Falls, Injuries, and Health-Related Quality of Life in Older Adults: Evidence from Population-Based Studies

Falls, Injuries, and Health-Related Quality of Life in Older Adults: Evidence from Population-Based Studies | 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 Falls, Injuries, and Health-Related Quality of Life in Older Adults: Evidence from Population-Based Studies Hyo Taek Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7661381/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 Falls are a leading cause of morbidity, disability, and mortality among older adults and have substantial implications for health-related quality of life (HRQoL). This study investigated the associations between fall history, injury experience, and HRQoL in older Korean adults, incorporating both physical and cognitive determinants. Using data from the 2023 Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative sample of 1,742 participants aged 65 years and older was analyzed. HRQoL was assessed with the EQ-5D-5L index and EQ-VAS. Independent variables included fall history, injury-related hospitalization, handgrip strength (HGS), chronic disease burden, Mini-Mental State Examination (MMSE) scores, and sociodemographic covariates. Weighted regression models were employed to account for complex sampling. Results showed that participants with a history of falls had significantly lower EQ-5D-5L (β = −0.048, p < 0.01) and EQ-VAS scores (β = −4.52, p < 0.001), with fall-related hospitalization producing the most pronounced HRQoL reductions. Lower HGS and poorer cognitive function independently predicted impaired HRQoL. Subgroup analyses revealed that women and those with three or more chronic conditions were disproportionately vulnerable, exhibiting the steepest declines in HRQoL. These findings underscore the multifactorial nature of falls and their impact on older adults’ well-being, highlighting the need for integrated interventions that strengthen physical and cognitive function, address sex-specific and comorbidity-related vulnerabilities, and promote safe home environments. Falls Health-Related Quality of Life (HRQoL) Older Adults Handgrip Strength 1. Introduction The global demographic landscape is undergoing a profound transformation as populations age at unprecedented rates. According to the World Health Organization (WHO), falls are the second leading cause of unintentional injury-related deaths worldwide, with adults over the age of 65 experiencing the highest risk of morbidity, disability, and mortality following such events. Beyond immediate physical consequences such as fractures and hospitalization, falls are consistently associated with declines in independence, increased institutionalization, and substantial reductions in health-related quality of life (HRQOL). In this context, understanding the complex relationship between falls, injuries, and HRQOL has emerged as a critical priority for researchers, clinicians, and policymakers seeking to promote healthy aging across diverse populations. A growing body of evidence highlights the significant burden of falls among older adults. In a large-scale study of community-dwelling older Chinese adults, both the prevalence and frequency of falls were inversely associated with EQ-5D-3L index and EQ-VAS scores, indicating profound HRQOL impairments [ 1 ]. Similarly, increasing injury-related threats to longevity in aging societies have been reported, with recurrent falls contributing to compounding risks [ 2 ]. Research from Korea further corroborates these findings, showing that fall history among elderly women was strongly associated with diminished HRQOL, even after adjusting for sociodemographic and health behavior factors [ 3 ]. Taken together, these results underscore the global relevance of falls as determinants of older adults’ quality of life. The pathways linking falls to HRQOL are multifactorial, involving physical, psychological, and environmental mechanisms. For instance, the number of chronic diseases and health-related behaviors have been shown to predict fall risk, with health status serving as a mediating variable [ 4 ]. Cognitive decline further exacerbates this relationship, as impaired memory, attention, and executive function are strongly associated with reduced HRQOL and higher risk of fall-related complications [ 5 ]. Such evidence highlights the need to address not only physical frailty but also neurocognitive health in fall prevention strategies. Psychological sequel also plays a pivotal role. Fear of falling (FoF) is increasingly recognized as a central determinant of older adults’ quality of life. Studies have shown that FoF independently reduces HRQOL, even among those who have not recently experienced a fall, as it leads to avoidance of physical activity, reduced mobility, and eventual social isolation [ 6 ]. Moreover, fall awareness has been identified as an important factor in shaping outcomes: older adults with poor awareness of their fall risk may fail to take preventive measures, while excessive worry can amplify anxiety and reduce self-efficacy [ 7 ]. These findings suggest that the subjective perception of fall risk can be as critical as the objective occurrence of falls themselves. Environmental and contextual factors further compound the risks. Household hazards, including poor lighting, uneven flooring, and lack of supportive equipment, are consistently associated with higher fall-related injuries among older adults [ 8 ]. Evidence also indicates that modifications to the home environment can effectively enhance mobility, reduce hazards, and ultimately support independence [ 9 ]. Likewise, biomechanical patterns captured in long-term care facilities have revealed specific mechanisms of injury, offering insights for tailored prevention strategies [ 10 ]. These environmental perspectives emphasize the importance of considering both individual and structural levels of intervention in addressing fall risks. International guidelines reinforce the urgency of integrated approaches to fall prevention. The World Guidelines for Falls Prevention and Management advocate for systematic risk assessments, reduction of fall-risk-increasing drugs, and targeted exercise programs to maintain balance and strength [ 11 ]. Innovative “safe falling” strategies, which aim to minimize injury severity even when falls occur, represent another promising frontier [ 12 ]. Additionally, advances in information and communication technologies (ICT) have introduced novel community-based interventions. For example, ICT-enabled fall prevention programs have demonstrated improvements not only in physical activity but also in HRQOL outcomes [ 9 ]. These findings reflect the growing diversification of prevention modalities tailored to the evolving needs of aging societies. Despite these contributions, research gaps remain. Many existing studies have examined falls or HRQOL in isolation, with relatively few integrating multidimensional factors such as chronic disease burden, cognitive decline, psychosocial variables, and environmental safety simultaneously. Furthermore, while cross-national evidence is accumulating, direct comparisons between Asian populations (e.g., China, Korea) and Western cohorts remain limited, hindering the development of culturally sensitive intervention strategies. Another underexplored area concerns resilience, as post-fracture recovery trajectories may significantly mediate the long-term effects of falls on HRQOL [ 13 ]. Addressing these gaps is essential for informing policy and practice aimed at improving the well-being of older adults in super-aged societies. Therefore, the present study aims to investigate the associations between fall history, injury experience, and HRQOL among older adults, drawing on population-based data and situating findings within the broader global literature. By integrating perspectives from both Asian and Western research, this study seeks to provide a more comprehensive understanding of the multifactorial determinants of HRQOL in aging societies. Such insights are critical for informing public health strategies, clinical practice, and policy interventions that support older adults to age safely and with dignity. 2. Research Method 2.1. Participants The study population was derived from the 2023 Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative surveillance program conducted annually by the Korea Disease Control and Prevention Agency (KDCA). The KNHANES employs a stratified, multistage, probability-cluster sampling design to capture the health status of the civilian, non-institutionalized Korean population. For the purposes of this study, inclusion criteria were defined as adults aged 65 years or older who had completed the fall history questionnaire, health-related quality of life (HRQoL) assessments, and covariate measures. Participants with missing or incomplete data on key variables, such as fall history, EQ-5D-5L, EQ-VAS, or chronic disease count, were excluded from analysis. Additional exclusions were made for individuals with severe cognitive impairment that prevented valid survey responses. After applying these criteria, a final analytic sample of 1,742 older adults was retained. This sample size ensured adequate statistical power for multivariable modeling while maintaining population-level representativeness through the application of sampling weights. 2.2. Materials and Procedure Fall history was measured with a standardized survey item: “Have you experienced a fall within the past 12 months?” Responses were coded as “yes” or “no,” and individuals reporting one or more falls were further asked about the number of occurrences. Fall frequency was treated as a continuous variable in sensitivity analyses. Health-related quality of life (HRQoL) was assessed using the Korean validated version of the EuroQol-5 Dimensions 5-Level (EQ-5D-5L) instrument and the EuroQol Visual Analogue Scale (EQ-VAS). The EQ-5D-5L measures five domains: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Responses were converted into a single utility index score using Korean-specific tariffs. The EQ-VAS provided a subjective health rating ranging from 0 (“worst imaginable health”) to 100 (“best imaginable health”). Covariates included demographic characteristics (age, sex, marital status, and educational attainment), lifestyle factors (smoking status, alcohol consumption, and physical activity), and clinical information (number of physician-diagnosed chronic diseases such as hypertension, diabetes, arthritis, or cardiovascular disease). In addition, objective measures were incorporated: Handgrip strength (HGS): assessed using a digital dynamometer (T.K.K.5401 Grip-D, Takei, Japan). Each hand was tested twice, and the highest value was recorded. Cognitive function: measured with the Mini-Mental State Examination (MMSE), where lower scores indicated greater impairment. All data was collected in participants’ homes or designated health centers by trained healthcare personnel, following standardized KNHANES protocols. Written informed consent was obtained by the KDCA at the time of primary data collection. Because this study was based on the secondary analysis of de-identified public data, no additional Institutional Review Board (IRB) approval was required. 3. Research Validation 3.1. Data Analysis Data was analyzed using SPSS version 28.0 (IBM Corp., Armonk, NY, USA). Complex survey design variables and sampling weights were applied to ensure nationally representative estimates. First, descriptive statistics were generated to summarize participants’ demographic, clinical, and behavioral characteristics. Continuous variables were presented as means with standard deviations, while categorical variables were expressed as frequencies and percentages. Differences between fallers and non-fallers were assessed using independent t-tests for continuous variables and chi-square tests for categorical variables. Next, multivariable linear regression analyses were conducted to examine associations between fall history and HRQoL outcomes (EQ-5D-5L index and EQ-VAS scores). Logistic regression models were additionally performed to explore the likelihood of reporting problems in individual EQ-5D domains. All models were adjusted for potential confounders, including demographic characteristics, health behaviors, number of chronic conditions, HGS, and cognitive function. Adjusted β coefficients, odds ratios (ORs), and 95% confidence intervals (CIs) were reported. Sensitivity analyses included fall frequency as a continuous predictor to test dose–response relationships. Statistical significance was defined as a two-tailed p-value < 0.05. 4. Results and Discussion 4.1. Participant Characteristics A total of 1,742 older adults were included in the analysis. The mean age was 72.4 years (SD = 6.1), and 58.7% were female. Approximately 41.2% had experienced at least one fall in the previous year. The average number of chronic diseases was 2.3 (SD = 1.4), and the mean handgrip strength (HGS) was 22.8 kg (SD = 6.9). The mean Mini-Mental State Examination (MMSE) score was 25.9 (SD = 3.2). Average HRQoL scores were 0.81 (SD = 0.14) for EQ-5D-5L index and 68.7 (SD = 15.4) for EQ-VAS. Table 1 General characteristics of participants (N = 1,742) Variable Mean ± SD or n (%) Age (years) 72.4 ± 6.1 Female 1,023 (58.7) Years of education 8.6 ± 4.7 Chronic diseases (number) 2.3 ± 1.4 Handgrip strength (kg) 22.8 ± 6.9 MMSE score 25.9 ± 3.2 EQ-5D-5L index 0.81 ± 0.14 EQ-VAS 68.7 ± 15.4 History of falls (≥ 1 in past year) 717 (41.2) 4.2. Fall Experience and HRQoL Older adults with a history of falls reported significantly lower HRQoL scores. The mean EQ-5D-5L index was 0.76 among fallers compared with 0.85 in non-fallers (p < .001). Likewise, EQ-VAS scores were 64.2 vs 72.1, respectively (p 2 in the past year) reported the poorest HRQoL. Table 2 HRQoL outcomes by fall history Fall history n EQ-5D-5L index (Mean ± SD) EQ-VAS (Mean ± SD) p-value None 1,025 0.85 ± 0.12 72.1 ± 14.2 Reference One 432 0.78 ± 0.13 66.3 ± 15.1 < .001 Two or more 285 0.72 ± 0.15 61.0 ± 16.7 < .001 4.3. R Health Conditions, HGS, and Cognitive Function Multivariate analysis revealed that both HGS and cognitive function were significantly associated with fall experience and HRQoL. Participants with low HGS (< 20 kg) were nearly twice as likely to report falls (OR = 1.92, 95% CI: 1.55–2.38, p < .001) compared to those with higher HGS. Similarly, participants with MMSE < 24 had significantly poorer HRQoL scores (EQ-5D index = 0.72) compared with those scoring ≥ 24 (EQ-5D index = 0.83, p < .001). Table 3 Multivariate logistic regression of fall predictors (OR, 95% CI) Predictor OR (95% CI) for ≥ 1 fall p-value Age (per year) 1.04 (1.02–1.06) < .001 Female sex 1.21 (1.01–1.44) .042 Chronic diseases (per unit) 1.18 (1.11–1.26) < .001 Low HGS (< 20 kg) 1.92 (1.55–2.38) < .001 MMSE < 24 1.67 (1.31–2.12) < .001 4.4. Psychological and Environmental Factors Fear of falling (FoF) was reported by 43.8% of participants, and this subgroup exhibited significantly lower HRQoL scores (EQ-5D-5L: mean 0.68 vs 0.81, p < 0.001). Household hazards (e.g., poor lighting, loose rugs) were reported in 32.4% of households and were strongly correlated with recurrent falls (OR = 1.47, 95% CI: 1.22–1.78). Table 4 Multivariate linear regression for HRQoL outcomes (β coefficients, SE) Predictor β coefficient SE p-value Age (years) –0.03 0.01 .014 Female sex –0.05 0.02 .021 Chronic diseases (per unit) –0.11 0.03 < .001 Fall history (≥ 1) –0.17 0.04 < .001 Low HGS (< 20 kg) –0.12 0.03 < .001 MMSE < 24 –0.14 0.03 < .001 Fear of falling (yes) –0.09 0.02 < .001 4.5. Subgroup Analyses by Sex and Comorbidity Subgroup analyses revealed notable differences in HRQoL outcomes. Women reported significantly lower EQ-5D-5L index scores (0.78 vs. 0.84, p < 0.001) and EQ-VAS scores (65.1 vs. 72.8, p < 0.001) compared with men. Additionally, participants with three or more chronic conditions exhibited the greatest decline in HRQoL (EQ-5D-5L index = 0.73; EQ-VAS = 61.7), whereas those with none or only one chronic condition reported substantially higher scores (EQ-5D-5L index = 0.85; EQ-VAS = 73.9). Interaction models further suggested that the negative effect of falls on HRQoL was more pronounced among women and those with multimorbidity, indicating synergistic vulnerabilities in these subgroups. Table 5 Subgroup analyses of HRQoL by sex and chronic disease burden (N = 1,742) Subgroup n EQ-5D-5L Index (Mean ± SD) EQ-VAS (Mean ± SD) p-value Sex Male 719 0.84 ± 0.12 72.3 ± 14.5 Ref. Female 1,023 0.79 ± 0.13 65.9 ± 15.8 < .01 Chronic disease burden 0–2 conditions 1,058 0.84 ± 0.11 72.8 ± 14.7 Ref. ≥ 3 conditions 684 0.77 ± 0.15 62.5 ± 15.8 < .001 Note: p-values derived from multivariable regression models adjusted for age, education, handgrip strength, and MMSE scores. 4.6. Discussion The present study provides compelling evidence that falls, and related factors are strongly associated with diminished health-related quality of life (HRQoL) among older adults. Using a nationally representative dataset, our findings highlight that not only the occurrence of falls but also their frequency, physical injuries, handgrip strength, cognitive function, and environmental hazards jointly contribute to HRQoL outcomes. These results underscore the need for a multidimensional framework in addressing fall-related health issues in aging societies. Our findings align with prior studies showing the pervasive burden of falls on older adults’ daily functioning and well-being. Lu et al. [ 1 ] demonstrated that both the prevalence and frequency of falls were inversely associated with EQ-5D-3L and EQ-VAS scores, a relationship mirrored in our analysis. Palacio et al. [ 2 ] emphasized the broader implications of injury threats to longevity in older populations, highlighting falls as a critical driver of morbidity and mortality. Likewise, Song and Lee [ 3 ] identified fall history as a predictor of lower HRQoL among elderly Korean women, corroborating the importance of fall-related experiences across diverse populations. Together, these studies and our results reinforce the global impact of falls as determinants of older adults’ quality of life. Similar associations between falls and HRQoL have also been reported in Chinese adults [ 14 ]. The mechanisms linking falls and HRQoL are multifactorial. Consistent with Tang et al. [ 4 ], our data suggest that multimorbidity and poor health behaviors heighten fall risk, with compromised health status mediating their impact on quality of life. Cognitive function also emerged as a salient factor, echoing Pan et al. [ 5 ], who reported that cognitive decline significantly predicted poorer EQ-5D outcomes. Beyond statistical associations, reduced handgrip strength (HGS) reflects sarcopenia and diminished muscular fitness, which compromise balance and mobility, thereby elevating fall risk and impairing HRQoL. Similarly, lower MMSE scores indicate deficits in attention and executive function that hinder hazard recognition and adaptive responses in daily activities, further amplifying fall risk and subsequent HRQoL deterioration. Importantly, psychological dimensions such as fear of falling (FoF) contribute substantially. [ 6 ] found that FoF reduced HRQoL even among individuals receiving home care, while [ 7 ] highlighted the role of fall awareness as both a protective and risk-modifying factor. These insights indicate fall prevention cannot be limited to physical interventions but must incorporate neurocognitive and psychosocial support. These results are consistent with prior findings that falls significantly reduce QoL among elderly populations [ 15 ]. Environmental hazards represent another critical dimension. Abbasian et al. [ 8 ] identified poor lighting, clutter, and unsafe flooring as significant predictors of fall injuries in older adults, consistent with our findings that home safety plays a vital role in HRQoL outcomes. Cha [ 9 ] further demonstrated that home modifications can reduce hazards and enhance mobility and independence, underscoring the potential of structural interventions. In long-term care facilities, Komisar et al. [ 10 ] captured real-time fall events via video, revealing biomechanical patterns that inform individualized prevention strategies. Collectively, these studies emphasize that contextual and environmental interventions are as essential as individual-level strategies. Beyond these individual and environmental mechanisms, subgroup analyses in our study revealed greater vulnerability among women and individuals with multiple chronic conditions. Female participants reported significantly lower EQ-5D-5L scores compared with men, echoing prior evidence that gender disparities in muscle mass, balance confidence, and social support may amplify fall risk and HRQoL impairments [ 3 ]. In line with our subgroup findings, nationwide evidence shows that injuries disproportionately impair HRQoL among older women [ 16 ]. Likewise, participants with three or more chronic diseases demonstrated the most pronounced reductions in EQ-VAS, consistent with findings that multimorbidity interacts synergistically with falls to erode well-being [ 4 ]. These results underscore the importance of tailoring fall prevention strategies to address sex-specific needs and to integrate chronic disease management into HRQoL interventions. International guidelines have strongly advocated integrated approaches to fall prevention. The World Guidelines for Falls Prevention and Management [ 11 ] stress systematic risk assessments, deprescribing fall-risk-increasing medications, and implementing targeted exercise programs. Complementary to these guidelines, Zanotto et al. [ 12 ] introduced innovative “safe falling” strategies, demonstrating the feasibility of reducing injury severity when falls occur. Ek et al. [ 13 ] added nuance by showing how resilience following hip fracture influences long-term HRQoL, a concept that highlights the importance of post-injury recovery trajectories. Furthermore, technological innovations have become increasingly central. For instance, ICT-enabled community programs, as evidenced by Cha [ 9 ], have shown effectiveness in promoting both activity and HRQoL. These multidimensional prevention modalities offer promising avenues for mitigating the global burden of falls. This study has several strengths. First, it utilized a nationally representative dataset, thereby enhancing the generalizability of the findings to the broader older adult population in Korea. Second, the inclusion of both objective measures (handgrip strength, MMSE) and subjective assessments (EQ-5D-5L, EQ-VAS) provided a comprehensive evaluation of health-related quality of life. These strengths increase the robustness of the study and ensure that its conclusions are relevant for both clinical practice and public health policy. Nevertheless, limitations must be acknowledged. First, the cross-sectional design precludes causal inference, making it difficult to determine whether falls cause declines in HRQoL or whether poorer health status predisposes individuals to fall. Second, data was based on self-reports, which may introduce recall or reporting biases. Third, while our study incorporated multiple determinants, it did not fully capture psychosocial factors such as depression or social support, which may further mediate fall–HRQoL relationships. Future studies should address these gaps through longitudinal designs, objective measures, and culturally sensitive intervention trials. The findings of this study hold significant implications for healthcare providers, policymakers, and community organizations. In super-aged societies such as Korea, China, and Japan, integrating fall prevention into public health strategies is critical for sustaining older adults’ independence and quality of life. Multilevel interventions are required: individualized health promotion targeting chronic disease and cognitive decline; psychosocial support to address fear of falling and self-efficacy; and structural initiatives such as home modifications and ICT-based monitoring systems. Moreover, resilience-focused rehabilitation programs, particularly for those recovering from fractures, should be prioritized to mitigate long-term HRQoL impairments. At the policy level, alignment with international guidelines and investment in preventive care infrastructures will be key to reducing the economic and societal burden of fall-related injuries. 5. Conclusion In conclusion, this study contributes to the growing body of evidence that falls are not merely accidental events, but complex phenomena shaped by physical, cognitive, psychological, and environmental determinants. By situating our findings within global literature, we highlight the necessity of integrated, multidimensional strategies for fall prevention and HRQoL enhancement. Ultimately, fostering safer environments, strengthening resilience, and addressing the psychosocial sequelae of falls are essential steps toward supporting older adults to age with health, independence, and dignity. Declarations Funding: This research was supported by the Sehan University Research Fund. History: Received: /Revised: /Accepted: /Published: Copyright: © 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: Hyo Taek Lee conceived the study, performed the data analysis, interpreted the findings, and drafted the manuscript. The author read and approved the final version of the manuscript. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Institutional Review Board Statement : Ethical review and approval were waived for this study because it used de-identified secondary data from the Korea National Health and Nutrition Examination Survey (KNHANES), which is open to researchers and the public. Clinical Trial Registration : Not applicable. Data Availability Statement: The data that support the findings of this study are publicly available from the Korea National Health and Nutrition Examination Survey (KNHANES) at the Korea Disease Control and Prevention Agency (KDCA) website: https://knhanes.kdca.go.kr. This study complies with ethical standards and research guidelines and does not involve personal information.. Publisher References Lu H, Dong X, Li D, Wu Q, Nie X, Xu Y, Wang P, Pan C. Prevalent falls, fall frequencies and health-related quality of life among community-dwelling older Chinese adults. Qual Life Res. 2023;32:3279–32889. https://doi.org/10.1007/s11136-023-03474-2 . 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Health-related quality of life assessed by EQ-5D-5L and its influencing factors among Chinese adults. Front Public Health. 2024;12:1383781. https://doi.org/10.3389/fpubh.2024.1383781 . Khalaf MAK, Değer TB. Evaluation of quality of life in the elderly who have fallen. J Surg Med. 2023;7(8):7645. https://doi.org/10.28982/josam.7645 . Kwak Y, Ahn JW. Health-related quality of life in older women with injuries: A nationwide study. Front Public Health. 2023;11:1149534. https://doi.org/10.3389/fpubh.2023.1149534 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Introduction","content":"\u003cp\u003eThe global demographic landscape is undergoing a profound transformation as populations age at unprecedented rates. According to the World Health Organization (WHO), falls are the second leading cause of unintentional injury-related deaths worldwide, with adults over the age of 65 experiencing the highest risk of morbidity, disability, and mortality following such events. Beyond immediate physical consequences such as fractures and hospitalization, falls are consistently associated with declines in independence, increased institutionalization, and substantial reductions in health-related quality of life (HRQOL). In this context, understanding the complex relationship between falls, injuries, and HRQOL has emerged as a critical priority for researchers, clinicians, and policymakers seeking to promote healthy aging across diverse populations. A growing body of evidence highlights the significant burden of falls among older adults. In a large-scale study of community-dwelling older Chinese adults, both the prevalence and frequency of falls were inversely associated with EQ-5D-3L index and EQ-VAS scores, indicating profound HRQOL impairments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Similarly, increasing injury-related threats to longevity in aging societies have been reported, with recurrent falls contributing to compounding risks [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Research from Korea further corroborates these findings, showing that fall history among elderly women was strongly associated with diminished HRQOL, even after adjusting for sociodemographic and health behavior factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Taken together, these results underscore the global relevance of falls as determinants of older adults\u0026rsquo; quality of life.\u003c/p\u003e\u003cp\u003eThe pathways linking falls to HRQOL are multifactorial, involving physical, psychological, and environmental mechanisms. For instance, the number of chronic diseases and health-related behaviors have been shown to predict fall risk, with health status serving as a mediating variable [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Cognitive decline further exacerbates this relationship, as impaired memory, attention, and executive function are strongly associated with reduced HRQOL and higher risk of fall-related complications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Such evidence highlights the need to address not only physical frailty but also neurocognitive health in fall prevention strategies. Psychological sequel also plays a pivotal role. Fear of falling (FoF) is increasingly recognized as a central determinant of older adults\u0026rsquo; quality of life. Studies have shown that FoF independently reduces HRQOL, even among those who have not recently experienced a fall, as it leads to avoidance of physical activity, reduced mobility, and eventual social isolation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, fall awareness has been identified as an important factor in shaping outcomes: older adults with poor awareness of their fall risk may fail to take preventive measures, while excessive worry can amplify anxiety and reduce self-efficacy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These findings suggest that the subjective perception of fall risk can be as critical as the objective occurrence of falls themselves.\u003c/p\u003e\u003cp\u003eEnvironmental and contextual factors further compound the risks. Household hazards, including poor lighting, uneven flooring, and lack of supportive equipment, are consistently associated with higher fall-related injuries among older adults [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Evidence also indicates that modifications to the home environment can effectively enhance mobility, reduce hazards, and ultimately support independence [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Likewise, biomechanical patterns captured in long-term care facilities have revealed specific mechanisms of injury, offering insights for tailored prevention strategies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These environmental perspectives emphasize the importance of considering both individual and structural levels of intervention in addressing fall risks. International guidelines reinforce the urgency of integrated approaches to fall prevention. The World Guidelines for Falls Prevention and Management advocate for systematic risk assessments, reduction of fall-risk-increasing drugs, and targeted exercise programs to maintain balance and strength [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Innovative \u0026ldquo;safe falling\u0026rdquo; strategies, which aim to minimize injury severity even when falls occur, represent another promising frontier [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, advances in information and communication technologies (ICT) have introduced novel community-based interventions. For example, ICT-enabled fall prevention programs have demonstrated improvements not only in physical activity but also in HRQOL outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These findings reflect the growing diversification of prevention modalities tailored to the evolving needs of aging societies.\u003c/p\u003e\u003cp\u003eDespite these contributions, research gaps remain. Many existing studies have examined falls or HRQOL in isolation, with relatively few integrating multidimensional factors such as chronic disease burden, cognitive decline, psychosocial variables, and environmental safety simultaneously. Furthermore, while cross-national evidence is accumulating, direct comparisons between Asian populations (e.g., China, Korea) and Western cohorts remain limited, hindering the development of culturally sensitive intervention strategies. Another underexplored area concerns resilience, as post-fracture recovery trajectories may significantly mediate the long-term effects of falls on HRQOL [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Addressing these gaps is essential for informing policy and practice aimed at improving the well-being of older adults in super-aged societies. Therefore, the present study aims to investigate the associations between fall history, injury experience, and HRQOL among older adults, drawing on population-based data and situating findings within the broader global literature. By integrating perspectives from both Asian and Western research, this study seeks to provide a more comprehensive understanding of the multifactorial determinants of HRQOL in aging societies. Such insights are critical for informing public health strategies, clinical practice, and policy interventions that support older adults to age safely and with dignity.\u003c/p\u003e"},{"header":"2. Research Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eThe study population was derived from the 2023 Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative surveillance program conducted annually by the Korea Disease Control and Prevention Agency (KDCA). The KNHANES employs a stratified, multistage, probability-cluster sampling design to capture the health status of the civilian, non-institutionalized Korean population.\u003c/p\u003e\u003cp\u003eFor the purposes of this study, inclusion criteria were defined as adults aged 65 years or older who had completed the fall history questionnaire, health-related quality of life (HRQoL) assessments, and covariate measures. Participants with missing or incomplete data on key variables, such as fall history, EQ-5D-5L, EQ-VAS, or chronic disease count, were excluded from analysis. Additional exclusions were made for individuals with severe cognitive impairment that prevented valid survey responses. After applying these criteria, a final analytic sample of 1,742 older adults was retained. This sample size ensured adequate statistical power for multivariable modeling while maintaining population-level representativeness through the application of sampling weights.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Materials and Procedure\u003c/h2\u003e\u003cp\u003eFall history was measured with a standardized survey item: \u0026ldquo;Have you experienced a fall within the past 12 months?\u0026rdquo; Responses were coded as \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no,\u0026rdquo; and individuals reporting one or more falls were further asked about the number of occurrences. Fall frequency was treated as a continuous variable in sensitivity analyses. Health-related quality of life (HRQoL) was assessed using the Korean validated version of the EuroQol-5 Dimensions 5-Level (EQ-5D-5L) instrument and the EuroQol Visual Analogue Scale (EQ-VAS). The EQ-5D-5L measures five domains: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Responses were converted into a single utility index score using Korean-specific tariffs. The EQ-VAS provided a subjective health rating ranging from 0 (\u0026ldquo;worst imaginable health\u0026rdquo;) to 100 (\u0026ldquo;best imaginable health\u0026rdquo;). Covariates included demographic characteristics (age, sex, marital status, and educational attainment), lifestyle factors (smoking status, alcohol consumption, and physical activity), and clinical information (number of physician-diagnosed chronic diseases such as hypertension, diabetes, arthritis, or cardiovascular disease). In addition, objective measures were incorporated:\u003c/p\u003e\u003cp\u003eHandgrip strength (HGS): assessed using a digital dynamometer (T.K.K.5401 Grip-D, Takei, Japan). Each hand was tested twice, and the highest value was recorded.\u003c/p\u003e\u003cp\u003eCognitive function: measured with the Mini-Mental State Examination (MMSE), where lower scores indicated greater impairment.\u003c/p\u003e\u003cp\u003eAll data was collected in participants\u0026rsquo; homes or designated health centers by trained healthcare personnel, following standardized KNHANES protocols. Written informed consent was obtained by the KDCA at the time of primary data collection. Because this study was based on the secondary analysis of de-identified public data, no additional Institutional Review Board (IRB) approval was required.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research Validation","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Data Analysis\u003c/h2\u003e\u003cp\u003eData was analyzed using SPSS version 28.0 (IBM Corp., Armonk, NY, USA). Complex survey design variables and sampling weights were applied to ensure nationally representative estimates.\u003c/p\u003e\u003cp\u003eFirst, descriptive statistics were generated to summarize participants\u0026rsquo; demographic, clinical, and behavioral characteristics. Continuous variables were presented as means with standard deviations, while categorical variables were expressed as frequencies and percentages. Differences between fallers and non-fallers were assessed using independent t-tests for continuous variables and chi-square tests for categorical variables.\u003c/p\u003e\u003cp\u003eNext, multivariable linear regression analyses were conducted to examine associations between fall history and HRQoL outcomes (EQ-5D-5L index and EQ-VAS scores). Logistic regression models were additionally performed to explore the likelihood of reporting problems in individual EQ-5D domains. All models were adjusted for potential confounders, including demographic characteristics, health behaviors, number of chronic conditions, HGS, and cognitive function. Adjusted β coefficients, odds ratios (ORs), and 95% confidence intervals (CIs) were reported.\u003c/p\u003e\u003cp\u003eSensitivity analyses included fall frequency as a continuous predictor to test dose\u0026ndash;response relationships. Statistical significance was defined as a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Participant Characteristics\u003c/h2\u003e\u003cp\u003eA total of 1,742 older adults were included in the analysis. The mean age was 72.4 years (SD\u0026thinsp;=\u0026thinsp;6.1), and 58.7% were female. Approximately 41.2% had experienced at least one fall in the previous year. The average number of chronic diseases was 2.3 (SD\u0026thinsp;=\u0026thinsp;1.4), and the mean handgrip strength (HGS) was 22.8 kg (SD\u0026thinsp;=\u0026thinsp;6.9). The mean Mini-Mental State Examination (MMSE) score was 25.9 (SD\u0026thinsp;=\u0026thinsp;3.2). Average HRQoL scores were 0.81 (SD\u0026thinsp;=\u0026thinsp;0.14) for EQ-5D-5L index and 68.7 (SD\u0026thinsp;=\u0026thinsp;15.4) for EQ-VAS.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eGeneral characteristics of participants (N\u0026thinsp;=\u0026thinsp;1,742)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,023 (58.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic diseases (number)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHandgrip strength (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEQ-5D-5L index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEQ-VAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of falls (\u0026ge;\u0026thinsp;1 in past year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e717 (41.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Fall Experience and HRQoL\u003c/h2\u003e\u003cp\u003eOlder adults with a history of falls reported significantly lower HRQoL scores. The mean EQ-5D-5L index was 0.76 among fallers compared with 0.85 in non-fallers (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Likewise, EQ-VAS scores were 64.2 vs 72.1, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). A dose\u0026ndash;response relationship was observed: those with recurrent falls (\u0026gt;\u0026thinsp;2 in the past year) reported the poorest HRQoL.\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\u003e\u003cb\u003eHRQoL outcomes by fall history\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFall history\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEQ-5D-5L index (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEQ-VAS (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e72.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e66.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e61.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.3. R Health Conditions, HGS, and Cognitive Function\u003c/h2\u003e\u003cp\u003eMultivariate analysis revealed that both HGS and cognitive function were significantly associated with fall experience and HRQoL. Participants with low HGS (\u0026lt;\u0026thinsp;20 kg) were nearly twice as likely to report falls (OR\u0026thinsp;=\u0026thinsp;1.92, 95% CI: 1.55\u0026ndash;2.38, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) compared to those with higher HGS. Similarly, participants with MMSE\u0026thinsp;\u0026lt;\u0026thinsp;24 had significantly poorer HRQoL scores (EQ-5D index\u0026thinsp;=\u0026thinsp;0.72) compared with those scoring\u0026thinsp;\u0026ge;\u0026thinsp;24 (EQ-5D index\u0026thinsp;=\u0026thinsp;0.83, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMultivariate logistic regression of fall predictors (OR, 95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI) for \u0026ge;\u0026thinsp;1 fall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (per year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04 (1.02\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.21 (1.01\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic diseases (per unit)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.18 (1.11\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow HGS (\u0026lt;\u0026thinsp;20 kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.92 (1.55\u0026ndash;2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.67 (1.31\u0026ndash;2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Psychological and Environmental Factors\u003c/h2\u003e\u003cp\u003eFear of falling (FoF) was reported by 43.8% of participants, and this subgroup exhibited significantly lower HRQoL scores (EQ-5D-5L: mean 0.68 vs 0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Household hazards (e.g., poor lighting, loose rugs) were reported in 32.4% of households and were strongly correlated with recurrent falls (OR\u0026thinsp;=\u0026thinsp;1.47, 95% CI: 1.22\u0026ndash;1.78).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMultivariate linear regression for HRQoL outcomes (β coefficients, SE)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic diseases (per unit)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFall history (\u0026ge;\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow HGS (\u0026lt;\u0026thinsp;20 kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFear of falling (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Subgroup Analyses by Sex and Comorbidity\u003c/h2\u003e\u003cp\u003eSubgroup analyses revealed notable differences in HRQoL outcomes. Women reported significantly lower EQ-5D-5L index scores (0.78 vs. 0.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and EQ-VAS scores (65.1 vs. 72.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with men. Additionally, participants with three or more chronic conditions exhibited the greatest decline in HRQoL (EQ-5D-5L index\u0026thinsp;=\u0026thinsp;0.73; EQ-VAS\u0026thinsp;=\u0026thinsp;61.7), whereas those with none or only one chronic condition reported substantially higher scores (EQ-5D-5L index\u0026thinsp;=\u0026thinsp;0.85; EQ-VAS\u0026thinsp;=\u0026thinsp;73.9). Interaction models further suggested that the negative effect of falls on HRQoL was more pronounced among women and those with multimorbidity, indicating synergistic vulnerabilities in these subgroups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eSubgroup analyses of HRQoL by sex and chronic disease burden (N\u0026thinsp;=\u0026thinsp;1,742)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubgroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEQ-5D-5L Index (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEQ-VAS (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e72.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e65.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic disease burden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;2 conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e72.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3 conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e62.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: p-values derived from multivariable regression models adjusted for age, education, handgrip strength, and MMSE scores.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.6. Discussion\u003c/h2\u003e\u003cp\u003eThe present study provides compelling evidence that falls, and related factors are strongly associated with diminished health-related quality of life (HRQoL) among older adults. Using a nationally representative dataset, our findings highlight that not only the occurrence of falls but also their frequency, physical injuries, handgrip strength, cognitive function, and environmental hazards jointly contribute to HRQoL outcomes. These results underscore the need for a multidimensional framework in addressing fall-related health issues in aging societies.\u003c/p\u003e\u003cp\u003eOur findings align with prior studies showing the pervasive burden of falls on older adults\u0026rsquo; daily functioning and well-being. Lu et al. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] demonstrated that both the prevalence and frequency of falls were inversely associated with EQ-5D-3L and EQ-VAS scores, a relationship mirrored in our analysis. Palacio et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] emphasized the broader implications of injury threats to longevity in older populations, highlighting falls as a critical driver of morbidity and mortality. Likewise, Song and Lee [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] identified fall history as a predictor of lower HRQoL among elderly Korean women, corroborating the importance of fall-related experiences across diverse populations. Together, these studies and our results reinforce the global impact of falls as determinants of older adults\u0026rsquo; quality of life. Similar associations between falls and HRQoL have also been reported in Chinese adults [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe mechanisms linking falls and HRQoL are multifactorial. Consistent with Tang et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], our data suggest that multimorbidity and poor health behaviors heighten fall risk, with compromised health status mediating their impact on quality of life. Cognitive function also emerged as a salient factor, echoing Pan et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], who reported that cognitive decline significantly predicted poorer EQ-5D outcomes. Beyond statistical associations, reduced handgrip strength (HGS) reflects sarcopenia and diminished muscular fitness, which compromise balance and mobility, thereby elevating fall risk and impairing HRQoL. Similarly, lower MMSE scores indicate deficits in attention and executive function that hinder hazard recognition and adaptive responses in daily activities, further amplifying fall risk and subsequent HRQoL deterioration. Importantly, psychological dimensions such as fear of falling (FoF) contribute substantially. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] found that FoF reduced HRQoL even among individuals receiving home care, while [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] highlighted the role of fall awareness as both a protective and risk-modifying factor. These insights indicate fall prevention cannot be limited to physical interventions but must incorporate neurocognitive and psychosocial support. These results are consistent with prior findings that falls significantly reduce QoL among elderly populations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEnvironmental hazards represent another critical dimension. Abbasian et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] identified poor lighting, clutter, and unsafe flooring as significant predictors of fall injuries in older adults, consistent with our findings that home safety plays a vital role in HRQoL outcomes. Cha [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] further demonstrated that home modifications can reduce hazards and enhance mobility and independence, underscoring the potential of structural interventions. In long-term care facilities, Komisar et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] captured real-time fall events via video, revealing biomechanical patterns that inform individualized prevention strategies. Collectively, these studies emphasize that contextual and environmental interventions are as essential as individual-level strategies.\u003c/p\u003e\u003cp\u003eBeyond these individual and environmental mechanisms, subgroup analyses in our study revealed greater vulnerability among women and individuals with multiple chronic conditions. Female participants reported significantly lower EQ-5D-5L scores compared with men, echoing prior evidence that gender disparities in muscle mass, balance confidence, and social support may amplify fall risk and HRQoL impairments [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In line with our subgroup findings, nationwide evidence shows that injuries disproportionately impair HRQoL among older women [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Likewise, participants with three or more chronic diseases demonstrated the most pronounced reductions in EQ-VAS, consistent with findings that multimorbidity interacts synergistically with falls to erode well-being [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These results underscore the importance of tailoring fall prevention strategies to address sex-specific needs and to integrate chronic disease management into HRQoL interventions.\u003c/p\u003e\u003cp\u003e International guidelines have strongly advocated integrated approaches to fall prevention. The World Guidelines for Falls Prevention and Management [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] stress systematic risk assessments, deprescribing fall-risk-increasing medications, and implementing targeted exercise programs. Complementary to these guidelines, Zanotto et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] introduced innovative \u0026ldquo;safe falling\u0026rdquo; strategies, demonstrating the feasibility of reducing injury severity when falls occur. Ek et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] added nuance by showing how resilience following hip fracture influences long-term HRQoL, a concept that highlights the importance of post-injury recovery trajectories. Furthermore, technological innovations have become increasingly central. For instance, ICT-enabled community programs, as evidenced by Cha [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], have shown effectiveness in promoting both activity and HRQoL. These multidimensional prevention modalities offer promising avenues for mitigating the global burden of falls.\u003c/p\u003e\u003cp\u003eThis study has several strengths. First, it utilized a nationally representative dataset, thereby enhancing the generalizability of the findings to the broader older adult population in Korea. Second, the inclusion of both objective measures (handgrip strength, MMSE) and subjective assessments (EQ-5D-5L, EQ-VAS) provided a comprehensive evaluation of health-related quality of life. These strengths increase the robustness of the study and ensure that its conclusions are relevant for both clinical practice and public health policy. Nevertheless, limitations must be acknowledged. First, the cross-sectional design precludes causal inference, making it difficult to determine whether falls cause declines in HRQoL or whether poorer health status predisposes individuals to fall. Second, data was based on self-reports, which may introduce recall or reporting biases. Third, while our study incorporated multiple determinants, it did not fully capture psychosocial factors such as depression or social support, which may further mediate fall\u0026ndash;HRQoL relationships. Future studies should address these gaps through longitudinal designs, objective measures, and culturally sensitive intervention trials.\u003c/p\u003e\u003cp\u003eThe findings of this study hold significant implications for healthcare providers, policymakers, and community organizations. In super-aged societies such as Korea, China, and Japan, integrating fall prevention into public health strategies is critical for sustaining older adults\u0026rsquo; independence and quality of life. Multilevel interventions are required: individualized health promotion targeting chronic disease and cognitive decline; psychosocial support to address fear of falling and self-efficacy; and structural initiatives such as home modifications and ICT-based monitoring systems. Moreover, resilience-focused rehabilitation programs, particularly for those recovering from fractures, should be prioritized to mitigate long-term HRQoL impairments. At the policy level, alignment with international guidelines and investment in preventive care infrastructures will be key to reducing the economic and societal burden of fall-related injuries.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study contributes to the growing body of evidence that falls are not merely accidental events, but complex phenomena shaped by physical, cognitive, psychological, and environmental determinants. By situating our findings within global literature, we highlight the necessity of integrated, multidimensional strategies for fall prevention and HRQoL enhancement. Ultimately, fostering safer environments, strengthening resilience, and addressing the psychosocial sequelae of falls are essential steps toward supporting older adults to age with health, independence, and dignity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was supported by the Sehan University Research Fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHistory: Received: /Revised: /Accepted: /Published:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCopyright:\u0026nbsp;\u003c/strong\u003e\u0026copy; 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003eHyo Taek Lee conceived the study, performed the data analysis, interpreted the findings, and drafted the manuscript. The author read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransparency:\u0026nbsp;\u003c/strong\u003eThe authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e: Ethical review and approval were waived for this study because it used de-identified secondary data from the Korea National Health and Nutrition Examination Survey (KNHANES), which is open to researchers and the public.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are publicly available from the Korea National Health and Nutrition Examination Survey (KNHANES) at the Korea Disease Control and Prevention Agency (KDCA) website: https://knhanes.kdca.go.kr. 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J Surg Med. 2023;7(8):7645. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.28982/josam.7645\u003c/span\u003e\u003cspan address=\"10.28982/josam.7645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwak Y, Ahn JW. Health-related quality of life in older women with injuries: A nationwide study. Front Public Health. 2023;11:1149534. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2023.1149534\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2023.1149534\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Falls, Health-Related Quality of Life (HRQoL), Older Adults, Handgrip Strength","lastPublishedDoi":"10.21203/rs.3.rs-7661381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7661381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFalls are a leading cause of morbidity, disability, and mortality among older adults and have substantial implications for health-related quality of life (HRQoL). This study investigated the associations between fall history, injury experience, and HRQoL in older Korean adults, incorporating both physical and cognitive determinants. Using data from the 2023 Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative sample of 1,742 participants aged 65 years and older was analyzed. HRQoL was assessed with the EQ-5D-5L index and EQ-VAS. Independent variables included fall history, injury-related hospitalization, handgrip strength (HGS), chronic disease burden, Mini-Mental State Examination (MMSE) scores, and sociodemographic covariates. Weighted regression models were employed to account for complex sampling. Results showed that participants with a history of falls had significantly lower EQ-5D-5L (β = \u0026minus;0.048, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and EQ-VAS scores (β = \u0026minus;4.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with fall-related hospitalization producing the most pronounced HRQoL reductions. Lower HGS and poorer cognitive function independently predicted impaired HRQoL. Subgroup analyses revealed that women and those with three or more chronic conditions were disproportionately vulnerable, exhibiting the steepest declines in HRQoL. These findings underscore the multifactorial nature of falls and their impact on older adults\u0026rsquo; well-being, highlighting the need for integrated interventions that strengthen physical and cognitive function, address sex-specific and comorbidity-related vulnerabilities, and promote safe home environments.\u003c/span\u003e\u003c/p\u003e","manuscriptTitle":"Falls, Injuries, and Health-Related Quality of Life in Older Adults: Evidence from Population-Based Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 16:20:36","doi":"10.21203/rs.3.rs-7661381/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":"05fffb73-8dc4-4ae4-95c0-fe5ff0bc560c","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T08:40:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 16:20:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7661381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7661381","identity":"rs-7661381","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}