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Relationship between hand grip strength and lung function among older adults in Korea: Secondary analysis of the Korea National Health and Nutrition Examination Survey

Hand grip strength and lung function in Korean older adults

Article information

J Korean Gerontol Nurs. 2025;27(3):285-294
Publication date (electronic) : 2025 August 29
doi : https://doi.org/10.17079/jkgn.2024.00696
Inkyung Park1orcid_icon, Minjae Lee,1orcid_icon, Kahyun Kim2orcid_icon
1Doctoral Student, College of Nursing, Ewha Womans University, Seoul, Korea
2Registered Nurse, Department of Nursing, Ewha Womans University Mokdong Hospital, Seoul, Korea
Corresponding author: Minjae Lee College of Nursing, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea TEL: +82-2-3277-2876 E-mail: leemj1992@gmail.com
Received 2024 December 29; Revised 2025 February 1; Accepted 2025 July 14.

Abstract

Purpose

South Korea is rapidly aging, with older adult care costs soaring and lung diseases prevalent among the older adults. The correlation between hand grip strength (HGS) and lung function is contested, demanding further research to assess its viability as a predictor of lung health in this growing demographic. In this study, we explored the relationship between HGS and lung function.

Methods

In this cross-sectional study, of 32,379 participants, we selected 3,362 aged 65 years or older, after exclusions. We assessed lung function through forced vital capacity (FVC) and forced expiratory volume (FEV1) measurements, and gauged HGS using a digital dynamometer. The analysis accounted for various covariates including medical history, lifestyle factors, and socioeconomic status, using complex sample analysis in SPSS.

Results

Analysis of 3,362 participants revealed that, after adjusting for relevant covariates, HGS retained a statistically significant correlation with FVC and FEV1, with regression coefficients of 0.03 and 0.02, respectively. These findings indicate that HGS may serve as a reliable indicator of respiratory health in an aging population.

Conclusion

Findings showed HGS independently aligned with FVC and FEV1, requiring a significant post-adjustment for confounders. Consistent with prior studies, results suggest HGS could indicate respiratory health in older adults. Despite divergent outcomes from earlier research, a persistent link between HGS and lung function underscores its value in geriatric screenings for early lung-decline detection.

Keywords: Aged; Muscle strength; Respiratory function tests

INTRODUCTION

1. Background

In 2022, South Korea had 9,018,000 older adults aged 65 and over, accounting for 17.5% of the population. By 2025, this is expected to rise to 20.6%, marking the country’s transformation into a super-aged society [1]. The rate of aging in South Korea is rapid, with only seven years required to exceed a 20% older adults’ population from the time it reached 14% in 2017—surpassing the 20% mark in 2024. This is a much shorter period compared to Austria (53 years), the United Kingdom (50 years), the United States (15 years), and Japan (10 years). In the first quarter of 2023, older adult care costs increased by 28.48% from the previous year, reaching 12.6 trillion won [2], placing a significant financial burden on the national economy. Notably, approximately 40% of patients in their 70s are affected by respiratory diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. Consequently, treatment costs for COPD have grown by an annual average of 4.8%, while those for lung cancer have increased by 9.2% [3].

Aging is associated with changes in the lungs and supporting extrapulmonary structures such as the chest wall, spine, and respiratory muscles, leading to compromised respiratory mechanics. These changes result in lower expiratory flow rates, increased air retention, a higher closing volume, and reduced gas exchange efficiency [4]. Lung function assessments show that forced expiratory volume (FEV1) declines by about 30 mL annually, and forced vital capacity (FVC) decreases by approximately 20 mL per year [5]. Subclinical reductions in lung function are associated with higher risks of all-cause and cardiovascular mortality, as well as the onset of cardiovascular diseases such as left heart failure, atrial fibrillation, and stroke [6]. Therefore, the timely identification of older adults at risk of lung function decline is crucial from a public health perspective.

Hand grip strength (HGS) is a simple, effective tool for diagnosing sarcopenia and assessing physical fitness and muscle strength in the older adults [7,8]. Its ease of measurement and minimal equipment requirements make it accessible and cost-effective for routine health checks. HGS exhibits a significant negative correlation with age, remaining stable until around 45 to 50 years, after which it declines rapidly [9]. Low HGS is associated with sarcopenia and an increased risk of cancer, as well as cardiovascular and respiratory diseases, and is a significant predictor of mortality [7,10,11].

Previous studies on the relationship between HGS and lung function have reported inconsistent findings. One study comparing COPD and non-COPD patients aged 40 years and above found no significant difference in HGS between the two groups after adjustment [12]. However, another study examining adults aged 40 years and older reported a significant positive association between HGS and spirometric parameters such as FVC and FEV1, indicating that muscle strength may influence pulmonary function in the general adult population [13]. Additionally, a systematic review and meta-analysis demonstrated that lower HGS is associated with increased COPD morbidity and a higher likelihood of hospitalization, emphasizing the potential role of HGS in assessing respiratory health risks [14]. Given these conflicting results, this knowledge gap highlights the need for further investigation to better understand how HGS could potentially serve as a predictive marker for respiratory health in the older adults.

2. Objectives

This study aims to explore the relationship between HGS and lung function using data from the 2016~2019 Korea National Health and Nutrition Examination Survey (KNHANES).

METHODS

Ethics statement: As this study used publicly available data, it was exempted from review by the Institutional Review Board (IRB) of Ewha Womans University (ewha-202311-0018-01).

1. Study Design

This study is a cross-sectional, descriptive analysis using secondary data from the KNHANES (2016~2019). It was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist (https://www.strobe-statement.org).

2. Data Sources

Data for this study were obtained from the KNHANES, conducted by the Korea Disease Control and Prevention Agency (KDCA). The KNHANES is a nationwide survey designed to assess the health and nutritional status of non-institutionalized individuals in the Republic of Korea. It includes a health interview, health examination, and nutrition survey for all household members.

3. Study Population

The KNHANES employs a stratified, multistage, clustered probability sampling design based on region size and demographic characteristics. Of the 32,379 participants in the survey, 6,691 were aged 65 years and older. We excluded 3,329 individuals with missing data for FVC and FEV1 (n=2,051), HGS measurements (n=578), or other adjustment variables (n=700), resulting in a final sample of 3,362 participants (Figure 1).

Figure 1.

Flow chart of subject inclusion and exclusion in the Korea National Health and Nutrition Examination Surveys (KNHANES). FEV1=Forced expiratory volume; FVC=Forced vital capacity.

4. Measures

1) Dependent variables: FVC, FEV1

The dependent variables in this study were FVC and FEV1, primary indicators of lung function. Since 2010, the KNHANES has conducted lung function tests on participants aged 40 years and older, measuring both FVC and FEV1. From 2016, these measurements were taken using Vyntus Spiro equipment (Vyaire Medical). The survey was suspended in 2020 due to the COVID-19 pandemic [15].

2) Independent variable: HGS

The independent variable was HGS. Since 2014, the KNHANES has conducted HGS tests on individuals aged 10 years and older using a digital grip strength dynamometer (T.K.K 5401; Takei Scientific Instruments), recording three measurements per hand. Following the 2019 Asian Working Group for Sarcopenia (AWGS) guidelines, the peak value from at least two attempts using both hands—or the dominant hand—should be recorded [7]. Accordingly, this study used the highest value from the six measurements. Due to COVID-19, the 2020 grip strength survey was limited to certain regions, resulting in an insufficient sample size for analysis, and the data were not released. Before HGS testing, participants underwent a screening process involving a physical examination (for arm, hand, or thumb defects; fractures; paralysis; casts; or bandages) and a questionnaire regarding recent surgeries, arthritis, carpal tunnel surgery, ability to participate, and any recent pain or discomfort [15].

3) Covariates

This study included a comprehensive set of covariates to assess the relationship between HGS and lung function in older adults. Medical conditions included hypertension, ischemic heart disease [16], diabetes mellitus [17], stroke [18], tuberculosis, asthma, lung cancer, rhinitis, sinusitis, and otitis media. Health behaviors included smoking status, high-risk alcohol consumption, aerobic physical activity [19,20], influenza vaccination [21], and health screening attendance. Socioeconomic factors included education and income levels. Additional covariates were body mass index (BMI), sex, and age [22]. Each covariate was selected based on its potential influence on health status and lung function, as indicated by previous research.

5. Data Analysis

The KNHANES employs a multistage, clustered probability sampling technique to represent the broader non-institutionalized Korean population. Weighting factors are assigned to individuals in the sample [15]. Accordingly, all statistical analyses in this study applied complex sample analysis methods. Subgroup analyses by sex and HGS quartiles were conducted to examine potential interactions. Statistical analyses were performed using IBM SPSS Statistics, version 28 (IBM Corp.). We evaluated the mean and standard error of FEV1 and FVC for each clinical group using a complex-sample general linear model. After adjusting for covariates, we analyzed the association of HGS with FEV1 and FVC. To assess differences in FEV1 and FVC between groups, we used a complex-sample general linear model with a 95% confidence interval (CI). All analyses incorporated sample weights to reflect national population projections. Two-sided p-values were reported, with values less than .05 considered statistically significant.

RESULTS

1. Demographic and Clinical Characteristics of Participants

Among adults aged 65 years and older who participated in the KNHANES, individuals with missing data for study variables or with defects or surgery on their wrists, hands, or fingers were excluded, resulting in a final sample of 3,362 participants. Table 1 presents differences in FVC and FEV1 based on the demographic and clinical characteristics of the study population. The sample included 1,548 males (46.5%) and 1,814 females (53.5%). Significant differences in average FVC and FEV1 (both p<.001) were observed between sexes, with men showing higher values. Age also showed significant differences in average FVC and FEV1 (both p<.001) across the groups aged 65~69, 70~74, and 75 years or older, with values decreasing with age. BMI categories exhibited significant differences in FVC and FEV1 (both p<.001), with the underweight group (BMI<18.5 kg/m2) having the lowest scores. A total of 359 people (11.8%) had an education level above college, and this group exhibited significantly higher average FVC and FEV1 (both p<.001) compared to those with lower education levels. Significant differences were also observed between low-income (2,435 participants; 70.5%) and high-income groups in average FVC and FEV1 (both p<.001). Health behaviors such as aerobic exercise, smoking, high-risk alcohol use, and hypertension also exhibited significant differences in FVC and FEV1 (all p<.001).

Demographics and Clinical Characteristics (N*=3,362)

2. Lung Function by Health Conditions

Regarding health conditions, 964 participants (27.5%) had diabetes mellitus, with significant differences in average FVC (p=.026) and FEV1 (p=.013) between those with and without the condition. Additionally, 129 participants (4.0%) had a history of stroke, and FEV1 was significantly lower in this group (p=.004). Among the 228 participants (6.5%) with tuberculosis, no significant difference in FVC was observed; however, FEV1 was significantly lower compared to those without tuberculosis (p<.001). Participants with asthma (n=153; 4.4%) had significantly lower mean FVC and FEV1 (both p<.001) than those without asthma. Of the 2,833 participants (84.8%) who received the influenza vaccine in the past year, both FVC (p=.002) and FEV1 (p=.033) were significantly lower compared to unvaccinated individuals. Conversely, the 2,527 participants (74.8%) who had undergone a health examination in the previous two years exhibited significantly higher mean FVC and FEV1 (both p<.001) than those who had not. Other comorbidities included ischemic heart disease (252 participants; 7.6%), lung cancer (16; 0.5%), rhinitis (228; 6.9%), sinusitis (160; 5.0%), and otitis media (149; 4.6%). None of these conditions exhibited significant differences in average FVC or FEV1.

3. Differences in Lung Function across HGS Quartiles

We also investigated the mean FVC and FEV1 across HGS quartiles by sex (Table 2). For male participants, the regression coefficients for FVC and FEV1 relative to HGS were 2.16 (95% CI: 1.97~2.34, R2=.15, p<.001) and 1.40 (95% CI: 1.24~1.56, R2=.12, p<.001), respectively. For female participants, the values were 1.83 (95% CI: 1.73~1.93, R2=.11, p<.001) and 1.41 (95% CI: 1.32~1.50, R2=.08, p<.001), respectively. Significant differences in average FVC and FEV1 (both p<.001) were observed across HGS quartiles for both sexes, with higher grip strength corresponding to higher FVC and FEV1 (Figure 2).

Pulmonary Function According to Hand Grip Strength (Unadjusted) (N*=3,362)

Figure 2.

Mean FVC and FEV1 according to hand grip strength quartile. FEV1=Forced expiratory volume; FVC=Forced vital capacity.

4. Association between HGS and Lung Function Adjusted for Covariates

The relationship between HGS and lung function remained significant after adjusting for covariates, including age, BMI, education, income, physical activity, smoking, alcohol use, health conditions, vaccination status, and health examination participation (Table 3). For male participants, the regression coefficients for FVC and FEV1 relative to HGS were 0.03 (95% CI: 0.02~0.04, R2=.25, p<.001) and 0.02 (95% CI: 0.01~0.02, R2=.27, p<.001), respectively. For female participants, the coefficients were 0.02 (95% CI: 0.01~0.02, R2=.26, p<.001) and 0.01 (95% CI: 0.01~0.02, R2=.27, p<.001), respectively.

Pulmonary Function According to Hand Grip Strength (Adjusted*) (N†=3,362)

DISCUSSION

We investigated the association between HGS and lung function in a representative sample of older Korean adults. Our analysis revealed that HGS, as measured by HGS and spirometric parameters, was independently and positively associated with FVC and FEV1 after adjusting for potential confounding variables. These results are consistent with previous studies using KHNANES data, demonstrating that HGS, as a measure of sarcopenia, may indicate a decline in lung function in older adults [13]. Moreover, our findings align with prior research from KHNANES that, after adjusting for age, smoking, and physical activity, found a decrease in lung function (FVC and FEV1) associated with loss of skeletal muscle mass [23]. Recent research has also explored the causal association between HGS and lung function using two-sample Mendelian randomization analysis [24], confirming that higher HGS is significantly associated with stronger lung function. Additionally, there was a significant association between both right and left HGS and FVC and FEV1, while no significant association was found between HGS and the FEV1/FVC ratio. Conversely, Jeong et al. [12] found no significant association between HGS and lung function in individuals with COPD, highlighting the complexity of this relationship and the need to consider confounders.

Despite these contradictory results, the relationship between HGS and lung function can be explained by age-related loss of skeletal muscle mass, which includes respiratory muscles such as the diaphragm [25]. Respiratory muscle strength plays a crucial role in coordinating the interaction between lung function and muscular force to maintain proper ventilation [26]. In line with this, multiple studies have identified HGS as an indicator of overall muscular strength [7,27]. Reductions in skeletal muscle mass and neuromuscular function contribute to weakened respiratory muscles and decreased lung function. The progressive loss of fast-twitch (Type II) muscle fibers with aging is associated with declining inspiratory and expiratory muscle strength, ultimately impairing pulmonary function. Recent evidence suggests that HGS is positively correlated with pulmonary function measures such as FEV₁ and FVC, highlighting its potential as a clinical marker of respiratory health [28,29]. Similarly, respiratory sarcopenia is characterized by deterioration and thinning of muscle fibers affecting not only respiratory muscles, such as the diaphragm, but also systemic skeletal muscles as part of the aging process [30,31]. Since both handgrip and respiratory muscles rely on Type II fibers for forceful contractions, their age-related deterioration may contribute to the parallel decline in HGS and respiratory muscle strength [32]. Respiratory sarcopenia results in decreased generation of respiratory force [33] and impaired lung function [34]. Furthermore, research has shown that peak HGS is not only correlated with inspiratory and expiratory pressures but may also serve as a predictor of ventilatory capacity and pulmonary resilience in older adults [35].

The current study supports the use of HGS as a predictive tool for lung function, which is especially valuable for evaluating respiratory muscle capacity in clinical settings [28,36,37]. Integrating HGS assessment into routine nursing practice could enable early detection of respiratory decline, allowing for timely interventions such as inspiratory muscle training (IMT) and pulmonary rehabilitation. In geriatric health assessments, HGS screening could help identify high-risk individuals who may benefit from preventive strategies, including structured exercise programs and education on maintaining muscle strength.

Recent evidence also suggests that lower HGS is associated with increased risk of all-cause mortality, cardiovascular mortality, and disability, reinforcing its value as a clinical screening tool for overall health status [38]. Given its strong association with pulmonary function, HGS could serve as a practical screening tool in clinical nursing practice to identify older adults at risk of declining lung function. Routine HGS assessment in primary care and community health settings could enable early detection and timely intervention through IMT or resistance exercises. Furthermore, HGS screening could be integrated into geriatric assessment programs for a more comprehensive evaluation of musculoskeletal and pulmonary health. To maximize the effectiveness of HGS assessment, community education programs should promote awareness among older adults about the importance of maintaining muscle strength for respiratory health. Studies show that structured strength training programs can significantly improve HGS, which may translate into improved respiratory function and reduced health risks [38]. Health professionals can use visual tools, demonstrations, and simple strength tests to help individuals understand how HGS reflects their overall health. Practical strategies—such as handgrip exercises, resistance training, and IMT—can be introduced to improve both HGS and lung function. Encouraging older adults to engage in regular muscle-strengthening activities may contribute to better functional capacity and reduced risk of respiratory complications.

This study has several limitations. Its cross-sectional design limits the ability to infer causality, and the absence of 2019 data due to the COVID-19 pandemic further restricts generalizability. These limitations emphasize the need for continued post-pandemic research to better understand the relationship between HGS and lung function. Nonetheless, our study adds to the literature by providing normative data on HGS in an older adult population and highlights its potential as an independent indicator of various health outcomes.

CONCLUSION

This research investigates the correlation between HGS and lung function in an aging Korean population. Using a nationally representative dataset, our study highlights the potential of HGS as a meaningful biomarker in clinical evaluations and public health interventions. In particular, our findings suggest that HGS may offer insights into the respiratory health of older adults, with practical implications for the early detection and management of pulmonary decline. Despite limitations due to the cross-sectional design and the loss of data from 2019 because of the COVID-19 pandemic, the study’s findings on the correlation between muscular strength and lung capacity highlight the need for continued investigation into the potential of HGS as a predictor of mortality and cardiovascular risk. To further differentiate this study from previous research, future investigations could explore differences in the relationship between HGS and lung function among specific subgroups, such as patients with COPD compared to the general aging population. Additionally, analyzing the interplay between HGS, lung function, and comorbidities such as diabetes, hypertension, or sarcopenia may provide deeper insight into disease-specific mechanisms. Longitudinal studies examining temporal changes in HGS and respiratory health over time could help clarify causality and support the development of targeted interventions. This knowledge could inform health strategies aimed at enhancing the quality of life and reducing the disease burden in the aging population.

Notes

Authors' contribution

Conceptualization or/and methodology - IP, ML, and KK; Data curation or/and analysis - IP, ML, and KK; Writing–original draft - IP, ML, and KK; Writing–review & editing - IP and ML.

Conflict of interest

No existing or potential conflict of interest relevant to this article was reported.

Funding

None.

Data availability

The data can be accessed at the following URL: https://knhanes.kdca.go.kr/knhanes/main.do

Acknowledgements

None.

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© 2025 Korean Gerontological Nursing Society

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/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Figure 1.

Figure 1.

Flow chart of subject inclusion and exclusion in the Korea National Health and Nutrition Examination Surveys (KNHANES). FEV1=Forced expiratory volume; FVC=Forced vital capacity.

Figure 2.

Figure 2.

Mean FVC and FEV1 according to hand grip strength quartile. FEV1=Forced expiratory volume; FVC=Forced vital capacity.

Table 1.

Demographics and Clinical Characteristics (N*=3,362)

Variable Category n* (%)† FVC
 FEV1
Mean±SE‡ p-value‡ Mean±SE‡ p-value‡
All N/A 3,362 (100.0) 2.9 N/A 2.1 N/A
Sex Male 1,548 (46.5) 3.5±0.02 <.001 2.4±0.02 <.001
Female 1,814 (53.5) 2.5±0.01 1.9±0.01
Age (years) 65~69 1,305 (37.6) 3.1±0.02 <.001 2.3±0.02 <.001
70~74 1,001 (31.1) 2.9±0.03 2.1±0.02
≥75 1,056 (31.3) 2.7±0.02 1.9±0.02
BMI (kg/m2) <18.5 50 (1.4) 2.8±0.11 <.001 1.9±0.12 <.001
18.5~24.9 2,023 (61.4) 3.0±0.02 2.2±0.01
25.0~29.9 1,161 (33.8) 2.9±0.02 2.1±0.02
≥30.0 128 (3.4) 2.6±0.06 2.0±0.04
Education level <College 3,003 (88.2) 2.9±0.01 <.001 2.1±0.01 <.001
≥College 359 (11.8) 3.3±0.04 2.4±0.03
Income level Low 2,435 (70.5) 2.9±0.02 <.001 2.1±0.01 <.001
High 927 (29.5) 3.0±0.03 2.2±0.02
Aerobic exercise Yes 1,202 (37.0) 3.1±0.03 <.001 2.2±0.02 <.001
No 2,160 (63.0) 2.9±0.02 2.1±0.01
Smoking Yes 289 (8.9) 3.4±0.06 <.001 2.3±0.04 <.001
No 3073 (91.1) 2.9±0.01 2.1±0.01
High-risk alcohol use Yes 169 (5.3) 3.5±0.06 <.001 2.5±0.05 <.001
No 3,193 (94.7) 2.9±0.01 2.1±0.01
HTN Yes 2,131 (63.1) 2.9±0.02 <.001 2.1±0.01 <.001
No 1,231 (36.9) 3.0±0.02 2.2±0.02
DM Yes 964 (27.5) 2.9±0.03 .026 2.1±0.02 .013
No 2,398 (72.5) 3.0±0.02 2.2±0.01
Stroke Yes 129 (4.0) 2.8±0.06 .069 2.0±0.05 .004
No 3,233 (96.0) 2.9±0.01 2.1±0.01
IHD Yes 252 (7.6) 2.9±0.05 .997 2.1±0.04 .586
No 3,110 (92.4) 2.9±0.02 2.1±0.01
Tuberculosis Yes 228 (6.5) 2.9±0.06 .850 2.0±0.05 <.001
No 3,134 (93.5) 2.9±0.02 2.2±0.01
Asthma Yes 153 (4.4) 2.6±0.06 <.001 1.7±0.04 <.001
No 3,209 (95.6) 3.0±0.01 2.2±0.01
Lung cancer Yes 16 (0.5) 3.1±0.27 .558 2.0±0.13 .171
No 3,346 (99.5) 2.9±0.01 2.1±0.01
Influenza vaccination Yes 2,833 (84.8) 2.9±0.02 .002 2.1±0.01 .033
No 529 (15.2) 3.0±0.04 2.2±0.03
Checkup Yes 2,527 (74.8) 3.0±0.02 <.001 2.2±0.01 <.001
No 835 (25.2) 2.8±0.03 2.0±0.02
Rhinitis Yes 228 (6.9) 3.0±0.05 .671 2.1±0.04 .979
No 3,134 (93.1) 2.9±0.02 2.1±0.01
Sinusitis Yes 160 (5.0) 2.9±0.06 .237 2.1±0.04 .416
No 3,202 (95.0) 2.9±0.01 2.1±0.01
Otitis media Yes 149 (4.6) 2.9±0.06 .739 2.1±0.04 .734
No 3,213 (95.4) 2.9±0.01 2.1±0.01
*

Unweighted count;

†

weighted %;

‡

by complex samples general linear model; BMI=Body mass index; DM=Diabetes mellitus; FEV1=Forced expiratory volume; FVC=Forced vital capacity; HTN=Hypertension; IHD=Ischemic heart disease; N/A=Not applicable; SE=Standard error.

Table 2.

Pulmonary Function According to Hand Grip Strength (Unadjusted) (N*=3,362)

Variable Category n* (%)† FVC
 FEV1
Regression coefficient (95% CI)‡ or Mean±SE§ p-value Regression coefficient (95% CI)‡ or Mean±SE§ p-value
Male All 1,548 (100.0) 2.16 (1.97, 2.34) <.001 1.40 (1.24, 1.56) <.001
Q1 (≤30.4) 392 (25.3) 3.2±0.03 <.001 2.2±0.03 <.001
Q2 (30.5~34.6) 382 (25.0) 3.4±0.03 2.4±0.02
Q3 (34.7~38.7) 389 (25.4) 3.6±0.03 2.5±0.03
Q4 (≥38.8) 385 (24.3) 3.8±0.04 2.7±0.03
Female All 1,814 (100.0) 1.83 (1.73, 1.93) <.001 1.41 (1.32, 1.50) <.001
Q1 (≤18.0) 450 (24.3) 2.3±0.02 <.001 1.7±0.02 <.001
Q2 (18.1~21.0) 457 (25.1) 2.4±0.02 1.8±0.02
Q3 (21.1~23.9) 452 (24.9) 2.5±0.02 1.9±0.02
Q4 (≥24.0) 455 (25.7) 2.6±0.02 2.0±0.02
*

Unweighted count;

†

weighted %;

‡

by complex samples general linear model;

§

by complex samples general linear regression analysis; CI=Confidence interval; FEV1=Forced expiratory volume; FVC=Forced vital capacity; SE=Standard error.

Table 3.

Pulmonary Function According to Hand Grip Strength (Adjusted*) (N†=3,362)

Variable FVC
 FEV1
Regression Coefficient‡ 95% CI‡ p-value‡ Regression Coefficient‡ 95% CI‡ p-value‡
Male 0.03 0.02, 0.04 <.001 0.02 0.01, 0.02 <.001
Female 0.02 0.01, 0.02 <.001 0.01 0.01, 0.02 <.001
*

Adjusted by age, body mass index, education level, income level, aerobic exercise, smoking, high-risk alcohol use, hypertension, diabetes mellitus, stroke, tuberculosis, asthma, influenza vaccination, health screening;

†

unweighted count;

‡

by complex samples general linear regression analysis; CI=Confidence interval; FEV1=Forced expiratory volume; FVC=Forced vital capacity.