ABSTRACT Despite a wide range of components and criteria affecting travel behavior presented through empirical research, the results of these studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel behavior due to the differences in various physical developments. The required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan in Tehran, Iran, as the old, conventional, and new neighborhoods, respectively. ANOVA test was exerted to analyze the significant difference between different development patterns in three neighborhoods. Dunnett's T3 was applied to determine which neighborhood caused the difference between groups. Also, the factors affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered that factors such as car ownership, dependence and pro-liking for private cars, density and access to educational centers and parks, access to medical and service centers, and variety and density of retail stores had been introduced as the factors affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a residence, dependence, and pro-liking for other than a private car, having a license, number of children under five years old, and age have influenced travel behavior regardless of the variation between neighborhoods. Extended Abstract Introduction Finding factors affecting travel behavior has been one of the main concerns of transportation planners. However, in the last two decades, the importance of the influence of the features of the built environment, including land use, along with demographic-economic characteristics, travel behavior, and attitudes of people, has been raised by urban planners. Studies seek to find factors affecting travel behavior, especially land use characteristics. Despite presenting a wide range of components and criteria affecting travel behavior, the results of the studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel behavior due to the differences in various physical developments. In order to do this, it must first be determined whether the study areas/different development patterns have a significant difference in terms of travel behavior or not. In case of a positive answer to the previous question, the following question is which study areas caused this difference. The next question arises: -Which physical and non-physical characteristics affect travel behavior due to distinctions between different development patterns?   Methodology The present research method is analytical and experimental based on quantitative methods. This research chose the frequency of travel by private car, public transportation, and walking as the travel behavior. According to the research's purpose, indicators and criteria affecting travel behavior were extracted after reviewing the theoretical and experimental literature. Then, the required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan as the old, conventional, and new neighborhoods, respectively. The questionnaire was compiled as a Likert scale in five parts of travel information, demographic-economic characteristics, perceptual characteristics of land use, travel habits, and access preferences of people in choosing their residence. ANOVA test was used to analyze the significant difference between different groups of a characteristic (here, different development patterns or the three case studies). Dunnett T3 was exerted to determine which neighborhood caused the difference between groups. Also, the factors affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered which factors affecting travel behavior were due to the differences in study areas and which factors affect travel behavior regardless of development patterns.   Results and discussion This research aims to identify the factors affecting travel behavior due to the differences in development patterns. In this regard, the findings in line with the first research question show that the frequency of three modes of travel, by private car, transportation, and pedestrian, differ significantly in the three neighborhoods. Furthermore, ANOVA test results depict that there is a significant difference between these three neighborhoods in terms of factors affecting travel behavior, such as perceptually environmental characteristics of the neighborhood, dependence and pro-liking for personal cars, variety and density of retail stores, density and access to educational units and parks, access to medical and service centers, and car ownership. Finally, by comparing the results of the ANOVA test with the regression analysis assessing the relationship between physical and non-physical factors (the same indicators in the same study areas) with travel behavior, the factors affecting travel behavior owing to different development patterns were identified. Factors such as car ownership, dependence and pro-liking for private cars, density and access to educational units and parks, access to medical and service centers, and variety and density of retail stores have been introduced as the factors affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a place of residence, dependence, and pro-liking for other than a private car, having a certificate, number of children under five years old, and age have influenced on travel behavior regardless of the variation between neighborhoods (different physical development patterns).   Conclusion In In order to discover the factors affecting travel behavior due to the differences in patterns of physical development, this research has provided a more detailed analysis of the factors affecting travel behavior. It has achieved more accurate components than previous studies in this regard. Detailed analysis of studies related to travel behavior and finding the main components affecting it, considering the extent of variables and data, can pave the way for professionals in transportation planning and urban planning, in addition to providing detailed methods and criteria in the related literature.   Funding There is no funding support.   Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

The recognition of physical fitness and functional capacity of military personnel is one of the important concerns of the armed forces of the country. This study aimed to compare the factors of physical fitness including flexibility, agility, speed, aerobic power, anaerobic power and explosive power among the graduates. For this purpose, 100 students selected from among athletes in Imam Ali University and Shahid Sattari Aeronautical University based on convenience sampling method. Then, all subjects were taken rast test, Sit and reach Hugo corrected test, 36m speed test, Sargent jump test, Illinois Test and Cooper test in three sessions in three days (one week between each session). To analyze the data, Kolmogorov-Smirnov test was first used to determine the normality of the data and the independent t-test was used to examine the differences between the two groups and the SPSS software (V. 22) was used. The results showed that there is a significant difference between the two universities in the speed variables (p = 0. 0001), agility (p = 0. 0001), explosive power (p = 0. 0001), aerobic power (p = 0. 0001) and anaerobic power (p = 0. 0001). According to the results, it is concluded that physical fitness factors are different among the graduates of Imam Ali University and Shahid Sattari Aeronautical University.

Paying much attention to one's appearance and body is one of the major consequences of today's consumer society which has manifested itself in going on diets, body building, and losing weight. The present article aims to study the factors that affect this tendency among women residing in Isfahan. To do this, 340 women aged 20-42 were selected using a stratified sampling technique. Bourdieu's and Goffman's ideas along with those of Giddens and exchange theory were used to construct a theoretical framework that associated certain variables to tendency toward physical fitness. The findings showed that the mean for tendency toward physical fitness was 43.2 (expected mean was 33). The results also showed that a woman's perceptions of men's views regarding women's shapes, wanting to be liked by others, and usage of mass media were positively associated with tendency toward physical fitness. The association between satisfaction with one's body and tendency toward physical fitness was reverse. No association was found between age, and socio-economic status and tendency toward physical fitness. Regression analysis indicated that three independent variables (women's perceptions of men's views, wanting to be liked, and spouse's education) together could explain 24 percent of the variance of the dependent variable.

Background and Aim: Studies in sport, exercise and physical activity have shown that self-perceived physical fitness is an important predictor of behavior and plays an important role in continuing or stopping physical activity. The purpose of this study was to investigate the relationship between perceived and actual physical fitness in military personnel. Methods: A total of 77 military personnel were selected randomly and divided to four groups (group 1: 20‒ 30 years, group 2: 31‒ 40 years, group 3: 41‒ 50 years and group 4: 51‒ 60 years). Participants completed the questionnaire of study in 4 parts: personal information, self-perceived physical fitness scale, short form of Marsh Physical Self-Description Questionnaire and mental toughness questionnaire. Physical fitness tests were then performed according to the questionnaires. Results: The mean age of military staff was 38 years, the average height was 175 cm, and the average weight was 78 kg. There was a significant correlation between perceived and actual physical fitness. But this correlation was significant only in group 2 (31‒ 40 years). There was also a significant correlation between perceived physical conditions, perceived muscular condition and perceived physical scale in comparison with actual conditions. The correlation coefficient between perceived muscular condition and actual condition was higher than the other subscales. There was no significant relationship between perceived flexibility and actual flexibility. Partial correlation also showed that the relationship between self-perceived physical fitness and actual physical fitness is affected by mental toughness, physical self-perceived and level of physical activity. Conclusion: There is a relationship between perceived and actual physical fitness, but this relationship is not equal in all subscales. Perceived physical fitness is affected by age, psychological characteristics and the level of physical activity. It is necessary to consider perceived physical fitness according to importance of self-perceived in life.

Background: Adolescence is an important period of growth and development. We aimed to analyz the factors affecting the subjective health and physical fitness perceptions of Korean adolescents. Methods: This cross-sectional study used data from 2,599 Korean adolescents who participated in the Korea National Life and Sports Surveys during 2020-2022, along with secondary data analysis. The study included frequency analysis of the demographic variables and chi-square tests to examine the relationship between the variables and subjective health and fitness perceptions. Multivariate logistic regression was also used to explore the factors affecting adolescent subjective perceptions. Results: The study revealed significant factors influencing health perceptions in Korean adolescents, including male gender, lack of regular physical activity, adequate rest, adherence to a consistent diet, and use of nutritional supplements (P<0.05). Conclusion: Key factors, such as rest, diet, and nutritional supplements should be considered while developing policies that support adolescent health. Unfortunately, there has been a decline in the physical activity of adolescents in Korea. Therefore, a variety of physical activity programs that consider the characteristics of each student need to be developed to address adolescent health issues.

Introduction: Diabetes interfere the glucose homeostasis and reduces physical fitness and brain health. This study was performed with aim to compare some physical fitness and physiologic factors in overweight diabetic and non-diabetic elderly women. Methods: This descriptive-analytical study was performed on 34 elderly women in Khorramabad city in 2019. One week before performing physical fitness tests, blood sample was taken in fasting state to measure serum levels of glucose, irisin, HbA1c and BDNF. Five days before doing the study, the subjects were familiarized with the method of performing the tests. Then, on the day of the study, some physical fitness tests special for the elderly were performed. Data were analyzed by Independent t-test and Pearson correlation coefficient test. P< 0. 05 was considered statistically significant. Results: There was no significant difference between the diabetic and non-diabetic groups in all physical fitness variables (p>0. 05). However, all physiologic variables showed a significant difference (p<0. 05). Also, in the non-diabetic group, there was a significant negative correlation between BDNF and HbA1c (p = 0. 0005 and r =-0. 824) and irisin and HbA1c (p = 0. 0005 and r =-0. 734) and a significant positive correlation between BDNF and irisin (p = 0. 004 and r = 0. 608). But in the diabetic group, there was a significant positive correlation between glucose and HbA1c (p =0. 028 and r =0. 586) and irisin and BDNF (p = 0. 0005 and r = 0. 853) and a significant negative correlation between glucose and irisin (P = 0. 015 and r =-0. 636), BDNF and HbA1c (p = 0. 002 and r =-0. 759) and irisin and HbA1c (p = 0. 002 and r =-0. 752). Conclusion: It seems that the level of physical fitness of overweight non-diabetic and diabetic elderly women is the same. But the non-diabetic group was better in physiological variables. Also, irisin plays more important role than BDNF in diabetes.

One of the correct ways of selecting and directing the youth toward the right athletic activity is assessing their inherited physical and motor characteristics. The Scope of this study was to compare the physical characteristics (height, weight and max heart rate), physical fitness factors (max aerobic capacity, flexibility, abdominal muscle endurance and grip strength), motor fitness factors (Speed, agility and auditory response time), among 16 pairs of identical twin sisters (Experimental group), and 18 pairs of non -identical twin sisters (Control group), from the city of Rashet with an age range of 14-17 years. Both contort and experimental groups were selected randomly and non -randomly based on specific criteria such as being monozygotic (MZ) or not under the supervision of a specialist. All Variables were investigated under similar condition, using standard and common field tests. The hypothesis of this study was also investigated by using Pearson coefficient correlation, dependent and independent (t) test with (α=0/05), using Spss program.