Psychometric Properties and Cross-Cultural Adaptation of the 6-item UCLA Loneliness Scale among Older Iranian People

Psychometric Properties and Cross-Cultural Adaptation of the 6-item UCLA Loneliness Scale among Older Iranian People | 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 Psychometric Properties and Cross-Cultural Adaptation of the 6-item UCLA Loneliness Scale among Older Iranian People Farzaneh Bahadori, Abdolrahim Asadollahi, Ogholgol Ghajari, Mahsa Yarelahi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5420810/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Loneliness is common in old individuals, and the UCLA loneliness scale is one of the most reliable tools for measuring loneliness worldwide. The present study aimed to investigate the psychometric properties of the 6-item UCLA loneliness scale in the Iranian older population. Methods In this psychometric study, we outline the translation and validation of the 6-item UCLA loneliness scale among 612 older adults with a mean age of 68.2 ± 7.2 years (females = 60.9%) in 2023. The participants were selected via stratified random sampling. Data were collected through face‒to-face interviews via the UCLA Loneliness Scale, Loneliness Scale, Oxford Happiness Scale, and demographic questionnaire. The data were analyzed via SPSS version 26 and AMOS version 25 software. The content validity, construct validity, and internal consistency were investigated, and ROC analysis and convergent validity were also assessed. Results Approximately two-thirds of the participants were married, and three-fourths had at least one chronic condition. The EFA assigned a two-factor solution for the UCLA loneliness scale, confirmed by CFA (GFI = 0.90, CFI = 0.91, and RMSEA = 0.056). Internal consistency was confirmed by the ICCs, Cronbach’s alphas, and McDonald’s Omega values (a ≥ 0.90). The ROC analysis indicated an exact cutoff value for older adults with and without severe loneliness with high sensitivity and specificity. Conclusion The Persian version of the 6-Item UCLA loneliness scale presented adequate psychometric properties and could be used to confidently measure loneliness in community-dwelling older adults. in Geriatric Health Department of Gerontology Faculty of Health Shiraz University of Medical Sciences Shiraz Iran Figures Figure 1 Background Loneliness is an unpleasant feeling caused by a discrepancy between desired and perceived levels of social connectedness[ 1 ]. Accordingly, a person may experience loneliness despite social interactions because interactions with others alone cannot prevent loneliness [ 2 ]. Individuals of all ages can experience loneliness. However, older adults are more prone to social isolation and loneliness than any other age group for a variety of reasons, such as the death of a partner, retirement, an empty nest, a decline in functions, and health problems [ 3 , 4 ]. Loneliness is a major health problem for older adults. Studies have shown that loneliness is significantly associated with depression, functional defects, high blood pressure, suicide, limited mobility [ 5 ], low quality of life, poor sleep quality, poor cognitive ability[ 6 , 7 ], and mortality[ 8 ]. In addition, loneliness increases the risk of malnutrition in older adults by 4.2 times [ 9 ]. According to a recent review, the prevalence of loneliness among older adults in different European countries varies from 3–34%. Aging is considered a risk factor for loneliness, and approximately 50% of people aged 80 years and older report loneliness[ 10 ]. According to a recent study, loneliness is prevalent among older Iranian adults, particularly those who live alone and have underlying health conditions[ 11 ]. Considering the high prevalence of loneliness in old individuals and its negative impact on older adults' health, it is necessary to evaluate loneliness using valid scales. To date, several scales have been created to measure loneliness, but the most commonly used scale in the general population is the UCLA Loneliness Scale [ 12 ]. It was developed by Russell and colleagues in 1978 to assess the social and emotional dimensions of loneliness [ 13 ]. This scale has become a significant scale in clinical settings and research because studies have reported its acceptable validity and reliability across different countries [ 12 ]. Similarly, an evaluation of the 20-item version of this scale among Iranian adults revealed good psychometric properties[ 14 ]. However, the large number of scale items causes physicians and researchers to face problems during interviews or assessments of older adults. For example, in the phone-based study of Hosseini Moghaddam et al. (2021), some older adults ended phone conversation before completing the scale because of exhaustion [ 15 ]. Illiteracy and low levels of education in older adults also lead to more problems completing the scale[ 16 ]. However, in cases where the aim is to measure psychological concepts such as loneliness, an interview is the only way to examine an individual's subjective situation[ 17 ]. Therefore, to measure loneliness in older adults, it is better to use shorter but valid versions of the scale rather than longer versions[ 18 ]. Several shortened versions of the University of California (UCLA) Loneliness Scale have been developed to reduce the length of the questionnaire and increase response rates in clinical and longitudinal studies [ 12 ]. Recently, Wongpakaran et al. (2020) validated the 20-item scale and developed the 6-item Revised UCLA Loneliness Scale (RULS-6) in a study with 719 participants [ 19 ]. The RULS-6 scale has good validity and reliability and can identify feelings of loneliness in clinical and community settings. Another advantage of the RULS-6 scale is that it is not affected by the gender or age of the subjects. Additionally, the RULS-6 scale had a high correlation (r = 0.88) with the 20-item version [ 19 ]. Given that developing tools is time-consuming and costly, valid existing tools should be translated, culturally adapted, and assessed for psychometric properties in the target country[ 20 ]. Adapting the RULS-6 scale will increase the breadth of our knowledge of the loneliness phenomenon in older adults, identify the true prevalence of loneliness in old age, and provide the basis for designing interventions and preventive measures to alleviate loneliness in aging individuals[ 21 ]. Hence, the present study was conducted to psychometrically validate the 6-item California Loneliness Questionnaire in Iranian older adults. Methods The present study is a psychometric study conducted in 2023. On the basis of the reported prevalence of loneliness in Iranian older adults by Asadollahi et al.[ 11 ] and considering an alpha level of 0.05 and a precision of 0.1%, the sample size was estimated to be 600 participants. After the 4% drop rate was considered, it increased to 624 participants. Shiraz and Gonbad-e Kavus were selected for the study because of the rapid growth rate of their older adult population. A stratified random sampling method was used to select the study participants. Shiraz city and Gonbad-e Kavus city have 30 and 7 comprehensive health centers, respectively. According to the urban maps, each city was divided into four regions (North, South, East, and West), and one health center was randomly selected from each region. A total of 78 older adults were randomly selected from each center via the integrated electronic health record system (SIB). Researchers reviewed the inclusion criteria by calling registered phone numbers in electronic health records to invite eligible older adults to participate in the study. The inclusion criteria were being 60 years or older, residing in Shiraz, scoring 7 or higher on the Abbreviated Mental Test (AMT) (indicating normal cognitive status), having the ability to communicate verbally, and being willing to participate in the study. The exclusion criteria included incomplete questionnaire responses and withdrawal from the study. Eligibility was assessed through telephone contact, and those who met the criteria were invited for in-person interviews. Eligible participants completed the demographic questionnaire, the Persian version of the GLFS, the abbreviated mental health assessment questionnaire, and the informed consent form through face‒to-face interviews. Data were collected from older adults' homes through face‒to-face interviews from early July to mid-October 2023. Instruments A researcher-made demographic questionnaire, the RULS-6 scale, the DeJung-Gierveld Loneliness Questionnaire, and the Happiness Questionnaire were used for data collection. Demographic questionnaire: This questionnaire includes questions about participants' gender, age, education level, living situation, and perceived health status. 6-Item UCLA Loneliness Scale: Wongpakaran et al. (2020) designed the California Loneliness Questionnaire 6-item version (RULS-6). The scale is scored on a four-point Likert scale from never (0) to always (3), with higher scores indicating more loneliness. Their study revealed a high correlation of 0.88 (p < 0.001) between the RULS-6 and the longer 20-item version, as well as with other scales. Cronbach's alpha for the RULS-6 ranges from 0.72 to 0.84 for various samples[ 19 ]. The DeJang-Gierveld loneliness questionnaire: This questionnaire, developed by DeJang and Ventilberg in 2006, assesses loneliness via eleven questions with a three-point Likert scale: "no" (0), "somewhat" (1), and "yes" (2)[ 22 ]. Scores range from 0 to 11, with higher scores indicating greater loneliness. Hosseinabadi et al. (2020) reported an internal consistency coefficient of 0.69 in the older Iranian population for this questionnaire[ 21 ]. Oxford Happiness Questionnaire: This eight-question tool, developed by Hill and Argyle (2002), evaluates happiness via a six-point Likert scale ranging from strongly disagree (score 1) to strongly agree (score 6). Total scores range from 8–48, with higher scores indicating greater happiness[ 23 ]. Dehshiri et al. (2016) reported a 0.90 correlation between this abbreviated questionnaire and its longer version (29-item questionnaire), with a Cronbach's alpha of 0.71[ 24 ]. In the present study, the Cronbach's alphas for the Giervild Loneliness and Oxford Happiness questionnaires were 0.80 and 0.72, respectively. Study implementation After necessary permission was obtained, the World Health Organization protocol was used to translate and validate the 6-item UCLA loneliness scale [ 25 ]. Two independent Persian translators first translated the original English questionnaire. The translators then met to review and create a common version. Face validity was assessed qualitatively through interviews with 10 older adults with varying education levels. Older adults evaluated the difficulty, appropriateness, and clarity of the questions, providing suggestions for improvement. After confirming face validity, the content validity of the 6-item UCLA loneliness scale was examined via the content validity ratio (CVR) with the participation of 5 older adults and 5 gerontologists. According to the Lawshe table, the acceptable CVR limit with 10 raters is 0.62[26), and in this study, all the questions exceeded a validity ratio of 0.91. After confirming its validity, each translator independently translated the questionnaire into English. The translations were then merged under the supervision of two faculty members from Shiraz University of Medical Sciences to create a version that closely resembled the original version. To determine construct validity, exploratory factor analysis (EFA) with varimax rotation was conducted with 612 older participants to identify questionnaire factors. The sample adequacy and correlation coefficients between questions were assessed via the Kaiser‒Meyer‒Olkin (KMO) test and Bartlett sphericity test, respectively. Subsequently, confirmatory factor analysis (CFA) was performed via AMOS software version 24 and the generalized least squares (GLS) method to evaluate the model fit indices. In the third stage, the internal consistency of the questionnaire was tested by determining McDonald's omega coefficient, Cronbach's alpha, and Pearson correlation. The scale's internal consistency was assessed by computing the intracluster correlation coefficient (ICC). In the final stage, the cutoff points of the questionnaire were determined via receiver operating characteristic (ROC) curve analysis, computing Youden's J coefficient, DOR index, BACC index, DIFF coefficient, and D coefficient. The study followed the Helsinki Declaration and STROBE guidelines[ 27 ], received approval from the Ethics Committee of Golestan University of Medical Sciences (IR.GOUMS.REC.1401.038), and obtained informed consent from all participants. Results Participant Description Out of 630 participants in the study, 12 were excluded because of incomplete questionnaires. The mean age of the 612 older participants was 68.2 ± 7.2 years (females = 60.9% & males = 39.1%). Most of the participants had a diploma (22.2%), were married (66.7%), had very good perceived health (43.1%), had at least one chronic condition (78.6%), had no multimorbidity (56.5%), and were Fars (75.5%). Approximately 22.9% of the participants lived alone, and among them, 37.9% lived alone for more than six years. The mean scores of loneliness and happiness were 3.6 ± 4.4 (for the 6-item UCLA loneliness scale), 5.5 ± 3.0 (for the DeJong Gierveld loneliness scale), and 25.7 ± 3.4, respectively. As shown in Table 1 , a one-way analysis of variance (ANOVA) and independent samples t test revealed that loneliness was greater in older adults with increasing age, chronic conditions, multimorbidity, longer chronic conditions, poor health, living alone, not married, and a lower level of education. The loneliness mean scores did not differ by gender. Table 1 One-way ANOVA for Demographic and Health Factors (n = 612) Variables Categories Mean (SD) Source of Variation Sum of Squares df Mean Square F ES: Omega Sqr P value Age 60–75 years 3.3(4.0) Between Groups 276.439 2 138.220 7.253 0.020 0.001 * 75–85 years 5.0(5.5) Within Groups 11605.907 609 19.057 85 + years 3.6(4.6) Total 11882.346 611 Gender male 5.2(5.3) Between Groups 0.678 1 0.678 0.025 0.003 0.865 ** Within Groups 7301.367 611 23.327 female 5.1(4.6) Total 7302.044 612 Chronic Condition Yes 5.5(4.8) Between Groups 611.405 1 611.405 13.943 0.084 0.001 ** Within Groups 6990.639 611 22.334 No 2.6 (3.9) Total 7302.044 612 Multimorbidity Yes 6.0 (4.8) Between Groups 354.740 1 354.740 15.982 0.045 0.000 ** No 3.8 (4.4) Within Groups 6947.304 611 22.615 Total 7302.044 612 Chronic Condition Duration 1 Year or less 4.6 (4.5) Between Groups 487.757 3 162.586 7.420 0.057 0.001 * 2–5 years 4.4 (4.4) 5–10 years 6.1 (4.7) Within Groups 6814.288 611 21.911 10 + years 9.9 (6.8) Total 7302.044 612 Perceived Health Weak 9.44(6.4) Between Groups 632.874 3 210.958 9.837 0.077 0.001 * Moderate 4.6(4.6) Good 6.3 (4.9) Within Groups 6669.171 611 21.444 Very Good 4.37 (4.1) Total 7302.044 612 Living Alone Yes 10.7 (4.4) Between Groups 2087.712 1 2087.712 125.612 0.283 0.001 ** Within Groups 5615.332 611 16.659 No 3.9 (3.9) Total 7302.044 612 Marital Status Divorced 7.8 (3.0) Between Groups 2650.993 5 530.615 35.225 0.352 0.001 * Widows 9.5 (4.5) Separate 9.5(4.7) Within Groups 4651.052 615 15.052 Married 3.2 (3.5) Single 8.7 (5.0) Total 7302.044 612 Other 13.0 (7.0) Education Illiterate 6.6 (5.8) Between Groups 356.523 5 71.305 3.172 0.001 0.008 * Only Read & Write 6.4 (5.3) Elementary 5.6 (4.4) Within Groups 6945.522 615 71.305 Mid school 5.0 (4.3) High school 3.7 (3.2) Total 7302.044 612 Graduated 4.9 (4.6) * ANOVA Test ** Independent Samples T Test [Please enter Table 1 here] Validity In the present study, the skewness was between ± 1, and an acceptable score was considered to be less than 2, indicating that the data were normally distributed [ 28 ]. The correlation matrix revealed that most item correlations were above 0.41, and the Kaiser–Meyer–Olkin value (KMO) was 0.854 (P < 0.001), which is greater than the recommended threshold of 0.6 [ 29 ]. On the basis of the EFA, the 6-item UCLA loneliness scale has two factors. Factor 1 (questions 1–3) and factor 2 (questions 4–6). We conducted a confirmatory factor analysis (CFA) via AMOS-24 to evaluate the proposed two-factor structure of the 6-item UCLA loneliness scale. The results, summarized in Table 2 , show a significant chi-square (p < 0.001), a relative chi-square of 15.314, a goodness-of-fit index (GFI) of 0.90, a comparative fit index (CFI) of 0.91, and a root mean square error of approximation (RMSEA) of 0.056. According to Furr (2011), the results support the two-factor structure as acceptable [ 30 ]. The final model is illustrated in Fig. 1 . Table 2 The goodness-of-fit indices of the extracted model for the 6-item UCLA Chi-Square df chi2/df n Sig. * CFI GFI RMSEA 15.314 5 3.06 612 0.004 0.91 0.90 0.056 * Two-sided Chi-squared test, P ≤ .05 Reliability The 6 items demonstrated moderate to high internal consistency with each other and total scores (Table 3 ). All path coefficients were significant at the p < 0.001 level. The 6-Item UCLA scale demonstrated excellent reliability, with a Cronbach’s alpha of 0.90, McDonald’s omega of 0.91 for the entire scale, and an ICC of 0.90 (p < 0.001). Table 3 Correlation between UCLA loneliness scores and total loneliness and happiness scores Variables 1 2 3 4 5 6 UCLA Loneliness F1 F2 Item_1 1 Item_2 0.766 1 Item_3 0.632 0.668 1 Item_4 0.639 0.642 0.743 1 Item_5 0.536 0.538 0.619 0.727 1 Item_6 0.479 0.548 0.638 0.702 0.760 1 UCLA Loneliness 0.810 0.837 0.848 0.883 0.828 0.820 1 F1 0.902 0.923 0.844 0.751 0.628 0.618 0.931 1 F2 0.608 0.636 0.736 0.893 0.915 0.908 0.931 0.735 1 De Jong Gierveld Loneliness Scale 0.441 0.419 0.505 0.499 0.441 0.438 0.544 0.504 0.507 HAPPINESS -0.147 -0.160 -0.307 -0.247 -0.309 -0.274 -0.283 -0.223 -0.306 * All correlation is significant at the 0.05 level (2-tailed). ROC Curve Analysis and Cutoff Points Table 4 shows the area under the ROC curve (AUC), sensitivity, specificity, and cutoff points for the 6-item UCLA Loneliness Scale. The optimal cutoff point for identifying severe loneliness in older adults is 12.5. Youden’s J, D value (Euclidean distance), and DIFF indices are used to assess biomarker effectiveness, with values close to 1 for J and near 0 for D and DIFF indicating the best cutoff point[ 31 ]. As indicated in Table 4 , the estimated cutoff points are acceptable. Table 4 AUC, sensitivity, specificity, and Youden's index for possible cutoff points of the 6-Item UCLA Variable AUC Cutoff Point (≥) Sensitivity Specificity Youden's J Distance Sqrt. (K-Index) DIFF LR+ LR- BACC DOR UCLA 0.691 12.5 0.980 0.947 0.927 0.023 0.033 0.033 -0.035 1.454 0.947 AUC, area under curve; CI, confidence interval; DIFF, abs (sensitivity– specificity); D Value or K-Index, Sqrt ((1-Sensitivity) ² + (1-Specificity) ²); LR, likelihood ratio; BACC, Balance of Accuracy; DOR, Diagnostic Odds Ratio Discussion This study inspected the psychometric characteristics of the short version of the UCLA loneliness scale and its cutoff points for loneliness levels in older adults in Iran. The results indicated acceptable internal consistency, reliability, structure, and convergent validity ( P ≤ 0.05). On the basis of the results of this study, the goodness-of-fit indices, such as the CFI and GFI, were greater than 0.9, which is acceptable. In the study of EKŞİ et al. (2022) in Turkey, which was conducted on a youth age group, the 6-item version of the 6-item UCLA loneliness scale was shown to have good fit indices[ 32 ]. In addition, Wongpakaran et al. (2020) reported a high model fit for the scale. Neto et al. (2014) also investigated the psychometrics of another 6-question version of the UCLA loneliness scale in Portuguese older adults, which had higher model fit indices than did the current scale, but its item-total correlation and reliability (Cronbach's alpha) were lower[ 33 ]. Unlike the study of EKŞİ et al., which reported that the 6-item UCLA loneliness scale has a single-factor structure[ 32 ], the results of CFA in the present study revealed that the scale has a two-factor structure, which explains 76% of the variance in loneliness. According to studies conducted with non-older adult participants, the scale explains only 42–54% of the variance in loneliness [ 32 ]. This finding shows that loneliness is likely perceived differently in old age than in other age groups. However, most studies have examined the scale’s psychometric properties across age groups. This makes it difficult to compare the results of the present study with those of other studies. Criterion validity was also investigated in the current study with the De Jong Gierveld Loneliness Scale and the Oxford Happiness Scale, which had a moderate positive correlation with the former and a weak negative correlation with the latter. EKŞİ et al. (2022) and Wongpakaran et al. (2020) reported high correlations between the 6-item version and the 3-item and 20-item versions of the UCLA Loneliness Scale, respectively [ 19 , 32 ]. The Cronbach’s alpha coefficient is one of the most commonly used methods to determine the reliability of a scale, and the reliability of the 6-Item UCLA loneliness scale was excellent (≥ 0.90). In addition to the Cronbach’s alpha coefficient, the scale reliability was examined by McDonald’s Omega, an optimal measure of reliability. This scale reliability indicator was excellent (> 0.90). The obtained Cronbach's alpha coefficients in the studies of EKŞİ et al. (2022) and Wongpakaran et al. (2020) were equal to 0.84, which showed good reliability for the scale[ 32 ]. In addition, the moderate positive item-total correlation (> 0.80) and moderate to very strong positive correlations of the items with each other in the present study indicate the high internal consistency of the tool. Additionally, EKŞİ et al. (2022) reported a very strong item-total correlation [ 32 ]. One advantage of the present study was the introduction of a cutoff point for the total scale score, which was not considered in previous studies. On this basis, the obtained cutoff point (12.5) has high sensitivity and specificity and can differentiate older adults with severe and nonsevere feelings of loneliness. Additionally, the relationships between loneliness and demographic and health variables were investigated in the present study. The study results revealed that loneliness was greater in older adults with increasing age, chronic conditions, multimorbidity, longer chronic conditions, poor perceived health, living alone, not being married, and a lower level of education. The sequence of events that occur with age increases loneliness in older adults. Retirement, chronic diseases, inability to self-care, and the loss of spouses and friends cause older adults' small social networks to decrease. Limited social networks and reduced social interactions eventually lead to an increase in loneliness with increasing age [ 34 ]. In the present study, the mean loneliness scores did not differ according to gender. A systematic review of longitudinal studies of risk factors for loneliness in older adults revealed that, in bivariate analyses, five out of six articles reported an increased risk of loneliness for women, whereas in multivariable analyses, only 2 out of 15 articles reported a positive association between loneliness and being a woman. In addition, 2 out of 15 articles reported a negative association between loneliness and being a woman. This finding suggests that gender in itself is likely not a risk factor for loneliness and that the relationship between gender and loneliness in old age is influenced by other factors, such as chronic diseases, being widowed, and depression. Unsurprisingly, these factors are more common in older women than in younger women [ 34 ]. In conclusion, the scale is a valid and reliable measurement tool for measuring and evaluating loneliness in older adults. An advantage of this study is the introduction of a sensitive cutoff point to differentiate severe loneliness in older adults. Conclusion The psychometric properties of the short version of the UCLA loneliness scale were assessed in the present study. On the basis of these results, the Persian version of the 6-item UCLA loneliness scale is a valid and reliable tool that can be utilized in community-based settings to measure older adults’ loneliness. Researchers suggest further studies to validate the 6-item UCLA loneliness scale in older adults residing in other settings. To generalize the results of the present study to other subgroups of older populations, such as those in nursing homes, sufficient precautions should be taken, especially regarding the cutoff point score. Abbreviations CFA Confirmatory Factor Analysis EFA Exploratory Factor Analysis AUC Area under the ROC Curve ROC Receiver operating characteristics ICC Intra-class Correlation Coefficient RMSEA Root Mean Square Error of Approximation GFI Goodness of Fit Index CFI Comparative of Fit Index GLS Generalized Least Squares KMO Kaiser-Meyer-Olkin CVR Content Validity Ratios ANOVA One-way Analysis of Variance UCLA University of California Loneliness Scale RULS-6 6-item Revised UCLA Loneliness Scale AMT Abbreviated Mental Test Declarations Ethical approval and consent to participate This study protocol was approved by the ethics committee of Golestan University of Medical Sciences (IR.GOUMS.REC.1401.038). Informed consent for participation in the study was obtained from all participants. The ethics committee approved the procedure for verbal consent since the study was observational and respected the code of ethics as stated in the declarations of Helsinki (2013). Consent for publication None. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Golestan University of Medical Sciences. Author Contribution M.Y. and F.B. wrote the main manuscript text and A.A. prepared figure 1 and tables 1-4. All authors reviewed the manuscript.A.A. and A.GH. assisted in the conceptualization and design of the study, oversaw the data collection, F.B. and MY collected the data, performed the data file preparation. A.A. interpreted the data, analyzed it, and extracted the results. All the authors read and approved the final manuscript. Acknowledgments Our warm thanks go to Goleston University of Medical Sciences and Shiraz University of Medical Sciences for their patience and participation in the study. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author (MY) upon reasonable request. References Perlman D. 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Cruchinho P, López-Franco MD, Capelas ML, Almeida S, Bennett PM, Miranda da Silva M, et al. Translation, Cross-Cultural Adaptation, and Validation of Measurement Instruments: A Practical Guideline for Novice Researchers. J Multidisciplinary Healthc. 2024;17(null):2701–28. https://doi.org/10.2147/JMDH.S419714 . Hosseinabadi R, Foroughan M, Ghaed Amini Harouni GR, Zeidali Beiranvand R, Pournia Y. Psychometric Properties of the Persian Version of the 6-item De Jong Gierveld Loneliness Scale in Iranian Community-dwelling Older Persons. Salmand: Iran J Ageing. 2020;15(3):338–49. https://doi.org/10.32598/sija.15.3.2515.2 . Gierveld JDJ, Tilburg TV. A 6-item scale for overall, emotional, and social loneliness: Confirmatory tests on survey data. Res aging. 2006;28(5):582–98. https://doi.org/https://doi.org/10.1177/0164027506289723 . Hills P, Argyle M. The Oxford Happiness Questionnaire: a compact scale for the measurement of psychological well-being. Pers Indiv Differ. 2002;33(7):1073–82. https://doi.org/https://doi.org/10.1016/S0191-8869(01)00213-6 . Dehshiri G, Akbari Balootbangan A, Najafi M, Moghadamzadeh A. Psychometric properties of the oxford happiness questionnaire short form in university students. Educational Meas Evaluation Stud. 2016;5(12):9–26. http://jresearch.sanjesh.org/article_20525_064b2d55d4ed799383df78a4f2e5ba9c.pdf . WHO. WHOQOL: Measuring Quality of Life 2020 [ https://www.who.int/tools/whoqol/whoqol-bref/docs/default-source/publishing-policies/whoqol-100-guidelines/translation-methodology Lawshe C. A Quantitative Approach to Content Validity. Personnel psychology/Berrett-Koehler; 1975. Cuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31–4. Mishra P, Pandey CM, Singh U, Gupta A, Sahu C, Keshri A. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth. 2019;22(1):67–72. https://doi.org/10.4103/aca.ACA_157_18 . Kaiser HF. An index of factorial simplicity. Psychometrika. 1974;39(1):31–6. https://doi.org/10.1007/BF02291575 . Furr M. Scale construction and psychometrics for social and personality psychology. 2011. https://doi.org/https://doi.org/10.4135/9781446287866 Zhou X-H, Obuchowski NA, McClish DK. Statistical methods in diagnostic medicine. Wiley; 2014. EKŞİ H. Adaptation of ruls-6 loneliness scale (6-item short form) into turkish: a validity and reliability study. 2022. https://doi.org/http://dx.doi.org /10.18844/cerj.v12i4.7466 Neto F. Psychometric analysis of the short-form UCLA Loneliness Scale (ULS-6) in older adults. Eur J Ageing. 2014;11:313–9. https://doi.org/https://doi.org/10.1007/s10433-014-0312-1 . Dahlberg L, McKee KJ, Frank A, Naseer M. A systematic review of longitudinal risk factors for loneliness in older adults. Aging & Mental Health. 2022;26(2):225 – 49.10.1080/13607863.2021.1876638.http://jresearch.sanjesh.org/article_20525_20064b 20522d20555d20524ed799383df799378a799384f799382e799385ba7 99389c.pdf 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-5420810","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378726142,"identity":"df246712-1117-4f92-9e79-835c22ae9e7d","order_by":0,"name":"Farzaneh Bahadori","email":"","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Farzaneh","middleName":"","lastName":"Bahadori","suffix":""},{"id":378726143,"identity":"58300b01-02d2-4b99-9eb0-d2b3b6860789","order_by":1,"name":"Abdolrahim Asadollahi","email":"","orcid":"","institution":"Shiraz University of Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Abdolrahim","middleName":"","lastName":"Asadollahi","suffix":""},{"id":378726144,"identity":"66a3afc2-12b9-41e0-897c-babc9490268f","order_by":2,"name":"Ogholgol Ghajari","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ogholgol","middleName":"","lastName":"Ghajari","suffix":""},{"id":378726145,"identity":"26373b4f-bb00-40b6-89bb-e053a7b54fa1","order_by":3,"name":"Mahsa Yarelahi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYFACHijN3sDATKIWngMka5FIIFKLbvvZwx9+MByWN5/5xvBzQYUNA397dwJeLWZn8tIkexgOG865nWMsPeNMGoPEmbMb8Gs5kGMGdNthxhnSOQbSvG2HGQwkcgloOf/G+OMfhsP2MyTPGP8mTssNoOFAWxJnSPCYEWnLjTdm0jIG6ckzeNLKrHnOpPEQ9sv5HOOPbyqsbWewH958m6fCRo6/vRe/FggwaAYSHAYgJg8BpXBQB8TsD4hVPQpGwSgYBSMMAAAPZ0NPqW5dUAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mahsa","middleName":"","lastName":"Yarelahi","suffix":""}],"badges":[],"createdAt":"2024-11-09 08:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5420810/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5420810/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71565384,"identity":"65e82d67-160a-4238-9713-c9ba9189e2be","added_by":"auto","created_at":"2024-12-16 17:35:37","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePath Diagram for the Confirmatory Factor Analysis of UCLA, 6-Items\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5420810/v1/d782c4f76859ee3820d41a42.jpeg"},{"id":71567099,"identity":"fb8d4c15-d600-44ba-8682-d497ac71dac6","added_by":"auto","created_at":"2024-12-16 17:51:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":864804,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5420810/v1/cdd60780-24af-40dc-9989-10aa06d1b934.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychometric Properties and Cross-Cultural Adaptation of the 6-item UCLA Loneliness Scale among Older Iranian People","fulltext":[{"header":"Background","content":"\u003cp\u003eLoneliness is an unpleasant feeling caused by a discrepancy between desired and perceived levels of social connectedness[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Accordingly, a person may experience loneliness despite social interactions because interactions with others alone cannot prevent loneliness [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Individuals of all ages can experience loneliness. However, older adults are more prone to social isolation and loneliness than any other age group for a variety of reasons, such as the death of a partner, retirement, an empty nest, a decline in functions, and health problems [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLoneliness is a major health problem for older adults. Studies have shown that loneliness is significantly associated with depression, functional defects, high blood pressure, suicide, limited mobility [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], low quality of life, poor sleep quality, poor cognitive ability[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and mortality[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, loneliness increases the risk of malnutrition in older adults by 4.2 times [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to a recent review, the prevalence of loneliness among older adults in different European countries varies from 3\u0026ndash;34%. Aging is considered a risk factor for loneliness, and approximately 50% of people aged 80 years and older report loneliness[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to a recent study, loneliness is prevalent among older Iranian adults, particularly those who live alone and have underlying health conditions[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the high prevalence of loneliness in old individuals and its negative impact on older adults' health, it is necessary to evaluate loneliness using valid scales. To date, several scales have been created to measure loneliness, but the most commonly used scale in the general population is the UCLA Loneliness Scale [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It was developed by Russell and colleagues in 1978 to assess the social and emotional dimensions of loneliness [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This scale has become a significant scale in clinical settings and research because studies have reported its acceptable validity and reliability across different countries [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, an evaluation of the 20-item version of this scale among Iranian adults revealed good psychometric properties[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the large number of scale items causes physicians and researchers to face problems during interviews or assessments of older adults. For example, in the phone-based study of Hosseini Moghaddam et al. (2021), some older adults ended phone conversation before completing the scale because of exhaustion [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Illiteracy and low levels of education in older adults also lead to more problems completing the scale[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, in cases where the aim is to measure psychological concepts such as loneliness, an interview is the only way to examine an individual's subjective situation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, to measure loneliness in older adults, it is better to use shorter but valid versions of the scale rather than longer versions[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral shortened versions of the University of California (UCLA) Loneliness Scale have been developed to reduce the length of the questionnaire and increase response rates in clinical and longitudinal studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recently, Wongpakaran et al. (2020) validated the 20-item scale and developed the 6-item Revised UCLA Loneliness Scale (RULS-6) in a study with 719 participants [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The RULS-6 scale has good validity and reliability and can identify feelings of loneliness in clinical and community settings. Another advantage of the RULS-6 scale is that it is not affected by the gender or age of the subjects. Additionally, the RULS-6 scale had a high correlation (r\u0026thinsp;=\u0026thinsp;0.88) with the 20-item version [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven that developing tools is time-consuming and costly, valid existing tools should be translated, culturally adapted, and assessed for psychometric properties in the target country[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Adapting the RULS-6 scale will increase the breadth of our knowledge of the loneliness phenomenon in older adults, identify the true prevalence of loneliness in old age, and provide the basis for designing interventions and preventive measures to alleviate loneliness in aging individuals[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Hence, the present study was conducted to psychometrically validate the 6-item California Loneliness Questionnaire in Iranian older adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe present study is a psychometric study conducted in 2023. On the basis of the reported prevalence of loneliness in Iranian older adults by Asadollahi et al.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and considering an alpha level of 0.05 and a precision of 0.1%, the sample size was estimated to be 600 participants. After the 4% drop rate was considered, it increased to 624 participants.\u003c/p\u003e \u003cp\u003eShiraz and Gonbad-e Kavus were selected for the study because of the rapid growth rate of their older adult population. A stratified random sampling method was used to select the study participants. Shiraz city and Gonbad-e Kavus city have 30 and 7 comprehensive health centers, respectively. According to the urban maps, each city was divided into four regions (North, South, East, and West), and one health center was randomly selected from each region.\u003c/p\u003e \u003cp\u003eA total of 78 older adults were randomly selected from each center via the integrated electronic health record system (SIB). Researchers reviewed the inclusion criteria by calling registered phone numbers in electronic health records to invite eligible older adults to participate in the study. The inclusion criteria were being 60 years or older, residing in Shiraz, scoring 7 or higher on the Abbreviated Mental Test (AMT) (indicating normal cognitive status), having the ability to communicate verbally, and being willing to participate in the study. The exclusion criteria included incomplete questionnaire responses and withdrawal from the study. Eligibility was assessed through telephone contact, and those who met the criteria were invited for in-person interviews. Eligible participants completed the demographic questionnaire, the Persian version of the GLFS, the abbreviated mental health assessment questionnaire, and the informed consent form through face‒to-face interviews. Data were collected from older adults' homes through face‒to-face interviews from early July to mid-October 2023.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInstruments\u003c/h2\u003e \u003cp\u003eA researcher-made demographic questionnaire, the RULS-6 scale, the DeJung-Gierveld Loneliness Questionnaire, and the Happiness Questionnaire were used for data collection.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDemographic questionnaire: This questionnaire includes questions about participants' gender, age, education level, living situation, and perceived health status.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e6-Item UCLA Loneliness Scale: Wongpakaran et al. (2020) designed the California Loneliness Questionnaire 6-item version (RULS-6). The scale is scored on a four-point Likert scale from never (0) to always (3), with higher scores indicating more loneliness. Their study revealed a high correlation of 0.88 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the RULS-6 and the longer 20-item version, as well as with other scales. Cronbach's alpha for the RULS-6 ranges from 0.72 to 0.84 for various samples[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe DeJang-Gierveld loneliness questionnaire: This questionnaire, developed by DeJang and Ventilberg in 2006, assesses loneliness via eleven questions with a three-point Likert scale: \"no\" (0), \"somewhat\" (1), and \"yes\" (2)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Scores range from 0 to 11, with higher scores indicating greater loneliness. Hosseinabadi et al. (2020) reported an internal consistency coefficient of 0.69 in the older Iranian population for this questionnaire[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOxford Happiness Questionnaire: This eight-question tool, developed by Hill and Argyle (2002), evaluates happiness via a six-point Likert scale ranging from strongly disagree (score 1) to strongly agree (score 6). Total scores range from 8\u0026ndash;48, with higher scores indicating greater happiness[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Dehshiri et al. (2016) reported a 0.90 correlation between this abbreviated questionnaire and its longer version (29-item questionnaire), with a Cronbach's alpha of 0.71[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In the present study, the Cronbach's alphas for the Giervild Loneliness and Oxford Happiness questionnaires were 0.80 and 0.72, respectively.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy implementation\u003c/h3\u003e\n\u003cp\u003eAfter necessary permission was obtained, the World Health Organization protocol was used to translate and validate the 6-item UCLA loneliness scale [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Two independent Persian translators first translated the original English questionnaire. The translators then met to review and create a common version. Face validity was assessed qualitatively through interviews with 10 older adults with varying education levels.\u003c/p\u003e \u003cp\u003eOlder adults evaluated the difficulty, appropriateness, and clarity of the questions, providing suggestions for improvement. After confirming face validity, the content validity of the 6-item UCLA loneliness scale was examined via the content validity ratio (CVR) with the participation of 5 older adults and 5 gerontologists. According to the Lawshe table, the acceptable CVR limit with 10 raters is 0.62[26), and in this study, all the questions exceeded a validity ratio of 0.91. After confirming its validity, each translator independently translated the questionnaire into English. The translations were then merged under the supervision of two faculty members from Shiraz University of Medical Sciences to create a version that closely resembled the original version.\u003c/p\u003e \u003cp\u003eTo determine construct validity, exploratory factor analysis (EFA) with varimax rotation was conducted with 612 older participants to identify questionnaire factors. The sample adequacy and correlation coefficients between questions were assessed via the Kaiser‒Meyer‒Olkin (KMO) test and Bartlett sphericity test, respectively. Subsequently, confirmatory factor analysis (CFA) was performed via AMOS software version 24 and the generalized least squares (GLS) method to evaluate the model fit indices.\u003c/p\u003e \u003cp\u003eIn the third stage, the internal consistency of the questionnaire was tested by determining McDonald's omega coefficient, Cronbach's alpha, and Pearson correlation. The scale's internal consistency was assessed by computing the intracluster correlation coefficient (ICC). In the final stage, the cutoff points of the questionnaire were determined via receiver operating characteristic (ROC) curve analysis, computing Youden's J coefficient, DOR index, BACC index, DIFF coefficient, and D coefficient. The study followed the Helsinki Declaration and STROBE guidelines[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], received approval from the Ethics Committee of Golestan University of Medical Sciences (IR.GOUMS.REC.1401.038), and obtained informed consent from all participants.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Description\u003c/h2\u003e \u003cp\u003eOut of 630 participants in the study, 12 were excluded because of incomplete questionnaires. The mean age of the 612 older participants was 68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 years (females\u0026thinsp;=\u0026thinsp;60.9% \u0026amp; males\u0026thinsp;=\u0026thinsp;39.1%). Most of the participants had a diploma (22.2%), were married (66.7%), had very good perceived health (43.1%), had at least one chronic condition (78.6%), had no multimorbidity (56.5%), and were Fars (75.5%). Approximately 22.9% of the participants lived alone, and among them, 37.9% lived alone for more than six years. The mean scores of loneliness and happiness were 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 (for the 6-item UCLA loneliness scale), 5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 (for the DeJong Gierveld loneliness scale), and 25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4, respectively. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a one-way analysis of variance (ANOVA) and independent samples t test revealed that loneliness was greater in older adults with increasing age, chronic conditions, multimorbidity, longer chronic conditions, poor health, living alone, not married, and a lower level of education. The loneliness mean scores did not differ by gender.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-way ANOVA for Demographic and Health Factors (n\u0026thinsp;=\u0026thinsp;612)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eES: Omega Sqr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e60\u0026ndash;75 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3.3(4.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBetween Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e276.439\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e138.220\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e75\u0026ndash;85 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.0(5.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWithin Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e11605.907\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e609\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e19.057\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e85\u0026thinsp;+\u0026thinsp;years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3.6(4.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e11882.346\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e611\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003emale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e5.2(5.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBetween Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.678\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.678\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.865\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eWithin Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e7301.367\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e611\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e23.327\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003efemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e5.1(4.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e7302.044\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e612\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eChronic Condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e5.5(4.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBetween Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e611.405\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e611.405\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e13.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWithin Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e6990.639\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e611\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e22.334\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.6 (3.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e7302.044\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e612\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMultimorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6.0 (4.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBetween Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e354.740\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e354.740\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e15.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e3.8 (4.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWithin Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e6947.304\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e611\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e22.615\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e7302.044\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e612\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eChronic Condition Duration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1 Year or less\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4.6 (4.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eBetween Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e487.757\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e162.586\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e7.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e2\u0026ndash;5 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4.4 (4.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e5\u0026ndash;10 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6.1 (4.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWithin Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e6814.288\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e611\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e21.911\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10\u0026thinsp;+\u0026thinsp;years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e9.9 (6.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e7302.044\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e612\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePerceived Health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeak\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e9.44(6.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eBetween Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e632.874\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e210.958\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003e9.837\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eModerate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4.6(4.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGood\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6.3 (4.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWithin Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e6669.171\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e611\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e21.444\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVery Good\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4.37 (4.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e7302.044\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e612\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eLiving Alone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e10.7 (4.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBetween Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e2087.712\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e2087.712\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003e125.612\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eWithin Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e5615.332\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e611\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e16.659\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e3.9 (3.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e7302.044\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e612\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDivorced\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e7.8 (3.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eBetween Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e2650.993\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e530.615\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cem\u003e35.225\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWidows\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e9.5 (4.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSeparate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e9.5(4.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eWithin Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e4651.052\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e615\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e15.052\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMarried\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3.2 (3.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSingle\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e8.7 (5.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e7302.044\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e612\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOther\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e13.0 (7.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIlliterate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6.6 (5.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eBetween Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e356.523\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e71.305\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e3.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOnly Read \u0026amp; Write\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6.4 (5.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eElementary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.6 (4.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eWithin Groups\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e6945.522\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e615\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e71.305\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMid school\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.0 (4.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHigh school\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3.7 (3.2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e7302.044\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e612\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGraduated\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4.9 (4.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e* ANOVA Test\u003c/p\u003e \u003cp\u003e** Independent Samples T Test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Please enter Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidity\u003c/h3\u003e\n\u003cp\u003eIn the present study, the skewness was between \u0026plusmn;\u0026thinsp;1, and an acceptable score was considered to be less than 2, indicating that the data were normally distributed [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The correlation matrix revealed that most item correlations were above 0.41, and the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin value (KMO) was 0.854 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which is greater than the recommended threshold of 0.6 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. On the basis of the EFA, the 6-item UCLA loneliness scale has two factors. Factor 1 (questions 1\u0026ndash;3) and factor 2 (questions 4\u0026ndash;6).\u003c/p\u003e \u003cp\u003eWe conducted a confirmatory factor analysis (CFA) via AMOS-24 to evaluate the proposed two-factor structure of the 6-item UCLA loneliness scale. The results, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, show a significant chi-square (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a relative chi-square of 15.314, a goodness-of-fit index (GFI) of 0.90, a comparative fit index (CFI) of 0.91, and a root mean square error of approximation (RMSEA) of 0.056. According to Furr (2011), the results support the two-factor structure as acceptable [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The final model is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe goodness-of-fit indices of the extracted model for the 6-item UCLA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003echi2/df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e* Two-sided Chi-squared test, P\u0026thinsp;\u0026le;\u0026thinsp;.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReliability\u003c/h2\u003e \u003cp\u003eThe 6 items demonstrated moderate to high internal consistency with each other and total scores (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All path coefficients were significant at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 level. The 6-Item UCLA scale demonstrated excellent reliability, with a Cronbach\u0026rsquo;s alpha of 0.90, McDonald\u0026rsquo;s omega of 0.91 for the entire scale, and an ICC of 0.90 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between UCLA loneliness scores and total loneliness and happiness scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUCLA Loneliness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eItem_1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eItem_2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eItem_3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eItem_4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eItem_5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eItem_6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUCLA Loneliness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDe Jong Gierveld Loneliness Scale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHAPPINESS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e* All correlation is significant at the 0.05 level (2-tailed).\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eROC Curve Analysis and Cutoff Points\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the area under the ROC curve (AUC), sensitivity, specificity, and cutoff points for the 6-item UCLA Loneliness Scale. The optimal cutoff point for identifying severe loneliness in older adults is 12.5. Youden\u0026rsquo;s J, D value (Euclidean distance), and DIFF indices are used to assess biomarker effectiveness, with values close to 1 for J and near 0 for D and DIFF indicating the best cutoff point[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As indicated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the estimated cutoff points are acceptable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAUC, sensitivity, specificity, and Youden's index for possible cutoff points of the 6-Item UCLA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCutoff Point (\u0026ge;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYouden's J\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDistance Sqrt. (K-Index)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDIFF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLR+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLR-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBACC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDOR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAUC, area under curve; CI, confidence interval; DIFF, abs (sensitivity\u0026ndash; specificity); D Value or K-Index, Sqrt ((1-Sensitivity) \u0026sup2; + (1-Specificity) \u0026sup2;); LR, likelihood ratio; BACC, Balance of Accuracy; DOR, Diagnostic Odds Ratio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study inspected the psychometric characteristics of the short version of the \u003cb\u003eUCLA loneliness\u003c/b\u003e scale and its cutoff points for loneliness levels in older adults in Iran. The results indicated acceptable internal consistency, reliability, structure, and convergent validity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eOn the basis of the results of this study, the goodness-of-fit indices, such as the CFI and GFI, were greater than 0.9, which is acceptable. In the study of EKŞİ et al. (2022) in Turkey, which was conducted on a youth age group, the 6-item version of the 6-item UCLA loneliness scale was shown to have good fit indices[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, Wongpakaran et al. (2020) reported a high model fit for the scale. Neto et al. (2014) also investigated the psychometrics of another 6-question version of the UCLA loneliness scale in Portuguese older adults, which had higher model fit indices than did the current scale, but its item-total correlation and reliability (Cronbach's alpha) were lower[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Unlike the study of EKŞİ et al., which reported that the 6-item UCLA loneliness scale has a single-factor structure[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], the results of CFA in the present study revealed that the scale has a two-factor structure, which explains 76% of the variance in loneliness. According to studies conducted with non-older adult participants, the scale explains only 42\u0026ndash;54% of the variance in loneliness [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This finding shows that loneliness is likely perceived differently in old age than in other age groups. However, most studies have examined the scale\u0026rsquo;s psychometric properties across age groups. This makes it difficult to compare the results of the present study with those of other studies.\u003c/p\u003e \u003cp\u003eCriterion validity was also investigated in the current study with the De Jong Gierveld Loneliness Scale and the Oxford Happiness Scale, which had a moderate positive correlation with the former and a weak negative correlation with the latter. EKŞİ et al. (2022) and Wongpakaran et al. (2020) reported high correlations between the 6-item version and the 3-item and 20-item versions of the UCLA Loneliness Scale, respectively [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Cronbach\u0026rsquo;s alpha coefficient is one of the most commonly used methods to determine the reliability of a scale, and the reliability of the 6-Item UCLA loneliness scale was excellent (\u0026ge;\u0026thinsp;0.90). In addition to the Cronbach\u0026rsquo;s alpha coefficient, the scale reliability was examined by McDonald\u0026rsquo;s Omega, an optimal measure of reliability. This scale reliability indicator was excellent (\u0026gt;\u0026thinsp;0.90). The obtained Cronbach's alpha coefficients in the studies of EKŞİ et al. (2022) and Wongpakaran et al. (2020) were equal to 0.84, which showed good reliability for the scale[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, the moderate positive item-total correlation (\u0026gt;\u0026thinsp;0.80) and moderate to very strong positive correlations of the items with each other in the present study indicate the high internal consistency of the tool. Additionally, EKŞİ et al. (2022) reported a very strong item-total correlation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne advantage of the present study was the introduction of a cutoff point for the total scale score, which was not considered in previous studies. On this basis, the obtained cutoff point (12.5) has high sensitivity and specificity and can differentiate older adults with severe and nonsevere feelings of loneliness.\u003c/p\u003e \u003cp\u003eAdditionally, the relationships between loneliness and demographic and health variables were investigated in the present study. The study results revealed that loneliness was greater in older adults with increasing age, chronic conditions, multimorbidity, longer chronic conditions, poor perceived health, living alone, not being married, and a lower level of education. The sequence of events that occur with age increases loneliness in older adults. Retirement, chronic diseases, inability to self-care, and the loss of spouses and friends cause older adults' small social networks to decrease. Limited social networks and reduced social interactions eventually lead to an increase in loneliness with increasing age [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, the mean loneliness scores did not differ according to gender. A systematic review of longitudinal studies of risk factors for loneliness in older adults revealed that, in bivariate analyses, five out of six articles reported an increased risk of loneliness for women, whereas in multivariable analyses, only 2 out of 15 articles reported a positive association between loneliness and being a woman. In addition, 2 out of 15 articles reported a negative association between loneliness and being a woman. This finding suggests that gender in itself is likely not a risk factor for loneliness and that the relationship between gender and loneliness in old age is influenced by other factors, such as chronic diseases, being widowed, and depression. Unsurprisingly, these factors are more common in older women than in younger women [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, the scale is a valid and reliable measurement tool for measuring and evaluating loneliness in older adults. An advantage of this study is the introduction of a sensitive cutoff point to differentiate severe loneliness in older adults.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe psychometric properties of the short version of the UCLA loneliness scale were assessed in the present study. On the basis of these results, the Persian version of the 6-item UCLA loneliness scale is a valid and reliable tool that can be utilized in community-based settings to measure older adults\u0026rsquo; loneliness. Researchers suggest further studies to validate the 6-item UCLA loneliness scale in older adults residing in other settings. To generalize the results of the present study to other subgroups of older populations, such as those in nursing homes, sufficient precautions should be taken, especially regarding the cutoff point score.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExploratory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the ROC Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntra-class Correlation Coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGoodness of Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative of Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Least Squares\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKMO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaiser-Meyer-Olkin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eContent Validity Ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOne-way Analysis of Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUCLA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversity of California Loneliness Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRULS-6\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e6-item Revised UCLA Loneliness Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbbreviated Mental Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was approved by the ethics committee of Golestan University of Medical Sciences (IR.GOUMS.REC.1401.038). Informed consent for participation in the study was obtained from all participants. The ethics committee approved the procedure for verbal consent since the study was observational and respected the code of ethics as stated in the declarations of Helsinki (2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Golestan University of Medical Sciences.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eM.Y. and F.B. wrote the main manuscript text and A.A. prepared figure 1 and tables 1-4. All authors reviewed the manuscript.A.A. and A.GH. assisted in the conceptualization and design of the study, oversaw the data collection, F.B. and MY collected the data, performed the data file preparation. A.A. interpreted the data, analyzed it, and extracted the results. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eOur warm thanks go to \u003cem\u003eGoleston University of Medical Sciences\u003c/em\u003e and \u003cem\u003eShiraz University of Medical Sciences\u003c/em\u003e for their patience and participation in the study.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author (MY) upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePerlman D. 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Aging \u0026amp; Mental Health. 2022;26(2):225\u0026thinsp;\u0026ndash;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u0026thinsp;49.10.1080/13607863.2021.1876638.http://jresearch.sanjesh.org/article_20525_20064b 20522d20555d20524ed799383df799378a799384f799382e799385ba7 99389c.pdf\u003c/span\u003e\u003cspan address=\"http://\u0026thinsp;49.10.1080/13607863.2021.1876638.http://jresearch.sanjesh.org/article_20525_20064b20522d20555d20524ed799383df799378a799384f799 382e799385ba799389c.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"in Geriatric Health, Department of Gerontology, Faculty of Health, Shiraz University of Medical Sciences, Shiraz, Iran","lastPublishedDoi":"10.21203/rs.3.rs-5420810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5420810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLoneliness is common in old individuals, and the UCLA loneliness scale is one of the most reliable tools for measuring loneliness worldwide. The present study aimed to investigate the psychometric properties of the 6-item UCLA loneliness scale in the Iranian older population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this psychometric study, we outline the translation and validation of the 6-item UCLA loneliness scale among 612 older adults with a mean age of 68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 years (females\u0026thinsp;=\u0026thinsp;60.9%) in 2023. The participants were selected via stratified random sampling. Data were collected through face‒to-face interviews via the UCLA Loneliness Scale, Loneliness Scale, Oxford Happiness Scale, and demographic questionnaire. The data were analyzed via SPSS version 26 and AMOS version 25 software. The content validity, construct validity, and internal consistency were investigated, and ROC analysis and convergent validity were also assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eApproximately two-thirds of the participants were married, and three-fourths had at least one chronic condition. The EFA assigned a two-factor solution for the UCLA loneliness scale, confirmed by CFA (GFI\u0026thinsp;=\u0026thinsp;0.90, CFI\u0026thinsp;=\u0026thinsp;0.91, and RMSEA\u0026thinsp;=\u0026thinsp;0.056). Internal consistency was confirmed by the ICCs, Cronbach\u0026rsquo;s alphas, and McDonald\u0026rsquo;s Omega values (a\u0026thinsp;\u0026ge;\u0026thinsp;0.90). The ROC analysis indicated an exact cutoff value for older adults with and without severe loneliness with high sensitivity and specificity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe Persian version of the 6-Item UCLA loneliness scale presented adequate psychometric properties and could be used to confidently measure loneliness in community-dwelling older adults.\u003c/p\u003e","manuscriptTitle":"Psychometric Properties and Cross-Cultural Adaptation of the 6-item UCLA Loneliness Scale among Older Iranian People","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 17:35:33","doi":"10.21203/rs.3.rs-5420810/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":"e024e2f0-00e9-41df-b5ab-91cc7acfb409","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-16T17:35:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 17:35:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5420810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5420810","identity":"rs-5420810","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}