Using compositional data analysis to explore accumulation of sedentary behavior, physical activity and youth health

Associations between 24-h movement behaviours and BMI in Chinese primary- and middle- school students

Purposes

This cross-sectional study aimed to examine the associations between the 24-h movement behaviours and body mass index (BMI) of students from China by using compositional data analysis.

Methods

A total of 389 students aged 6-16 years participated in this study. Accelerometers were used to measure moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LPA), sedentary behaviour (SED), and sleep. Weight and height were objectively measured. The association between 24-h movement and BMI was analyzed by using compositional data analysis.

Results

Time reallocation using minutes and proportions created major differences to the results. Reallocating 10 min from other movement behaviours to MVPA was associated with decreased BMI z-score of 1.372 to 0.158 among primary-school students. Reallocating 10 min from sleep and SED to MVPA, and from sleep and SED to LPA were associated with decreased BMI z-score of 0.505 to 0.017 among middle-school students. Reallocating 10% of time from all other components to SED and sleep were associated with a higher BMI z-score by 0.148 (95%CI: 0.020; 0.276) and 0.125 (95%CI: 0.046; 0.204), while reallocating time to MVPA was associated with a decrease in BMI z-scores of 0.132 (95%CI: -0.193; -0.070) among primary-school students. Reallocating 10% of time from all other components to SED was associated with a higher BMI z-score of 0.254 (95%CI: 0.165; 0.345), whereas reallocating time to MVPA and LPA was associated with a decrease in BMI z-scores of 0.039 (95%CI: -0.073; -0.005) and 0.093 (95%CI: -0.153; -0.033) among middle-school students.

Conclusion

Research results of 10-min one-to-one reallocation may be treated cautiously due to uneven distribution of time in 24-h movement behaviours. Based on the results of 10% one-to-remaining reallocation, replacing SED with MVPA may be an appropriate target for adiposity intervention in primary-school students, while increasing MVPA or LPA at the expense of SED may be effective in controlling adiposity of middle-school students in China.

Association between Stress and Physical Fitness of University Students Post-COVID-19 Pandemic

Post-COVID-19 pandemic, most universities changed their educational model from online courses to onsite learning, allowing students to attend regular face-to-face classes. These changes can cause stress in students, which affects physical fitness. The aim of this study was to investigate the relationship between stress levels and physical fitness in female university students. The participants were 101 female university students, 18-23 years of age. All participants completed the Suan Prung Stress Test-60 (SPST-60). The physical fitness test included body composition, cardiorespiratory fitness, as well as musculoskeletal fitness. Multiple linear regression analysis was used to determine the associations between SPST-60 scores and physical fitness. A p-value < 0.05 was considered statistically significant. We found a negative correlation between the sources of stress scores, here environment, and maximal oxygen consumption (β = -0.291; 95% CI, -0.551, -0.031). We also found that symptoms of stress scores in the parasympathetic and sympathetic nervous systems were positively associated with waist-hip circumference ratio (WHR) (β = 0.010; 95% CI, 0.002, 0.017 and β = 0.006; 95% CI, 0.000, 0.012, respectively). Moreover, the symptoms of stress, here emotion, were positively associated with the WHR (β = 0.005; 95 %CI, 0.001, 0.009) and negatively associated with upper extremity muscle strength (β = -0.005; 95% CI, -0.009, 0.000). The results of this study confirmed the associations between stress levels in the post-COVID-19 pandemic era and WHR, maximal oxygen consumption, and upper extremity muscle strength. As a result, stress reduction or prevention alternatives should be considered in order to maintain physical fitness and prevent stress disorders.

Association between Sleep, Sedentary Time, Physical Activity, and Adiposity in Adolescents: A Prospective Observational Study

PURPOSE

This study aimed to examine the effects of substituting sedentary time with sleep or physical activity on adiposity in a longitudinal sample of adolescents.

METHODS

Adolescents (10-16 yr) were recruited for a prospective observational cohort. Parents and adolescents reported demographic characteristics and pubertal development. Accelerometry was used to measure sleep, physical activity, and sedentary time. Adiposity was quantified with imaging techniques. Isotemporal substitution modeling was conducted to examine the effect of substituting 10 min of sedentary time with sleep or differing intensities of physical activity. Results were stratified by sex and race and adjusted for covariates.

RESULTS

A total of 217 adolescents provided complete measures at both baseline and 2 yr later (58.1% White, 51.8% girls; 12.9 ± 1.9 yr at baseline). Sleep was negatively related to adiposity 2 yr later when considering other movement behaviors, but substituting baseline sedentary time with sleep was not related to future adiposity ( P > 0.05). In boys and non-White adolescents, substituting sedentary time with vigorous-intensity physical activity was related to lower adiposity 2 yr later ( P < 0.05). Substituting sedentary time for moderate- to vigorous-intensity physical activity was not associated with future adiposity.

CONCLUSIONS

Substituting sedentary time with vigorous-intensity physical activity was related to lower adiposity in later adolescence in certain groups. Opportunities to promote an adequate balance of sleep, sedentary time, and physical activity in all adolescents are encouraged for optimal development.

Epidemiological Study of Physical Activity, Negative Moods, and Their Correlations among College Students

OBJECTIVE

Negative moods in college students have caused frequent extreme behaviors. This study analyzed the current status and correlation between physical activity and negative moods in college students.

METHODS

A cross-sectional study design was used in the present research. Data on college students' physical activity and negative moods were collected using the Sports Questionnaire Star software. The questionnaires were administered to college students in five colleges and universities in Henan and Jiangsu Provinces, China, and a total of 3711 correctly completed questionnaires were returned. Data on sociological and demographic information, the International Physical Activity Questionnaire Short Form (IPAQ-SF), and the Depression Anxiety and Stress Scale (DASS) were collected. The research was conducted in December 2021.

RESULTS

The low-intensity physical activity rate among college students was 55.56%, while depression, anxiety, and stress symptoms were detected in 35.14%, 65.29%, and 10.99%of the college students, respectively. Depression (K = 35.58, p < 0.001) and anxiety (K = 15.56, p < 0.001) rates were significantly different among the different physical activity intensity groups. The proportion of students who perform high-intensity physical activities was lower than those who perform low- and moderate-intensity physical activities.

CONCLUSION

Low physical activity and high anxiety are evident among college students, and prolonged moderate-to-low-intensity physical activity (including static behavior) induces depression and anxiety. In the future, further studies can be conducted on improving the physical activity intensity of college students, improving the mental health monitoring and intervention systems of college students, and exploring the dose-effect relationship between physical activity and negative moods.

Intervention effects on children's movement behaviour accumulation as a result of the Transform-Us! school- and home-based cluster randomised controlled trial

Background

It is unknown if and how children’s movement behaviour accumulation patterns change as a result of physical activity and/or sedentary behaviour interventions. It is important to establish the effectiveness of interventions targeting changes in such accumulation patterns. This study aimed to investigate the effect of the Transform-Us! school- and home-based intervention program on children’s movement behaviour accumulation patterns, focusing on sporadic accumulation versus time in bouts.

Methods

Baseline and post-intervention (18 months) accelerometer data from the Transform-Us! 2 × 2 factorial design cluster randomised controlled trial was used (Melbourne, 2010–2012; analytical sample n = 267; aged 8–9 years). Linear mixed models were fitted to examine effects of three different interventions (targeting increases in physical activity [PA-I], reductions in sedentary time [SB-I], or both [PA + SB-I]) compared to a usual practice (control) group on post-intervention movement behaviour accumulation compositions with eight components, including sporadic time and bouts of sedentary time, and light-, moderate- and vigorous-intensity physical activity.

Results

Intervention effects on distribution of time in the post-intervention waking movement behaviour accumulation composition (adjusted for baseline composition) were small and not significant. However, visual inspection of the change in compositions over time revealed that only groups with a sedentary behaviour intervention component (SB-I and PA + SB-I) reduced time in sedentary bouts, compared to the overall sample compositional mean. In addition, the SB-I group was the only group with an increase in vigorous-intensity physical activity. The combined intervention group (PA + SB-I) was characterized by the largest proportional increase in MPA bouts. The usual practice group was characterized by the largest proportional increases in both sporadic and bouts of sedentary time.

Conclusions

This study showed some early evidence to suggest that the “break up your sitting” message may result in greater impact than the “move more” message. Future research, including larger sample sizes, should investigate if this type of messaging is indeed more effective in changing movement behaviours and ultimately child health.

Trial registration

International Standard Randomized Controlled Trial Number ISRCTN83725066; Australian New Zealand Clinical Trials Registry Number ACTRN12609000715279.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-022-01314-z.

Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys

BACKGROUND

Large-scale health surveys often consider sociodemographic characteristics and several health indicators influencing physical activity that often vary across subpopulations. Data in a survey for some small subpopulations are often not representative of the larger population.

OBJECTIVE

We developed a multilevel regression and poststratification (MRP) model to estimate leisure-time physical activity across Brazilian state capitals and evaluated whether the MRP outperforms single-level regression estimates based on the Brazilian cross-sectional national survey VIGITEL (2018).

METHODS

We used various approaches to compare the MRP and single-level model (complete-pooling) estimates, including cross-validation with various subsample proportions tested.

RESULTS

MRP consistently had predictions closer to the estimation target than single-level regression estimations. The mean absolute errors were smaller for the MRP estimates than single-level regression estimates with smaller sample sizes. MRP presented substantially smaller uncertainty estimates compared to single-level regression estimates. Overall, the MRP was superior to single-level regression estimates, particularly with smaller sample sizes, yielding smaller errors and more accurate estimates.

CONCLUSION

The MRP is a promising strategy to predict subpopulations' physical activity indicators from large surveys. The observations present in this study highlight the need for further research, which could, potentially, incorporate more information in the models to better interpret interactions and types of activities across target populations.