the effects of PBs manipulation on recovery and the putative mechanisms are encouraged.

Device-measured physical activity, sedentary behaviour and cardiometabolic health and fitness across occupational groups: a systematic review and meta-analysis

BACKGROUND

With approximately 8 hours of one's waking day spent at work, occupational tasks and environments are important influencers on an individual's physical activity (PA) and sedentary behaviours. Little research has compared device-measured physical activity, sedentary behaviour and cardiometabolic outcomes between occupational groups.

OBJECTIVE

To compare device-measured movement (sedentary time [ST], light intensity physical activity [LPA], moderate-to-vigorous intensity physical activity [MVPA], and steps) across occupations. The secondary objective was to examine whether cardiometabolic and fitness outcomes differed by occupation.

METHODS

Five bibliographic databases were searched to identify all studies which included working age, employed adults from high-income countries, and reported on device-measured movement within occupations. Risk of bias within and across studies was assessed. Results were synthesized using meta-analyses and narrative syntheses.

RESULTS

The review includes 132 unique studies with data from 15,619 participants. Working adults spent ~ 60% of their working and waking time engaged in sedentary behaviour; a very small proportion (~ 4%) of the day included MVPA. On average, workers accumulated 8124 steps/day. Office and call center workers' steps/day were among the lowest, while those of postal delivery workers were highest. Office workers had the greatest ST and the lowest time in LPA both at work and during wakeful time. However, office workers had the greatest minutes sent in MVPA during wakeful hours. Laborers had the lowest ST and spent a significantly greater proportion of their work time in LPA and MVPA. Healthcare and protective services workers had higher levels of LPA at work compared to other occupations. Workers in driving-based occupations tended to have a higher body mass index and blood pressure.

CONCLUSION

This review identifies that occupational and wakeful time PA and ST differed between occupations. Future studies are needed to assess whether patterns differ by age and sex, describe leisure-time movement and movement patterns, and the relationship with cardiometabolic health.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42017070448 .

Off-Training Levels of Physical Activity and Sedentary Behavior in Young Athletes: Preliminary Results during a Typical Week

The level of physical activity (PA) and sedentary behavior (SED) off-training of young athletes may reveal the quality of recovery from training and highlight health related issues. Thus, the aim was to identify and describe young athletes' PA and SED off-training, according to daily life activities. Eight athletes (15.7 ± 2 years, 1.72 ± 0.6 m height, 62.9 ± 10.2 kg) of a sport talent program wore on their waist a tri-axial accelerometer (ActiGraph^® wGT9X-link, Shalimar, FL, USA) at 30 Hz for 15 consecutive days, and reported their schedule. A two-step cluster analysis classified three groups according to sedentary PA and MVPA. The Sedentary (56.9%), presented the highest sedentary PA (mean [CI], 37.37 [36.45⁻38.29] min/hour); The Hazardous (19.4%) had the lowest values of sedentary and MVPA (10.07 [9.41⁻10.36] min/hour and 8.67 [7.64⁻9.70] min/hour, respectively). Balanced (23.7%) had the highest MVPA (28.61 [27.16⁻30.07] min/hour). Sedentary had the lowest count of home time associated (20%) and higher school (26%) time when compared to the Hazardous (13%). The Balanced showed the highest count of school (61%) and home time (47%). Different profiles for young athletes revealed alarming behavior in the associations with sedentary PA, sitting and SED breaks, which may influence performance and health.

Sedentary Behavior among National Elite Rowers during Off-Training-A Pilot Study

The aim of this pilot study was to analyze the off-training physical activity (PA) profile in national elite German U23 rowers during 31 days of their preparation period. The hours spent in each PA category (i.e., sedentary: <1.5 metabolic equivalents (MET); light physical activity: 1.5–3 MET; moderate physical activity: 3–6 MET and vigorous intense physical activity: >6 MET) were calculated for every valid day (i.e., >480 min of wear time). The off-training PA during 21 weekdays and 10 weekend days of the final 11-week preparation period was assessed by the wrist-worn multisensory device Microsoft Band II (MSBII). A total of 11 rowers provided valid data (i.e., >480 min/day) for 11.6 week days and 4.8 weekend days during the 31 days observation period. The average sedentary time was 11.63 ± 1.25 h per day during the week and 12.49 ± 1.10 h per day on the weekend, with a tendency to be higher on the weekend compared to weekdays (p = 0.06; d = 0.73). The average time in light, moderate and vigorous PA during the weekdays was 1.27 ± 1.15, 0.76 ± 0.37, 0.51 ± 0.44 h per day, and 0.67 ± 0.43, 0.59 ± 0.37, 0.53 ± 0.32 h per weekend day. Light physical activity was higher during weekdays compared to the weekend (p = 0.04; d = 0.69). Based on our pilot study of 11 national elite rowers we conclude that rowers display a considerable sedentary off-training behavior of more than 11.5 h/day.

Leisure-time Physical Activity and Sedentary Behaviour in Older People: The Influence of Sport Involvement on Behaviour Patterns in Later Life

Given the dramatic demographic change underway in most industrialized nations, the health of older adults is a major concern, particularly given the prevalence of sedentary behaviours and physical inactivity among ageing populations. Researchers have suggested sport participation in later life promotes other health-related behaviours, however, these relationships are poorly understood. It is possible for individuals to be classified as sufficiently active and still spend most of their day involved in sedentary pursuits. Moreover, there is little information on older sport participants' use of time compared to leisurely active or inactive peers and whether type of physical activity involvement is associated with differences in older adults' behaviour patterns. With this in mind, data from 1,723 respondents (65 years and older) who completed the sport module of the 2010 Canadian General Social Survey-Time Use were used to investigate the influence of physical activity involvement (competitive sport vs. non-competitive sport vs. physically active leisure vs. inactivity) on time spent in leisure-time physical activity and sedentary behaviours. Results indicated that competitive sport participants spent less time engaging in sedentary behaviours compared to the physically active leisure or inactive respondents; however, sport participants (both competitive and non-competitive) also spent less time engaging in leisure-time physical activities than the physically active leisure group. Implications of these findings to assumptions related to the activity levels of older sport participants, suggestions for future research, and considerations for sport-related interventions aimed at enhancing health in older adulthood are discussed.