Weekly physical activity patterns of university students: Are athletes more active than non-athletes?

Relationships between grit and lifestyle factors in undergraduate college students during the COVID-19 pandemic

Objective: To investigate the influence of grit on physical activity, sleep and diet in undergraduate students during the COVID-19 pandemic. Participants: 125 undergraduate students participated in the study. Participants completed an online survey while enrolled in an online class during June, 2020. Methods: The online questionairre featured the following validated survey instruments: International Physical Activity Questionnaire Short Form, Pittsburgh Sleep Quality Index, the Rapid Eating Assessment for Participants Short Form and the grit short form. Effects of grit on lifestyle behaviors were explored using several statistical approaches. Results: We found that individuals with more grit reported better eating and sleeping behaviors compared to those with less grit. However, we did not find an influence of grit on physical activity or sitting time. Conclusion: Our findings support that personality traits encompassed in grit contribute to college students reporting healthier dietary habits and better sleep quality during a global pandemic.

Athlete students lead a healthier life than their non-athlete peers: A cross-sectional study of health behaviors, depression, and perceived health status among university students

Some studies show that athlete students are more likely to engage in health-risk behaviors with negative health consequences, while others suggest that they lead a healthier life than their non-athlete peers. Given these inconsistent results, this study aims to compare health behaviors, depression, and perceived health status between athlete and non-athlete students, and explore the associations between health behaviors and health outcomes. An online questionnaire survey including Heath Habits Scale for five health-risk behaviors and five health-promoting behaviors, Patient Health Questionnaire-9 (PHQ-9), and 5-point scale for perceived health status was conducted in Beijing Sports University in March 2021. Data from 372 athlete students and 252 non-athlete students aging from 18 to 22 were included in this study. Chi-squared tests and t-tests were used to determine differences between athlete and non-athlete samples, and logistic regression analyses were conducted to examine the associations of health behaviors with depression and perceived health status. The significance level was p < 0.05. The results show that compared with non-athlete students, athlete students perform better in health habits (10.01 vs. 8.27), report lower proportion of depression (44.6% vs. 54.4%) and higher proportion of good health (77.2% vs. 55.6%). Health behaviors, such as getting adequate sleeping, participating in vigorous physical activity, overeating, and smoking, were significantly associated with health outcomes of athlete students. The findings may contribute to the better understanding of health behaviors in athlete students and warrant continued attention on mental health and health habits in this population.

Physical activity measured by accelerometry among adolescents participating in sports clubs and non-participating peers

AbstractThe purpose of this descriptive cross-sectional study is to describe the amount and intensity of physical activity (PA) measured by accelerometry among adolescents participating in organized sports (SCP) and age-matched non-participating peers (NP). SCPs (332) and NPs (139) wore an accelerometer on the hip for 7 days. PA was reported using the 1 min exponential moving average. The current moderate-to-vigorous physical activity (MVPA) recommendation of at least an average of 60 minutes of MVPA daily was reached by 85% of SCPs and 45% of NPs (p < 0.001). During training days, the MVPA times among SCPs ranged from 153 ± 39 min in males and 109 ± 35 min in females participating in basketball to 113 ± 33 min in males participating in floorball and 83 ± 32 min in females participating in gymnastics. -Sports participation contributes rather strongly to the accumulation of the recommended amount of MVPA. During training days SCPs, except for females participating in gymnastics, accumulated more MVPA than NPs. During non-training days only males participating in cross country skiing and females participating in track and field accumulated more MVPA than NPs.

Independent and Combined Associations between Physical Activity and Sedentary Time with Sleep Quality among Chinese College Students

OBJECTIVE

To investigate the independent and combined associations between physical activity and sedentary time with sleep quality among Chinese college students.

METHODS

A cross-sectional study was conducted among Chinese college students (N = 2347; M age = 20 years; 67.1% students were female). We used the International Physical Activity Questionnaire-Short Form, the Adolescent Sedentary Activity Questionnaire, and the Pittsburgh Sleep Quality Index to assess the subjects' physical activity level, sedentary time, and sleep quality, respectively. Analyses were conducted using a multivariate logistic regression model.

RESULT

Of the total participants, 48.6% had poor sleep quality and 10% were at low physical activity levels, and the mean (±SD) sedentary time was 5.33 ± 2.34 h/day. No significant association was found between physical activity and sleep quality (OR = 1.27, 95% CI: 0.95~1.70) among Chinese college students after adjustment for potential confounders. Sedentary time was significantly positively associated with poor sleep quality (OR = 1.37, 95% CI: 1.14~1.65). The risks for those with a low physical activity level and high sedentary time, and a moderate physical activity level and high sedentary time were 2.78 (OR = 2.78, 95% CI: 1.61~4.80) and 1.49 (OR = 1.49, 95% CI: 1.13~1.95) times higher, respectively, than those with a high physical activity level and low sedentary time.

CONCLUSION

Among Chinese college students, high sedentary time was significantly negatively associated with sleep quality. A low physical activity level was insignificantly associated with sleep quality. A moderate physical activity level and high sedentary time, and a low physical activity level and high sedentary time were interactively associated with increased risks of poor sleep quality, respectively.

The Consumption of a Synbiotic Does Not Affect the Immune, Inflammatory, and Sympathovagal Parameters in Athletes and Sedentary Individuals: A Triple-Blinded, Randomized, Place-bo-Controlled Pilot Study

This investigation aimed to identify the effect of a synbiotic in athletes and sedentary people, and their potential varying responses regarding the immune system, autonomic regulation and body composition. Twenty-seven participants were involved in the protocol: 14 sedentary and 13 semi-professional soccer players. Both groups were randomly divided into an experimental and control group. A synbiotic (Gasteel Plus^®, Heel España S.A.U.) comprising a blend of probiotic strains, including Bifidobacterium lactis CBP-001010, Lactobacillus rhamnosus CNCM I-4036, and Bifidobacterium longum ES1, was administered to the experimental group, and a placebo was given to the control group for 30 days. Heart rate variability, body composition, and immune/inflammatory cytokines were determined. Statistically significant differences were observed between sedentary individuals and athletes in heart rate variability but not between the experimental and control groups. A difference between the athletic and sedentary group is observed with the influence of training on the effects of the synbiotic on the levels of fat mass and body-fold sum. No significant differences were shown in cytokines after the protocol study. No changes occur with the synbiotic treatment between the athlete and sedentary groups, while no negative effect was produced. Further research will be necessary to see chronic effects in the analyzed biomarkers.

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.

Sedentary time among undergraduate students: A systematic review

To systematically review and synthesize all relevant literature to assess the range of average daily sedentary time (ST) of undergraduate students enrolled in university and college programs across the world. Methods: Published, peer-reviewed articles were included if they: (a) were in English; (b) had a study population that consisted entirely of undergraduate students; (c) reported a daily or weekly ST for undergraduate students; and (d) included a measure of ST using a valid sedentary behavior questionnaire or accelerometers. Results: Twenty-three studies were included in the review. A wide range of ST (0.75 to 14.35 hours per day) was reported. Mean ST as measured by domain-specific questionnaires and accelerometers was 11.10 hours per day and 10.69 hours per day, respectively. Conclusion: Findings suggest that some undergraduate students participate in high levels of ST. Additional research, as well as interventions focused on reducing ST among postsecondary students, are needed.

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.

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.

The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review

Background: Physical activity (PA) is paramount for human health and well-being. However, there is a lack of information regarding the types of PA and the way they can exert an influence on functional and mental health as well as quality of life. Studies have measured and classified PA type in controlled conditions, but only provided limited insight into the validity of classifiers under real-life conditions. The advantage of utilizing the type dimension and the significance of real-life study designs for PA monitoring brought us to conduct a systematic literature review on PA type detection (PATD) under real-life conditions focused on three main criteria: methods for detecting PA types, using accelerometer data collected by portable devices, and real-life settings.

Method: The search of the databases, Web of Science, Scopus, PsycINFO, and PubMed, identified 1,170 publications. After screening of titles, abstracts and full texts using the above selection criteria, 21 publications were included in this review.

Results: This review is organized according to the three key elements constituting the PATD process using real-life datasets, including data collection, preprocessing, and PATD methods. Recommendations regarding these key elements are proposed, particularly regarding two important PA classes, i.e., posture and motion activities. Existing studies generally reported high to near-perfect classification accuracies. However, the data collection protocols and performance reporting schemes used varied significantly between studies, hindering a transparent performance comparison across methods.

Conclusion: Generally, considerably less studies focused on PA types, compared to other measures of PA assessment, such as PA intensity, and even less focused on real-life settings. To reliably differentiate the basic postures and motion activities in real life, two 3D accelerometers (thigh and hip) sampling at 20 Hz were found to provide the minimal sensor configuration. Decision trees are the most common classifier used in practical applications with real-life data. Despite the significant progress made over the past year in assessing PA in real-life settings, it remains difficult, if not impossible, to compare the performance of the various proposed methods. Thus, there is an urgent need for labeled, fully documented, and openly available reference datasets including a common evaluation framework.

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.

Correlates of sedentary behaviour in university students: A systematic review

High levels of sedentary behaviour are associated with negative health-related outcomes. However, there is limited evidence on the variables influencing sedentary behaviour in university students. The aim of this systematic review was to identify the intrapersonal, interpersonal, environmental, and time correlates of sedentary behaviour in university students. Records from 12 electronic databases were screened by two independent reviewers. Inclusion criteria included: (i) peer-reviewed articles written in English, Spanish, or French; (ii) studies including undergraduate or postgraduate university students; (iii) studies reporting on the association between sedentary behaviour and at least one variable. The protocol is registered in PROSPERO (CRD42017074198). A total of 126 studies published between 1994 and 2017 met the inclusion criteria. The primary measure of sedentary behaviour was self-reported screen time (61%), followed by total sitting time (28%). Most studies were cross-sectional (86%). After excluding high risk of bias studies (58%), only three intrapersonal variables were sufficiently investigated (≥4) to determine an association with sedentary behaviour: physical activity (negative association with sitting time), obesity markers (indeterminate associations with TV viewing), and gender - female (null associations with total sitting time and screen time). Overall, most of the reported correlates of sedentary behaviour were intrapersonal, non-modifiable factors. Further research on modifiable correlates covering all socio-ecologic levels is required to inform future intervention development. In addition, longitudinal studies are needed to enable the identification of determinants. Improvements in designing and reporting future studies are recommended to help strengthen the available evidence and facilitate future reviewing efforts.