Development and application of active aging scale for rural older adults living alone

Development and application of active aging scale for rural older adults living alone | 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 Development and application of active aging scale for rural older adults living alone Shufang Liao, Shasha Li, Liying Dong, Jianyi Bao, Yue Li, Yingxue Xi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4952208/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background The issue of active aging among older adults living alone in rural areas is becoming increasingly complex worldwide, and China is no exception. However, more specialized assessment tools are needed to evaluate active aging in this population. This study aims to develop and validate an active aging scale for rural older adults living alone (AAS-ROALA) in China, providing a theoretical foundation for research in this area. Methods The scale was developed in three phases—a preliminary version, a test version, and a final refined version—a cross-sectional survey of 480 rural older adults living alone in two cities in China in April and May 2024. The scale was tested for item analysis, content validity, structural validity, and internal reliability via a cross-sectional survey design. Results The newly developed scale has thirty-two items across five dimensions: independent autonomy, self-regulation, active participation, economic security, and collaborative assistance. The I-CVI ranged from 0.813–1.000, and the S-CVI/Ave was 0.929. EFA identified five factors with a cumulative variance of 61.60%. The CFA showed a good model fit. The Cronbach’s α, McDonald’s ω, split-half coefficient, and retest reliability for the total scale were 0.928, 0.935, 0.815, and 0.874, respectively. Conclusion The findings show that the AAS-ROALA is a valid and appropriate instrument to inform in-depth studies of active aging among rural older adults living alone. Active aging Rural older adults living alone Psychometric properties Scale development Validity and reliability Figures Figure 1 Figure 2 Figure 3 Introduction According to data released by the National Bureau of Statistics of China in January 2024, the population aged 60 and above has reached 297 million, accounting for 21.1% of the total population [ 1 ]. Among them, there are more than 150 million older adults living alone, and it is expected that by 2030, this number will exceed 200 million, indicating that the problem of the aging of older adults living alone in China is becoming increasingly severe. Data from the fifth, sixth, and seventh national population censuses revealed that China’s rural older adult population living alone reached 4.93 million, 8.12 million, and 15.33 million in 2000, 2010, and 2020, respectively, and that the percentage of rural older adult households living alone in 2020 was approximately 3.5 times that of 2000, whereas that of cities and towns was only approximately 1.9 times and 2.5 times greater [ 2 ], indicating that the proportion of rural older adult households living alone is accelerating. The “14th Five-Year Plan for the Development of the National Aging Career and Elderly Service System” points out that a preliminary pattern has been formed to actively respond to population aging across society [ 3 ] and that promoting active aging among rural older adults is critical to actively improving their ability to maintain health. The World Health Organization (WHO) concept emphasizes active aging as enabling older adults to participate actively and healthily in social, economic, cultural, and public affairs to become creators of social wealth and contributors to social development [ 4 ]. The policy encourages stimulating economic, social, and cultural potential so that resources can be effectively used to serve society, giving older people a sense of achievement and value. Active aging is an academic and policy concept and an international consensus on action guidelines for aging societies [ 5 ]. Therefore, focusing on the active aging of rural older adults living alone is a guarantee for innovating and promoting a diversified supply pattern of elderly health services. Rural older adults living alone are still characterized by “three lows” and “three highs” regarding health, participation, and security in active aging. “Three lows” refer to low self-assessments of health [ 6 ], social participation [ 7 ], and economic security [ 8 ], whereas “three highs” indicate a high prevalence of multiple chronic conditions [ 9 ], active dedication [ 10 ], and passive coping capacity [ 11 ]. This population is defined as those aged 60 years and above living in rural areas without children, spouses, or other caregivers around them for more than six consecutive months due to being unmarried, divorced, widowed, or separated [ 12 ]. The tendency of young and middle-aged people to migrate to urban areas has led to an unequal distribution of resources for old-age care between urban and rural areas. This imbalance exacerbates the gap between the supply and demand of elderly care services in rural areas, posing a significant challenge to active aging for older people living alone [ 13 ]. Previous studies have investigated active aging among older people [ 14 ]. However, there is a lack of research on active aging among vulnerable rural older adults living alone, making it difficult to analyze their characteristics and connotations. This approach is not conducive to formulating and practicing active aging policies. At present, no specialized assessment tools exist for the active aging of rural older adults living alone. The active aging scale for Thai adults [ 15 ] reflects only the respondents’ self-assessment of active aging. Moreover, more objective evaluation indicators, such as the living environment and cultural characteristics, are lacking. The active aging scale of Jyvaskyla University [ 16 ], the active aging scale of nursing homes [ 17 ], the active aging scale of urban areas [ 18 ], and the active aging scale of South Korea [ 19 ] are more suitable for comprehensive evaluation of urban active aging. Chinese scholars developed the active aging questionnaire in rural areas [ 20 ], which mainly applies to the general characteristics of rural older adults and does not accurately assess those living alone. Given the particular characteristics of rural older adults living alone, the existing active aging assessment tools still cannot accurately address the current problems of active aging among rural older adults living alone. Therefore, to assess the active aging of rural older adults living alone effectively, it is necessary to construct a set of assessment scales suitable for the active aging of rural older adults living alone. This study constructed critical elements of active aging for rural older adults living alone (Fig. 1 ) on the basis of self-determination theory (autonomy, competence, and relatedness) and the three-pillar conceptual framework of active aging (health, participation, and security). The specifics of these elements were identified through semistructured interviews. The study was divided into three phases (Fig. 2 ): the first phase involved the development of a preliminary version, including literature analysis and semistructured interviews; the second phase covered the development of a test version, which was accomplished through two rounds of Delphi surveys and preexperimentation; and the third phase focused on the refinement of the final version, using cross-sectional surveys and model estimation methods. This study provides an evaluation tool for in-depth research on the active aging status of rural older adults living alone. Methods Design and Participants This was a cross-sectional study. We randomly selected two provinces from China’s central and eastern regions: Jiangxi Province and Zhejiang Province. We then randomly chose Nanchang city (Jiangxi) and Huzhou city (Zhejiang) as specific study cities. The differences in geography, economic development, and cultural traditions between Nanchang and Huzhou have Chinese characteristics, reflecting the current aging situation of older adults living alone in rural China. The inclusion criteria included individuals who (i) were aged 60 years or above. (ii) Continuous residence in rural areas for at least six months; (iii) unmarried, divorced, or widowed without live-in partners or relatives; (iv) adequate communication abilities; (v) no prior participation in similar surveys or interventions; and (vi) willingness to accept follow-up visits and tracking surveys. The exclusion criteria were (i) suffering from severe acute or chronic diseases; (ii) cognitive dysfunction; (iii) deafness, blindness, aphasia, or inability to complete the questionnaire even with assistance; (iv) not living in rural households; and (v) family members or relatives providing comprehensive care and lacking the authenticity of the living alone state. Psychometric testing procedures Phase I: Development of the preliminary version This study conducted a thorough literature search with target keywords (e.g., rural older adults living alone and actively aged), including Chinese and English databases such as CBM, CNKI, Vip Database, Wanfang Database, PubMed, Web of Science, Embase, EBSCO, CINAHL, and Google Scholar. We aimed to find studies on rural older adults living alone and actively aged, published until October 2023. After screening, we selected 37 articles covering quantitative and qualitative research for detailed review. The review focused on active aging among rural older adults living alone. Two researchers independently screened and resolved differences through discussion to ensure accuracy and credibility. The study conducted semistructured interviews with older adults living alone in rural areas. To ensure representativeness, participants were selected on the basis of gender, age, occupation, and cultural level, following the maximum difference sampling method. The interview outline was reviewed and revised by two gerontology research experts. Preinterviews with two rural older adults living alone were conducted to clarify and adjust the questions. The finalized interview outline included the following questions: (i) How has living alone affected your life? Are these influences positive or negative? (ii) How are you doing? How do you keep fit? When health problems arise, what will you do? (iii) What activities were organized in the village? What did you join? Do you benefit from participating in activities? (iv) Have you received help in your daily life? Does this help come from family members, friends, neighbors, or the government? What kind of help affects you most? (v) What other services or support should the village committee provide to help you enjoy your old age? This study selected interviewees on the basis of specific criteria and determined the number via data saturation, meaning that we stopped collecting data when no new themes emerged during the analysis. The study included thirteen interviewees, each lasting thirty to sixty minutes, with two experienced interviewers. The researcher maintained neutrality, avoided leading questions, and noted nonverbal cues. Two researchers independently analyzed the data via content analysis [21], resolving disagreements through discussion to eliminate personal biases and ensure data relevance and significance. Phase 2: Development of the test version On the basis of the purpose and feasibility of the study [22], sixteen experts were invited to review and revise forty-six initial items in March 2024. These experts had at least ten years of experience in gerontological or geriatric nursing, a background in active aging research or scale development, and were at an associate senior level or higher, and they participated voluntarily. They employed a 5-point Likert scale to evaluate the items. Items were included if their average significance was greater than or equal to 3.5 and their coefficient of variation was less than or equal to 0.25, along with a review of language and presentation. To ensure the representativeness and acceptability of the scale items, according to the suggestion of Oksenberg et al. [23], the test sample should be between twenty-five and seventy-five. Therefore, this study conducted a presurvey of thirty older adults living alone in rural areas. The investigator guided the participants in completing the scale and provided feedback on any difficulties or ambiguities. These thirty rural older adults were then excluded from further psychometric evaluation. Phase 3: Development of the final version Setting. This research stage adopted purposive sampling from April to May 2024. The aim of this study was to understand the living conditions and health status of rural older adults aged 60 years and above living alone in central and eastern China. Sample size. The sample size was ten times the number of items recommended by Myers et al. [24]. There were thirty-eight items in this study, and we additionally considered 25% invalid responses. A total of 507 questionnaires were distributed in this study (10*38/0.75), and 480 were validly returned, with a valid return rate of 94.7%. Finally, 240 cases were included in the exploratory factor analysis (EFA), and 240 cases were included in the validation factor analysis (CFA), which met the sample size requirements [25]. Ethical standards The study was approved by the Ethics Committee. All participants provided informed consent. The study followed the principles of the World Medical Association Declaration of Helsinki. Data analysis Statistical analysis of the data was conducted via IBM SPSS Statistics version 25.0 (IBM Corp., Armonk, NY, USA) and IBM SPSS Amos version 25.0 (IBM Corp., Armonk, NY, USA) to analyze the collected data. The normality of the data was assessed via the absolute skewness and kurtosis values for each dataset. In both datasets, the absolute values of skewness and kurtosis ranged from 0.002--1.459 and 0.008--1.449, respectively. With skewness and kurtosis values between -2 and +2 for all the items, the data met the normality assumption and could be used for factor analysis [26]. Item analysis. Scale items’ discriminatory power and differentiation were tested via the critical ratio, Cronbach’s α coefficient, discrete trend, and correlation coefficient methods. Items were deleted on the basis of the following criteria: (i) Critical ratio method: Items with nonsignificant differences in t test results or t values less than 3 when comparing high- and low-scoring groups were deleted. (ii) Cronbach’s α coefficient method: Items were deleted if their removal significantly increased the total Cronbach’s α coefficient of the scale. (iii) Discrete trend method: Items with a standard deviation less than 0.75 or a coefficient of variation (CV) less than 0.15 were considered for deletion. (iv) Correlation coefficient method: Items with a correlation (r) less than 0.4 or scores not significantly different from the total score were considered for deletion. Content validity. The item-level content validity index (I-CVI) and scale-level content validity index (S-CVI) were calculated on the basis of expert evaluations via a 4-point Lynn scale [27]. An I-CVI greater than or equal to 0.78 and an S-CVI greater than or equal to 0.90 were acceptance criteria [28]. Structural validity. EFA and CFA were used to test the scale’s structural validity. Before factor analysis, the Kaiser‒Meyer‒Olkin (KMO) test (standardized at 0.80) and Bartlett’s sphericity test (p < 0.001) were used to check data suitability. EFA uses principal component analysis and maximum variance rotation without limiting the number of factors. The criteria for judgment were as follows: (i) extraction of factors with eigenvalues greater than 1; (ii) cumulative variance contribution ratio greater than 50%; (iii) each factor having greater than or equal to three items; (iv) item loading greater than 0.40 on one factor and less than 0.40 on others; and (v) adhering to the fragmentation plot test. Items with loadings less than or equal to 0.4 or high on multiple factors were excluded [29]. For CFA, multiple fit metrics were used to assess model fit: chi-square degrees of freedom ratio (χ 2 /df) less than 3.00; root mean square error of approximation (RMSEA) less than or equal to 0.08; incremental fit index (IFI) and comparative fit index (CFI) greater than or equal to 0.90; and parsimonious normed fit index (PNFI) and parsimonious goodness-of-fit index (PGFI) greater than or equal to 0.50 [30]. Convergent validity was considered good if factor loadings exceeded 0.5, average variance extracted (AVE) values were above 0.5, and composite reliability (CR) was greater than 0.7. Discriminant validity was confirmed if the square root of the AVE for each factor was more significant than the correlation coefficients between that factor and the other factors. Reliability. Cronbach’s α coefficient, the Guttman split-half coefficient, and McDonald’s ω coefficient were used to evaluate internal consistency. Thirty participants were retested after three weeks, and intragroup correlation coefficients (ICCs) were used to assess retest and interrater reliability. Coefficient evaluation criteria: a coefficient greater than or equal to 0.9 indicates high reliability; 0.8 to less than 0.9 indicates good reliability; 0.7 to less than 0.8 indicates acceptable reliability; 0.6 to less than 0.7 indicates barely acceptable reliability; and less than 0.6 indicates unsatisfactory reliability [31]. Results Phase 1: Development of the preliminary version After conducting a thorough review of the literature and interviews and drawing from self-determination theory and the conceptual framework of active aging, we identified five critical dimensions of active aging for rural older adults living alone: (i) Independent autonomy (ten items): Refers to the ability to live independently and care for oneself in daily life, including making autonomous decisions. (ii) Self-regulation (ten items): Adapting to negative emotions caused by chronic diseases or physical aging through self-adjustment to achieve emotional balance and inner peace. (iii) Active participation (ten items): This factor involves actively engaging in social and cultural activities on a sustained basis. (iv) Economic security (six items): To obtain sufficient economic support for older adults to ensure a high quality of life later. (v) Collaborative assistance (ten items): Cooperation and mutual assistance from various parties to obtain support for older adults regarding life care and health care. We initially developed a measurement library containing forty-six items, rigorously reviewing and translating all the items, ensuring clarity. Phase 2: Development of the test version Delphi study. After the first round of expert consultation, eleven items were removed on the basis of the experts’ ratings. These items included “I insist on doing manual work or odd jobs,” “I often worry for unknown reasons,” and “My home environment is safe.” Four items were added, such as “I know my health status,” and some were revised according to the experts’ recommendations. Following the second round of expert assessment, “I often feel worthless” and “The village organizes regular sports and health education” were removed, and “I rarely lose sleep” was added. Additionally, item descriptions were adjusted on the basis of the experts’ opinions. The coefficients of variation for all the items decreased after these revisions [32]. Preinvestigation. All thirty participants agreed that the questionnaire was easy to understand. It took an average of eight to ten minutes to complete the questionnaire. Finally, the test scale consisted of five dimensions: independent autonomy (eight items), self-regulation (eight items), active participation (ten items), economic security (five items), and collaborative assistance (seven items), totaling thirty-eight items. Phase 3: D evelopment of the final version In this study, a random splitting method was used for factor analysis. The final sample (n=480) was randomly divided into two equal subsets, dataset A (n=240) and dataset B (n=240), via IBM SPSS Statistics. Table 1 shows the sample characteristics. Most participants were female (57.7%), had a primary school education or below (86.7%), were widowed (65.9%), and had been living alone for 1–5 years (47.3%). The average age was approximately 70 years; most had 2–3 children (44.4%), and only 89 (18.5%) had religious beliefs. Financial support mainly came from pensions (48.5%), and most had a monthly income of less than 500 yuan. Additionally, 43.8% had one chronic disease. Both datasets were similar to the total sample, with no significant differences. Item analysis. The results show that, according to the critical ratio, Cronbach’s α coefficient, and discrete trend methods, all scale items meet the retention criteria. Only six items with r values less than 0.4 were deleted because they did not meet the retention criteria. Content validity. The scale showed good content validity, with an I-CVI ranging from 0.813--1.000 and an S-CVI/Ave of 0.929. Structural validity. EFA revealed that the KMO value was 0.848, and Bartlett’s sphericity test was significant [χ 2 (496) =4163.736, P<0.001], indicating suitability for factor analysis. Five factors with eigenvalues greater than 1 explained 61.60% of the variance. The scree plot confirmed a clear change after the fifth factor. All item loadings were greater than 0.40 across factors ( Table 2 ). The CFA showed item loadings ranging from 0.630 to 0.890, all greater than 0.500 ( Figure 3 ). The model fit indices were χ 2 /df = 2.053, RMSEA = 0.066, IFI = 0.905, CFI = 0.904, PNFI = 0.719, and PGFI = 0.685, indicating good fit for the five-factor model. All standardized factor loadings were greater than 0.6 and significant. AVE values for each factor greater than 0.5 and CRs greater than 0.8 indicated good convergent validity. The square roots of AVEs were greater than the interfactor correlations, confirming good discriminant validity ( Schedule 1 and Schedule 2) . Reliability. The Cronbach’s α for the total scale was 0.928, ranging from 0.828 to 0.909 across dimensions. The split-half reliability for the total scale was 0.815, with dimensions ranging from 0.813 to 0.883. McDonald’s ω was 0.935, ranging from 0.887 to 0.928 for the dimensions. The ICC for the total scale was 0.874, ranging from 0.705 to 0.890 for the dimensions ( Schedule 3 ). The final version of the AAS-ROALA This study developed the AAS-ROALA with thirty-two items and five dimensions: independent autonomy (seven items), self-regulation (seven items), active participation (eight items), economic security (four items), and collaborative assistance (six items). Each item was assessed via a 5-point Likert scale (1~5 stands for “not at all” to “completely”). The total scores ranged from 32--160, with higher scores indicating higher levels of active aging among rural older adults living alone. Discussion Rapid development, a large base, aging, chronic comorbidities, and a lack of spiritual comfort are characteristics of the aging of rural old adults living alone, and their active aging has also become increasingly complex [33]. The development of an active aging scale specifically for this population is urgent and essential. This study strictly adheres to the scale development process [34]. First, on the basis of the theoretical framework of active aging and self-determination theory, we gained an in-depth understanding of the current situation of active aging among rural older adults living alone at home and abroad through a literature review and semistructured in-depth interviews, from which we distilled the relevant content to form an initial pool of items. Sixteen experts were subsequently invited for two expert consultation and discussion rounds. As a result of these modifications, a preliminary version of the scale was formed. Through a presurvey of thirty rural older adults living alone, the scale had a moderate number of items. It was easy to implement and understand, indicating that the scale had good operationalization and feasibility [35]. To our knowledge, this is the first assessment tool to monitor and assess the aging of older adults living alone in rural areas. The establishment scale consists of five dimensions and thirty-two items, including seven on independent autonomy, seven on self-adjustment, eight on active participation, four on economic security, and six on collaborative assistance. The first element of the AAS-ROALA is independent autonomy, which focuses on managing daily challenges and independent decision-making. For rural older adults living alone, their strong independence allows them to manage daily life and maintain good health without help. This autonomy improves their quality of life, reduces their dependence on external support, and increases their self-esteem [36]. The second factor is self-regulation, which helps rural older adults living alone manage life changes and mood swings. Effective self-regulation promotes mental health and social integration for those who feel isolated or stressed [37]. The third factor is active participation. Active participation in social and cultural activities helps enhance the social connections of older persons living alone in rural areas, expanding their networks and strengthening their sense of belonging [7]. The fourth factor is economic security, which ensures stability for older adults living alone in rural areas with limited resources. It supports access to essential services, improves quality of life, and reduces social isolation [38]. The final factor is collaborative assistance, which emphasizes cooperation among older persons in the community to promote cohesion. This cooperation ensures personal safety and social support while promoting overall development and stability within the community [39]. Therefore, by establishing the multidimensional tool of the AAS-ROALA, we can analyze the differences in the active aging of rural older adults living alone across multiple dimensions. To develop this new scale, it is critical to clearly define and operationalize active aging in a population of rural older adults living alone [35]. This study utilizes health, participation, and security as the core framework and incorporates elements of self-determination theory to define and quantify active aging among this group. The uniqueness of this approach is the precise delineation and quantification of five key dimensions, specifically for rural older adults living alone, as opposed to simply incorporating the factors of active aging from the literature [40]. These dimensions were customized to the specific needs and context of rural older adults living alone, providing clear definitions and quantitative criteria for the study. Introducing self-determination theory into active aging research can provide insight into how older adults fare with respect to independent autonomy, self-adjustment, active participation, economic security, and collaborative assistance and the interrelationships among these factors. This approach considers the specific characteristics of rural older adults living alone, providing a pathway and direction for promoting active aging research in this population. To our surprise, thirteen questionnaire items were removed after two rounds of expert consultation, and five new items were added. In the independent autonomy dimension, “I can adapt to feeling lonely” was deleted because of repetition. “I insist on doing manual work or odd jobs” was also removed because it may not fit most of the population. Two new items, “I take medicine correctly as instructed,” were added to reflect better health management and self-determination. For self-regulation, five items were removed, including “I often worry for unknown reasons,” owing to their relation to mental health issues, limitations in individual action, and subjective ambiguity influenced by multiple factors. Three new items were added. “I can manage chronic illness discomfort,” “I rarely lose sleep,” and “My memory decline will not bring trouble to my life” emphasize the actual response and adaptability of older adults in the face of health and cognitive challenges, which not only captures the core features of active aging more accurately but also takes into account the actual situation of quality of life and self-perception. With respect to active participation, “I follow current events through channels such as TV or mobile phones” was deleted because of varying media habits among older persons. For economic security, “I know the government’s economic subsidy policy for older adults” was removed because of its broad and unspecified nature. Four items, including “My home environment is safe,” were deleted since their macrolevel evaluations depend on the social context and may vary according to geographical and community characteristics. These adjustments aim to increase the scale’s accuracy and relevance, reflecting the core characteristics of active aging among older adults living alone in rural areas. Four item analysis techniques, including the critical ratio method and correlation analysis, were used in this study to comprehensively assess and determine the representativeness and discriminability of the scale items to optimize the scale item pool. Six items had low correlations (r values less than 0.4) with the scale’s total score, were less representative of the dimensions, and may not have effectively reflected the scale’s measurement objectives or the concepts of the dimensions. After discussion and analysis, it was decided to delete them. Moreover, cross-validation was conducted via EFA and CFA for the five-factor structure of active aging among rural older adults living alone, with the sample randomly divided into two sections. The I-CVI ranged from 0.813--1.000, and the S-CVI/Ave was 0.929. The content validity of the scale is good. EFA identified five factors with a cumulative variance of 61.60%. The scale had a Cronbach’s α of 0.928, split-half reliability of 0.815, and McDonald’s ω of 0.935, indicating strong internal consistency. The test-retest reliability after three weeks was 0.874, indicating good temporal stability. The results suggest that the scale has good content validity, structural validity, and reliability in assessing active aging among rural older adults living alone. Despite its strengths, this study has several limitations. First, the study sample was drawn from central and eastern China, making the sample representation geographically limited. Regional differences and cultural factors inherent in rural communities may limit the generalizability of the findings. Future studies should validate the scale in multiple regions with large samples in different geographic locations and cultural contexts to ensure broader scale applicability. Additionally, the cross-sectional design of this study did not allow for the complete elucidation of causal associations between the AAS-ROALA and health outcomes in older adults. Future research efforts could utilize this tool and explore longitudinal trends to reveal more intrinsic associations of the AAS-ROALA. Conclusion This study developed and validated an active aging measurement instrument for rural older adults living alone in China. The scale achieved high levels of acceptability, reliability, and validity. Rooted in self-determination theory and the concept of active aging, the scale highlights five key elements: independent autonomy, self-regulation, active participation, economic security, and collaborative assistance. These factors are crucial for comprehensively evaluating the active aging of rural older adults living alone and provide a theoretical reference for the study of precise intervention countermeasures to promote their active aging. Declarations Funding This work was supported by the National Natural Science Foundation of China (No. 72204084) and the Postgraduate Research and Innovation Project of Huzhou University (No. 2024KYCX82). Author Contribution Study conception and design: SL and SL. Data collection and evaluation: SL, LD, JB, YL, YX, XS, GG, and XY. Data extraction and analysis: SL, SL, and JB. Manuscript draft: SL, SL, LD, JB, YL, YX, XS, GG, and XY. Critical revision of important intellectual content: SL, SL, LD, YC, and JB. 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Psychol Assess. 2000;12:287–97. https://doi.org/10.1037//1040-3590.12.3.287 . Klein RB. Principles and practice of structural equation modeling. New York, NY.: Guilford Press; 2015. Sainani KL. Reliability statistics. PM R. 2017;9:622–8. https://doi.org/10.1016/j.pmrj.2017.05.001 . Hasson F, Keeney S, Mckenna H. Research guidelines for the delphi survey technique. J Adv Nurs. 2000;32:1008-15. https://doi.org/. Hou B, Zhang H. Latent profile analysis of depression among older adults living alone in China. J Affect Disord. 2023;325. https://doi.org/10.1016/j.jad.2022.12.154 . :378 – 85. Devellis R, Devellis R, Devellis RF. Scale development: theory and applications. 1991. Jebb AT, Ng V, Tay L. A review of key likert scale development advances: 1995–2019. Front Psychol. 2021;12:637547. https://doi.org/10.3389/fpsyg.2021.637547 . Aalto UL, Knuutila M, Lehti T, Jansson A, Kautiainen H, Ohman H, Strandberg T, Pitkala KH. Being actively engaged in life in old age: determinants, temporal trends, and prognostic value. Aging Clin Exp Res. 2023;35:1557–63. https://doi.org/10.1007/s40520-023-02440-9 . Akatsuka E, Tadaka E. Development of a resilience scale for oldest-old age (rso). BMC Geriatr. 2021;2110.1186/s12877-021-02036-w. Kwan C, Tam HC. what if i die and no one notices? a qualitative study exploring how living alone and in poverty impacts the health and well-being of older people in hong kong. Int J Environ Res Public Health. 2022;19:15856. https://doi.org/10.3390/ijerph192315856 . Burnette D, Ye X, Cheng Z, Ruan H. Living alone, social cohesion, and quality of life among older adults in rural and urban China: a conditional process analysis. Int Psychogeriatr. 2021;33:469–79. https://doi.org/10.1017/S1041610220001210 . Marzo RR, Khanal P, Shrestha S, Mohan D, Myint PK, Su TT. Determinants of active aging and quality of life among older adults: systematic review. Front Public Health. 2023;11:1193789. https://doi.org/10.3389/fpubh.2023.1193789 . Tables Table 1 to 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files table.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Aug, 2024 Editor assigned by journal 23 Aug, 2024 Submission checks completed at journal 23 Aug, 2024 First submitted to journal 21 Aug, 2024 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. 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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-4952208","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344041682,"identity":"92e97ae7-9d49-4c73-8670-b6476e254329","order_by":0,"name":"Shufang Liao","email":"","orcid":"","institution":"Huzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shufang","middleName":"","lastName":"Liao","suffix":""},{"id":344041683,"identity":"8e3493ea-d637-451f-b2f3-4ce57b42b243","order_by":1,"name":"Shasha 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14:10:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4952208/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4952208/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65091474,"identity":"bf89bab8-5ceb-4d99-a5b9-2c1a3f431f41","added_by":"auto","created_at":"2024-09-23 13:52:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32862,"visible":true,"origin":"","legend":"\u003cp\u003eFramework diagram of self-determination theory and active aging.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4952208/v1/88966eb5eccce5991837e333.png"},{"id":65091477,"identity":"baa525b4-19ae-4d24-8e87-2ce2e7e6c6d0","added_by":"auto","created_at":"2024-09-23 13:52:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33371,"visible":true,"origin":"","legend":"\u003cp\u003eProduction stage of active aging scale for rural older adults living alone.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4952208/v1/a82456bf0fa74c1d8bae4ae6.png"},{"id":65091478,"identity":"7ca2a1c9-5205-4a11-a08a-a2e9557180d4","added_by":"auto","created_at":"2024-09-23 13:52:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64635,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized path coefficient diagram of the questionnaire.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4952208/v1/4b7872f954c1b58def2f706f.png"},{"id":65092212,"identity":"db39758f-dbe3-453c-90c4-9446d89e8c39","added_by":"auto","created_at":"2024-09-23 14:00:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":628233,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4952208/v1/5f2b1cb2-5793-4307-af2c-d8e4fe876e8a.pdf"},{"id":65091475,"identity":"03c94041-6899-4026-a790-764eb06ba28a","added_by":"auto","created_at":"2024-09-23 13:52:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41063,"visible":true,"origin":"","legend":"","description":"","filename":"table.docx","url":"https://assets-eu.researchsquare.com/files/rs-4952208/v1/85ab71f97a1320bd79a14e3d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and application of active aging scale for rural older adults living alone","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to data released by the National Bureau of Statistics of China in January 2024, the population aged 60 and above has reached 297\u0026nbsp;million, accounting for 21.1% of the total population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among them, there are more than 150\u0026nbsp;million older adults living alone, and it is expected that by 2030, this number will exceed 200\u0026nbsp;million, indicating that the problem of the aging of older adults living alone in China is becoming increasingly severe. Data from the fifth, sixth, and seventh national population censuses revealed that China\u0026rsquo;s rural older adult population living alone reached 4.93\u0026nbsp;million, 8.12\u0026nbsp;million, and 15.33\u0026nbsp;million in 2000, 2010, and 2020, respectively, and that the percentage of rural older adult households living alone in 2020 was approximately 3.5 times that of 2000, whereas that of cities and towns was only approximately 1.9 times and 2.5 times greater [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], indicating that the proportion of rural older adult households living alone is accelerating.\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;14th Five-Year Plan for the Development of the National Aging Career and Elderly Service System\u0026rdquo; points out that a preliminary pattern has been formed to actively respond to population aging across society [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and that promoting active aging among rural older adults is critical to actively improving their ability to maintain health. The World Health Organization (WHO) concept emphasizes active aging as enabling older adults to participate actively and healthily in social, economic, cultural, and public affairs to become creators of social wealth and contributors to social development [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The policy encourages stimulating economic, social, and cultural potential so that resources can be effectively used to serve society, giving older people a sense of achievement and value. Active aging is an academic and policy concept and an international consensus on action guidelines for aging societies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, focusing on the active aging of rural older adults living alone is a guarantee for innovating and promoting a diversified supply pattern of elderly health services.\u003c/p\u003e \u003cp\u003eRural older adults living alone are still characterized by \u0026ldquo;three lows\u0026rdquo; and \u0026ldquo;three highs\u0026rdquo; regarding health, participation, and security in active aging. \u0026ldquo;Three lows\u0026rdquo; refer to low self-assessments of health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], social participation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and economic security [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], whereas \u0026ldquo;three highs\u0026rdquo; indicate a high prevalence of multiple chronic conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], active dedication [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and passive coping capacity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This population is defined as those aged 60 years and above living in rural areas without children, spouses, or other caregivers around them for more than six consecutive months due to being unmarried, divorced, widowed, or separated [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The tendency of young and middle-aged people to migrate to urban areas has led to an unequal distribution of resources for old-age care between urban and rural areas. This imbalance exacerbates the gap between the supply and demand of elderly care services in rural areas, posing a significant challenge to active aging for older people living alone [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Previous studies have investigated active aging among older people [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, there is a lack of research on active aging among vulnerable rural older adults living alone, making it difficult to analyze their characteristics and connotations. This approach is not conducive to formulating and practicing active aging policies.\u003c/p\u003e \u003cp\u003eAt present, no specialized assessment tools exist for the active aging of rural older adults living alone. The active aging scale for Thai adults [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reflects only the respondents\u0026rsquo; self-assessment of active aging. Moreover, more objective evaluation indicators, such as the living environment and cultural characteristics, are lacking. The active aging scale of Jyvaskyla University [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the active aging scale of nursing homes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the active aging scale of urban areas [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and the active aging scale of South Korea [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] are more suitable for comprehensive evaluation of urban active aging. Chinese scholars developed the active aging questionnaire in rural areas [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which mainly applies to the general characteristics of rural older adults and does not accurately assess those living alone. Given the particular characteristics of rural older adults living alone, the existing active aging assessment tools still cannot accurately address the current problems of active aging among rural older adults living alone. Therefore, to assess the active aging of rural older adults living alone effectively, it is necessary to construct a set of assessment scales suitable for the active aging of rural older adults living alone.\u003c/p\u003e \u003cp\u003eThis study constructed critical elements of active aging for rural older adults living alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) on the basis of self-determination theory (autonomy, competence, and relatedness) and the three-pillar conceptual framework of active aging (health, participation, and security). The specifics of these elements were identified through semistructured interviews. The study was divided into three phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): the first phase involved the development of a preliminary version, including literature analysis and semistructured interviews; the second phase covered the development of a test version, which was accomplished through two rounds of Delphi surveys and preexperimentation; and the third phase focused on the refinement of the final version, using cross-sectional surveys and model estimation methods. This study provides an evaluation tool for in-depth research on the active aging status of rural older adults living alone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eDesign and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a cross-sectional study. We randomly selected two provinces from China\u0026rsquo;s central and eastern regions: Jiangxi Province and Zhejiang Province. We then randomly chose Nanchang city (Jiangxi) and Huzhou city (Zhejiang) as specific study cities. The differences in geography, economic development, and cultural traditions between Nanchang and Huzhou have Chinese characteristics, reflecting the current aging situation of older adults living alone in rural China. The inclusion criteria included individuals who (i) were aged 60 years or above. (ii) Continuous residence in rural areas for at least six months; (iii) unmarried, divorced, or widowed without live-in partners or relatives; (iv) adequate communication abilities; (v) no prior participation in similar surveys or interventions; and (vi) willingness to accept follow-up visits and tracking surveys. The exclusion criteria were (i) suffering from severe acute or chronic diseases; (ii) cognitive dysfunction; (iii) deafness, blindness, aphasia, or inability to complete the questionnaire even with assistance; (iv) not living in rural households; and (v) family members or relatives providing comprehensive care and lacking the authenticity of the living alone state.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychometric testing procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhase I: Development of the preliminary version\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;study conducted a thorough literature search with target keywords (e.g., rural older adults living alone and\u0026nbsp;actively aged), including Chinese and English databases such as CBM, CNKI, Vip Database, Wanfang Database, PubMed, Web of Science, Embase, EBSCO, CINAHL, and Google Scholar. We aimed to find\u0026nbsp;studies\u0026nbsp;on rural older adults living alone and\u0026nbsp;actively aged, published until October 2023. After screening, we selected 37 articles covering quantitative and qualitative research for detailed review. The review focused on active aging among rural older adults living alone. Two researchers independently screened and resolved differences through discussion to ensure accuracy and credibility.\u003c/p\u003e\n\u003cp\u003eThe study conducted\u0026nbsp;semistructured\u0026nbsp;interviews with older adults living alone in rural areas. To ensure representativeness, participants\u0026nbsp;were selected on the basis of\u0026nbsp;gender, age, occupation, and cultural level, following the maximum difference sampling method. The interview outline was reviewed and revised by two gerontology research experts. Preinterviews with two rural older adults living alone were conducted to clarify and adjust the questions. The finalized interview outline included the following questions:\u0026nbsp;(i) How has living alone affected your life? Are these influences positive or negative? (ii) How are you doing? How do you keep fit? When health problems arise, what will you do? (iii) What activities were organized in the village? What did you join? Do you benefit from participating in activities? (iv) Have you received help in your daily life?\u0026nbsp;Does this help come from family members, friends, neighbors, or the government? What kind of help affects you most? (v) What other services or support should the village committee provide to help you enjoy your old age?\u003c/p\u003e\n\u003cp\u003eThis study selected interviewees on\u0026nbsp;the basis of specific criteria and determined the number\u0026nbsp;via\u0026nbsp;data saturation, meaning\u0026nbsp;that we stopped collecting data when no new themes emerged during the analysis. The study included thirteen interviewees, each lasting thirty to sixty minutes, with two experienced interviewers. The researcher maintained neutrality, avoided leading questions, and noted\u0026nbsp;nonverbal\u0026nbsp;cues. Two researchers independently analyzed the data\u0026nbsp;via\u0026nbsp;content analysis\u0026nbsp;[21], resolving disagreements through discussion to eliminate personal biases and ensure data relevance and significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhase 2: Development of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe test version\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the basis of\u0026nbsp;the purpose and feasibility of the study\u0026nbsp;[22],\u0026nbsp;sixteen experts were invited to review and revise forty-six initial items in March 2024. These experts had at least ten years of experience in gerontological or geriatric nursing, a background in active aging research or scale development, and were\u0026nbsp;at an associate senior level or higher, and they participated voluntarily. They employed a 5-point\u0026nbsp;Likert scale to evaluate the items. Items were included if their average significance was\u0026nbsp;greater\u0026nbsp;than or equal to 3.5 and their coefficient of variation was less than or equal to 0.25, along with a review of language and presentation.\u003c/p\u003e\n\u003cp\u003eTo\u0026nbsp;ensure the representativeness and acceptability of the scale items, according to the suggestion of Oksenberg et al.\u0026nbsp;[23], the test sample should be between twenty-five and seventy-five. Therefore, this study conducted a\u0026nbsp;presurvey of\u0026nbsp;thirty older adults living alone\u0026nbsp;in rural areas. The investigator guided the participants in completing the scale and provided feedback on any difficulties or ambiguities. These thirty rural older adults were then excluded from further psychometric evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhase 3: Development of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe final version\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSetting. This research stage adopted purposive sampling from April to May 2024.\u0026nbsp;The aim of this study was to understand the living conditions and health status of rural older adults aged 60 years and above living alone in central and eastern China.\u003c/p\u003e\n\u003cp\u003eSample size. The sample size was ten times the number of items recommended by Myers et al.\u0026nbsp;[24]. There were thirty-eight items in this study, and we additionally considered 25% invalid responses. A total of 507 questionnaires were distributed in this study (10*38/0.75), and 480 were validly returned, with a valid return rate of 94.7%. Finally, 240 cases were included in the exploratory factor analysis (EFA), and 240 cases were included in the validation factor analysis (CFA), which met the sample size requirements\u0026nbsp;[25].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee. All participants provided informed consent. The study followed the principles of the World Medical Association Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis of the data was conducted via\u0026nbsp;IBM SPSS Statistics version 25.0 (IBM Corp., Armonk, NY, USA) and IBM SPSS Amos version 25.0 (IBM Corp., Armonk, NY, USA)\u0026nbsp;to analyze the collected data. The normality of the data was assessed via the absolute skewness and kurtosis values for each dataset. In both datasets, the absolute values of skewness and kurtosis ranged from 0.002--1.459 and 0.008--1.449, respectively. With skewness and kurtosis values between -2 and +2 for all the\u0026nbsp;items, the data met the normality assumption and could be used for factor analysis\u0026nbsp;[26].\u003c/p\u003e\n\u003cp\u003eItem analysis. Scale items\u0026rsquo; discriminatory power and differentiation were tested via the critical ratio, Cronbach\u0026rsquo;s \u0026alpha; coefficient, discrete trend, and correlation coefficient methods.\u0026nbsp;Items\u0026nbsp;were deleted on the basis of the following criteria: (i) Critical ratio method:\u0026nbsp;Items\u0026nbsp;with nonsignificant differences in t test results or t values less than 3 when comparing high- and low-scoring groups were deleted. (ii) Cronbach\u0026rsquo;s \u0026alpha; coefficient method:\u0026nbsp;Items\u0026nbsp;were deleted if their removal significantly increased the total Cronbach\u0026rsquo;s \u0026alpha; coefficient of the scale. (iii) Discrete trend method:\u0026nbsp;Items\u0026nbsp;with a standard deviation less than 0.75 or a coefficient of variation (CV) less than 0.15 were considered for deletion. (iv) Correlation coefficient method:\u0026nbsp;Items\u0026nbsp;with a correlation (r) less than 0.4 or scores not significantly different from the total score were considered for deletion.\u003c/p\u003e\n\u003cp\u003eContent validity.\u0026nbsp;The item-level content validity index (I-CVI) and scale-level content validity index (S-CVI) were calculated on\u0026nbsp;the basis of expert evaluations\u0026nbsp;via\u0026nbsp;a 4-point Lynn scale\u0026nbsp;[27]. An\u0026nbsp;I-CVI greater than or equal to 0.78 and\u0026nbsp;an S-CVI greater than or equal to 0.90 were acceptance criteria\u0026nbsp;[28].\u003c/p\u003e\n\u003cp\u003eStructural validity. EFA and CFA were used to test the scale\u0026rsquo;s structural validity. Before factor analysis, the Kaiser‒Meyer‒Olkin (KMO) test (standardized at 0.80) and Bartlett\u0026rsquo;s sphericity test (p \u0026lt; 0.001) were used to check data suitability. EFA uses principal component analysis and maximum variance rotation without limiting the number of factors. The criteria for judgment were as follows: (i) extraction of factors with eigenvalues greater than 1; (ii) cumulative variance contribution ratio greater than 50%; (iii) each factor having greater than or equal to three items; (iv) item loading greater than 0.40 on one factor and less than 0.40 on others; and (v) adhering to the fragmentation plot test. Items with loadings less than or equal to 0.4 or high on multiple factors were excluded\u0026nbsp;[29]. For CFA, multiple fit metrics were used to assess model fit: chi-square degrees of freedom ratio (\u0026chi;\u003csup\u003e2\u003c/sup\u003e/df) less than 3.00; root mean square error of approximation (RMSEA) less than or equal to 0.08; incremental fit index (IFI) and comparative fit index (CFI) greater than or equal to 0.90; and parsimonious normed fit index (PNFI) and parsimonious goodness-of-fit index (PGFI) greater than or equal to\u0026nbsp;0.50\u0026nbsp;[30]. Convergent validity was considered good if factor loadings exceeded 0.5, average variance extracted (AVE) values were above 0.5, and composite reliability (CR) was greater than 0.7. Discriminant validity was confirmed if the square root of the AVE for each factor was more significant than the correlation coefficients between that factor and the other factors.\u003c/p\u003e\n\u003cp\u003eReliability. Cronbach\u0026rsquo;s \u0026alpha; coefficient, the Guttman split-half coefficient, and McDonald\u0026rsquo;s \u0026omega; coefficient were used to evaluate internal consistency. Thirty participants were retested after three weeks, and intragroup correlation coefficients (ICCs) were used to assess retest and interrater reliability. Coefficient evaluation criteria: a coefficient greater than or equal to 0.9 indicates high reliability; 0.8 to less than 0.9 indicates good reliability; 0.7 to less than 0.8 indicates acceptable reliability; 0.6 to less than 0.7 indicates barely acceptable reliability; and less than 0.6 indicates unsatisfactory reliability [31].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhase 1: Development of the preliminary version\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter conducting a thorough review of the literature and interviews and drawing from self-determination theory and the conceptual framework of active aging, we identified five critical dimensions of active aging for rural older adults living alone: (i) Independent autonomy (ten items): Refers to the ability to live independently and care for oneself in daily life, including making autonomous decisions. (ii) Self-regulation (ten items): Adapting to negative emotions caused by chronic diseases or physical aging through self-adjustment to achieve emotional balance and inner peace. (iii) Active participation (ten items): This factor involves actively engaging in social and cultural activities on a sustained basis. (iv) Economic security (six items): To obtain sufficient economic support for older adults to ensure a high quality of life later. (v) Collaborative assistance (ten items): Cooperation and mutual assistance from various parties to obtain support for older adults regarding life care and health care. We initially developed a measurement library containing forty-six items, rigorously reviewing and translating all the\u0026nbsp;items, ensuring clarity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhase 2: Development of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe test version\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDelphi study. After the first round of expert consultation, eleven items were removed on the basis of the experts\u0026rsquo; ratings. These items included \u0026ldquo;I insist on doing manual work or odd jobs,\u0026rdquo; \u0026ldquo;I often worry for unknown reasons,\u0026rdquo; and \u0026ldquo;My home environment is safe.\u0026rdquo; Four items were added, such as \u0026ldquo;I know my health status,\u0026rdquo; and some were revised according to the experts\u0026rsquo; recommendations. Following the second round of expert assessment, \u0026ldquo;I often feel worthless\u0026rdquo; and \u0026ldquo;The village organizes regular sports and health education\u0026rdquo; were removed, and \u0026ldquo;I rarely lose sleep\u0026rdquo; was added. Additionally, item descriptions were adjusted on the basis of the experts\u0026rsquo; opinions. The coefficients of variation for all the items decreased after these revisions [32].\u003c/p\u003e\n\u003cp\u003ePreinvestigation. All thirty participants agreed that the questionnaire was easy to understand. It took an average of\u0026nbsp;eight\u0026nbsp;to ten minutes to complete the questionnaire. Finally, the test scale consisted of five dimensions:\u0026nbsp;independent autonomy\u0026nbsp;(eight items),\u0026nbsp;self-regulation\u0026nbsp;(eight\u0026nbsp;items), active participation (ten items), economic security (five items), and\u0026nbsp;collaborative assistance\u0026nbsp;(seven items), totaling thirty-eight items.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhase 3: D\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eevelopment of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe final version\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a random splitting method was used for factor analysis. The final sample (n=480) was randomly divided into two equal subsets,\u0026nbsp;dataset A (n=240) and\u0026nbsp;dataset B (n=240),\u0026nbsp;via\u0026nbsp;IBM SPSS Statistics. \u003cstrong\u003eTable 1\u003c/strong\u003e shows the sample characteristics. Most participants were female (57.7%), had\u0026nbsp;a primary school education or below (86.7%), were widowed (65.9%), and had been living alone for\u0026nbsp;1\u0026ndash;5\u0026nbsp;years (47.3%). The average age was\u0026nbsp;approximately 70 years; most had\u0026nbsp;2\u0026ndash;3\u0026nbsp;children (44.4%), and only 89 (18.5%) had religious beliefs. Financial support mainly came from pensions (48.5%), and most had a monthly income of less than 500 yuan. Additionally, 43.8% had one chronic disease. Both datasets were similar to the total sample, with no significant differences.\u003c/p\u003e\n\u003cp\u003eItem analysis.\u0026nbsp;The results show that,\u0026nbsp;according to the critical ratio, Cronbach\u0026rsquo;s \u0026alpha; coefficient, and discrete trend methods, all scale items meet\u0026nbsp;the retention criteria. Only six items with r\u0026nbsp;values less than 0.4 were deleted\u0026nbsp;because they did not meet\u0026nbsp;the retention criteria.\u003c/p\u003e\n\u003cp\u003eContent validity. The scale showed good content validity,\u0026nbsp;with\u0026nbsp;an I-CVI ranging from 0.813--1.000 and\u0026nbsp;an S-CVI/Ave of 0.929.\u003c/p\u003e\n\u003cp\u003eStructural validity. EFA\u0026nbsp;revealed that the KMO value was 0.848, and Bartlett\u0026rsquo;s sphericity test was significant [\u0026chi;\u003csup\u003e2\u003c/sup\u003e(496) =4163.736, P\u0026lt;0.001], indicating suitability for factor analysis. Five factors with eigenvalues greater than 1\u0026nbsp;explained 61.60% of the variance.\u0026nbsp;The scree plot confirmed a clear change after the fifth factor.\u0026nbsp;All item loadings were greater than 0.40 across factors (\u003cstrong\u003eTable 2\u003c/strong\u003e). The CFA showed item loadings ranging from 0.630 to 0.890, all greater than 0.500 (\u003cstrong\u003eFigure 3\u003c/strong\u003e). The model fit indices were \u0026chi;\u003csup\u003e2\u003c/sup\u003e/df = 2.053, RMSEA = 0.066, IFI = 0.905, CFI = 0.904, PNFI = 0.719, and PGFI = 0.685, indicating good fit for the five-factor model. All standardized factor loadings were greater than 0.6 and significant. AVE values for each factor greater than 0.5 and CRs greater than 0.8 indicated good convergent validity. The square roots of AVEs were greater than the interfactor correlations, confirming good discriminant validity (\u003cstrong\u003eSchedule 1 and Schedule 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eReliability. The Cronbach\u0026rsquo;s \u0026alpha; for the total scale was 0.928, ranging from 0.828 to 0.909 across dimensions. The split-half reliability for the total scale was 0.815, with dimensions ranging from 0.813 to 0.883. McDonald\u0026rsquo;s \u0026omega; was 0.935, ranging from 0.887 to 0.928 for the dimensions. The\u0026nbsp;ICC for the total scale was 0.874, ranging from 0.705 to 0.890 for the dimensions\u0026nbsp;(\u003cstrong\u003eSchedule 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe final version of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe AAS-ROALA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study developed the AAS-ROALA with thirty-two items and five dimensions: independent autonomy (seven items), self-regulation (seven items), active participation (eight items), economic security (four items), and collaborative assistance (six items). Each item was assessed via a 5-point Likert scale (1~5 stands for \u0026ldquo;not at all\u0026rdquo; to \u0026ldquo;completely\u0026rdquo;). The total scores ranged from 32--160, with higher scores indicating higher levels of active aging among rural older adults living alone.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRapid development, a large base, aging, chronic comorbidities, and a lack of spiritual comfort are characteristics of the aging of rural old adults living alone, and their active aging has also become increasingly complex\u0026nbsp;[33]. The development of an active aging scale specifically for this population is urgent and essential. This study strictly adheres to the scale development process\u0026nbsp;[34]. First, on the basis of the theoretical framework of active aging and self-determination theory, we gained an in-depth understanding of the current situation of active aging among rural older adults living alone at home and abroad through a literature review and semistructured in-depth interviews, from which we distilled the relevant content to form an initial pool of items. Sixteen experts were subsequently invited for two expert consultation and discussion rounds. As a result of these modifications, a preliminary version of the scale was formed. Through a presurvey of thirty rural older adults living alone, the scale had a moderate number of items. It was easy to implement and understand, indicating that the scale had good operationalization and feasibility\u0026nbsp;[35]. To our knowledge, this is the first assessment tool to monitor and assess the aging of older adults living alone in rural areas. The establishment scale consists of five dimensions and thirty-two items, including seven on independent autonomy, seven on self-adjustment, eight on active participation, four on economic security, and six on collaborative assistance.\u003c/p\u003e\n\u003cp\u003eThe first element of the AAS-ROALA is\u0026nbsp;independent autonomy, which focuses on managing daily challenges and independent decision-making. For rural older adults living alone, their strong independence allows them to manage daily life and maintain good health without help. This autonomy improves their quality of life, reduces their dependence on external support, and increases their self-esteem\u0026nbsp;[36]. The second factor is self-regulation, which helps rural older adults living alone manage life changes and mood swings. Effective self-regulation promotes mental health and social integration for those who feel isolated or stressed\u0026nbsp;[37]. The third factor is active participation. Active participation in social and cultural activities helps enhance the social connections of older persons living alone in rural areas, expanding their networks and strengthening their sense of belonging\u0026nbsp;[7]. The fourth factor is economic security, which ensures stability for older adults living alone in rural areas with limited resources. It supports access to essential services, improves quality of life, and reduces social isolation\u0026nbsp;[38]. The final factor is collaborative assistance, which emphasizes cooperation among older persons in the community to promote cohesion. This cooperation ensures personal safety and social support while promoting overall development and stability within the community\u0026nbsp;[39]. Therefore, by establishing the multidimensional tool of the AAS-ROALA, we\u0026nbsp;can analyze the differences in the active aging of rural older adults living alone across multiple\u0026nbsp;dimensions.\u003c/p\u003e\n\u003cp\u003eTo develop this new scale, it is critical to clearly define and operationalize active aging in a population of rural older adults living alone\u0026nbsp;[35].\u0026nbsp;This study utilizes health, participation, and security as the core framework and incorporates elements of self-determination theory to define and quantify active aging among this group. The uniqueness of this approach is the precise delineation and quantification of five key dimensions, specifically for rural older adults living alone, as opposed to simply incorporating the factors of active aging from the literature\u0026nbsp;[40].\u0026nbsp;These dimensions were customized to the specific needs and context of rural older adults living alone, providing clear definitions and quantitative criteria for the study. Introducing self-determination theory into active aging research can provide insight into how older adults fare with respect to independent autonomy, self-adjustment, active participation, economic security, and collaborative assistance and the interrelationships among these factors. This approach considers the specific characteristics of rural older adults living alone, providing a pathway and direction for promoting active aging research in this population.\u003c/p\u003e\n\u003cp\u003eTo our surprise, thirteen questionnaire items were removed after two rounds of expert consultation, and five new items were added. In the independent autonomy dimension, \u0026ldquo;I can adapt to feeling lonely\u0026rdquo; was deleted because of repetition. \u0026ldquo;I insist on doing manual work or odd jobs\u0026rdquo; was also removed because it may not fit most of the population. Two new items, \u0026ldquo;I take medicine correctly as instructed,\u0026rdquo; were added to reflect better health management and self-determination. For self-regulation, five items were removed, including \u0026ldquo;I often worry for unknown reasons,\u0026rdquo; owing to their relation to mental health issues, limitations in individual action, and subjective ambiguity influenced by multiple factors. Three new items were added. \u0026ldquo;I can manage chronic illness discomfort,\u0026rdquo; \u0026ldquo;I rarely lose sleep,\u0026rdquo; and \u0026ldquo;My memory decline will not bring trouble to my life\u0026rdquo; emphasize the actual response and adaptability of older adults in the face of health and cognitive challenges, which not only captures the core features of active aging more accurately but also takes into account the actual situation of quality of life and self-perception. With respect to active participation, \u0026ldquo;I follow current events through channels such as TV or mobile phones\u0026rdquo; was deleted because of varying media habits among older persons. For economic security, \u0026ldquo;I know the government\u0026rsquo;s economic subsidy policy for older adults\u0026rdquo; was removed because of its broad and unspecified nature. Four items, including \u0026ldquo;My home environment is safe,\u0026rdquo; were deleted since their macrolevel evaluations depend on the social context and may vary according to geographical and community characteristics. These adjustments aim to increase the scale\u0026rsquo;s accuracy and relevance, reflecting the core characteristics of active aging among older adults living alone in rural areas.\u003c/p\u003e\n\u003cp\u003eFour item analysis techniques, including the critical ratio method and correlation analysis, were used in this study to comprehensively assess and determine the representativeness and discriminability of the scale items to optimize the scale item pool. Six items had low correlations (r values less than 0.4) with the scale\u0026rsquo;s total score, were less representative of the dimensions, and may not have effectively reflected the scale\u0026rsquo;s measurement objectives or the concepts of the dimensions. After discussion and analysis, it was decided to delete them. Moreover, cross-validation was conducted via EFA and CFA for the five-factor structure of active aging among rural older adults living alone, with the sample randomly divided into two sections. The I-CVI ranged from 0.813--1.000, and the S-CVI/Ave was 0.929. The content validity of the scale is good. EFA identified five factors with a cumulative variance of 61.60%. The scale had a Cronbach\u0026rsquo;s \u0026alpha; of 0.928, split-half reliability of 0.815, and McDonald\u0026rsquo;s \u0026omega; of 0.935, indicating strong internal consistency. The test-retest reliability after three weeks was 0.874, indicating good temporal stability. The results suggest that the scale has good content validity, structural validity, and reliability in assessing active aging among rural older adults living alone.\u003c/p\u003e\n\u003cp\u003eDespite its strengths, this study has several limitations. First, the study sample was drawn from central and eastern China, making the sample representation geographically limited. Regional differences and cultural factors inherent in rural communities may limit the generalizability of the findings. Future studies should validate the scale in multiple regions with large samples in different geographic locations and cultural contexts to ensure broader scale applicability. Additionally, the cross-sectional design of this study did not allow for the complete elucidation of causal associations between the AAS-ROALA and health outcomes in older adults. Future research efforts could utilize this tool and explore longitudinal trends to reveal more intrinsic associations of the AAS-ROALA.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed and validated an active aging measurement instrument for rural older adults living alone in China. The scale achieved high levels of acceptability, reliability, and validity. Rooted in self-determination theory and the concept of active aging, the scale highlights five key elements: independent autonomy, self-regulation, active participation, economic security, and collaborative assistance. These factors are crucial for comprehensively evaluating the active aging of rural older adults living alone and provide a theoretical reference for the study of precise intervention countermeasures to promote their active aging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 72204084) and the Postgraduate Research and Innovation Project of Huzhou University (No. 2024KYCX82).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy conception and design: SL and SL. Data collection and evaluation: SL, LD, JB, YL, YX, XS, GG, and XY. Data extraction and analysis: SL, SL, and JB. Manuscript draft: SL, SL, LD, JB, YL, YX, XS, GG, and XY. Critical revision of important intellectual content: SL, SL, LD, YC, and JB. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the research team, the peer reviewers, and all the older adults who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eData from the census. In 2024 [EB/OL]. [2024-01-07]. National bureau of statistics, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stats.gov.cn/xxgk/jd/sjjd2020/202401/t20240118_1946711.html\u003c/span\u003e\u003cspan address=\"https://www.stats.gov.cn/xxgk/jd/sjjd2020/202401/t20240118_1946711.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L. Study on the characteristics and changing trend of the elderly population living alone in China. 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Front Public Health. 2023;11:1193789. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2023.1193789\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2023.1193789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Active aging, Rural older adults living alone, Psychometric properties, Scale development, Validity and reliability","lastPublishedDoi":"10.21203/rs.3.rs-4952208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4952208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe issue of active aging among older adults living alone in rural areas is becoming increasingly complex worldwide, and China is no exception. However, more specialized assessment tools are needed to evaluate active aging in this population. This study aims to develop and validate an active aging scale for rural older adults living alone (AAS-ROALA) in China, providing a theoretical foundation for research in this area.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe scale was developed in three phases\u0026mdash;a preliminary version, a test version, and a final refined version\u0026mdash;a cross-sectional survey of 480 rural older adults living alone in two cities in China in April and May 2024. The scale was tested for item analysis, content validity, structural validity, and internal reliability via a cross-sectional survey design.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe newly developed scale has thirty-two items across five dimensions: independent autonomy, self-regulation, active participation, economic security, and collaborative assistance. The I-CVI ranged from 0.813\u0026ndash;1.000, and the S-CVI/Ave was 0.929. EFA identified five factors with a cumulative variance of 61.60%. The CFA showed a good model fit. The Cronbach\u0026rsquo;s α, McDonald\u0026rsquo;s ω, split-half coefficient, and retest reliability for the total scale were 0.928, 0.935, 0.815, and 0.874, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings show that the AAS-ROALA is a valid and appropriate instrument to inform in-depth studies of active aging among rural older adults living alone.\u003c/p\u003e","manuscriptTitle":"Development and application of active aging scale for rural older adults living alone","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-23 13:52:46","doi":"10.21203/rs.3.rs-4952208/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-23T07:35:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-23T04:09:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-23T04:08:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-08-21T14:08:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a07ec31e-51d2-4885-86f8-1bdd17d2a093","owner":[],"postedDate":"September 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-23T14:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-23 13:52:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4952208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4952208","identity":"rs-4952208","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}