Journal of Korean Society of Dental Hygiene (J Korean Soc Dent Hyg)
4
Weeks in Review
2
Weeks to Publication
Journal of Korean Society of Dental Hygiene
Open access, Peer Reviewed
pISSN 2287-1705, eISSN 2288-2294
Weeks in Review
Weeks to Publication
Journal of Korean Society of Dental Hygiene Open access, Peer Reviewed
pISSN 2287-1705, eISSN 2288-2294
Hye-Sook Choi
Department of Dental Technology, Kyungdong University
Correspondence to Hye-Sook Choi, Department of Dental Technology, Kyungdong University, 815 Gyeonhwon-ro, Munmak-eup, Wonjusi, Gangwon-do, 26495, Korea. Tel: +82-33-738-1304, Fax: +82-33-738-1209, E-mail: chs@kduniv.ac.kr
Volume 26, Number 1, Pages 117–28, February 2026.
J Korean Soc Dent Hyg 2026;26(1):117–28. https://doi.org/10.13065/jksdh.2026.26.1.13
Received on January 14, 2026, Revised on February 02, 2026, Accepted on February 02, 2026, Published on February 28, 2026.
Copyright © 2026 Journal of Korean Society of Dental Hygiene.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/licenses/by-nc/4.0).
Objectives: We analyzed factors impacting health-related quality of life (HRQoL) among Korean older adults, as measured by the EuroQol 5 Dimensions (EQ-5D), and compared trends over time. Methods: Data were obtained from the Korea National Health and Nutrition Examination Survey (KNHANES). Using data collected every four years from 2016 to 2024, we examined factors associated with older adults’ quality of life during each survey period. Participants were aged 65 years or older, with a sample size of 1,578 in 2016, 1,681 in 2020, and 1,951 in 2024. Complex sample logistic regression was used for statistical analyses. Results: Spanning the survey periods, sex and activity limitations consistently emerged as significant determinants of HRQoL. In 2016 and 2024, economic activity, stress, and unmet dental care were also significant factors, whereas in 2020, smoking and chewing discomfort were significant. Conclusions: HRQoL in older adults is influenced by interactions between structural factors (sex and activity limitations) and environmental and behavioral factors (economic activity, mental health, and oral health). Issues related to dental care access, such as unmet dental treatment needs, exert long-term impacts on quality of life, underscoring the need for integrated elderly health policies and early interventions to support health management.
Aged, Dental care, Nutrition survey, Oral health, Quality of life
Health-related quality of life (HRQoL) refers to the aspects of quality of life that are directly associated with an individual’s physical, mental, and social health status [1]. It extends beyond the mere presence or absence of disease, comprehensively reflecting an individual’s perceived overall health and functional capacity in daily life. HRQoL is considered an important indicator for assessing health status in older adults [2]. In later life, the prevalence of chronic diseases and declines in physical function tend to significantly reduce HRQoL, highlighting the need for a systematic analysis of its determinants [3,4].
Oral health enables a balanced diet and adequate nutritional intake by maintaining proper masticatory and swallowing functions. This contributes to the preservation of immune function and physical capacity, thereby playing a crucial role in promoting healthy aging [5]. In contrast, deterioration in oral function can reduce the enjoyment of eating and limit opportunities for social interaction, potentially leading to psychological issues such as depression and social isolation. From this perspective, oral health is regarded as an important determinant of overall quality of life in older adults, extending beyond a mere component of physical health [6,7].
In addition, unmet dental care needs have been reported as a major factor contributing to a decline in oral health–related quality of life, particularly by significantly increasing psychological discomfort and social limitations [8,9]. Older adults with unmet dental care needs tend to have lower HRQoL scores, resulting in an overall reduction in quality of life [8]. These findings indicate that timely and adequate dental care plays a crucial role not only in maintaining oral function but also in supporting daily activities and overall psychosocial well-being [10].
In Korea, HRQoL is commonly assessed using the EQ-5D instrument, following the standardized guidelines provided by the Korea Disease Control and Prevention Agency [11]. The EQ-5D evaluates five domains: mobility, self-care, usual activities, pain/ discomfort, and anxiety/depression. Its simple yet comprehensive structure facilitates large-scale epidemiological assessments and the evaluation of healthcare policies. Additionally, the use of country-specific value weights enables comparisons of health status across different time periods and population groups [12].
Previous studies have reported that higher oral health-related quality of life (OHRQoL) is significantly associated with higher HRQoL, indicating a strong link between oral health and overall health perception [13,14]. Conversely, oral health problems, such as tooth loss or reduced masticatory function can contribute to the deterioration of general health and lower HRQoL. Declining oral function is also associated with reduced happiness and increased psychological distress or depressive symptoms [6,7,15]. Moreover, various socioeconomic factors, including smoking, stress, economic activity, and activity limitations, have been shown to influence HRQoL. These findings indicate that health-related quality of life in older adults is shaped by a complex interplay of oral health status and broader social and economic determinants.
However, most previous domestic studies have been cross-sectional or have focused on specific time points or populations, limiting the ability to comprehensively examine temporal changes in HRQoL and its associated factors [5,16]. In particular, studies that consistently use the same indicators for periodic comparisons are relatively scarce.
This study utilizes data from the nationally representative Korea National Health and Nutrition Examination Survey (KNHANES), collected at four-year intervals from 2016 to 2024. The aim of the study is to analyze the key factors influencing HRQoL, measured by the EQ-5D among older adults at each survey period, and to compare the results across years at a descriptive level. Through this approach, the study seeks to systematically identify the primary determinants of quality of life in older adults and to provide foundational evidence to support the development of healthcare policies aimed at improving oral health and overall HRQoL in the elderly population.
This study utilized data from the health survey component of the Korea National Health and Nutrition Examination Survey (KNHANES) to analyze factors associated with the health-related quality of life (HRQoL) of older adults. Data were drawn from the first year of the 7th survey cycle (2016), the second year of the 8th cycle (2020), and the third year of the 9th cycle (2024). These years were selected because key health variables, including the EQ-5D, were consistently measured, allowing reliable comparisons across survey cycles. In addition, the samples from these years provide adequate representation of the older population, enabling stable estimation of oral health and HRQoL outcomes. The total number of survey participants was 8,150 in 2016, 7,359 in 2020, and 6,997 in 2024, among whom adults aged 65 years and older were included in the analysis, comprising 1,578, 1,681, and 1,951 participants in 2016, 2020, and 2024, respectively.
The dependent variable was the EQ-5D index, a standardized instrument developed to assess overall health status. The EQ-5D consists of five dimensions: self-care, mobility, usual activities, pain/discomfort, and anxiety/depression, each rated on a three-level scale (1=no problems, 2=moderate problems, 3=severe problems). EQ-5D was converted into an index score using the Time TradeOff (TTO) value set. According to the measurement criteria for the EQ-5D for Koreans presented by the Korea Disease Control and Prevention Agency, when all five dimensions have a response of “1”, it is considered a full health state, and the EQ-5D value at that time is 1. If there is a response of “2” or “3”, EQ-5D=1−h, and h is calculated using the following weighted formula. Here, M2 represents the response “2” , and M3 represents the response “3”, and the remaining terms are defined in the same way. Finally, N3 indicates that there is a response of “3”. For example, when the worst health state occurs and all five dimensions are “3”, EQ5D=−0.171, and the value ranges from −0.171 to 1, meaning that the worse the health status, the smaller the value [17].
h = 0.05 ＋ 0.096(M2) ＋ 0.418(M3) ＋ 0.046(SC2) ＋ 0.136(SC3) ＋ 0.051(UA2) ＋ 0.208(UA3) ＋ 0.037(PD2) ＋ 0.151(PD3) ＋ 0.043(AD2) ＋ 0.158(AD3) ＋ 0.05(N3).
The outcome variable, the EQ-5D index, was dichotomized into a “high” group (EQ-5D index=1) and a “low” group (EQ-5D index<1). This dichotomization facilitated interpretation of the results and identification of key factors associated with HRQoL among older adults [18].
The explanatory variables included sociodemographic characteristics, health-related psychological and behavioral factors, and oral health-related factors. Sociodemographic characteristics comprised sex (male, female), region (urban, rural), co-habitation(yes, no), income level (high, upper-middle, lower-middle, low), education level (below elementary, middle school, high school, college or higher), and economic activity status (yes, no).
Health-related psychological and behavioral factors included alcohol consumption, smoking experience, stress perception, aerobic physical activity, and activity limitations. Alcohol consumption was categorized as drinking at least once per month or lifelong non-drinker, and smoking experience as yes or no. Stress Perception was originally measured on a four-point scale (very much, much, a little, almost none) and dichotomized into ‘low’ and ‘high’ stress. Aerobic physical activity was classified as ‘high’ if participants met the recommended weekly thresholds (≥150 minutes of moderate-intensity activity, ≥75 minutes of vigorousintensity activity, or a combination of moderate and vigorous activity, with 1 minute of vigorous activity counted as 2 minutes of moderate activity), and ‘low’ if not. Activity limitations were coded as yes or no.
Oral health-related factors included chewing discomfort, speech difficulty, and unmet dental care needs within the past year. Chewing discomfort was measured on a five-point scale (from very uncomfortable to not uncomfortable at all) and categorized as present if participants reported ‘very uncomfortable’ or ‘uncomfortable’ speech difficulty and unmet dental care were coded as yes or no.
Statistical analyses were conducted using IBM SPSS Statistics (ver 26.0; IBM Corp., Armonk, NY, USA) with complex sample procedures. The complex sample design was specified by defining stratification (variance estimation stratum: Kstrata), clustering (primary sampling units: PSU), and applying the combined health interview and examination survey weights (Wt_itvex). Variables were categorized into three groups: sociodemographic characteristics, health-related factors, and oral health-related factors. Frequency analyses, descriptive statistics, and cross-tabulations were performed for each survey year. In addition, complex sample logistic regression analyses were performed for each survey year to identify significant factors associated with quality of life among older adults. However, formal analyses of year-to-year differences using year × independent variable interaction terms were not conducted, and comparisons across years were interpreted descriptively. Statistical significance was defined as p<0.05.
The characteristics of respondents by survey year are presented in <Table 1>. Among sociodemographic factors, the proportion of women was higher in 2016 (57.8%), 2020 (56.8%), 2024 (55.9%). A majority of respondents resided in urban areas (2016: 76.3%, 2020: 77.3%, 2024: 73.7%). Most participants lived with others (2016: 79.9%, 2020: 78.5%, 2024: 73.6%). Regarding family income, the largest group fell into the low-income category, though this proportion decreased over time (2016: 49.5%, 2020: 41.8%, 2024: 38.4%), and the majority had an education level of elementary school or below, with a declining trend (2016: 59.6%, 2020: 49.5%, 2024: 44.2%). Most respondents were not engaged in economic activities, with percentages decreasing (2016: 68.7%, 2020: 63.2%, 2024: 59.9%).
Among health-related psychological and behavioral factors, most respondents reported consuming less than one alcoholic drink per month (2016: 66.1%, 2020: 70.2%, 2024: 65.3%), were past smokers or non-smokers (2016: 90.3%, 2020: 90.8%, 2024: 90.3%), perceived low levels of stress (2016: 81.5%, 2020: 85.5%, 2024: 85.3%), engaged in low levels of aerobic physical activity (2016: 65.6%, 2020: 66.8%, 2024: 67.3%), and reported no activity limitations (2016: 80.4%, 2020: 85.4%, 2024: 77.1%).
Regarding oral health-related factors, respondents reported chewing discomfort (2016: 44.1%, 2020: 33.1%, 2024: 22.6%), speech difficulty (2016: 21.2%, 2020: 13.7%, 2024: 9.8%), and unmet dental care (2016: 35.8%, 2020: 34.0%, 2024: 28.3%). The mean healthrelated quality of life (EQ-5D) gradually increased over time, with values of 0.479 in 2016, 0.531 in 2020, and 0.550 in 2024.
The changes in health-related quality of life (EQ-5D) according to respondent characteristics by survey year are presented in <Table 2>.
In 2016, statistically significant differences in EQ-5D scores were observed according to sex, region, co-habitation, income, education level, economic activity status, smoking, stress, aerobic physical activity, activity limitations, chewing discomfort, speech difficulty, and unmet dental care (p<0.05). Among sociodemographic factors, men had significantly higher scores than women (p<0.001), and urban residents scored higher than rural residents (p<0.05). Participants living with others had higher scores (p<0.001), and higher income and education levels were associated with increased scores (p<0.001). Individuals engaged in economic activities also demonstrated higher EQ-5D scores (p<0.001).
Regarding health-related behaviors in 2016, non-drinkers scored significantly higher (p<0.001), non-smokers scored significantly higher (p<0.001), participants with low scored levels had higher scores (p<0.001), individuals engaging in aerobic physical activity scored higher (p<0.001), and those without activity limitations scored higher (p<0.001). In terms of oral health, respondents without chewing discomfort (p<0.001), without speech difficulty (p<0.01), and without unmet dental care (p<0.001) had significantly higher EQ-5D scores.
In 2020, EQ-5D scores differed significantly base on sex, region, co-habitation, income, education level, economic activity status, alcohol consumption, smoking, aerobic physical activity, activity limitations, chewing discomfort, speech difficulty, and unmet dental care (p<0.05). Among sociodemographic factors, men had significantly higher scores than women (p<0.001), and urban residents scored higher than rural residents (p<0.05). Participants living with others had higher scores (p<0.01), and higher income and education levels were associated with higher scores (income:p<0.001; education: p<0.001). Those engaged in economic activities also had higher scores (p<0.01).
Regarding health-related behaviors in 2020, non-drinkers scored higher (p<0.01), non-smokers scored higher (p<0.001), participants with low stress scored higher (p <0.001), those engaging in aerobic physical activity scored higher (p<0.001), and those without activity limitations had higher scores (p<0.001). For oral health, respondents without chewing discomfort (p<0.001), without speech difficulty (p<0.01), and without unmet dental care (p<0.001) had significantly higher scores.
In 2024, statistically significant differences in EQ-5D scores were observed base on sex, co-habitation, income, education level, Economic activity status, alcohol consumption, smoking, stress, aerobic physical activity, activity limitations, chewing discomfort, speech difficulty, and unmet dental care (p<0.05). Among sociodemographic factors, men had significantly higher EQ-5D scores than women (p<0.001), and participants living with others had significantly higher scores than those living alone (p<0.05). Higher income and education levels were associated with significantly higher scores (income: p<0.01; education: p<0.001), and individuals engaged in economic activities also had higher scores (p<0.001).
In terms of health-related behaviors, non-drinkers had significantly higher EQ-5D scores (p<0.001), whereas past or non-smokers had significantly lower EQ-5D scores (p<0.001). Participants reporting low stress levels had higher scores (p<0.001), those engaging in aerobic physical activity scored higher (p<0.05), and individuals without activity limitations had significantly higher scores (p<0.001). Regarding oral health, respondents without chewing discomfort (p<0.001), without speech difficulty (p<0.05), and without unmet dental care (p<0.001) demonstrated significantly higher EQ-5D scores.
The factors influencing health-related quality of life (HRQoL) were analyzed using complex-sample multiple logistic regression. The results are presented in <Table 3>.
In 2016, the factors significantly associated with HRQoL were sex, economic activity status, Stress perception, activity limitations, speech difficulty, and unmet dental care. Men had 2.439(1.772–3.356, p<0.001)times higher odds of better HRQoL than women, and participants engaged in economic activities had 1.605(1.171–2.201, p<0.01)times higher odds. Individuals experiencing low stress had 1.997(1.372–2.908, p<0.001)times higher odds, while those without activity limitations had 5.354(3.588–7.987, p<0.001)times higher odds. Participants without speech difficulty had 2.050(1.309–3.208, p<0.01)times higher odds, and those without unmet dental care had 1.834(1.277–2.634, p<0.01)times higher odds of better HRQoL.
In 2020, the significant predictors of HRQoL were sex, smoking status, activity limitations, and chewing discomfort. Men had 1.516(1.071–2.147, p<0.05)times higher odds of better HRQoL than women, and non-smokers had 2.090(1.127–3.877, p<0.05)times higher odds compared to smokers. Participants without activity limitations had 8.831(5.525–14.115, p<0.001)times higher odds, and those without chewing discomfort had 1.726(1.191–2.502, p<0.01)times higher odds of better HRQoL.
In 2024, the factors significantly associated with HRQoL among older adults were sex, economic activity status, stress perception, activity limitations, and unmet dental care. Compared to women, men had 1.762(1.303–2.383, p<0.001 )times higher odds of better HRQoL, and participants engaged in economic activities had 1.770(1.312–2.388, p<0.001)times higher odds. Individuals reporting low stress had 2.617(1.688–4.057, p<0.001)times higher odds, those without activity limitations had 3.341(2.339–4.773, p<0.001)times higher odds, and participants without unmet dental care had 1.716(1.248–2.359, p<0.01)times higher odds of better HRQoL .
Table 1. General characteristics [n(%)]
| 2016 | 2020 | 2024 | ||
|---|---|---|---|---|
| Characteristic socio-demographic factors | ||||
| Sex | Male | 695(42.2) | 724(43.2) | 812(44.1) |
| Female | 883(57.8) | 957(56.8) | 1139(55.9) | |
| Region | Urban | 1145(76.3) | 1197(77.3) | 1361(73.7) |
| Rural | 433(23.7) | 484(22.7) | 590(26.3) | |
| Co-habitation | No | 361(20.1) | 421(21.5) | 527(26.4) |
| Yes | 1217(79.9) | 1260(78.5) | 1424(73.6) | |
| Income level | Low | 776(49.5) | 732(41.8) | 808(38.4) |
| Lower-middle | 430(25.9) | 488(29.0) | 566(30.5) | |
| Upper-middle | 211(14.3) | 278(18.2) | 373(20.3) | |
| High | 149(10.4) | 165(11.1) | 188(10.8) | |
| Education level | Below elementary | 894(59.6) | 704(49.5) | 839(44.2) |
| Middle school | 214(14.4) | 243(18.1) | 367(20.1) | |
| High school | 233(15.7) | 278(21.4) | 411(23.3) | |
| College or higher | 140(10.4) | 149(11.0) | 207(12.3) | |
| Economic activity status | Yes | 481(31.3) | 512(36.8) | 694(40.1) |
| No | 1002(68.7) | 862(63.2) | 1035(59.9) | |
| Health-related psychological & behavioral factors | ||||
| Alcohol consumption | Drinks | 1008(66.1) | 1139(70.0) | 1222(65.3) |
| Abstinent (less than 1 drink per month) | 519(33.9) | 505(30.0) | 606(34.7) | |
| Smoking experience | Past smoker/non-smoker | 1380(90.3) | 1493(90.8) | 1684(91.8) |
| Smoker | 146(9.7) | 148(9.2) | 144(8.2) | |
| Stress perception | Low | 1233(81.5) | 1363(83.9) | 1561(86.3) |
| High | 289(18.5) | 275(16.1) | 265(13.7) | |
| Aerobic physical activity | Low | 975(65.6) | 916(66.8) | 1201(67.3) |
| High | 506(34.4) | 454(33.2) | 527(32.7) | |
| Activity limitations | Yes | 303(19.6) | 202(14.6) | 437(22.9) |
| No | 1201(80.4) | 1185(85.4) | 1386(77.1) | |
| Oral health-related factors | ||||
| Chewing discomfort | No | 842(55.9) | 1100(66.9) | 1335(73.4) |
| Yes | 682(44.1) | 541(33.1) | 493(26.6) | |
| speech difficulty | No | 1207(78.8) | 1407(86.3) | 1638(90.2) |
| Yes | 323(21.2) | 234(13.7) | 190(9.8) | |
| Unmet dental care | Yes | 418(35.8) | 427(34.0) | 415(28.3) |
| No | 802(64.2) | 826(66.0) | 1015(71.7) | |
| Health-related quality of life (EQ-5D) | ||||
| EQ5D | Low | 769(52.1) | 660(46.8) | 1153(62.1) |
| High | 695(47.9) | 723(53.2) | 671(37.9) | |
| Mean±SD | 0.479 ± 0.017 | 0.531 ± 0.017 | 0.550 ± 0.007 |
SD: standard deviation
by complex sample frequency analysis & descriptive statistics, proportion (%): weight.
Table 2. EQ-5D by respondents’ characteristics [n(%)]
| Category | Characteristics | Division | 2016 | 2020 | 2024 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Low | High | p* | Low | High | p* | Low | High | p* | |||
| Characteristic socio-demographic factors | Sex | Male | 261(37.7) | 396(62.3) | <0.001 | 240(36.6) | 375(63.4) | <0.001 | 390(49.7) | 377(50.3) | <0.001 |
| Female | 535(62.5) | 299(37.5) | 420(55.0) | 348(45.0) | 763(72.2) | 294(27.8) | |||||
| Region | Urban | 554(50.0) | 533(50.0) | 0.016 | 457(45.1) | 543(54.9) | 0.023 | 806(61.1) | 481(38.9) | 0.210 | |
| Rural | 242(59.2) | 162(40.8) | 203(53.0) | 180(47.0) | 347(64.9) | 190(35.1) | |||||
| Co-habitation | No | 207(61.7) | 130(38.3) | <0.001 | 183(55.6) | 143(44.4) | 0.001 | 323(66.5) | 153(33.5) | 0.044 | |
| Yes | 589(49.7) | 565(50.3) | 477(44.5) | 580(55.5) | 830(60.6) | 518(39.4) | |||||
| Income level | Low | 433(58.7) | 296(41.3) | <0.001 | 325(56.9) | 244(43.1) | <0.001 | 491(66.6) | 230(33.4) | 0.003 | |
| Lower-middle | 204(49.3) | 199(50.7) | 182(43.8) | 228(56.2) | 347(64.0) | 202(36.0) | |||||
| Upper-middle | 90(42.5) | 114(57.5) | 98(38.8) | 148(61.2) | 203(55.2) | 162(44.8) | |||||
| High | 65(42.6) | 81(57.4) | 49(31.9) | 99(68.1) | 110(55.6) | 76(44.4) | |||||
| Education level | Below elementary | 543(60.2) | 345(39.8) | <0.001 | 398(56.9) | 306(43.1) | <0.001 | 583(69.5) | 256(30.5) | <0.001 | |
| Middle school | 97(42.6) | 117(57.4) | 111(43.9) | 132(56.1) | 234(62.6) | 133(37.4) | |||||
| High school | 87(37.5) | 144(62.5) | 100(34.8) | 178(65.2) | 226(54.2) | 185(45.8) | |||||
| College or higher | 55(37.4) | 85(62.6) | 47(30.1) | 102(69.9) | 110(50.1) | 97(49.9) | |||||
| Economic activity status | Yes | 204(41.8) | 277(58.2) | <0.001 | 213(39.9) | 299(60.1) | 0.002 | 385(54.0) | 301(46.0) | <0.001 | |
| No | 580(56.4) | 414(43.6) | 443(50.9) | 419(49.1) | 680(68.0) | 320(32.0) | |||||
| Health-related psychological & behavioral factors | Alcohol consumption | Drinks | 556(57.0) | 409(43.0) | 0.855 | 490(51.9) | 458(48.1) | <0.001 | 820(66.9) | 398(33.1) | <0.001 |
| Abstinent | 226(41.8) | 280(58.2) | 170(35.4) | 264(64.6) | 333(53.2) | 273(46.8) | |||||
| Smoking experience | Past/non-smoker | 713(51.9) | 621(48.1) | <0.001 | 594(46.3) | 663(53.7) | 0.327 | 1082(63.7) | 598(36.3) | <0.001 | |
| Smoker | 70(51.0) | 67(49.0) | 66(52.0) | 59(48.0) | 71(44.2) | 73(55.8) | |||||
| Stress perception | Low | 569(46.7) | 621(53.3) | <0.001 | 514(43.7) | 643(56.3) | <0.001 | 927(58.7) | 630(41.3) | <0.001 | |
| High | 211(74.6) | 66(25.4) | 145(64.1) | 79(35.9) | 225(83.5) | 40(16.5) | |||||
| Aerobic physical activity | Low | 563(56.5) | 407(43.5) | <0.001 | 476(51.9) | 440(48.1) | <0.001 | 759(64.3) | 410(35.7) | 0.029 | |
| High | 225(43.9) | 280(56.1) | 176(35.7) | 278(64.3) | 304(58.1) | 211(41.9) | |||||
| Activity limitations | Yes | 255(85.1) | 44(14.9) | <0.001 | 169(85.0) | 31(15.0) | <0.001 | 374(84.3) | 63(15.7) | <0.001 | |
| No | 541(44.1) | 650(55.9) | 491(40.4) | 692(59.6) | 778(55.5) | 608(44.5) | |||||
| Oral health-related factors | Chewing discomfort | No | 369(43.7) | 446(56.3) | <0.001 | 389(40.8) | 564(59.2) | <0.001 | 784(58.0) | 569(42.0) | <0.001 |
| Yes | 414(62.3) | 241(37.7) | 271(59.1) | 182(40.9) | 369(73.6) | 122(26.4) | |||||
| Speech difficulty | No | 570(47.4) | 591(52.6) | <0.001 | 544(45.0) | 644(55.0) | 0.003 | 1013(61.0) | 623(39.0) | 0.013 | |
| Yes | 213(68.8) | 96(31.2) | 116(58.7) | 78(41.3) | 140(72.5) | 48(27.5) | |||||
| Unmet dental care | Yes | 264(66.1) | 141(33.9) | <0.001 | 207(54.7) | 156(45.3) | <0.001 | 314(73.7) | 100(26.3) | <0.001 | |
| No | 361(44.4) | 408(55.6) | 308(43.3) | 401(56.7) | 611(59.1) | 403(40.9) | |||||
*by complex sample chi-square test, proportion (%): weight.
Table 3. Factors affecting quality of life [OR(95% CI)]
| Category | Characteristics | Division | 2016 | 2020 | 2024 | |||
|---|---|---|---|---|---|---|---|---|
| OR(95% CI) | p* | OR(95% CI) | p* | OR(95% CI) | p* | |||
| Characteristic socio-demographic factors | Sex | Male | 2.439(1.772-3.356) | 0.000 | 1.516(1.071-2.147) | 0.019 | 1.762(1.303-2.383) | 0.000 |
| Female | ||||||||
| Region | Urban | 1.452(0.970-2.171) | 0.070 | 1.371(0.962-1.952) | 0.080 | 1.219(0.884-1.682) | 0.226 | |
| Rural | ||||||||
| Co-habitation | No | 0.919(0.624-1.355) | 0.669 | 0.864(0.555-1.344) | 0.515 | 0.885(0.630-1.243) | 0.480 | |
| Yes | ||||||||
| Income level | Low | 1.021(0.599-1.741) | 0.939 | 0.623(0.343-1.130) | 0.119 | 1.309(0.776-2.209) | 0.312 | |
| Lower-middle | 1.049(0.600-1.833) | 0.866 | 0.751(0.414-1.363) | 0.346 | 1.127(0.694-1.832) | 0.628 | ||
| Upper-middle | 1.070(0.592-1.934) | 0.823 | 0.646(0.382-1.092) | 0.103 | 1.300(0.794-2.199) | 0.289 | ||
| High | ||||||||
| Education level | Below elementary | 0.923(0.589-1.447) | 0.727 | 0.664(0.371-1.188) | 0.167 | 0.631(0.395-1.008) | 0.054 | |
| Middle school | 1.493(0.849-2.627) | 0.164 | 0.797(0.425-1.494) | 0.478 | 0.629(0.382-1.034) | 0.067 | ||
| High school | 1.280(0.730-2.244) | 0.389 | 1.251(0.663-2.360) | 0.489 | 0.751(0.486-1.161) | 0.197 | ||
| College or higher | ||||||||
| Economic activity status | Yes | 1.605(1.171-2.201) | 0.003 | 1.333(0.984-1.807) | 0.064 | 1.770(1.312-2.388) | 0.000 | |
| No | ||||||||
| Health-related psychological & behavioral factors | Alcohol consumption | Drinks | 0.951(0.667-1.356) | 0.779 | 0.788(0.541-1.146) | 0.212 | 0.936(0.691-1.269) | 0.671 |
| Abstinent | ||||||||
| Smoking experience | Past/non-smoker | 1.063(0.620-1.824) | 0.823 | 2.090(1.127-3.877) | 0.020 | 0.736(0.482-1.123) | 0.154 | |
| Smoker | ||||||||
| Stress perception | Low | 1.997(1.372-2.908) | 0.000 | 1.490(0.951-2.337) | 0.082 | 2.617(1.688-4.057) | 0.000 | |
| High | ||||||||
| Aerobic physical activity | Low | 0.764(0.552-1.056) | 0.103 | 0.730(0.520-1.043) | 0.085 | 0.792(0.597-1.052) | 0.107 | |
| High | ||||||||
| Activity limitations | Yes | 5.354(3.588-7.987) | 0.000 | 8.831(5.525-14.115) | 0.000 | 3.341(2.339-4.773) | 0.000 | |
| No | ||||||||
| Oral health-related factors | Chewing discomfort | No | 1.047(0.728-1.505) | 0.806 | 1.726(1.191-2.502) | 0.004 | 1.333(0.950-1.869) | 0.096 |
| Yes | ||||||||
| speech difficulty | No | 2.050(1.309-3.208) | 0.002 | 0.758(0.469-1.226) | 0.258 | 0.754(0.438-1.299) | 0.309 | |
| Yes | ||||||||
| Unmet dental care | Yes | 1.834(1.277-2.634) | 0.001 | 1.025(0.729-1.440) | 0.888 | 1.716(1.248-2.359) | 0.001 | |
| No | ||||||||
OR: odds ratio; CI: confidence interval
*by complex sample logistic regression
This study used data from the 7th (2016) to the 9th (2024) Korea National Health and Nutrition Examination Survey to explore factors associated with quality of life among older adults and to descriptively compare their associations across survey periods.
Sex was significantly associated with quality of life in all survey periods, with men reporting relatively higher quality of life than women. This finding is consistent with previous studies [19–21], which have suggested that older women are more likely to experience a higher burden of chronic conditions, economic vulnerability, and reduced social roles in later life [22,23]. In the present study, major potential confounders such as age, education level, and household income were included in the multivariable logistic regression models, and the association between sex and quality of life remained statistically significant after adjustment.
Activity limitation consistently demonstrated a negative impact on QoL across all survey periods, aligning with previous research [24–26]. Reduced physical function associated with activity limitations can lead to decreased social participation and increased depressive symptoms, thereby broadly affecting overall QoL. These findings underscore the importance of community-based support and healthcare services aimed at maintaining physical function and promoting independent daily living.
In 2016, economic activity, stress perception, speech difficulty, and unmet dental care were found to significantly affect quality of life among older adults, consistent with findings from previous studies [5,11,27]. However, in the 2020 survey, the effects of these factors decreased or were no longer statistically significant, while smoking and chewing discomfort emerged as newly significant variables. This change can be interpreted in the context of the COVID-19 pandemic, which coincided with the survey period and resulted in restrictions on external activities and reduced utilization of healthcare services, thereby influencing older adults’ daily lives and health behaviors.
According to statistics from the Ministry of Health and Welfare and the Health Insurance Review and Assessment Service, outpatient visits and dental visits among older adults declined during the pandemic, limiting opportunities for regular preventive and therapeutic oral care [28,29]. Social distancing and reduced face-to-face interactions may have lowered the perceived impact of communication difficulties, such as speech problems, while the shift toward home-centered meals may have relatively increased the importance of masticatory function.
In 2024, similar to 2016, economic activity, stress, and unmet dental care were significant predictors of QoL, consistent with prior studies [9,11,27]. Notably, unmet dental care remained significant both before and after the pandemic, underscoring the persistent structural impact of oral healthcare accessibility on QoL in older adults.
Overall, the quality of life among older adults is influenced not by a single factor, but by the complex interaction of various factors that change according to social environments and health conditions over time. Accordingly, public health policies for the elderly should focus on persistent factors such as sex and physical functional limitations, while also developing timely and context-specific strategies to address variable factors, including economic activity, mental health, and oral health. Specifically, to reduce unmet dental care in the elderly, approaches such as mobile dental clinics, home-visit oral care services, reduction of out-of-pocket costs, and provision of vouchers for vulnerable populations are warranted. From a functional maintenance perspective, programs that combine community-based physical exercise with oral exercises, as well as integration with rehabilitation services, can help prevent declines in both physical and oral function. Furthermore, to enhance mental health and stress management, integrated screening at primary care facilities or public health centers should be implemented, and linking these efforts with programs that support economic activity and social participation—combining work, community engagement, and health management services—can contribute to improving the overall quality of life for older adults.
Several limitations of this study should be considered. First, this study employed a repeated cross-sectional design using data from the Korea National Health and Nutrition Examination Survey, which limits the ability to infer causal relationships between quality of life and its associated factors. Because analyses were conducted separately for each survey year, direct statistical comparisons of effect sizes across years or assessments of within-individual changes over time were not possible. Also, differences in sample characteristics across survey years, as well as broader social and environmental changes—such as those related to the COVID-19 pandemic—may have influenced health behaviors, healthcare utilization, and response patterns, thereby affecting the observed associations. In addition, the proportion of older adults with a high EQ-5D score decreased in 2024, and further research is needed to determine whether this decline is temporary or driven by other factors.
Despite these limitations, this study has important strengths, including the use of nationally representative data over an extended time period to examine factors related to quality of life among older adults. Future research should employ longitudinal designs to better clarify temporal relationships between oral health and quality of life and consider pooled modeling approaches with interaction terms or causal inference methods to more rigorously assess variations in associations across time.
This study analyzed factors related to the quality of life measured by EQ-5D among older adults using data from the Korea National Health and Nutrition Examination Survey (KNHANES) spanning from 2016 (7th wave) to 2024 (9th wave), collected at fouryear intervals. The results are as follows.
1. In 2016, sex, economic activity status, stress perception, activity limitations, speech difficulty, and unmet dental care significantly affected quality of life (p<0.05).
2. In 2020, sex, smoking, activity limitations, and Chewing discomfort were significantly associated with quality of life (p<0.05).
3. In 2024, sex, economic activity status, stress perception, activity limitations, and unmet dental care significantly influenced quality of life (p<0.05).
4. Across all survey periods, sex(p<0.05) and activity limitations(p<0.05) were consistently associated with quality of life among older adults.
These findings suggest that the quality of life in older adults is influenced by an interaction of structural factors, such as sex and physical functional limitations, with environmental and behavioral factors, including economic activity, mental health, and oral health. In particular, issues related to access to dental care-such as unmet dental needs-have a long-term impact on quality of life. This study emphasizes that integrated public health policies and early interventions are essential for supporting comprehensive health management for older adults, including oral health.
The author fully participated in the work performed and documented truthfully.
The authors declared no conflicts of interest.
None.
This study was approved by the Institutional Review Board (IRB) of Korea Disease Control and Prevention Agency (KDCA) Research Ethics Committee (IRB No. 2013-07CON-03-4C, 2018-01-03-C-A, 2018-01-03-4C-A).
In the case of KNHANES 6th(2016)-9th(2024) Data can be obtained from National Health and Nutrition Examination Survey website (https://knhanes.kdca.go.kr) repository source.
This research was supported by Kyungdong University Research Fund, 2024.
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