Introduction
With their hectic schedules and lengthy screen time, university students are highly prone to developing sedentary behavior, which involves prolonged sitting alongside limited physical tasks.[1] Several studies have revealed that when transitioning to university studies, students commonly decrease their participation in physical exercise while allocating more time to physical inactivity,[2,3] attributed to consequential shifts in lifestyle and psychosocial factors.[4] Sedentary behavior refers to activities characterized by diminished energy expenditure, typically at an intensity level under 1.5 metabolic equivalents (METs).[5] In the scientific research context, “sitting time” defines sedentary behavior (SB) rather than merely low physical activity (PA) levels.[6]
The World Health Organization (WHO) highlights the significance of regular national surveillance of the population’s physical exertion through the Global Action Plan on PA 2018–2030.[7] According to the data from the WHO, the percentage of inadequate exercise among adults aged 18 years and older in Indonesia is 22.57%. Meanwhile, an alarming 86.38% of the teenagers aged 11–17 years in Indonesia fail to participate in sufficient exercise.[8] Insufficient PA has been connected to lower quality of life (QoL) in young boys and increased mortality rates among females. Many studies have explored how PA affects the QoL in people, both those who are generally healthy and those dealing with health issues. Notably, Ou et al.’s study found a positive link between leisure time PA and QoL.[9]
The benefits of involvement in physical pursuits for the vitality and emotional equilibrium of younger people are currently widely acknowledged. As a result, global public health authorities have released guidelines for younger people regarding physical movement. Engaging in leisure-time PA significantly affects the daily QoL, particularly for individuals who see themselves as unhealthy.[9] It is well known that a lack of exercise contributes to weight gain. The research findings have shown an undeniable connection between the intensity of physical exertion and adolescent abdominal obesity.[10] However, the connection between the PA and abdominal obesity is less clear-cut in other research.[11] Some research findings found no correlation between screen time and obesity indicators,[12] whereas others revealed a correlation between lack of PA and excess abdominal weight.[13-16]
We identified research gaps/needs as topics because there was insufficient evidence, hence authors called for further research. Surbakti et al.’s study, employing one-way ANOVA test analysis, demonstrated a significant association (P = 0.007) between PA and the mean of body mass index (BMI). In addition, the study conducted by showed no correlation between PA and SB at the baseline (r = −0.02). Intriguingly, after 1 year, a weak but positive correlation emerged (r = 0.12).[17] Furthermore, a study by Sallis et al. in children and adolescents revealed a weak negative association between PA and a high degree of SB.[18] Concerning Nowak et al.’s study, there was no evident connection, either positive or negative, between leisure time activity and the QoL.[19] According to Sohoolian et al., there is a noticeable negative connection between BMI and PA, particularly among students who are overweight.[20]
It is noteworthy that excessive SB and insufficient PA can lead to an increased likelihood of obesity among children and adolescents. Not only that but also these factors can also elevate the risk of health issues such as morbidity and mortality, heart-related conditions, and diabetes mellitus type 2 in adolescents. Furthermore, SB has been associated with unhealthy eating habits and lifestyle choices.[21-24] This paper presents findings on the association between physique index, activity levels, and inactive lifestyle among Saudi youngsters residing in Surakarta, Central Java, Indonesia.
Methods
Study design
This was a cross-sectional study.
Setting
This study was done in Surakarta, Central Java, Indonesia, specifically within the school setting from January to February 2023. The questionnaire was uploaded to Google Form and made accessible online through an active uniform resource locator. To reach the potential respondents, the researchers collaborated with a college program to distribute an online questionnaire through popular social media platforms such as WhatsApp and Telegram.
Participants
The study focused on participants aged 17–25, encompassing high school and college students who willingly chose to participate. In addition, this study excluded students with medical conditions such as respiratory conditions, metabolic disorders, malignancies, heart-related ailments, bone fractures, liver diseases, or other health issues requiring medical care. Furthermore, students with physical challenges were also excluded. Participation was entirely voluntary, and individuals who decided not to participate were not included in the study. Likewise, respondents who agreed to take part signed an electronic consent (e-consent) form. Throughout the 2 months, a total of 252 responses were collected.
Variables
The initial set of seven variables: age, sibling quantity, maternal educational attainment, paternal educational attainment, aggregate score of physical engagement, sedentary habits composite score, total inactivity on weekdays, and overall inactive duration on weekends.
Data sources
Students were requested to fill out the online questionnaire link after the researchers obtained the anthropometric indicators of the students. In Phase 1, participants wore light clothing, took off their shoes, and had their weight and height measured by the researcher at the classroom. The participant’s body weight was assessed using a digital body weighing scale, with an accuracy of up to 0.1 kg. They removed their shoes and socks before stepping on the equipment. Similarly, their height was determined with precision up to 0.1 cm. They were asked to maintain an erect head position, keep their arms alongside their bodies, release tension in their shoulders, and stand tall with their backs against a stadiometer rule. BMI calculated as body weight divided by squared height (kg/m2) was classified based on Asian-Pacific cutoff points with the following categories: underweight (<18.5), normal weight (18.5–22.9), overweight (23–24.9), and obesity (≥25). All measurements were conducted twice to ensure precision, and the mean value was employed for subsequent analysis.[25] In Phase 2, participants complete the online questionnaire. Participants filled out an online questionnaire after an anthropometric examination. For this phase, we used data collection process comprising with signature electronic consent (e-consent) form and questionnaire. The e-consent form must be known by the guardian for students under 18 years of age because it requires the guardian to make the decision to become a respondent. The researcher asked for the subject’s consent to become a respondent without any elements of coercion from various parties.
The questionnaire was derived and adapted from the PA Questionnaire for Adolescents (PAQ-A) and the Adolescent Sedentary Activity Questionnaire (ASAQ). It consisted of a total of 26 statement items, presented in short and multiple-choice formats, and organized into three distinct sections. The first part included seven demographic questions about respondents’ age group, gender, siblings, parent’s education, and weight and height information. Meanwhile, the second part comprised 11 questions about the PA of respondents, and the third part included eight questions regarding participants’ sedentary lifestyle. To ensure the reliability and stability of the gathered data, a careful assessment of internal consistency was conducted using Cronbach’s alpha. Internal consistency of PAQ-A was acceptable (α = 0.61). For the final score of the PAQ-A, test–retest reliability showed intraclass correlation coefficient (ICC) = 0.73, which is moderate reliability to support the reliability of the PAQ-A in this target population. The PAQ-A’s moderate correlation (r = 0.41) provided support for the construct validity of the PAQ-A. In this study, the internal consistency of the ASAQ was acceptable (α = 0.75). The test–retest reliability for ASAQ showed ICC = 0.55 was considered moderate. Construct validity of the ASAQ of this study was moderate correlation (r = 0.71).
Bias
To address potential social desirability bias and rater bias in this study, several measures were implemented. First, anonymity was ensured throughout the data collection process. Respondents were provided with the opportunity to complete the questionnaires anonymously, fostering a sense of privacy and reducing the likelihood of socially desirable responses. In addition, respondents were encouraged to fill out the questionnaires in a private and uninterrupted setting, further promoting honest feedback. To mitigate rater bias, efforts were made to minimize the influence of data collection staff perceptions. This involved standardization of procedures to ensure consistency in measurements and interactions with participants. The study was conducted as a cross-sectional investigation within the school setting in Surakarta, Central Java, Indonesia. Distribution of the online questionnaire was facilitated through college programs and popular social media platforms to reach a diverse sample. Overall, these strategies aimed to enhance the reliability and validity of the data collected by minimizing the impact of social desirability bias and rater bias.
Statistical analysis
The data were checked, entered, and analyzed using STATA/MP 13.0 (Stata Corporation, Texas, USA) on a computer, with results presented at 95% confidence intervals (CI). In addition, the results were expressed using the mean, standard deviation, percentage, and frequency. Furthermore, a multiple linear regression model was utilized to ascertain the correlation between variables, where a P < 0.05 was deemed statistically valid.
Ethical consideration
All participants provided e-consent after being informed, and the information they provided was kept private. This study received ethical approval from the Al-Azhar Islamic University Mataram Ethical Committee (decision number: 142/EC-04/FK-06/UNIZAR/XII/2022).
Results
Table 1 illustrates the descriptive analysis pertaining to the sociodemographic attributes of the study participants. The participants had an average age of 20.24 ± 0.21 years. Of the participants, 17.46% are female, whereas 82.54% are male. Most of the participants had parents who had received a formal education, with an equal distribution of both parents at every level.
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Table 1: Characteristics of the participants
The individuals’ general anthropometric measurements, comprising their height, weight, and BMI, are displayed in Table 2. The height average of the participants was 158.3 ± 0.41 cm, and their weight average was 52 ± 0.71 kg. The mean BMI was 20.5 ± 0.24 kg/m2. The majority of participants (118/46.83%) have normal weight, 32.54% are underweight, 9.13% are overweight, 7.94% are obese, and 3.57% are classified as obesity Type 2.
Table 2: Anthropometric and body mass index measurements of the participants
Table 3 demonstrates the distribution of the participants’ degrees of PA. The study group displayed a lack of overall engagement in PA, with only 19.05% classified as fulfilling PA criteria. Notably, a significant majority (80.95%) were unable to meet the minimum stipulation of at least 420 min of moderate-to-vigorous PA weekly.[26,27]
Table 3: The allocation of participants according to their physical activity levels
The average amount of time spent sedentary on weekdays was 595.47 ± 165.06 min per day, whereas on weekends, it was 615.47 ± 206.74 min per day [Table 4]. During weekdays, the mean of social (chatting, talking, or social interaction using media social and telephone) activities was 228.80 ± 82.64 min/day. Meanwhile, over the weekend, the average of social activities was 223.57 ± 88.50 min per day.
Table 4: Sedentary behavior of participants on weekdays and weekends
BMI was computed utilizing multiple regression models, and Table 5 indicates the relationships among sociodemographic characteristics, physical exercises, and SB. The initial set of seven variables employed in the simple linear regression model encompassed age (years), sibling quantity, maternal educational attainment, paternal educational attainment, aggregate score of physical engagement, sedentary habits composite score, total inactivity on weekdays, and overall inactive duration on weekends. Age, total physical movement, and sedentary habits on both weekdays and weekends had P < 0.05, which indicated a significant relationship between these four predictors. The analysis indicated that BMI was positively correlated with age and PA, while exhibiting a negative association with SBs on both weekdays and weekends. The positive association can be interpreted that as the value of the age and PA increases, the mean of the BMI also tends to increase. Meanwhile, a negative association suggests that as the values of SBs on weekdays and weekends increase, the mean BMI tends to decrease.
Table 5: The association among sociodemographic profiles, physical exercises, and sedentary behavior with body mass index explored using simple, and multiple regression analyses
Discussion
The current study examined the BMI of college students aged 17–25 from Surakarta, Indonesia, to determine the prevalence of overweight and obesity, as well as their contributing variables. The mean BMI from moderate activity was 20.5 ± 0.24, with the majority of respondents classified as normal weight (46.83%). However, the findings of this research demonstrated high rates of sedentary habits as well as insufficient levels of physical movement among the respondents. Most of the youngsters (80.95%) were unable to meet the suggested physical exercise level of at least 420 min of MVPA weekly, whereas only 19.05% of the respondents classified as achieving physical exercise recommendations. Based on a previous survey, the majority of teenagers do not adhere to the most recent PA recommendations. The information is sourced from 298 school-based surveys carried out in 2016, encompassing 1.6 million students aged 11–17 across 146 nations, territories, and regions.[10]
The total of sedentary during weekends was 615.47 ± 206.74 min, whereas on weekdays, it was 595.47 ± 165.06 min. The majority of respondents spent the most time on social activities, including chatting, talking, or social interaction using media social and telephone, both on weekdays (228.80 ± 82.64) and weekends (223.57 ± 88.50). Our analysis displayed that most (91.67%) of the college students in this research spent more than 5 h per day engaged in sedentary activities on weekdays, and 92.06% did it on weekends. Besides, the participants spent approximately 2 h per day looking at screens on both weekdays and weekends. Meanwhile, it was disclosed that sociodemographic factors, including the educational levels of participants’ mothers and fathers, or the number of siblings, exhibited no significant correlation (P > 0.05). According to the descriptive information, approximately half of the participants had educated parents with tertiary education. These results may have been influenced by the homogeneity of the sample.
The most significant factors affecting the respondents’ BMI are age (β =0.041; CI 95% = 0.00–0.08; P = 0.03), PA (β = 0.02; CI 95% = 2.03–2.08; P = 0.02), and negatively associated with SBs on weekdays (β = −1.39; CI 95% = −4.77–1.98; P = 0.01) and weekends (β = −2.23; CI 95% = −9.19 [−1.27]; P = 0.01). This was similar with the research done by Alghadir etal., that explained the health of Saudi adolescents is jeopardized by high levels of SB, low levels of physical activities, and high consumption of high-fat fast foods and sugary drinks.[28] Besides, Said and Shaab Alibrahim found a strong correlation between breakfast consumption, PA, SB, and BMI. The two most significant sedentary activities impacting BMI among participants were using laptops and playing video games.[29] Sedentary behavior is defined as spending the majority of one’s time sitting down, watching television, or playing video games. This type of behavior has a low MET value and results in minimal PA.[30] Advancing age was also recognized as a pivotal factor contributing to physical inactivity. As young adults age, there is a tendency for them to participate in less PA and display increased SBs. This observation aligns with findings previously reported in Saudi Arabia.[31-34] The recent study found that PA in individuals with obesity is more strongly influenced by implicit attitudes compared to the general population.[35]
Limitation
One limitation of this study is that it only examined a small number of colleges in Surakarta and did not include other cities in Indonesia. The constraint is because of time limitations and a limited number of researchers in the study, making it impractical to boost the number of universities and include more cities. In addition, the study would gain strength and provide more information regarding the anthropometric measurements if dietary intake were included as a measurement. In addition, even though the study’s questionnaire was verified, caution should be exercised when interpreting the findings. Hence, certain limitations must be considered, and the precision of the collected data relies on the subject’s ability to comprehend the questionnaire, as well as their capacity to accurately recall and report information. Last but not least, biological maturation characteristics were not measured, which could be seen as a weakness in this study. We recommend including this variable in future studies involving populations with similar characteristics.
Conclusion
The findings of this research highlighted that most of the college students in Surakarta had normal weight. However, the majority of female adolescents did not reach the guidelines for PA, which called for at least 420 min of MVPA per week. On weekdays and weekends, the majority of the participants engaged in sedentary activities for the bulk of their spare time. Age, lack of PA, and excessive sedentary time on weekdays and weekend were associated with BMI in college students with other relevant characteristics. Reducing sitting or laying down time among adolescents at weekdays or weekends could potentially reduce the risk of higher BMI.
Authors’ contributions
Tri Yuniarti: Concepts, design, manuscript review. Musta’in: definition of intellectual content, manuscript review. Rita Benya Adriani: Literature search, manuscript review. Aris Widiyanto: Data acquisition, data analysis. Joko Tri Atmojo: Statistical analysis, manuscript preparation. Santy Irene Putri: Manuscript preparation, manuscript editing.
Data availability statement
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Acknowledgement
The author extends appreciation to all participants who willingly participated in this study.
References
1. Cotten E, Prapavessis H. Increasing nonsedentary behaviors in university students using text messages:Randomized controlled trial. JMIR Mhealth Uhealth 2016;4:e99.
2. Vella-Zarb RA, Elgar FJ. The 'freshman 5':A meta-analysis of weight gain in the freshman year of college. J Am Coll Health 2009;58:161–6.
3. Crombie AP, Ilich JZ, Dutton GR, Panton LB, Abood DA. The freshman weight gain phenomenon revisited. Nutr Rev 2009;67:83–94.
4. El Ansari W, Oskrochi R, Haghgoo G. Are students'symptoms and health complaints associated with perceived stress at university?Perspectives from the United Kingdom and Egypt. Int J Environ Res Public Health 2014;11:9981–10002.
5. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary Behavior Research Network (SBRN) –Terminology consensus project process and outcome. Int J Behav Nutr Phys Act 2017;14:75.
6. Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, et al. Amount of time spent in sedentary behaviors in the United States, 2003-2004. Am J Epidemiol 2008;167:875–81.
7. World Health Organization (WHO) Global Action Plan on Physical Activity 2018-2030:More Active People for a Healthier World. Geneva:World Health Organization;2019.
8. Global Health Observatory (GHO) Data Prevalence of Insufficient Physical Activity Among Adults Aged 18+Years. Geneva:Global Health Observatory (GHO) Data;2019.
9. Ou HT, Su CT, Luh WM, Lin CY. 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. Appl Res Qual Life 2017;12:799–812.
10. Al-Hazzaa HM, Abahussain NA, Al-Sobayel HI, Qahwaji DM, Musaiger AO. Lifestyle factors associated with overweight and obesity among Saudi adolescents. BMC Public Health 2012;12:354.
11. Haas GM, Liepold E, Schwandt P. Metabolic risk factors, leisure time physical activity, and nutrition in German children and adolescents. Cholesterol 2012;2012:370850.
12. Altenburg TM, Singh AS, van Mechelen W, Brug J, Chinapaw MJ. Direction of the association between body fatness and self-reported screen time in Dutch adolescents. Int J Behav Nutr Phys Act 2012;9:4.
13. Dumith SC, Muniz LC, Tassitano RM, Hallal PC, Menezes AM. Clustering of risk factors for chronic diseases among adolescents from Southern Brazil. Prev Med 2012;54:393–6.
14. Chinapaw M, Altenburg T, Brug J. Sedentary behaviour and health in children –Evaluating the evidence. Prev Med 2015;70:1–2.
15. Carson V, Hunter S, Kuzik N, Gray CE, Poitras VJ, Chaput JP, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth:An update. Appl Physiol Nutr Metab 2016;41:S240–65.
16. Byun W, Dowda M, Pate RR. Associations between screen-based sedentary behavior and cardiovascular disease risk factors in Korean youth. J Korean Med Sci 2012;27:388–94.
17. Surbakti UH, Sari MI, Wijaya DD. The association between physical activity, sedentary behaviour and body mass index in students. Stem Cell Oncol 2018;1:179–84.
18. Sallis JF, Bull F, Guthold R, Heath GW, Inoue S, Kelly P, et al. Progress in physical activity over the Olympic quadrennium. Lancet 2016;388:1325–36.
19. Nowak PF, Bożek A, Blukacz M. Physical activity, sedentary behavior, and quality of life among university students. Biomed Res Int 2019;2019:1–10.
20. Sohoolian M, Alizadeh A, Shiri-Shahsava MR, Amerzadeh M, Razzaghi A, Javadi M. Weight status and its relationship with corona fear, food diversity, and physical activity in children and adolescents. Asian J Soc Health Behav 2024;6:174–81. Available from:
https://www.shbonweb.com/text.asp?2020/3/1/1/280554. [Last accessed on 2023 Dec 21].
21. Leech RM, McNaughton SA, Timperio A. The clustering of diet, physical activity and sedentary behavior in children and adolescents:A review. Int J Behav Nutr Phys Act 2014;11:4.
22. Kumar B, Robinson R, Till S. Physical activity and health in adolescence. Clin Med (Lond) 2015;15:267–72.
23. Mozafarian N, Motlagh ME, Heshmat R, Karimi S, Mansourian M, Mohebpour F, et al. Factors associated with screen time in Iranian children and adolescents:The CASPIAN-IV study. Int J Prev Med 2017;8:31.
24. Imran TF, Ommerborn M, Clark C, Correa A, Dubbert P, Gaziano JM, et al. Television viewing time, physical activity, and mortality among African Americans. Prev Chronic Dis 2018;15:E10.
25. Lim JU, Lee JH, Kim JS, Hwang YI, Kim TH, Lim SY, et al. Comparison of World Health Organization and Asia-Pacific body mass index classifications in COPD patients. Int J Chron Obstruct Pulmon Dis 2017;12:2465–75.
26. López-Gil JF, Brazo-Sayavera J, de Campos W, Yuste Lucas JL. Meeting the physical activity recommendations and its relationship with obesity-related parameters, physical fitness, screen time, and Mediterranean diet in schoolchildren. Children (Basel) 2020;7:263.
27. Saint-Maurice PF, Welk GJ, Beyler NK, Bartee RT, Heelan KA. Calibration of self-report tools for physical activity research:The physical activity questionnaire (PAQ). BMC Public Health 2014;14:461.
28. Alghadir AH, Iqbal ZA, A Gabr S. The relationships of watching television, computer use, physical activity, and food preferences to body mass index:Gender and nativity differences among adolescents in Saudi Arabia. Int J Environ Res Public Health 2021;18:9915.
29. Said MA, Shaab Alibrahim M. Physical activity, sedentary behaviors, and breakfast eating as factors influencing BMI in Saudi students, aged 10 to 15 years. Ann Med 2022;54:1459–72.
30. Tremblay MS, Leblanc AG, Janssen I, Kho ME, Hicks A, Murumets K, et al. Canadian sedentary behaviour guidelines for children and youth. Appl Physiol Nutr Metab 2011;36:59–64.
31. Mahmoud W, Ahmed AS, Elnaggar RK. Physical activity in overweight and obese adults in Al-kharj, Saudi Arabia. Eur J Sci Res 2016;137:236–45.
32. Caspersen CJ, Pereira MA, Curran KM. Changes in physical activity patterns in the United States, by sex and cross-sectional age. Med Sci Sports Exerc 2000;32:1601–9.
33. Alqahtani BA, Alenazi AM, Alhowimel AS, Elnaggar RK. The descriptive pattern of physical activity in Saudi Arabia:Analysis of national survey data. Int Health 2021;13:232–9.
34. Al-Nozha MM, Al-Hazzaa HM, Arafah MR, Al-Khadra A, Al-Mazrou YY, Al-Maatouq MA, et al. Prevalence of physical activity and inactivity among Saudis aged 30-70 years. A population-based cross-sectional study. Saudi Med J 2007;28:559–68.
35. Cheng OY, Yam CL, Cheung NS, Lee PL, Ngai MC, Lin CY. Extended theory of planned behavior on eating and physical activity. Am J Health Behav 2019;43:569–81.
