Physical Activity Factors Research Articles

Psychometric Properties of a Wellness Behavior Rating Scale for Young Adolescents.

We examined the psychometric properties of the Physical Activity, Nutrition, and Technology (PANT) survey, developed by researchers to track weight management behaviors among youth. Data from 2,039 middle school students (M age = 12.4, SD = .5; 51.4% girls) were analyzed to explore and then confirm the factor structure of the PANT survey. We also examined the bivariate associations between the PANT survey, body mass index (BMI), and the Progressive Aerobic Cardiorespiratory Endurance Run (PACER). Results suggest that the PANT survey is comprised of two factors-Physical Activity and Healthy Choices-each with adequate internal consistency (α = .79 and 0.86, respectively). The Physical Activity subscale appears to be significantly associated with both z-BMI (r = -0.10, p < .001) and the PACER (r = 0.33, p < .001) in the anticipated directions, but the criterion validity of the Healthy Choices subscale is less clear. We discuss these findings and explore future directions for developing meaningful self-report wellness behavior scales for youth.

Factors Affecting Inequality in the Quality Diets: A Scoping Review

Objectivesthe current dietary pattern is found increasingly unsafe, unstable, and unequal for a huge part of the populations. Disadvantaged populations were usually known by less healthy diets and were at a higher risk of disease in comparison with high socioeconomic groups. The current scoping review study aims at indicating the effective factors on inequality in the quality of diets. Methodsthe academic databases, including Scopus, Web of science, PubMed, Scientific Information Database, Islamic World Science Citation Center, Google scholar search engine, World Health Organization, and the website of the European Union, until April 2021, were systematically reviewed. We used vote counting technique to identify the effective factors causing inequality in the quality of diets. ResultsThe factors that have caused inequality in the quality of diets were divided into 3 categories of demographic, lifestyle, and socioeconomic. It was found that any increase in age, income, education, different ethnic groups, smoking, and occupational statues increased inequality in diet quality. Also, as a factor physical activity, could reduce inequality in diet quality. Moreover, type of residence in terms of access to food, dominant available food, and culture of the area could cause inequality in diet quality. ConclusionsAccording to the results of this study, the effective factors on inequality in the quality of diet are demographic and socioeconomic factors that cannot be manipulated by policy makers. Nevertheless, increasing the knowledge of individuals, improving their lifestyle, and providing subsidies to poorer individuals reduce inequality in the quality of the diets.

Effect of physical activity and sedentary sitting time on psychological quality of life of people with and without disabilities; A survey from Saudi Arabia.

BackgroundMental and psychological health issues are on the rise globally. People with disabilities are at greater risk of poor psychological quality of life especially after covid-19 pandemic. Along with other factors physical activity (PA) may have a significant effect on mental health. This study aims to analyze the difference of PA participation and sitting time among people with and without disabilities and their association with psychological quality of life.MethodsA standard questionnaire was used to collect the data from disabled and non-disabled participants above 15 years of age. Bivariate and multivariate analysis was performed to yield statistical results.ResultsTotal study sample consisted of 359 participants (67.7% without disability and 32.3% with disability). Participants without disabilities reported a significantly better psychological quality of life (QOL) (Mean score = 68) as compared to the ones with disabilities (Mean score = 61), (p < 0.01). There was significant difference between the sitting time of two groups with longer sitting time among people with disabilities (6.1 h/day) as compared to non-disabled (5.3 h). Optimum level of PA was strongly associated with better psychological quality of life among individuals without disabilities (p = 0.00). Younger age (p = 0.00) and being single (p = 0.01) were significant predictors of poor psychological health among non-disables. Increase in sedentary sitting time was significantly associated with poor psychological quality of life among both groups.ConclusionsTailored health policies to encourage PA and reduce sitting hours should be formulated to improve psychological health with special focus on individuals with disabilities. Future studies with large sample size are recommended to validate the current results and further explore the difference in association of PA and psychological wellbeing in people with and without disabilities.

Dynamical Indicator of Human Body’s Physical Endurance

Physical endurance is the time span between the beginning of physical activity by an individual and the termination because of exhaustion. Physical endurance involves a multifaceted behaviour which can be understood by complexities. Everyone performs physical activity in order to sustain-life. However, the number of activities done are largely subject to personal choice and varies from person to person as well as for a given person over time. Physical activity like meditation/exercises are positively related to physical fitness. One needs to understand relation between physical activity, exercise, physical fitness and health. These activities can be partitioned mutually exclusively into many different ways. This paper categorizes daily physical activity into three broad subdivisions based on amount of body movements taking place are: (i) light, (ii) moderate and (iii) high intensity. These three characterizations are considered to be mutually exclusive and sum up to total energy spent by an individual. The behavior of the three factors physical activity, heart and energy generated is analyzed with the help of Fast Lyapunov indicator (FLI), Dynamic Lyapunov indicator (DLI), Small alignment index (SALI). FLI’s increase for chaotic orbits for values of R=20, Q=70 for the case of high intensity exercises and to linearly regular orbits for values of R=5, Q=8 and R=10, Q=12 in the cases of light and moderate exercises respectively. SALI’s alters through non-zero value for R=20, Q=70 while it tends to zero for values of R=5, Q=8 and R=10, Q=12. DLI’s the largest Eigen values form a definite pattern/curve for n=2000 for values of R=5, Q=8 and n=100 for R=10, Q=12 respectively as the motion stays regular plus dispersed randomly as the motion is chaotic for n=60 and for R=20, Q=70.

Prediction Equation For Estimating Total Daily Energy Requirements Of Special Operations Personnel

Special Operations Forces (SOF) engage in a variety of military tasks with many producing high energy expenditures, resulting in undesired energy deficits and loss of body mass. Therefore, the ability to accurately estimate daily energy requirements would be useful to generate adequate feeding regimens to support maintenance of energy balance during military operations. PURPOSE: Generate a predictive equation estimating energy requirements of SOF. METHODS: Retrospective analysis of data collected from SOF personnel engaged in 12 different SOF training scenarios. Energy expenditure and total body water were determined using the doubly-labeled water (DLW) technique. Physical activity level was determined as daily energy expenditure divided by resting metabolic rate. Physical activity level was broken into quartiles (0 = mission prep, 1 = common warrior tasks, 2 = battle drills, 3 = specialized intense activity) to generate a physical activity factor (PAF). Regression analysis was used to construct two predictive equations (Model A; body mass and PAF, Model B; fat-free mass and PAF) estimating daily energy expenditures. RESULTS: Average measured energy expenditure during SOF training was 4468 (range: 3700 to 6300) Kcal·d-1. Regression analysis revealed that physical activity level (r = 0.91; P < 0.05) and body mass (r = 0.28; P < 0.05; Model A), or fat-free mass (FFM; r = 0.32; P < 0.05; Model B) were the factors that most highly predicted energy expenditures. Predictive equations coupling PAF with body mass (Model A) and FFM (Model B), were correlated (r = 0.74 and r = 0.76, respectively) and did not differ (mean ± SEM: Model A; 4463 ± 65 Kcal·d-1, Model B; 4462 ± 61 Kcal·d-1) from DLW measured energy expenditures. CONCLUSION: By quantifying and grouping SOF training exercises into activity factors, SOF energy requirements can be predicted with reasonable accuracy and these equations can be used by dietetic/logistical personnel to plan appropriate feeding regimens to meet SOF nutritional requirements across their various mission profile. Disclaimer: The views expressed in this abstract are those of the authors and do not reflect the official policy of the Department of Army, Department of Defense, or the U.S. Government.

Prediction equation for estimating total daily energy requirements of special operations personnel

BackgroundSpecial Operations Forces (SOF) engage in a variety of military tasks with many producing high energy expenditures, leading to undesired energy deficits and loss of body mass. Therefore, the ability to accurately estimate daily energy requirements would be useful for accurate logistical planning.PurposeGenerate a predictive equation estimating energy requirements of SOF.MethodsRetrospective analysis of data collected from SOF personnel engaged in 12 different SOF training scenarios. Energy expenditure and total body water were determined using the doubly-labeled water technique. Physical activity level was determined as daily energy expenditure divided by resting metabolic rate. Physical activity level was broken into quartiles (0 = mission prep, 1 = common warrior tasks, 2 = battle drills, 3 = specialized intense activity) to generate a physical activity factor (PAF). Regression analysis was used to construct two predictive equations (Model A; body mass and PAF, Model B; fat-free mass and PAF) estimating daily energy expenditures.ResultsAverage measured energy expenditure during SOF training was 4468 (range: 3700 to 6300) Kcal·d-1. Regression analysis revealed that physical activity level (r = 0.91; P < 0.05) and body mass (r = 0.28; P < 0.05; Model A), or fat-free mass (FFM; r = 0.32; P < 0.05; Model B) were the factors that most highly predicted energy expenditures. Predictive equations coupling PAF with body mass (Model A) and FFM (Model B), were correlated (r = 0.74 and r = 0.76, respectively) and did not differ [mean ± SEM: Model A; 4463 ± 65 Kcal·d− 1, Model B; 4462 ± 61 Kcal·d− 1] from DLW measured energy expenditures.ConclusionBy quantifying and grouping SOF training exercises into activity factors, SOF energy requirements can be predicted with reasonable accuracy and these equations used by dietetic/logistical personnel to plan appropriate feeding regimens to meet SOF nutritional requirements across their mission profile.

Causes and risk factors for anaphylaxis

Anaphylaxis is in most cases an IgE-dependent immunologic reaction. Mast cells are activated and release several mediators. Recent data about possible triggers of anaphylaxis indicate a clear age-dependency. The most frequent triggers of anaphylaxis in children are foods; in adults venom and drugs predominate. In 2006 an anaphylaxis registry was established in German-speaking countries. In the registry the triggers, circumstances, and treatment measures are collected from patients with anaphylaxis. However, the registry cannot supply epidemiological data like prevalence or incidence rates since the registration of cases is based on collaboration with allergy centers only. Similarly, other approaches to obtain data on the epidemiology of anaphylaxis are problematic given that allergic reactions of varying severity are covered by a number of codes in the ICD-10. Research in the field of anaphylaxis is focused on the identification of risk factors. Several data indicate the relevance of co-factors and augmentation factors in well-defined patient groups. Among these factors physical activity, infection, alcohol and additives are relevant. In the future a unique coding system with a subtype analysis regarding the triggers and severity should help to provide data on the epidemiology of anaphylaxis. Furthermore the mechanisms of co-factors and identification of biomarkers for risk assessment are important research areas for the future.