https://orcid.org/0000-0001-5026-9399
Figures
Abstract
Background
As a country’s future leaders and pioneers, University students must live with healthy habits. In order to achieve a healthy lifestyle, Physical activity and Quality of Life can serve as suitable indices to study. The purpose of the study was to clarify how physical activity (PA), Body mass index (BMI) and gender relate to the quality of life (QOL) of students of Medical Sciences University by using a structural equation model.
Methods
The research was a cross-sectional study. The number of participants was 225 students of the University. The participants answered three questionnaires, including Demographic, International Physical Activity Questionnaire (IPAQ short form), and Quality of Life Questionnaire (SF-12), BMI was calculated by anthropometric measures, as well. The Structural equation model (SEM) method was employed. The Fitness of the proposed pattern was measured using the following indexes: chi-square/degree of freedom ratio (CMIN/DF), Normed Fit Index (NFI), comparative fit index (CFI), the goodness of fit index (GFI), and standardized root mean squared residual (SRMR). In the analysis the significant level was considered as P < 0.05.
Results
PA (r = -0.726, P<0.001) and QOL (r = -0.405, P<0.001) have significantly inverse relationship with BMI, whereas the QOL and PA were proven to be positively related (r = 0.357, P<0.001). Moreover, the results signify gender as a moderator in the relationship between PA and QOL (Δχ2 (10) = 19.903, P = 0.030) and also the mediatory role of BMI among students. BMI affects the QOL in men (P < 0.001, β = -0.307) more in compare to women women (P = 0.324, β = -0.158).
Citation: Hoseini M, Bardoon S, Bakhtiari A, Adib-Rad H, Omidvar S (2022) Structural model of the relationship between physical activity and students’ quality of life: Mediating role of body mass index and moderating role of gender. PLoS ONE 17(8): e0273493. https://doi.org/10.1371/journal.pone.0273493
Editor: Mohammad Hossein Ebrahimi, Shahrood University of Medical Sciences, ISLAMIC REPUBLIC OF IRAN
Received: July 10, 2021; Accepted: August 10, 2022; Published: August 26, 2022
Copyright: © 2022 Hoseini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets used and/or analyzed during the current study are not publicly available due to restrictions of Research Ethical Committee of Babol University of Medical Sciences. The data will be available from Dr. Reza Ghadimi (rezaghadimi@yahoo.com) on reasonable request.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Physical activity can be defined as body movement escalating into a considerable rise in energy consumption, in compare to resting. Appearing in many forms, the amount of physical activity is affected by its frequency, intensity, and duration. Despite the great importance of the physical activity to the well-being of individuals, it is reported that almost 30% of women and 25% of men are physically inactive; in other words, more than one- quarter of the world’s population is inactive [1, 2]. It is reported that physical inactivity can be considered a major risk factor affecting the mortality of various diseases and is associated with almost 3.2 million deaths, annually [3]. Physical inactivity leads to weakened general health and is hence being recognized by the medical and health community, rapidly [4]. It is known that PA has a positive impact on various health indicators [5, 6]. Regular physical activity encourages a better QOL, as well as reduced chances of morbidity and mortality in diseases [7, 8]. PA can be effective on multiple aspects of individuals’ QOL [9–11]. Researchers suggest that individuals with a higher level of PA tend to have a better score on QOL when compared to people with less physical activity [12–15]. In addition to that, physical activity also affects an individual’s physical, psychological as well as emotional health [16]. This can be seen in different stages of life [17]. Study by Javadivala exhibited that there was a significant and positive relation between PA and QOL in menopausal women [18]. The obesity trends give rise to estimations such that by the year 2030, 65 million and 11 million obese adults will be living in the US and the UK, respectively [19]. The prevalence of obesity has been reported to be ranged from 22 to 48% in eastern countries [20–22]. WHO has reported that almost 2 billion adults are overweight out of which 650 million suffered from obesity. This report dates back to 2016, following a Global action plan on physical activity which has been planned through the years 2018 to 2030 to encourage a healthier lifestyle with increased physical activity [2]. The World Health Organization (WHO) aims to diminish the prevalence of physical inactivity by 15% by the year 2030 [23]. The WHO explains that the positive side of the concept of health, is a positive attitude of society towards the improvement and upkeep of health as the main condition for social well-being [24].
QOL consists of multiple domains. It can be associated with positive feelings such as happiness, wealth, success, health and satisfaction [25]. QOL can be assumed as a crucial tool in health care. The World Health Organization Quality of Life assessment (WHOQOL) group has defined QOL as "an individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns" [26].
Sedentary behavior is a crucial health issue in children and adolescents, which is intensified with age [27]. University students often experience a sitting time of over 9 hours a day which could set obesity in motion [28]. It is assumed that a large number of college students follow an unhealthy lifestyle (Alcohol over-consumption, tobacco use, sedentary behavior, etc.) [29, 30]. The level of physical activity starts to significantly decrease during adolescence, and the period of life in which the turn from adolescence into adulthood happens is very important [31]. Several studies have shown that physically active people have a better quality of life than inactive people. However, the improvement in quality of life was only observed in participants with high levels of physical activity [13, 14, 32]. There are many other factors which have effects on individuals’ quality of life. A study found that gender was not a determinant of correlations between PA and QOL [33] and it was contrary with the results of other studies [34, 35]. It seems that the role of gender in the relationship between PA and quality of life is still poorly understood. In addition, in two US population studies that used single-item life satisfaction measures, obesity was associated with decreased levels of life satisfaction [36, 37], while a Danish epidemiological study, controlled for several lifestyle factors, including body mass index (BMI), found a positive and independent association between self-reported physical activity and satisfaction in life [38]. Therefore, it was assumed that there might be a relationship between PA and QOL, with mediating or moderating effects of BMI and gender.
Materials and methods
The present study was a cross-sectional study. The participants included all students studying at the Babol University of Medical Sciences (BUMS). Multistage sampling was applied to select the subjects. All the university majors were 22 such as medicine, dentistry, nursing and midwifery, public health etc. were initially considered as clusters, and then among them ten majors were selected by simple random sampling. In the next step, participants were selected conveniently from the chosen clusters. We invited 25 students from each majors to participate in the study. The response rate was 90 percent and finally the data of 225 students who provided informed consent for study participation, was included in the study.
203 participants were initially assumed sufficient for this study to achieve a power of 80% at alphas level of 0.05 based on correlation coefficient of 0.196. In addition to that, a 10% drop-out rate was assumed, resulting in a sample size of 225.
Inclusion criteria were being a student at BUMS. Exclusion criteria were disability imposing limitations to physical activity, diagnosis with diseases with an effect on BMI (Diabetes, Thyroid, etc.), depression and other similar psychological disorders, the experience of a sorrowful incident in the past 3 months limiting physical activity or hindering the personal quality of life.
Ethics approval and consent to participate
The study was approved by the Research Ethical Committee of Babol University of Medical Sciences (MUBABOL.HRI. REC.1399.061). Written informed consent was taken from all the participants.
Instruments
We used three questionnaires including:
1. Demographic characteristics questionnaire: including age, marital status (single, married), residential status (Dormitory, house along with others, house alone), gender (female, male) and occupation (yes, no), family financial status (low, middle and high), etc. For assessing family financial status participants answered a question: (what is your family financial status?)Anthropometric measurements: The BMI was calculated through the division of weight in kilograms by the square of height, measured in meters. The weight was measured through a portable electronic scale, with an accuracy of 0.1 kg, and the height was assessed using a stadiometer with an accuracy of 0.1 cm. WHO classification was used to classify the participants’ BMI [39].
2. International Physical Activity Questionnaire (IPAQ): The short version of the International
Physical Activity Questionnaire IPAQ (IPAQ-SF) was employed to assess the physical activity of individuals. The IPAQ-SF is a scale with 7 items. It includes questions on physical activities related to work, transportation, housework, and leisure time during the past 7 days. Metabolic equivalents (METs)-min were calculated by multiplying the duration of activity in minutes by the coefficient of the activity level (1.3 for sitting, 3.3 for walking, 4 for moderate, and 8 for vigorous activities) [40]. The total Metabolic Equivalents (METs) estimation was utilized to divide the participants into different groups. The division was such that participants experiencing moderate activity, vigorous activity, or walking during the past 7 days, with a score under 600 MET-min/weeks were placed in the low physical activity group. Similarly, participants scoring between 600 and 3000 MET-min/week were placed in the moderate physical activity group, and those with over 3000 MET-min/week were placed in the high physical activity group. This version of IPAQ has been proven to have validity and reliability with co-relation factors of 0.77 and 0.95 [41–43]. The Persian version of this questionnaire has been reported to have Alpha Cronbach 0.7 and reliability was assessed by test re-test [44].
3. QOL questionnaire SF-12 is a self-reported questionnaire including 12 items from the original 36 items questionnaire (SF-36). The 12 questions assess 8 domains physical functioning 2 items, role physical 2 items, bodily pain 1 item, general health 1 item, vitality 1 item, social functioning 1 item, role emotional 2 items and mental health 2 items. The response categories for items ranged from 2 to 6 point scales and raw scores for items are ranging from 1 to 6. After recoding raw scores for some items (that are Bodily Pain, General Health, Vitality, and one item from Mental Health); then the raw scores could be transformed in order to provide eight scale scores each ranging from 0 (the worst) to 100 (the best). The physical component summary score (PCS) from the Physical Health domain and the mental component summary score (MCS) from the Mental Health domain the scores could range from 0 to 100 with higher scores indicating a better QOL. Montazeri et al have evaluated the validity of the Persian version of this questionnaire [45].
We tested two hypotheses:
1. The effects of physical activity on quality of life are mediated by BMI.
2. The mediated effects of physical activity on QOL through BMI are moderated by gender.
Statistical analysis
Research structural model.
To assess the suggested pattern between physical activity and students’ quality of life, we conducted structural equation modeling (Maximum likelihood estimation method) using AMOS version 24. Index fitting was performed using Chi-square and Chi-square degree of freedom ratio index (CMIN/DF), Parsimonious Normed Fit Index (PNFI), Comparative Fit Index (CFI), Parsimonious Comparative Fit Index (PCFI), Incremental Fit Index (IFI), Goodness-of-Fit Index (GFI) and Root-Mean-Square Error of Approximation (RMSEA). In addition to that, we have applied multi-group analysis through measured weight models comparison to study the moderating effects of gender, as a variable. To assess the research hypotheses, the assumptions were studied, prior to the application of the structural equations method. We have utilized the univariate and multivariate data distribution to analyze the normal and outlier data, separately. The test of univariate and multivariate normality and multicollinearity indicated the data were normally distributed (Mahalanobis d-squared p values more than .001) and multicollinearity was not an issue (VIF <5) [46].”
Results
The present study comprised 121 (53.8%) females and 104 (46.2%) male participants, with an average age of 23.06±2.63 and the age range of 19 to 37 years. The results exhibited that 93.3% of the students were single, 13.8% were employed and 57.4% were in the average or poor economic status group. The results depict that the mean BMI (kg/m2), mean physical activity (METs), and the mean quality of life of the participants were 23.86±3.87, 2008.28±2703.1, and 42.39±3.68, respectively. Interestingly, 34.8% of the subjects were overweight or obese and 32.1% had low PA.
The results exhibited that physical activity levels have a significant relationship with gender, occupation, smoking, frequency of exercise, duration of exercise, and BMI (Table 1).
Pearson correlation test showed that PA (r = -0.726, P<0.001) and QOL (r = -0.405, P<0.001) exhibited significantly inverse relationship with BMI, whereas QOL and PA were proven to be positively related (r = 0.357, P<0.001) (Table 2).
The Table 3, indicates that the proposed fitting indices were relatively weak because of collinearity. In order to improve the fit of the proposed model, correlation of the residuals was employed. Correlation values greater than 4 were considered to be the correction indicator. The fitting for the suggested pattern was validated by the presented indices. The fitting indices for the final pattern prove that the fitting is valid.
)e.g.,χ2 = 115.459; RMSEA = .075; CFI = .914; IFI = .933; GFI = .992; PNFI = .520; PCFI = .914)
QOL can present 41% (R2 = .409) of the changes associated with physical activity and BMI in this model.
Fig 1 shows the standard coefficients of the research model.
The standardized regression coefficient showed that PA had a significant and inverse relationship with BMI (P < 0.001, β = -0.671) and a significant and direct relationship with QOL (P < 0.001, β = 0.501). In addition to these relations, BMI has also proven to be inversely related to QOL (P < 0.001, β = -0.396). Bootstrap testing showed BMI has a mediatory role in the relationship between PA and QOL (P < 0.001, Indirect Effects = 0.265) (Table 4).
Path coefficients of the research model with respect to gender have been presented in Table 5. The fitting indices validate the proposed pattern in both genders, as shown in Table 5. It can be perceived that the effect of BMI on QOL was significant in men (P < 0.001, β = -0.307) in compare to women (P = 0.324, β = -0.158). The other paths however exhibit no difference between the two genders. The measured weight model (in two models unconstrained and constrained) results signify gender as a moderator in the relationship between physical activity and QOL (Δχ2 (10) = 19.903, P = 0.030) and also the mediatory role of BMI among students. In the modified model, by drawing a correlation between the errors (e1-e2 and e2-e4) the fit indices was improved (Fig 2).
The results also showed that the indirect effect of BMI on physical activity and quality of life in men (β = 0.203, P < .001) was higher and more significant than in women (β = 0.104, P < .001). Other independent variables were not included in the model.
Discussion
The study aimed to analyze the relationships among PA level, BMI, and gender in the QOL of Students.
The present study demonstrated that PA had a direct positive relationship with QOL, inverse relationship with BMI, and BMI had an inverse relationship with QOL. Furthermore, Physical activity had an indirect relationship with QOL as well. In addition, BMI mediated the influence of physical activity on QOL. Finally, gender had a moderator role in the relationships. The present study shows that physical activity is positively related to the QOL, which is in accordance with similar studies [8, 48, 49]. The hypothesis suggesting the predictability of QOL through physical activity with the mediatory role of BMI was proven to be true. The results are also in agreement with similar studies which supported the idea that the intensity of physical activity plays a significant role in QOL [50, 51]. It is reported that the motivation and adherence to physical activity are affected by multiple factors, such as age, sex, occupation, socioeconomic status and psychosocial state [49, 52].
We have also studied the relationship between PA levels and some other variables. We found a significant relationship between physical activity levels and gender, occupation, smoking, and BMI. A study by Cicek also suggests such a positive relationship which is in accordance with our results [53]. Furthermore, it was suggested that the intensity of physical activity was significantly affected by gender which is also in accordance with our results [53–55]. In the present study, the mean MET was different in different gender.
Contrary to the results of this research, another study suggests that the level of PA does not affect QOL [32]. However, this difference can be justified by the fact that the QOL is affected by multiple factors in older people and this suggests the importance of the age range of the participants.
In comparison with the gender, similar results have been reported in a study in Poland, which suggested that male adolescents are more active than females (32). Even more, studies can be found, verifying the higher activity levels in men (33).
We found a significant difference in BMI according to gender. Similar research in Turkey also reported a significant relationship between BMI and gender. It was perceived that males were at a higher risk for obesity, compared to females; these results are contrary to similar research that report a higher prevalence of obesity among females [56, 57]. The gender-related differences in our study can be attributed to Iranian culture such as eating outside and the increased eating of high-calorie food which is more prevalent among males than females.
The present study showed that a significant inverse relationship exists between physical activity and BMI; this is while the relationship between physical activity and QOL was seen to be direct. However, the PA was seen to be higher in male participants, despite higher BMI. The results of χ2 test signify a relationship between BMI and intensity of physical activity (P = 0.018).
While other studies suggest a poorer QOL in obese people [58, 59], the present study proves that BMI affects the QOL in men more than in women). The effect of BMI is significant in men and insignificant in women.
The present study proved BMI as a mediator in the relationship between PA and QOL. Similarly, the mediatory role of BMI was also seen in studies regarding children’s PA [60–63]. In a study by Bottcher et al., it was reported that weight-loss, leads to a diminish in the BMI, which in turn leads to a higher QOL score [64]. Although there are not many studies that discuss the relationship by considering a mediatory role, another study suggests that regular physical activity had a relationship with QOL with no effect from BMI values [65]. These contradictions could be due to the difference in the method of statistical analysis.
A research by Kim et al. shed light on the fact that the department of the study had a relationship with physical activity. Their work reported that students studying sports had a higher PA level and hence, better QOL scores [66].
The results indicate that most of the participants are in the normal BMI range. Student life invokes poor health habits, such as a reduction in physical activity [67].
The model suggests that health education programs focused on promoting regular PA and controlling BMI may be effective at improving QOL in young adults and college-aged populations. It can be said that the promotion of higher physical activity could prove to be beneficial to the QOL of college students. The moderating role of gender suggests an unhealthier lifestyle among college boys which could be due to the cultural practice in Iran.
The limitations of this study include the generalizability of the results to other samples and populations, and cross-sectional research conduction which may affect the relationship between the variables studied.
The implication of the research:
The results of this study could shed light on the possible factors, affecting the QOL. This information can be used by the health system to encourage higher levels of physical activity, culminating in a refined QOL.
Conclusion
The present study showed that the PA levels among students of BUMS were relatively low. We assume that an improved PA through proper exercise, as well as changes in lifestyle, could serve as a method to improve the QOL in the population.
Acknowledgments
The authors would like to thank the students who participated in this study. We also would like to thank Mr. Kord bagheri for his supports with data analysis. Finally, we appreciate student research committee and Health Research Center of Babol University of Medical Sciences for approving the study.
References
- 1. Grujic S. Ucestalost i problemi kvantifikacije gojaznosti predskolske dece [The frequency and problems of quantification overweight and preschool children] Faculty of Sport and Tourism, Novi Sad. TIMS Acta. 2011; 5: 31–36. Serbian.
- 2.
https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (Accessed 30 May 2021).
- 3. Organization WH. New physical activity recommendations for reducing disease and prevent deaths. 2011.
- 4. Orsega-Smith EM, Payne LL, Mowen AJ, Ho C-H, Godbey GC. The role of social support and self-efficacy in shaping the leisure time physical activity of older adults. Journal of Leisure research. 2007;39(4):705–27.
- 5. Humphreys BR, McLeod L, Ruseski JE. Physical activity and health outcomes: evidence from Canada. Health economics. 2014;23(1):33–54. pmid:23364850
- 6. Sushames A, van Uffelen JG, Gebel K. Do physical activity interventions in Indigenous people in Australia and New Zealand improve activity levels and health outcomes? A systematic review. International Journal of Behavioral Nutrition and Physical Activity. 2016;13(1):1–16. pmid:28003015
- 7. Elavsky S. Physical activity, menopause, and quality of life: the role of affect and self-worth across time. Menopause (New York, NY). 2009;16(2):265.
- 8. Wolin KY, Glynn RJ, Colditz GA, Lee I-M, Kawachi I. Long-term physical activity patterns and health-related quality of life in US women. American journal of preventive medicine. 2007;32(6):490–9.
- 9. Lobo A, Santos P, Carvalho J, Mota J. Relationship between intensity of physical activity and health‐related quality of life in Portuguese institutionalized elderly. Geriatrics & gerontology international. 2008;8(4):284–90. pmid:19149841
- 10. Guedes DP, Hatmann AC, Martini FAN, Borges MB, Bernardelli R Jr. Quality of life and physical activity in a sample of Brazilian older adults. Journal of Aging and Health. 2012;24(2):212–26. pmid:21750225
- 11. Rejeski WJ, Mihalko SL. Physical activity and quality of life in older adults. The Journals of Gerontology Series A: Biological sciences and medical sciences. 2001;56(suppl_2):23–35. pmid:11730235
- 12. Puciato D, Rozpara M, Mynarski W, Łoś A, Królikowska B. Physical activity of adult residents of Katowice and selected determinants of their occupational status and socio-economic characteristics. Medycyna pracy. 2013;64(5):649–57.
- 13. Chai W, Nigg CR, Pagano IS, Motl RW, Horwath C, Dishman RK. Associations of quality of life with physical activity, fruit and vegetable consumption, and physical inactivity in a free living, multiethnic population in Hawaii: a longitudinal study. International Journal of Behavioral Nutrition and Physical Activity. 2010;7(1):1–6.
- 14. Omorou YA, Erpelding M-L, Escalon H, Vuillemin A. Contribution of taking part in sport to the association between physical activity and quality of life. Quality of life research. 2013;22(8):2021–9. pmid:23345023
- 15. McAuley E, Elavsky S, Jerome GJ, Konopack JF, Marquez DX. Physical activity-related well-being in older adults: social cognitive influences. Psychology and aging. 2005;20(2):295. pmid:16029093
- 16. Li GS-F, Lu FJ, Wang AH-H. Exploring the relationships of physical activity, emotional intelligence and health in Taiwan college students. Journal of Exercise Science & Fitness. 2009;7(1):55–63.
- 17. Ohrnberger J, Fichera E, Sutton M. The relationship between physical and mental health: A mediation analysis. Social science & medicine. 2017;195:42–9. pmid:29132081
- 18. Javadivala Z, Kousha A, Allahverdipour H, ASGHARI JM, TALEBIAN H. Modeling the relationship between physical activity and quality of life in menopausal-aged women: a cross-sectional study. 2013.
- 19. Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and economic burden of the projected obesity trends in the USA and the UK. The Lancet. 2011;378(9793):815–25. pmid:21872750
- 20. Esteghamati A, Meysamie A, Khalilzadeh O, Rashidi A, Haghazali M, Asgari F, et al. Third national Surveillance of Risk Factors of Non-Communicable Diseases (SuRFNCD-2007) in Iran: methods and results on prevalence of diabetes, hypertension, obesity, central obesity, and dyslipidemia. BMC public health. 2009;9(1):1–10.
- 21. Alsaif MA, Hakim IA, Harris RB, Alduwaihy M, Al-Rubeaan K, Al-Nuaim AR, et al. Prevalence and risk factors of obesity and overweight in adult Saudi population. Nutrition Research. 2002;22(11):1243–52.
- 22. Fouad M, Rastam S, Ward K, Maziak W. Prevalence of obesity and its associated factors in Aleppo, Syria. Prevention and Control. 2006;2(2):85–94. pmid:18040524
- 23.
Organization WH. Global action plan on physical activity 2018–2030: more active people for a healthier world: World Health Organization; 2019.
- 24. Group W. The World Health Organization quality of life assessment (WHOQOL): position paper from the World Health Organization. Social science & medicine. 1995;41(10):1403–9.
- 25. Al-Naggar RA, Osman MT, Musa R, Malaysia TMU. Quality of life among university students in a single Malaysian institute. Pensee Journal. 2013;75(10).
- 26.
https://www.who.int/tools/whoqol.
- 27. Mielgo-Ayuso J, Aparicio-Ugarriza R, Castillo A, Ruiz E, Avila JM, Aranceta-Bartrina J, et al. Sedentary behavior among Spanish children and adolescents: findings from the ANIBES study. BMC Public Health. 2017;17(1):1–9.
- 28. Castro O, Bennie J, Vergeer I, Bosselut G, Biddle SJ. How sedentary are university students? A systematic review and meta-analysis. Prevention Science. 2020;21(3):332–43. pmid:31975312
- 29. Von Ah D, Ebert S, Ngamvitroj A, Park N, Kang DH. Predictors of health behaviours in college students. Journal of advanced nursing. 2004;48(5):463–74. pmid:15533084
- 30. Stock C, McAlaney J, Pischke C, Vriesacker B, Van Hal G, Akvardar Y, et al. Student estimations of peer alcohol consumption: Links between the Social Norms Approach and the Health Promoting University concept. Scandinavian Journal of Public Health. 2014;42(15_suppl):52–9. pmid:25416574
- 31. Telama R, Yang X, Viikari J, Välimäki I, Wanne O, Raitakari O. Physical activity from childhood to adulthood: a 21-year tracking study. American journal of preventive medicine. 2005;28(3):267–73. pmid:15766614
- 32. Krzepota J, Biernat E, Florkiewicz B. The relationship between levels of physical activity and quality of life among students of the university of the third age. Central European journal of public health. 2015;23(4):335. pmid:26841148
- 33. Puciato D, Rozpara M, Borysiuk Z. Physical activity as a determinant of quality of life in working-age people in Wrocław, Poland. International journal of environmental research and public health. 2018;15(4):623. pmid:29596312
- 34. Ou H-t Su C-T, Luh W-M Lin C-Y. Knowing is half the battle: The association between leisure-time physical activity and quality of life among four groups with different self-perceived health status in Taiwan. Applied Research in Quality of Life. 2017;12(4):799–812.
- 35. Kotarska K, Nowak MA, Nowak L, Król P, Sochacki A, Sygit K, et al. Physical activity and quality of life of university students, their parents and grandparents in poland—selected determinants. International Journal of Environmental Research and Public Health. 2021;18(8):3871. pmid:33917082
- 36. Strine TW, Chapman DP, Balluz LS, Moriarty DG, Mokdad AH. The associations between life satisfaction and health-related quality of life, chronic illness, and health behaviors among US community-dwelling adults. Journal of community health. 2008;33(1):40–50.
- 37. Wadsworth T, Pendergast PM. Obesity (sometimes) matters: The importance of context in the relationship between obesity and life satisfaction. Journal of Health and Social Behavior. 2014;55(2):196–214. pmid:24872467
- 38. Schnohr P, Kristensen TS, Prescott E, Scharling H. Stress and life dissatisfaction are inversely associated with jogging and other types of physical activity in leisure time—The Copenhagen City Heart Study. Scandinavian journal of medicine & science in sports. 2005;15(2):107–12. pmid:15773865
- 39. Weir CB, Jan A. BMI classification percentile and cut off points. 2019.
- 40. Meeus M, Van Eupen I, Willems J, Kos D, Nijs J. Is the International Physical Activity Questionnaire-short form (IPAQ-SF) valid for assessing physical activity in Chronic Fatigue Syndrome? Disability and rehabilitation. 2011;33(1):9–16. pmid:20446802
- 41. Maddison R, Mhurchu CN, Jiang Y, Vander Hoorn S, Rodgers A, Lawes CM, et al. International physical activity questionnaire (IPAQ) and New Zealand physical activity questionnaire (NZPAQ): a doubly labelled water validation. International Journal of Behavioral Nutrition and Physical Activity. 2007;4(1):62.
- 42. Hagströmer M, Oja P, Sjöström M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public health nutrition. 2006;9(6):755–62. pmid:16925881
- 43. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Medicine & science in sports & exercise. 2003;35(8):1381–95. pmid:12900694
- 44. Moghaddam MB, Aghdam FB, Jafarabadi MA, Allahverdipour H, Nikookheslat SD, Safarpour S. The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: content and construct validity, factor structure, internal consistency and stability. World applied sciences journal. 2012;18(8):1073–80.
- 45. Montazeri A, Vahdaninia M, Mousavi SJ, Omidvari S. The Iranian version of 12-item Short Form Health Survey (SF-12): factor structure, internal consistency and construct validity. BMC public health. 2009;9(1):341. pmid:19758427
- 46.
Esposito Vinzi V, Chin WW, Henseler J, Wang H. Handbook of partial least squares: Concepts, methods and applications: Heidelberg, Dordrecht, London, New York: Springer; 2010.
- 47.
Kline R. Data preparation and psychometrics review. Principles and practice of structural equation modeling 4th ed New York, NY: Guilford. 2016.
- 48. Gill DL, Hammond CC, Reifsteck EJ, Jehu CM, Williams RA, Adams MM, et al. Physical activity and quality of life. Journal of Preventive Medicine and Public Health. 2013;46(Suppl 1):S28. pmid:23412703
- 49. Xiao Y, Wang H, Zhang T, Ren X. Psychosocial predictors of physical activity and health-related quality of life among Shanghai working adults. Health and quality of life outcomes. 2019;17(1):1–9.
- 50. Haegele JA, Famelia R, Lee J. Health-related quality of life, physical activity, and sedentary behavior of adults with visual impairments. Disability and rehabilitation. 2017;39(22):2269–76. pmid:27636881
- 51. Wu XY, Han LH, Zhang JH, Luo S, Hu JW, Sun K. The influence of physical activity, sedentary behavior on health-related quality of life among the general population of children and adolescents: A systematic review. PloS one. 2017;12(11):e0187668. pmid:29121640
- 52. Barber FD, editor Effects of social support on physical activity, self-efficacy, and quality of life in adult cancer survivors and their caregivers. Oncol Nurs Forum; 2013.
- 53. Çiçek G. Quality of life and physical activity among university students. 2018.
- 54. Silva GdSFd, Bergamaschine R, Rosa M, Melo C, Miranda R, Bara Filho M. Evaluation of the physical activity level of undergraduation students of health/biology fields. Revista Brasileira de Medicina do Esporte. 2007;13(1):39–42.
- 55. Yahia N, Abdallah A, Achkar A, Rizk S. Physical activity and smoking habits in relation to weight status among lebanese university students. International Journal of Health Research. 2010;3(1):21–7.
- 56. Hamaideh SH, Al‐Khateeb RY, Al‐Rawashdeh AB. Overweight and obesity and their correlates among Jordanian adolescents. Journal of Nursing Scholarship. 2010;42(4):387–94. pmid:21091621
- 57. Ferreira Marques CD, de Cássia Ribeiro Silva R, Machado MEC, de Santana MLP, de Andrade Cairo RC, de Jesús Pinto E, et al. The prevalence of overweight and obesity in adolescents in Bahia, Brazil. Nutrición Hospitalaria. 2013;28(2). pmid:23822703
- 58. Going RT, Spencer SM, Krummel DA. Effect of obesity on health-related quality of life among Appalachian elderly. Southern medical journal. 2003;96(6):552–8. pmid:12938781
- 59. Hassan M, Joshi A, Madhavan S, Amonkar M. Obesity and health-related quality of life: a cross-sectional analysis of the US population. International journal of obesity. 2003;27(10):1227. pmid:14513071
- 60. Basterfield L, Adamson AJ, Frary JK, Parkinson KN, Pearce MS, Reilly JJ, et al. Longitudinal study of physical activity and sedentary behavior in children. Pediatrics. 2011;127(1):e24–e30. pmid:21173005
- 61. Colley RC, Garriguet D, Janssen I, Craig CL, Clarke J, Tremblay MS. Physical activity of Canadian children and youth: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Health reports. 2011;22(1):15. pmid:21510586
- 62. Hills AP, Andersen LB, Byrne NM. Physical activity and obesity in children. British journal of sports medicine. 2011;45(11):866–70. pmid:21836171
- 63. Waters E, de Silva‐Sanigorski A, Burford BJ, Brown T, Campbell KJ, Gao Y, et al. Interventions for preventing obesity in children. Cochrane database of systematic reviews. 2011(12). pmid:22161367
- 64. Bottcher LB, Bandeira PFR, Vieira NB, Zaia V, de Almeida RL. Quality of life, BMI, and physical activity in bariatric surgery patients: a structural equation model. Obesity surgery. 2020;30(8):2927–34. pmid:32347516
- 65. Kruger J, Bowles HR, Jones D, Ainsworth BE, Kohl HW III. Health-related quality of life, BMI and physical activity among US adults (18 years): National Physical Activity and Weight Loss Survey, 2002. International journal of obesity. 2007;31(2):321.
- 66. Kim I, Choi H, Davis AH. Health-related quality of life by the type of physical activity in Korea. Journal of community health nursing. 2010;27(2):96–106. pmid:20437290
- 67. Skoro V, Stojanovic N, Banjari I, editors. Nutritional status of students of the University of Osijek. Proceedings of the 1st Student Congress of Clinical Nutrition and Dietotherapy Medical Faculty, University of Rijeka.