scholarly journals Fracture risk across a wide range of physical activity levels, from sedentary individuals to elite athletes

Bone ◽  
2021 ◽  
pp. 116128
Author(s):  
Karl Stattin ◽  
Jonas Höijer ◽  
Ulf Hållmarker ◽  
John A. Baron ◽  
Susanna C. Larsson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Fracture Risk ◽  
Elite Athletes ◽  
Activity Levels ◽  
Wide Range ◽  
Physical Activity Levels

scholarly journals Effects of Different Physical Activity Levels during a Single Day on Energy Intake, Appetite, and Energy Balance: A Preliminary Study

Nutrients ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 690
Author(s):  
Yoichi Hatamoto ◽  
Rie Takae ◽  
Ryoma Goya ◽  
Eichi Yoshimura ◽  
Yasuki Higaki ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Balance ◽  
Energy Intake ◽  
Random Order ◽  
Activity Levels ◽  
Metabolic Chamber ◽  
Interval Exercise ◽  
Wide Range ◽  
Physical Activity Levels ◽  
Preliminary Study

We aimed to investigate the effects of a wide range of daily physical activity (PA) levels on energy balance (EB), energy intake (EI), and appetite. Nine young men completed three different PA levels in a metabolic chamber in a random order: (1) no exercise (Low-PA); (2) 25 min walking seven times (Mid-PA); and (3) 25 min running seven times (High-PA) within a 24 h period. Interval exercise (25 min exercise and 35 min rest) was performed three times in the morning and four times in the afternoon. The exercise intensities were 21.6% and 53.7% V ˙ O2 peak for the Mid-PA and High-PA days, respectively. Participants were served three standardized meals and a buffet for dinner. The 24 h EB was calculated as 24 h energy expenditure (EE) minus 24 h EI. The 24 h EEs for the Low-PA, Mid-PA, and High-PA days were 1907 ± 200, 2232 ± 240, and 3224 ± 426 kcal, respectively, with significant differences observed among the three conditions (p < 0.01 for Low-PA vs. Mid-PA, Low-PA vs. High-PA, and Mid-PA vs. High-PA, respectively). The 24 h EIs for the Low-PA, Mid-PA, and High-PA days were 3232 ± 528, 2991 ± 617, and 3337 ± 684 kcal, and were unaffected by PA levels (p = 0.115). The 24 h EBs were 1324 ± 441 kcal (Low-PA), 759 ± 543 kcal (Mid-PA), and 113 ± 430 kcal (High-PA), with significant differences observed between Low-PA vs. Mid-PA (p = 0.0496), Low-PA vs. High-PA (p ≤ 0.01), and Mid-PA vs. High-PA (p = 0.017) conditions. The EB in the Low-PA group was the highest of the three conditions. Appetite perception did not differ among the study days, however there was an interaction trend (p = 0.078, time × condition). Thus, significantly different daily PA did not affect 24 h EI, however markedly affected 24 h EB, implying that EB is not automatically matched during a single day.

The Association Among Social Support, Self-Efficacy, Use of Mobile Apps, and Physical Activity: Structural Equation Models With Mediating Effects (Preprint)

2018 ◽  
Author(s):  
Taotao Wang ◽  
Mengyuan Ren ◽  
Ying Shen ◽  
Xiaorou Zhu ◽  
Xing Zhang ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Support ◽  
College Students ◽  
Self Efficacy ◽  
Structural Equation ◽  
Mobile Apps ◽  
Mediating Effect ◽  
Activity Levels ◽  
Support Level ◽  
Physical Activity Levels

BACKGROUND Physical inactivity is a risk factor for chronic noncommunicable diseases. Insufficient physical activity has become an important public health problem worldwide. As mobile apps have rapidly developed, physical activity apps have the potential to improve the level of physical activity among populations. OBJECTIVE This study aimed to evaluate the effect of physical activity apps on levels of physical activity among college students. METHODS A Web-based questionnaire was used to survey college students in Beijing from December 27, 2017, to January 5, 2018. According to a previous survey, 43% of college students using physical activity apps and 36% of those who never used such apps achieved the physical activity recommendations. In this study, the sample size was calculated to be 500. The questionnaire consisted of 5 parts: the use of physical activity apps, sports habits, social support, self-efficacy, and social demographic information. Structural equation modeling was used to test the relationships between the use of physical activity apps, self-efficacy, social support, and level of physical activity. RESULTS Of the 1245 participants, 384 college students (30.8%) used physical activity apps (in the past month). Of these 384 students, 191 (49.7%) gained new friends via the app. College students who were using physical activity apps had a higher level of physical activity and higher scores for social support and self-efficacy (<italic>P</italic>&lt;.001) than those who did not use such apps. The use of physical activity apps significantly affected the mediating effect of physical activity level through social support (beta=.126; <italic>P</italic>&lt;.001) and self-efficacy (beta=.294; <italic>P</italic>&lt;.001). Gender played an important role in app use, self-efficacy, and physical activity in the mediation model: male users spent more time on physical activity and had higher self-efficacy scores (<italic>P</italic>&lt;.001). CONCLUSIONS This study focused on college students in Beijing and found that the use of physical activity apps is associated with higher physical activity levels among these students. This effect is mainly through the mediation effect of social support and self-efficacy, rather than the direct effect of physical activity apps. The use of physical activity apps is associated with a higher social support level and higher self-efficacy score. Furthermore, a high social support level and high self-efficacy score are associated with higher physical activity levels.

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