Analysis of Physical, Psychological, and Lifestyle Factors Affecting Falls in Older Adults: A Study Based on the Korea National Health and Nutrition Examination Survey
Department of Nursing, College of Health Sciences, Daejin University, 1007, Hoguk-ro, Pocheon-si 11159, Gyeonggi-do, Republic of Korea
J. Ageing Longev. 2025, 5(4), 53; https://doi.org/10.3390/jal5040053 (registering DOI)
Submission received: 2 November 2025
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Revised: 14 November 2025
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Accepted: 26 November 2025
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Published: 29 November 2025
Abstract
Falls are a major cause of morbidity in aging populations; this study examined physical, psychological, and lifestyle correlates of falls among older Korean adults. Using 2022 KNHANES data, we conducted a cross-sectional analysis of adults aged ≥65 years (n = 612). Fall in the past year was the outcome; multivariable logistic regression and ROC analyses evaluated candidate predictors. Lower weekly working hours (<12) (OR = 3.11, 95% CI 1.23–7.88), insufficient physical activity (<150 min/week) (2.49, 1.03–5.99), reduced grip strength (<15 kg) (2.23, 1.14–4.35), low diastolic blood pressure (<69 mmHg) (2.06, 1.09–3.89), elevated LDL cholesterol (≥150 mg/dL) (3.06, 1.49–6.28), and depressive symptoms (PHQ-9 ≥ 3) (3.02, 1.52–6.00) were independently associated with higher fall odds. Age ≥ 75 years, alcohol use, anxiety (GAD-7 ≥ 3), vitamin D ≤ 3 ng/mL, and vitamin E ≤ 7 mg/L were not significant in adjusted models. Discrimination was modest across individual markers (AUCs 0.55–0.65); model fit was acceptable (Nagelkerke R2 = 0.262; Hosmer–Lemeshow p = 0.318). These findings suggest that screening for low muscle strength, depressive symptoms, hypotension, and high LDL cholesterol—alongside promoting physical activity and social engagement through work—may help identify and manage fall risk in community-dwelling older adults. Causal inference is not supported due to the cross-sectional design.
1. Introduction
Falls are one of the most critical health challenges faced by older adults, representing a major cause of injury-related morbidity and mortality [1]. Globally, falls are a leading contributor to disability in older populations, resulting in a wide range of physical injuries, from minor bruises to severe fractures and head trauma [1,2]. Beyond the immediate physical harm, falls have long-term implications for psychological health, often leading to increased anxiety, fear of falling, and depression [3]. Such psychological effects contribute to decreased physical activity, further reducing mobility and increasing the risk of subsequent falls [4]. This cycle highlights the need for effective, multidimensional approaches to fall prevention.
The significance of fall prevention is particularly relevant in aging societies like South Korea, where the proportion of older adults (aged 65 years and older) is rapidly increasing. In 2021, this age group accounted for 16.5% of the population, a figure projected to rise to 24% by 2030 [5]. As the population ages, the societal and economic burden of falls is expected to grow exponentially [6], underscoring the need for evidence-based strategies to mitigate fall risks and support healthy aging.
Numerous studies have identified well-established physical predictors of falls in older adults, including low body mass index (BMI), reduced grip strength, impaired balance, and poor mobility [6,7,8]. However, other potentially important factors—such as depressive symptoms, diastolic blood pressure, LDL cholesterol levels, and weekly working hours—have received comparatively less attention in previous fall risk research [9]. Several studies have focused on single physical or clinical markers (e.g., mobility, balance, or blood pressure) rather than examining the combined influence of physical, psychological, and lifestyle determinants within a single analytic framework [10]. This single-factor focus limits the development of comprehensive, multidomain fall-prevention strategies.
These variables were included in the present study based on plausible physiological mechanisms. For example, elevated LDL cholesterol may reflect vascular dysfunction affecting mobility, and low diastolic blood pressure may increase the risk of orthostatic hypotension, leading to instability and falls in older adults [11]. Medication use, including sedatives and antihypertensive drugs, is another area warranting further investigation. While certain medications are known to affect balance and coordination [12], their impact on fall risks in combination with other factors remains unclear. Furthermore, many existing interventions focus on physical rehabilitation or educational programs [13,14], often neglecting the psychological and lifestyle components of fall risk. These limitations highlight the need for a more integrated approach to fall prevention.
Previous studies have often examined specific domains of fall risk in isolation—such as physical function, mental health, or metabolic factors—and many have used limited or non-representative samples [15]. Few studies have simultaneously evaluated physical, psychological, and lifestyle determinants, including underexplored markers such as LDL cholesterol and weekly working hours, in a nationally representative sample of older adults. Based on multifactorial models of fall risk, we hypothesized that both traditional physical indicators (e.g., grip strength, blood pressure) and less-studied physiological and lifestyle factors (e.g., LDL cholesterol, depressive symptoms, weekly working hours) would show independent associations with falls
Therefore, this study aimed to examine the physical, psychological, and lifestyle factors associated with falls among community-dwelling older adults in South Korea using 2022 KNHANES data. By evaluating both well-established and underrecognized predictors within a single multivariable model, we sought to provide multidomain, practice-modifiable indicators to inform integrated fall-prevention strategies.
2. Materials and Methods
2.1. Study Design
This retrospective cross-sectional study analyzed data from the 2022 Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative health and nutrition survey conducted annually by the Korea Disease Control and Prevention Agency. We examined associations between physical, psychological, and lifestyle factors and the occurrence of falls among older adults. KNHANES is a nationally representative, ongoing cross-sectional survey conducted by the Korea Disease Control and Prevention Agency using a multistage, stratified, cluster sampling design of the non-institutionalized Korean population. Health interviews, physical examinations, and nutrition surveys are administered by trained staff.
2.2. Study Population
The study population consisted of individuals aged 65 years and older who participated in the 2022 KNHANES. Participants with missing data on variables related to physical or psychological factors were excluded from the analysis. The final sample size included a sufficient number of participants to meet the study’s objectives and ensure statistical validity. All participants aged ≥65 years with complete data on the variables of interest were included in the analysis (final n = 612). Because this is a secondary analysis of an existing national survey, an a priori sample size calculation was not performed during data collection.
2.3. Ethical Considerations
This study was approved by the Institutional Review Board of Daejin University (Approval No. 1040656-202412-HR-01-05). The analysis used publicly available, de-identified data from the Korea National Health and Nutrition Examination Survey (KNHANES), and the Korea Disease Control and Prevention Agency obtained informed consent from all participants at the time of the original survey. No additional contact with participants was required for this secondary analysis. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Reporting followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for cross-sectional studies.
2.4. Variable Selection and Definitions
This study included various physical, psychological, and lifestyle factors that could potentially influence fall risks in older adults. The variables were defined as follows: The dependent variable was the occurrence of falls within the past year, assessed through the KNHANES question, “Have you experienced a fall in the past year?” Responses were categorized as binary variables, with 1 indicating a fall and 0 indicating no fall. The independent variables included physical, psychological, and lifestyle factors.
2.4.1. Physical Factors
Physical factors comprised body mass index (BMI), grip strength, bone density, balance, and mobility. BMI was calculated by dividing weight (kg) by the square of height (m2), and both underweight and overweight statuses were considered potential fall risk factors due to their associations with physical frailty and mobility impairments. Grip strength was measured in kilograms (kg) and used as an indicator of physical function, with lower values reflecting muscle loss and increased physical vulnerability in older adults. It was selected over lower limb muscle strength due to its ease of measurement, strong correlation with total body muscle mass, and proven predictive value for mobility and balance impairments in older adults. Previous research has demonstrated that grip strength can serve as a practical and reliable proxy for overall physical frailty and fall risk [16]. Bone density was analyzed as a measure of osteoporosis risk, which is closely related to the likelihood of fractures and fall-related injuries. Balance was assessed as the ability to maintain equilibrium, a critical factor directly influencing fall risk. Mobility was measured through walking speed, with slower walking speeds associated with diminished physical function and an increased likelihood of falls.
2.4.2. Psychological Factors
Psychological factors included depression scores, anxiety levels, and cognitive function. Depression was evaluated using the Patient Health Questionnaire-9 (PHQ-9), which assessed the severity of depressive symptoms. Higher depression scores were associated with reduced physical activity and increased fall risks. Anxiety levels were assessed through survey items, as anxiety can impair attention during daily activities, thereby heightening the risk of falls. Cognitive function was evaluated using the Mini-Mental State Examination (MMSE), which measured cognitive decline and its relationship with fall risks. Cognitive impairment, including diminished awareness of surroundings, was considered a significant contributing factor to falls.
2.4.3. Lifestyle Factors
Lifestyle factors included physical activity levels, nutritional status, and medication use. LDL cholesterol was included as a metabolic marker based on its association with cardiovascular health. Elevated LDL levels may impair blood circulation and vascular integrity, potentially affecting cerebral perfusion, balance, and mobility. While it is not traditionally recognized as a direct fall risk factor, recent evidence suggests a possible link between dyslipidemia and functional decline in older adults [17]. Physical activity levels were classified based on weekly activity duration, with lower levels of activity associated with decreased muscle strength and impaired balance, increasing fall risks. Nutritional status was assessed using dietary intake data provided by KNHANES, focusing on factors such as deficiencies in essential nutrients. Medication use, including sedatives, tranquilizers, and antihypertensive drugs, was examined for its potential contribution to increased fall risks. This comprehensive approach to variable selection provided a multidimensional framework for analyzing the factors influencing fall risks among older adults.
2.5. Data Analysis
Data analysis was conducted using IBM SPSS Statistics version 29.0 (IBM Corp., Armonk, NY, USA). Continuous variables were examined for normality using the Shapiro–Wilk test, histograms, and Q–Q plots. Because most variables approximated a normal distribution and sample sizes in each group were sufficient, independent t-tests were used to compare means between participants with and without fall experiences. Categorical variables were compared using chi-square (χ2) tests. To identify factors independently associated with falls, we performed multivariable logistic regression analysis including physical, psychological, and lifestyle variables that were clinically relevant and/or showed associations in univariable analyses. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Model fit was evaluated using Nagelkerke R2 and the Hosmer–Lemeshow goodness-of-fit test. Statistical significance was set at p < 0.05 (two-sided).
3. Results
3.1. Demographic, Physical, and Psychological Characteristics Associated with Fall Experiences in Older Adults
Among adults aged 65 years and older, several physical, psychological, and lifestyle factors differed significantly between those with and without fall experience (Table 1). Older adults who had experienced a fall had fewer weekly working hours (6.85 vs. 10.90 h; p = 0.008), lower diastolic blood pressure (70.68 vs. 73.09 mmHg; p = 0.022), and weaker grip strength (17.85 vs. 21.35 kg; p = 0.008) than those without falls. They also reported higher depressive symptoms (PHQ-9: 4.69 vs. 2.82; p < 0.001) and anxiety symptoms (GAD-7: 3.38 vs. 1.81; p = 0.008) and were less likely to meet the recommended 150 min of physical activity per week (χ2 = 2.642, p = 0.022).
In contrast, other sociodemographic and health-related variables, including age, sex, residential area, BMI, systolic blood pressure, perceived stress, smoking status, alcohol consumption, sleep duration, and comorbid conditions (hypertension, diabetes, and dyslipidemia), did not differ significantly between the two groups (Table 1).
3.2. Nutritional Intake and Biochemical Factors Associated with Fall Experiences in Older Adults
Regarding nutritional intake and biochemical markers, older adults with fall experience had significantly lower vitamin D intake (2.26 vs. 3.10 µg/day; p = 0.024) and higher LDL cholesterol levels (110.0 vs. 100.6 mg/dL; p = 0.046) compared with those without falls (Table 2).
In contrast, no significant between-group differences were observed for total energy intake, macronutrients (carbohydrates, fat, and protein), other vitamins and minerals, or other biochemical parameters, including fasting blood glucose, HbA1c, total cholesterol, HDL cholesterol, triglycerides, liver and renal function tests, complete blood counts, uric acid, and hs-CRP (Table 2).
3.3. Predictive Analysis of Falls Among Older Adults: AUC and Logistic Regression Results
The area under the curve (AUC) analysis for predicting fall experiences in adults aged 65 years and older yielded the following results. PHQ-9 scores > 3 showed the highest predictive value with an AUC of 0.653 (95% CI: 0.614–0.691, p = 0.0001), sensitivity of 71.15%, and specificity of 61.79%. GAD-7 scores > 3 had an AUC of 0.618 (95% CI: 0.579–0.657, p = 0.003), sensitivity of 44.23%, and specificity of 77.5%, demonstrating its significance as a predictive factor. Grip strength < 15 kg was also a significant predictor, with an AUC of 0.619 (95% CI: 0.579–0.657, p = 0.003), sensitivity of 44.23%, and specificity of 76.61%. Weekly working hours < 12 showed an AUC of 0.554 (95% CI: 0.513–0.596, p = 0.0022), sensitivity of 67.35%, and specificity of 47.16%. DBP < 69 mmHg yielded an AUC of 0.593 (95% CI: 0.552–0.632, p = 0.0451), physical activity < 150 min per week had an AUC of 0.572 (95% CI: 0.531–0.611, p = 0.03), and age < 75 years showed an AUC of 0.586 (95% CI: 0.546–0.625, p = 0.0493), all indicating significant predictive power. In contrast, vitamin D < 3 ng/mL (AUC = 0.541, p = 0.358) and vitamin E < 7 mg/L (AUC = 0.534, p = 0.428) were not significantly associated with fall prediction (Table 3).
3.4. Factors Associated with Falls: Logistic Regression Results
The logistic regression analysis revealed several significant associations with fall experiences. Weekly working hours < 12 h significantly increased the likelihood of falls (OR = 3.108, 95% CI: 1.226–7.876, p = 0.017). Physical activity of <150 min per week was also associated with an increased risk of falls (OR = 2.487, 95% CI: 1.032–5.993, p = 0.045). Grip strength < 15 kg significantly increased fall risk (OR = 2.227, 95% CI: 1.139–4.353, p = 0.019), as did DBP < 69 mmHg (OR = 2.055, 95% CI: 1.086–3.888, p = 0.027). Additionally, LDL cholesterol levels ≥ 150 mg/dL (OR = 3.055, 95% CI: 1.486–6.281, p = 0.002) and PHQ-9 scores ≥ 3 (OR = 3.021, 95% CI: 1.522–5.995, p = 0.002) were significantly associated with increased fall risk. Conversely, age ≥ 75 years (p = 0.125), alcohol consumption (p = 0.898), GAD-7 scores ≥ 3 (p = 0.055), vitamin D levels ≤ 3 ng/mL (p = 0.093), and vitamin E levels ≤ 7 mg/L (p = 0.883) showed no significant associations with fall experiences. The model’s explanatory power, as indicated by Nagelkerke R2, was 0.262, suggesting that the independent variables explained approximately 26.2% of the variance in the dependent variable. The Hosmer–Lemeshow goodness-of-fit test yielded a p-value of 0.318, indicating that the model fit the data well (Table 4).
4. Discussion
The findings of this study underscore the complex interplay of physical, psychological, and lifestyle factors that contribute to fall risks among older adults. In particular, the identification of LDL cholesterol and weekly working hours as significant fall predictors represents a novel contribution, as these factors have not been widely examined in previous fall-related research. By examining a nationally representative sample from the KNHANES dataset, this research provides valuable insights into the multifaceted nature of fall risks, offering a foundation for developing targeted interventions.
4.1. Physical Factors
Physical factors emerged as significant predictors of fall risks in this study. Reduced grip strength was strongly associated with increased fall risks, highlighting its role as a key indicator of overall muscle function and mobility. Grip strength is closely linked to lower-body strength, which is essential for maintaining balance and preventing falls [16]. This finding is consistent with prior research [17] emphasizing the importance of muscle strength in fall prevention. Interventions aimed at improving muscle strength, such as resistance training programs, could play a critical role in reducing fall risks. Low DBP (<69 mmHg) was another significant predictor, suggesting that blood pressure levels may influence balance and coordination. Hypotension, particularly in postural changes, may impair cerebral perfusion, increasing the likelihood of falls [11,18]. The association between low diastolic blood pressure and falls is also biologically plausible, as lower DBP may reflect reduced perfusion pressure and increased vulnerability to orthostatic hypotension, dizziness, and transient cerebral ischemia in older individuals [19]. Monitoring and managing blood pressure in older adults could thus be a vital component of fall-prevention strategies. Elevated LDL cholesterol (≥150 mg/dL) was also significantly associated with increased fall risks. Our finding that elevated LDL cholesterol was independently associated with fall risk adds to a relatively small body of literature linking metabolic dysregulation with functional decline in older adults [10,20]. Dyslipidemia may contribute to atherosclerosis, endothelial dysfunction, and impaired cerebral perfusion, which in turn can affect balance, gait stability, and neuromuscular control, thereby increasing susceptibility to falls. While the direct mechanisms remain unclear, high LDL cholesterol may reflect underlying cardiovascular risks that affect mobility and balance [21]. This finding underscores the importance of regular health screenings for cardiovascular risk factors in older adults. Although LDL cholesterol is not traditionally considered a direct fall risk factor, our results suggest it may serve as an indirect indicator of functional decline. This opens new possibilities for understanding the vascular contributions to physical instability.
4.2. Psychological Factors
The study found a strong association between depression (PHQ-9 scores ≥ 3) and fall risks. Depression can lead to reduced physical activity, impaired concentration, and decreased motivation [11], all of which contribute to increased fall risks [22]. The significant role of depression highlights the need for integrating mental health support into fall-prevention programs. Counseling, cognitive–behavioral therapy, and community-based mental health initiatives could help address this critical risk factor. Interestingly, anxiety (GAD-7 scores ≥ 3) did not show a strong independent association with fall risks in this study. This contrasts with previous research [23,24] suggesting that anxiety may indirectly increase fall risks by reducing attention and increasing fear of falling. The discrepancy may be due to differences in measurement tools or cultural factors influencing the expression of anxiety among older adults.
4.3. Lifestyle Factors and Fall Risk
Reduced physical activity (<150 min/week) and low weekly working hours (<12 h/week) were significant lifestyle predictors of fall risks. Physical inactivity is well-documented as a risk factor for falls, as it contributes to muscle atrophy, decreased balance, and reduced coordination [25]. Encouraging regular physical activity through structured exercise programs, walking groups, or community fitness initiatives could significantly reduce fall risks. Furthermore, the identification of limited weekly working hours as a risk factor suggests that social engagement through work may play a protective role by maintaining physical activity, daily structure, and social interaction [26]. Older adults who remain engaged in work-related activities may preserve muscle strength and balance and may also have higher levels of cognitive and psychosocial stimulation, all of which can contribute to fall prevention. Interventions that promote active aging, such as volunteer programs or part-time employment opportunities, could help mitigate this risk [27,28]. Furthermore, the inclusion of weekly working hours as a fall-related factor offers a novel behavioral dimension that links reduced social engagement to physical vulnerability. This has not been widely reported in previous epidemiological studies on falls.
4.4. Implications for Nursing Practice
The findings of this study highlight the need for a multidimensional approach to fall prevention. Nursing interventions should address the physical, psychological, and lifestyle components of fall risks, tailoring strategies to the individual needs of older adults. Simple screening tools, such as grip strength tests and PHQ-9 assessments, could be used to identify high-risk individuals. Integrating these assessments into routine health check-ups could enable early intervention.
Effective fall-prevention strategies are best delivered through interdisciplinary collaboration among nurses, physiotherapists, occupational therapists, and community health professionals. Such collaboration can combine strength and balance training, cardiovascular risk management, and psychosocial support tailored to the functional status and living environments of older adults.
Community-based programs that combine physical rehabilitation, mental health support, and education on fall-prevention strategies could be particularly effective. These programs should also consider environmental factors, such as home safety modifications, to further reduce fall risks. Taken together, our results highlight LDL cholesterol and weekly working hours as underrecognized yet readily assessable indicators that complement traditional physical and psychological risk factors. By examining these multidomain predictors within a single multivariable model using nationally representative KNHANES data, this study offers novel insights for risk stratification and targeted fall-prevention strategies in community-dwelling older adults.
4.5. Limitations and Future Directions
While this study identified several significant predictors of falls, it is important to acknowledge the possibility of unmeasured confounding variables. Factors such as cognitive impairment, environmental hazards (e.g., housing conditions), and detailed medication use (e.g., psychotropic drugs) were not included in the analysis due to data limitations. These unmeasured variables may have influenced the observed associations. Future studies should incorporate a broader range of risk factors using longitudinal designs to better establish causal relationships and validate the findings of this cross-sectional study. In addition, although we used a nationally representative survey, our analytic sample was limited to 612 older adults because we restricted the analysis to participants aged ≥65 years with complete data on key physical, psychological, and lifestyle variables. This modest sample size may have reduced the precision of some estimates and increased the possibility of type II error, particularly for subgroup comparisons. Furthermore, the cross-sectional nature of this secondary analysis precludes establishing temporal or causal relationships between the identified risk factors and falls. It is therefore possible that some associations reflect reverse causation (for example, prior falls leading to reduced physical activity or weekly working hours), and unmeasured or residual confounding cannot be entirely ruled out.
5. Conclusions
In summary, this nationally representative study identified reduced grip strength, low diastolic blood pressure, elevated LDL cholesterol, depressive symptoms, insufficient physical activity, and limited weekly working hours as independent correlates of falls in older Korean adults. Clinically, these findings suggest that routine fall risk assessments in primary care and community settings should incorporate simple measures of muscle strength, mood (PHQ-9), blood pressure, and LDL cholesterol, as well as brief questions about work-related activity and physical activity. Integrating these multidomain indicators into nursing assessments may support more targeted screening, follow-up, and multidisciplinary interventions, including strength-training programs, lipid management, blood pressure monitoring, and psychosocial support. Future research should use prospective designs to confirm the temporal relationships between these risk factors and fall events, explore the role of environmental and psychosocial determinants (e.g., home hazards, social isolation), and test multidimensional intervention packages that combine physical, metabolic, and psychological components. Such work will be crucial for refining fall-prevention guidelines and promoting healthy aging in rapidly aging societies.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Daejin University (protocol code 1040656-202412-HR-01-05; approval date: 9 December 2024).
Informed Consent Statement
Patient consent was waived because the analysis used publicly available, de-identified secondary data from KNHANES in accordance with KDCA policies.
Data Availability Statement
Data supporting the findings are available from the Korea National Health and Nutrition Examination Survey (KNHANES) website of the Korea Disease Control and Prevention Agency (KDCA). Access may require user registration per KDCA policy.
Acknowledgments
We thank the KDCA for providing access to KNHANES data.
Conflicts of Interest
The author declares no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Abbreviations
The following abbreviations are used in this manuscript:
| BMI | Body mass index |
| BUN | Blood urea nitrogen |
| CI | Confidence interval |
| DBP | Diastolic blood pressure |
| GAD-7 | Generalized Anxiety Disorder-7 |
| Hb | Hemoglobin |
| Hct | Hematocrit |
| HDL-C | High-density lipoprotein cholesterol |
| hs-CRP | High-sensitivity C-reactive protein |
| IRB | Institutional Review Board |
| KDCA | Korea Disease Control and Prevention Agency |
| KNHANES | Korea National Health and Nutrition Examination Survey |
| LDL-C | Low-density lipoprotein cholesterol |
| MMSE | Mini-Mental State Examination |
| OR | Odds ratio |
| PHQ-9 | Patient Health Questionnaire-9 |
| RBC | Red blood cell |
| ROC | Receiver operating characteristic |
| R2 | Nagelkerke R-squared |
| SBP | Systolic blood pressure |
| SD | Standard deviation |
| TG | Triglycerides |
| WBC | White blood cell |
| WHO | World Health Organization |
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Table 1.
Comparison of demographic, health, and lifestyle factors between older adults with and without fall experiences.
Table 1.
Comparison of demographic, health, and lifestyle factors between older adults with and without fall experiences.
| Variables | Falls (n = 52) Mean (SD) or n (%) | No Falls (n = 560) Mean (SD) or n (%) | X2 or t | p |
|---|---|---|---|---|
| Age, years | 75.06 (5.370) | 73.60 (5.100) | −1.96 | 0.033 |
| Gender (male) | 15 (28.8) | 215 (38.4) | 1.849 | 0.174 |
| Residence (urban) | 30 (57.7) | 360 (64.3) | 0.895 | 0.344 |
| Weekly working hours | 6.85 (15.525) | 10.90 (16.381) | 2.438 | 0.008 |
| Hypertension, yes | 34 (65.4) | 322 (57.5) | 2.746 | 0.601 |
| Dislipidemia, yes | 16 (30.8) | 221 (39.5) | 1.769 | 0.622 |
| Diabetes, yes | 10 (19.2) | 133 (23.8) | 2.851 | 0.240 |
| BMI, kg/m2 | 24.08 (3.519) | 23.92 (3.226) | −0.315 | 0.377 |
| SBP, mmHg | 125.76 (20.979) | 128.79 (17.662) | 1.161 | 0.123 |
| DBP, mmHg | 70.68 (9.200) | 73.09 (8.174) | 2.012 | 0.022 |
| Average grip strength, kg | 17.85 (8.515) | 21.349 (10.027) | 2.438 | 0.008 |
| PHQ-9 scores | 4.69 (4.591) | 2.82 (3.864) | −3.304 | <0.001 |
| GAD-7 scores | 3.38 (4.442) | 1.81 (3.621) | −2.474 | 0.008 |
| Perceived stress rate | 3.25 (1.454) | 3.18 (1.137) | −0.443 | 0.329 |
| Current smoker, yes | 4 (8.0) | 58 (10.6) | 0.334 | 0.388 |
| Drinking, yes | 16 (30.8) | 247 (44.1) | 3.454 | 0.078 |
| Average sleep duration | 6.63 (1.657) | 6.72 (1.579) | 0.378 | 0.353 |
| Physical activity ≥ 150 | 6 (11.5) | 65 (11.6) | 2.642 | 0.022 |
| Strength training | 1 (1.9) | 22 (3.9) | 0.529 | 0.402 |
Note. Values are presented as mean ± standard deviation (SD) or n (%). Group differences were tested using independent t-tests for continuous variables and chi-square (χ2) tests for categorical variables. BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; PHQ-9 = Patient Health Questionnaire-9; GAD-7 = Generalized Anxiety Disorder-7.
Table 2.
Comparison of nutritional intake and biochemical factors between older adults with and without fall experiences.
Table 2.
Comparison of nutritional intake and biochemical factors between older adults with and without fall experiences.
| Variables | Falls (n = 49) Mean (SD) or n (%) | No Falls (n = 528) Mean (SD) or n (%) | X2 or t | p |
|---|---|---|---|---|
| Energy Intake (kcal/day) | 1528.21 (708.296) | 1543.99 (594.261) | 0.175 | 0.431 |
| Carbohydrate Intake (g/day) | 263.85 (119.319) | 254.98 (96.849) | −0.601 | 0.274 |
| Fat Intake (g/day) | 28.44 (22.229) | 31.18 (21.708) | 0.843 | 0.252 |
| Protein Intake (g/day) | 53.27 (29.207) | 54.34 (25.214) | 0.281 | 0.389 |
| Water Intake (mL/day) | 819.01 (470.039) | 781.42 (451.663) | −0.555 | 0.289 |
| Calcium Intake (mg/day) | 475.80 (320.148) | 465.12 (294.847) | −0.444 | 0.329 |
| Phosphorus Intake (mg/day) | 902.23 (463.995) | 904.35 (396.762) | 0.035 | 0.486 |
| Sodium Intake (mg/day) | 2646.23 (1708.957) | 2769.50 (1731.573) | 0.477 | 0.317 |
| Potassium Intake (mg/day) | 2557.46 (1436.319) | 2551.68 (1214.668) | −0.031 | 0.487 |
| Magnesium Intake (mg/day) | 288.79(154.514) | 287.16 (130.108) | −0.072 | 0.472 |
| Zinc Intake (mg/day) | 8.99 (4.503) | 8.91 (3.865) | −0.145 | 0.442 |
| Iron Intake (mg/day) | 7.48 (4.636) | 7.92 (4.938) | 0.592 | 0.277 |
| Vitamin A (µg/day) | 314.45 (260.631) | 356.06 (366.628) | 0.776 | 0.219 |
| Vitamin B Complex (mg/day) | 0.86 (0.528) | 0.90 (0.494) | 0.506 | 0.307 |
| Vitamin C Intake (mg/day) | 87.10 (153.514) | 79.89 (55.841) | −0.624 | 0.304 |
| Vitamin D Intake (µg/day) | 2.258 (2.753) | 3.104 (4.292) | 1.394 | 0.024 |
| Vitamin E Intake (mg/day) | 5.282 (2.788) | 5.814 (3.351) | 1.109 | 0.134 |
| Fasting Glucose (mg/dL) | 104.44 (15.827) | 106.26 (25.082) | 0.495 | 0.31 |
| HbA1c (%) | 5.84 (0.607) | 5.92 (0.896) | 0.67 | 0.252 |
| Total Cholesterol (mg/dL) | 179.85 (41.795) | 171.53 (39.913) | −1.379 | 0.084 |
| HDL-C (mg/dL) | 51.90 (10.832) | 54.37 (13.467) | 1.235 | 0.109 |
| LDL-C (mg/dL) | 109.96 (38.800) | 100.60 (36.588) | −1.69 | 0.046 |
| TG (mg/dL) | 126.44 (52.670) | 119.62 (59.082) | −0.773 | 0.22 |
| AST (U/L) | 22.15 (8.465) | 24.78 (12.233) | 1.459 | 0.073 |
| ALT (U/L) | 17.17 (9.922) | 19.94 (13.312) | 1.411 | 0.079 |
| Hemoglobin (Hb, g/dL) | 13.03 (1.294) | 13.20 (1.476) | 0.764 | 0.223 |
| Hematocrit (Hct, %) | 39.74 (3.548) | 40.11 (4.118) | 0.602 | 0.274 |
| BUN (mg/dL) | 16.29 (5.231) | 17.45 (5.939) | 1.303 | 0.096 |
| Creatinine (mg/dL) | 0.79 (0.224) | 0.84 (0.268) | 1.261 | 0.104 |
| WBC (103/µL) | 6.29 (2.042) | 6.14 (1.768) | −0.488 | 0.314 |
| RBC (106/µL) | 4.27 (0.425) | 4.30 (0.472) | 0.547 | 0.292 |
| Platelet Count (103/µL) | 242.55 (69.506) | 233.88 (62.256) | −0.907 | 0.182 |
| Uric Acid (mg/dL) | 4.70 (1.714) | 4.84 (1.467) | 0.613 | 0.27 |
| hs-CRP (mg/L) | 2.74 (7.240) | 1.84 (5.001) | −1.108 | 0.134 |
Note. Values are presented as mean ± standard deviation (SD) or n (%). Group differences were tested using independent t-tests for continuous variables and chi-square (χ2) tests for categorical variables. SD = standard deviation; χ2 = chi-square; LDL-C = low-density lipoprotein cholesterol; HDL-C = high-density lipoprotein cholesterol; TG = triglycerides; AST = aspartate aminotransferase; ALT = alanine aminotransferase; Hb = hemoglobin; Hct = hematocrit; BUN = blood urea nitrogen; WBC = white blood cell; RBC = red blood cell; hs-CRP = high-sensitivity C-reactive protein.
Table 3.
Area under the curve (AUC) analysis of predictors for falls in older adults.
| Variables | Cut off Value | AUC | 95% CI | Sensitivity | Sensitivity | p |
|---|---|---|---|---|---|---|
| Age | 75 years | 0.586 | 0.546–0.625 | 65.38 | 59.64 | 0.0493 |
| DBP | 69 mmHg | 0.593 | 0.552–0.632 | 53.85 | 68.23 | 0.0451 |
| LDL Cholesterol | 150 mg/dL | 0.564 | 0.523–0.605 | 25 | 89.61 | 0.015 |
| Physical Activity Minutes per Week | 150 min | 0.572 | 0.531–0.611 | 88.46 | 27.86 | 0.03 |
| GAD-7 scores | 3 | 0.618 | 0.579–0.657 | 44.23 | 77.5 | 0.003 |
| PHQ-9 scores | 3 | 0.653 | 0.614–0.691 | 71.15 | 61.79 | 0.0001 |
| Weekly Working Hours | 12 h | 0.554 | 0.513–0.596 | 67.35 | 47.16 | 0.0022 |
| Vitamin D | 3 ng/mL | 0.541 | 0.499–0.582 | 36.73 | 76.89 | 0.358 |
| Vitamin E | 7 mg/L | 0.534 | 0.493–0.575 | 83.67 | 27.84 | 0.428 |
| Grip Strength | 15 kg | 0.619 | 0.579–0.657 | 44.23 | 76.61 | 0.003 |
Note: AUC = area under the curve; CI = confidence interval; DBP = diastolic blood pressure; LDL = low-density lipoprotein cholesterol; GAD-7 = Generalized Anxiety Disorder-7; PHQ-9 = Patient Health Questionnaire-9.
Table 4.
Logistic regression analysis of factors associated with falls in older adults.
| Variable | β | p | OR | 95% CI | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| Age > 75 years | 0.572 | 0.125 | 1.772 | 0.853 | 3.681 |
| Weekly Working Hours < 12 h | 1.134 | 0.017 | 3.108 | 1.226 | 7.876 |
| Physical Activity < 150 min per Week | 0.911 | 0.045 | 2.487 | 1.032 | 5.993 |
| Current Drinker | −0.045 | 0.898 | 0.956 | 0.482 | 1.896 |
| Grip Strength < 15 kg | 0.8 | 0.019 | 2.227 | 1.139 | 4.353 |
| DBP < 69 mmHg | 0.72 | 0.027 | 2.055 | 1.086 | 3.888 |
| LDL Cholesterol ≥ 150 mg/dL | 1.117 | 0.002 | 3.055 | 1.486 | 6.281 |
| PHQ-9 scores ≥ 3 | 1.105 | 0.002 | 3.021 | 1.522 | 5.995 |
| GAD-7 scores ≥ 3 | 0.653 | 0.055 | 1.922 | 0.987 | 3.743 |
| Vitamin D < 3 ng/mL | 0.609 | 0.093 | 1.838 | 0.903 | 3.741 |
| Vitamin E < 7 mg/L | 0.058 | 0.883 | 1.06 | 0.489 | 2.299 |
Model statistics: Nagelkerke R2 = 0.262; Hosmer–Lemeshow Test, p = 0.318. Note: β = regression coefficient; OR = odds ratio; CI = confidence interval; DBP = diastolic blood pressure; LDL = low-density lipoprotein cholesterol; PHQ-9 = Patient Health Questionnaire-9; GAD-7 = Generalized Anxiety Disorder-7.
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Jang, K. Analysis of Physical, Psychological, and Lifestyle Factors Affecting Falls in Older Adults: A Study Based on the Korea National Health and Nutrition Examination Survey. J. Ageing Longev. 2025, 5, 53. https://doi.org/10.3390/jal5040053
AMA Style
Jang K. Analysis of Physical, Psychological, and Lifestyle Factors Affecting Falls in Older Adults: A Study Based on the Korea National Health and Nutrition Examination Survey. Journal of Ageing and Longevity. 2025; 5(4):53. https://doi.org/10.3390/jal5040053
Chicago/Turabian StyleJang, Kyeongmin. 2025. "Analysis of Physical, Psychological, and Lifestyle Factors Affecting Falls in Older Adults: A Study Based on the Korea National Health and Nutrition Examination Survey" Journal of Ageing and Longevity 5, no. 4: 53. https://doi.org/10.3390/jal5040053
APA StyleJang, K. (2025). Analysis of Physical, Psychological, and Lifestyle Factors Affecting Falls in Older Adults: A Study Based on the Korea National Health and Nutrition Examination Survey. Journal of Ageing and Longevity, 5(4), 53. https://doi.org/10.3390/jal5040053
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