Assessing the quality of life of university students during COVID-19 lockdown: A structural equation modelling approach

Assessing the quality of life of university students during COVID-19 lockdown: A structural equation modelling approach | 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 Assessing the quality of life of university students during COVID-19 lockdown: A structural equation modelling approach Tanjirul Islam, Md. Ashfikur Rahman, Tayeeba Tabussum Anni, Israt Jahan Juie, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4219581/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 Purpose In the face of the upsurge of the COVID-19 pandemic global students including those of Bangladesh are forced to go into distance learning mode due to the lockdown or social isolation that is being imposed. The present study was intended to evaluate the impact of distance education on the quality of life (QOL) among Bangladeshi university students that are exerted due to the COVID-19 lockdown. Methods We used the World Health Organization Quality of Life (WHOQOL)-Bref questionnaire which was distributed among students from four universities in Bangladesh using electronic platforms such as WhatsApp, Facebook, and Email. The scores of the WHOQOL-Bref were converted into a linear scale from 0 to 100 and recorded (0–70) as low/moderate quality of life whereas (≥ 70) were coded as high quality of life. Results The study obtained an excellent internal consistency of WHOQOL-Bref (α = 0.878). The mean QOL (0-100) among the participants was 78.29 ± 11.59 with a median of (73.35, IQR: 42.53–88.30). All domains showed a strong to moderate correlation with the overall quality of life score. The domain most affected by isolation was the psychological domain, and the social relationship domain showed the weakest correlation with the overall quality of life scores. In the regression analysis, factors such as increased Internet use, watching more TV, participating in classes with zooms, and residing with a family of more than three members were found to be associated with having a good quality of life. Conclusions The study pointed out that, while there are no alternatives to keep the educational system functioning thus distance learning during this overwhelming COVID-19 situation, more interactive platforms such as Zoom, the promotion of more internet and television use can be of value to retain the good quality of life among the students in this overwhelming condition. Lockdown Quality of Life COVID-19 University Students Bangladesh Figures Figure 1 Figure 2 Introduction The emergence of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), commonly known as COVID-19 has put the world health system including those of community living and education in crisis [ 1 – 3 ]. It has affected almost all the continents and it was first declared as a pandemic on 11 march 2020 by the World Health Organization (WHO)[ 4 ]. As a result, to get control over COVID-19, movement restriction was implied worldwide as containment measure is one of the most preferred ways to reduce the effect of this predicament to ratify the coronavirus[ 5 ]. Soon after COVID-19 was declared as a pandemic local government imposed lockdown to restrict public movement except in emergency situation to flatten the COVID-19 infection curve[ 6 ]. Worldwide, education institutions are being forced to shut down to halt the spread of coronavirus. According to United Nations Educational, Scientific and Cultural Organization (UNESCO), there are around 1.2 billion students around the world have been affected by school and university shut down due to the COVID-19 pandemic[ 7 ]. This made the governing bodies think of alternative ways of teaching during the pandemic situation. Which ultimately makes the way for web-based learning or e-learning or distance learning to move from traditional offline teaching practices[ 8 ]. Mental health and physical health problems due to excess stress is a very common concern of student life and this impeding crisis has worsened the situation to a great extent[ 9 ]. In higher education students’ health behaviors are often negatively influenced by stressful events[ 10 ]. Earlier studies established that profound health behaviors often triggered by large-scale emergencies i.e. COVID-19 pandemic[ 9 , 11 ]. There are some other studies that also support the cause of increasing mental health distress during emergencies [ 12 – 14 ]. Most of the university students are at risk of elevated stress and mental health concerns as they are experiencing disruption of their established daily schedules, the current study sought a large amount of concentration, focus, and determinations which is very hard to come by in this stressful situation[ 15 , 16 ]. Social isolation has a major negative impact on quality of life, and technology can help to alleviate this discomfort however, the efficiency and effects of distance learning on the quality of life are questionable. To the best of our knowledge, there are no studies carried out in Bangladesh exploring the impact of COVID-19 on the quality of life among university students. In light of this, the present study was an endeavour to evaluate the impact of lockdown on the quality of life (QOL) of Bangladeshi university students exerted due to the COVID-19. Methods Data source and ethical considerations The study followed a cross-sectional design, which involved the anonymous opinion of the university students from seven higher education institutions of Bangladesh. Data were collected through an online e-questionnaire (Google® Forms) from 25th November to 25th December 2020. This research project was approved by the authority of Development Studies Discipline, Khulna University, Bangladesh. It is notable to mention that, in order to reduce the transmission of coronavirus (COVID-19) among the students, all the educational institutions were closed from 17th March to till the study period in Bangladesh. The survey was administered among the university students through using different social media platforms such as Messenger, WhatsApp, and Email. The inclusion criteria were: Willing to participate in the study and being a university student. Research instruments The previously validated World Health Organization Quality of Life (WHOQOL)-Bref questionnaire used in the current study to measure the quality of life of university students. The questionnaire was incorporated with four blocks of questions where each of the blocks contains different measurements of questions related to study aims. Block 1, contains background characteristics of the students such as; age, sex, semester, shift, and extracurricular activities prior to COVID-19 and block 2, contains questions regarding study practices prior to and during COVID-19, as well as the medium of instructions of distance learning (TV, Smartphone, Zoom, streaming media, etc.) amid COVID-19. Block 3, contains questions regarding accessibility and activities performed during COVID-19 while block 4 contains the questions of the WHOQOL-Bref questionnaire. The WHOQOL-Bref is an instrument developed by the WHO in 1998 to measure the quality of life through a shortened version of a longer pre-existing questionnaire, called the WHOQOL-100[ 25 ]. WHOQOL-Bref comprises 26 questions that can be answered using a five-point Likert scale, of which 24 questions cover four domains (physical health, psychological well-being, social relationships and environment), and the other two items measure self-assessed quality of life[ 26 ]. All the questions in the survey were mandatory, therefore, without answering any items students were not able to move in the next questions. The English version of the questionnaire was first translated to Bengali language and then back translated to English by two researchers to ensure the contents' consistency. The questionnaire was then piloted among a small sample (n = 10) of university students to refine the language in the final version. The contents of the questionnaire were approved by the participants from a pilot study without requiring any changes in wording or sentence structure. Statistical analysis Data from the surveys were exported to a Microsoft Excel sheet and encoded where necessary and analyzed using the software Statistical Package for Social Sciences (SPSS.25) Windows version. The scores of the WHOQOL-Bref were converted into a linear scale from 0 to 100 and recoded (0–70) as low/moderate quality of life whereas (≥ 70) were coded as high quality of life. The mean, standard deviations (St. deviation), and Cronbach’s alpha were calculated. The Pearson correlation test was used to measure the association between the items. Two categories (low/moderate quality of life [0–70] and high quality of life [70–100]) were examined with all items of the first three blocks of the questionnaire applying Pearson’s Chi-squared test where the statistically significance set (p < 0.05). The variables which were found significant were taken for the final logistic regression model to analyze each block of items. Assessing the PLS-SEM algorithms model The theoretical outline, which is portrayed according to the SEM tactic, is shown in Fig. 1 . The theoretical framework is primarily concerned with the establishment and integration of physical, psychological, social, and environmental domains. Structural equation modeling (SEM) is designed with the two-phase model (i) measurement model and (ii) structural model. The measurement model depicts the inner relationships between latent variables and observed variables, and the latter, along with the structural model, is used to investigate loadings and estimate indicators[ 27 – 29 ]. The conceptual framework model was run using Smart PLS 2.0, and the structural framework used in the hypothesis testing segment is shown in Fig. 2 . Smart PLS consists of a series of standard metrics, such as loading indicators, composite reliability, average variance extracted (AVE), path coefficients, inner structural correlations, latent variable scores, t values, etc. A structural procedure has been introduced to investigate loading and eliminating indications (loadings < 0.70) was adopted[ 30 ]. In the context of the study, we utilized a reflexive measurement framework. In line with the majority of other behavioral research [ 31 , 32 ], a set of assessment criteria that can quantify the credibility and viability of the PLS-SEM model with recessive criterion was proposed. Which are as follows- Justification of potential endogenous variable’s variance Checking factor loading of Inner-model Justifying loadings of outer model and their significance Indicator reliability Internal consistency reliability Convergent validity Discriminant validity Assessing Structural Path Significance in Bootstrapping Results Social isolation reduces the quality of life of university students Table 1 , shows the reliability analysis of the WHOQOL-Bref of the current study. The mean quality of life of university students after social isolation and lockdown was 78.29 ± 11.59 with a median of 73.35 (IQR 42.53 to 88.30) points. Cronbach's alpha exhibited acceptable internal validity of the construct (α = 0.878), and we observed no exclusion of any domain significantly reduced and increased these values. All domains showed a strong to moderate correlation with the overall quality-of-life score. The domain most affected by isolation was the psychological domain (r = 0.886; α = 0.888; p < 0.001), at the same time the social relationship domain showed the weakest correlation with the overall quality-of-life score (r = 0.630; α = 0.614; p < 0.001) (Table 1 ). Table 1 Reliability analysis, descriptive and inferential analysis of the WHOQOL-Bref among university students during the COVID-19 pandemic Mean ± SD Cronbach’s Alpha Correlation with WHOQOL-Bref WHOQOL-Bref 78.29 ± 11.59 0.878 a D1 Physical Health 71.81 ± 22.03 0.614 b p < 0.001 (r = 0.630) c D2 Psychological 69.97 ± 19.64 0.888 b p < 0.001 (r = 0.886) c D3 Social Relationships 72.19 ± 13.14 0.801 b p < 0.001 (r = 0.762) c D4 Environmental Health 70.60 ± 16.26 0.777 b p < 0.001 (r = 0.860) c D5 Self Perception 62.04 ± 14.23 0.800 b p < 0.001 (r = 0.666) c a Cronbach's alpha. b Cronbach's alpha when excluding the item. c Pearson correlation with the overall domain (D). A total of 533 university students who participated in this current study were staying at home after the COVID-19 lockdown and maintaining social isolation. More than half of the study participants (n = 292, 54.8%) were between the ages of 18 and 21, with a mean age of 21.6 ± 1.9 years. An about two-thirds of them were males (n = 307, 57.6%). Of the total (n = 148, 27.8%) students were in their first year of education. The majority of the students living in a home have more than or equal to three family members in the same home (Table 2 ). Table 2 Sociodemographic characteristics of the study participants Variables Total Quality of Life p -value Up to 70 > 70 Age of Participants 0.712 18–21 292 (54.8) 232 (53.8) 60 (58.8) 22–23 150 (28.1) 123 (28.5) 27 (26.5) ≥ 24 91 (17.1) 76 (17.6) 15 (14.7) Sex of the Participants 0.633 Male 307 (57.6) 257 (59.6) 50 (49.0) Female 226 (42.4) 174 (40.4) 52 (51.0) Semester 0.561 First Year 148 (27.8) 135 (31.3) 13 (12.7) Second Year 116 (21.8) 88 (20.4) 28 (27.5) Third Year 117 (22.0) 100 (23.2) 17 (16.7) Fourth Year 62 (11.6) 41 (9.5) 21 (20.6) Masters 90 (16.9) 67 (15.5) 23 (22.5) Number of Family Member in the Same Home < 0.001 Up to 3 103 (19.3) 80(18.6) 23(22.5) ≥ 3 430 (80.7) 351(81.4) 79(77.5) The study hours of most students before COVID-19 lockdown involved either 2–4 hours (n = 222, 41.7%) or up to 1 hour/d (n = 161, 30.2%) per day. But due to lockdown for COVID-19, the numbers were reversed, with the highest prevalence of study hours up to 1 hour per day (n = 241, 45.2%) and 2–4 hours (n = 148, 27.8%), respectively. Most of the students reported that after COVID-19 lockdown the extent of using internet significantly increased (n = 465, 87.2%), similarly using of cell phones increased notably (n = 429, 80.5%), and watching TV increased reported by (n = 218, 40.9%) (Table 3 ). Table 3 Impact of educational activities on the quality of life of university students before and after social isolation for COVID-19 Variables Total Quality of Life p -value Up to 70 > 70 Study Hour Prior to COVID-19 Lockdown 0 .940 Up to 1h/d 161 (30.2) 129 (29.9) 32 (31.4) From 1 to 2h/d 150 (28.1) 121 (28.1) 29928.4) From 2 to 4h/d 222 (41.7) 181 (42.0) 41 (40.2) Study Hour After COVID-19 Lockdown 0.247 Up to 1h/d 241 (45.2) 197 (45.7) 44 (43.1) From 1 to 2h/d 144 (27.0) 110 (25.5) 34 (33.3) From 2 to 4h/d 148 (27.8) 124 (28.8) 24 (23.5) Study Time During Lockdown 0.306 Reduced 391 (73.4) 322 (74.7) 69 (67.6) Unchanged 74 (13.9) 58 (13.5) 16 (15.7) Increased 68 (12.8) 51 (11.8) 17 (16.7) Internet Use After COVID-19 Lockdown 0.011 Reduced 30 (5.6) 25(5.8) 5(4.9) Unchanged 38 (7.1) 33(7.7) 5(4.9) Increased 465 (87.2) 373(86.5) 92(90.2) Cell Phone Use After COVID-19 Lockdown 0.287 Reduced 43 (8.1) 33(7.7) 10(9.8) Unchanged 61 (11.4) 49(11.4) 12(11.8) Increased 429 (80.5) 349(81.0) 80(78.4) Frequency of Watching TV After COVID-19 Lockdown 0.314 Reduced 122 (22.9) 98(22.7) 24(23.5) Unchanged 193 (36.2) 159(36.9) 34(33.3) Increased 218 (40.9) 174(40.4) 44(43.1) This study examined all students during the lockdown era who attended at least some form of distance learning or used any of the following technologies. A total of (n = 480, 90.1%) students attended virtual classes using online platforms, such as Zoom (Table 4 ). Cell phones (n = 461.86.5%) and computers/laptops (n = 266, 49.9%) were the most commonly used devices for accessing distance educational materials and content. The study environment most often used to access distance education content and materials was the study room (n = 393, 73.7%), followed by the bedroom (n = 356, 66.8%), outdoor (n = 326, 61.2%), dining room (n = 251, 47.1%) and kitchen room (n = 172, 32.3%). Compared to students without attending online classes, the high quality of life was significantly higher among students with virtual classes via online platforms, like Zoom (p = 0.001). The students who have attended distance education activities in the study room were more likely to have a high quality of life than the students who have not done such activities in that study room (p = 0.003) (Table 4 ). Table 4 Effect of distance education on the quality of life of university students during social isolation due to COVID-19 Variables Total Quality of Life p -value Up to 70 > 70 DL through Zoom 0.001 Yes 480 (90.1) 383(88.9) 97(95.1) No 53 (9.9) 48(11.1) 5(4.9) Access to DL through Cell Phone 0.371 Yes 461 (86.5) 370 (85.8) 91 (89.2) No 72 (13.5) 61 (14.2) 11 (10.8) Access to DL at Computer 0.021 Yes 266 (49.9) 213(49.4) 53(52.0) No 267 (50.1) 218(50.6) 49(48.0) Access to DL in the Study Room 0.003 Yes 393 (73.7) 315(73.1) 78(76.5) No 140 (26.3) 116(26.9) 24(23.5) Access to DL in the Bedroom 0.255 Yes 356 (66.8) 283 (65.7) 73 (71.6) No 177 (33.2) 148 (34.3) 29 (28.4) Access to DL in the Dining Room 0.831 Yes 251 (47.1) 202 (46.9) 49 (48.0) No 282 (52.9) 229 (53.1) 53 (52.0) Access to DL in the Kitchen 0.336 Yes 172 (32.3) 135 (31.3) 37 (36.3) No 361 (67.7) 296 (68.7) 65 (63.7) Access to DL in Outdoor 0.621 Yes 326 (61.2) 265 (61.5) 61 (59.8) No 207 (38.8) 166 (38.5) 41 (40.2) In the logistic regression analysis, students who participated in distance education activities via virtual platforms using Zoom (aOR = 2.45, 95% CI: 1.92–3.53, p = 0.003), increased the frequency of using the Internet (aOR = 1.65, 95% CI: 1.48–2.67, p = 0.025), increased watching TV (aOR = 1.14, 95% CI: 1.12–2.09, p = 0.011) and family members (aOR = 1.75, 95% CI: 1.43–2.31, p = 0.015) above three at the same home increased the chances of good quality of life, regardless of other variables (Table 5 ). On the other hand, factors associated with decreasing the chances of a good quality of life include male and second-and fourth-year students. Table 5 Multiple logistic regression analysis of modifying factors on quality of life among university students amid COVID-19 Quality of Life Age of Participants Total AOR (95% CI) p -value 18–21 292 (54.8) 1.23 (0.63–2.39) 0.544 22–23 150 (28.1) 1.21 (0.58–2.51) 0.609 ≥ 24 (RC) 91 (17.1) 1.00 Sex of the Participants Male 307 (57.6) 0.63 (0.39–0.99) 0.047 Female (RC) 226 (42.4) 1.00 Semester First Year (RC) 148 (27.8) 1.00 Second Year 116 (21.8) 0.28 (0.13–0.49) 0.001 Third Year 117 (22.0) 0.96 (0.50–1.85) 0.910 Fourth Year 62 (11.6) 0.48 (0.23–0.98) 0.044 Masters 90 (16.9) 1.64 (0.78–3.41) 0.190 Internet Use After COVID-19 Lockdown Reduced (RC) 30 (5.6) 1.00 Unchanged 38 (7.1) 0.63 (0.13–3.09) 0.569 Increased 465 (87.2) 1.65 (1.48–2.67) 0.025 Cell Phone Use After COVID-19 Lockdown Reduced (RC) 43 (8.1) 1.00 Unchanged 61 (11.4) 0.80 (0.26–2.48) 0.703 Increased 429 (80.5) 1.46 (0.18–1.17) 0.102 Frequency of Watching TV After COVID-19 Lockdown Reduced (RC) 122 (22.9) 1.00 Unchanged 193 (36.2) 0.89 (0.48–1.67) 0.719 Increased 218 (40.9) 1.14 (1.12–2.09) 0.011 DL through Zoom Yes 480 (90.1) 2.45 (1.92–3.53) 0.003 No (RC) 53 (9.9) 1.00 Access to DL at Computer Yes 266 (49.9) 1.21 (0.76–1.92) 0.432 No (RC) 267 (50.1) 1.00 Access to DL in Outdoor Yes 326 (61.2) 0.89 (0.56–1.42) 0.627 No (RC) 207 (38.8) 1.00 Number of Family Member in the Same Home Up to 3 (RC) 103 (19.3) 1.00 > 3 430 (80.7) 1.75 (1.43–2.31) 0.015 PLS-SEM results By observing Fig. 1 , we can quantify the above-mentioned assessment criterion. Quality of life has a coefficient of determination R 2 of 0.73, indicating that the combined effects of Physical Aspects, Physiological Aspects, Social Aspects, and Environmental Aspects accounted for at least 73% of the total. Which is well above the accepted values of 0.6 or higher as recommended[ 33 ]. The internal construction of the framework revealed that social aspects have the most substantial impact on Quality of Life (0.446), followed by Psychological Aspects (0.329), Environmental aspects (0.198) and Physical Aspects (0.237). These outcomes indicate the structural path of all the hypothesized constructs contains significant correlational impacts, which is well above the expected values of 0.2[ 28 , 29 , 33 ]. Since, the relationship between environmental aspects and quality of life (QOL) is very close to the expected value and well passed the threshold value of 0.1, therefore, in the context of the study, we assumed it passed the significance test as suggested[ 32 , 34 ]. As a result, the study concluded that: social and psychological aspects can strongly predict QOL, whereas only physical aspects predict QOL moderately. On the other hand, environmental aspects have a lower impact on quantifying the QOL than other indicators. In order to construct the correlational impacts among the exogenous variables (inner model) with the outer model, we carefully explored the outer model loading of the proposed contract. In addition, as with most other behavioral research, we investigated the reliability and validity of the latent constructs in order to provide a well-established framework. More specifically, we used the criteria of Indicator Reliability, Internal Consistency Reliability, Convergent validity, and Discriminant validity in particular. Table 7 represents that all the indicators contain well above the accepted values of 0.4 and close to the preferred values of 0.7 as suggested[ 35 ]. Table 6 Descriptive statistics of key variables. Constructs Variables Mean Standard deviation Physical Aspects Phy_Q3 2.32 0.82 Phy_Q4 3.89 0.92 Phy_Q10 2.12 0.89 Phy_Q15 3.90 0.78 Phy_Q16 2.15 0.63 Phy_Q17 4.10 1.04 Phy_Q18 2.82 0.93 Psychological Aspects Psy_Q5 4.12 1.45 Psy_Q6 3.94 0.89 Psy_Q7 3.13 1.60 Psy_Q11 2.41 1.11 Psy_Q19 3.09 1.04 Psy_Q26 4.06 0.81 Social Aspects So_Q20 4.01 0.91 So_Q21 3.78 0.79 So_Q22 3.36 0.72 Environmental Aspects En_Q8 3.37 0.94 En_Q9 4.03 0.81 En_Q12 4.12 0.82 En_Q13 4.35 0.72 En_Q14 4.87 0.69 En_Q23 4.31 0.71 En_Q24 4.18 0.67 En_Q25 4.14 0.83 The measurement model has a good fit (see exact values below Table 7 ) as indicated by the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI) above the recommended level of 0.90, and Root Mean Square Error Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR) below the cut-off value of 0.08[ 32 , 33 ]. As the measurement model is now established, we can proceed with the tactics called "Composite Reliability". Interestingly, social science research utilized the measurement of "Cronbach’s alpha" to maintain a satisfactory reliability of the internal model’s consistency, but in PLS-SEM it could provide narrowed views towards the visual construct of a potential measurement model. Therefore, measuring the "Composite Reliability" with higher values than 0.6 should be used as a substitute[ 33 , 36 ]. Table 7 Validity of constructs (Reflective Outer Models) Constructs Variables Loadings Indicator Reliability (λ) Composite reliability AVE Physical Dimensions Phy_Q3 0.693 0.480249 0.732 0.625 Phy_Q4 0.787 0.619369 Phy_Q10 0.898 0.806404 Phy_Q15 0.799 0.638401 Phy_Q16 0.798 0.636804 Phy_Q17 0.759 0.576081 Phy_Q18 0.788 0.620944 Physiological Dimensions Psy_Q5 0.883 0.779689 0.863 0.781 Psy_Q6 0.901 0.811801 Psy_Q7 0.883 0.779689 Psy_Q11 0.855 0.731025 Psy_Q19 0.875 0.765625 Psy_Q26 0.903 0.815409 Social Dimensions So_Q20 0.812 0.659344 0.818 0.725 So_Q21 0.732 0.868624 So_Q22 0.804 0.646416 Environmental dimension En_Q8 0.721 0.519841 0.737 0.642 En_Q9 0.832 0.692224 En_Q12 0.798 0.636804 En_Q13 0.899 0.808201 En_Q14 0.798 0.636804 En_Q23 0.901 0.811801 En_Q24 0.701 0.491401 En_Q25 0.732 0.535824 Model-fit: CFI = 0.94, TLI = 0.94, RMSEA = 0.07, SRMR = 0.07 Table 7 indicates that the proposed construct contains a satisfactory level of internal consistency as all the values of Composite Reliability are higher than 0.6, which reflects that all the three latent variables are well connected for quantifying QOL. The Average Variance Extracted (AVE) of every observed construct has been explored to verify the convergent validity. Table 7 also confirmed that all constructs' AVE scores were observed to be greater than the expected level of 0.5, confirming the potential model's convergent validity. For evaluating the discriminant validity of the construct, the square root of AVE of every indicator should be utilized to measure the discriminant validity. If the value will account for a higher level than the correlational value, the discriminant validity can be maintained[ 37 ]. Table 8 confirmed the entire hypothesized construct has passed the discriminant validity test. Correlational values of Physical aspects, for example, are 0.791, which is significantly higher than values in its column. Table 8 Fornell-Larcker Criterion Analysis for Checking Discriminant Validity Physical Aspects Physical Aspects Physical Aspects Physical Aspects Physical Aspects 0.791 Physiological Aspects 0.723 0.884 Social Aspects 0.604 0.709 0.851 Environmental Aspects 0.587 0.646 0.699 0.801 Finally, the study utilized bootstrapping criterion for measuring the associated t statistics. In this process, an enormous quantity of subsamples (e.g., 5000) is reserved for commencing the novel sample by replacing them for giving a bootstrap standard error, which in return provides an estimated t-values for connotation testing of the structural path. The Bootstrap outcome estimates the normality of data. Using a two-tailed t-test with a significance level of 5%, the path coefficient will be significant if the T-statistics is larger than 1.96. Table 4 and Table 5 indicated that all the estimated t statistics are larger than the expected values of 1.96, which confines that the proposed possessed a good connection between the observed and potential constructs[ 36 , 38 ]. As per all the evaluation, we can conclude that there is a well-established model loading within the outer and inner models with highly significant correlational values. Based upon the analysis, the study fosters some policy recommendations which are demonstrated in the following section. Table 9 Bootstrap results of the model (inner model) T-statistics Physical Aspects → Quality of Life 5.788 Physiological Aspects → Quality of Life 12.986 Social Aspects → Quality of Life 19.027 Environmental Aspects → Quality of Life 6.263 Table 10 Bootstrap results of the model (outer model) Frame/ construct Indicator Total sample estimate Mean of sub-sample Standard error t-Statistics Physical Dimensions Phy_Q3 0.298 0.278 0.0099 15.208 Phy_Q4 0.439 0.409 0.0102 11.043 Phy_Q10 0.398 0.308 0.0075 8.233 Phy_Q15 0.358 0.348 0.0092 14.344 Phy_Q16 0.281 0.261 0.0087 11.089 Phy_Q17 0.346 0.336 0.0076 6.233 Phy_Q18 0.278 0.208 0.0071 5.532 Physiological Dimensions Psy_Q5 0.261 0.261 0.0043 29.129 Psy_Q6 0.246 0.244 0.0093 27.142 Psy_Q7 0.279 0.271 0.0031 22.328 Psy_Q11 0.255 0.245 0.0071 26.491 Psy_Q19 0.319 0.309 0.0040 26.189 Psy_Q26 0.302 0.292 0.0091 21.186 Social Dimensions So_Q20 0.313 0.203 0.0018 28.754 So_Q21 0.409 0.399 0.0094 26.302 So_Q22 0.346 0.246 0.0031 29.062 Environmental dimension En_Q8 0.234 0.234 0.0018 11.038 En_Q9 0.345 0.345 0.0071 10.602 En_Q12 0.166 0.166 0.0031 9.223 En_Q13 0.419 0.403 0.0018 8.766 En_Q14 0.373 0.334 0.0071 7.402 En_Q23 0.289 0.289 0.0092 6.030 En_Q24 0.357 0.341 0.0091 4.089 En_Q25 0.323 0.311 0.0057 3.089 Table 11 Parameters of the structural model Hypothesis Total sample estimate Mean of sub-sample Standard error t-Statistics Notes Physical Dimensions-Quality of Life 0.679 0.606 0.039 10.240 Accepted Physiological Dimensions-Quality of life 0.874 0.814 0.066 25.411 Accepted Social Dimensions-Quality of life 0.823 0.812 0.049 28.039 Accepted Environmental dimension-Quality of Life 0.683 0.894 0.089 7.530 Accepted Discussion This cross-sectional study was carried out among the university students to explore the distance learning and relevant quality of life amid the COVID-19 pandemic. This study not only provides a detailed examination of an unexplored learning technique used during the COVID-19 pandemic, but also identifies some important areas for further research relevant to the quality of distance education. A total of 533 students from four universities participated in the online survey and filled-up the questionnaire while (n = 480, 90.1%) students reported they had attended distance learning using online platforms. Overall, we found that the domain most affected by isolation was the psychological domain, and the social relationship domain showed the lowest correlation with the overall quality of life scores. In the regression analysis, factors such as increased Internet use, watching more TV, participating in classes with zooms and residing with a family more than three members were found to be associated with having a good quality of life. Evidence suggests that a long time of social isolation and prolonged lockdown hampers mental well-being[ 39 ]. Because of confinement in the family for long period may lead to conflicts among family members leading to anger and boredom which can interfere with good quality of life. In our study, the psychological domain had the strongest influence on the quality of life, while the physical health domain had the weakest association with the overall quality of life scores, indicating that social isolation has a significant impact on the psychological profile. This is because when people undergoing 10 days of social quarantine or isolation are often affected by psycho-emotional changes in mental health[ 40 ]. In our study, 80.86% of students had a low/moderate quality of life, due to social confinement, which is similar to the findings of a previous study carried out in Brazil[ 39 ]. The growing number of COVID-19 cases around the world, including Bangladesh, is instilling fear not only among students but also among the general public which has a direct effect on the quality of life[ 41 ]. A recent study carried out in Bangladesh also demonstrated significant fear of COVID-19 among the people in Bangladesh[ 42 ]. Therefore, as demonstrated in this study, the psychological domains of WHOQOL-Bref (most affected domain) may be related to the fear of COVID-19 among the students. Previous research has shown that social isolation and prolonged lockdown have negative effects on children and adolescents [ 43 ]. But to reduce virus transmission among students, the government of Bangladesh had to shut all educational institutions and thereby encouraged distance learning via online platforms. As a result, there was enough spare time among the students to use cellphones and the Internet, leading to alleviating unhappiness in life and better interacting with friends, classmates, and society[ 44 ]. The current study also found that good quality of life was significantly associated with several factors such as increased Internet use, viewing of more TV, participating classes using zoom platform and living with a family with more than three members. The zoom platform is sophisticated and possesses advanced interaction mechanisms to connect people, even over long distances, effectively improve social communication, minimize distance, and encourage interaction between students and teachers[ 45 ]. Also, when using the Internet on Facebook or via social media, students are in touch with their friends to feel like they are not alone and to relieve their unhappiness[ 46 ]. At the same time, watching various TV shows is linked with a lower risk of feeling lonely for an extended period of time would be an effective way for students to get away from the doldrums exerted due to isolation and lockdown[ 47 ]. Students living in a house where more family members reside exhibited to have a good quality of life, this would be because, in these circumstances, people can talk and chat face-to-face, they can play several indoor games and gossip is an effective way to get them away from loneliness[ 48 , 49 ]. The strengths and limitations of this current study must be acknowledged. First, this is the first study carried out in Bangladesh to measure the quality of life of university students during the COVID-19 lockdown. Second, appropriate statistical techniques we applied to measure the WHOQOL-BREF, while the advanced SEM model we incorporated in the current study. Despite its strengths, the first limitation is that we designed this study to be cross-sectional, which means that causal relationships were not established. Furthermore, non-random sampling and online technique limit the generalizability of the findings. In addition, we had done the study with a comparatively smaller sample within a short period of time so a detailed study is further recommended. Despite our short-time findings, we suggest that future research with a longer isolation period be conducted in order to determine the true impact of significant confinement such as is occurring today. Conclusion Due to the overwhelming COVID-19 pandemic, the education sector is highly affected while students around the world undergoing social isolation and lockdown on a global scale and forced to adopt distance learning approach. The present study highlighted that, while distance education is recommended during this overwhelming situation, it should be delivered using interactive platforms such as zoom to improve quality of life. Also, the use of the internet and TV viewing needs to be promoted among the students to preserve the quality of life amid this pandemic with distance learning in place. Declarations Funding: We didn’t receive any funding for this current study. Competing interests: No potential conflict of interest was reported by the author(s). Availability of data and materials: The data of the study is not publicly sharable, but can be given upon appropriate request. Code availability: Not Applicable Ethical approval and consent: This study was approved by Development Studies Discipline, Khulna University. Informed consent was gained from all individual participants included in the study. Consent for publications: Not Applicable. Author’s contribution Conceptualization: Md. Ashfikur Rahman; Tanjirul Islam. Data curation: Md. Ashfikur Rahman; Tanjirul Islam. Formal analysis: Md. Ashfikur Rahman; Mortuja Mahmud Tohan; Tanjirul Islam. Methodology: Sakib Al Hassan; Md Amirul Islam; Abdul Elah Al-Mahde. Software: Md. Ashfikur Rahman; Mortuja Mahmud Tohan. Supervision: Md. Ashfikur Rahman Validation: Tanjirul Islam; Md Amirul Islam Writing – original draft: Md. Ashfikur Rahman; Tanjirul Islam; Tayeeba Tabussum Anni; Israt Jahan Juie; Abdul Elah Al-Mahde; Sakib Al Hassan; Md Amirul Islam; Mortuja Mahmud Tohan Writing – review & editing: Md. Ashfikur Rahman; Tanjirul Islam; Tayeeba Tabussum Anni; Israt Jahan Juie; Abdul Elah Al-Mahde; Sakib Al Hassan; Md Amirul Islam; Mortuja Mahmud Tohan References Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet. 2020;395: 1225–1228. Unted Nations. Policy Brief : Education during COVID-19 and beyond. 2020. Schleicher A. COVID-19 ON EDUCATION INSIGHTS FROM GLANCE 2020. 2020. World Health Organization (WHO). Director-General’s opening remarks at the media briefng on COVID19 -March 2020. De Brouwer E, Raimondi D, Moreau Y. Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries. medRxiv. 2020. Shammi M, Bodrud-Doza M, Islam ARMT, Rahman MM. Strategic assessment of COVID-19 pandemic in Bangladesh: comparative lockdown scenario analysis, public perception, and management for sustainability. Environ Dev Sustain. 2020; 1–44. UNESCO. COVID-19 Educational Disruption and Response. 2020 [cited 18 Apr 2021]. Available: https://en.unesco.org/news/covid-19-educational-disruption-and-response Radha R, Mahalakshmi K, Kumar VS, Saravanakumar AR. E-Learning during lockdown of Covid-19 pandemic: A global perspective. Int J Control Autom. 2020;13: 1088–1099. Zhou L, Wu S, Zhou M, Li F. 'School’s Out, But Class’ On’, The Largest Online Education in the World Today: Taking China’s Practical Exploration During The COVID-19 Epidemic Prevention and Control As an Example. But Class’ On’, Larg Online Educ World Today Tak China’s Pract Explor Dur COVID-19 Epidemic Prev Control As an Ex (March 15, 2020). 2020. Ali M, Allihyani M, Abdulaziz A, Alansari S, Faqeh S, Kurdi A, et al. What just happened? Impact of on-campus activities suspension on pharmacy education during COVID-19 lockdown–A students’ perspective. Saudi Pharm J. 2021;29: 59–66. Rapanta C, Botturi L, Goodyear P, Guàrdia L, Koole M. Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity. Postdigital Sci Educ. 2020;2: 923–945. Alam MA. Challenges and Possibilities of Online Education during Covid-19. 2020; 12–14. doi:10.20944/preprints202006.0013.v1 Houlden S, Veletsianos G. Coronavirus pushes universities to switch to online classes—but are they ready. Conserv. 2020. Ali A, Ramay MI, Shahzad M. Key factors for determining student satisfaction in distance learning courses: A study of Allama Iqbal Open University (AIOU) Islamabad, Pakistan. Turkish Online J Distance Educ. 2011;12: 114–127. doi:10.17718/tojde.10766 Abel R. Implementing best practices in online learning. Educ Q. 2005;28: 75–77. Crawford J, Butler-Henderson K, Rudolph J, Malkawi B, Glowatz M, Burton R, et al. COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. J Appl Learn Teach. 2020;3: 1–20. Huckins JF, DaSilva AW, Wang W, Hedlund E, Rogers C, Nepal SK, et al. Mental health and behavior of college students during the early phases of the COVID-19 pandemic: longitudinal smartphone and ecological momentary assessment study. J Med Internet Res. 2020;22: e20185. Du C, Zan MCH, Cho MJ, Fenton JI, Hsiao PY, Hsiao R, et al. Health behaviors of higher education students from 7 countries: poorer sleep quality during the COVID-19 pandemic predicts higher dietary risk. Clocks & sleep. 2021;3: 12–30. Mattioli A V, Sciomer S, Cocchi C, Maffei S, Gallina S. Quarantine during COVID-19 outbreak: Changes in diet and physical activity increase the risk of cardiovascular disease. Nutr Metab Cardiovasc Dis. 2020;30: 1409–1417. Vlahov D, Galea S, Ahern J, Resnick H, Kilpatrick D. Sustained increased consumption of cigarettes, alcohol, and marijuana among Manhattan residents after September 11, 2001. Am J Public Health. 2004;94: 253–254. Neria Y, Olfson M, Gameroff MJ, Wickramaratne P, Gross R, Pilowsky DJ, et al. The mental health consequences of disaster-related loss: findings from primary care one year after the 9/11 terrorist attacks. Psychiatry Interpers Biol Process. 2008;71: 339–348. Bromet EJ, Atwoli L, Kawakami N, Navarro-Mateu F, Piotrowski P, King AJ, et al. Post-traumatic stress disorder associated with natural and human-made disasters in the World Mental Health Surveys. Psychol Med. 2017;47: 227. Murphy MH, Carlin A, Woods C, Nevill A, MacDonncha C, Ferguson K, et al. Active students are healthier and happier than their inactive peers: the results of a large representative cross-sectional study of university students in Ireland. J Phys Act Heal. 2018;15: 737–746. Ji M, An R, Qiu Y, Guan C. The Impact of Natural Disasters on Dietary Intake. Am J Health Behav. 2020;44: 26–39. Group W. Development of the World Health Organization WHOQOL-BREF quality of life assessment. Psychol Med. 1998;28: 551–558. Fleck M, Lousada S, Xavier M, Chachamovich E, Vieira G, Santos L, et al. Application of the portuguese version of the instrument for the assessment of the quality of life of the World Health Organization (WHOQOL-100). Rev Saude Publica. 1999;33: 198–205. Wong KK-K. Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark Bull. 2013;24: 1–32. Sarkar A, Qian L, Peau AK. Structural equation modeling for three aspects of green business practices: a case study of Bangladeshi RMG’s industry. Environ Sci Pollut Res. 2020;27: 35750–35768. Sarker T, Sarkar A, Rabbany MG, Barmon M, Roy R, Rahman MA, et al. Evaluation of preventive, supportive and awareness building measures among international students in China in response to COVID-19: a structural equation modeling approach. Glob Heal Res Policy. 2021;6: 1–10. Black WC, Babin BJ, Anderson RE. Multivariate data analysis: A global perspective. Pearson; 2010. May D, Arancibia S, Behrendt K, Adams J. Preventing young farmers from leaving the farm: Investigating the effectiveness of the young farmer payment using a behavioural approach. Land use policy. 2019;82: 317–327. Munim ZH, Noor T. Young people’s perceived service quality and environmental performance of hybrid electric bus service. Travel Behav Soc. 2020;20: 133–143. Hair JF, Sarstedt M, Pieper TM, Ringle CM. The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plann. 2012;45: 320–340. Anderson JC, Gerbing DW. Structural equation modeling in practice: A review and recommended two-step approach. Psychol Bull. 1988;103: 411. Hulland J. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strateg Manag J. 1999;20: 195–204. Bagozzi RP, Yi Y. On the evaluation of structural equation models. J Acad Mark Sci. 1988;16: 74–94. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18: 39–50. Abdi H. Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev Comput Stat. 2010;2: 97–106. Silva PG de B, de Oliveira CAL, Borges MMF, Moreira DM, Alencar PNB, Avelar RL, et al. Distance learning during social seclusion by COVID-19: Improving the quality of life of undergraduate dentistry students. Eur J Dent Educ. 2020; 1–11. doi:10.1111/eje.12583 Hawryluck L, Gold WL, Robinson S, Pogorski S, Galea S, Styra R. SARS control and psychological effects of quarantine, Toronto, Canada. Emerg Infect Dis. 2004;10: 1206. Schoene D, Heller C, Aung YN, Sieber CC, Kemmler W, Freiberger E. A systematic review on the influence of fear of falling on quality of life in older people: is there a role for falls? Clin Interv Aging. 2019;14: 701. Mistry SK, Ali ARMM, Akther F, Yadav UN, Harris MF. Exploring fear of COVID-19 and its correlates among older adults in Bangladesh. 2021; 1–9. Wang G, Zhang Y, Zhao J, Zhang J, Jiang F. Mitigate the effects of home confinement on children during the COVID-19 outbreak. Lancet. 2020;395: 945–947. Hawton A, Green C, Dickens AP, Richards SH, Taylor RS, Edwards R, et al. The impact of social isolation on the health status and health-related quality of life of older people. Qual Life Res. 2011;20: 57–67. Redondo T. The digital economy: Social interaction technologies–an overview. 2015. Guo H. Linking loneliness and use of social media. University of Helsinki Faculty. 2018. Teh JKL, Tey NP. Effects of selected leisure activities on preventing loneliness among older Chinese. SSM - Popul Heal. 2019;9: 100479. doi:10.1016/j.ssmph.2019.100479 Rahman MS, Rahman MA, Ali M, Rahman MS, Maniruzzaman M, Yeasmin MA, et al. Determinants of depressive symptoms among older people in Bangladesh. J Affect Disord. 2020;264. doi:10.1016/j.jad.2019.12.025 Mistry SK, Ali ARMM, Hossain MB, Yadav UN, Ghimire S, Rahman MA, et al. Exploring depressive symptoms and its associates among Bangladeshi older adults amid COVID-19 pandemic: findings from a cross-sectional study. Soc Psychiatry Psychiatr Epidemiol. 2021. doi:10.1007/s00127-021-02052-6 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-4219581","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301459790,"identity":"31c81c52-67b2-4bfb-868b-63508c8c6584","order_by":0,"name":"Tanjirul Islam","email":"data:image/png;base64,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","orcid":"","institution":"Khulna University","correspondingAuthor":true,"prefix":"","firstName":"Tanjirul","middleName":"","lastName":"Islam","suffix":""},{"id":301459791,"identity":"92b91d61-76ab-4e38-92cc-248c6500c040","order_by":1,"name":"Md. Ashfikur Rahman","email":"","orcid":"","institution":"Khulna University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Ashfikur","lastName":"Rahman","suffix":""},{"id":301459792,"identity":"c86c6a58-49c3-42c1-b966-921cfcd6d24d","order_by":2,"name":"Tayeeba Tabussum Anni","email":"","orcid":"","institution":"Khulna University","correspondingAuthor":false,"prefix":"","firstName":"Tayeeba","middleName":"Tabussum","lastName":"Anni","suffix":""},{"id":301459793,"identity":"276c1d4f-be30-4cb5-bb96-791764454d96","order_by":3,"name":"Israt Jahan Juie","email":"","orcid":"","institution":"Khulna University","correspondingAuthor":false,"prefix":"","firstName":"Israt","middleName":"Jahan","lastName":"Juie","suffix":""},{"id":301459794,"identity":"67401998-989b-4afa-be6b-a15ed71ba4d7","order_by":4,"name":"Abdul Elah Al-Mahde","email":"","orcid":"","institution":"Khulna University","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"Elah","lastName":"Al-Mahde","suffix":""},{"id":301459795,"identity":"7317a959-b730-4642-ab59-f4ddf0b1a369","order_by":5,"name":"Sakib Al Hassan","email":"","orcid":"","institution":"Khulna University","correspondingAuthor":false,"prefix":"","firstName":"Sakib","middleName":"Al","lastName":"Hassan","suffix":""},{"id":301459797,"identity":"312d8618-76ba-4727-a602-3bdaafb75114","order_by":6,"name":"Md Amirul Islam","email":"","orcid":"","institution":"Khulna University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Amirul","lastName":"Islam","suffix":""},{"id":301459798,"identity":"48169140-ea0c-4ad9-b395-b0a9939a6711","order_by":7,"name":"Mortuja Mahmud Tohan","email":"","orcid":"","institution":"Khulna University","correspondingAuthor":false,"prefix":"","firstName":"Mortuja","middleName":"Mahmud","lastName":"Tohan","suffix":""}],"badges":[],"createdAt":"2024-04-04 19:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4219581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4219581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56547926,"identity":"587e58ae-0693-4692-a05d-ddad46d87986","added_by":"auto","created_at":"2024-05-15 15:39:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48822,"visible":true,"origin":"","legend":"\u003cp\u003eProposed SEM model\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4219581/v1/07c38c28c7478b57b4240f7a.png"},{"id":56547927,"identity":"3aad34c6-7d08-46c5-87d1-7be039a12e99","added_by":"auto","created_at":"2024-05-15 15:39:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53483,"visible":true,"origin":"","legend":"\u003cp\u003ePLS-SEM model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4219581/v1/afe575f1f2e301617aa5e084.png"},{"id":81312508,"identity":"e7d8f111-143d-459d-b839-cfe277573143","added_by":"auto","created_at":"2025-04-24 15:39:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2269032,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4219581/v1/7fe4dd11-3480-4685-9614-7fde6fe6cad9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the quality of life of university students during COVID-19 lockdown: A structural equation modelling approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe emergence of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), commonly known as COVID-19 has put the world health system including those of community living and education in crisis [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It has affected almost all the continents and it was first declared as a pandemic on 11 march 2020 by the World Health Organization (WHO)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a result, to get control over COVID-19, movement restriction was implied worldwide as containment measure is one of the most preferred ways to reduce the effect of this predicament to ratify the coronavirus[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Soon after COVID-19 was declared as a pandemic local government imposed lockdown to restrict public movement except in emergency situation to flatten the COVID-19 infection curve[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWorldwide, education institutions are being forced to shut down to halt the spread of coronavirus. According to United Nations Educational, Scientific and Cultural Organization (UNESCO), there are around 1.2\u0026nbsp;billion students around the world have been affected by school and university shut down due to the COVID-19 pandemic[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This made the governing bodies think of alternative ways of teaching during the pandemic situation. Which ultimately makes the way for web-based learning or e-learning or distance learning to move from traditional offline teaching practices[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMental health and physical health problems due to excess stress is a very common concern of student life and this impeding crisis has worsened the situation to a great extent[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In higher education students\u0026rsquo; health behaviors are often negatively influenced by stressful events[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Earlier studies established that profound health behaviors often triggered by large-scale emergencies i.e. COVID-19 pandemic[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. There are some other studies that also support the cause of increasing mental health distress during emergencies [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Most of the university students are at risk of elevated stress and mental health concerns as they are experiencing disruption of their established daily schedules, the current study sought a large amount of concentration, focus, and determinations which is very hard to come by in this stressful situation[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocial isolation has a major negative impact on quality of life, and technology can help to alleviate this discomfort however, the efficiency and effects of distance learning on the quality of life are questionable. To the best of our knowledge, there are no studies carried out in Bangladesh exploring the impact of COVID-19 on the quality of life among university students. In light of this, the present study was an endeavour to evaluate the impact of lockdown on the quality of life (QOL) of Bangladeshi university students exerted due to the COVID-19.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData source and ethical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study followed a cross-sectional design, which involved the anonymous opinion of the university students from seven higher education institutions of Bangladesh. Data were collected through an online e-questionnaire (Google\u0026reg; Forms) from 25th November to 25th December 2020. This research project was approved by the authority of Development Studies Discipline, Khulna University, Bangladesh. It is notable to mention that, in order to reduce the transmission of coronavirus (COVID-19) among the students, all the educational institutions were closed from 17th March to till the study period in Bangladesh. The survey was administered among the university students through using different social media platforms such as Messenger, WhatsApp, and Email. The inclusion criteria were: Willing to participate in the study and being a university student.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eResearch instruments\u003c/h2\u003e\n\u003cp\u003eThe previously validated World Health Organization Quality of Life (WHOQOL)-Bref questionnaire used in the current study to measure the quality of life of university students. The questionnaire was incorporated with four blocks of questions where each of the blocks contains different measurements of questions related to study aims. Block 1, contains background characteristics of the students such as; age, sex, semester, shift, and extracurricular activities prior to COVID-19 and block 2, contains questions regarding study practices prior to and during COVID-19, as well as the medium of instructions of distance learning (TV, Smartphone, Zoom, streaming media, etc.) amid COVID-19. Block 3, contains questions regarding accessibility and activities performed during COVID-19 while block 4 contains the questions of the WHOQOL-Bref questionnaire. The WHOQOL-Bref is an instrument developed by the WHO in 1998 to measure the quality of life through a shortened version of a longer pre-existing questionnaire, called the WHOQOL-100[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. WHOQOL-Bref comprises 26 questions that can be answered using a five-point Likert scale, of which 24 questions cover four domains (physical health, psychological well-being, social relationships and environment), and the other two items measure self-assessed quality of life[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. All the questions in the survey were mandatory, therefore, without answering any items students were not able to move in the next questions. The English version of the questionnaire was first translated to Bengali language and then back translated to English by two researchers to ensure the contents' consistency. The questionnaire was then piloted among a small sample (n\u0026thinsp;=\u0026thinsp;10) of university students to refine the language in the final version. The contents of the questionnaire were approved by the participants from a pilot study without requiring any changes in wording or sentence structure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eData from the surveys were exported to a Microsoft Excel sheet and encoded where necessary and analyzed using the software Statistical Package for Social Sciences (SPSS.25) Windows version. The scores of the WHOQOL-Bref were converted into a linear scale from 0 to 100 and recoded (0\u0026ndash;70) as low/moderate quality of life whereas (\u0026ge;\u0026thinsp;70) were coded as high quality of life. The mean, standard deviations (St. deviation), and Cronbach\u0026rsquo;s alpha were calculated. The Pearson correlation test was used to measure the association between the items. Two categories (low/moderate quality of life [0\u0026ndash;70] and high quality of life [70\u0026ndash;100]) were examined with all items of the first three blocks of the questionnaire applying Pearson\u0026rsquo;s Chi-squared test where the statistically significance set (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The variables which were found significant were taken for the final logistic regression model to analyze each block of items.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eAssessing the PLS-SEM algorithms model\u003c/h2\u003e\n\u003cp\u003eThe theoretical outline, which is portrayed according to the SEM tactic, is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The theoretical framework is primarily concerned with the establishment and integration of physical, psychological, social, and environmental domains. Structural equation modeling (SEM) is designed with the two-phase model (i) measurement model and (ii) structural model. The measurement model depicts the inner relationships between latent variables and observed variables, and the latter, along with the structural model, is used to investigate loadings and estimate indicators[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. The conceptual framework model was run using Smart PLS 2.0, and the structural framework used in the hypothesis testing segment is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Smart PLS consists of a series of standard metrics, such as loading indicators, composite reliability, average variance extracted (AVE), path coefficients, inner structural correlations, latent variable scores, t values, etc. A structural procedure has been introduced to investigate loading and eliminating indications (loadings\u0026thinsp;\u0026lt;\u0026thinsp;0.70) was adopted[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the context of the study, we utilized a reflexive measurement framework. In line with the majority of other behavioral research [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], a set of assessment criteria that can quantify the credibility and viability of the PLS-SEM model with recessive criterion was proposed. Which are as follows-\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n\u003cli\u003e\n\u003cp\u003eJustification of potential endogenous variable\u0026rsquo;s variance\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eChecking factor loading of Inner-model\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJustifying loadings of outer model and their significance\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIndicator reliability\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInternal consistency reliability\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConvergent validity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDiscriminant validity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssessing Structural Path Significance in Bootstrapping\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eSocial isolation reduces the quality of life of university students\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, shows the reliability analysis of the WHOQOL-Bref of the current study. The mean quality of life of university students after social isolation and lockdown was 78.29\u0026thinsp;\u0026plusmn;\u0026thinsp;11.59 with a median of 73.35 (IQR 42.53 to 88.30) points. Cronbach's alpha exhibited acceptable internal validity of the construct (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.878), and we observed no exclusion of any domain significantly reduced and increased these values. All domains showed a strong to moderate correlation with the overall quality-of-life score. The domain most affected by isolation was the psychological domain (r\u0026thinsp;=\u0026thinsp;0.886; \u0026alpha;\u0026thinsp;=\u0026thinsp;0.888; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), at the same time the social relationship domain showed the weakest correlation with the overall quality-of-life score (r\u0026thinsp;=\u0026thinsp;0.630; \u0026alpha;\u0026thinsp;=\u0026thinsp;0.614; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eReliability analysis, descriptive and inferential analysis of the WHOQOL-Bref among university students during the COVID-19 pandemic\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCorrelation with WHOQOL-Bref\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWHOQOL-Bref\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.29\u0026thinsp;\u0026plusmn;\u0026thinsp;11.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.878\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD1 Physical Health\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.81\u0026thinsp;\u0026plusmn;\u0026thinsp;22.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.614\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (r\u0026thinsp;=\u0026thinsp;0.630)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD2 Psychological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.97\u0026thinsp;\u0026plusmn;\u0026thinsp;19.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.888\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (r\u0026thinsp;=\u0026thinsp;0.886)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD3 Social Relationships\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.19\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.801\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (r\u0026thinsp;=\u0026thinsp;0.762)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD4 Environmental Health\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.60\u0026thinsp;\u0026plusmn;\u0026thinsp;16.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.777\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (r\u0026thinsp;=\u0026thinsp;0.860)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD5 Self Perception\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.04\u0026thinsp;\u0026plusmn;\u0026thinsp;14.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.800\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (r\u0026thinsp;=\u0026thinsp;0.666)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eCronbach's alpha.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eCronbach's alpha when excluding the item.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003ePearson correlation with the overall domain (D).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA total of 533 university students who participated in this current study were staying at home after the COVID-19 lockdown and maintaining social isolation. More than half of the study participants (n\u0026thinsp;=\u0026thinsp;292, 54.8%) were between the ages of 18 and 21, with a mean age of 21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 years. An about two-thirds of them were males (n\u0026thinsp;=\u0026thinsp;307, 57.6%). Of the total (n\u0026thinsp;=\u0026thinsp;148, 27.8%) students were in their first year of education. The majority of the students living in a home have more than or equal to three family members in the same home (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSociodemographic characteristics of the study participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQuality of Life\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUp to 70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge of Participants\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.712\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u0026ndash;21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e292 (54.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e232 (53.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (58.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u0026ndash;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150 (28.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (28.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (26.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91 (17.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76 (17.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (14.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSex of the Participants\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.633\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e307 (57.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e257 (59.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (49.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e226 (42.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174 (40.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 (51.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSemester\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.561\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFirst Year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148 (27.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135 (31.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (12.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecond Year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116 (21.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88 (20.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (27.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThird Year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117 (22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 (23.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (16.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFourth Year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (11.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (20.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMasters\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90 (16.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67 (15.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (22.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNumber of Family Member in the Same Home\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUp to 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103 (19.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80(18.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23(22.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e430 (80.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e351(81.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79(77.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe study hours of most students before COVID-19 lockdown involved either 2\u0026ndash;4 hours (n\u0026thinsp;=\u0026thinsp;222, 41.7%) or up to 1 hour/d (n\u0026thinsp;=\u0026thinsp;161, 30.2%) per day. But due to lockdown for COVID-19, the numbers were reversed, with the highest prevalence of study hours up to 1 hour per day (n\u0026thinsp;=\u0026thinsp;241, 45.2%) and 2\u0026ndash;4 hours (n\u0026thinsp;=\u0026thinsp;148, 27.8%), respectively. Most of the students reported that after COVID-19 lockdown the extent of using internet significantly increased (n\u0026thinsp;=\u0026thinsp;465, 87.2%), similarly using of cell phones increased notably (n\u0026thinsp;=\u0026thinsp;429, 80.5%), and watching TV increased reported by (n\u0026thinsp;=\u0026thinsp;218, 40.9%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eImpact of educational activities on the quality of life of university students before and after social isolation for COVID-19\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQuality of Life\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUp to 70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Hour Prior to COVID-19 Lockdown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e.940\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUp to 1h/d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e161 (30.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (29.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (31.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrom 1 to 2h/d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e150 (28.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121 (28.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29928.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrom 2 to 4h/d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e222 (41.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181 (42.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (40.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Hour After COVID-19 Lockdown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.247\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUp to 1h/d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e241 (45.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197 (45.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44 (43.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrom 1 to 2h/d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e144 (27.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 (25.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrom 2 to 4h/d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e148 (27.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124 (28.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (23.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Time During Lockdown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.306\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReduced\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e391 (73.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e322 (74.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69 (67.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnchanged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e74 (13.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58 (13.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (15.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreased\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e68 (12.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51 (11.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (16.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInternet Use After COVID-19 Lockdown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReduced\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30 (5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnchanged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e38 (7.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33(7.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreased\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e465 (87.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e373(86.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92(90.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCell Phone Use After COVID-19 Lockdown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.287\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReduced\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e43 (8.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33(7.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10(9.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnchanged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e61 (11.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49(11.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12(11.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreased\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e429 (80.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e349(81.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80(78.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFrequency of Watching TV After COVID-19 Lockdown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.314\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReduced\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e122 (22.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98(22.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24(23.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnchanged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e193 (36.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159(36.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34(33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreased\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e218 (40.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174(40.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44(43.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis study examined all students during the lockdown era who attended at least some form of distance learning or used any of the following technologies. A total of (n\u0026thinsp;=\u0026thinsp;480, 90.1%) students attended virtual classes using online platforms, such as Zoom (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Cell phones (n\u0026thinsp;=\u0026thinsp;461.86.5%) and computers/laptops (n\u0026thinsp;=\u0026thinsp;266, 49.9%) were the most commonly used devices for accessing distance educational materials and content. The study environment most often used to access distance education content and materials was the study room (n\u0026thinsp;=\u0026thinsp;393, 73.7%), followed by the bedroom (n\u0026thinsp;=\u0026thinsp;356, 66.8%), outdoor (n\u0026thinsp;=\u0026thinsp;326, 61.2%), dining room (n\u0026thinsp;=\u0026thinsp;251, 47.1%) and kitchen room (n\u0026thinsp;=\u0026thinsp;172, 32.3%). Compared to students without attending online classes, the high quality of life was significantly higher among students with virtual classes via online platforms, like Zoom (p\u0026thinsp;=\u0026thinsp;0.001). The students who have attended distance education activities in the study room were more likely to have a high quality of life than the students who have not done such activities in that study room (p\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEffect of distance education on the quality of life of university students during social isolation due to COVID-19\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQuality of Life\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUp to 70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDL through Zoom\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e480 (90.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e383(88.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97(95.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (9.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48(11.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to DL through Cell Phone\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.371\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e461 (86.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e370 (85.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91 (89.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72 (13.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61 (14.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (10.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to DL at Computer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e266 (49.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e213(49.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53(52.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e267 (50.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e218(50.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49(48.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to DL in the Study Room\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e393 (73.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e315(73.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78(76.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140 (26.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116(26.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24(23.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to DL in the Bedroom\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.255\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e356 (66.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e283 (65.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73 (71.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e177 (33.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148 (34.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (28.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to DL in the Dining Room\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.831\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e251 (47.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202 (46.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (48.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e282 (52.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229 (53.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (52.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to DL in the Kitchen\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.336\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e172 (32.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135 (31.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (36.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e361 (67.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e296 (68.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65 (63.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to DL in Outdoor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.621\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e326 (61.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e265 (61.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61 (59.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e207 (38.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166 (38.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (40.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the logistic regression analysis, students who participated in distance education activities via virtual platforms using Zoom (aOR\u0026thinsp;=\u0026thinsp;2.45, 95% CI: 1.92\u0026ndash;3.53, p\u0026thinsp;=\u0026thinsp;0.003), increased the frequency of using the Internet (aOR\u0026thinsp;=\u0026thinsp;1.65, 95% CI: 1.48\u0026ndash;2.67, p\u0026thinsp;=\u0026thinsp;0.025), increased watching TV (aOR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.12\u0026ndash;2.09, p\u0026thinsp;=\u0026thinsp;0.011) and family members (aOR\u0026thinsp;=\u0026thinsp;1.75, 95% CI: 1.43\u0026ndash;2.31, p\u0026thinsp;=\u0026thinsp;0.015) above three at the same home increased the chances of good quality of life, regardless of other variables (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). On the other hand, factors associated with decreasing the chances of a good quality of life include male and second-and fourth-year students.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultiple logistic regression analysis of modifying factors on quality of life among university students amid COVID-19\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQuality of Life\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge of Participants\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u0026ndash;21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e292 (54.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.23 (0.63\u0026ndash;2.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.544\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u0026ndash;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150 (28.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21 (0.58\u0026ndash;2.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.609\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;24 (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91 (17.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSex of the Participants\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e307 (57.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63 (0.39\u0026ndash;0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e226 (42.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSemester\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFirst Year (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148 (27.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecond Year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116 (21.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28 (0.13\u0026ndash;0.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThird Year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117 (22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96 (0.50\u0026ndash;1.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.910\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFourth Year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (11.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48 (0.23\u0026ndash;0.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMasters\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90 (16.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.64 (0.78\u0026ndash;3.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInternet Use After COVID-19 Lockdown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReduced (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnchanged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (7.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63 (0.13\u0026ndash;3.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.569\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreased\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e465 (87.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.65 (1.48\u0026ndash;2.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCell Phone Use After COVID-19 Lockdown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReduced (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (8.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnchanged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61 (11.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80 (0.26\u0026ndash;2.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.703\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreased\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e429 (80.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.46 (0.18\u0026ndash;1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFrequency of Watching TV After COVID-19 Lockdown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReduced (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (22.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnchanged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e193 (36.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89 (0.48\u0026ndash;1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.719\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreased\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e218 (40.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14 (1.12\u0026ndash;2.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDL through Zoom\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e480 (90.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.45 (1.92\u0026ndash;3.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (9.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to DL at Computer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e266 (49.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21 (0.76\u0026ndash;1.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.432\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e267 (50.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to DL in Outdoor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e326 (61.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89 (0.56\u0026ndash;1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.627\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e207 (38.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNumber of Family Member in the Same Home\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUp to 3 (RC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103 (19.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e430 (80.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.75 (1.43\u0026ndash;2.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003ePLS-SEM results\u003c/h2\u003e\n\u003cp\u003eBy observing Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, we can quantify the above-mentioned assessment criterion. Quality of life has a coefficient of determination R\u003csup\u003e2\u003c/sup\u003e of 0.73, indicating that the combined effects of Physical Aspects, Physiological Aspects, Social Aspects, and Environmental Aspects accounted for at least 73% of the total. Which is well above the accepted values of 0.6 or higher as recommended[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. The internal construction of the framework revealed that social aspects have the most substantial impact on Quality of Life (0.446), followed by Psychological Aspects (0.329), Environmental aspects (0.198) and Physical Aspects (0.237). These outcomes indicate the structural path of all the hypothesized constructs contains significant correlational impacts, which is well above the expected values of 0.2[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Since, the relationship between environmental aspects and quality of life (QOL) is very close to the expected value and well passed the threshold value of 0.1, therefore, in the context of the study, we assumed it passed the significance test as suggested[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. As a result, the study concluded that: social and psychological aspects can strongly predict QOL, whereas only physical aspects predict QOL moderately. On the other hand, environmental aspects have a lower impact on quantifying the QOL than other indicators. In order to construct the correlational impacts among the exogenous variables (inner model) with the outer model, we carefully explored the outer model loading of the proposed contract. In addition, as with most other behavioral research, we investigated the reliability and validity of the latent constructs in order to provide a well-established framework. More specifically, we used the criteria of Indicator Reliability, Internal Consistency Reliability, Convergent validity, and Discriminant validity in particular. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e represents that all the indicators contain well above the accepted values of 0.4 and close to the preferred values of 0.7 as suggested[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive statistics of key variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConstructs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard deviation\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Aspects\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q10\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q15\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q16\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q17\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q18\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsychological Aspects\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q11\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q19\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q26\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Aspects\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSo_Q20\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSo_Q21\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSo_Q22\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Aspects\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q9\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q12\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q13\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q14\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q23\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q24\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q25\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe measurement model has a good fit (see exact values below Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) as indicated by the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI) above the recommended level of 0.90, and Root Mean Square Error Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR) below the cut-off value of 0.08[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. As the measurement model is now established, we can proceed with the tactics called \"Composite Reliability\". Interestingly, social science research utilized the measurement of \"Cronbach\u0026rsquo;s alpha\" to maintain a satisfactory reliability of the internal model\u0026rsquo;s consistency, but in PLS-SEM it could provide narrowed views towards the visual construct of a potential measurement model. Therefore, measuring the \"Composite Reliability\" with higher values than 0.6 should be used as a substitute[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eValidity of constructs (Reflective Outer Models)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConstructs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLoadings\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIndicator\u003c/p\u003e\n\u003cp\u003eReliability (\u0026lambda;)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComposite reliability\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAVE\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Dimensions\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.693\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.480249\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e0.732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e0.625\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.787\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.619369\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q10\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.898\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.806404\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q15\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.638401\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q16\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.636804\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q17\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.576081\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q18\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.788\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.620944\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhysiological Dimensions\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.779689\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e0.863\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e0.781\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.901\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.811801\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.779689\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q11\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.855\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.731025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q19\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.875\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.765625\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q26\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.903\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.815409\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Dimensions\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSo_Q20\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.812\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.659344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.818\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.725\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSo_Q21\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.868624\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSo_Q22\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.804\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.646416\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental dimension\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.721\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.519841\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e0.737\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e0.642\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q9\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.832\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.692224\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q12\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.636804\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q13\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.899\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.808201\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q14\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.636804\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q23\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.901\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.811801\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q24\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.701\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.491401\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q25\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.535824\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel-fit: CFI\u0026thinsp;=\u0026thinsp;0.94, TLI\u0026thinsp;=\u0026thinsp;0.94, RMSEA\u0026thinsp;=\u0026thinsp;0.07, SRMR\u0026thinsp;=\u0026thinsp;0.07\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e indicates that the proposed construct contains a satisfactory level of internal consistency as all the values of Composite Reliability are higher than 0.6, which reflects that all the three latent variables are well connected for quantifying QOL. The Average Variance Extracted (AVE) of every observed construct has been explored to verify the convergent validity. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e also confirmed that all constructs' AVE scores were observed to be greater than the expected level of 0.5, confirming the potential model's convergent validity. For evaluating the discriminant validity of the construct, the square root of AVE of every indicator should be utilized to measure the discriminant validity. If the value will account for a higher level than the correlational value, the discriminant validity can be maintained[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e confirmed the entire hypothesized construct has passed the discriminant validity test. Correlational values of Physical aspects, for example, are 0.791, which is significantly higher than values in its column.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFornell-Larcker Criterion Analysis for Checking Discriminant Validity\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhysical Aspects\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhysical Aspects\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhysical Aspects\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhysical Aspects\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhysical Aspects\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.791\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhysiological Aspects\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.723\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.884\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Aspects\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.604\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.851\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Aspects\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.699\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.801\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFinally, the study utilized bootstrapping criterion for measuring the associated t statistics. In this process, an enormous quantity of subsamples (e.g., 5000) is reserved for commencing the novel sample by replacing them for giving a bootstrap standard error, which in return provides an estimated t-values for connotation testing of the structural path. The Bootstrap outcome estimates the normality of data. Using a two-tailed t-test with a significance level of 5%, the path coefficient will be significant if the T-statistics is larger than 1.96. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e indicated that all the estimated t statistics are larger than the expected values of 1.96, which confines that the proposed possessed a good connection between the observed and potential constructs[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. As per all the evaluation, we can conclude that there is a well-established model loading within the outer and inner models with highly significant correlational values. Based upon the analysis, the study fosters some policy recommendations which are demonstrated in the following section.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab9\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBootstrap results of the model (inner model)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eT-statistics\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhysical Aspects \u0026rarr; Quality of Life\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e5.788\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhysiological Aspects \u0026rarr; Quality of Life\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e12.986\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Aspects \u0026rarr; Quality of Life\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e19.027\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Aspects \u0026rarr; Quality of Life\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e6.263\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab10\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBootstrap results of the model (outer model)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrame/\u003c/p\u003e\n\u003cp\u003econstruct\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIndicator\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal sample estimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean of sub-sample\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003et-Statistics\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Dimensions\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.278\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.208\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.439\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.043\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q10\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.398\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.233\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q15\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.348\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.344\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q16\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.281\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.261\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0087\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q17\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.233\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhy_Q18\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.278\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.532\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhysiological Dimensions\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.261\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.261\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.129\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.142\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.328\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q11\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.255\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.245\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.491\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q19\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.309\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.189\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePsy_Q26\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.186\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Dimensions\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSo_Q20\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.313\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.754\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSo_Q21\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.302\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSo_Q22\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental dimension\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.038\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q9\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.602\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q12\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.223\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q13\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.766\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q14\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.373\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.402\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q23\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.289\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.289\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.030\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q24\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.357\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.341\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEn_Q25\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab11\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eParameters of the structural model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHypothesis\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal sample estimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean of sub-sample\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003et-Statistics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNotes\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical Dimensions-Quality of Life\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.679\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.606\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.240\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysiological Dimensions-Quality of life\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.814\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.411\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial Dimensions-Quality of life\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.823\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.812\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnvironmental dimension-Quality of Life\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.683\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.894\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.530\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study was carried out among the university students to explore the distance learning and relevant quality of life amid the COVID-19 pandemic. This study not only provides a detailed examination of an unexplored learning technique used during the COVID-19 pandemic, but also identifies some important areas for further research relevant to the quality of distance education.\u003c/p\u003e \u003cp\u003eA total of 533 students from four universities participated in the online survey and filled-up the questionnaire while (n\u0026thinsp;=\u0026thinsp;480, 90.1%) students reported they had attended distance learning using online platforms. Overall, we found that the domain most affected by isolation was the psychological domain, and the social relationship domain showed the lowest correlation with the overall quality of life scores. In the regression analysis, factors such as increased Internet use, watching more TV, participating in classes with zooms and residing with a family more than three members were found to be associated with having a good quality of life.\u003c/p\u003e \u003cp\u003eEvidence suggests that a long time of social isolation and prolonged lockdown hampers mental well-being[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Because of confinement in the family for long period may lead to conflicts among family members leading to anger and boredom which can interfere with good quality of life. In our study, the psychological domain had the strongest influence on the quality of life, while the physical health domain had the weakest association with the overall quality of life scores, indicating that social isolation has a significant impact on the psychological profile. This is because when people undergoing 10 days of social quarantine or isolation are often affected by psycho-emotional changes in mental health[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In our study, 80.86% of students had a low/moderate quality of life, due to social confinement, which is similar to the findings of a previous study carried out in Brazil[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The growing number of COVID-19 cases around the world, including Bangladesh, is instilling fear not only among students but also among the general public which has a direct effect on the quality of life[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A recent study carried out in Bangladesh also demonstrated significant fear of COVID-19 among the people in Bangladesh[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, as demonstrated in this study, the psychological domains of WHOQOL-Bref (most affected domain) may be related to the fear of COVID-19 among the students.\u003c/p\u003e \u003cp\u003ePrevious research has shown that social isolation and prolonged lockdown have negative effects on children and adolescents [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. But to reduce virus transmission among students, the government of Bangladesh had to shut all educational institutions and thereby encouraged distance learning via online platforms. As a result, there was enough spare time among the students to use cellphones and the Internet, leading to alleviating unhappiness in life and better interacting with friends, classmates, and society[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study also found that good quality of life was significantly associated with several factors such as increased Internet use, viewing of more TV, participating classes using zoom platform and living with a family with more than three members.\u003c/p\u003e \u003cp\u003eThe zoom platform is sophisticated and possesses advanced interaction mechanisms to connect people, even over long distances, effectively improve social communication, minimize distance, and encourage interaction between students and teachers[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Also, when using the Internet on Facebook or via social media, students are in touch with their friends to feel like they are not alone and to relieve their unhappiness[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. At the same time, watching various TV shows is linked with a lower risk of feeling lonely for an extended period of time would be an effective way for students to get away from the doldrums exerted due to isolation and lockdown[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Students living in a house where more family members reside exhibited to have a good quality of life, this would be because, in these circumstances, people can talk and chat face-to-face, they can play several indoor games and gossip is an effective way to get them away from loneliness[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strengths and limitations of this current study must be acknowledged. First, this is the first study carried out in Bangladesh to measure the quality of life of university students during the COVID-19 lockdown. Second, appropriate statistical techniques we applied to measure the WHOQOL-BREF, while the advanced SEM model we incorporated in the current study. Despite its strengths, the first limitation is that we designed this study to be cross-sectional, which means that causal relationships were not established. Furthermore, non-random sampling and online technique limit the generalizability of the findings. In addition, we had done the study with a comparatively smaller sample within a short period of time so a detailed study is further recommended. Despite our short-time findings, we suggest that future research with a longer isolation period be conducted in order to determine the true impact of significant confinement such as is occurring today.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDue to the overwhelming COVID-19 pandemic, the education sector is highly affected while students around the world undergoing social isolation and lockdown on a global scale and forced to adopt distance learning approach. The present study highlighted that, while distance education is recommended during this overwhelming situation, it should be delivered using interactive platforms such as zoom to improve quality of life. Also, the use of the internet and TV viewing needs to be promoted among the students to preserve the quality of life amid this pandemic with distance learning in place.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eWe didn\u0026rsquo;t receive any funding for this current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data of the study is not publicly sharable, but can be given upon appropriate request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent:\u0026nbsp;\u003c/strong\u003eThis study was approved by Development Studies Discipline, Khulna University. Informed consent was gained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publications:\u0026nbsp;\u003c/strong\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u0026nbsp;\u003c/strong\u003eMd. Ashfikur Rahman; Tanjirul Islam.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData curation:\u0026nbsp;\u003c/strong\u003eMd. Ashfikur Rahman; Tanjirul Islam.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormal analysis:\u0026nbsp;\u003c/strong\u003eMd. Ashfikur Rahman; Mortuja Mahmud Tohan; Tanjirul Islam.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u0026nbsp;\u003c/strong\u003eSakib Al Hassan; Md Amirul Islam; Abdul Elah Al-Mahde.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware:\u0026nbsp;\u003c/strong\u003eMd. Ashfikur Rahman; Mortuja Mahmud Tohan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupervision:\u0026nbsp;\u003c/strong\u003eMd. Ashfikur Rahman\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation:\u0026nbsp;\u003c/strong\u003eTanjirul Islam; Md Amirul Islam\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026ndash; original draft:\u0026nbsp;\u003c/strong\u003eMd. Ashfikur Rahman; Tanjirul Islam; Tayeeba Tabussum Anni; Israt Jahan Juie; Abdul Elah Al-Mahde; Sakib Al Hassan; Md Amirul Islam; Mortuja Mahmud Tohan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026ndash; review \u0026amp; editing:\u0026nbsp;\u003c/strong\u003eMd. Ashfikur Rahman; Tanjirul Islam; Tayeeba Tabussum Anni; Israt Jahan Juie; Abdul Elah Al-Mahde; Sakib Al Hassan; Md Amirul Islam; Mortuja Mahmud Tohan\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRemuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet. 2020;395: 1225\u0026ndash;1228. \u003c/li\u003e\n\u003cli\u003eUnted Nations. Policy Brief : Education during COVID-19 and beyond. 2020. \u003c/li\u003e\n\u003cli\u003eSchleicher A. COVID-19 ON EDUCATION INSIGHTS FROM GLANCE 2020. 2020. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). Director-General\u0026rsquo;s opening remarks at the media briefng on COVID19 -March 2020. \u003c/li\u003e\n\u003cli\u003eDe Brouwer E, Raimondi D, Moreau Y. Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries. medRxiv. 2020. \u003c/li\u003e\n\u003cli\u003eShammi M, Bodrud-Doza M, Islam ARMT, Rahman MM. Strategic assessment of COVID-19 pandemic in Bangladesh: comparative lockdown scenario analysis, public perception, and management for sustainability. Environ Dev Sustain. 2020; 1\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eUNESCO. COVID-19 Educational Disruption and Response. 2020 [cited 18 Apr 2021]. Available: https://en.unesco.org/news/covid-19-educational-disruption-and-response\u003c/li\u003e\n\u003cli\u003eRadha R, Mahalakshmi K, Kumar VS, Saravanakumar AR. E-Learning during lockdown of Covid-19 pandemic: A global perspective. Int J Control Autom. 2020;13: 1088\u0026ndash;1099. \u003c/li\u003e\n\u003cli\u003eZhou L, Wu S, Zhou M, Li F. \u0026apos;School\u0026rsquo;s Out, But Class\u0026rsquo; On\u0026rsquo;, The Largest Online Education in the World Today: Taking China\u0026rsquo;s Practical Exploration During The COVID-19 Epidemic Prevention and Control As an Example. But Class\u0026rsquo; On\u0026rsquo;, Larg Online Educ World Today Tak China\u0026rsquo;s Pract Explor Dur COVID-19 Epidemic Prev Control As an Ex (March 15, 2020). 2020. \u003c/li\u003e\n\u003cli\u003eAli M, Allihyani M, Abdulaziz A, Alansari S, Faqeh S, Kurdi A, et al. What just happened? Impact of on-campus activities suspension on pharmacy education during COVID-19 lockdown\u0026ndash;A students\u0026rsquo; perspective. Saudi Pharm J. 2021;29: 59\u0026ndash;66. \u003c/li\u003e\n\u003cli\u003eRapanta C, Botturi L, Goodyear P, Gu\u0026agrave;rdia L, Koole M. Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity. Postdigital Sci Educ. 2020;2: 923\u0026ndash;945. \u003c/li\u003e\n\u003cli\u003eAlam MA. Challenges and Possibilities of Online Education during Covid-19. 2020; 12\u0026ndash;14. doi:10.20944/preprints202006.0013.v1\u003c/li\u003e\n\u003cli\u003eHoulden S, Veletsianos G. Coronavirus pushes universities to switch to online classes\u0026mdash;but are they ready. Conserv. 2020. \u003c/li\u003e\n\u003cli\u003eAli A, Ramay MI, Shahzad M. Key factors for determining student satisfaction in distance learning courses: A study of Allama Iqbal Open University (AIOU) Islamabad, Pakistan. Turkish Online J Distance Educ. 2011;12: 114\u0026ndash;127. doi:10.17718/tojde.10766\u003c/li\u003e\n\u003cli\u003eAbel R. Implementing best practices in online learning. Educ Q. 2005;28: 75\u0026ndash;77. \u003c/li\u003e\n\u003cli\u003eCrawford J, Butler-Henderson K, Rudolph J, Malkawi B, Glowatz M, Burton R, et al. COVID-19: 20 countries\u0026rsquo; higher education intra-period digital pedagogy responses. J Appl Learn Teach. 2020;3: 1\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eHuckins JF, DaSilva AW, Wang W, Hedlund E, Rogers C, Nepal SK, et al. Mental health and behavior of college students during the early phases of the COVID-19 pandemic: longitudinal smartphone and ecological momentary assessment study. J Med Internet Res. 2020;22: e20185. \u003c/li\u003e\n\u003cli\u003eDu C, Zan MCH, Cho MJ, Fenton JI, Hsiao PY, Hsiao R, et al. Health behaviors of higher education students from 7 countries: poorer sleep quality during the COVID-19 pandemic predicts higher dietary risk. Clocks \u0026amp; sleep. 2021;3: 12\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eMattioli A V, Sciomer S, Cocchi C, Maffei S, Gallina S. Quarantine during COVID-19 outbreak: Changes in diet and physical activity increase the risk of cardiovascular disease. Nutr Metab Cardiovasc Dis. 2020;30: 1409\u0026ndash;1417. \u003c/li\u003e\n\u003cli\u003eVlahov D, Galea S, Ahern J, Resnick H, Kilpatrick D. Sustained increased consumption of cigarettes, alcohol, and marijuana among Manhattan residents after September 11, 2001. Am J Public Health. 2004;94: 253\u0026ndash;254. \u003c/li\u003e\n\u003cli\u003eNeria Y, Olfson M, Gameroff MJ, Wickramaratne P, Gross R, Pilowsky DJ, et al. The mental health consequences of disaster-related loss: findings from primary care one year after the 9/11 terrorist attacks. Psychiatry Interpers Biol Process. 2008;71: 339\u0026ndash;348. \u003c/li\u003e\n\u003cli\u003eBromet EJ, Atwoli L, Kawakami N, Navarro-Mateu F, Piotrowski P, King AJ, et al. Post-traumatic stress disorder associated with natural and human-made disasters in the World Mental Health Surveys. Psychol Med. 2017;47: 227. \u003c/li\u003e\n\u003cli\u003eMurphy MH, Carlin A, Woods C, Nevill A, MacDonncha C, Ferguson K, et al. Active students are healthier and happier than their inactive peers: the results of a large representative cross-sectional study of university students in Ireland. J Phys Act Heal. 2018;15: 737\u0026ndash;746. \u003c/li\u003e\n\u003cli\u003eJi M, An R, Qiu Y, Guan C. The Impact of Natural Disasters on Dietary Intake. Am J Health Behav. 2020;44: 26\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003eGroup W. Development of the World Health Organization WHOQOL-BREF quality of life assessment. Psychol Med. 1998;28: 551\u0026ndash;558. \u003c/li\u003e\n\u003cli\u003eFleck M, Lousada S, Xavier M, Chachamovich E, Vieira G, Santos L, et al. Application of the portuguese version of the instrument for the assessment of the quality of life of the World Health Organization (WHOQOL-100). Rev Saude Publica. 1999;33: 198\u0026ndash;205. \u003c/li\u003e\n\u003cli\u003eWong KK-K. Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark Bull. 2013;24: 1\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eSarkar A, Qian L, Peau AK. Structural equation modeling for three aspects of green business practices: a case study of Bangladeshi RMG\u0026rsquo;s industry. Environ Sci Pollut Res. 2020;27: 35750\u0026ndash;35768. \u003c/li\u003e\n\u003cli\u003eSarker T, Sarkar A, Rabbany MG, Barmon M, Roy R, Rahman MA, et al. Evaluation of preventive, supportive and awareness building measures among international students in China in response to COVID-19: a structural equation modeling approach. Glob Heal Res Policy. 2021;6: 1\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eBlack WC, Babin BJ, Anderson RE. Multivariate data analysis: A global perspective. Pearson; 2010. \u003c/li\u003e\n\u003cli\u003eMay D, Arancibia S, Behrendt K, Adams J. Preventing young farmers from leaving the farm: Investigating the effectiveness of the young farmer payment using a behavioural approach. Land use policy. 2019;82: 317\u0026ndash;327. \u003c/li\u003e\n\u003cli\u003eMunim ZH, Noor T. Young people\u0026rsquo;s perceived service quality and environmental performance of hybrid electric bus service. Travel Behav Soc. 2020;20: 133\u0026ndash;143. \u003c/li\u003e\n\u003cli\u003eHair JF, Sarstedt M, Pieper TM, Ringle CM. The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plann. 2012;45: 320\u0026ndash;340. \u003c/li\u003e\n\u003cli\u003eAnderson JC, Gerbing DW. Structural equation modeling in practice: A review and recommended two-step approach. Psychol Bull. 1988;103: 411. \u003c/li\u003e\n\u003cli\u003eHulland J. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strateg Manag J. 1999;20: 195\u0026ndash;204. \u003c/li\u003e\n\u003cli\u003eBagozzi RP, Yi Y. On the evaluation of structural equation models. J Acad Mark Sci. 1988;16: 74\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eFornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18: 39\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eAbdi H. Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev Comput Stat. 2010;2: 97\u0026ndash;106. \u003c/li\u003e\n\u003cli\u003eSilva PG de B, de Oliveira CAL, Borges MMF, Moreira DM, Alencar PNB, Avelar RL, et al. Distance learning during social seclusion by COVID-19: Improving the quality of life of undergraduate dentistry students. Eur J Dent Educ. 2020; 1\u0026ndash;11. doi:10.1111/eje.12583\u003c/li\u003e\n\u003cli\u003eHawryluck L, Gold WL, Robinson S, Pogorski S, Galea S, Styra R. SARS control and psychological effects of quarantine, Toronto, Canada. Emerg Infect Dis. 2004;10: 1206. \u003c/li\u003e\n\u003cli\u003eSchoene D, Heller C, Aung YN, Sieber CC, Kemmler W, Freiberger E. A systematic review on the influence of fear of falling on quality of life in older people: is there a role for falls? Clin Interv Aging. 2019;14: 701. \u003c/li\u003e\n\u003cli\u003eMistry SK, Ali ARMM, Akther F, Yadav UN, Harris MF. Exploring fear of COVID-19 and its correlates among older adults in Bangladesh. 2021; 1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eWang G, Zhang Y, Zhao J, Zhang J, Jiang F. Mitigate the effects of home confinement on children during the COVID-19 outbreak. Lancet. 2020;395: 945\u0026ndash;947. \u003c/li\u003e\n\u003cli\u003eHawton A, Green C, Dickens AP, Richards SH, Taylor RS, Edwards R, et al. The impact of social isolation on the health status and health-related quality of life of older people. Qual Life Res. 2011;20: 57\u0026ndash;67. \u003c/li\u003e\n\u003cli\u003eRedondo T. The digital economy: Social interaction technologies\u0026ndash;an overview. 2015. \u003c/li\u003e\n\u003cli\u003eGuo H. Linking loneliness and use of social media. University of Helsinki Faculty. 2018. \u003c/li\u003e\n\u003cli\u003eTeh JKL, Tey NP. Effects of selected leisure activities on preventing loneliness among older Chinese. SSM - Popul Heal. 2019;9: 100479. doi:10.1016/j.ssmph.2019.100479\u003c/li\u003e\n\u003cli\u003eRahman MS, Rahman MA, Ali M, Rahman MS, Maniruzzaman M, Yeasmin MA, et al. Determinants of depressive symptoms among older people in Bangladesh. J Affect Disord. 2020;264. doi:10.1016/j.jad.2019.12.025\u003c/li\u003e\n\u003cli\u003eMistry SK, Ali ARMM, Hossain MB, Yadav UN, Ghimire S, Rahman MA, et al. Exploring depressive symptoms and its associates among Bangladeshi older adults amid COVID-19 pandemic: findings from a cross-sectional study. Soc Psychiatry Psychiatr Epidemiol. 2021. doi:10.1007/s00127-021-02052-6\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":"Lockdown, Quality of Life, COVID-19, University Students, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-4219581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4219581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eIn the face of the upsurge of the COVID-19 pandemic global students including those of Bangladesh are forced to go into distance learning mode due to the lockdown or social isolation that is being imposed. The present study was intended to evaluate the impact of distance education on the quality of life (QOL) among Bangladeshi university students that are exerted due to the COVID-19 lockdown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used the World Health Organization Quality of Life (WHOQOL)-Bref questionnaire which was distributed among students from four universities in Bangladesh using electronic platforms such as WhatsApp, Facebook, and Email. The scores of the WHOQOL-Bref were converted into a linear scale from 0 to 100 and recorded (0\u0026ndash;70) as low/moderate quality of life whereas (\u0026ge;\u0026thinsp;70) were coded as high quality of life.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study obtained an excellent internal consistency of WHOQOL-Bref (α\u0026thinsp;=\u0026thinsp;0.878). The mean QOL (0-100) among the participants was 78.29\u0026thinsp;\u0026plusmn;\u0026thinsp;11.59 with a median of (73.35, IQR: 42.53\u0026ndash;88.30). All domains showed a strong to moderate correlation with the overall quality of life score. The domain most affected by isolation was the psychological domain, and the social relationship domain showed the weakest correlation with the overall quality of life scores. In the regression analysis, factors such as increased Internet use, watching more TV, participating in classes with zooms, and residing with a family of more than three members were found to be associated with having a good quality of life.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study pointed out that, while there are no alternatives to keep the educational system functioning thus distance learning during this overwhelming COVID-19 situation, more interactive platforms such as Zoom, the promotion of more internet and television use can be of value to retain the good quality of life among the students in this overwhelming condition.\u003c/p\u003e","manuscriptTitle":"Assessing the quality of life of university students during COVID-19 lockdown: A structural equation modelling approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 15:39:14","doi":"10.21203/rs.3.rs-4219581/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":"2614822b-d62a-4525-84d5-29efdb6e756a","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-24T15:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 15:39:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4219581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4219581","identity":"rs-4219581","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}