scholarly journals Isokinetic Testing: Sensitivity of the Force-Velocity Relationship Assessed through the Two-Point Method to Discriminate between Muscle Groups and Participants’ Physical Activity Levels

2020 ◽  
Vol 17 (22) ◽  
pp. 8570
Author(s):  
Danica Janicijevic ◽  
Olivera M. Knezevic ◽  
Amador Garcia-Ramos ◽  
Danilo Cvetic ◽  
Dragan M. Mirkov
Keyword(s):  
Physical Activity ◽  
Maximum Velocity ◽  
Point Method ◽  
Knee Extensors ◽  
Activity Levels ◽  
Force Output ◽  
Isokinetic Testing ◽  
Physical Activity Levels ◽  
Muscle Groups ◽  
Knee Flexors

Background: Isokinetic testing has been routinely used to assess the capacities of individual muscle groups. In this study we aimed to evaluate the sensitivity of the force-point (F-v) relationship assessed through the two-point method to discriminate between antagonist muscle groups and males with different physical activity levels. Methods: The concentric force output of the knee, hip, elbow, and shoulder flexors and extensors of 27 active and 13 non-active men was recorded at 60 and 180°/s to determine the F-v relationship parameters (maximum force [F0], maximum velocity [v0], and maximum power [Pmax]). Results: F0 and Pmax were higher for knee extensors (effect size [ES] = 1.97 and 0.57, respectively), hip extensors (ES = 2.52 and 0.77, respectively), and shoulder flexors (ES = 1.67 and 0.83, respectively) compared to their antagonist muscles, while v0 was higher for knee flexors compared to knee extensors (ES = 0.59). Active males revealed higher F0 for knee extensors (ES = 0.72) and knee flexors (ES = 0.83) and higher Pmax for knee flexors (ES = 0.70), elbow extensors (ES = 0.83) and shoulder extensors (ES = 0.36). Conclusions: The sensitivity of the two-point method for testing the maximal mechanical capacities was high for the knee, moderate for the hip and shoulder, and low for the elbow joint.

Muscle architecture and force-velocity relationships in humans

1984 ◽  
Vol 57 (2) ◽  
pp. 435-443 ◽  
Author(s):  
T. L. Wickiewicz ◽  
R. R. Roy ◽  
P. L. Powell ◽  
J. J. Perrine ◽  
V. R. Edgerton
Keyword(s):  
Peak Torque ◽  
Knee Extensors ◽  
Major Influence ◽  
Inhibitory Mechanism ◽  
Cross Sectional ◽  
Isokinetic Testing ◽  
Force Velocity ◽  
Muscle Groups ◽  
Knee Flexors

The in vivo torque-velocity relationships of the knee extensors (KE), knee flexors (KF), ankle plantarflexors (PF), and ankle dorsiflexors (DF) were determined in 12 untrained subjects using an isokinetic testing device (Cybex II). These data were then matched to the predicted maximum forces and shortening velocities derived from muscle architectural determinations made on three hemipelvectomies (36). The torque-velocity curves of all muscle groups resembled that predicted by Hill's (19, 20) equation except at the higher forces and lower velocities. The peak torques occurred at mean velocities ranging from 41–62 rad X s-1 for the KE, KF, and PF. Although the peak torque of the DF occurred at the isometric loading condition, it was also lower than that predicted by Hill's equation. The muscle fiber length and physiological cross-sectional area measurements indicate that the architecture of the human leg musculature has a major influence on the torque-velocity characteristics. These data corroborate previous findings (24) that some neural inhibitory mechanism exists in the control of the leg musculature, which limits the maximum forces that could be produced under optimal stimulating conditions.

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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