Influence of Living Spaces and Sociodemographic Factors on Older Adults' Mobility and Autonomy: A Cross-Sectional Study in Chile

Influence of Living Spaces and Sociodemographic Factors on Older Adults' Mobility and Autonomy: A Cross-Sectional Study in Chile | 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 Influence of Living Spaces and Sociodemographic Factors on Older Adults' Mobility and Autonomy: A Cross-Sectional Study in Chile Paula Moreno-Reyes, Antonia Espejo-Jeraldo, Rocío Perea-Colman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6531182/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 Mobility within living spaces is essential for active and healthy aging, supporting autonomy and overall quality of life. However, sociodemographic and environmental factors can significantly shape how older adults utilize these spaces. OBJECTIVE This study aimed to examine the relationship between living space utilization and sociodemographic characteristics in older adults, and to identify key barriers that may restrict mobility and independence. METHODOLOGY A cross-sectional study was conducted in Copiapó, Chile, involving 403 community-dwelling adults aged 60 to 91. The Life Space Assessment (LSA) questionnaire was used to evaluate spatial mobility across five levels, from bedroom to beyond city limits. Sociodemographic, health, and physical activity data were also collected. Multiple linear regression was applied to identify factors influencing mobility. RESULTS Most participants reported high independence, with frequent use of indoor spaces (97.5%) and immediate outdoor areas (91.4%). Mobility decreased in neighborhood (54%) and city-level environments. Regression analysis revealed that sex (B = -8.752, p = 0.004), age (B = -0.462, p = 0.009), and use of assistive devices (B = -19.539, p < 0.001) were significant negative predictors of mobility. Education and income were not statistically significant. CONCLUSIONS Older adults in this study demonstrated preserved autonomy in proximal environments, but reduced mobility in broader urban settings. These findings highlight the importance of creating age-friendly environments and targeted interventions to promote outdoor mobility and independence, particularly among older women and individuals using assistive devices. Aged Quality of Life Sociodemographic Factors Motor Activity Activities of Daily Living Active Aging Introduction Functional mobility is a key determinant of independence and the quality of life in older adults. As aging progresses, changes in physical capacity, health conditions, and environmental factors can restrict movement within living spaces, leading to a decline in autonomy and increased risk of social isolation. Mobility limitations are associated with reduced physical activity, greater dependence on caregivers, and a decline in overall well-being (Curcio et al., 2013 ). The concept of living space has been widely explored in gerontology as it provides insights into how the home and surrounding environment influence mobility, social participation, and health outcomes (Baker et al., 2003 ). However, mobility is not solely determined by physical capacity; factors such as neighborhood accessibility, perceived safety, and availability of resources play crucial roles in shaping movement patterns in older adults (Kuspinar et al., 2020 ). Active aging has been promoted as a strategy to preserve functional independence and social engagement; however, sociodemographic characteristics influence how older individuals interact with their surroundings. Variables such as gender, education level, marital status, and economic conditions affect the extent to which older adults access and utilize their living spaces. The World Health Organization (WHO) emphasizes that the environments in which individuals live and age significantly affect health, reinforcing the need to address structural barriers that limit mobility. Reduced access to outdoor spaces has been linked to diminished physical function and lower quality of life, reinforcing the importance of fostering mobility-friendly environments to counteract the “prisoners of space” phenomenon described by Rowles et al. (1981). Age-related conditions such as joint pain and visual impairment also contribute to a preference for indoor spaces, restricting access to external environments that promote greater mobility and independence Suri et al. ( 2021 ). Individuals who regularly utilize outdoor spaces demonstrate improved mobility and functional capacity, thereby reducing the risk of environmental confinement (Quimbaya & Borrero, 2016). Although the relationship between mobility and well-being has been studied extensively, there is limited research on how sociodemographic variables specifically affect the use of living spaces in different urban settings. Access to larger open spaces has been associated with better health outcomes and reduced isolation (Webber et al., 2010 ). However, little is known about how these factors interact in medium-sized cities, such as Copiapó and Chile. Understanding these patterns is essential for designing public policies and community interventions to promote autonomy and active aging. Therefore, this study aimed to examine the relationship between living space utilization and sociodemographic characteristics in older adults and identify barriers that may restrict mobility and independence. The Life Space Assessment (LSA) questionnaire, a validated instrument for measuring mobility patterns, was used to assess movement across different spatial levels (Baker et al., 2003 ). The findings from this research can contribute to the development of policies and programs that enhance mobility, encourage active lifestyles, and improve the overall quality of life in aging populations. Methods Type of study This descriptive cross-sectional study was conducted between July 2023 and August 2024 in Copiapó, Chile. The study protocol was approved by the Scientific Ethics Committee of Universidad de Atacama (No. 06/23). Participants A total of 403 participants were selected through simple random probability sampling from the 2022 Fonasa database, which registered 15,302 older adults in local family health centers (CESFAM). The sample size was determined using a 95% confidence level, 5% margin of error, and estimated population proportion of 50% to ensure representativeness. Eligibility criteria included individuals aged 60 years or older, self-sufficient, residing in the Atacama Region, cognitively capable of understanding and answering the questionnaires, and who provided informed consent. Exclusion criteria were nursing home residents, individuals with neurodegenerative diseases affecting mobility, and those with severe cognitive impairment. Data loss resulted from incomplete questionnaires, voluntary withdrawal, or non-responses. Instruments : The Life Space Assessment (LSA) questionnaire was used to evaluate mobility and living space utilization (Baker et al., 2003 ). The LSA assesses mobility across five zones: inside the bedroom, inside the home but outside the bedroom, the neighborhood, the city, and beyond city limits. Mobility frequency was categorized from less than once a week to daily, and the levels of assistance ranged from independent movement to needing support. The total LSA score was obtained by multiplying the frequency by the assistance level per zone and summing the results. Scores were classified as restricted living space (0–29), medium living space (30–59), or large living space (≥ 60). Procedures Data were collected between July 2023 and August 2024 and informed consent was obtained beforehand. Demographic variables included age, sex, weight, height, BMI, marital status, education level, years of schooling, residence, comorbidities, medication use, recent surgeries, use of assistive devices, cohabitation, and caregiving responsibilities. Socioeconomic factors such as monthly per capita income and employment status were also evaluated. Statistical analysis Data analysis was conducted using the JASP software. Descriptive statistics, including means, standard deviations, frequencies, and percentages were calculated. The Shapiro-Wilk test was used to assess data distribution. A multiple linear regression model was employed to identify the factors influencing mobility, incorporating t-tests and stepwise regression with a 95% confidence interval (p < 0.05). Results Results Sociodemographic Characteristics The sample consisted predominantly of female participants (68%), with a mean age of 69.6 ± 6.7 years. The vast majority of participants resided in urban areas (99.9%) and lived with companions (84.9%). Educational attainment remained low, as 51% of the participants had only completed basic education. Monthly income ranged between 200,001 and 500,000 CLP for 46.4% of participants, reflecting economic constraints. Table 1 provides detailed sociodemographic information including marital status, place of residence, and household composition. Physical Status and Activity An analysis of body mass index (BMI) showed that the majority of participants (72.5%) were overweight or obese, while only 27.5% had a normal BMI. Although 69.2% of the participants reported engaging in regular physical activity, the intensity and frequency of exercise might be insufficient to counteract metabolic risks. The use of assistive devices was minimal (7.2%), which indicated a high level of functional independence (92.8%). Comorbidities averaged 1.5 ± 1.2, while medication use was reported by 73.2% of the participants. Additionally, 61.5% of the participants had no recent surgical history and 97% did not require caregiving support. Table 1 outlines the data on comorbidities, medication use, and levels of physical activity. Table 1 Table 1 : Socio-demographic characteristics of the surveyed elderly, including mean and standard deviation (SD) for age, weight, height, BMI, years of schooling, and number of comorbidities. Frequencies (n) and percentages (%) are presented for marital status, education level, residence, employment, income, use of technical assistance, physical activity, BMI categories, medication use, surgeries, and caregiving. *Chilean Pesos. Variable male (n = 127) female (n = 276) Total (n = 403) Marital Status Married 88 (69.4) 101 (36.6) 189 (42.5) Single 14 (11.1) 86 (31.1) 100 (27.5) Widowed 11 (8.3) 74 (26.8) 85 (23.5) Divorced 14 (11.1) 15 (5.5) 29 (6.5) Educational Level Basic 46 (36.1) 150 (54.3) 196 (51.0) Secondary 71 (55.6) 84 (30.5) 155 (35.0) Higher Education 10 (8.3) 42 (15.2) 52 (14.0) Years of Education 10.6 ± 3.1 10.1 ± 3.8 10.2 ± 3.7 Place of Residence Urban 123 (97.2) 274 (99.4) 397 (99.9) Rural 4 (2.8) 2 (0.6) 6 (1.0) Who do you live with? Alone 7 (5.6) 54 (19.5) 61 (15.1) Accompanied 120 (94.4) 222 (80.5) 342 (84.9) Employment Retired 92 (72.2) 247 (89.6) 339 (84.1) Working 35 (27.8) 29 (10.4) 64 (15.9) Income 0 to 200.000 32 (25.0) 109 (39.6) 141 (35.0) 200.001 to 500.000 56 (44.4) 131 (47.6) 187 (46.4) 500.001 to 800.000 21 (16.7) 25 (9.1) 46 (11.4) 800.001 to 1.000000 11 (8.3) 3 (1.2) 14 (3.5) > 1.000000 7 (5.6) 8 (2.4) 15 (3.7) Technical Assistance Does not use 113 (88.6) 261 (94.5) 374 (92.8) Does use 14 (11.4) 15 (5.5) 29 (7.2) Physical Activity Does not do 53 (41.7) 71 (25.6) 124 (30.8) Does do 74 (58.3) 205 (74.4) 279 (69.2) Number of Comorbidities 1.0 ± 0.9 1.6 ± 1.2 1.5 ± 1.2 Medicines Does not take 46 (36.1) 62 (22.6) 108 (26.8) Does take 81 (63.9) 214 (77.4) 295 (73.2) Use of Living Spaces The Life Space Assessment (LSA) questionnaire indicated that 97.5% of participants used indoor spaces outside the bedroom daily, while 91.4% regularly accessed immediate outdoor areas, such as patios and terraces. However, mobility decreased as distance increased, with 54% reporting daily access to neighborhood spaces, whereas areas beyond city limits were accessed daily by only 0.8% of the participants. Table 2 presents a detailed breakdown of the utilization of living spaces. Daily engagement was highest in indoor and immediate outdoor spaces, whereas activities beyond the neighborhood occurred less frequently. For level 5, 50.8% of the participants engaged in activities one to three times per week, and only 0.8% reported daily participation. The need for assistive devices or personal support remained minimal at lower LSA levels (> 97% independence), although it slightly increased for spaces outside the city. LSA scores showed a steady rise up to level 4 (18.8 ± 7.8), reflecting greater mobility demands. However, a decline was observed at level 5 (15.2 ± 6.5), suggesting limited engagement in high-intensity activities or lower participation in distant environments. Table 2 Description of the levels of use of living spaces. Level 1: In other rooms of the house, different from the bedroom where they sleep. Level 2: In an area outside the house, such as a terrace or patio, hallway of an apartment building, garage, their own garden, or entrance of the house. Level 3: In places within their neighbourhood that are not their own garden or apartment building. Level 4: Places outside their neighbourhood but within the city. Level 5: Places outside the city. N = 403 How many times? Level 1 Level 2 Level 3 Level 4 Level 5 0 0 (0.0) 2 (0.5) 7 (1.7) 4 (1.0) 109 (27.0) < 1 time/week 1 (0.2) 4 (1.0) 10 (2.5) 36 (8.9) 150 (37.2) 1–3 times/week 3 (0.7) 19 (4.7) 72 (17.9) 125 (31.0) 131 (32.5) 4–6 times/week 4 (1.0) 10 (2.5) 38 (9.4) 67 (16.6) 6 (1.5) Daily 395 (98.0) 368 (91.3) 276 (68.5) 171 (42.4) 7 (1.7) Have you used aids or equipment or required assistance from another person? Level 1 Level 2 Level 3 Level 4 Level 5 0 0 (0.0) 2 (0.5) 7 (1.7) 4 (1.0) 109 (27.0) Help from others 3 (0.7) 2 (0.5) 5 (1.2) 16 (4.0) 14 (3.5) Equipment only 8 (2.0) 9 (2.2) 8 (2.0) 9 (2.2) 9 (2.2) None 392 (97.3) 390 (96.8) 383 (95.0) 374 (92.8) 271 (67.2) Factors Affecting Living Space Use Table 3 presents the relationships between living space use and various sociodemographic factors. The multiple regression model identified sex (B = -8.752, t = -2.909, p = 0.004) and age (B = -0.462, t = -2.646, p = 0.009) as significant negative predictors of mobility, indicating that women and older adults demonstrated lower LSA scores. Conversely, variables such as education level (B = 0.378, p = 0.807), place of residence (B = 12.630, p = 0.239), and income (B = 2.137, p = 0.082) were not statistically significant. The strongest negative impact on mobility was associated with the use of assistive devices (B = -19.539, t = -4.352, p < 0.001), reinforcing the relationship between functional dependence and restricted mobility. The regression model accounted for 20.3% of the variance in mobility scores (R² = 0.203), indicating that while sex, age, and assistive device use significantly influenced LSA scores, other unexamined factors may also contribute to variations in mobility patterns. These findings highlight a clear trend of reduced mobility with age, particularly among women, while also suggesting that financial and educational factors may have a lesser impact on movement patterns than previously assumed. Table 3 The following stepwise correlation between the Life Space Assessment (LSA) and socio-demographic variables was established. A multiple linear regression model was constructed and the findings indicated that the primary explanatory factors for the utilisation of living space were the absence of technical assistance (β=-.326; p < .001), male gender (β=-.237), and younger age (β=-.208). The model demonstrated a variance explained (coefficient of determination) of 20.3% (R2 = 0.203; F = 17.869, gl = 3; p < .001). Unstandardized coefficients Standardized coefficients 95% confidence interval for B Model B Std. Error Beta t Sig. Lower Limit Upper Limit (Constant) 97.130 20.645 4.705 0.000 56.406 137.854 Gender -8.752 3.009 -0.203 -2.909 0.004 -14.687 -2.817 Age -0.462 0.174 -0.185 -2.646 0.009 -0.806 -0.117 Educational Level 0.378 1.546 0.016 0.244 0.807 -2.673 3.428 Residence 12.630 10.696 0.076 1.181 0.239 -8.468 33.729 Household Coexistence 0.412 2.819 0.010 0.146 0.884 -5.149 5.974 Employment Status 0.116 3.450 0.002 0.034 0.973 -6.690 6.922 Economic Income 2.137 1.224 0.117 1.746 0.082 -0.277 5.550 Technical Assistance -19.539 4.490 -0.290 -4.352 0.000 -28.396 -10.683 Physical Activity 4.195 2.421 0.114 1.733 0.085 -0.581 8.972 Marital Status 1.847 2.311 0.055 0.799 0.425 -2.711 6.406 Dependent Variable : Total Score Discussion This study analyzed the mobility patterns of older adults in Copiapó, Chile, using the Life Space Assessment (LSA) questionnaire, a validated tool widely applied in gerontological research to evaluate mobility and its association with health, quality of life, and social participation [8]. We found that age, gender, and marital status significantly influence mobility, highlighting the complex interplay between sociodemographic variables and functional independence in aging populations. The sample was predominantly female (82%), with an average age of 69.6 ± 6.7 years, consistent with demographic trends indicating longer female life expectancy due to biological and social factors (Osorio-Parraguez et al., 2022 ; Pinho-Gomes et al., 2023 ). However, women tend to experience poorer health outcomes than men (Austad et al., 2020 ). Gender differences in body composition were evident, with men presenting with higher body mass and height, influencing BMI classifications and mobility patterns. While increased BMI in men often reflects greater muscle mass, in women, it is more commonly associated with adipose accumulation, which negatively affects functional mobility and chronic disease risk (Wells et al., 2008 ). A high prevalence of overweight and obesity (72.5%) has been observed, reinforcing concerns regarding their effects on mobility and chronic disease susceptibility (Guh et al., 2009 ). Despite 71.5% of the participants engaging in physical activity, their intensity may not be sufficient to counteract poor dietary habits or metabolic conditions. These findings underscore the need for interventions that integrate nutritional and physical activity strategies to enhance mobility and overall health outcomes. Marital status and educational level have emerged as relevant factors. While most men were married (69.4%), a significant proportion of women were widowed (26.8%) or single (31.1%), potentially influencing the social support systems. Married older adults benefit from stronger social connections, which enhance mobility and overall well-being (Paul & Verma, 2016 ). Additionally, women exhibited lower educational attainment, with 54.3% completing only primary education compared to 36.1% of men. Lower educational levels have been linked to reduced access to health information and poorer quality of life (Sánchez-Rodríguez et al., 2020 ). Most participants lived in urban areas (99%) and with companions (80.5%), aligning with evidence suggesting that social and familial cohabitation positively affect mobility. Research indicates that living with others promotes greater engagement in activities outside the home, reduces social isolation, and improves functional independence (Che Had et al., 2023 ; Freiberger et al., 2020 ). The ability to make independent decisions and actively participate in social activities has been shown to significantly enhance emotional, social, and physical well-being (González Viloria et al., 2014 ). Consequently, social support promotes independence and improves the quality of life of older individuals. Additionally, easy access to essential health services highlights the importance of communal living in maximizing overall wellbeing. Economic and employment conditions also play a role in mobility outcomes. Most participants were retired (86.5%), with a higher proportion of retired women (89.6%) than of men (72.2%). The majority reported a monthly income of between 200,001 and 500,000 CLP (47%), with only 6% earning over 1,000,000 CLP. Economic limitations have been associated with restricted healthcare access, slower recovery from adverse health conditions, and a reduced ability to meet essential medical needs (Ikeda et al., 2019 ). Economic stability is crucial for ensuring access to healthcare services, maintaining independence, and promoting overall well-being in older adults. Despite high self-reported physical activity levels (71.5%), overweight (47%) and obesity (25%) remained prevalent, with only 27% of participants falling within the normal weight range. These findings emphasize the need to enhance the quality and intensity of exercise intervention. Studies have suggested that physically challenging environments improve health outcomes in older adults (Ludlow & Roth, 2011 ). Additionally, urbanization and limited recreational spaces contribute to sedentary lifestyles and exacerbate obesity risk (Malo-Serrano et al., 2017 ). Comorbidity analysis revealed a relatively low average of chronic conditions (1.5 ± 1.2), with 75% of cases managed through medication. While the prevalence of comorbidities is not particularly high, polypharmacy remains a concern, particularly among older women with lower educational levels who often experience greater functional decline (Baker et al., 2003 ; Sánchez-Rodríguez et al., 2020 ). Notably, only 3% of the participants reported receiving additional care, highlighting potential gaps in healthcare provision. The analysis of living space utilization revealed that 97.5% of participants frequently used indoor rooms beyond the bedroom, while 91.4% regularly accessed outdoor areas, such as patios and terraces. However, engagement declined for neighbourhood spaces (54%) and areas beyond city limits (19%). These results suggest that while older adults remain active within their immediate environments, access to larger external spaces is limited, potentially because of environmental barriers, poor public space design, or lack of accessibility (Maresova et al., 2023 ; Webber et al., 2010 ). A key finding was the limited reliance on assistance for mobility, with 97% of the participants reporting full independence. This suggests a high level of functional autonomy, which is a crucial factor in maintaining a good quality of life in aging populations. Greater independence in self-care and mobility has been linked to improved well-being and personal satisfaction (Loredo-Figueroa et al., 2016 ). Multiple linear regression analysis provided insights into the factors that influence mobility. The absence of technical aid was strongly associated with greater mobility (β=-0.326, p < 0.001), reinforcing the importance of functional independence in enabling movement across different environments. Individuals who do not require canes, walkers, or other assistive devices generally engage more in outdoor activities, enhance social participation, and reduce isolation risks [24]. Gender differences were evident, with men achieving significantly higher LSA scores (β=-0.237), reflecting greater mobility than women. Older men tend to experience fewer functional limitations and engage in outdoor activities more frequently, possibly due to social expectations and greater access to public spaces (Al Snih et al., 2002 ; Arber et al., 1999). Age was a significant predictor of reduced mobility (β=-0.208), with older participants using external living spaces less frequently. This decline is consistent with research linking age-related factors, such as sarcopenia, joint degeneration, and fall risks, to lower autonomy and social engagement (Clegg et al., 2013 ). Regarding the Limitations, this study's cross-sectional design restricts causal inferences. The overrepresentation of women restricts generalizability to male populations. Additionally, the lack of a detailed assessment of physical activity intensity limits the ability to determine its true impact on mobility and health outcomes. In conclusion, our results highlight the impact of functional autonomy, sex, and age on mobility in older adults. The non-use of assistive devices was strongly associated with greater independence, while sex disparities underscored the need for interventions that enhanced mobility in older women. These findings emphasize the importance of designing age-friendly environments that promote active ageing and improve accessibility to public spaces. Declarations Statement of Ethics The Scientific Ethics Committee of the Universidad de Atacama reviewed and approved the study protocol (Approval No. 06/23). All participants provided written informed consent before participating in the study. To protect privacy, we anonymized all personal identifiers throughout the data collection, analysis, and publication processes. This study followed the ethical principles set out in the Declaration of Helsinki and the institutional guidelines. Conflict of Interest Statement The authors have no conflicts of interest to disclose. Funding Sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contributions Conceptualization: Wilson Pastén-Hidalgo, Paula Moreno-Reyes Data curation: Antonia Espejo-Jeraldo, Rocío Perea-Colman, Carlos Doepking-Mella Formal analysis: Wilson Pastén-Hidalgo, Antonia Espejo-Jeraldo, Rocío Perea-Colman, Carlos Doepking-Mella. Investigation Methodology: Wilson Pastén-Hidalgo, Antonia Espejo-Jeraldo, Rocío Perea-Colman, Sergio Jiménez-Torres. Project administration: Wilson Pastén-Hidalgo, Paula Moreno-Reyes. Resources Software: Carlos Doepking-Mella, Sergio Jiménez-Torres. Supervision: Wilson Pastén-Hidalgo, Paula Moreno-Reyes. Visualization Writing – original draft: Wilson Pastén-Hidalgo, Paula Moreno-Reyes, Sergio Jiménez-Torres, Carlos Doepking-Mella. Writing – review & editing: Wilson Pastén-Hidalgo, Paula Moreno-Reyes, Sergio Jiménez-Torres, Carlos Doepking-Mella. Data Availability Statement The data from this study can be requested from the corresponding author. References Al Snih, S., Markides, K. S., Ray, L., Ostir, G. V., & Goodwin, J. S. J. J. o. t. A. G. S. (2002). Handgrip strength and mortality in older Mexican Americans. 50 (7), 1250-1256. . https://doi.org/10.1046/j.1532-5415.2002.50312.x Arber, S., Cooper, H. J. S. s., & medicine. (1999). 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Revisión de literatura. 21 , 271-277. https://doi.org/10.15446/rsap.V21n2.76678 Suri, A., Rosso, A. L., VanSwearingen, J., Coffman, L. M., Redfern, M. S., Brach, J. S., & Sejdić, E. J. T. J. o. G. S. A. (2021). Mobility of older adults: gait quality measures are associated with life-space assessment scores. 76 (10), e299-e306. https://doi.org/10.1093/gerona/glab151 Webber, S. C., Porter, M. M., & Menec, V. H. J. T. g. (2010). Mobility in older adults: a comprehensive framework. 50 (4), 443-450. https://doi.org/10.1093/geront/gnq013 Wells, J. C., Cole, T. J., & Treleaven, P. J. O. (2008). Age‐variability in body shape associated with excess weight: the UK National Sizing Survey. 16 (2), 435-441. https://doi.org/10.1038/oby.2007.62 Additional Declarations No competing interests reported. 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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-6531182","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454191588,"identity":"55c7cacf-9707-4373-bb67-62c38e45b0d1","order_by":0,"name":"Paula Moreno-Reyes","email":"","orcid":"","institution":"University of Atacama","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Moreno-Reyes","suffix":""},{"id":454191589,"identity":"24945a97-810d-48c7-b828-254e91afd65a","order_by":1,"name":"Antonia Espejo-Jeraldo","email":"","orcid":"","institution":"University of Atacama","correspondingAuthor":false,"prefix":"","firstName":"Antonia","middleName":"","lastName":"Espejo-Jeraldo","suffix":""},{"id":454191590,"identity":"e9e4995f-39f3-4a0b-a8d8-8271cb03b61e","order_by":2,"name":"Rocío Perea-Colman","email":"","orcid":"","institution":"University of Atacama","correspondingAuthor":false,"prefix":"","firstName":"Rocío","middleName":"","lastName":"Perea-Colman","suffix":""},{"id":454191591,"identity":"fa960ba0-56f5-47cd-b6fd-4a948a2adba4","order_by":3,"name":"Sergio Jiménez-Torres","email":"","orcid":"","institution":"University of Atacama","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Jiménez-Torres","suffix":""},{"id":454191592,"identity":"1c3300bd-7759-449a-a8d6-9aa792c374b5","order_by":4,"name":"Carlos Doepking-Mella","email":"","orcid":"","institution":"University of Atacama","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Doepking-Mella","suffix":""},{"id":454191593,"identity":"89bf5123-6d03-4d74-a9c7-200c15fab3fe","order_by":5,"name":"Wilson Pastén-Hidalgo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDACZuYGJF4FAyOQy3gAvxZGmBZmID4D1sKAXwsDshbGNiK0GBxnbP7wcQdDHn//+YOPC+cdlu1vYH6AX8thxjbJmWcYiiVuJDMbz9x22HjGATYDvFrMgFqYedsYEhtuMLNJ8247nNhwgAe/w4Bamj+DtMw/f5j9N++cw4nzidDSIA3SsuFAMhszb8NhIIOAFnuwX9okig1vJBtL8xxLN954mIBfJPsPH/7wsc0mT+78wYefeWqsZecdb374AJ8WKJBIQLCZiVAPAgkEVYyCUTAKRsHIBQBtJ06k/2Es9gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Atacama","correspondingAuthor":true,"prefix":"","firstName":"Wilson","middleName":"","lastName":"Pastén-Hidalgo","suffix":""}],"badges":[],"createdAt":"2025-04-25 19:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6531182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6531182/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89461325,"identity":"54b6bf9f-759d-4bbc-ac19-d6f54ee11497","added_by":"auto","created_at":"2025-08-20 08:02:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":774700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6531182/v1/eea6a301-214a-40c9-b9bf-d13af0fa0236.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of Living Spaces and Sociodemographic Factors on Older Adults' Mobility and Autonomy: A Cross-Sectional Study in Chile","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFunctional mobility is a key determinant of independence and the quality of life in older adults. As aging progresses, changes in physical capacity, health conditions, and environmental factors can restrict movement within living spaces, leading to a decline in autonomy and increased risk of social isolation. Mobility limitations are associated with reduced physical activity, greater dependence on caregivers, and a decline in overall well-being (Curcio et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The concept of living space has been widely explored in gerontology as it provides insights into how the home and surrounding environment influence mobility, social participation, and health outcomes (Baker et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, mobility is not solely determined by physical capacity; factors such as neighborhood accessibility, perceived safety, and availability of resources play crucial roles in shaping movement patterns in older adults (Kuspinar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eActive aging has been promoted as a strategy to preserve functional independence and social engagement; however, sociodemographic characteristics influence how older individuals interact with their surroundings. Variables such as gender, education level, marital status, and economic conditions affect the extent to which older adults access and utilize their living spaces. The World Health Organization (WHO) emphasizes that the environments in which individuals live and age significantly affect health, reinforcing the need to address structural barriers that limit mobility. Reduced access to outdoor spaces has been linked to diminished physical function and lower quality of life, reinforcing the importance of fostering mobility-friendly environments to counteract the \u0026ldquo;prisoners of space\u0026rdquo; phenomenon described by Rowles et al. (1981). Age-related conditions such as joint pain and visual impairment also contribute to a preference for indoor spaces, restricting access to external environments that promote greater mobility and independence Suri et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Individuals who regularly utilize outdoor spaces demonstrate improved mobility and functional capacity, thereby reducing the risk of environmental confinement (Quimbaya \u0026amp; Borrero, 2016).\u003c/p\u003e \u003cp\u003eAlthough the relationship between mobility and well-being has been studied extensively, there is limited research on how sociodemographic variables specifically affect the use of living spaces in different urban settings. Access to larger open spaces has been associated with better health outcomes and reduced isolation (Webber et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, little is known about how these factors interact in medium-sized cities, such as Copiap\u0026oacute; and Chile. Understanding these patterns is essential for designing public policies and community interventions to promote autonomy and active aging. Therefore, this study aimed to examine the relationship between living space utilization and sociodemographic characteristics in older adults and identify barriers that may restrict mobility and independence. The Life Space Assessment (LSA) questionnaire, a validated instrument for measuring mobility patterns, was used to assess movement across different spatial levels (Baker et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The findings from this research can contribute to the development of policies and programs that enhance mobility, encourage active lifestyles, and improve the overall quality of life in aging populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cstrong\u003eType of study\u003c/strong\u003e \u003cp\u003eThis descriptive cross-sectional study was conducted between July 2023 and August 2024 in Copiap\u0026oacute;, Chile. The study protocol was approved by the Scientific Ethics Committee of Universidad de Atacama (No. 06/23).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipants\u003c/strong\u003e \u003cp\u003eA total of 403 participants were selected through simple random probability sampling from the 2022 Fonasa database, which registered 15,302 older adults in local family health centers (CESFAM). The sample size was determined using a 95% confidence level, 5% margin of error, and estimated population proportion of 50% to ensure representativeness.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eEligibility criteria included individuals aged 60 years or older, self-sufficient, residing in the Atacama Region, cognitively capable of understanding and answering the questionnaires, and who provided informed consent. Exclusion criteria were nursing home residents, individuals with neurodegenerative diseases affecting mobility, and those with severe cognitive impairment. Data loss resulted from incomplete questionnaires, voluntary withdrawal, or non-responses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInstruments\u003c/b\u003e: The Life Space Assessment (LSA) questionnaire was used to evaluate mobility and living space utilization (Baker et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The LSA assesses mobility across five zones: inside the bedroom, inside the home but outside the bedroom, the neighborhood, the city, and beyond city limits. Mobility frequency was categorized from less than once a week to daily, and the levels of assistance ranged from independent movement to needing support. The total LSA score was obtained by multiplying the frequency by the assistance level per zone and summing the results. Scores were classified as restricted living space (0\u0026ndash;29), medium living space (30\u0026ndash;59), or large living space (\u0026ge;\u0026thinsp;60).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProcedures\u003c/strong\u003e \u003cp\u003eData were collected between July 2023 and August 2024 and informed consent was obtained beforehand. Demographic variables included age, sex, weight, height, BMI, marital status, education level, years of schooling, residence, comorbidities, medication use, recent surgeries, use of assistive devices, cohabitation, and caregiving responsibilities. Socioeconomic factors such as monthly per capita income and employment status were also evaluated.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical analysis\u003c/strong\u003e \u003cp\u003eData analysis was conducted using the JASP software. Descriptive statistics, including means, standard deviations, frequencies, and percentages were calculated. The Shapiro-Wilk test was used to assess data distribution. A multiple linear regression model was employed to identify the factors influencing mobility, incorporating t-tests and stepwise regression with a 95% confidence interval (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cstrong\u003eResults Sociodemographic Characteristics\u003c/strong\u003e \u003cp\u003eThe sample consisted predominantly of female participants (68%), with a mean age of 69.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7 years. The vast majority of participants resided in urban areas (99.9%) and lived with companions (84.9%). Educational attainment remained low, as 51% of the participants had only completed basic education. Monthly income ranged between 200,001 and 500,000 CLP for 46.4% of participants, reflecting economic constraints. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides detailed sociodemographic information including marital status, place of residence, and household composition.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePhysical Status and Activity\u003c/strong\u003e \u003cp\u003eAn analysis of body mass index (BMI) showed that the majority of participants (72.5%) were overweight or obese, while only 27.5% had a normal BMI. Although 69.2% of the participants reported engaging in regular physical activity, the intensity and frequency of exercise might be insufficient to counteract metabolic risks. The use of assistive devices was minimal (7.2%), which indicated a high level of functional independence (92.8%). Comorbidities averaged 1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2, while medication use was reported by 73.2% of the participants. Additionally, 61.5% of the participants had no recent surgical history and 97% did not require caregiving support. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the data on comorbidities, medication use, and levels of physical activity.\u003c/p\u003e \u003c/p\u003e \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e: Socio-demographic characteristics of the surveyed elderly, including mean and standard deviation (SD) for age, weight, height, BMI, years of schooling, and number of comorbidities. Frequencies (n) and percentages (%) are presented for marital status, education level, residence, employment, income, use of technical assistance, physical activity, BMI categories, medication use, surgeries, and caregiving. *Chilean Pesos.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003emale (n\u0026thinsp;=\u0026thinsp;127)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003efemale (n\u0026thinsp;=\u0026thinsp;276)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;403)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e88 (69.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e101 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e189 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e14 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e86 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e100 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e11 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e74 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e85 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e14 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e15 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e29 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eEducational Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eBasic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e46 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e150 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e196 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e71 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e84 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e155 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eHigher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e10 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e42 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e52 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eYears of Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003ePlace of Residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e123 (97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e274 (99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e397 (99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e4 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e6 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eWho do you live with?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eAlone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e7 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e54 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e61 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eAccompanied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e120 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e222 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e342 (84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e92 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e247 (89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e339 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e35 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e29 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e64 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 24.8182%;\"\u003e\n \u003cp\u003e0 to 200.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.3198%;\"\u003e\n \u003cp\u003e32 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.6614%;\"\u003e\n \u003cp\u003e109 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.9691%;\"\u003e\n \u003cp\u003e141 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 24.8182%;\"\u003e\n \u003cp\u003e200.001 to 500.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.3198%;\"\u003e\n \u003cp\u003e56 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.6614%;\"\u003e\n \u003cp\u003e131 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.9691%;\"\u003e\n \u003cp\u003e187 (46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 24.8182%;\"\u003e\n \u003cp\u003e500.001 to 800.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.3198%;\"\u003e\n \u003cp\u003e21 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.6614%;\"\u003e\n \u003cp\u003e25 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.9691%;\"\u003e\n \u003cp\u003e46 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 24.8182%;\"\u003e\n \u003cp\u003e800.001 to 1.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.3198%;\"\u003e\n \u003cp\u003e11 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.6614%;\"\u003e\n \u003cp\u003e3 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.9691%;\"\u003e\n \u003cp\u003e14 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 24.8182%;\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.3198%;\"\u003e\n \u003cp\u003e7 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 13.6614%;\"\u003e\n \u003cp\u003e8 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.9691%;\"\u003e\n \u003cp\u003e15 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eTechnical Assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eDoes not use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e113 (88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e261 (94.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e374 (92.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eDoes use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e14 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e15 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e29 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003ePhysical Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eDoes not do\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e53 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e71 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e124 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eDoes do\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e74 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e205 (74.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e279 (69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eNumber of Comorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eMedicines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eDoes not take\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e46 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e62 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e108 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 19.1259%;\"\u003e\n \u003cp\u003eDoes take\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e81 (63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 14.2306%;\"\u003e\n \u003cp\u003e214 (77.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\" style=\"width: 12.6368%;\"\u003e\n \u003cp\u003e295 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003e \u003cstrong\u003eUse of Living Spaces\u003c/strong\u003e \u003cp\u003eThe Life Space Assessment (LSA) questionnaire indicated that 97.5% of participants used indoor spaces outside the bedroom daily, while 91.4% regularly accessed immediate outdoor areas, such as patios and terraces. However, mobility decreased as distance increased, with 54% reporting daily access to neighborhood spaces, whereas areas beyond city limits were accessed daily by only 0.8% of the participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a detailed breakdown of the utilization of living spaces. Daily engagement was highest in indoor and immediate outdoor spaces, whereas activities beyond the neighborhood occurred less frequently. For level 5, 50.8% of the participants engaged in activities one to three times per week, and only 0.8% reported daily participation. The need for assistive devices or personal support remained minimal at lower LSA levels (\u0026gt;\u0026thinsp;97% independence), although it slightly increased for spaces outside the city. LSA scores showed a steady rise up to level 4 (18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8), reflecting greater mobility demands. However, a decline was observed at level 5 (15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5), suggesting limited engagement in high-intensity activities or lower participation in distant environments.\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\u003eDescription of the levels of use of living spaces. Level 1: In other rooms of the house, different from the bedroom where they sleep. Level 2: In an area outside the house, such as a terrace or patio, hallway of an apartment building, garage, their own garden, or entrance of the house. Level 3: In places within their neighbourhood that are not their own garden or apartment building. Level 4: Places outside their neighbourhood but within the city. Level 5: Places outside the city.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;403\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow many times?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLevel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLevel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLevel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLevel 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eLevel 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109 (27.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150 (37.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131 (32.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e395 (98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e368 (91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276 (68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you used aids or equipment or required assistance from another person?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLevel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLevel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLevel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLevel 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eLevel 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109 (27.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHelp from others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (2.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e392 (97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390 (96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e383 (95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e374 (92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e271 (67.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFactors Affecting Living Space Use\u003c/strong\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the relationships between living space use and various sociodemographic factors. The multiple regression model identified sex (B = -8.752, t = -2.909, p\u0026thinsp;=\u0026thinsp;0.004) and age (B = -0.462, t = -2.646, p\u0026thinsp;=\u0026thinsp;0.009) as significant negative predictors of mobility, indicating that women and older adults demonstrated lower LSA scores. Conversely, variables such as education level (B\u0026thinsp;=\u0026thinsp;0.378, p\u0026thinsp;=\u0026thinsp;0.807), place of residence (B\u0026thinsp;=\u0026thinsp;12.630, p\u0026thinsp;=\u0026thinsp;0.239), and income (B\u0026thinsp;=\u0026thinsp;2.137, p\u0026thinsp;=\u0026thinsp;0.082) were not statistically significant. The strongest negative impact on mobility was associated with the use of assistive devices (B = -19.539, t = -4.352, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reinforcing the relationship between functional dependence and restricted mobility.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe regression model accounted for 20.3% of the variance in mobility scores (R\u0026sup2; = 0.203), indicating that while sex, age, and assistive device use significantly influenced LSA scores, other unexamined factors may also contribute to variations in mobility patterns. These findings highlight a clear trend of reduced mobility with age, particularly among women, while also suggesting that financial and educational factors may have a lesser impact on movement patterns than previously assumed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe following stepwise correlation between the Life Space Assessment (LSA) and socio-demographic variables was established. A multiple linear regression model was constructed and the findings indicated that the primary explanatory factors for the utilisation of living space were the absence of technical assistance (β=-.326; p\u0026thinsp;\u0026lt;\u0026thinsp;.001), male gender (β=-.237), and younger age (β=-.208). The model demonstrated a variance explained (coefficient of determination) of 20.3% (R2\u0026thinsp;=\u0026thinsp;0.203; F\u0026thinsp;=\u0026thinsp;17.869, gl\u0026thinsp;=\u0026thinsp;3; p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnstandardized coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eStandardized coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% confidence interval for B\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eStd. Error\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBeta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSig.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eLower Limit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eUpper Limit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e137.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-14.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-8.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Coexistence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical Assistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-19.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-28.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-10.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDependent Variable\u003c/b\u003e: Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed the mobility patterns of older adults in Copiap\u0026oacute;, Chile, using the Life Space Assessment (LSA) questionnaire, a validated tool widely applied in gerontological research to evaluate mobility and its association with health, quality of life, and social participation [8]. We found that age, gender, and marital status significantly influence mobility, highlighting the complex interplay between sociodemographic variables and functional independence in aging populations.\u003c/p\u003e \u003cp\u003eThe sample was predominantly female (82%), with an average age of 69.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7 years, consistent with demographic trends indicating longer female life expectancy due to biological and social factors (Osorio-Parraguez et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pinho-Gomes et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, women tend to experience poorer health outcomes than men (Austad et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Gender differences in body composition were evident, with men presenting with higher body mass and height, influencing BMI classifications and mobility patterns. While increased BMI in men often reflects greater muscle mass, in women, it is more commonly associated with adipose accumulation, which negatively affects functional mobility and chronic disease risk (Wells et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA high prevalence of overweight and obesity (72.5%) has been observed, reinforcing concerns regarding their effects on mobility and chronic disease susceptibility (Guh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Despite 71.5% of the participants engaging in physical activity, their intensity may not be sufficient to counteract poor dietary habits or metabolic conditions. These findings underscore the need for interventions that integrate nutritional and physical activity strategies to enhance mobility and overall health outcomes.\u003c/p\u003e \u003cp\u003eMarital status and educational level have emerged as relevant factors. While most men were married (69.4%), a significant proportion of women were widowed (26.8%) or single (31.1%), potentially influencing the social support systems. Married older adults benefit from stronger social connections, which enhance mobility and overall well-being (Paul \u0026amp; Verma, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, women exhibited lower educational attainment, with 54.3% completing only primary education compared to 36.1% of men. Lower educational levels have been linked to reduced access to health information and poorer quality of life (S\u0026aacute;nchez-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost participants lived in urban areas (99%) and with companions (80.5%), aligning with evidence suggesting that social and familial cohabitation positively affect mobility. Research indicates that living with others promotes greater engagement in activities outside the home, reduces social isolation, and improves functional independence (Che Had et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Freiberger et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The ability to make independent decisions and actively participate in social activities has been shown to significantly enhance emotional, social, and physical well-being (Gonz\u0026aacute;lez Viloria et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Consequently, social support promotes independence and improves the quality of life of older individuals. Additionally, easy access to essential health services highlights the importance of communal living in maximizing overall wellbeing.\u003c/p\u003e \u003cp\u003eEconomic and employment conditions also play a role in mobility outcomes. Most participants were retired (86.5%), with a higher proportion of retired women (89.6%) than of men (72.2%). The majority reported a monthly income of between 200,001 and 500,000 CLP (47%), with only 6% earning over 1,000,000 CLP. Economic limitations have been associated with restricted healthcare access, slower recovery from adverse health conditions, and a reduced ability to meet essential medical needs (Ikeda et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Economic stability is crucial for ensuring access to healthcare services, maintaining independence, and promoting overall well-being in older adults.\u003c/p\u003e \u003cp\u003eDespite high self-reported physical activity levels (71.5%), overweight (47%) and obesity (25%) remained prevalent, with only 27% of participants falling within the normal weight range. These findings emphasize the need to enhance the quality and intensity of exercise intervention. Studies have suggested that physically challenging environments improve health outcomes in older adults (Ludlow \u0026amp; Roth, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, urbanization and limited recreational spaces contribute to sedentary lifestyles and exacerbate obesity risk (Malo-Serrano et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComorbidity analysis revealed a relatively low average of chronic conditions (1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2), with 75% of cases managed through medication. While the prevalence of comorbidities is not particularly high, polypharmacy remains a concern, particularly among older women with lower educational levels who often experience greater functional decline (Baker et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; S\u0026aacute;nchez-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, only 3% of the participants reported receiving additional care, highlighting potential gaps in healthcare provision.\u003c/p\u003e \u003cp\u003eThe analysis of living space utilization revealed that 97.5% of participants frequently used indoor rooms beyond the bedroom, while 91.4% regularly accessed outdoor areas, such as patios and terraces. However, engagement declined for neighbourhood spaces (54%) and areas beyond city limits (19%). These results suggest that while older adults remain active within their immediate environments, access to larger external spaces is limited, potentially because of environmental barriers, poor public space design, or lack of accessibility (Maresova et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Webber et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA key finding was the limited reliance on assistance for mobility, with 97% of the participants reporting full independence. This suggests a high level of functional autonomy, which is a crucial factor in maintaining a good quality of life in aging populations. Greater independence in self-care and mobility has been linked to improved well-being and personal satisfaction (Loredo-Figueroa et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultiple linear regression analysis provided insights into the factors that influence mobility. The absence of technical aid was strongly associated with greater mobility (β=-0.326, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reinforcing the importance of functional independence in enabling movement across different environments. Individuals who do not require canes, walkers, or other assistive devices generally engage more in outdoor activities, enhance social participation, and reduce isolation risks [24].\u003c/p\u003e \u003cp\u003eGender differences were evident, with men achieving significantly higher LSA scores (β=-0.237), reflecting greater mobility than women. Older men tend to experience fewer functional limitations and engage in outdoor activities more frequently, possibly due to social expectations and greater access to public spaces (Al Snih et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Arber et al., 1999). Age was a significant predictor of reduced mobility (β=-0.208), with older participants using external living spaces less frequently. This decline is consistent with research linking age-related factors, such as sarcopenia, joint degeneration, and fall risks, to lower autonomy and social engagement (Clegg et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the Limitations, this study's cross-sectional design restricts causal inferences. The overrepresentation of women restricts generalizability to male populations. Additionally, the lack of a detailed assessment of physical activity intensity limits the ability to determine its true impact on mobility and health outcomes. In conclusion, our results highlight the impact of functional autonomy, sex, and age on mobility in older adults. The non-use of assistive devices was strongly associated with greater independence, while sex disparities underscored the need for interventions that enhanced mobility in older women. These findings emphasize the importance of designing age-friendly environments that promote active ageing and improve accessibility to public spaces.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eStatement of Ethics\u003c/p\u003e\n\u003cp\u003eThe Scientific Ethics Committee of the Universidad de Atacama reviewed and approved the study protocol (Approval No. 06/23). All participants provided written informed consent before participating in the study. To protect privacy, we anonymized all personal identifiers throughout the data collection, analysis, and publication processes. This study followed the ethical principles set out in the Declaration of Helsinki and the institutional guidelines.\u003c/p\u003e\n\u003cp\u003eConflict of Interest Statement\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003eFunding Sources\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions \u003c/p\u003e\n\u003cp\u003eConceptualization: Wilson Pastén-Hidalgo, Paula Moreno-Reyes\u003c/p\u003e\n\u003cp\u003eData curation: Antonia Espejo-Jeraldo, Rocío Perea-Colman, Carlos Doepking-Mella\u003c/p\u003e\n\u003cp\u003eFormal analysis: Wilson Pastén-Hidalgo, Antonia Espejo-Jeraldo, Rocío Perea-Colman, Carlos Doepking-Mella. \u003c/p\u003e\n\u003cp\u003eInvestigation Methodology: Wilson Pastén-Hidalgo, Antonia Espejo-Jeraldo, Rocío Perea-Colman, Sergio Jiménez-Torres.\u003c/p\u003e\n\u003cp\u003eProject administration: Wilson Pastén-Hidalgo, Paula Moreno-Reyes.\u003c/p\u003e\n\u003cp\u003eResources Software: Carlos Doepking-Mella, Sergio Jiménez-Torres.\u003c/p\u003e\n\u003cp\u003eSupervision: Wilson Pastén-Hidalgo, Paula Moreno-Reyes.\u003c/p\u003e\n\u003cp\u003eVisualization Writing – original draft: Wilson Pastén-Hidalgo, Paula Moreno-Reyes, Sergio Jiménez-Torres, Carlos Doepking-Mella.\u003c/p\u003e\n\u003cp\u003eWriting – review \u0026amp; editing: Wilson Pastén-Hidalgo, Paula Moreno-Reyes, Sergio Jiménez-Torres, Carlos Doepking-Mella.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from this study can be requested from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl Snih, S., Markides, K. 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Mobility of older adults: gait quality measures are associated with life-space assessment scores.\u003cem\u003e \u003c/em\u003e\u003cem\u003e76\u003c/em\u003e(10), e299-e306. https://doi.org/10.1093/gerona/glab151 \u003c/li\u003e\n\u003cli\u003eWebber, S. C., Porter, M. M., \u0026amp; Menec, V. H. J. T. g. (2010). Mobility in older adults: a comprehensive framework.\u003cem\u003e 50\u003c/em\u003e(4), 443-450. https://doi.org/10.1093/geront/gnq013 \u003c/li\u003e\n\u003cli\u003eWells, J. C., Cole, T. J., \u0026amp; Treleaven, P. J. O. (2008). Age‐variability in body shape associated with excess weight: the UK National Sizing Survey.\u003cem\u003e 16\u003c/em\u003e(2), 435-441. https://doi.org/10.1038/oby.2007.62 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aged, Quality of Life, Sociodemographic Factors, Motor Activity, Activities of Daily Living, Active Aging","lastPublishedDoi":"10.21203/rs.3.rs-6531182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6531182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMobility within living spaces is essential for active and healthy aging, supporting autonomy and overall quality of life. However, sociodemographic and environmental factors can significantly shape how older adults utilize these spaces.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBJECTIVE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to examine the relationship between living space utilization and sociodemographic characteristics in older adults, and to identify key barriers that may restrict mobility and independence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODOLOGY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional study was conducted in Copiapó, Chile, involving 403 community-dwelling adults aged 60 to 91. The Life Space Assessment (LSA) questionnaire was used to evaluate spatial mobility across five levels, from bedroom to beyond city limits. Sociodemographic, health, and physical activity data were also collected. Multiple linear regression was applied to identify factors influencing mobility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost participants reported high independence, with frequent use of indoor spaces (97.5%) and immediate outdoor areas (91.4%). Mobility decreased in neighborhood (54%) and city-level environments. Regression analysis revealed that sex (B = -8.752, p = 0.004), age (B = -0.462, p = 0.009), and use of assistive devices (B = -19.539, p \u0026lt; 0.001) were significant negative predictors of mobility. Education and income were not statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOlder adults in this study demonstrated preserved autonomy in proximal environments, but reduced mobility in broader urban settings. These findings highlight the importance of creating age-friendly environments and targeted interventions to promote outdoor mobility and independence, particularly among older women and individuals using assistive devices.\u003c/p\u003e","manuscriptTitle":"Influence of Living Spaces and Sociodemographic Factors on Older Adults' Mobility and Autonomy: A Cross-Sectional Study in Chile","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 08:22:49","doi":"10.21203/rs.3.rs-6531182/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":"8d78e594-ccc1-4ac3-82ab-5c0134ad6851","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-20T07:54:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 08:22:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6531182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6531182","identity":"rs-6531182","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}