The Persian version of the Older Adults Lifestyle Scale: A Translation and Psychometrics

The Persian version of the Older Adults Lifestyle Scale: A Translation and Psychometrics | 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 The Persian version of the Older Adults Lifestyle Scale: A Translation and Psychometrics Fatemeh Mehriyan, Neda Ahmadzadeh Tori, Hamid Sharif-Nia, Samaneh Pourhadi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6740618/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 The global increase in the elderly population necessitates reliable tools to assess lifestyle factors that influence healthy aging. Existing Western lifestyle assessment instruments often lack cultural relevance for non-Western populations, including Iran. This study aimed to translate, culturally adapt, and validate the Persian version of the Older Adults Lifestyle Scale (P-OALS) to provide a contextually appropriate tool for evaluating lifestyle behaviors among Iranian older adults. Methods In a cross-sectional methodological study, 397 adults aged ≥ 60 were recruited from Babol, Iran. The P-OALS was translated using the Forward-Backward method and rigorously evaluated for psychometric properties. Face and content validity were assessed by expert review (CVI > 0.79, CVR > 0.62). Construct validity was examined through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Reliability was tested via internal consistency (Cronbach’s α) and test-retest stability (ICC), while convergent/discriminant validity was analyzed using Composite Reliability (CR > 0.7) and Average Variance Extracted (AVE > 0.5). Results EFA revealed a robust four-factor structure—Quality of Relationships (6 items), Preventive Behaviors (5 items), Nutrition (4 items), and Physical Activity (4 items)—accounting for 55.4% of the total variance. CFA confirmed excellent model fit (CFI = 0.923, RMSEA = 0.064). The P-OALS demonstrated strong reliability (α = 0.86, ICC = 0.859) and validity, with all subscales meeting psychometric benchmarks. Conclusion The P-OALS is a valid, reliable, and culturally adapted instrument for assessing the lifestyle of Iranian older adults. Its concise format and contextual relevance make it valuable for research and clinical practice. Future studies should explore its applicability in broader populations and longitudinal settings to further establish generalizability. Elderly Lifestyle Questionnaire Psychometrics Translation Validation Figures Figure 1 Introduction Aging represents an inevitable biological process marked by complex biological, psychological, and social transformations across the lifespan. Recent decades have witnessed remarkable advancements in medical and healthcare sectors, leading to increased life expectancy and a consequent global surge in older adult populations ( 1 ). According to World Health Organization (WHO) projections, the population aged 65 and older will surpass 800 million by 2025 ( 2 ), with developing nations accounting for 70% of this demographic shift ( 3 ). Current estimates suggest that by 2030, elderly populations in these regions will expand nine fold( 4 ). Iran's latest census data indicate that over 10% of the population is now aged 60 or older, with this proportion expected to grow substantially in coming decades ( 5 ). This rapid demographic transition presents significant economic, social, and public health challenges that demand urgent attention. The expanding elderly cohort has been accompanied by a rising prevalence of age-related health conditions, including diabetes, cardiovascular diseases, and mental health disorders ( 6 ). While these conditions can substantially diminish the quality of life, growing evidence demonstrates that lifestyle modifications may effectively prevent disease progression and enhance overall well-being ( 7 ). Lifestyle encompasses a constellation of habitual behaviors, with healthy lifestyles specifically comprising health-promoting activities such as balanced nutrition, regular physical activity, effective stress management, positive social engagement, and avoidance of harmful behaviors. These behavioral patterns emerge from dynamic interactions between individual life opportunities and stable variables including ethnicity, age, socioeconomic status, and socialization experiences ( 8 ). Consequently, personal choices and available opportunities fundamentally influence the development of health-related habits and behaviors( 9 ). Proactive health strategies incorporating physical activity, nutritional awareness, and stress reduction techniques can significantly mitigate health risks in older populations ( 10 ). By consciously adopting healthy lifestyle practices, individuals can actively influence their aging trajectory, fostering vitality, independence, and sustained well-being during later life stages. The intricate relationship between aging processes and lifestyle choices underscores the transformative potential of preventive measures in determining quality of life among elderly populations. Given the well-established connections between aging, chronic conditions, and lifestyle factors, a comprehensive assessment of older adults' attitudes and behaviors toward lifestyle components provides healthcare professionals with valuable insights for evaluating living patterns and designing targeted interventions ( 11 ). While Western societies have developed numerous lifestyle assessment instruments - including the Health-Promoting Lifestyle Profile II (HPLP-II) ( 12 ), Short Form Health Survey (SF-36) ( 13 ), WHOQOL-OLD ( 14 ), and Physical Activity Scale for the Elderly (PASE) ( 15 ) - these tools frequently prove culturally inappropriate for populations with distinct social characteristics. In Iran, where significant sociocultural differences exist and many older adults have limited formal education, the implementation of complex assessment tools presents considerable challenges. Although several Persian-language questionnaires have been developed, including Eshaghi's 46-item Elderly Lifestyle Assessment ( 16 ) and Montazeri's Healthy Lifestyle Questionnaire for the Elderly (HEL) ( 17 ), these instruments either lack comprehensive lifestyle coverage or prove excessively lengthy for practical use. This underscores the pressing need for culturally sensitive, easily comprehensible assessment tools tailored to Iran's elderly population. Cultural context plays a pivotal role in shaping lifestyle behaviors and their assessment. The translation and validation of the Older Adults Lifestyle Scale (OALS) within Iran's sociocultural framework offers significant potential for identifying lifestyle-associated factors among elderly Iranians. Cultural variations substantially impact questionnaire interpretation and response patterns, necessitating rigorous validation of assessment tools for specific populations. Presently, the psychometric properties of the Persian version of OALS (P-OALS) remain unevaluated within Iranian populations. This validation study contributes to ongoing instrument refinement while supporting cross-cultural applicability in geriatric research. The current study aimed to establish the reliability and validity of P-OALS among the Iranian elderly through comprehensive translation and psychometric evaluation. By developing a culturally adapted assessment tool, this research provides clinicians and researchers with an effective instrument for evaluating lifestyle patterns in Persian-speaking elderly populations, ultimately facilitating the development of targeted, culturally appropriate interventions to promote healthy aging. Methods Study Design and Setting This cross-sectional methodological study was conducted in Babul, Iran, between February and March 2025. The research comprised two sequential phases: ( 1 ) translation of the healthy lifestyle scale and ( 2 ) psychometric evaluation of its Persian version. Data collection occurred across multiple healthcare settings, including hospitals, clinics, and affiliated health institutions in Babul. Participants, Sample Size, and data collection The target population consisted of community-dwelling adults aged ≥ 60 years residing in Babul. Inclusion criteria required participants to meet the age threshold and provide voluntary informed consent. Exclusion criteria encompassed cognitive impairment, severe comorbid conditions, or unwillingness to participate. Following MacCallum's recommendations for psychometric studies ( 18 ), we established a minimum sample requirement of 200 cases. To accommodate separate samples for construct validation analyses, we recruited 397 participants through convenience sampling. This approach ensured adequate statistical power for both exploratory and confirmatory analyses. Trained researchers administered the questionnaires through structured face-to-face sessions, beginning with a thorough explanation of the study objectives to ensure participant understanding. Under direct researcher supervision, participants completed the questionnaires, a methodological approach designed to achieve multiple objectives: maintaining exceptionally high response rates exceeding 95%, providing immediate clarification for any participant questions or uncertainties, and minimizing missing data through real-time verification of responses during the completion process. This supervised administration protocol enhanced data quality while maintaining standardized conditions across all participants. Original Questionnaire The study employed the OALS, originally developed by Dr. Luana Ferreira at the Federal University of Juiz de For a ( 8 ). This instrument contains 19 items organized into four distinct subscales: preventive behaviors (5 items), nutrition (4 items), physical activity (4 items), and quality of social relationships (6 items). Each item utilizes a standardized 5-point Likert scale (1 = never to 5 = always), yielding potential total scores ranging from 19 to 95 points. The scoring methodology involves a simple summation of all item responses, with higher aggregate scores reflecting more favorable lifestyle patterns. Translation Procedure Following formal authorization from the original scale developer, we implemented a rigorous Forward-Backward translation protocol to ensure linguistic and conceptual equivalence. The process commenced with two parallel translations performed by independent bilingual translators: one translator without specialized medical training (to preserve natural language usage) and one academic translator from a medical university (to maintain technical accuracy). These translations underwent systematic comparison and harmonization by a panel of three bilingual researchers to produce a consensus Persian version. An independent translator, blinded to the original English version, then performed a back-translation of this preliminary Persian version into English. The back-translated version underwent meticulous review by the original developer, Dr. Luana Ferreira, who provided expert feedback on semantic discrepancies and conceptual equivalencies. Through an iterative revision process incorporating this feedback, we achieved final approval of the Persian version. Psychometric Evaluation Face Validity Assessment The preliminary questionnaire underwent face validity testing through a dual evaluation process involving both target population representatives and subject matter experts. Ten older adults completed the instrument while providing detailed feedback on items of clarity, transparency, linguistic appropriateness, and comprehensibility of the items, which led to necessary modifications. Then, a panel of health professionals conducted evaluations focusing on: the content, clarity, readability, simplicity, understandability of the questions, and practical administration considerations. All revisions were carefully documented and reviewed to ensure they maintained conceptual equivalence with the source questionnaire while optimizing cultural appropriateness for the Iranian elderly. Content Validity Assessment To assess content validity, the Persian version of the questionnaire was reviewed by a panel of experts. Content validity was evaluated both qualitatively (assessing wording, grammar, and relevance of items) and quantitatively by calculating the Content Validity Ratio (CVR) and Content Validity Index (CVI), as detailed below: To assess the questionnaire's content validity, 10 experts in gerontology, health education, and psychometrics were recruited. They evaluated the instrument by completing it and providing feedback based on the CVI. For the CVR, each item was rated using three options: (a) essential , (b) useful but not essential , or (c) not essential . The CVR was calculated using Lawshe’s formula: *(Ne − N/2)/(N/2)*, where Ne = number of experts selecting "essential" and N = total experts. The cutoff value was determined using Lawshe’s table, which specifies a minimum CVR of 0.62 for 10 experts ( 19 ). For the CVI evaluation, the experts independently assessed each item based on three criteria—simplicity, specificity, and clarity—using a 4-point Likert scale. The CVI score was calculated as the percentage of experts who rated an item as 3 or 4 (indicating high relevance). Items with a CVI > 0.79 were retained as highly appropriate, while those scoring 0.70–0.79 were revised for improvement. Items with a CVI < 0.70 were discarded for failing to meet the minimum validity threshold ( 20 ). Construct Validity The factorial structure of the scale was examined through a two-stage analytical approach. First, EFA was conducted using SPSS software to identify potential factor structures. This was followed by CFA in AMOS software to verify the emerging structure, with model fit indices evaluated to assess structural adequacy. For construct validity assessment, the original sample (N = 397) was randomly split into two subsets. The first subset (n = 200) underwent Maximum Likelihood EFA with Promax rotation (Kaiser normalization) to explore the underlying factor structure. Data suitability for factor analysis was confirmed through the Kaiser-Meyer-Olkin measure (KMO > 0.8) and Bartlett's Test of Sphericity (p < 0.01). Exploratory Factor Analysis A purposive sample of 200 eligible older adults from Babol completed both the OALS and a demographic questionnaire assessing age, education level, occupation, and number of children. Sampling adequacy was verified through Kaiser-Meyer-Olkin (KMO = 0.70–0.80 [good]; 0.80–0.90 [excellent]) and Bartlett's tests of Sphericity (p < 0.001) ( 21 ). Maximum likelihood estimation with Promax rotation was employed for factor extraction. We applied the following retention criteria: minimum factor loading of 0.30, communality threshold of 0.20, and adherence to the three-indicator rule requiring at least three items per factor ( 22 ). Confirmatory Factor Analysis A separate sample of 197 older adults completed the OALS and demographic questionnaire. Model fit was assessed using multiple indices: Incremental Fit Index (IFI > 0.90), Comparative Fit Index (CFI > 0.90), Root Mean Square Error of Approximation (RMSEA 0.80), Parsimony Comparative Fit Index (PCFI > 0.50), and Parsimony Normed Fit Index (PNFI) ( 23 ). Convergent and Discriminant Validity The OALS was assessed for both convergent and discriminant validity. Convergent validity was established using two criteria: Composite Reliability (CR > 0.7) and Average Variance Extracted (AVE > 0.5). For discriminant validity, we employed the Heterotrait-Monotrait Ratio of Correlations (HTMT) method, with all HTMT values required to be below 0.85 to confirm discriminant validity ( 24 ). Normality, Outliers, and Missing Values Univariate normality was evaluated using skewness (± 3) and kurtosis (± 8) thresholds. Multivariate outliers were identified via Mahalanobis D ² (p < 0.001), while multivariate normality was assessed using Mardia’s coefficient of multivariate kurtosis. Missing data were handled through multiple imputations and replaced with mean values ( 25 ). Reliability Assessment Internal consistency was evaluated using Cronbach's alpha, with values between 0.70–0.80 considered acceptable ( 26 ). Stability was assessed through test-retest reliability in a subsample of 20 older adults who completed the OALS twice at a 2-week interval. The Intraclass Correlation Coefficient (ICC) was calculated to examine consistency between administrations. Analyses were conducted using SPSS-AMOS 27 and JASP 0.18.0.0 Feasibility and Acceptability Feasibility was evaluated through completion time analysis and psychometric evaluation. The average completion time ranged from 10–15 minutes, demonstrating good practicality. Acceptability was assessed through participant feedback and questionnaire response patterns. Results Demographic Characteristics The sample consisted of 397 older adults (mean age = 69.5 ± 8.6 years), with 62.5% (n = 248) women and 37.5% (n = 149) men. Most participants were married (75.25%, n = 301), while 24.75% (n = 99) were single. A majority (61.7%, n = 245) reported a history of chronic illness, and most had no formal education. Exploratory Factor Analysis Maximum Likelihood EFA with Promax rotation revealed excellent sampling adequacy (KMO = 0.84) and significant correlations (Bartlett's test = 3537.002, p < 0.001). The parallel analysis identified four factors accounting for 55.4% of the total variance: ( 1 ) Quality of Relationships (6 items, eigenvalue = 3.148), ( 2 ) Preventive Behaviors (5 items, eigenvalue = 2.785), ( 3 ) Nutrition (4 items, eigenvalue = 2.371), and ( 4 ) Physical Activity (4 items, eigenvalue = 2.223) (Table 1 ). Table 1 Factor Structure of the Persian version of the Older Adults Lifestyle Scale: Results of Maximum Likelihood Exploratory Factor Analysis (n = 200) Factors Item Factor loading h 2 ʎ Variance Alpha Quality of relationships Q17. Do you feel loved by your family members? 0.838 0.376 3.148 16.6 0.856 Q18. Do you feel you have people you can trust? 0.803 0.370 Q19. Do you have people you can talk to? 0.766 0.665 Q16. Do you have a good relationship with the people you live with daily? 0.680 0.726 Q14. Do you have a good relationship with your family? 0.626 0.562 Q15. Do you feel supported by your friends? 0.570 0.445 Preventive behaviors Q4. Are you concerned about controlling your blood pressure? 0.899 0.721 2.785 14.7 0.814 Q3. Are you concerned about controlling your blood sugar levels? 0.851 0.683 Q5. Do you visit the doctor regularly? 0.696 0.472 Q2. Do you perform preventive examinations such as mammograms, prostate exams, and others? 0.533 0.685 Q1. Do you care about preventing diseases such as diabetes, hypertension, obesity, and other illnesses? 0.488 0.611 Food/diet Q7. How often do you consume fresh foods such as rice, beans, fruits, vegetables, eggs, meat, milk, and others? 0.869 0.318 2.371 12.5 0.810 Q8. Do you consume fruits and vegetables? 0.850 0.642 Q9. Do you consider your diet healthy? 0.654 0.477 Q6. Are you seeking to adopt a healthier and more nutritious diet? 0.460 0.422 Physical activity Q10. Do you engage in physical activity? 0.828 0.533 2.223 11.7 0.802 Q13. Do you have the habit of walking at least 30 minutes a day? 0.819 0.673 Q11 Do you carry out daily activities such as gardening, housework, walking, and others requiring movement? 0.773 0.608 Q12. Are you concerned about maintaining regular physical activity? 0.415 0.542 Abbreviations h2: Communalities, λ: Eigenvalues Confirmatory Factor Analysis CFA was performed on the second random subsample (n = 197) to validate the factor structure identified through MLEFA (Fig. 1 ). The model demonstrated good fit: χ²(58) = 155.220, p < 0.001; χ²/df = 2.676; CFI = 0.923; NFI = 0.848; IFI = 0.925; TLI = 0.902; RMSEA = 0.064 (90% CI: 0.052–0.077). All fit indices met established thresholds for model acceptability. Convergent and Discriminant Validity All four factors demonstrated strong internal consistency (α > 0.70), with the total scale showing excellent reliability (α = 0.86). Stability was confirmed by high test-retest reliability (ICC = 0.859, 95% CI [0.812–0.906]). Convergent validity was supported with composite reliability (CR > 0.70) and average variance extracted (AVE > 0.50) for all constructs. Discriminant validity was established using the Heterotrait-Monotrait ratio (HTMT < 0.85). (Table 2 ). Table 2 Convergent Validity and Construct Reliability of the Persian Older Adults Lifestyle Scale (n = 197) Factors α Ω CR MaxR AVE 1 0.856 0.859 0.856 0.869 0.501 2 0.814 0.822 0.821 0.833 0.481 3 0.810 0.815 0.830 0.857 0.553 4 0.802 0.808 0.815 0.848 0.534 Abbreviations α: Cronbach’s alpha, Ω: McDonald’s omega Reliability All constructs demonstrated strong reliability, with Cronbach's α > 0.70, McDonald's ω > 0.70, composite reliability (CR) > 0.70, and MaxR(H) > 0.70. Average inter-item correlations (AIC) ranged from 0.20–0.40, indicating appropriate internal consistency. Discussion At present, few studies have systematically examined the lifestyle of older adults in Iran—a gap largely attributed to the lack of validated, culturally tailored instruments suitable for this population. The primary goal of the current study was to adapt and psychometrically validate the OALS for Persian-speaking older adults, thus creating the P-OALS. This rigorous process incorporated best practices in cross-cultural adaptation and psychometric validation, yielding a tool that is both reliable and culturally sensitive for use among the Iranian elderly. This research significantly contributes to the methodological literature by addressing the structural, content, and construct validity of the P-OALS. The exploratory factor analysis identified a stable four-factor structure: ( 1 ) Quality of Relationships (6 items), ( 2 ) Preventive Behaviors (5 items), ( 3 ) Nutrition (4 items), and ( 4 ) Physical Activity (4 items)—accounting for 55.4% of the total variance. The respective eigenvalues were 3.148, 2.785, 2.371, and 2.223, affirming the multidimensionality of the lifestyle construct in this population. Confirmatory factor analysis confirmed the model’s fit, with all indices (CFI, RMSEA, IFI, etc.) meeting accepted thresholds, thus establishing robust construct validity. These findings are in line with the original instrument developed by Ferreira et al. ( 8 ) and echoed by other international adaptations, which report strong factorial validity across various cultural contexts ( 27 ). Despite methodological differences, the consistent validation across diverse populations highlights the tool’s flexibility and potential for global use. Reliability analysis reinforced these results: Cronbach’s alpha values for each subscale ranged from 0.80 to 0.85, demonstrating high internal consistency. The instrument also showed excellent stability over time, with an intraclass correlation coefficient (ICC) of 0.859, confirming its test-retest reliability. Notably, the item-level analysis indicated that each of the 19 items contributed meaningfully to the construct without redundancy—deleting any item would not improve internal consistency, validating the scale’s completeness. Among the extracted factors, Quality of Relationships emerged as the primary dimension, explaining the largest share of variance. This subscale reflects the level of emotional and social support perceived by older adults through family and peer relationships. In addition to the psychological benefits, high-quality relationships are linked to improved physical health outcomes such as reduced stress, enhanced social engagement, and even cognitive resilience ( 25 ). These findings support prior literature asserting that frequent intergenerational contact and emotional closeness foster psychological well-being and stability ( 28 , 29 ). In the Iranian context, where older adults are culturally revered and family ties remain strong, this factor’s prominence reinforces the sociocultural salience of interpersonal connectedness. However, contrasting evidence from Daniel et al. ( 30 ) underscores a shift in other societies, where technology-mediated communication has become a key source of emotional support among the elderly. This discrepancy may reflect not only generational differences in technology use but also fundamental cultural values. In Iran, traditional family structures and in-person interaction still predominate, potentially accounting for the stronger emphasis on physical relational networks. From a practical perspective, the P-OALS is a highly feasible instrument: it is brief (10–15 minutes to complete), user-friendly even among older adults with low literacy, and yields actionable insights for healthcare professionals. Its cross-cultural validation supports broader applicability in geriatric health research, while its psychometric robustness ensures consistent measurement across varying demographics. Despite the methodological rigor and robust findings, this study is not without limitations. The study was conducted exclusively among older adults, and therefore, the results may not be generalizable to other populations. Furthermore, as the present study's population was limited to Babol (a city in Mazandaran province, Iran), the generalizability of its findings to the entire Iranian population may be influenced, given that Iran is a country with diverse social cultures. Additionally, the data collection process during the present study was restricted to a single time point, making it impossible to determine how Older Adults Lifestyle (OAL) changes over time. Conversely, despite its novelty, the implementation of exploratory factor analysis to identify factors related to OAL, along with the calculation of McDonald's omega coefficient and Cronbach's alpha, are considered significant strengths of this study. In conclusion, the current study fills a critical gap by providing a psychometrically sound, culturally adapted tool for assessing the lifestyle of older Iranian adults. It affirms the P-OALS as both a theoretically grounded and practically viable instrument for clinical, policy, and research applications. Furthermore, it underscores the importance of context-specific validation to ensure meaningful assessment in diverse aging populations. Abbreviations AGFI Adjusted Goodness of Fit Index AMOS Analysis of Moment Structure CFA Confirmatory Factor Analysis CFI Comparative Fit Index CMIN/DF Minimum Discrepancy Function by Degrees of Freedom Divided COSMIN Consensus-Based Standards for the Selection of Health Status Measurement Instruments CVI Content Validity Index CVR Content Validity Rate DF Degree of Freedom FMI Functional Independence Measure ICC Intraclass Correlation Coefficient IFI Incremental Fit Index IS Impact Score GFI Goodness of Fit Index MLEFA Maximum Likelihood Exploratory Factor Analysis NFI Normed Fit Index NNFI Non-Normed Fit Index OALS Older Adult Lifestyle Scale P P-Value PASE Physical Activity Scale for the Elderly PNFI Parsimonious Normed Fit Index RMR Root Mean Square Residual RMSEA Root Mean Square Error of Approximation SPSS Statistical Package for the Social Sciences χ2 Chi-Square χ2/df Ratio of Chi Square to Degrees of Freedom Declarations Ethics approval and consent to participate The Ethics Committee of Babol University of Medical Sciences (Babol, Iran) gave its approval to this study (Ethics code: IR.MUBABOL.HRI.REC.1403.298). The participants were given a thorough explanation of the study’s goals and methods, as well as assurances that their participation was entirely voluntary. Written Informed consent was obtained from all subjects and/or their legal guardian(s). Permissions to use the data collection instruments were obtained from their developers. All procedures adhered to the appropriate guidelines and regulations. Consent for publication The authors and participants have given their consent for the publication of the study. Availability of data and materials The data set used in this study will be available based on reasoned request. Competing interests The authors declare no competing interests . Funding No funds, grants, or other support was received. Author contributions Performance of data gathering: FM and SP; Planning and supervision of the work: NAT and HSH; Performance of the analysis: HSH and MM; Manuscript draft: MG, and SP, and All authors; and comment on the final manuscript: NAT and MM and All authors. Acknowledgements We would like to express our gratitude to all those who contributed to this project at various stages. We are especially grateful to Dr. Luana Ferreira at the Federal University of Juiz de Fora for her professional collaboration in this study. We also extend our sincere thanks to all public health and geriatrics specialists who participated in the questionnaire validation process. Clinical trial number: not applicable.’ Clinical trial number: not applicable References Razeghi Nasrabad HB, Rashidi F. Physical and mental health status of the elderly in the context of age structural transition: A Study in Khorramabad, Iran. J Social Continuity Change (JSCC). 2023;2(1):45–67. 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Navigating the Digital Divide: Exploring the Drivers, Drawbacks, and Prospects of Social Interaction Technologies′ Adoption and Usage Among Older Adults During COVID-19. J Aging Res. 2025;2025(1):7625097. 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-6740618","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475713012,"identity":"f0cb31ae-b2ef-4277-856a-6aa1343ef7ce","order_by":0,"name":"Fatemeh Mehriyan","email":"","orcid":"","institution":"Babol University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Mehriyan","suffix":""},{"id":475713013,"identity":"600bded6-9344-4596-bfac-7b35961e4136","order_by":1,"name":"Neda Ahmadzadeh Tori","email":"","orcid":"","institution":"Babol University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Neda","middleName":"Ahmadzadeh","lastName":"Tori","suffix":""},{"id":475713014,"identity":"21d38012-e3d3-4ffb-bf8d-6f5b8a3c8245","order_by":2,"name":"Hamid Sharif-Nia","email":"","orcid":"","institution":"Mazandaran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Sharif-Nia","suffix":""},{"id":475713015,"identity":"3cd104a2-3eac-43e8-ae49-9c53d890ae31","order_by":3,"name":"Samaneh Pourhadi","email":"","orcid":"","institution":"Babol University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Samaneh","middleName":"","lastName":"Pourhadi","suffix":""},{"id":475713016,"identity":"01835b7d-77ad-4735-8f2a-5132c0b2c3d5","order_by":4,"name":"Mina Galeshi","email":"","orcid":"","institution":"Babol University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mina","middleName":"","lastName":"Galeshi","suffix":""},{"id":475713017,"identity":"02069de5-0d69-485d-b936-4e6ae25635ed","order_by":5,"name":"Mostafa maleki","email":"data:image/png;base64,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","orcid":"","institution":"in Health Education and Promotion, Shahrekord University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"maleki","suffix":""}],"badges":[],"createdAt":"2025-05-24 19:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6740618/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6740618/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85379822,"identity":"70ed5543-c928-4e21-8d09-8c10e956fe5b","added_by":"auto","created_at":"2025-06-25 09:04:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147581,"visible":true,"origin":"","legend":"\u003cp\u003eConfirmatory Factor Analysis of the Persian Older Adults Lifestyle Scale: Standardized Factor Loadings and Model Structure (n=197)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6740618/v1/bf75d4c3b010a882cb572240.png"},{"id":90798794,"identity":"70c8cb19-6c4f-45d2-9ee0-5cc0e45ce27a","added_by":"auto","created_at":"2025-09-08 09:39:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":965069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6740618/v1/9de75aed-299e-4b49-a9ce-39532997d2d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Persian version of the Older Adults Lifestyle Scale: A Translation and Psychometrics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAging represents an inevitable biological process marked by complex biological, psychological, and social transformations across the lifespan. Recent decades have witnessed remarkable advancements in medical and healthcare sectors, leading to increased life expectancy and a consequent global surge in older adult populations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). According to World Health Organization (WHO) projections, the population aged 65 and older will surpass 800\u0026nbsp;million by 2025 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), with developing nations accounting for 70% of this demographic shift (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Current estimates suggest that by 2030, elderly populations in these regions will expand nine fold(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Iran's latest census data indicate that over 10% of the population is now aged 60 or older, with this proportion expected to grow substantially in coming decades (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This rapid demographic transition presents significant economic, social, and public health challenges that demand urgent attention.\u003c/p\u003e \u003cp\u003eThe expanding elderly cohort has been accompanied by a rising prevalence of age-related health conditions, including diabetes, cardiovascular diseases, and mental health disorders (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). While these conditions can substantially diminish the quality of life, growing evidence demonstrates that lifestyle modifications may effectively prevent disease progression and enhance overall well-being (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Lifestyle encompasses a constellation of habitual behaviors, with healthy lifestyles specifically comprising health-promoting activities such as balanced nutrition, regular physical activity, effective stress management, positive social engagement, and avoidance of harmful behaviors. These behavioral patterns emerge from dynamic interactions between individual life opportunities and stable variables including ethnicity, age, socioeconomic status, and socialization experiences (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Consequently, personal choices and available opportunities fundamentally influence the development of health-related habits and behaviors(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Proactive health strategies incorporating physical activity, nutritional awareness, and stress reduction techniques can significantly mitigate health risks in older populations (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy consciously adopting healthy lifestyle practices, individuals can actively influence their aging trajectory, fostering vitality, independence, and sustained well-being during later life stages. The intricate relationship between aging processes and lifestyle choices underscores the transformative potential of preventive measures in determining quality of life among elderly populations. Given the well-established connections between aging, chronic conditions, and lifestyle factors, a comprehensive assessment of older adults' attitudes and behaviors toward lifestyle components provides healthcare professionals with valuable insights for evaluating living patterns and designing targeted interventions (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile Western societies have developed numerous lifestyle assessment instruments - including the Health-Promoting Lifestyle Profile II (HPLP-II) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), Short Form Health Survey (SF-36) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), WHOQOL-OLD (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and Physical Activity Scale for the Elderly (PASE) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) - these tools frequently prove culturally inappropriate for populations with distinct social characteristics. In Iran, where significant sociocultural differences exist and many older adults have limited formal education, the implementation of complex assessment tools presents considerable challenges. Although several Persian-language questionnaires have been developed, including Eshaghi's 46-item Elderly Lifestyle Assessment (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and Montazeri's Healthy Lifestyle Questionnaire for the Elderly (HEL) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), these instruments either lack comprehensive lifestyle coverage or prove excessively lengthy for practical use. This underscores the pressing need for culturally sensitive, easily comprehensible assessment tools tailored to Iran's elderly population.\u003c/p\u003e \u003cp\u003eCultural context plays a pivotal role in shaping lifestyle behaviors and their assessment. The translation and validation of the Older Adults Lifestyle Scale (OALS) within Iran's sociocultural framework offers significant potential for identifying lifestyle-associated factors among elderly Iranians. Cultural variations substantially impact questionnaire interpretation and response patterns, necessitating rigorous validation of assessment tools for specific populations. Presently, the psychometric properties of the Persian version of OALS (P-OALS) remain unevaluated within Iranian populations. This validation study contributes to ongoing instrument refinement while supporting cross-cultural applicability in geriatric research.\u003c/p\u003e \u003cp\u003eThe current study aimed to establish the reliability and validity of P-OALS among the Iranian elderly through comprehensive translation and psychometric evaluation. By developing a culturally adapted assessment tool, this research provides clinicians and researchers with an effective instrument for evaluating lifestyle patterns in Persian-speaking elderly populations, ultimately facilitating the development of targeted, culturally appropriate interventions to promote healthy aging.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis cross-sectional methodological study was conducted in Babul, Iran, between February and March 2025. The research comprised two sequential phases: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) translation of the healthy lifestyle scale and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) psychometric evaluation of its Persian version. Data collection occurred across multiple healthcare settings, including hospitals, clinics, and affiliated health institutions in Babul.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants, Sample Size, and data collection\u003c/h3\u003e\n\u003cp\u003eThe target population consisted of community-dwelling adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years residing in Babul. Inclusion criteria required participants to meet the age threshold and provide voluntary informed consent. Exclusion criteria encompassed cognitive impairment, severe comorbid conditions, or unwillingness to participate.\u003c/p\u003e \u003cp\u003eFollowing MacCallum's recommendations for psychometric studies (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), we established a minimum sample requirement of 200 cases. To accommodate separate samples for construct validation analyses, we recruited 397 participants through convenience sampling. This approach ensured adequate statistical power for both exploratory and confirmatory analyses.\u003c/p\u003e \u003cp\u003eTrained researchers administered the questionnaires through structured face-to-face sessions, beginning with a thorough explanation of the study objectives to ensure participant understanding. Under direct researcher supervision, participants completed the questionnaires, a methodological approach designed to achieve multiple objectives: maintaining exceptionally high response rates exceeding 95%, providing immediate clarification for any participant questions or uncertainties, and minimizing missing data through real-time verification of responses during the completion process. This supervised administration protocol enhanced data quality while maintaining standardized conditions across all participants.\u003c/p\u003e\n\u003ch3\u003eOriginal Questionnaire\u003c/h3\u003e\n\u003cp\u003eThe study employed the OALS, originally developed by Dr. Luana Ferreira at the Federal University of Juiz de For a (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This instrument contains 19 items organized into four distinct subscales: preventive behaviors (5 items), nutrition (4 items), physical activity (4 items), and quality of social relationships (6 items). Each item utilizes a standardized 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;never to 5\u0026thinsp;=\u0026thinsp;always), yielding potential total scores ranging from 19 to 95 points. The scoring methodology involves a simple summation of all item responses, with higher aggregate scores reflecting more favorable lifestyle patterns.\u003c/p\u003e\n\u003ch3\u003eTranslation Procedure\u003c/h3\u003e\n\u003cp\u003eFollowing formal authorization from the original scale developer, we implemented a rigorous Forward-Backward translation protocol to ensure linguistic and conceptual equivalence. The process commenced with two parallel translations performed by independent bilingual translators: one translator without specialized medical training (to preserve natural language usage) and one academic translator from a medical university (to maintain technical accuracy). These translations underwent systematic comparison and harmonization by a panel of three bilingual researchers to produce a consensus Persian version. An independent translator, blinded to the original English version, then performed a back-translation of this preliminary Persian version into English. The back-translated version underwent meticulous review by the original developer, Dr. Luana Ferreira, who provided expert feedback on semantic discrepancies and conceptual equivalencies. Through an iterative revision process incorporating this feedback, we achieved final approval of the Persian version.\u003c/p\u003e\n\u003ch3\u003ePsychometric Evaluation\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFace Validity Assessment\u003c/h2\u003e \u003cp\u003eThe preliminary questionnaire underwent face validity testing through a dual evaluation process involving both target population representatives and subject matter experts. Ten older adults completed the instrument while providing detailed feedback on items of clarity, transparency, linguistic appropriateness, and comprehensibility of the items, which led to necessary modifications. Then, a panel of health professionals conducted evaluations focusing on: the content, clarity, readability, simplicity, understandability of the questions, and practical administration considerations. All revisions were carefully documented and reviewed to ensure they maintained conceptual equivalence with the source questionnaire while optimizing cultural appropriateness for the Iranian elderly.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eContent Validity Assessment\u003c/h3\u003e\n\u003cp\u003eTo assess content validity, the Persian version of the questionnaire was reviewed by a panel of experts. Content validity was evaluated both qualitatively (assessing wording, grammar, and relevance of items) and quantitatively by calculating the Content Validity Ratio (CVR) and Content Validity Index (CVI), as detailed below:\u003c/p\u003e \u003cp\u003eTo assess the questionnaire's content validity, 10 experts in gerontology, health education, and psychometrics were recruited. They evaluated the instrument by completing it and providing feedback based on the CVI. For the CVR, each item was rated using three options: (a) \u003cem\u003eessential\u003c/em\u003e, (b) \u003cem\u003euseful but not essential\u003c/em\u003e, or (c) \u003cem\u003enot essential\u003c/em\u003e. The CVR was calculated using Lawshe\u0026rsquo;s formula: *(Ne\u0026thinsp;\u0026minus;\u0026thinsp;N/2)/(N/2)*, where \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;number of experts selecting \"essential\" and \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;total experts. The cutoff value was determined using Lawshe\u0026rsquo;s table, which specifies a minimum CVR of 0.62 for 10 experts (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the CVI evaluation, the experts independently assessed each item based on three criteria\u0026mdash;simplicity, specificity, and clarity\u0026mdash;using a 4-point Likert scale. The CVI score was calculated as the percentage of experts who rated an item as 3 or 4 (indicating high relevance). Items with a CVI\u0026thinsp;\u0026gt;\u0026thinsp;0.79 were retained as highly appropriate, while those scoring 0.70\u0026ndash;0.79 were revised for improvement. Items with a CVI\u0026thinsp;\u0026lt;\u0026thinsp;0.70 were discarded for failing to meet the minimum validity threshold (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eConstruct Validity\u003c/h3\u003e\n\u003cp\u003eThe factorial structure of the scale was examined through a two-stage analytical approach. First, EFA was conducted using SPSS software to identify potential factor structures. This was followed by CFA in AMOS software to verify the emerging structure, with model fit indices evaluated to assess structural adequacy.\u003c/p\u003e \u003cp\u003eFor construct validity assessment, the original sample (N\u0026thinsp;=\u0026thinsp;397) was randomly split into two subsets. The first subset (n\u0026thinsp;=\u0026thinsp;200) underwent Maximum Likelihood EFA with Promax rotation (Kaiser normalization) to explore the underlying factor structure. Data suitability for factor analysis was confirmed through the Kaiser-Meyer-Olkin measure (KMO\u0026thinsp;\u0026gt;\u0026thinsp;0.8) and Bartlett's Test of Sphericity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExploratory Factor Analysis\u003c/h2\u003e \u003cp\u003eA purposive sample of 200 eligible older adults from Babol completed both the OALS and a demographic questionnaire assessing age, education level, occupation, and number of children. Sampling adequacy was verified through Kaiser-Meyer-Olkin (KMO\u0026thinsp;=\u0026thinsp;0.70\u0026ndash;0.80 [good]; 0.80\u0026ndash;0.90 [excellent]) and Bartlett's tests of Sphericity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Maximum likelihood estimation with Promax rotation was employed for factor extraction. We applied the following retention criteria: minimum factor loading of 0.30, communality threshold of 0.20, and adherence to the three-indicator rule requiring at least three items per factor (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory Factor Analysis\u003c/h2\u003e \u003cp\u003eA separate sample of 197 older adults completed the OALS and demographic questionnaire. Model fit was assessed using multiple indices: Incremental Fit Index (IFI\u0026thinsp;\u0026gt;\u0026thinsp;0.90), Comparative Fit Index (CFI\u0026thinsp;\u0026gt;\u0026thinsp;0.90), Root Mean Square Error of Approximation (RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Adjusted Goodness-of-Fit Index (AGFI\u0026thinsp;\u0026gt;\u0026thinsp;0.80), Parsimony Comparative Fit Index (PCFI\u0026thinsp;\u0026gt;\u0026thinsp;0.50), and Parsimony Normed Fit Index (PNFI) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConvergent and Discriminant Validity\u003c/h2\u003e \u003cp\u003eThe OALS was assessed for both convergent and discriminant validity. Convergent validity was established using two criteria: Composite Reliability (CR\u0026thinsp;\u0026gt;\u0026thinsp;0.7) and Average Variance Extracted (AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.5). For discriminant validity, we employed the Heterotrait-Monotrait Ratio of Correlations (HTMT) method, with all HTMT values required to be below 0.85 to confirm discriminant validity (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNormality, Outliers, and Missing Values\u003c/h2\u003e \u003cp\u003eUnivariate normality was evaluated using skewness (\u0026plusmn;\u0026thinsp;3) and kurtosis (\u0026plusmn;\u0026thinsp;8) thresholds. Multivariate outliers were identified via Mahalanobis \u003cem\u003eD\u003c/em\u003e\u0026sup2; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while multivariate normality was assessed using Mardia\u0026rsquo;s coefficient of multivariate kurtosis. Missing data were handled through multiple imputations and replaced with mean values (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eReliability Assessment\u003c/h2\u003e \u003cp\u003eInternal consistency was evaluated using Cronbach's alpha, with values between 0.70\u0026ndash;0.80 considered acceptable (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Stability was assessed through test-retest reliability in a subsample of 20 older adults who completed the OALS twice at a 2-week interval. The Intraclass Correlation Coefficient (ICC) was calculated to examine consistency between administrations. Analyses were conducted using SPSS-AMOS 27 and JASP 0.18.0.0\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFeasibility and Acceptability\u003c/h2\u003e \u003cp\u003eFeasibility was evaluated through completion time analysis and psychometric evaluation. The average completion time ranged from 10\u0026ndash;15 minutes, demonstrating good practicality. Acceptability was assessed through participant feedback and questionnaire response patterns.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Characteristics\u003c/h2\u003e \u003cp\u003eThe sample consisted of 397 older adults (mean age\u0026thinsp;=\u0026thinsp;69.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6 years), with 62.5% (n\u0026thinsp;=\u0026thinsp;248) women and 37.5% (n\u0026thinsp;=\u0026thinsp;149) men. Most participants were married (75.25%, n\u0026thinsp;=\u0026thinsp;301), while 24.75% (n\u0026thinsp;=\u0026thinsp;99) were single. A majority (61.7%, n\u0026thinsp;=\u0026thinsp;245) reported a history of chronic illness, and most had no formal education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eExploratory Factor Analysis\u003c/h2\u003e \u003cp\u003eMaximum Likelihood EFA with Promax rotation revealed excellent sampling adequacy (KMO\u0026thinsp;=\u0026thinsp;0.84) and significant correlations (Bartlett's test\u0026thinsp;=\u0026thinsp;3537.002, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The parallel analysis identified four factors accounting for 55.4% of the total variance: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Quality of Relationships (6 items, eigenvalue\u0026thinsp;=\u0026thinsp;3.148), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Preventive Behaviors (5 items, eigenvalue\u0026thinsp;=\u0026thinsp;2.785), (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Nutrition (4 items, eigenvalue\u0026thinsp;=\u0026thinsp;2.371), and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Physical Activity (4 items, eigenvalue\u0026thinsp;=\u0026thinsp;2.223) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eFactor Structure of the Persian version of the Older Adults Lifestyle Scale: Results of Maximum Likelihood Exploratory Factor Analysis (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eʎ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlpha\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eQuality of\u003c/p\u003e \u003cp\u003erelationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ17. Do you feel loved by your family members?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e3.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ18. Do you feel you have people you can trust?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ19. Do you have people you can talk to?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ16. Do you have a good relationship with the people you live with daily?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ14. Do you have a good relationship with your family?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ15. Do you feel supported by your friends?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePreventive\u003c/p\u003e \u003cp\u003ebehaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4. Are you concerned about controlling your blood pressure?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3. Are you concerned about controlling your blood sugar levels?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ5. Do you visit the doctor regularly?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2. Do you perform preventive examinations such as mammograms, prostate exams, and others?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1. Do you care about preventing diseases such as diabetes, hypertension, obesity, and other illnesses?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFood/diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7. How often do you consume fresh foods such as rice, beans, fruits, vegetables, eggs, meat, milk, and others?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ8. Do you consume fruits and vegetables?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9. Do you consider your diet healthy?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ6. Are you seeking to adopt a healthier and more nutritious diet?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ10. Do you engage in physical activity?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ13. Do you have the habit of walking at least 30 minutes a day?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ11 Do you carry out daily activities such as gardening, housework, walking, and others requiring movement?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ12. Are you concerned about maintaining regular physical activity?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations h2: Communalities, λ: Eigenvalues\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory Factor Analysis\u003c/h2\u003e \u003cp\u003eCFA was performed on the second random subsample (n\u0026thinsp;=\u0026thinsp;197) to validate the factor structure identified through MLEFA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The model demonstrated good fit: χ\u0026sup2;(58)\u0026thinsp;=\u0026thinsp;155.220, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.676; CFI\u0026thinsp;=\u0026thinsp;0.923; NFI\u0026thinsp;=\u0026thinsp;0.848; IFI\u0026thinsp;=\u0026thinsp;0.925; TLI\u0026thinsp;=\u0026thinsp;0.902; RMSEA\u0026thinsp;=\u0026thinsp;0.064 (90% CI: 0.052\u0026ndash;0.077). All fit indices met established thresholds for model acceptability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eConvergent and Discriminant Validity\u003c/h2\u003e \u003cp\u003eAll four factors demonstrated strong internal consistency (α\u0026thinsp;\u0026gt;\u0026thinsp;0.70), with the total scale showing excellent reliability (α\u0026thinsp;=\u0026thinsp;0.86). Stability was confirmed by high test-retest reliability (ICC\u0026thinsp;=\u0026thinsp;0.859, 95% CI [0.812\u0026ndash;0.906]). Convergent validity was supported with composite reliability (CR\u0026thinsp;\u0026gt;\u0026thinsp;0.70) and average variance extracted (AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.50) for all constructs. Discriminant validity was established using the Heterotrait-Monotrait ratio (HTMT\u0026thinsp;\u0026lt;\u0026thinsp;0.85). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eConvergent Validity and Construct Reliability of the Persian Older Adults Lifestyle Scale (n\u0026thinsp;=\u0026thinsp;197)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΩ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaxR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations α: Cronbach\u0026rsquo;s alpha, Ω: McDonald\u0026rsquo;s omega\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eReliability\u003c/h2\u003e \u003cp\u003eAll constructs demonstrated strong reliability, with Cronbach's α\u0026thinsp;\u0026gt;\u0026thinsp;0.70, McDonald's ω\u0026thinsp;\u0026gt;\u0026thinsp;0.70, composite reliability (CR)\u0026thinsp;\u0026gt;\u0026thinsp;0.70, and MaxR(H)\u0026thinsp;\u0026gt;\u0026thinsp;0.70. Average inter-item correlations (AIC) ranged from 0.20\u0026ndash;0.40, indicating appropriate internal consistency.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAt present, few studies have systematically examined the lifestyle of older adults in Iran\u0026mdash;a gap largely attributed to the lack of validated, culturally tailored instruments suitable for this population. The primary goal of the current study was to adapt and psychometrically validate the OALS for Persian-speaking older adults, thus creating the P-OALS. This rigorous process incorporated best practices in cross-cultural adaptation and psychometric validation, yielding a tool that is both reliable and culturally sensitive for use among the Iranian elderly.\u003c/p\u003e \u003cp\u003eThis research significantly contributes to the methodological literature by addressing the structural, content, and construct validity of the P-OALS. The exploratory factor analysis identified a stable four-factor structure: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Quality of Relationships (6 items), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Preventive Behaviors (5 items), (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Nutrition (4 items), and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Physical Activity (4 items)\u0026mdash;accounting for 55.4% of the total variance. The respective eigenvalues were 3.148, 2.785, 2.371, and 2.223, affirming the multidimensionality of the lifestyle construct in this population. Confirmatory factor analysis confirmed the model\u0026rsquo;s fit, with all indices (CFI, RMSEA, IFI, etc.) meeting accepted thresholds, thus establishing robust construct validity.\u003c/p\u003e \u003cp\u003eThese findings are in line with the original instrument developed by Ferreira et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and echoed by other international adaptations, which report strong factorial validity across various cultural contexts (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Despite methodological differences, the consistent validation across diverse populations highlights the tool\u0026rsquo;s flexibility and potential for global use.\u003c/p\u003e \u003cp\u003eReliability analysis reinforced these results: Cronbach\u0026rsquo;s alpha values for each subscale ranged from 0.80 to 0.85, demonstrating high internal consistency. The instrument also showed excellent stability over time, with an intraclass correlation coefficient (ICC) of 0.859, confirming its test-retest reliability. Notably, the item-level analysis indicated that each of the 19 items contributed meaningfully to the construct without redundancy\u0026mdash;deleting any item would not improve internal consistency, validating the scale\u0026rsquo;s completeness.\u003c/p\u003e \u003cp\u003eAmong the extracted factors, Quality of Relationships emerged as the primary dimension, explaining the largest share of variance. This subscale reflects the level of emotional and social support perceived by older adults through family and peer relationships. In addition to the psychological benefits, high-quality relationships are linked to improved physical health outcomes such as reduced stress, enhanced social engagement, and even cognitive resilience (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These findings support prior literature asserting that frequent intergenerational contact and emotional closeness foster psychological well-being and stability (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In the Iranian context, where older adults are culturally revered and family ties remain strong, this factor\u0026rsquo;s prominence reinforces the sociocultural salience of interpersonal connectedness.\u003c/p\u003e \u003cp\u003eHowever, contrasting evidence from Daniel et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) underscores a shift in other societies, where technology-mediated communication has become a key source of emotional support among the elderly. This discrepancy may reflect not only generational differences in technology use but also fundamental cultural values. In Iran, traditional family structures and in-person interaction still predominate, potentially accounting for the stronger emphasis on physical relational networks.\u003c/p\u003e \u003cp\u003eFrom a practical perspective, the P-OALS is a highly feasible instrument: it is brief (10\u0026ndash;15 minutes to complete), user-friendly even among older adults with low literacy, and yields actionable insights for healthcare professionals. Its cross-cultural validation supports broader applicability in geriatric health research, while its psychometric robustness ensures consistent measurement across varying demographics.\u003c/p\u003e \u003cp\u003eDespite the methodological rigor and robust findings, this study is not without limitations. The study was conducted exclusively among older adults, and therefore, the results may not be generalizable to other populations. Furthermore, as the present study's population was limited to Babol (a city in Mazandaran province, Iran), the generalizability of its findings to the entire Iranian population may be influenced, given that Iran is a country with diverse social cultures. Additionally, the data collection process during the present study was restricted to a single time point, making it impossible to determine how Older Adults Lifestyle (OAL) changes over time. Conversely, despite its novelty, the implementation of exploratory factor analysis to identify factors related to OAL, along with the calculation of McDonald's omega coefficient and Cronbach's alpha, are considered significant strengths of this study.\u003c/p\u003e \u003cp\u003eIn conclusion, the current study fills a critical gap by providing a psychometrically sound, culturally adapted tool for assessing the lifestyle of older Iranian adults. It affirms the P-OALS as both a theoretically grounded and practically viable instrument for clinical, policy, and research applications. Furthermore, it underscores the importance of context-specific validation to ensure meaningful assessment in diverse aging populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAGFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Goodness of Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Moment Structure\u003c/p\u003e \u003c/div\u003e \u003c/div\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\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMIN/DF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinimum Discrepancy Function by Degrees of Freedom Divided\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOSMIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsensus-Based Standards for the Selection of Health Status Measurement Instruments\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eContent Validity Index\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 Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDegree of Freedom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFunctional Independence Measure\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\u003eIntraclass Correlation Coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIncremental Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImpact Score\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\"\u003eMLEFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum Likelihood Exploratory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormed Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNNFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-Normed Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOALS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOlder Adult Lifestyle Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePASE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhysical Activity Scale for the Elderly\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePNFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParsimonious Normed Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Residual\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\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eχ2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChi-Square\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eχ2/df\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRatio of Chi Square to Degrees of Freedom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of Babol University of Medical Sciences (Babol, Iran) gave its approval to this study (Ethics code: IR.MUBABOL.HRI.REC.1403.298). The participants were given a thorough explanation of the study\u0026rsquo;s goals and methods, as well as assurances that their participation was entirely voluntary. Written Informed consent was obtained from all subjects and/or their legal guardian(s). Permissions to use the data collection instruments were obtained from their developers. All procedures adhered to the appropriate guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors and participants have given their consent for the publication of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data set used in this study will be available based on reasoned request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funds, grants, or other support was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerformance of data gathering: FM and SP; Planning and supervision of the work: NAT and HSH; Performance of the analysis: HSH and MM; Manuscript draft: MG, and SP, and All authors; and comment on the final manuscript: NAT and MM and All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all those who contributed to this project at various stages. We are especially grateful to\u0026nbsp;Dr. Luana Ferreira\u0026nbsp;at the Federal University of Juiz de Fora for her professional collaboration in this study. We also extend our sincere thanks to all public health and geriatrics specialists who participated in the questionnaire validation process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u0026rsquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRazeghi Nasrabad HB, Rashidi F. Physical and mental health status of the elderly in the context of age structural transition: A Study in Khorramabad, Iran. J Social Continuity Change (JSCC). 2023;2(1):45\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYahyavi Dizaj J, Tajvar M, Mohammadzadeh Y. The effect of the presence of an elderly member on health care costs of Iranian households. Iran J Ageing. 2020;14(4):462\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBloom DE, Canning D, Fink G. Implications of population ageing for economic growth. Oxf Rev Econ Policy. 2010;26(4):583\u0026ndash;612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBloom DE, Chatterji S, Kowal P, Lloyd-Sherlock P, McKee M, Rechel B, et al. Macroeconomic implications of population ageing and selected policy responses. Lancet. 2015;385(9968):649\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirzaie M, Darabi S. Population aging in Iran and rising health care costs. Iran J Ageing. 2017;12(2):156\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohaqeqi Kamal SH, Basakha M. Prevalence of chronic diseases among the older adults in Iran: Does socioeconomic status matter? 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The mediating effect of social support between self-perceptions of aging and fear of dementia in community-dwelling older adults. Geriatr Nurs. 2025;62:194\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatey D, Chivers S. Navigating the Digital Divide: Exploring the Drivers, Drawbacks, and Prospects of Social Interaction Technologies\u0026prime; Adoption and Usage Among Older Adults During COVID-19. J Aging Res. 2025;2025(1):7625097.\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":"Elderly, Lifestyle, Questionnaire, Psychometrics, Translation, Validation","lastPublishedDoi":"10.21203/rs.3.rs-6740618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6740618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe global increase in the elderly population necessitates reliable tools to assess lifestyle factors that influence healthy aging. Existing Western lifestyle assessment instruments often lack cultural relevance for non-Western populations, including Iran. This study aimed to translate, culturally adapt, and validate the Persian version of the Older Adults Lifestyle Scale (P-OALS) to provide a contextually appropriate tool for evaluating lifestyle behaviors among Iranian older adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn a cross-sectional methodological study, 397 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 were recruited from Babol, Iran. The P-OALS was translated using the Forward-Backward method and rigorously evaluated for psychometric properties. Face and content validity were assessed by expert review (CVI\u0026thinsp;\u0026gt;\u0026thinsp;0.79, CVR\u0026thinsp;\u0026gt;\u0026thinsp;0.62). Construct validity was examined through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Reliability was tested via internal consistency (Cronbach\u0026rsquo;s α) and test-retest stability (ICC), while convergent/discriminant validity was analyzed using Composite Reliability (CR\u0026thinsp;\u0026gt;\u0026thinsp;0.7) and Average Variance Extracted (AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.5).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEFA revealed a robust four-factor structure\u0026mdash;Quality of Relationships (6 items), Preventive Behaviors (5 items), Nutrition (4 items), and Physical Activity (4 items)\u0026mdash;accounting for 55.4% of the total variance. CFA confirmed excellent model fit (CFI\u0026thinsp;=\u0026thinsp;0.923, RMSEA\u0026thinsp;=\u0026thinsp;0.064). The P-OALS demonstrated strong reliability (α\u0026thinsp;=\u0026thinsp;0.86, ICC\u0026thinsp;=\u0026thinsp;0.859) and validity, with all subscales meeting psychometric benchmarks.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe P-OALS is a valid, reliable, and culturally adapted instrument for assessing the lifestyle of Iranian older adults. Its concise format and contextual relevance make it valuable for research and clinical practice. Future studies should explore its applicability in broader populations and longitudinal settings to further establish generalizability.\u003c/p\u003e","manuscriptTitle":"The Persian version of the Older Adults Lifestyle Scale: A Translation and Psychometrics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 09:04:23","doi":"10.21203/rs.3.rs-6740618/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":"bf43006e-de92-4e59-92d2-8f77dc4261f5","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T09:39:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 09:04:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6740618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6740618","identity":"rs-6740618","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}