INTRODUCTION

Older adults are vulnerable to injury, with falls representing the most common cause [1]. Falls reduce quality of life among older adults [2], and severe falls can lead to functional decline, loss of independence, fear of falling, social isolation, and death [3,4]. In Korea, 15.9–25.1% of community-dwelling older adults experience falls annually [5], and the incidence increases substantially with age [6,7]. As Korea is experiencing population aging at an unprecedented pace worldwide, both the incidence of falls and the associated social burden are expected to increase further as the older population continues to grow.

General obesity is typically considered a risk factor for falls. However, previous studies in older adults have reported inconsistent findings, including increased fall risk [8–10], U-shaped associations [8,11], and no or inverse associations [12–14], indicating that the relationship remains a topic of debate. These inconsistencies may reflect differences in socio-demographic characteristics (e.g., race/ethnicity), disease-related factors, and health-related characteristics across study populations. In particular, variation in the operational definition of obesity has been identified as an important methodological factor contributing to these discrepancies [15].

Among indicators used to assess obesity in large-scale population surveys, general obesity measured by body mass index (BMI) and central obesity defined by waist circumference (WC) are the most common. Recently, concerns have been raised that BMI, which does not distinguish between muscle and fat mass, may be inappropriate for assessing obesity in older adults [15,16]. In contrast, WC, an indicator of body fat distribution and abdominal obesity, has been reported to improve the prediction of various health outcomes in older adults [17–19]. Regarding falls, emerging evidence suggests that WC may be a stronger predictor than BMI among older adults with obesity [15,16]. However, clear evidence supporting WC as a stronger predictor of falls than BMI remains limited. Furthermore, although several studies in Korea have examined the relationship between BMI and falls among older adults [8,13,20], research on the association between WC and falls remains limited [15].

Therefore, this study aimed to investigate the association between obesity and falls among community-dwelling older adults in Korea and to determine whether BMI or WC better predicts fall risk, overall and according to sex and age group.

METHODS

Study Population

We used data from the seventh Korea National Health and Nutrition Examination Survey (KNHANES VII, 2016–2018). KNHANES, conducted annually by the Korea Disease Control and Prevention Agency (KDCA), is a nationwide, population-based survey designed to assess health-related behaviors, health status, and nutritional intake among Korean adults. The survey employs a stratified, multistage, clustered probability sampling design to obtain a representative sample of civilian, non-institutionalized adults in Korea. The raw data were obtained after completion of the required application process through the KNHANES website.

For this study, we selected participants aged ≥65 years from KNHANES VII, which includes the most recent data containing measurements for height, weight, and WC, as these are required to assess BMI and WC. Of the 4956 older adults aged ≥65 years, we excluded 418 individuals with missing data on falls, 46 with missing obesity-related indicators (BMI or WC), and 114 with missing covariates. A total of 4378 participants were included in the final analysis.

Measures

Falls

The presence of falls was assessed through self-reported interviews. Participants were asked, “Has there been a fall or slip requiring treatment at a hospital or emergency room during the past year?” Those who responded “yes” were classified as having experienced a fall [21].

Obesity

Obesity was assessed using BMI as an indicator of general obesity and WC as an indicator of central obesity; both were measured during the KNHANES health examination survey. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²), and classification followed the criteria of the Korean Society for the Study of Obesity [22]. Individuals with a BMI <18.5 were classified as underweight, those with a BMI of 18.5–22.9 as normal weight, those with a BMI of 23.0–24.9 as overweight, and those with a BMI ≥25.0 as obese. Central obesity was defined using WC cutoff values recommended by the Korean Society for the Study of Obesity [22]: ≥90 cm for males and ≥85 cm for females.

Covariates

Covariates included socio-demographic characteristics and health-related variables. Socio-demographic characteristics included sex (male, female), age (65–74, ≥75 years), education level (elementary school, middle school, high school, college/university), household income (lower, lower-middle, upper-middle, upper), employment status (yes, no), and living arrangement (living alone vs. living with family). Health-related variables included self-rated health status (good, normal, poor), current smoking (yes, no), current alcohol consumption (yes, no), aerobic physical activity (yes, no), number of chronic diseases (0, 1–2, ≥3), and number of medications (0, 1–2, ≥3). Aerobic physical activity was defined as ≥150 minutes of moderate-intensity activity, ≥75 minutes of vigorous-intensity activity, or an equivalent combination per week [23]. Chronic diseases included physician-diagnosed hypertension, dyslipidemia, stroke, myocardial infarction or angina pectoris, osteoarthritis or rheumatoid arthritis, osteoporosis, diabetes mellitus, and cancers (stomach, liver, colon, breast, cervix, and lung).

Statistical Analysis

Data were analyzed using SPSS version 30.0 (IBM Corp., Armonk, NY, USA), and R version 4.5.2 (R Foundation for Statistical Computing, Vienna, Austria), with appropriate weighting to account for the complex sampling design in accordance with KDCA guidelines. Participant characteristics were presented as unweighted frequencies and weighted percentages. Differences in falls according to participant characteristics were assessed using the Rao–Scott chi-square test. Complex-sample logistic regression analyses were conducted to estimate odds ratios and 95% confidence intervals (CIs) for the associations between obesity (defined by BMI and WC) and falls. Analyses were stratified by sex and age group and adjusted for potential confounders, including socio-demographic and health-related factors. Predictive accuracy was evaluated using Harrell’s C-index. Differences in predictive performance between WC and BMI were calculated as the ΔC-index (WC–BMI), and statistical significance was assessed using the DeLong test. A 2-sided p-value <0.05 was considered statistically significant.

Ethics Statement

This study used anonymized secondary data from the KNHANES. The KDCA, which oversees the survey, obtained approval from its institutional review board (IRB), and informed consent was obtained from all participants at the time of data collection. Additional ethical approval for this study was obtained from the IRB of Chungbuk National University (IRB No. CBNU-2026-A-0001).

RESULTS

Among the 4378 participants, 3.6% (n=157) had experienced a fall in the preceding year. Falls were more likely to be experienced by females (4.1 vs. 2.3%), individuals aged 65–74 years (3.9 vs. 2.8%), and participants who lived alone (4.7 vs. 3.0%). In addition, the proportion of falls increased across BMI categories (underweight, normal weight, overweight, and obese: 1.6, 2.8, 3.4, and 4.4%, respectively) and was higher among those with central obesity (4.7 vs. 2.4%) (Table 1).

Table 2 presents the logistic regression analyses examining the associations between falls and both general and central obesity. BMI-defined general obesity was not significantly associated with falls in the multivariable-adjusted model. In contrast, WC-defined central obesity was significantly associated with falls in the multivariable-adjusted model (adjusted odds ratio [aOR], 1.82; 95% CI, 1.20 to 2.76).

When the analyses were stratified by sex, general obesity was not significantly associated with falls in either males or females. In contrast, central obesity was significantly associated with falls in both males (aOR, 2.21; 95% CI, 1.07 to 4.55) and females (aOR, 1.64; 95% CI, 1.03 to 2.61) (Table 3).

When the analyses were stratified by age group, central obesity was significantly associated with falls among adults aged 65–74 years (aOR, 1.79; 95% CI, 1.09 to 2.93), whereas no significant association was observed among those aged ≥75 years (Table 4).

Overall, WC showed slightly higher predictive ability for falls than BMI (ΔC-index=0.047, p=0.044). In subgroup analyses stratified by sex and age group, the ΔC-index values were positive but did not reach statistical significance (Table 5).

DISCUSSION

This study examined the associations between obesity and falls among community-dwelling older adults in Korea, with a direct comparison of the predictive performance of BMI and WC. To our knowledge, this is the first large-scale study to evaluate whether central obesity provides additional predictive value beyond general obesity, with stratification by sex and age group.

Our main finding was that WC-defined central obesity showed a more consistent association with falls than BMI-defined general obesity and provided modest but statistically significant incremental predictive value beyond BMI. Although the improvement in predictive ability was small, these results suggest that incorporating WC into fall risk assessment may improve risk classification compared with relying on BMI alone.

In our study, BMI-defined general obesity was not significantly associated with falls, consistent with previous studies reporting no significant relationship between BMI-defined obesity and falls in older adults [12,15,16,24]. The absence of a significant association between BMI-defined general obesity and falls in older adults may be attributable to BMI’s inability to reflect body composition accurately. Aging is typically accompanied by decreased muscle mass and increased fat mass, which can lead to sarcopenic obesity even without substantial changes in body weight or BMI [25,26]. Because BMI is calculated solely from weight and height, it cannot distinguish between fat mass and lean mass, potentially misclassifying individuals with high body fat as having a normal BMI [27]. Recent studies have raised concerns that BMI may be an inappropriate measure of obesity in older adults [28,29], and our findings support this perspective. Therefore, healthcare providers should be cautious about using BMI alone to assess health risks, including fall risk, associated with obesity in older adults [16]. Nonetheless, some studies have reported a significant association between BMI-defined general obesity and falls in older adults [8,10,30,31], highlighting the need for further research.

In contrast, WC, which reflects central fat accumulation, has consistently been reported to be significantly associated with falls in older adults [15,16,24]. In this study, WC-defined central obesity was significantly associated with falls in all analyses except those involving participants aged ≥75 years, partially supporting previous evidence that abdominal obesity is an important indicator of fall risk. The association between central obesity and falls may be explained by several mechanisms. Excess abdominal fat shifts the body’s center of mass forward, resulting in postural instability, impaired balance [15,16,32], and inefficient gait patterns, including reduced walking speed [33,34]. Moreover, abdominal obesity is associated with systemic inflammation, oxidative stress, and insulin resistance, which may accelerate muscle loss, exacerbate muscle weakness, and impair neuromuscular function [35,36]. Therefore, the increased fall risk observed in individuals with central obesity likely reflects not only increased body weight but also altered fat distribution and functional impairments affecting balance, posture, and gait.

A notable finding of this study is that, among adults aged ≥75 years, neither general obesity nor central obesity was significantly associated with falls. This finding suggests that, in very old adults, factors other than obesity—such as frailty, multimorbidity, polypharmacy, sensory deficits, and prior falls—may play a more prominent role in fall risk. Recent studies have emphasized the importance of age-stratified analyses within older populations [37,38]. Such stratification is necessary because treating all adults aged ≥65 years as a single older population may overlook age-related differences within this heterogeneous group [37]. Indeed, risk factors for falls differ across age groups among older adults [39,40]. Future research should examine age-specific associations between obesity and falls in older adults to inform tailored interventions and policies.

The prevalence of obesity varied according to the definition used. In this study, BMI-defined general obesity was 25.5%, whereas WC-defined central obesity was 42.2%, consistent with previous studies reporting a higher prevalence of abdominal obesity than general obesity in older populations [15,16]. This finding suggests that BMI, which does not account for age-related increases in fat mass and decreases in muscle mass, may substantially underestimate obesity in older adults [15]. This is particularly relevant for individuals with a normal BMI but excess central fat. Although some studies have reported associations between BMI-defined obesity and falls [8,10,30,31], BMI alone may be insufficient in this population. Furthermore, international guidelines define obesity as BMI ≥30 kg/m², whereas a lower cutoff (BMI ≥25 kg/m²) is recommended for Asian populations because of differences in body composition and metabolic risk. In our sample, only 66 participants met the BMI ≥30 kg/m² criterion, limiting statistical power for reliable analysis. Therefore, we applied the Korean BMI criteria. Differences in cutoff values should be considered when comparing results across studies, and future research, including larger numbers of individuals with BMI ≥30 kg/m² in Asian populations, is warranted.

From a public health perspective, indicators that improve risk prediction are particularly important for designing fall-prevention strategies. In addition, because WC can be measured easily without specialized equipment, incorporating routine WC assessment into clinical practice may be a practical and meaningful approach to fall risk screening.

This study has several limitations. First, the cross-sectional design precludes causal inference regarding the relationships among general obesity, central obesity, and falls, making it unclear whether obesity contributes to falls or whether falls lead to changes in body composition. Second, selective survival or response bias among adults aged ≥75 years may have influenced the observed associations, as participants in this age group may represent a healthier subset of the population. Third, some fall-related variables and covariates were self-reported and may therefore be subject to reporting errors or recall bias. Fourth, because this study used secondary data, residual confounding and unmeasured variables cannot be fully ruled out. Future longitudinal studies are needed to clarify the causal relationship between obesity and falls and to further evaluate the predictive utility of WC. Despite these limitations, this study is meaningful as the first to analyze the association between obesity and falls using nationally representative data in Korea and to compare whether general obesity or central obesity better predicts falls.

In conclusion, WC-defined central obesity showed a more consistent association with falls than BMI and offered modest but meaningful additional predictive value. These findings suggest that, among Korean older adults, WC may be a more useful anthropometric measure than BMI for assessing fall risk. Central obesity may therefore warrant prioritization as a screening measure for fall risk in older adults, and individuals with central obesity may benefit from targeted fall-prevention strategies. Prioritizing central obesity in fall risk screening may help strengthen prevention strategies for the older adult population.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

None.

Author Contributions

Conceptualization: Kim S, Im JS, Ha B. Data curation: Kim S, Ha B. Formal analysis: Kim S, Ha B. Funding acquisition: None. Methodology: Kim S, Im JS, Ha B. Project administration: Kim S, Ha B. Visualization: Kim S, Ha B. Writing – original draft: Kim S, Im JS, Ha B. Writing – review & editing: Kim S, Im JS, Ha B.

REFERENCES

• 1. Houry D, Florence C, Baldwin G, Stevens J, McClure R. The CDC Injury Center’s response to the growing public health problem of falls among older adults. Am J Lifestyle Med 2016;10(1):74-77. https://doi.org/10.1177/1559827615600137ArticlePubMed
• 2. Choo J, Kim EK. Health-related quality of life of fallers vs. non-fallers in community-dwelling elderly people. J Muscle Joint Health 2012;19(3):373-382. (Korean) https://doi.org/10.5953/JMJH.2012.19.3.373Article
• 3. Kelsey JL, Procter-Gray E, Hannan MT, Li W. Heterogeneity of falls among older adults: implications for public health prevention. Am J Public Health 2012;102(11):2149-2156. https://doi.org/10.2105/AJPH.2012.300677ArticlePubMed
• 4. Sekaran NK, Choi H, Hayward RA, Langa KM. Fall-associated difficulty with activities of daily living in functionally independent individuals aged 65 to 69 in the United States: a cohort study. J Am Geriatr Soc 2013;61(1):96-100. https://doi.org/10.1111/jgs.12071ArticlePubMedPMC
• 5. Kim T, Choi SD, Xiong S. Epidemiology of fall and its socioeconomic risk factors in community-dwelling Korean elderly. PLoS One 2020;15(6):e0234787. https://doi.org/10.1371/journal.pone.0234787ArticlePubMedPMC
• 6. Kim MY, Kim Y. Comparison of factors influencing fall recurrence in the young-old and old-old: a cross-sectional nationwide study in South Korea. BMC Geriatr 2022;22(1):520. https://doi.org/10.1186/s12877-022-03172-7ArticlePubMedPMC
• 7. Kim JY, Lee YW, Ham OK. Factors related to fall in elderly patients with osteoporosis. Korean J Adult Nurs 2009;21(2):257-267. (Korean)
• 8. Kim SY, Kim MS, Sim S, Park B, Choi HG. Association between obesity and falls among Korean adults: a population-based cross-sectional study. Medicine (Baltimore) 2016;95(12):e3130. https://doi.org/10.1097/MD.0000000000003130ArticlePubMedPMC
• 9. Yi SW, Kim YM, Won YJ, Kim SK, Kim SH. Association between body mass index and the risk of falls: a nationwide population-based study. Osteoporos Int 2021;32(6):1071-1078. https://doi.org/10.1007/s00198-020-05725-1ArticlePubMed
• 10. Zhao X, Yu J, Hu F, Chen S, Liu N. Association of body mass index and waist circumference with falls in Chinese older adults. Geriatr Nurs 2022;44: 245-250. https://doi.org/10.1016/j.gerinurse.2022.02.020ArticlePubMed
• 11. Ogliari G, Ryg J, Andersen-Ranberg K, Scheel-Hincke LL, Masud T. Association between body mass index and falls in community-dwelling men and women: a prospective, multinational study in the Survey of Health, Ageing and Retirement in Europe (SHARE). Eur Geriatr Med 2021;12(4):837-849. https://doi.org/10.1007/s41999-021-00485-5ArticlePubMed
• 12. Hermenegildo-López Y, Sandoval-Insausti H, Donat-Vargas C, Banegas JR, Rodríguez-Artalejo F, Guallar-Castillón P. General and central obesity operate differently as predictors of falls requiring medical care in older women: a population-based cohort study in Spain. Age Ageing 2021;50(1):213-219. https://doi.org/10.1093/ageing/afaa164ArticlePubMed
• 13. Yeom J, Na HJ. Risk factors of falls among Korean elderly. J Korea Gerontol Soc 2012;32(2):577-592. (Korean)
• 14. Zhang N, Lu SF, Zhou Y, Zhang B, Copeland L, Gurwitz JH. Body mass index, falls, and hip fractures among nursing home residents. J Gerontol A Biol Sci Med Sci 2018;73(10):1403-1409. https://doi.org/10.1093/gerona/gly039ArticlePubMed
• 15. Shim GY, Yoo MC, Soh Y, Chon J, Won CW. Obesity, physical performance, balance confidence, and falls in community-dwelling older adults: results from the Korean frailty and aging cohort study. Nutrients 2024;16(5):614. https://doi.org/10.3390/nu16050614ArticlePubMedPMC
• 16. Cho BY, Seo DC, Lin HC, Lohrmann DK, Chomistek AK. BMI and central obesity with falls among community-dwelling older adults. Am J Prev Med 2018;54(4):e59-e66. https://doi.org/10.1016/j.amepre.2017.12.020ArticlePubMed
• 17. Qiao T, Luo T, Pei H, Yimingniyazi B, Aili D, Aimudula A, et al. Association between abdominal obesity indices and risk of cardiovascular events in Chinese populations with type 2 diabetes: a prospective cohort study. Cardiovasc Diabetol 2022;21(1):225. https://doi.org/10.1186/s12933-022-01670-xArticlePubMedPMC
• 18. Sahakyan KR, Somers VK, Rodriguez-Escudero JP, Hodge DO, Carter RE, Sochor O, et al. Normal-weight central obesity: implications for total and cardiovascular mortality. Ann Intern Med 2015;163(11):827-835. https://doi.org/10.7326/M14-2525ArticlePubMedPMC
• 19. Zhang Z, Zhao L, Lu Y, Meng X, Zhou X. Association between Chinese visceral adiposity index and risk of stroke incidence in middle-aged and elderly Chinese population: evidence from a large national cohort study. J Transl Med 2023;21(1):518. https://doi.org/10.1186/s12967-023-04309-xArticlePubMedPMC
• 20. Jun HJ, Choi JY. Factors influencing fall experiences among the older adults in community: using the 2021 Community Health Survey. Korean J Occup Health Nurs 2023;32(2):79-88. (Korean)https://doi.org/10.5807/kjohn.2023.32.2.79Article
• 21. Go YJ, Lee DC, Lee HJ. Association between handgrip strength asymmetry and falls in elderly Koreans: a nationwide population-based cross-sectional study. Arch Gerontol Geriatr 2021;96: 104470. https://doi.org/10.1016/j.archger.2021.104470ArticlePubMed
• 22. Kim BY, Kang SM, Kang JH, Kang SY, Kim KK, Kim KB, et al. 2020 Korean Society for the Study of Obesity guidelines for the management of obesity in Korea. J Obes Metab Syndr 2021;30(2):81-92. https://doi.org/10.7570/jomes21022ArticlePubMedPMC
• 23. Korea Disease Control and Prevention Agency. National Health and Nutrition Survey raw data usage guidelines 7th 2016–2018; [cited 2025 Jun 17]. Available from: https://knhanes.kdca.go.kr/knhanes/postSendPage.do?url=/dataAnlsGd/utztnGdDtl.do&postparam=%7B%22menuId%22:%22100310021001%22,%22utztnGdbkDataNo%22:%2298%22%7D (Korean)
• 24. Jyväkorpi SK, Urtamo A, Kivimäki M, Strandberg TE. Association of nutritional components with falls in oldest-old men: the Helsinki Businessmen Study (HBS). Exp Gerontol 2020;142: 111105. https://doi.org/10.1016/j.exger.2020.111105ArticlePubMed
• 25. Kim E. Health behavior and nutritional characteristics of obese and morbid obese older adults: based on the 9th Korea National Health and Nutrition Survey Data. 2022;J East West Nurs Res 2025;31(1):27-35. (Korean)https://doi.org/10.14370/jewnr.2025.31.1.27Article
• 26. Kim SK, Kwon YH, Cho JH, Lee DY, Park SE, Oh HG, et al. Changes in body composition according to age and sex among young non-diabetic Korean adults: the Kangbuk Samsung Health Study. Endocrinol Metab (Seoul) 2017;32(4):442-450. https://doi.org/10.3803/EnM.2017.32.4.442ArticlePubMedPMC
• 27. Choi KM. Sarcopenia and sarcopenic obesity. Korean J Intern Med 2016;31(6):1054-1060. https://doi.org/10.3904/kjim.2016.193ArticlePubMedPMC
• 28. Lv Y, Mao C, Gao X, Ji JS, Kraus VB, Yin Z, et al. The obesity paradox is mostly driven by decreased noncardiovascular disease mortality in the oldest old in China: a 20-year prospective cohort study. Nat Aging 2022;2(5):389-396. https://doi.org/10.1038/s43587-022-00201-3ArticlePubMed
• 29. Wannamethee SG, Atkins JL. Muscle loss and obesity: the health implications of sarcopenia and sarcopenic obesity. Proc Nutr Soc 2015;74(4):405-412. https://doi.org/10.1017/S002966511500169XArticlePubMed
• 30. Mitchell RJ, Lord SR, Harvey LA, Close JC. Associations between obesity and overweight and fall risk, health status and quality of life in older people. Aust N Z J Public Health 2014;38(1):13-18. https://doi.org/10.1111/1753-6405.12152ArticlePubMed
• 31. Trevisan C, Crippa A, Ek S, Welmer AK, Sergi G, Maggi S, et al. Nutritional status, body mass index, and the risk of falls in community-dwelling older adults: a systematic review and meta-analysis. J Am Med Dir Assoc 2019;20(5):569-582. https://doi.org/10.1016/j.jamda.2018.10.027ArticlePubMed
• 32. Neri SG, Gadelha AB, de David AC, Ferreira AP, Safons MP, Tiedemann A, et al. The association between body adiposity measures, postural balance, fear of falling, and fall risk in older community-dwelling women. J Geriatr Phys Ther 2019;42(3):E94-E100. https://doi.org/10.1519/JPT.0000000000000165ArticlePubMed
• 33. Máximo RO, Santos JL, Perracini MR, Oliveira C, Duarte YA, Alexandre TD. Abdominal obesity, dynapenia and dynapenic-abdominal obesity as factors associated with falls. Braz J Phys Ther 2019;23(6):497-505. https://doi.org/10.1016/j.bjpt.2018.10.009ArticlePubMedPMC
• 34. Zhang L, Liu S, Wang W, Sun M, Tian H, Wei L, et al. Dynapenic abdominal obesity and the effect on long-term gait speed and falls in older adults. Clin Nutr 2022;41(1):91-96. https://doi.org/10.1016/j.clnu.2021.11.011ArticlePubMed
• 35. Alexandre TD, Scholes S, Santos JL, de Oliveira C. Dynapenic abdominal obesity as a risk factor for worse trajectories of ADL disability among older adults: the ELSA cohort study. J Gerontol A Biol Sci Med Sci 2019;74(7):1112-1118. https://doi.org/10.1093/gerona/gly182ArticlePubMedPMC
• 36. Hong SH, Choi KM. Sarcopenic obesity, insulin resistance, and their implications in cardiovascular and metabolic consequences. Int J Mol Sci 2020;21(2):494. https://doi.org/10.3390/ijms21020494ArticlePubMedPMC
• 37. Ha B, Kim J, Kim S. Incidence and correlates of IADL disability in old Koreans: comparison of young-old and old-old. J Korea Acad Ind Coop Soc 2022;23(10):146-156. (Korean)https://doi.org/10.5762/KAIS.2022.23.10.146Article
• 38. Lee EJ, Lee ES. Factors related to fear of falling by age group in community-dwelling mid to late-adults. J East West Nurs Res 2022;28(2):122-131. (Korean)https://doi.org/10.14370/jewnr.2022.28.2.122Article
• 39. Kakara R, Bergen G, Burns E. Understanding the association of older adult fall risk factors by age and sex through factor analysis. J Appl Gerontol 2023;42(7):1662-1671. https://doi.org/10.1177/07334648231154881ArticlePubMedPMC
• 40. Park NJ, Shin YS. Predictors of accidental falls in the community-dwelling elderly by age. J Korean Acad Community Health Nurs 2019;30(2):141-149. (Korean)https://doi.org/10.12799/jkachn.2019.30.2.141Article

Figure & Data

References

Citations

Citations to this article as recorded by