scholarly journals Physical Activity Spectrum Discriminant Analysis – a Method to Compare Detailed Patterns Between Groups

2021 ◽  
Author(s):  
Jonatan Fridolfsson ◽  
Daniel Arvidsson ◽  
Lars Bo Andersen ◽  
Ola Thorsson ◽  
Per Wollmer ◽  
...  
Keyword(s):  
Physical Activity ◽  
Discriminant Analysis ◽  
Activity Spectrum

scholarly journals Cognitive Strategies and Physical Activity in Older Adults: A Discriminant Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Nathalie André ◽  
Claude Ferrand ◽  
Cédric Albinet ◽  
Michel Audiffren
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Discriminant Analysis ◽  
Cognitive Functions ◽  
Attentional Control ◽  
Cognitive Strategies ◽  
Predictor Variables ◽  
Stepwise Discriminant Analysis ◽  
Correct Prediction ◽  
Internal Memory

Background. Although a number of studies have examined sociodemographic, psychosocial, and environmental determinants of the level of physical activity (PA) for older people, little attention has been paid to the predictive power of cognitive strategies for independently living older adults. However, cognitive strategies have recently been considered to be critical in the management of day-to-day living. Methods. Data were collected from 243 men and women aged 55 years and older living in France using face-to-face interviews between 2011 and 2013. Results. A stepwise discriminant analysis selected five predictor variables (age, perceived health status, barriers’ self-efficacy, internal memory, and attentional control strategies) of the level of PA. The function showed that the rate of correct prediction was 73% for the level of PA. The calculated discriminant function based on the five predictor variables is useful for detecting individuals at high risk of lapses once engaged in regular PA. Conclusions. This study highlighted the need to consider cognitive functions as a determinant of the level of PA and, more specifically, those cognitive functions related to executive functions (internal memory and attentional control), to facilitate the maintenance of regular PA. These results are discussed in relation to successful aging.

Multidimensional Correlates of Falls in Older Women

1998 ◽  
Vol 47 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Diane L. Gill ◽  
Kathleen Williams ◽  
Lavon Williams ◽  
Wayne A. Hale
Keyword(s):  
Physical Activity ◽  
Discriminant Analysis ◽  
Older Women ◽  
Well Being ◽  
Multiple Factors ◽  
Functional Reach ◽  
Increase Risk ◽  
Risk Of Falls ◽  
Multifaceted Interventions ◽  
Levels Of Functioning

Older women who had fallen within the last year (n = 63) were compared with those who had not fallen ( n = 67) on several psychological and motor measures. Both fallers and nonfallers demonstrated high levels of functioning. Discriminant analysis results indicated that a combination of variables, including physical activity and both psychological (general well-being, self-efficacy) and motor (functional reach, mobility) measures differentiated fallers and nonfallers. Results suggest that falling is a multidimensional phenomenon, that small declines on multiple factors may increase risk of falls, and that multifaceted interventions may help maintain high levels of functioning and prevent declines often associated with increased age.

scholarly journals Cross-Sectional Associations of Total Daily Volume and Activity Patterns across the Activity Spectrum with Cardiometabolic Risk Factors in Children and Adolescents

2020 ◽  
Vol 17 (12) ◽  
pp. 4286
Author(s):  
Simone J. J. M. Verswijveren ◽  
Karen E. Lamb ◽  
Anna Timperio ◽  
Jo Salmon ◽  
Rohan M. Telford ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Cardiometabolic Risk ◽  
Sedentary Time ◽  
Activity Patterns ◽  
Cardiometabolic Risk Factors ◽  
Cross Sectional ◽  
Intensity Physical Activity ◽  
Activity Spectrum ◽  
Vigorous Intensity

Sedentary and physical activity patterns (bouts/breaks) may be important for cardiometabolic health in early life. This study aimed to examine cross-sectional associations of total daily volume and patterns across the activity spectrum with cardiometabolic risk factors in youth aged 7–13 years. Objectively measured accelerometer and cardiometabolic risk factor data were pooled from two studies (n = 1219; 69% valid accelerometry). Total daily volume of sedentary time and light-, moderate-, and vigorous-intensity physical activity was determined. Time in sustained bouts and median bout lengths of all intensities and breaks in sedentary time were also calculated. Outcomes included body mass index, waist circumference, blood pressure, blood lipids, and a cardiometabolic summary score. Regression models revealed beneficial associations between total daily volumes of moderate- and vigorous-intensity physical activity and cardiometabolic risk. Time spent in ≥1 min vigorous-intensity physical activity bouts was beneficially associated with cardiometabolic risk, yet this disappeared after adjusting for total vigorous-intensity physical activity and confounders. Time accumulated in light- (≥1 min; ≥5 min) and moderate-intensity (≥1 min) physical activity bouts was detrimentally associated with cardiometabolic risk. Total daily volume and activity patterns may have implications for cardiometabolic risk early in life. Sporadic physical activity may be more beneficial for health than sustained physical activity.

scholarly journals Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Wendong Xiao ◽  
Yingjie Lu
Keyword(s):  
Physical Activity ◽  
Discriminant Analysis ◽  
Frequency Domain ◽  
Extreme Learning Machine ◽  
Activity Recognition ◽  
Support Vector ◽  
Kernel Discriminant Analysis ◽  
Time Frequency ◽  
Physical Activity Recognition ◽  
Learning Machine

Wearable sensor based human physical activity recognition has extensive applications in many fields such as physical training and health care. This paper will be focused on the development of highly efficient approach for daily human activity recognition by a triaxial accelerometer. In the proposed approach, a number of features, including the tilt angle, the signal magnitude area (SMA), and the wavelet energy, are extracted from the raw measurement signal via the time domain, the frequency domain, and the time-frequency domain analysis. A nonlinear kernel discriminant analysis (KDA) scheme is introduced to enhance the discrimination between different activities. Extreme learning machine (ELM) is proposed as a novel activity recognition algorithm. Experimental results show that the proposed KDA based ELM classifier can achieve superior recognition performance with higher accuracy and faster learning speed than the back-propagation (BP) and the support vector machine (SVM) algorithms.

Analysis of Course Type, Gender, and Personal Incentives to Exercise

1994 ◽  
Vol 78 (1) ◽  
pp. 155-159 ◽  
Author(s):  
Mel E. Finkenberg ◽  
James M. Dinucci ◽  
Sandra L. McCune ◽  
E. Donice McCune
Keyword(s):  
Physical Activity ◽  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Canonical Discriminant Analysis ◽  
Men And Women ◽  
Independent Variables ◽  
Significant Difference ◽  
Dependent Variables ◽  
And Gender ◽  
Main Effects

206 women and 88 men enrolled in classes requiring different amounts of physical activity were administered the Personal Incentives for Exercise Questionnaire A two-way multivariate analysis of variance, with course type and gender as the categorical independent variables and 10 subscale scores representing incentives to exercise as the multivariate dependent variables, was completed. Canonical discriminant analysis was used to identify which of the incentives is most useful in discriminating among participants in active and less active classes or between men and women. For main effects, analysis indicated an over-all significant difference for both course type and gender. Men scored higher than women on activity and on competition, for example, while women scored higher on appearance and weight management.

scholarly journals Registration and Analysis of Acceleration Data to Recognize Physical Activity

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Marcin Kołodziej ◽  
Andrzej Majkowski ◽  
Paweł Tarnowski ◽  
Remigiusz J. Rak ◽  
Dominik Gebert ◽  
...  
Keyword(s):  
Physical Activity ◽  
Discriminant Analysis ◽  
Physical Activities ◽  
Quadratic Discriminant Analysis ◽  
Activity Type ◽  
Statistical Parameters ◽  
Acceleration Signal ◽  
Acceleration Data ◽  
Acceleration Signals

The purpose of the article is to check whether the acceleration signals recorded by a smartphone help identify a user’s physical activity type. The experiments were performed using the application installed in a smartphone, which was located on the hip of a subject. Acceleration signals were recorded for five types of physical activities (running, standing, going up the stairs, going down the stairs, and walking) for four users. The statistical parameters of the signal were used to extract features from the acceleration signal. In order to classify the type of activity, the quadratic discriminant analysis (QDA) was used. The accuracy of the user-independent classification for five types of activities was 83%. The accuracy of the user-dependent classification was in the range from 90% to 95%. The presented results indicate that the acceleration signal recorded by the device placed on the hip of a user allows us to effectively distinguish among several types of physical activity.

scholarly journals Discriminating Bangladeshi Adults by Simultaneous Prevalence of Obesity and Diabetes

2020 ◽  
Vol 2 (2) ◽  
pp. 19-26
Author(s):  
KC Bhuyan ◽  
Keyword(s):  
Physical Activity ◽  
Discriminant Analysis ◽  
Marital Status ◽  
Socioeconomic Factors ◽  
Odds Ratio ◽  
Health Hazard ◽  
Diabetic Patients ◽  
Obese Adults ◽  
Obesity And Diabetes ◽  
Prevalence Of Obesity

The present analysis was to identify the socioeconomic factors responsible for prevalence of obesity and diabetes simultaneously among adults of 18 years and above residing in both urban and rural localities of Bangladesh. Accordingly, information was collected from 960 adults by some doctors and nurses from and nearby their working places. Among the investigated adults, 66.9% were diabetic patients and 20.2% of them were obese. In the sample, total obese adults were 29.3%. Obesity and diabetes were significantly associated. Prevalence of obesity and diabetes were significantly associated with age, marital status and utilization of time. Income was the most responsible factor for this simultaneous health hazard followed by expenditure, physical activity, marital status, religion and occupation. This conclusion was drawn from the results of odds ratio and discriminant analysis.

Physical activity and body composition of adolescent boys

JAMA ◽  
1966 ◽  
Vol 197 (11) ◽  
pp. 891-893 ◽  
Author(s):  
L. P. Novak
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Adolescent Boys
Sign in / Sign up

Export Citation Format

Share Document