Prevalence of Smartphone Addiction and Its Association with Socio-economic, Physical, and Psychological Conditions: A Cross-Sectional Study among University of Bangladesh

Prevalence of Smartphone Addiction and Its Association with Socio-economic, Physical, and Psychological Conditions: A Cross-Sectional Study among University of Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence of Smartphone Addiction and Its Association with Socio-economic, Physical, and Psychological Conditions: A Cross-Sectional Study among University of Bangladesh Md. Al Mamunur Rashid, Nazrul Islam Mridha, Zaman Mia, Saim Hossen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9424184/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Smartphone addiction (SA), characterized by excessive and uncontrolled use, poses significant physical, psychological, and social challenges, particularly among university students. In Bangladesh, where SA prevalence reaches 61.4% among young adults, socio-economic stressors, lifestyle factors, and mental health issues amplify risks. This study investigates SA prevalence and its associations with socio-economic, physical, and psychological conditions among university students, addressing a critical gap in developing country contexts. Methods: A cross-sectional survey was conducted at Noakhali Science and Technology University, Bangladesh, involving 450 students from four faculties. Data were collected using a structured questionnaire, including the Smartphone Addiction Scale-Short Version (SAS-SV) for SA (cutoffs: ≥31 males, ≥33 females), and self-reported measures for sleep, physical activity, sedentary behavior, and psychological conditions (anxiety, depression, stress, worry). Chi-square tests and logistic regression analyzed associations and predictors, with p<0.05 for significance, using SPSS v27. Results: SA prevalence was 63.8% (286/450), with males (75.5%) showing higher rates than females (50.7%, AOR=0.19, p<0.001). Significant associations were found with academic year (4th-year: 75.7%, χ²=16.63, p=0.002), faculty (Social Science: 72.7%, χ²=15.59, p=0.001), paternal education (secondary: AOR=19.21, p<0.001), financial dependency (AOR=0.26, p<0.001), sleep (<5 hours: 87.7% addicted, χ²=188.50, p<0.001), physical activity (4–5 times/week: AOR=0.07, p<0.001), and psychological conditions (e.g., anxiety nearly every day: AOR=46.34, p<0.001; worry: AOR=253.40, p<0.001). Psychological factors were the strongest predictors, explaining 49.5–67.8% of SA variance. Findings align with local studies (61.4% prevalence) but exceed global estimates (23.3%). Conclusion: High Smartphone addiction prevalence among Bangladeshi university students is driven by psychological distress, socio-economic constraints, and lifestyle factors. Interventions should prioritize mental health support, promote moderate physical activity, and address socio-economic stressors through awareness and counseling. Longitudinal studies are needed to clarify causal pathways. Smartphone addiction university students Bangladesh socio-economic factors psychological conditions sleep physical activity 1. Introduction Smartphone addiction, often referred to as problematic smartphone use, is characterized by excessive and uncontrolled smartphone use that disrupts daily life, leading to negative physical, psychological, and social consequences [1]. It is defined as a behavioral addiction involving compulsive behavior, withdrawal symptoms, tolerance, and functional impairment, similar to substance-related dependencies. The American Psychological Association describes addiction as a state of psychological or physical dependence on behaviors or substances, and smartphone addiction fits this framework due to its impact on daily functioning [2]. Global prevalence rates of smartphone addiction vary widely [3]. A systematic review reported a median prevalence of 23.3% (interquartile range 14–31%) among children and young people, with rates as high as 41.93% among Asian medical students [4]. In specific populations, prevalence ranges from 9.3% in Iran to 48% in Malaysia, influenced by socio-cultural factors and smartphone accessibility. For instance, high smartphone ownership (e.g., 78% in Malaysia) and internet penetration correlate with higher smartphone addiction rates [1]. These variations underscore the need for region-specific studies to comprehend the contextual drivers of smartphone addiction. University students are particularly vulnerable to smartphone addiction due to their reliance on smartphones for academic, social, and entertainment purposes. Studies consistently show high smartphone addiction prevalence among this group. A study in Jordan found 88.7% of university students exhibited psychological distress linked to SA, with 59.1% showing severe mental health issues [5]. In Malaysia, 40.6–48% of students were classified as smartphone-addicted, driven by constant connectivity and social media use [1]. A cross-sectional study in Türkiye reported increased smartphone addiction during the COVID-19 pandemic, attributed to online education and social isolation. These studies often use validated tools like the Smartphone Addiction Scale-Short Version (SAS-SV) to measure smartphone addiction, with cutoffs (e.g., ≥31 for males, ≥33 for females) identifying addiction [6]. Gender differences are notable, with some studies indicating higher smartphone addiction among females due to social media use, while others report higher rates among males. Smartphone addiction is strongly associated with lifestyle factors, particularly sleep and physical activity. Smartphone-addicted individuals are more likely to sleep ≤6 hours per night (38.1%) and stay awake late due to smartphone use (88.9%), leading to poor sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) [7]. This is linked to delayed melatonin production from screen exposure and compulsive nighttime use. Physical inactivity is also prevalent among smartphone addiction groups (26.7%), with overuse correlating with musculoskeletal pain (e.g., neck, shoulders, eyes) and higher rates of obesity (32.5%) [8]. Conversely, leisure physical activity can moderate the negative effects of smartphone addiction, reducing loneliness and improving health outcomes [9]. These findings suggest that lifestyle interventions could impact smartphone addictions. Smartphone addiction is closely linked to psychological conditions, forming a bidirectional relationship. Studies consistently report higher rates of anxiety (40.7%), depression (40.0%), and stress among smartphone-addicted individuals compared to non-addicted groups [5]. The “Fear of Missing Out” (FoMO) and nomophobia (anxiety from being without a smartphone) are key drivers, as compulsive notification-checking disrupts mental well-being [8]. Depression and anxiety may also predispose individuals to smartphone addiction, as smartphones are used as a coping mechanism for negative emotions. These psychological factors underscore smartphone addictions impact on mental health. In Bangladesh, smartphone addiction is a growing public health concern, particularly among young adults. A cross-sectional study of 440 young adults (July 2021–February 2022) found a 61.4% smartphone addiction prevalence rate, with significant predictors including male gender, age ≤25, unemployment, and large family size (≥8). Smartphone-addicted participants reported higher anxiety (40.7%), depression (40.0%), insomnia, physical inactivity, and obesity, with compulsive use during daily activities (e.g., eating, driving) [8]. Another study highlighted Smartphone addiction impact on quality of life, linking it to social isolation and FoMO [10]. These studies, primarily cross-sectional and using the SAS-SV, indicate a high burden of Smartphone addiction among Bangladeshi youth but are limited by self-reported data and convenience sampling, reducing generalizability. Smartphone addiction is a pressing issue among university students, who are at high risk due to academic pressures, social media reliance, and widespread smartphone access. In Bangladesh, where 61.4% of young adults show smartphone addiction [8] the lack of studies limits understanding of causal pathways. This study is critical to address this gap, particularly in a developing country context where socio-economic stressors (e.g., family income, financial status) and cultural factors may exacerbate smartphone addiction. The bidirectional relationship between smartphone addiction and mental health, coupled with its impact on sleep and physical health, necessitates targeted interventions. By exploring mediators of physical activity, this study will inform evidence-based strategies to reduce smartphone addictions, improving student well-being and academic performance. The findings will contribute to global smartphone addiction research and support public health policies in Bangladesh. This study aims to assess the prevalence of smartphone addiction among university students in Bangladesh. Examine the associations between socio-economic factors (e.g., family income, financial status), physical conditions (e.g., sleep quality, physical activity), and psychological conditions (e.g., anxiety, depression, stress, loneliness) with smartphone addiction. This study also identifies potential gender differences and lifestyle moderators (e.g., physical activity) in smartphone addiction prevalence and impact. 2. Methodology 2.1 Study Design, Site, and Participants This study employed a cross-sectional survey design to investigate the associations between smartphone Addiction on socio-economic, physical, and psychological conditions among university students. The study relied on quantitative research methods, collecting data through structured questionnaires designed to measure Smartphone addiction and psychological conditions. Validated tools were utilized for data collection, ensuring the reliability and accuracy of the findings. The research was conducted at Noakhali Science and Technology University (NSTU), a public university located in the Noakhali district of southern Bangladesh. NSTU is a growing institution, with 33 academic departments under six faculties and two institutes. As of January 2025, the university had a total student population of 6,437 students pursuing courses across various disciplines. The university's diverse academic environment made it an ideal setting for exploring the multifaceted issues of smartphone addiction on socio-economic, physical, and psychological conditions. The target population for the study consisted of four students from the four faculties (Social Science & Humanities, Science, Business Study, Engineering & Technology) at NSTU. Initially, a sample size of 373 students was determined using Yamane’s Formula (n = 𝑁/1+𝑁∗ 𝑒2), for sample size calculation, assuming a 95% confidence level[11]. The final dataset comprises a total of 450 data. 2.2 Measurement and Data Collection Tools Based on the previous studies, a structured questionnaire was prepared in English, which was then translated into Bengali (local language) for better understanding. To assess the study's efficacy, data from 19 students (5% of the total sample) were collected for a pilot study prior to the final data collection. A designated member of the Social Science experts also reviewed the questionnaire. Based on the findings of the pilot study and the comments of the reviewers, the questionnaire was modified. However, the findings of the pilot survey were excluded from the final study. Cronbach's alpha, with a value of .870, was found, which indicated the reliability of the questionnaire. The final questionnaire had two parts. The first section was based on socio-demographic data such as gender, family income, and parental educational backgrounds. The second section consisted of the Smartphone Addiction Scale, the Self-developed lifestyle, well-being, and psychological condition questionnaire. 2.2.1 Sociodemographic Variables Sociodemographic factors measured in this study included gender, age, academic year, faculty, monthly family income, and parental education. Participants also reported their financial status (dependent or independent). 2.2.2 Smartphone Addiction Smartphone addiction was assessed using the Smartphone Addiction Scale-Short Version (SAS-SV), a validated instrument widely used in similar research studies. The SAS-SV contains 10 items rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The total scores range from 10 to 50, with higher scores indicating higher levels of smartphone addiction. A gender-specific cut-off was used, with a score of 31 or higher for males and 33 or higher for females [12]. The Cronbach’s alpha for the SAS-SV in this study was 0.85, demonstrating good internal consistency. 2.2.3 Physical Health and Lifestyle Physical health was measured through questions related to sleep quality, physical activity, and sedentary behavior. Sleep duration was categorized into three groups: ≤6 hours (insufficient), 7-9 hours (adequate), and ≥10 hours (excessive). Physical activity was categorized into three levels: no physical activity, irregular physical activity, and regular physical activity (at least 3 times per week). Sedentary behavior was assessed by the number of hours spent sitting daily. 2.2.4 Psychological Conditions A self-developed questionnaire was designed to assess key psychological symptoms commonly experienced by individuals under stress. The questionnaire included four items addressing emotional and cognitive symptoms: Feeling nervous, anxious, or on edge; Not being able to stop or control worrying; Little interest or pleasure in doing things; Feeling down, depressed, or hopeless. Participants responded using a 4-point Likert scale: Not at all, Several days, More than half the days, and Nearly every day. Higher scores indicated greater psychological distress. 2.3 Ethical statement This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethical Committee of Noakhali Science and Technology University (NSTU) (Reference no: NSTU/SCI/EC/2025/453, dated January 15, 2025). Written informed consent was obtained from all participants before data collection. Participants were fully informed about the study’s objectives, procedures, potential risks, and their right to withdraw at any stage without penalty. To maintain confidentiality, no personal identifiers (e.g., names, student IDs) were collected; instead, each participant was assigned a unique anonymized ID. Physical questionnaires were securely stored in a locked facility at NSTU, accessible only to the research team. Digital data were encrypted and stored on password-protected servers at NSTU, with access limited to the principal investigator and co-authors. In line with NSTU’s ethical guidelines, all data will be retained for five years following publication, after which they will be permanently destroyed (paper records shredded and digital files deleted). Participants who reported severe psychological distress during data collection were referred to NSTU’s counseling services to ensure appropriate ethical care. All procedures adhered to international standards for protecting participant privacy, autonomy, and data security, as outlined by the World Medical Association [13]. 2.4 Data Analysis Techniques All data were entered and analyzed using IBM SPSS Statistics Version 27. Descriptive statistics (frequencies, percentages) were used to summarize the sociodemographic characteristics of the participants, as well as the levels of smartphone addiction, physical health, and psychological well-being. To assess the associations between smartphone addiction and socio-economic, physical, and psychological factors, chi-square tests were performed for categorical variables. A Logistic regression was conducted to identify significant predictors of smartphone addiction. The level of statistical significance was set at p < 0.05. 3. Results 3.1 Characteristics of the Study Participants Table 1 summarizes the demographic, socioeconomic, Lifestyle, Well-being, and Mental health characteristics of the study participants (N=450). The sample comprised slightly more males, 51.8%, and females, 48.2%. The largest proportion of the sample is 4th-year students, 35.1%, followed by Master's students, 24.7%, and 2nd-year students, 18.0%. 1st and 3 rd year students make up smaller percentages (13.8% and 8.4% respectively). Regarding academic faculty, Social Science 40.7% represents the largest group, followed by Engineering 29.1%, Science 18.4%, and Business 11.8%. In terms of parental education, a significant portion of fathers have a Bachelor's degree 21.8% or no formal education 20.9%. For mothers, secondary education is 25.8% is the most common, followed by higher secondary education (20.2%) and Bachelor's degrees 19.6%. It's notable that a smaller percentage of mothers, 3.6%, have a Master's degree compared to fathers, 13.6%. The majority of the sample reports a monthly family income above 40,000 (32.4%), followed by 30,001 - 40,000 (26.4%) and 20,001 - 30,000 (22.0%). Most individuals in the sample are dependent, 66.0% financially. On average, a considerable portion of the sample sleeps 6-7 hours per night, 29.1%, though a notable percentage sleeps less than 5 hours, 23.6%. Regarding physical activity, the most common frequency is once a week, 29.1%, or 2-3 times a week, 28.9%. Only a small percentage engages in physical activity every day (6.2%), while 8.7% never participate. The majority of individuals spend sitting 5-7 hours, 29.1%, or 7-9 hours, 25.6%, indicating a significant amount of sedentary time. A substantial number of respondents report feeling nervous, anxious, or on edge nearly every day, 50.7%. Similarly, a high percentage indicates little interest or pleasure in doing things nearly every day, 42.2% or more than half the days, 35.8%. Concerning worrying, more than half the days, 42.0%, and nearly every day, 32.4%, are the most frequent responses for not being able to stop or control worrying. Finally, a considerable portion of the sample reports feeling down, depressed, or hopeless more than half the days, 44.4%, or nearly every day, 30.4%. 3.2 Associations between Smartphone Addiction with Socio-economic, Physical, and Psychological Conditions. Table 1 presents Smartphone addiction in this sample is significantly associated with a range of demographic and well-being factors, as evidenced by statistically significant chi-square values p<0.05. Gender shows a strong association (χ2=29.944, p<0.001), with one gender exhibiting a significantly higher proportion of addiction. The academic year is also a significant factor (χ2=16.633, p=0.002), with 4th-year students showing the highest addiction rate at 75.9%. Similarly, academic faculty is linked to addiction (χ2=15.586, p=0.001), where Social Science (72.7% addicted) and Business (66.0% addicted) faculties display higher rates. Parental education levels, for both father (χ2=40.983, p<0.001) and mother (χ2=28.389, p<0.001), are significantly associated; for instance, 92.2% of individuals whose fathers have higher secondary education are addicted, and 79.1% of those whose mothers have higher secondary education are addicted. Personal financial status also plays a role (χ2=9.930, p =0.002), with dependent individuals showing a higher addiction rate (68.7%). Furthermore, lifestyle and well-being factors demonstrate robust connections to smartphone addiction. Sleep duration is highly significant (χ2=188.500, p<0.001), as individuals sleeping less than 5 hours (87.7% addicted) or 6-7 hours (88.5% addicted) exhibit very high addiction rates. Physical activity levels are also crucial (χ2=33.761, p<0.001), with those exercising once a week 77.1% addicted or never 71.8% addicted, being more prone to addiction. The amount of time spent sitting is strongly associated (χ2=59.217, p<0.001), with notably 96.4% of those sitting less than 3 hours being addicted. Finally, various mental health indicators show compelling relationships: feeling nervous, anxious, or on edge (χ2=127.862, p<0.001), not being able to stop or control worrying (χ2=69.521, p<0.001), experiencing little interest or pleasure (χ2=35.919, p<0.001), and feeling down, depressed, or hopeless (χ2=98.077, p<0.001) are all profoundly linked to higher rates of smartphone addiction, with those feeling nervous nearly every day showing an 82.5% addiction rate, and those feeling down or hopeless nearly every day at 69.5% addiction. Notably, monthly family income was the only variable that did not demonstrate a statistically significant association with smartphone addiction χ2=3.927, p=0.416. Table 1. Associations between Smartphone Addiction with Socio-economic, Physical, and Psychological Conditions. Variable Total Sample (n; %) Smartphone Addiction Normal (n; %) Addicted (n; %) X² Value P-value Gender Male 233; 51.8% 57; 24.5% 176; 75.5% 29.944 <0.001 Female 217; 48.2% 107; 49.3% 110; 50.7% Year 1st year 62; 13.8% 26; 41.9% 36; 58.1% 16.633 0.002 2nd year 81; 18.0% 37; 45.7% 44; 54.3% 3rd year 38; 8.4% 15; 39.5% 23; 60.5% 4th year 158; 35.1% 38; 24.1% 120; 75.9% Masters 111; 24.7% 48; 43.2% 63; 56.8% Faculty Social Science 183; 40.7% 50; 27.3% 133; 72.7% 15.586 0.001 Science 83; 18.4% 32; 38.6% 51; 61.4% Business 53; 11.8% 18; 34.0% 35; 66.0% Engineering 131; 29.1% 64; 48.9% 67; 51.1% Father’s Education No formal education 94; 20.9% 42; 44.7% 52; 55.3% 40.983 <0.001 Primary education 55; 12.2% 18; 32.7% 37; 67.3% Secondary education 65; 14.4% 21; 32.3% 44; 67.7% Higher secondary education 77; 17.1% 6; 7.8% 71; 92.2% Bachelor’s degree 98; 21.8% 49; 50.0% 49; 50.0% Master’s degree 61; 13.6% 28; 45.9% 33; 54.1% Mother’s Education No formal education 65; 14.4% 25; 38.5% 40; 61.5% 28.389 <0.001 Primary education 74; 16.4% 16; 21.6% 58; 78.4% Secondary education 116; 25.8% 54; 46.6% 62; 53.4% Higher secondary education 91; 20.2% 19; 20.9% 72; 79.1% Bachelor’s degree 88; 19.6% 41; 46.6% 47; 53.4% Master’s degree 16; 3.6% 9; 56.3% 7; 43.8% Monthly Family Income Below 10,000 37; 8.2% 17; 45.9% 20; 54.1% 3.927 0.416 10,000 - 20,000 49; 10.9% 22; 44.9% 27; 55.1% 20,001 - 30,000 99; 22.0% 32; 32.3% 67; 67.7% 30,001 - 40,000 119; 26.4% 41; 34.5% 78; 65.5% Above 40,000 146; 32.4% 52; 35.6% 94; 64.4% Personal Financial Status Dependent 297; 66.0% 93; 31.3% 204; 68.7% 9.930 0.002 Independent 153; 34.0% 71; 46.4% 82; 53.6% Sleep on average per night Less than 5 hours 106; 23.6% 13; 12.3% 93; 87.7% 188.500 <0.001 5-6 hours 72; 16.0% 21; 29.2% 51; 70.8% 6-7 hours 131; 29.1% 15; 11.5% 116; 88.5% 7-8 hours 67; 14.9% 52; 77.6% 15; 22.4% More than 8 hours 74; 16.4% 63; 85.1% 11; 14.9% Physical Activity Never 39; 8.7% 11; 28.2% 28; 71.8% 33.761 <0.001 Once a week 131; 29.1% 30; 22.9% 101; 77.1% 2-3 times a week 130; 28.9% 43; 33.1% 87; 66.9% 4-5 times a week 122; 27.1% 61; 50.0% 61; 50.0% Every day 28; 6.2% 19; 67.9% 9; 32.1% Spend sitting (e.g., studying, using a computer) Less than 3 hours 84; 18.7% 3; 3.6% 81; 96.4% 59.217 <0.001 3-5 hours 87; 19.3% 34; 39.1% 53; 60.9% 5-7 hours 131; 29.1% 53; 40.5% 78; 59.5% 7-9 hours 115; 25.6% 64; 55.7% 51; 44.3% More than 9 hours 33; 7.3% 10; 30.3% 23; 69.7% Feeling nervous, anxious, or on edge. Not at all 76; 16.9% 61; 80.3% 15; 19.7% 127.862 <0.001 Several days 74; 16.4% 47; 63.5% 27; 36.5% More than half the days 72; 16.0% 16; 22.2% 56; 77.8% Nearly every day 228; 50.7% 40; 17.5% 188; 82.5% Not being able to stop or control worrying Not at all 59; 13.1% 47; 79.7% 12; 20.3% 69.521 <0.001 Several days 56; 12.4% 21; 37.5% 35; 62.5% More than half the days 189; 42.0% 70; 37.0% 119; 63.0% Nearly every day 146; 32.4% 26; 17.8% 120; 82.2% Little interest or pleasure in doing things Not at all 45; 10.0% 7; 15.6% 38; 84.4% 35.919 <0.001 Several days 54; 12.0% 25; 46.3% 29; 53.7% More than half the days 161; 35.8% 83; 51.6% 78; 48.4% Nearly every day 190; 42.2% 49; 25.8% 141; 74.2% Feeling down, depressed, or hopeless Not at all 79; 17.6% 64; 81.0% 15; 19.0% 98.077 <0.001 Several days 34; 7.6% 17; 50.0% 17; 50.0% More than half the days 200; 44.4% 61; 30.5% 139; 69.5% Nearly every day 137; 30.4% 22; 16.1% 115; 83.9% 3.3 Predictive Factors of Socio-economic, Physical, and Psychological Conditions with Smartphone Addiction. The logistic regression model fits the data well, with a -2 Log Likelihood of 282.813. The Cox & Snell R² (0.495) and Nagelkerke R² (0.678) indicate that the model explains about 49.5% to 67.8% of the variance in smartphone addiction. Socioeconomic factors exhibit varied impacts on the risk of smartphone addiction. Regarding gender, being female is associated with a significantly lower risk of smartphone addiction, with an Adjusted Odds Ratio (AOR) of 0.188 (p<0.001), indicating females are approximately 81.2% less likely to be addicted compared to males after controlling for other variables. Among academic years, 3rd-year students face a higher risk of smartphone addiction compared to Master's students, with an AOR of 2.760 (p=0.034), suggesting they are 2.76 times more likely to be addicted. In terms of faculty, students in the Science faculty demonstrate a substantially higher risk of smartphone addiction compared to those in Engineering, with an AOR of 11.222 (p<0.001), indicating over 11 times the likelihood of addiction. Regarding father's education, having a father with primary education leads to a 5.382 times higher risk of smartphone addiction (AOR =5.382, p=0.002) compared to a father with a Master's degree. Even more striking, having a father with secondary education presents a nearly 20-fold increased risk (AOR=19.208, p<0.001). Conversely, mothers' education levels did not show significant associations in the adjusted model. When examining monthly family income, lower income brackets are associated with a significantly higher risk of smartphone addiction; for instance, individuals in the 10,000–20,000 income bracket face an over 12-fold increased risk (AOR = 12.146, p < 0.001) compared to those earning above 40,000. Interestingly, being dependent financially appears to be a protective factor against smartphone addiction compared to being independent, with an AOR of 0.258 (p < 0.001), meaning dependent individuals are about 74.2% less likely to be addicted. Regarding physical conditions, the relationship with smartphone addiction presents some counterintuitive findings. While typically associated with better health, the data suggests that compared to sleeping more than 8 hours, sleeping less than 5 hours (AOR = 0.181, p = 0.001), 6–7 hours (AOR = 0.047, p < 0.001), and 7–8 hours (AOR = 0.024, p < 0.001) are all associated with a significantly lower risk of smartphone addiction. This implies that sleeping more than 8 hours might be a risk factor in this context, or that the reference category choice warrants reconsideration. In terms of physical activity, engaging 4–5 times a week is associated with a significantly lower risk of addiction (AOR=0.068, p<0.001) compared to exercising every day, suggesting a potential nuanced relationship where daily exercise might not be the most protective. For sedentary time, all categories of sitting less than "more than 9 hours" show a significantly lower risk of smartphone addiction, indicating that spending extensive time sitting is a risk factor, with "less than 3 hours" sitting still having a lower risk compared to the reference (AOR=0.119, p=0.002). Finally, psychological conditions emerge as exceptionally strong predictors of smartphone addiction risk. Compared to not feeling nervous, anxious, or on edge at all, experiencing these feelings nearly every day dramatically increases the risk of smartphone addiction by over 46 times (AOR=46.343, p<0.001). Similarly, the inability to stop or control worrying nearly every day is associated with an astounding 253-fold increased risk of smartphone addiction (AOR=253.395, p<0.001) compared to not worrying at all. Experiencing little interest or pleasure in doing things nearly every day is also a strong risk factor, as other categories show significantly lower risk compared to this reference (e.g., "not at all" has an AOR of 0.068, p < 0.001). Lastly, feeling down, depressed, or hopeless nearly every day is linked to an approximately 67-fold higher risk of smartphone addiction (AOR = 67.150, p < 0.001) compared to never feeling this way, as shown in Table 2. Table 2. Predictive Factors of Socio-economic, Physical, and Psychological Conditions with Smartphone Addiction. Variables Smartphone Addiction OR 95% CI P-value AOR 95% CI (AOR) P -value Gender Male Reference Female 0.333 0.223–0.497 <0.001 0.188 0.097–0.363 <0.001 Year Masters Reference 1st year 0.859 0.441–1.674 0.655 1.084 0.452–2.600 0.856 2nd year 1.107 0.486–2.522 0.808 1.202 0.374–3.863 0.758 3rd year 2.281 1.224–4.250 0.009 2.760 1.079–7.060 0.034 4th year 0.948 0.505–1.778 0.868 1.055 0.458–2.430 0.899 Faculty Engineering Reference Social Science 0.599 0.346–1.037 0.067 1.756 0.687–4.485 0.240 Science 0.731 0.380–1.407 0.348 11.222 3.389–37.159 <0.001 Business 0.394 0.245–0.631 <0.001 0.802 0.388–1.659 0.551 Father’s Education Master’s degree Reference No formal education 1.660 0.829–3.326 0.153 1.829 0.794–4.210 0.156 Primary education 1.692 0.875–3.274 0.118 5.382 1.875–15.447 0.002 Secondary education 9.558 3.782–24.156 <0.001 19.208 6.139–60.098 <0.001 Higher secondary education 0.808 0.458–1.425 0.461 0.619 0.257–1.494 0.286 Bachelor’s degree 0.952 0.498–1.818 0.881 0.939 0.374–2.360 0.894 Mother’s Education Master’s degree Reference No formal education 2.266 1.075–4.776 0.032 1.192 0.428–3.318 0.737 Primary education 0.718 0.387–1.332 0.293 0.593 0.231–1.523 0.278 Secondary education 2.368 1.163–4.821 0.017 2.323 0.807–6.685 0.118 Higher secondary education 0.716 0.373–1.375 0.316 1.199 0.428–3.358 0.729 Bachelor’s degree 0.486 0.161–1.471 0.202 0.997 0.212–4.694 0.997 Monthly Family Income Above 40,000 Reference Below 10,000 1.043 0.443–2.459 0.923 1.642 0.537–5.018 0.384 10,000–20,000 1.780 0.823–3.850 0.143 12.146 3.802–38.801 <0.001 20,001–30,000 1.617 0.765–3.420 0.209 6.492 2.130–19.790 0.001 30,001–40,000 1.537 0.741–3.188 0.249 3.445 1.256–9.451 0.016 Personal Financial Status Independent Reference Dependent 0.527 0.352–0.787 0.002 0.258 0.130–0.512 <0.001 Sleep on average per night More than 8 hours Reference Less than 5 hours 0.339 0.157–0.734 0.006 0.181 0.068–0.479 0.001 5–6 hours 1.081 0.490–2.385 0.847 0.974 0.387–2.451 0.955 6–7 hours 0.040 0.018–0.091 <0.001 0.047 0.018–0.124 <0.001 7–8 hours 0.024 0.010–0.058 <0.001 0.024 0.009–0.063 <0.001 Engage in physical activity Every day Reference Never 1.323 0.590–2.967 0.497 0.841 0.281–2.515 0.756 Once a week 0.795 0.362–1.747 0.568 0.562 0.192–1.640 0.291 2–3 times a week 0.393 0.180–0.859 0.019 0.679 0.241–1.909 0.462 4–5 times a week 0.186 0.065–0.535 0.002 0.068 0.017–0.273 <0.001 Spend sitting (e.g., studying, using a computer) More than 9 hours Reference Less than 3 hours 0.058 0.017–0.198 <0.001 0.119 0.031–0.459 0.002 3–5 hours 0.055 0.016–0.182 <0.001 0.108 0.028–0.418 0.001 5–7 hours 0.030 0.009–0.099 <0.001 0.083 0.022–0.313 <0.001 7–9 hours 0.085 0.022–0.336 <0.001 0.095 0.019–0.474 0.004 Feeling nervous, anxious, or on edge Not at all Reference Several days 2.336 1.118–4.882 0.024 3.658 1.324–10.111 0.012 More than half the days 14.233 6.445–31.434 <0.001 40.641 12.971–127.336 <0.001 Nearly every day 19.113 9.879–36.979 <0.001 46.343 17.321–123.994 <0.001 Not being able to stop or control worrying Not at all Reference Several days 6.528 2.837–15.021 <0.001 22.405 6.123–81.983 <0.001 More than half the days 6.658 3.309–13.399 <0.001 24.828 8.164–75.508 <0.001 Nearly every day 18.077 8.432–38.756 <0.001 253.395 62.703–1024.021 <0.001 Little interest or pleasure in doing things Nearly every day Reference Not at all 0.214 0.081–0.562 0.002 0.068 0.019–0.244 <0.001 Several days 0.173 0.073–0.410 <0.001 0.167 0.053–0.527 0.002 More than half the days 0.530 0.222–1.264 0.152 0.247 0.079–0.776 0.017 Feeling down, depressed, or hopeless Not at all Reference Several days 4.267 1.776–10.249 0.001 9.654 1.994–46.743 0.005 More than half the days 9.722 5.138–18.397 <0.001 3.006 1.165–7.753 0.023 Nearly every day 22.303 10.813–46.002 <0.001 67.150 22.008–204.887 <0.001 4. Discussion This study provides a comprehensive examination of smartphone addiction (SA) among university students in Bangladesh, contributing to the limited literature on this issue in a developing country context. By investigating socio-economic, physical, and psychological factors, the study addresses a critical gap in understanding the multifaceted drivers of SA in a high-risk population. Key findings reveal a high prevalence of SA (63.8% overall, based on 286/450 participants classified as addicted per Table 1), with significant associations across gender, academic year, faculty, parental education, financial dependency, sleep duration, physical activity, sedentary time, and psychological conditions (anxiety, depression, stress). Notably, psychological factors, such as feeling nervous or unable to control worrying nearly every day, emerged as the strongest predictors of SA, with adjusted odds ratios (AORs) as high as 253.395 for worrying. These findings underscore the interplay of socio-economic stressors, lifestyle factors, and mental health in driving SA, offering insights for targeted interventions in university settings. The article's reported prevalence of 63.8% is closely aligned with "Smartphone addiction: Physical, Mental and Cultural Aspects of Smartphone Use among Young Adults in Bangladesh" which found 61.4% prevalence among 440 young adults, with data collected between July 2021 and February 2023. This thesis used a mixed-methods approach, including an online survey, and identified socio-demographic predictors like being male (OR=1.79), aged ≤25 (OR=2.19), unemployed (OR=1.95), and belonging to large families (OR=3.44), which resonate with the article's findings [14]. However, another study, "Understanding the drivers of smartphone addiction among university students: a perspective from Bangladesh," reported a lower prevalence of 28.4% among 384 students from public and private universities, using unequal stratified random sampling and a structured questionnaire [15]. The article's higher prevalence aligns more closely with detailed local studies, indicating robustness in the context of Bangladesh's high smartphone penetration and youth usage patterns. Globally, a systematic review by [16], "Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review" reported a median prevalence of 23.3% (interquartile range 14.0–31.2%) among children and young people, which the article cites and aligns with for broader comparisons. This suggests the article's findings are contextually higher, possibly due to the university student focus and Bangladesh's specific socio-economic conditions. The article found significant associations between SA and socio-economic factors, such as gender (males 75.5% addicted vs. females 50.7%, χ²=29.944, p<0.001), with females having an 81.2% lower risk (AOR=0.188, p<0.001). This aligns with studies like Arefin et al. (2017), "Impact of Smartphone Addiction on Academic Performance of Business Students: A Case Study" [17], which identified socio-economic factors influencing SA among business students in Bangladesh, particularly highlighting parental education's role. The article's finding that lower paternal education (e.g., secondary education, AOR=19.208, p<0.001) increases SA risk is consistent, suggesting limited resources for alternative activities may drive addiction. Financial dependency was a protective factor (AOR=0.258, p<0.001), possibly due to parental oversight, which aligns with global studies like [18], "Smartphone Addiction Prevalence and Its Association on Academic Performance, Physical Health, and Mental Well-Being among University Students in Saudi Arabia" noting employment status impacts addiction. However, monthly family income showed no significant association (χ²=3.927, p=0.416), which contrasts with some studies, suggesting context-specific findings in Bangladesh. The article highlights strong links between SA and physical conditions, such as sleep duration (e.g., <5 hours, 87.7% addicted, χ²=188.500, p<0.001) and physical activity (e.g., once a week, 77.1% addicted, χ²=33.761, p<0.001). Logistic regression showed counterintuitive findings, with shorter sleep durations associated with lower risk (e.g., 8 hours) capturing compensatory sleep. This aligns with Ratan (2023), which reported addicted individuals had lower quality of life in physical domains and more discomforts (e.g., eye discomfort 57.5%), supporting the article's findings. Regarding physical activity, while the chi-square analysis indicated a significant association, with those exercising once a week or never being more prone to addiction, the adjusted model presented a nuanced picture. Engaging in physical activity 4-5 times a week was associated with a significantly lower risk of addiction compared to exercising every day. A systematic reviews like [19], linking exercise to reduced SA. High sedentary time (>9 hours, 69.7% addicted) increasing risk (e.g., <3 hours sitting, AOR=0.119, p=0.002) aligns with global trends, as noted in "Smartphone Addiction and Associated Health Outcomes in Adult Populations: A Systematic Review," reinforcing the article's focus on lifestyle impacts. This counterintuitive finding suggests that daily intense exercise might not be the most protective, or that a balanced, moderate frequency of physical activity (4-5 times a week) is optimal for mitigating smartphone addiction risk. This contrasts with some studies that found no significant correlation between smartphone addiction and physical activity [20], while others suggest a negative correlation where higher addiction is linked to lower physical activity [21]. The amount of time spent sitting also showed a strong association, with extensive sedentary time being a risk factor. The adjusted model confirmed that all categories of sitting less than "more than 9 hours" showed a significantly lower risk, reinforcing that prolonged sedentary behavior is detrimental. Psychological conditions emerged as exceptionally strong predictors of smartphone addiction risk in this study, aligning with extensive global research [20]. Experiencing nervousness, anxiety, or being on edge nearly every day dramatically increased the risk of smartphone addiction by over 46 times. The inability to stop or control worrying nearly every day was associated with an astounding 253-fold increased risk. This is strongly supported by [22], reporting OR=3.05 for anxiety and OR=3.17 for depression, and [8], finding 40.7% anxiety and 40.0% depression among addicted individuals. The article's findings (50.7% anxious, 44.4% depressed) are slightly higher, possibly due to the university student focus and academic pressures, aligning with studies, cited in ResearchGate results, linking SA to higher anxiety and depression. Similarly, experiencing little interest or pleasure, and feeling down, depressed, or hopeless nearly every day, were linked to substantially higher risks (67-fold). These findings strongly support the notion of a "vicious cycle" where psychological distress drives problematic smartphone use as a maladaptive coping mechanism, which in turn exacerbates these negative emotional states [23]. This highlights the critical need to address underlying mental health issues when developing interventions for smartphone addiction. Based on our findings, several recommendations can be made. Given the strong predictive power of psychological conditions, interventions should prioritize mental health support for university students, focusing on anxiety, depression, and stress management. This could involve accessible counseling services, stress reduction workshops, and promoting healthy coping mechanisms that do not involve excessive smartphone use. Educational campaigns are crucial to raise awareness about the risks of smartphone addiction, particularly among male students and those in specific academic years or faculties identified as high-risk. Furthermore, promoting balanced lifestyle habits, including adequate sleep hygiene and regular, moderate physical activity, is essential. Universities and policymakers should consider creating environments that encourage offline social engagement and recreational activities, addressing the lack of play areas and recreational facilities mentioned in the Bangladeshi context [24]. Finally, future research should delve deeper into the nuanced relationships observed, particularly the counterintuitive findings regarding sleep duration and physical activity, possibly by refining measurement tools and exploring mediating factors. This study, while providing valuable insights, has several limitations. Its cross-sectional design prevents the establishment of causality, meaning we can identify associations but not definitively determine if smartphone addiction causes, or is caused by, the associated factors [24]. The reliance on self-report scales may introduce social desirability bias, where participants might underreport problematic behaviors. The study was conducted within a specific university context in Bangladesh, which may limit the generalizability of the findings to other student populations or the broader general population. The contradictory findings between chi-square associations and adjusted odds ratios for certain socio-economic and lifestyle factors (e.g., family income, personal financial status, sleep duration, physical activity) highlight the complexity of these relationships and suggest the need for further research with more refined methodologies and longitudinal designs to clarify these dynamics. Future research should address these gaps through mixed-methods designs and broader sampling. 5. Conclusion The examination of smartphone addiction reveals a pervasive and escalating public health concern, particularly among young adults and university students globally. While not yet a formal clinical diagnosis, its behavioral manifestations strikingly mirror those of established addictions, characterized by subjective distress and significant functional impairment rather than merely high usage duration. This nuanced understanding is crucial for accurate assessment and the development of effective interventions. Prevalence rates are notably high among university students worldwide, with studies consistently reporting substantial percentages of students exhibiting problematic smartphone use. This issue profoundly impacts academic performance, leading to lower grades, increased procrastination, and reduced productivity, thereby serving as a critical indicator of functional impairment in this demographic. The physical and psychological dimensions of smartphone addiction are deeply interconnected. Problematic smartphone use is consistently and robustly linked to poor sleep quality, contributing to sleep disturbances and creating a harmful bidirectional cycle. Crucially, poor sleep exacerbates the psychological consequences of smartphone addiction, particularly anxiety, positioning sleep hygiene as a vital intervention point. While the direct relationship with physical activity remains inconsistent across studies, smartphone addiction is consistently associated with elevated levels of behavioral, sensory, and cognitive fatigue. This suggests fatigue acts as a significant intermediary, impacting overall well-being and warranting greater attention in research. Psychologically, smartphone addiction is strongly correlated with anxiety, depression, stress, and loneliness. This relationship is often bidirectional, with individuals using smartphones as a maladaptive coping mechanism, inadvertently deepening both the addiction and their underlying psychological distress. The social paradox of digital connectivity is particularly striking: while smartphones offer a means of connection, their excessive use can paradoxically lead to increased social isolation and loneliness by diminishing face-to-face interactions. This highlights the need to foster real-world social engagement. Methodological inconsistencies in defining and measuring problematic use versus general screen time, especially concerning loneliness, underscore the need for more refined research approaches. Socio-economic factors present a complex picture. High family income can predict smartphone addiction in some student populations, possibly due to increased access and affordability, while employment status and perceived social mobility also play roles. The influence of these factors appears to be context-dependent, highlighting the need for culturally sensitive investigations. In Bangladesh, smartphone addiction among university students is a significant concern, with prevalence rates comparable to or higher than global averages. Unique socio-economic and cultural factors, such as increased wealth, limited recreational facilities, and social isolation, contribute to this issue. The reported consequences include severe impacts on academic performance, daily activities, and physical health symptoms like eye strain and headaches. Collectively, these findings underscore the urgent need for comprehensive, multi-faceted interventions. Future research should focus on clarifying the intricate relationships between socio-economic factors, lifestyle habits, and psychological conditions, particularly within specific cultural contexts like Bangladesh. Developing targeted strategies that address not only smartphone use patterns but also underlying psychological vulnerabilities, promote healthy lifestyle choices, and foster real-world social connections will be essential for mitigating the adverse effects of smartphone addiction and improving the overall well-being of university students. Declarations Data availability statement The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding This study did not receive any funding support. Acknowledgment The authors thank all the study participants. Author Contributions Conceptualization-M.A.M.R. and M.M.H.; methodology- M.A.M.R. and M.Z.M.; formal analysis M.A.M.R. and S.H.; original draft preparation- N.I.M, S.H, M.I.I.R and M.Z.M; writing- M.A.M.R., S.H., M.M.H and M.Z.M.; SPSS and testing- M.A.M.R and N.I.M.; review and editing- N.I.M, M.I.I.R. All the authors have read and agreed to publish this version of the manuscript. Conflict of interest The authors do not have any conflicts of interest. Clinical Trial Number Not applicable. Ethics approval and consent to participate The authors affirm their unwavering commitment to ethical standards, ensuring that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2013. The study protocol was approved by the Ethical Committee of Noakhali Science and Technology University (Reference no: NSTU/SCI/EC/ 2025/453). Informed consent was obtained from all individual participants included in the study, reaffirming the Authors' commitment to transparency and ethical conduct. Consent for publication Not applicable. 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Int J Environ Res Public Health 19:10439 ERGÖNÜL E, KESKİN T, ERGAN M, BAŞKURT F, BAŞKURT Z (2024) The Impact of Smartphone Addiction on Physical Activity, Fatigue, and Sleep Quality among Rural Health Science Students. https://doi.org/10.21203/RS.3.RS-5341122/V1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9424184","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627368668,"identity":"2e3645ec-0278-4e44-a580-275f31a9744f","order_by":0,"name":"Md. 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Introduction","content":"\u003cp\u003eSmartphone addiction, often referred to as problematic smartphone use, is characterized by excessive and uncontrolled smartphone use that disrupts daily life, leading to negative physical, psychological, and social consequences [1]. It is defined as a behavioral addiction involving compulsive behavior, withdrawal symptoms, tolerance, and functional impairment, similar to substance-related dependencies. The American Psychological Association describes addiction as a state of psychological or physical dependence on behaviors or substances, and smartphone addiction fits this framework due to its impact on daily functioning [2]. Global prevalence rates of smartphone addiction vary widely [3]. A systematic review reported a median prevalence of 23.3% (interquartile range 14\u0026ndash;31%) among children and young people, with rates as high as 41.93% among Asian medical students [4]. In specific populations, prevalence ranges from 9.3% in Iran to 48% in Malaysia, influenced by socio-cultural factors and smartphone accessibility. For instance, high smartphone ownership (e.g., 78% in Malaysia) and internet penetration correlate with higher smartphone addiction rates [1]. These variations underscore the need for region-specific studies to comprehend the contextual drivers of smartphone addiction.\u003c/p\u003e\n\u003cp\u003eUniversity students are particularly vulnerable to smartphone addiction due to their reliance on smartphones for academic, social, and entertainment purposes. Studies consistently show high smartphone addiction prevalence among this group. A study in Jordan found 88.7% of university students exhibited psychological distress linked to SA, with 59.1% showing severe mental health issues [5]. In Malaysia, 40.6\u0026ndash;48% of students were classified as smartphone-addicted, driven by constant connectivity and social media use [1]. A cross-sectional study in T\u0026uuml;rkiye reported increased smartphone addiction during the COVID-19 pandemic, attributed to online education and social isolation. These studies often use validated tools like the Smartphone Addiction Scale-Short Version (SAS-SV) to measure smartphone addiction, with cutoffs (e.g., \u0026ge;31 for males, \u0026ge;33 for females) identifying addiction [6]. Gender differences are notable, with some studies indicating higher smartphone addiction among females due to social media use, while others report higher rates among males.\u003c/p\u003e\n\u003cp\u003eSmartphone addiction is strongly associated with lifestyle factors, particularly sleep and physical activity. Smartphone-addicted individuals are more likely to sleep \u0026le;6 hours per night (38.1%) and stay awake late due to smartphone use (88.9%), leading to poor sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) [7]. This is linked to delayed melatonin production from screen exposure and compulsive nighttime use. Physical inactivity is also prevalent among smartphone addiction groups (26.7%), with overuse correlating with musculoskeletal pain (e.g., neck, shoulders, eyes) and higher rates of obesity (32.5%) [8]. Conversely, leisure physical activity can moderate the negative effects of smartphone addiction, reducing loneliness and improving health outcomes [9]. These findings suggest that lifestyle interventions could impact smartphone addictions.\u003c/p\u003e\n\u003cp\u003eSmartphone addiction is closely linked to psychological conditions, forming a bidirectional relationship. Studies consistently report higher rates of anxiety (40.7%), depression (40.0%), and stress among smartphone-addicted individuals compared to non-addicted groups [5]. The \u0026ldquo;Fear of Missing Out\u0026rdquo; (FoMO) and nomophobia (anxiety from being without a smartphone) are key drivers, as compulsive notification-checking disrupts mental well-being [8]. Depression and anxiety may also predispose individuals to smartphone addiction, as smartphones are used as a coping mechanism for negative emotions. These psychological factors underscore smartphone addictions impact on mental health.\u003c/p\u003e\n\u003cp\u003eIn Bangladesh, smartphone addiction is a growing public health concern, particularly among young adults. A cross-sectional study of 440 young adults (July 2021\u0026ndash;February 2022) found a 61.4% smartphone addiction prevalence rate, with significant predictors including male gender, age \u0026le;25, unemployment, and large family size (\u0026ge;8). Smartphone-addicted participants reported higher anxiety (40.7%), depression (40.0%), insomnia, physical inactivity, and obesity, with compulsive use during daily activities (e.g., eating, driving) [8]. Another study highlighted Smartphone addiction impact on quality of life, linking it to social isolation and FoMO [10]. These studies, primarily cross-sectional and using the SAS-SV, indicate a high burden of Smartphone addiction among Bangladeshi youth but are limited by self-reported data and convenience sampling, reducing generalizability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSmartphone addiction is a pressing issue among university students, who are at high risk due to academic pressures, social media reliance, and widespread smartphone access. In Bangladesh, where 61.4% of young adults show smartphone addiction [8] the lack of studies limits understanding of causal pathways. This study is critical to address this gap, particularly in a developing country context where socio-economic stressors (e.g., family income, financial status) and cultural factors may exacerbate smartphone addiction. The bidirectional relationship between smartphone addiction and mental health, coupled with its impact on sleep and physical health, necessitates targeted interventions. By exploring mediators of physical activity, this study will inform evidence-based strategies to reduce smartphone addictions, improving student well-being and academic performance. The findings will contribute to global smartphone addiction research and support public health policies in Bangladesh. This study aims to assess the prevalence of smartphone addiction among university students in Bangladesh. Examine the associations between socio-economic factors (e.g., family income, financial status), physical conditions (e.g., sleep quality, physical activity), and psychological conditions (e.g., anxiety, depression, stress, loneliness) with smartphone addiction. This study also identifies potential gender differences and lifestyle moderators (e.g., physical activity) in smartphone addiction prevalence and impact.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003e\u003cstrong\u003e2.1 \u0026nbsp; \u0026nbsp; \u0026nbsp;Study Design, Site, and Participants \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a cross-sectional survey design to investigate the associations between smartphone Addiction on socio-economic, physical, and psychological conditions among university students. The study relied on quantitative research methods, collecting data through structured questionnaires designed to measure Smartphone addiction and psychological conditions. Validated tools were utilized for data collection, ensuring the reliability and accuracy of the findings. The research was conducted at Noakhali Science and Technology University (NSTU), a public university located in the Noakhali district of southern Bangladesh. NSTU is a growing institution, with 33 academic departments under six faculties and two institutes. As of January 2025, the university had a total student population of 6,437 students pursuing courses across various disciplines. The university's diverse academic environment made it an ideal setting for exploring the multifaceted issues of smartphone addiction on socio-economic, physical, and psychological conditions. The target population for the study consisted of four students from the four faculties (Social Science \u0026amp; Humanities, Science, Business Study, Engineering \u0026amp; Technology) at NSTU. Initially, a sample size of 373 students was determined using Yamane’s Formula (n = 𝑁/1+𝑁∗ 𝑒2), for sample size calculation, assuming a 95% confidence level[11]. The final dataset comprises a total of 450 data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 \u0026nbsp; \u0026nbsp; \u0026nbsp;Measurement and Data Collection Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the previous studies, a structured questionnaire was prepared in English, which was then translated into Bengali (local language) for better understanding. To assess the study's efficacy, data from 19 students (5% of the total sample) were collected for a pilot study prior to the final data collection. A designated member of the Social Science experts also reviewed the questionnaire. Based on the findings of the pilot study and the comments of the reviewers, the questionnaire was modified. However, the findings of the pilot survey were excluded from the final study. Cronbach's alpha, with a value of .870, was found, which indicated the reliability of the questionnaire.\u0026nbsp;The final questionnaire had two parts. The first section was based on socio-demographic data such as gender, family income, and parental educational backgrounds. The second section consisted of the Smartphone Addiction Scale, the Self-developed lifestyle, well-being, and psychological condition questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSociodemographic Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSociodemographic factors measured in this study included gender, age, academic year, faculty, monthly family income, and parental education. Participants also reported their financial status (dependent or independent).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSmartphone Addiction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmartphone addiction was assessed using the Smartphone Addiction Scale-Short Version (SAS-SV), a validated instrument widely used in similar research studies. The SAS-SV contains 10 items rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The total scores range from 10 to 50, with higher scores indicating higher levels of smartphone addiction. A gender-specific cut-off was used, with a score of 31 or higher for males and 33 or higher for females [12]. The Cronbach’s alpha for the SAS-SV in this study was 0.85, demonstrating good internal consistency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePhysical Health and Lifestyle\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhysical health was measured through questions related to sleep quality, physical activity, and sedentary behavior. Sleep duration was categorized into three groups: ≤6 hours (insufficient), 7-9 hours (adequate), and ≥10 hours (excessive). Physical activity was categorized into three levels: no physical activity, irregular physical activity, and regular physical activity (at least 3 times per week). Sedentary behavior was assessed by the number of hours spent sitting daily.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePsychological Conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA self-developed questionnaire was designed to assess key psychological symptoms commonly experienced by individuals under stress. The questionnaire included four items addressing emotional and cognitive symptoms: Feeling nervous, anxious, or on edge; Not being able to stop or control worrying; Little interest or pleasure in doing things; Feeling down, depressed, or hopeless. Participants responded using a 4-point Likert scale: Not at all, Several days, More than half the days, and Nearly every day. Higher scores indicated greater psychological distress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ethical statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethical Committee of Noakhali Science and Technology University (NSTU) (Reference no: NSTU/SCI/EC/2025/453, dated January 15, 2025). Written informed consent was obtained from all participants before data collection. Participants were fully informed about the study’s objectives, procedures, potential risks, and their right to withdraw at any stage without penalty. To maintain confidentiality, no personal identifiers (e.g., names, student IDs) were collected; instead, each participant was assigned a unique anonymized ID. Physical questionnaires were securely stored in a locked facility at NSTU, accessible only to the research team. Digital data were encrypted and stored on password-protected servers at NSTU, with access limited to the principal investigator and co-authors. In line with NSTU’s ethical guidelines, all data will be retained for five years following publication, after which they will be permanently destroyed (paper records shredded and digital files deleted). Participants who reported severe psychological distress during data collection were referred to NSTU’s counseling services to ensure appropriate ethical care. All procedures adhered to international standards for protecting participant privacy, autonomy, and data security, as outlined by the World Medical Association [13].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Data Analysis Techniques\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were entered and analyzed using IBM SPSS Statistics Version 27. Descriptive statistics (frequencies, percentages) were used to summarize the sociodemographic characteristics of the participants, as well as the levels of smartphone addiction, physical health, and psychological well-being. To assess the associations between smartphone addiction and socio-economic, physical, and psychological factors, chi-square tests were performed for categorical variables. A Logistic regression was conducted to identify significant predictors of smartphone addiction. The level of statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3.\tResults","content":"\u003cp\u003e\u003cstrong\u003e3.1 \u0026nbsp; \u0026nbsp; Characteristics of the Study Participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e summarizes the demographic, socioeconomic, Lifestyle, Well-being, and Mental health characteristics of the study participants (N=450). The sample comprised slightly more males, 51.8%, and females, 48.2%. The largest proportion of the sample is 4th-year students, 35.1%, followed by Master\u0026apos;s students, 24.7%, and 2nd-year students, 18.0%. 1st and 3\u003csup\u003erd\u003c/sup\u003e year students make up smaller percentages (13.8% and 8.4% respectively). Regarding academic faculty, Social Science 40.7% represents the largest group, followed by Engineering 29.1%, Science 18.4%, and Business 11.8%. In terms of parental education, a significant portion of fathers have a Bachelor\u0026apos;s degree 21.8% or no formal education 20.9%. For mothers, secondary education is 25.8% is the most common, followed by higher secondary education (20.2%) and Bachelor\u0026apos;s degrees 19.6%. It\u0026apos;s notable that a smaller percentage of mothers, 3.6%, have a Master\u0026apos;s degree compared to fathers, 13.6%. The majority of the sample reports a monthly family income above 40,000 (32.4%), followed by 30,001 - 40,000 (26.4%) and 20,001 - 30,000 (22.0%). Most individuals in the sample are dependent, 66.0% financially.\u003c/p\u003e\n\u003cp\u003eOn average, a considerable portion of the sample sleeps 6-7 hours per night, 29.1%, though a notable percentage sleeps less than 5 hours, 23.6%. Regarding physical activity, the most common frequency is once a week, 29.1%, or 2-3 times a week, 28.9%. Only a small percentage engages in physical activity every day (6.2%), while 8.7% never participate. The majority of individuals spend sitting 5-7 hours, 29.1%, or 7-9 hours, 25.6%, indicating a significant amount of sedentary time.\u003c/p\u003e\n\u003cp\u003eA substantial number of respondents report feeling nervous, anxious, or on edge nearly every day, 50.7%. Similarly, a high percentage indicates little interest or pleasure in doing things nearly every day, 42.2% or more than half the days, 35.8%. Concerning worrying, more than half the days, 42.0%, and nearly every day, 32.4%, are the most frequent responses for not being able to stop or control worrying. Finally, a considerable portion of the sample reports feeling down, depressed, or hopeless more than half the days, 44.4%, or nearly every day, 30.4%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 \u0026nbsp; \u0026nbsp;Associations between Smartphone Addiction with Socio-economic, Physical, and Psychological Conditions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003epresents Smartphone addiction in this sample is significantly associated with a range of demographic and well-being factors, as evidenced by statistically significant chi-square values p\u0026lt;0.05. Gender shows a strong association (\u0026chi;2=29.944, p\u0026lt;0.001), with one gender exhibiting a significantly higher proportion of addiction. The academic year is also a significant factor (\u0026chi;2=16.633, p=0.002), with 4th-year students showing the highest addiction rate at 75.9%. Similarly, academic faculty is linked to addiction (\u0026chi;2=15.586, p=0.001), where Social Science (72.7% addicted) and Business (66.0% addicted) faculties display higher rates. Parental education levels, for both father (\u0026chi;2=40.983, p\u0026lt;0.001) and mother (\u0026chi;2=28.389, p\u0026lt;0.001), are significantly associated; for instance, 92.2% of individuals whose fathers have higher secondary education are addicted, and 79.1% of those whose mothers have higher secondary education are addicted. Personal financial status also plays a role (\u0026chi;2=9.930, p =0.002), with dependent individuals showing a higher addiction rate (68.7%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, lifestyle and well-being factors demonstrate robust connections to smartphone addiction. Sleep duration is highly significant (\u0026chi;2=188.500, p\u0026lt;0.001), as individuals sleeping less than 5 hours (87.7% addicted) or 6-7 hours (88.5% addicted) exhibit very high addiction rates. Physical activity levels are also crucial (\u0026chi;2=33.761, p\u0026lt;0.001), with those exercising once a week 77.1% addicted or never 71.8% addicted, being more prone to addiction. The amount of time spent sitting is strongly associated (\u0026chi;2=59.217, p\u0026lt;0.001), with notably 96.4% of those sitting less than 3 hours being addicted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, various mental health indicators show compelling relationships: feeling nervous, anxious, or on edge (\u0026chi;2=127.862, p\u0026lt;0.001), not being able to stop or control worrying (\u0026chi;2=69.521, p\u0026lt;0.001), experiencing little interest or pleasure (\u0026chi;2=35.919, p\u0026lt;0.001), and feeling down, depressed, or hopeless (\u0026chi;2=98.077, p\u0026lt;0.001) are all profoundly linked to higher rates of smartphone addiction, with those feeling nervous nearly every day showing an 82.5% addiction rate, and those feeling down or hopeless nearly every day at 69.5% addiction. Notably, monthly family income was the only variable that did not demonstrate a statistically significant association with smartphone addiction \u0026chi;2=3.927, p=0.416.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eAssociations between Smartphone Addiction with Socio-economic, Physical, and Psychological Conditions.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample (n; %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmartphone Addiction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal (n; %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAddicted (n; %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u0026sup2; Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e233; 51.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57; 24.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e176; 75.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e29.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e217; 48.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e107; 49.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110; 50.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1st year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62; 13.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26; 41.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36; 58.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e16.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2nd year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81; 18.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37; 45.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44; 54.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3rd year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38; 8.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15; 39.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23; 60.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4th year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e158; 35.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38; 24.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120; 75.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111; 24.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48; 43.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63; 56.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFaculty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSocial Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e183; 40.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50; 27.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e133; 72.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e15.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eScience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83; 18.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32; 38.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51; 61.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53; 11.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18; 34.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35; 66.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngineering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131; 29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64; 48.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67; 51.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFather\u0026rsquo;s Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94; 20.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42; 44.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52; 55.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e40.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55; 12.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18; 32.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37; 67.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSecondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65; 14.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21; 32.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44; 67.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher secondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77; 17.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6; 7.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71; 92.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98; 21.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49; 50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49; 50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61; 13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28; 45.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33; 54.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMother\u0026rsquo;s Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65; 14.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25; 38.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40; 61.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e28.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74; 16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16; 21.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58; 78.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSecondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116; 25.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54; 46.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62; 53.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher secondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91; 20.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19; 20.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72; 79.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88; 19.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41; 46.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47; 53.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16; 3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9; 56.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7; 43.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly Family Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBelow 10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37; 8.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17; 45.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20; 54.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e3.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10,000 - 20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49; 10.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22; 44.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27; 55.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20,001 - 30,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99; 22.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32; 32.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67; 67.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30,001 - 40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119; 26.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41; 34.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78; 65.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbove 40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e146; 32.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52; 35.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94; 64.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonal Financial Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e297; 66.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93; 31.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e204; 68.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e9.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e153; 34.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71; 46.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82; 53.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep on average per night\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLess than 5 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106; 23.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13; 12.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93; 87.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e188.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5-6 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72; 16.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21; 29.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51; 70.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6-7 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131; 29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15; 11.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116; 88.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7-8 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67; 14.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52; 77.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15; 22.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than 8 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74; 16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63; 85.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11; 14.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical Activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39; 8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11; 28.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28; 71.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e33.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOnce a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131; 29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30; 22.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101; 77.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2-3 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130; 28.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43; 33.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87; 66.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4-5 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e122; 27.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61; 50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61; 50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEvery day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28; 6.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19; 67.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9; 32.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpend sitting (e.g., studying, using a computer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLess than 3 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84; 18.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3; 3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81; 96.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e59.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3-5 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87; 19.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34; 39.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53; 60.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5-7 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131; 29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53; 40.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78; 59.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7-9 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115; 25.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64; 55.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51; 44.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than 9 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33; 7.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10; 30.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23; 69.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeeling nervous, anxious, or on edge.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76; 16.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61; 80.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15; 19.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e127.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeveral days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74; 16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47; 63.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27; 36.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than half the days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72; 16.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16; 22.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56; 77.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNearly every day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e228; 50.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40; 17.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e188; 82.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot being able to stop or control worrying\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59; 13.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47; 79.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12; 20.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e69.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeveral days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56; 12.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21; 37.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35; 62.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than half the days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e189; 42.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70; 37.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119; 63.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNearly every day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e146; 32.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26; 17.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120; 82.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLittle interest or pleasure in doing things\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45; 10.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7; 15.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38; 84.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e35.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeveral days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54; 12.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25; 46.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29; 53.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than half the days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e161; 35.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83; 51.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78; 48.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNearly every day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190; 42.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49; 25.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e141; 74.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeeling down, depressed, or hopeless\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79; 17.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64; 81.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15; 19.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e98.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeveral days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34; 7.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17; 50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17; 50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than half the days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200; 44.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61; 30.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139; 69.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNearly every day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e137; 30.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22; 16.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115; 83.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003ePredictive Factors of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Socio-economic, Physical, and Psychological Conditions with Smartphone Addiction.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe logistic regression model fits the data well, with a -2 Log Likelihood of 282.813. The Cox \u0026amp; Snell R\u0026sup2; (0.495) and Nagelkerke R\u0026sup2; (0.678) indicate that the model explains about 49.5% to 67.8% of the variance in smartphone addiction. Socioeconomic factors exhibit varied impacts on the risk of smartphone addiction. Regarding gender, being female is associated with a significantly lower risk of smartphone addiction, with an Adjusted Odds Ratio (AOR) of 0.188 (p\u0026lt;0.001), indicating females are approximately 81.2% less likely to be addicted compared to males after controlling for other variables. Among academic years, 3rd-year students face a higher risk of smartphone addiction compared to Master\u0026apos;s students, with an AOR of 2.760 (p=0.034), suggesting they are 2.76 times more likely to be addicted. In terms of faculty, students in the Science faculty demonstrate a substantially higher risk of smartphone addiction compared to those in Engineering, with an AOR of 11.222 (p\u0026lt;0.001), indicating over 11 times the likelihood of addiction. Regarding father\u0026apos;s education, having a father with primary education leads to a 5.382 times higher risk of smartphone addiction (AOR =5.382, p=0.002) compared to a father with a Master\u0026apos;s degree. Even more striking, having a father with secondary education presents a nearly 20-fold increased risk (AOR=19.208, p\u0026lt;0.001). Conversely, mothers\u0026apos; education levels did not show significant associations in the adjusted model. When examining monthly family income, lower income brackets are associated with a significantly higher risk of smartphone addiction; for instance, individuals in the 10,000\u0026ndash;20,000 income bracket face an over 12-fold increased risk (AOR = 12.146, p \u0026lt; 0.001) compared to those earning above 40,000. Interestingly, being dependent financially appears to be a protective factor against smartphone addiction compared to being independent, with an AOR of 0.258 (p \u0026lt; 0.001), meaning dependent individuals are about 74.2% less likely to be addicted.\u003c/p\u003e\n\u003cp\u003eRegarding physical conditions, the relationship with smartphone addiction presents some counterintuitive findings. While typically associated with better health, the data suggests that compared to sleeping more than 8 hours, sleeping less than 5 hours (AOR = 0.181, p = 0.001), 6\u0026ndash;7 hours (AOR = 0.047, p \u0026lt; 0.001), and 7\u0026ndash;8 hours (AOR = 0.024, p \u0026lt; 0.001) are all associated with a significantly lower risk of smartphone addiction. This implies that sleeping more than 8 hours might be a risk factor in this context, or that the reference category choice warrants reconsideration. In terms of physical activity, engaging 4\u0026ndash;5 times a week is associated with a significantly lower risk of addiction (AOR=0.068, p\u0026lt;0.001) compared to exercising every day, suggesting a potential nuanced relationship where daily exercise might not be the most protective. For sedentary time, all categories of sitting less than \u0026quot;more than 9 hours\u0026quot; show a significantly lower risk of smartphone addiction, indicating that spending extensive time sitting is a risk factor, with \u0026quot;less than 3 hours\u0026quot; sitting still having a lower risk compared to the reference (AOR=0.119, p=0.002).\u003c/p\u003e\n\u003cp\u003eFinally, psychological conditions emerge as exceptionally strong predictors of smartphone addiction risk. Compared to not feeling nervous, anxious, or on edge at all, experiencing these feelings nearly every day dramatically increases the risk of smartphone addiction by over 46 times (AOR=46.343, p\u0026lt;0.001). Similarly, the inability to stop or control worrying nearly every day is associated with an astounding 253-fold increased risk of smartphone addiction (AOR=253.395, p\u0026lt;0.001) compared to not worrying at all. Experiencing little interest or pleasure in doing things nearly every day is also a strong risk factor, as other categories show significantly lower risk compared to this reference (e.g., \u0026quot;not at all\u0026quot; has an AOR of 0.068, p \u0026lt; 0.001). Lastly, feeling down, depressed, or hopeless nearly every day is linked to an approximately 67-fold higher risk of smartphone addiction (AOR = 67.150, p \u0026lt; 0.001) compared to never feeling this way, as shown in \u003cstrong\u003eTable 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePredictive Factors of Socio-economic, Physical, and Psychological Conditions with Smartphone Addiction.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmartphone Addiction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI (AOR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.223\u0026ndash;0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.097\u0026ndash;0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1st year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.441\u0026ndash;1.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.452\u0026ndash;2.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2nd year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.486\u0026ndash;2.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.374\u0026ndash;3.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3rd year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.224\u0026ndash;4.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.079\u0026ndash;7.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4th year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.505\u0026ndash;1.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.458\u0026ndash;2.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFaculty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngineering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSocial Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.346\u0026ndash;1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.687\u0026ndash;4.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eScience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.380\u0026ndash;1.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.389\u0026ndash;37.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.245\u0026ndash;0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.388\u0026ndash;1.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFather\u0026rsquo;s Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.829\u0026ndash;3.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.794\u0026ndash;4.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.875\u0026ndash;3.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.875\u0026ndash;15.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSecondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.782\u0026ndash;24.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.139\u0026ndash;60.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher secondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.458\u0026ndash;1.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.257\u0026ndash;1.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.498\u0026ndash;1.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.374\u0026ndash;2.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMother\u0026rsquo;s Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.075\u0026ndash;4.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.428\u0026ndash;3.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.387\u0026ndash;1.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.231\u0026ndash;1.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSecondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.163\u0026ndash;4.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.807\u0026ndash;6.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher secondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.373\u0026ndash;1.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.428\u0026ndash;3.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.161\u0026ndash;1.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.212\u0026ndash;4.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly Family Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbove 40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBelow 10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.443\u0026ndash;2.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.537\u0026ndash;5.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10,000\u0026ndash;20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.823\u0026ndash;3.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.802\u0026ndash;38.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20,001\u0026ndash;30,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.765\u0026ndash;3.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.130\u0026ndash;19.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30,001\u0026ndash;40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.741\u0026ndash;3.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.256\u0026ndash;9.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonal Financial Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.352\u0026ndash;0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.130\u0026ndash;0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep on average per night\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than 8 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLess than 5 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.157\u0026ndash;0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.068\u0026ndash;0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u0026ndash;6 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.490\u0026ndash;2.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.387\u0026ndash;2.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u0026ndash;7 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u0026ndash;0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u0026ndash;0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u0026ndash;8 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.010\u0026ndash;0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.009\u0026ndash;0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEngage in physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEvery day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.590\u0026ndash;2.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.281\u0026ndash;2.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOnce a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.362\u0026ndash;1.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.192\u0026ndash;1.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u0026ndash;3 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.180\u0026ndash;0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.241\u0026ndash;1.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u0026ndash;5 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.065\u0026ndash;0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u0026ndash;0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpend sitting (e.g., studying, using a computer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than 9 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLess than 3 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u0026ndash;0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.031\u0026ndash;0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u0026ndash;5 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.016\u0026ndash;0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.028\u0026ndash;0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u0026ndash;7 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.009\u0026ndash;0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.022\u0026ndash;0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u0026ndash;9 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.022\u0026ndash;0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.019\u0026ndash;0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeeling nervous, anxious, or on edge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeveral days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.118\u0026ndash;4.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.324\u0026ndash;10.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than half the days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.445\u0026ndash;31.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.971\u0026ndash;127.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNearly every day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.879\u0026ndash;36.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.321\u0026ndash;123.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot being able to stop or control worrying\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeveral days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.837\u0026ndash;15.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.123\u0026ndash;81.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than half the days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.309\u0026ndash;13.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.164\u0026ndash;75.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNearly every day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.432\u0026ndash;38.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e253.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.703\u0026ndash;1024.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLittle interest or pleasure in doing things\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNearly every day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.081\u0026ndash;0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.019\u0026ndash;0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeveral days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.073\u0026ndash;0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.053\u0026ndash;0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than half the days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.222\u0026ndash;1.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.079\u0026ndash;0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeeling down, depressed, or hopeless\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeveral days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.776\u0026ndash;10.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.994\u0026ndash;46.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than half the days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.138\u0026ndash;18.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.165\u0026ndash;7.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNearly every day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.813\u0026ndash;46.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.008\u0026ndash;204.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003eThis study provides a comprehensive examination of smartphone addiction (SA) among university students in Bangladesh, contributing to the limited literature on this issue in a developing country context. By investigating socio-economic, physical, and psychological factors, the study addresses a critical gap in understanding the multifaceted drivers of SA in a high-risk population. Key findings reveal a high prevalence of SA (63.8% overall, based on 286/450 participants classified as addicted per Table 1), with significant associations across gender, academic year, faculty, parental education, financial dependency, sleep duration, physical activity, sedentary time, and psychological conditions (anxiety, depression, stress). Notably, psychological factors, such as feeling nervous or unable to control worrying nearly every day, emerged as the strongest predictors of SA, with adjusted odds ratios (AORs) as high as 253.395 for worrying. These findings underscore the interplay of socio-economic stressors, lifestyle factors, and mental health in driving SA, offering insights for targeted interventions in university settings.\u003c/p\u003e\n\u003cp\u003eThe article\u0026apos;s reported prevalence of 63.8% is closely aligned with \u0026quot;Smartphone addiction: Physical, Mental and Cultural Aspects of Smartphone Use among Young Adults in Bangladesh\u0026quot; which found 61.4% prevalence among 440 young adults, with data collected between July 2021 and February 2023. This thesis used a mixed-methods approach, including an online survey, and identified socio-demographic predictors like being male (OR=1.79), aged \u0026le;25 (OR=2.19), unemployed (OR=1.95), and belonging to large families (OR=3.44), which resonate with the article\u0026apos;s findings [14]. However, another study, \u0026quot;Understanding the drivers of smartphone addiction among university students: a perspective from Bangladesh,\u0026quot; reported a lower prevalence of 28.4% among 384 students from public and private universities, using unequal stratified random sampling and a structured questionnaire [15]. The article\u0026apos;s higher prevalence aligns more closely with detailed local studies, indicating robustness in the context of Bangladesh\u0026apos;s high smartphone penetration and youth usage patterns. Globally, a systematic review by [16], \u0026quot;Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review\u0026quot; reported a median prevalence of 23.3% (interquartile range 14.0\u0026ndash;31.2%) among children and young people, which the article cites and aligns with for broader comparisons. This suggests the article\u0026apos;s findings are contextually higher, possibly due to the university student focus and Bangladesh\u0026apos;s specific socio-economic conditions.\u003c/p\u003e\n\u003cp\u003eThe article found significant associations between SA and socio-economic factors, such as gender (males 75.5% addicted vs. females 50.7%, \u0026chi;\u0026sup2;=29.944, p\u0026lt;0.001), with females having an 81.2% lower risk (AOR=0.188, p\u0026lt;0.001). This aligns with studies like Arefin et al. (2017), \u0026quot;Impact of Smartphone Addiction on Academic Performance of Business Students: A Case Study\u0026quot; [17], which identified socio-economic factors influencing SA among business students in Bangladesh, particularly highlighting parental education\u0026apos;s role. The article\u0026apos;s finding that lower paternal education (e.g., secondary education, AOR=19.208, p\u0026lt;0.001) increases SA risk is consistent, suggesting limited resources for alternative activities may drive addiction. Financial dependency was a protective factor (AOR=0.258, p\u0026lt;0.001), possibly due to parental oversight, which aligns with global studies like [18], \u0026quot;Smartphone Addiction Prevalence and Its Association on Academic Performance, Physical Health, and Mental Well-Being among University Students in Saudi Arabia\u0026quot; noting employment status impacts addiction. However, monthly family income showed no significant association (\u0026chi;\u0026sup2;=3.927, p=0.416), which contrasts with some studies, suggesting context-specific findings in Bangladesh.\u003c/p\u003e\n\u003cp\u003eThe article highlights strong links between SA and physical conditions, such as sleep duration (e.g., \u0026lt;5 hours, 87.7% addicted, \u0026chi;\u0026sup2;=188.500, p\u0026lt;0.001) and physical activity (e.g., once a week, 77.1% addicted, \u0026chi;\u0026sup2;=33.761, p\u0026lt;0.001). Logistic regression showed counterintuitive findings, with shorter sleep durations associated with lower risk (e.g., \u0026lt;5 hours, AOR=0.181, p=0.001), possibly due to the reference category (\u0026gt;8 hours) capturing compensatory sleep. This aligns with Ratan (2023), which reported addicted individuals had lower quality of life in physical domains and more discomforts (e.g., eye discomfort 57.5%), supporting the article\u0026apos;s findings.\u003c/p\u003e\n\u003cp\u003eRegarding physical activity, while the chi-square analysis indicated a significant association, with those exercising once a week or never being more prone to addiction, the adjusted model presented a nuanced picture. Engaging in physical activity 4-5 times a week was associated with a significantly lower risk of addiction compared to exercising every day. A systematic reviews like [19], \u0026nbsp; linking exercise to reduced SA. High sedentary time (\u0026gt;9 hours, 69.7% addicted) increasing risk (e.g., \u0026lt;3 hours sitting, AOR=0.119, p=0.002) aligns with global trends, as noted in \u0026quot;Smartphone Addiction and Associated Health Outcomes in Adult Populations: A Systematic Review,\u0026quot; reinforcing the article\u0026apos;s focus on lifestyle impacts. This counterintuitive finding suggests that daily intense exercise might not be the most protective, or that a balanced, moderate frequency of physical activity (4-5 times a week) is optimal for mitigating smartphone addiction risk. This contrasts with some studies that found no significant correlation between smartphone addiction and physical activity [20], while others suggest a negative correlation where higher addiction is linked to lower physical activity [21].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eThe amount of time spent sitting also showed a strong association, with extensive sedentary time being a risk factor. The adjusted model confirmed that all categories of sitting less than \u0026quot;more than 9 hours\u0026quot; showed a significantly lower risk, reinforcing that prolonged sedentary behavior is detrimental.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePsychological conditions emerged as exceptionally strong predictors of smartphone addiction risk in this study, aligning with extensive global research [20].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eExperiencing nervousness, anxiety, or being on edge nearly every day dramatically increased the risk of smartphone addiction by over 46 times. The inability to stop or control worrying nearly every day was associated with an astounding 253-fold increased risk. \u0026nbsp;This is strongly supported by [22], reporting OR=3.05 for anxiety and OR=3.17 for depression, and [8], finding 40.7% anxiety and 40.0% depression among addicted individuals. The article\u0026apos;s findings (50.7% anxious, 44.4% depressed) are slightly higher, possibly due to the university student focus and academic pressures, aligning with studies, cited in ResearchGate results, linking SA to higher anxiety and depression. Similarly, experiencing little interest or pleasure, and feeling down, depressed, or hopeless nearly every day, were linked to substantially higher risks (67-fold). These findings strongly support the notion of a \u0026quot;vicious cycle\u0026quot; where psychological distress drives problematic smartphone use as a maladaptive coping mechanism, which in turn exacerbates these negative emotional states [23].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eThis highlights the critical need to address underlying mental health issues when developing interventions for smartphone addiction.\u003c/p\u003e\n\u003cp\u003eBased on our findings, several recommendations can be made. Given the strong predictive power of psychological conditions, interventions should prioritize mental health support for university students, focusing on anxiety, depression, and stress management. This could involve accessible counseling services, stress reduction workshops, and promoting healthy coping mechanisms that do not involve excessive smartphone use. Educational campaigns are crucial to raise awareness about the risks of smartphone addiction, particularly among male students and those in specific academic years or faculties identified as high-risk. Furthermore, promoting balanced lifestyle habits, including adequate sleep hygiene and regular, moderate physical activity, is essential. Universities and policymakers should consider creating environments that encourage offline social engagement and recreational activities, addressing the lack of play areas and recreational facilities mentioned in the Bangladeshi context [24].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eFinally, future research should delve deeper into the nuanced relationships observed, particularly the counterintuitive findings regarding sleep duration and physical activity, possibly by refining measurement tools and exploring mediating factors.\u003c/p\u003e\n\u003cp\u003eThis study, while providing valuable insights, has several limitations. Its cross-sectional design prevents the establishment of causality, meaning we can identify associations but not definitively determine if smartphone addiction causes, or is caused by, the associated factors [24]. The reliance on self-report scales may introduce social desirability bias, where participants might underreport problematic behaviors.\u003csup\u003e\u0026nbsp;\u003c/sup\u003eThe study was conducted within a specific university context in Bangladesh, which may limit the generalizability of the findings to other student populations or the broader general population. The contradictory findings between chi-square associations and adjusted odds ratios for certain socio-economic and lifestyle factors (e.g., family income, personal financial status, sleep duration, physical activity) highlight the complexity of these relationships and suggest the need for further research with more refined methodologies and longitudinal designs to clarify these dynamics. Future research should address these gaps through mixed-methods designs and broader sampling.\u003c/p\u003e"},{"header":"5.\tConclusion","content":"\u003cp\u003eThe examination of smartphone addiction reveals a pervasive and escalating public health concern, particularly among young adults and university students globally. While not yet a formal clinical diagnosis, its behavioral manifestations strikingly mirror those of established addictions, characterized by subjective distress and significant functional impairment rather than merely high usage duration. This nuanced understanding is crucial for accurate assessment and the development of effective interventions. Prevalence rates are notably high among university students worldwide, with studies consistently reporting substantial percentages of students exhibiting problematic smartphone use. This issue profoundly impacts academic performance, leading to lower grades, increased procrastination, and reduced productivity, thereby serving as a critical indicator of functional impairment in this demographic.\u003c/p\u003e\n\u003cp\u003eThe physical and psychological dimensions of smartphone addiction are deeply interconnected. Problematic smartphone use is consistently and robustly linked to poor sleep quality, contributing to sleep disturbances and creating a harmful bidirectional cycle. Crucially, poor sleep exacerbates the psychological consequences of smartphone addiction, particularly anxiety, positioning sleep hygiene as a vital intervention point. While the direct relationship with physical activity remains inconsistent across studies, smartphone addiction is consistently associated with elevated levels of behavioral, sensory, and cognitive fatigue. This suggests fatigue acts as a significant intermediary, impacting overall well-being and warranting greater attention in research.\u003c/p\u003e\n\u003cp\u003ePsychologically, smartphone addiction is strongly correlated with anxiety, depression, stress, and loneliness. This relationship is often bidirectional, with individuals using smartphones as a maladaptive coping mechanism, inadvertently deepening both the addiction and their underlying psychological distress. The social paradox of digital connectivity is particularly striking: while smartphones offer a means of connection, their excessive use can paradoxically lead to increased social isolation and loneliness by diminishing face-to-face interactions. This highlights the need to foster real-world social engagement. Methodological inconsistencies in defining and measuring problematic use versus general screen time, especially concerning loneliness, underscore the need for more refined research approaches. Socio-economic factors present a complex picture. High family income can predict smartphone addiction in some student populations, possibly due to increased access and affordability, while employment status and perceived social mobility also play roles. The influence of these factors appears to be context-dependent, highlighting the need for culturally sensitive investigations.\u003c/p\u003e\n\u003cp\u003eIn Bangladesh, smartphone addiction among university students is a significant concern, with prevalence rates comparable to or higher than global averages. Unique socio-economic and cultural factors, such as increased wealth, limited recreational facilities, and social isolation, contribute to this issue. The reported consequences include severe impacts on academic performance, daily activities, and physical health symptoms like eye strain and headaches. Collectively, these findings underscore the urgent need for comprehensive, multi-faceted interventions. Future research should focus on clarifying the intricate relationships between socio-economic factors, lifestyle habits, and psychological conditions, particularly within specific cultural contexts like Bangladesh. Developing targeted strategies that address not only smartphone use patterns but also underlying psychological vulnerabilities, promote healthy lifestyle choices, and foster real-world social connections will be essential for mitigating the adverse effects of smartphone addiction and improving the overall well-being of university students.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any funding support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the study participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization-M.A.M.R. and M.M.H.; methodology- M.A.M.R. and M.Z.M.; formal analysis M.A.M.R. and S.H.; original draft preparation- N.I.M, S.H, M.I.I.R and M.Z.M; writing- M.A.M.R., S.H., M.M.H and M.Z.M.; SPSS and testing- M.A.M.R and N.I.M.; review and editing- N.I.M, M.I.I.R. All the authors have read and agreed to publish this version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors do not have any conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm their unwavering commitment to ethical standards, ensuring that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2013. The study protocol was approved by the Ethical Committee of Noakhali Science and Technology University (Reference no: NSTU/SCI/EC/ 2025/453). Informed consent was obtained from all individual participants included in the study, reaffirming the Authors\u0026apos; commitment to transparency and ethical conduct.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n \u003cli\u003eMohd Salleh Sahimi H, Norzan MH, Nik Jaafar NR, Sharip S, Ashraf A, Shanmugam K, Bistamam NS, Mohammad Arrif NE, Kumar S, Midin M (2022) Excessive smartphone use and its correlations with social anxiety and quality of life among medical students in a public university in Malaysia: A cross-sectional study. Front Psychiatry 13:956168\u003c/li\u003e\n \u003cli\u003eAzizi A, Emamian MH, Hashemi H, Fotouhi A (2024) Smartphone addiction in Iranian schoolchildren: a population-based study. Sci Rep 14:22304\u003c/li\u003e\n \u003cli\u003eZhang K, Guo H, Wang T, et al (2023) A bidirectional association between smartphone addiction and depression among college students: A cross-lagged panel model. Front Public Health 11:1083856\u003c/li\u003e\n \u003cli\u003eSohn S, Rees P, Wildridge B, Kalk NJ, Carter B (2019) Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry 19:356\u003c/li\u003e\n \u003cli\u003eAbuhamdah SMA, Naser AY (2023) Smart phone addiction and its mental health risks among university students in Jordan: a cross-sectional study. BMC Psychiatry 23:1\u0026ndash;9\u003c/li\u003e\n \u003cli\u003eAy MK, Topuzoğlu A, Altaş ZM (2024) Smartphone addiction and depression among health sciences students during COVID-19 pandemic. BMC Public Health 24:1\u0026ndash;13\u003c/li\u003e\n \u003cli\u003eSoni R, Upadhyay R, Jain M (2017) Prevalence of smart phone addiction, sleep quality and associated behaviour problems in adolescents. Int J Res Med Sci 5:515\u0026ndash;519\u003c/li\u003e\n \u003cli\u003eRatan ZA, Parrish AM, Alotaibi MS, Hosseinzadeh H (2022) Prevalence of Smartphone Addiction and Its Association with Sociodemographic, Physical and Mental Well-Being: A Cross-Sectional Study among the Young Adults of Bangladesh. Int J Environ Res Public Health 19:16583\u003c/li\u003e\n \u003cli\u003eDesouky DES, Abu-Zaid H (2020) Mobile phone use pattern and addiction in relation to depression and anxiety. Eastern Mediterranean Health Journal 26:692\u0026ndash;699\u003c/li\u003e\n \u003cli\u003eRatan ZA, Parrish AM, Alotaibi MS, Hosseinzadeh H (2025) Predictors of smartphone addiction and its effect on quality of life: a cross-sectional study among the young adults in Bangladesh. Front Digit Health 7:1351955\u003c/li\u003e\n \u003cli\u003eAdam AM (2020) Sample Size Determination in Survey Research. J Sci Res Rep 90\u0026ndash;97\u003c/li\u003e\n \u003cli\u003eKwon M, Kim DJ, Cho H, Yang S (2013) The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents. PLoS One 8:e83558\u003c/li\u003e\n \u003cli\u003eAssociation WM (2013) World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA 310:2191\u0026ndash;2194\u003c/li\u003e\n \u003cli\u003eRatan ZA (2025) Smartphone addiction: Physical, Mental and Cultural Aspects of Smartphone Use among Young Adults in Bangladesh.\u003c/li\u003e\n \u003cli\u003eMahmud A, Islam MR (2023) Understanding the drivers of smartphone addiction among university students: a perspective from Bangladesh. SN Social Sciences 3:1\u0026ndash;21\u003c/li\u003e\n \u003cli\u003eSohn S, Rees P, Wildridge B, Kalk NJ, Carter B (2019) Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry 19:1\u0026ndash;10\u003c/li\u003e\n \u003cli\u003eArefin MdS, Islam Md, Mustafi M, Afrin S, Islam N (2018) Impact of Smartphone Addiction on Academic Performance of Business Students: A Case Study. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3236301\u003c/li\u003e\n \u003cli\u003eAlotaibi MS, Fox M, Coman R, Ratan ZA, Hosseinzadeh H (2022) Smartphone Addiction Prevalence and Its Association on Academic Performance, Physical Health, and Mental Well-Being among University Students in Umm Al-Qura University (UQU), Saudi Arabia. Int J Environ Res Public Health 19:3710\u003c/li\u003e\n \u003cli\u003eRatan ZA, Parrish AM, Zaman S Bin, Alotaibi MS, Hosseinzadeh H (2021) Smartphone addiction and associated health outcomes in adult populations: A systematic review. Int J Environ Res Public Health 18:12257\u003c/li\u003e\n \u003cli\u003eMayerhofer D, Haider K, Amon M, G\u0026auml;chter A, O\u0026rsquo;Rourke T, Dale R, Humer E, Probst T, Pieh C (2024) The Association between Problematic Smartphone Use and Mental Health in Austrian Adolescents and Young Adults. Healthcare 12:600\u003c/li\u003e\n \u003cli\u003eQin X, Liu L, Yan Y, Guo X, Yang N, Li L (2025) Smartphone addiction and sleep quality in the physical activity-anxiety link: a mediation-moderation model. Front Public Health 13:1512812\u003c/li\u003e\n \u003cli\u003eSohn S, Rees P, Wildridge B, Kalk NJ, Carter B (2019) Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry 19:1\u0026ndash;10\u003c/li\u003e\n \u003cli\u003eAlbursan IS, Mohammad MF, Al-Barashdi HS, et al (2022) Smartphone Addiction among University Students in Light of the COVID-19 Pandemic: Prevalence, Relationship to Academic Procrastination, Quality of Life, Gender and Educational Stage. Int J Environ Res Public Health 19:10439\u003c/li\u003e\n \u003cli\u003eERG\u0026Ouml;N\u0026Uuml;L E, KESKİN T, ERGAN M, BAŞKURT F, BAŞKURT Z (2024) The Impact of Smartphone Addiction on Physical Activity, Fatigue, and Sleep Quality among Rural Health Science Students. https://doi.org/10.21203/RS.3.RS-5341122/V1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Smartphone addiction, university students, Bangladesh, socio-economic factors, psychological conditions, sleep, physical activity","lastPublishedDoi":"10.21203/rs.3.rs-9424184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9424184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSmartphone addiction (SA), characterized by excessive and uncontrolled use, poses significant physical, psychological, and social challenges, particularly among university students. In Bangladesh, where SA prevalence reaches 61.4% among young adults, socio-economic stressors, lifestyle factors, and mental health issues amplify risks. This study investigates SA prevalence and its associations with socio-economic, physical, and psychological conditions among university students, addressing a critical gap in developing country contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional survey was conducted at Noakhali Science and Technology University, Bangladesh, involving 450 students from four faculties. Data were collected using a structured questionnaire, including the Smartphone Addiction Scale-Short Version (SAS-SV) for SA (cutoffs: ≥31 males, ≥33 females), and self-reported measures for sleep, physical activity, sedentary behavior, and psychological conditions (anxiety, depression, stress, worry). Chi-square tests and logistic regression analyzed associations and predictors, with p\u0026lt;0.05 for significance, using SPSS v27.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e SA prevalence was 63.8% (286/450), with males (75.5%) showing higher rates than females (50.7%, AOR=0.19, p\u0026lt;0.001). Significant associations were found with academic year (4th-year: 75.7%, χ²=16.63, p=0.002), faculty (Social Science: 72.7%, χ²=15.59, p=0.001), paternal education (secondary: AOR=19.21, p\u0026lt;0.001), financial dependency (AOR=0.26, p\u0026lt;0.001), sleep (\u0026lt;5 hours: 87.7% addicted, χ²=188.50, p\u0026lt;0.001), physical activity (4–5 times/week: AOR=0.07, p\u0026lt;0.001), and psychological conditions (e.g., anxiety nearly every day: AOR=46.34, p\u0026lt;0.001; worry: AOR=253.40, p\u0026lt;0.001). Psychological factors were the strongest predictors, explaining 49.5–67.8% of SA variance. Findings align with local studies (61.4% prevalence) but exceed global estimates (23.3%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHigh Smartphone addiction prevalence among Bangladeshi university students is driven by psychological distress, socio-economic constraints, and lifestyle factors. Interventions should prioritize mental health support, promote moderate physical activity, and address socio-economic stressors through awareness and counseling. Longitudinal studies are needed to clarify causal pathways.\u003c/p\u003e","manuscriptTitle":"Prevalence of Smartphone Addiction and Its Association with Socio-economic, Physical, and Psychological Conditions: A Cross-Sectional Study among University of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 15:05:34","doi":"10.21203/rs.3.rs-9424184/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9be21551-4b78-4da7-8d4f-23be40b8ce25","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T15:05:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 15:05:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9424184","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9424184","identity":"rs-9424184","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}