scholarly journals Physical Activity of Preschool Children in COVID-19 Pandemic: Focusing on Activity Content and Exercise Intensity during Childcare

2021 ◽  
Vol 12 (05) ◽  
pp. 999-1010
Author(s):  
Mao Hashimoto ◽  
Takayuki Shishido ◽  
Satoru Kowa
2021 ◽  
Author(s):  
Lisa A. Bell ◽  
Peter Vuillermin ◽  
Anna Timperio ◽  
Anne‐Louise Ponsonby ◽  
Mimi L. K. Tang ◽  
...  

2016 ◽  
Vol 01 (02) ◽  
Author(s):  
Shelly K McCrady Spitzer ◽  
Vanessa Sagdalen ◽  
Chinmay U Manohar ◽  
James A Levine

2002 ◽  
Vol 95 (2) ◽  
pp. 407-415 ◽  
Author(s):  
Leila Oja ◽  
Toivo Jürimäe

The aim of this investigation was to study the relationships between physical activity, motor ability, and school readiness in 6-yr.-old children. In total, 294 healthy children from Tartu were studied (161 boys and 133 girls). The physical activity of children was reported by parents and teachers using the questionnaire of Harro. The motor ability of children was evaluated using various tests from the Eurofit test battery as well as the 3-min. endurance shuttle run test. The Controlled Drawing Observation test was used as a predictor of school readiness and development of mental abilities. Indoor physical activities predicted 19–25% of total variance in motor scores for these preschool children. Motor ability tests, which demand children's total attention and concentration, appear related to the chosen measures of school readiness.


2013 ◽  
Vol 184 (4) ◽  
pp. 589-601 ◽  
Author(s):  
Anne Soini ◽  
Tuija Tammelin ◽  
Arja Sääkslahti ◽  
Anthony Watt ◽  
Jari Villberg ◽  
...  

2020 ◽  
Vol 20 (2) ◽  
pp. 63-70
Author(s):  
Felipe de Ornelas ◽  
Danilo Rodrigues Batista ◽  
Vlademir Meneghel ◽  
Wellington Gonçalves Dias ◽  
Guilherme Borsetti Businari ◽  
...  

Physical inactivity is main cause of disease worldwide. Identify the physical exercise preference, resulting in increases adherence and future intention to perform physical activity. The preference of the intensity of exercise questionnaire (PRETIE-Q) is the main tool used to assess preference in physical exercise. Variables as age, body mass index (BMI), usual physical activity level (PAL), maximal oxygen uptake (VO2máx), can influence in PRETIE-Q answers. The purpose of this study was investigate if there is relation between preference for exercise intensity with maximal aerobic speed (MAS), PAL and heart rate variability (HRV) in postmenopausal women phase. Participated of study 30 subjects who answer PRETIE-Q together with analyses of MAS, PAL and HRV. Preference was large correlated with MAS (r = 0.63), PAL (r = 0.57) and HRVRMSSD (r = 0.52). Together, MAS (40.4%), PAL (10.7%) and HRVRMSSD (6.4%) explained 57.5% of the preference score. This results study allow to health professional, that prescribe physical exercise, understand that subjects with high aerobic capacity, cardiovagal modulation and usual PAL will have preference for high intensity exercise. In consequence, can increase the adherence to systematic practice of physical exercise. Conclude that preference of exercise intensity for women in postmenopausal phase is related with aerobic capacity, high HRV and physical activity level.


BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e042669
Author(s):  
Justyna Wyszyńska ◽  
Piotr Matłosz ◽  
Muhammad Asif ◽  
Agnieszka Szybisty ◽  
Paweł Lenik ◽  
...  

ObjectiveAssociations between self-reported sleep duration and obesity indices in children are well recognised; however, there are no studies on associations between objectively measured other sleep parameters and physical activity with body composition in preschoolers. Therefore, the aim of this study was to determine the associations between sleep parameters and moderate-to-vigorous physical activity (MVPA) with body composition indices in preschoolers using objective measures.DesignA cross-sectional study.ParticipantsThe study group consisted of 676 children aged 5–6 years, who were enrolled in kindergartens in the 2017/2018 school year.Outcome measuresSleep parameters and MVPA were measured using accelerometers for 7 days. Bioelectrical impedance analysis was used to estimate body composition.ResultsSleep duration and sleep efficiency were inversely associated with body fat percentage (BFP) (β=−0.013 and β from –0.311 to −0.359, respectively) and body mass index (BMI) (β from −0.005 to −0.006 and from −0.105 to –0.121, respectively), and directly associated with fat-free mass (FFM) (β from 0.010 to 0.011 and from 0.245 to 0.271, respectively) and muscle mass (β from 0.012 to 0.012 and from 0.277 to 0.307, respectively) in unadjusted and adjusted models. BFP was inversely associated with MVPA and positively associated with number of awakenings and sleep periods. Number of sleep periods was inversely associated with FFM, and positively with BMI and muscle mass. Correlation matrix indicated significant correlation between BFP, FFM and muscle mass with sleep duration, sleep efficiency, number of sleep periods and MVPA.ConclusionsPeriodic assessment of sleep parameters and MVPA in relation to body composition in preschool children may be considered, especially in those who are at risk for obesity.


2020 ◽  
Vol 32 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Catherine Draper ◽  
Simone A Tomaz ◽  
Caylee J Cook ◽  
Sasha S Jugdav ◽  
Candice Ramsammy ◽  
...  

Background: The International Study of Movement Behaviours in the Early Years, SUNRISE, was initiated to assess the extent to which young children meet movement behaviour guidelines (physical activity, sedentary behaviour, screen time, sleep). Objective: The South African SUNRISE pilot study assessed movement behaviours in preschool children from two low-income settings, and associations between these movement behaviours, adiposity, motor skills and executive function (EF). Methods: Preschool child/parent pairs (n = 89) were recruited from preschools in urban Soweto and rural Sweetwaters. Height and weight were measured to assess adiposity. Physical activity was assessed using accelerometers while sedentary behaviour, screen time and sleep were assessed via parent report. Fine and gross motor development were measured using the Ages and Stages Questionnaire-3, and EF was assessed using the Early Years Toolbox. Results: The proportion of children meeting the physical activity guideline was 84% , 66% met the sleep guideline ,48% met the screen time guideline , and 26% met all three guidelines. Rural children were more active, but spent more time on screens compared to urban children. Most children were on track for gross (96%) and fine motor (73%) development, and mean EF scores were in the expected range for all EF measures. EF was negatively associated with screen time, and gross motor skills were positively associated with physical activity. Conclusion: The South African SUNRISE study contributes to the growing literature on 24-hour movement behaviours in SA preschool children, and highlights that these behaviours require attention in this age group.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dale E. Rae ◽  
Simone A. Tomaz ◽  
Rachel A. Jones ◽  
Trina Hinkley ◽  
Rhian Twine ◽  
...  

Abstract Background The extent to which income setting or rural and urban environments modify the association between sleep and obesity in young children is unclear. The aims of this cross-sectional observational study were to (i) describe and compare sleep in South African preschool children from rural low-income (RL), urban low-income (UL) and urban high-income (UH) settings; and (ii) test for associations between sleep parameters and body mass index (BMI). Methods Participants were preschoolers (5.2 ± 0.7y, 49.5% boys) from RL (n = 111), UL (n = 65) and UH (n = 22) settings. Height and weight were measured. Sleep, sedentary behaviour and physical activity were assessed using accelerometery. Results UL children had higher BMI z-scores (median: 0.39; interquartile range: − 0.27, 0.99) than the UH (− 0.38; − 0.88, 0.11) and RL (− 0.08; − 0.83, 0.53) children (p = 0.001). The UL children had later bedtimes (p < 0.001) and wake-up times (p < 0.001) and shorter 24 h (p < 0.001) and nocturnal (p < 0.001) sleep durations than the RL and UH children. After adjusting for age, sex, setting, SB and PA, for every hour less sleep obtained (24 h and nocturnal), children were 2.28 (95% CI: 1.28–4.35) and 2.22 (95% CI: 1.27–3.85) more likely, respectively, to belong to a higher BMI z-score quartile. Conclusions Shorter sleep is associated with a higher BMI z-score in South African preschoolers, despite high levels of PA, with UL children appearing to be particularly vulnerable.


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