Abstract
Background
This cross-sectional study aimed to analyze temporal trends in handgrip strength (HGS) from 2014 to 2019 and explore potential contributors to such trends among Korean adults.
Methods
This study analyzed data from the Korea National Health and Nutrition Examination Survey, 2014–2019, among Korean adults aged ≥ 20 years (n = 20,038). Employing multiple linear regression models, we examined the influence of nutritional quality (mean nutrient adequacy ratio [MAR]), physical activity levels (metabolic equivalent of task [MET]), depressive symptoms, and sociodemographic factors on HGS. Higher MAR and MET values indicate better nutritional quality and greater physical activity, respectively. Temporal trends in HGS were also examined over the study period.
Results
A significant decline in HGS was observed over the study period, with a mean decrease of 2.28 kg (p < 0.001). This decline was paralleled by significant reductions in both physical activity (p for linear trend < 0.001) and nutritional quality (p for linear trend < 0.001). Specifically, MET values showed a notable decline, reflecting decreased engagement in both moderate and vigorous physical activities (p for linear trend < 0.001). Concurrently, a decline in MAR underscored a deterioration in dietary quality (p for linear trend < 0.001).
Conclusions
This study identified a significant decline in HGS among Korean adults between 2014 and 2019, in parallel with reductions in physical activity and nutritional quality. Efforts to reverse these trends are required for preserving functional health at the population level. Continuous monitoring of HGS trends might support the assessment of national health status.
Keywords: Hand grip strength, Nutrition assessment, Physical activity, Public health, Trend analysis
Handgrip strength (HGS) is recognized as a useful biomarker for various health conditions, such as diabetes, depressive disorder, and sarcopenia, and correlates with overall muscular strength, upper limb function, bone mineral density, diet, and physical activity.1) It has several advantages such as reliability, safety, and cost-effectiveness,2) providing consistent screening utility across multiple health-related domains.3,4)
The trend of average HGS among adults can indicate shifts in the physical function and general health of the population. Recently, Dufner et al.3) explored temporal trends in HGS across 14 high- and upper-middle-income countries from 1960 to 2017, analyzing data from over 2.5 million adults, and noted a decline in adult HGS since 2000. Similar trends of overall HGS decline and associated factors have been reported in other recent studies.5,6) For instance, Kang et al.7) revealed a significant decrease in HGS among Korean adolescents from 2014 to 2017, based on the Korea National Health and Nutrition Examination Survey (KNHANES) data, which represent the entire Korean population. However, large-scale studies analyzing temporal trends in average HGS among Korean adults are currently lacking. Therefore, this study aimed to analyze temporal trends in HGS using KNHANES data from 2014 to 2019. Additionally, considering the association of HGS with various lifestyle factors, such as physical activity level, nutritional factors, and depressive symptoms,8,9,10) this study also aimed to identify the factors related to the temporal trend of HGS, providing insights into potential areas for public health intervention and policy development.
METHODS
All KNHANES surveys received approval from the Institutional Review Board of the Korea Disease Control and Prevention Agency (KDCA) (IRB No. 2013-07CON-03-4C, 2013-12EXP-03-5C, 2018-01-03-P-A, 2018-01-03-C-A). Prior to data collection, all participants provided written informed consent. Our study was deemed exempt by the Institutional Review Board at Chungnam National University Sejong Hospital (IRB No. 2023-07-018-002), as it utilized publicly accessible data without identifiable information.
Study Design and Population
The KDCA has been conducting the KNHANES since 1998. This ongoing, cross-sectional survey aims to monitor the health and nutritional status of the non-institutionalized civilian population in Korea using a complex, multistage probability sampling design. The survey encompasses health interviews, examinations, and nutritional assessments. Further details on the study design, protocols, and data collection methods are publicly accessible via the KDCA’s KNHANES website (https://knhanes.kdca.go.kr/knhanes/main.do).
Among 31,525 adults aged ≥ 19 years who participated in all 3 KNHANES surveys from 2014 to 2019, those who met these criteria were excluded: no measurement of HGS (n = 2,693); pregnant or lactating (n = 266); restricted daily activity (n = 2,140); implausible energy intake (< 500 kcal or > 5,000 kcal) (n = 479); and missing data on covariates (n = 5,909). Our final analysis sample comprised 20,038 adults, including 8,517 men and 11,521 women (Fig. 1). Pregnant or lactating individuals were excluded to maintain statistical consistency, as their increased nutritional needs alter the standard evaluation criteria.11)
Fig. 1. PFlowchart of participants and inclusion criteria. KNHANES: Korea National Health and Nutrition Examination Survey.
Assessment of Main Variables
Anthropometric and hand grip strength measurements
Anthropometric data were accurately measured by trained examiners. Height was determined to the nearest 0.1 cm using a wall-mounted stadiometer, with participants standing barefoot in lightweight clothing. Weight was recorded to the nearest 0.1 kg under similar conditions. The body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters.
HGS was assessed using a digital hand dynamometer (TKK 5401, Takei), which has a measurement range of 5.0–100.0 kg and increments of 0.1 kg. This device features an adjustable grip span to accommodate different hand sizes. During the assessment, participants stood erect with their feet positioned hip-width apart and were instructed to look straight ahead. They extended their arm fully at their side without bending the elbow. Holding the dynamometer in a neutral position with the index finger at a 90° angle, participants performed the grip test. The procedure was conducted 3 times for each hand, beginning with the dominant hand. Participants were advised to apply their maximum grip strength steadily for at least 3 seconds without swinging the dynamometer or holding their breath. A minimum rest period of 30 seconds was allowed between attempts. The highest measurement out of the 6 trials was recorded as the participant’s HGS.
Dietary intake and mean nutrient adequacy ratio
One week following the health assessments, certified dietitians collected nutrition-related information through a 24-hour dietary recall conducted in the homes of the participants. Participants specified the amounts (in units of volume), time, and place of eating the foods and beverages consumed in the preceding 24 hours. The multiple-pass approach, with the assistance of a standard set of measuring guides, was used to obtain an accurate food recall. Daily intake of total energy and nutrients was estimated using the Korean Food Composition Table of the Rural Development Administration.12)
Nutrient adequacy ratio (NAR) was calculated based on nutrients with a recommended nutrient intake (RNI) level in the dietary reference intakes for Koreans: protein (g), vitamin A (µg retinol activity equivalent), vitamin B1 (mg), vitamin B2 (mg), niacin (mg), vitamin C (mg), calcium (mg), phosphorous (mg), and iron (mg). The NAR was calculated as the ratio of actual consumption of a selected nutrient to the RNI level. NARs were truncated at 1.0 because intakes of nutrients above the RNIs have no benefit. The mean nutrient adequacy ratio (MAR) was calculated as the sum of the NARs of all evaluated nutrients divided by the number of nutrients and expressed as a percentage; higher MAR values indicate better overall nutritional quality.
Physical activity level
The participants reported the frequency and duration of engaging in any physical activity, including work, transportation, exercise, or leisure, in a typical week, using the World Health Organization Global Physical Activity guidelines. For the physical activity level, the metabolic equivalent of task (MET; in min/wk) was calculated by multiplying the frequency, duration, and intensity of any physical activity engaged during a typical week (vigorous activities = 8 MET; moderate activities and walking = 4 MET); higher MET values indicate greater physical activity.
Depressive symptoms
The presence of depressive symptoms was defined as an affirmative response to having ever experienced depression or anxiety, as assessed using the EuroQol 5-dimension questionnaire.
Sociodemographic, lifestyle, and disease history characteristics
Sociodemographic indicators, lifestyle determinants, and medical history were meticulously evaluated for their potential impact on HGS within the Korean adult population. Demographic variables considered included age and sex. Socioeconomic attributes were assessed through education levels, equivalized monthly household income, and marital status. Lifestyle variables included smoking status, alcohol consumption, engagement in walking exercises, and participation in weight training. Furthermore, medical histories encompassing heart disease, stroke, cancer, hypertension, diabetes, dyslipidemia, and metabolic syndrome were incorporated and defined by specific diagnostic criteria. The comprehensive methodology and operational definitions applied to these variables are delineated in the Supplementary Material 1, providing a detailed context for our analysis.
Statistical Analysis
All analyses incorporated survey weights to account for the complex sampling design of the KNHANES. Baseline characteristics of the participants are described as means and standard errors (SEs) for continuous variables and percentage and SEs for categorical variables. The statistical differences in characteristics between men and women were tested using the analysis of variance for continuous variables and the Rao-Scott χ2 test for categorical variables.
Multivariable linear regression models were fitted to examine the association of the following factors with HGS: survey year (2014, 2015, 2016, 2017, 2018, and 2019), MAR (tertiles 1, 2, or 3), physical activity (< 600, 600- < 3,000, or ≥ 3,000 of METs/wk), depression (yes or no), age (years), sex (male or female), education (< high school graduate or ≥ high school graduate), monthly household income (quartiles of equivalized household income), current smoking (yes or no), current drinking (yes or no), total energy intake (kcal/day), and BMI. Multicollinearity was assessed using variance inflation factors (VIFs), all of which were below 10, indicating no evident multicollinearity among the covariates (Supplementary Table 1).
Linear regression models were applied to estimate the mean or prevalence, with 95% CIs, of the 3 main factors (diet, physical activity, and depression) across the survey year and treated the survey year as a continuous variable to test the statistical significance of linear trends over time. All tests were 2-sided, and the level of significance was set at 0.05 using PROC SURVEY procedures in SAS software version 9.4 (SAS Institute Inc.).
RESULTS
Data from a total of 20,038 participants were analyzed. There were statistically significant differences in several variables with respect to sex (Table 1).
Table 1. Demographic and Lifestyle Characteristics of Study Participants, KNHANES 2014–2019 (n = 20,083).
| Variable | Overall (n = 20,038) | Male (n = 8,517) | Female (n = 11,521) | p-value* | ||
|---|---|---|---|---|---|---|
| Demographic | ||||||
| Age (yr) | 45.2 ± 0.2 | 44.6 ± 0.2 | 45.8 ± 0.2 | < 0.001 | ||
| Age group, ≥ 65 yr (%) | 14.1 ± 0.3 | 13.8 ± 0.4 | 14.3 ± 0.4 | 0.230 | ||
| Socioeconomic | ||||||
| Education level (%) | < 0.001 | |||||
| < High school graduate | 17.9 ± 0.4 | 13.9 ± 0.5 | 22.1 ± 0.5 | |||
| ≥ High school graduate | 82.1 ± 0.4 | 86.1 ± 0.5 | 77.9 ± 0.5 | |||
| Household income (%) | < 0.001 | |||||
| Q1 | 11.9 ± 0.4 | 10.6 ± 0.4 | 13.1 ± 0.4 | |||
| Q2 | 23.2 ± 0.5 | 22.7 ± 0.6 | 23.8 ± 0.6 | |||
| Q3 | 30.0 ± 0.5 | 30.5 ± 0.6 | 29.6 ± 0.6 | |||
| Q4 | 34.9 ± 0.7 | 36.2 ± 0.8 | 33.5 ± 0.8 | |||
| Married (%) | 73.5 ± 0.5 | 69.0 ± 0.7 | 78.0 ± 0.6 | < 0.001 | ||
| Lifestyle | ||||||
| Current smoking (%) | 19.1 ± 0.4 | 33.2 ± 0.6 | 5.0 ± 0.3 | < 0.001 | ||
| Current drinking (%) | 79.6 ± 0.4 | 86.8 ± 0.4 | 72.4 ± 0.5 | < 0.001 | ||
| Walking exercise (%)† | 49.6 ± 0.5 | 50.8 ± 0.7 | 48.4 ± 0.6 | 0.003 | ||
| Weight training (%)‡ | 25.1 ± 0.4 | 33.5 ± 0.6 | 16.6 ± 0.4 | < 0.001 | ||
| Depressive mood (%) | 6.7 ± 0.2 | 4.4 ± 0.3 | 8.9 ± 0.3 | < 0.001 | ||
| Body mass index (kg/m2) | 23.8 ± 0.0 | 24.5 ± 0.0 | 23.1 ± 0.1 | < 0.001 | ||
| Diet | ||||||
| Energy (kcal/day) | 2,040 ± 8 | 2,357 ± 12 | 1,722 ± 8 | < 0.001 | ||
| Carbohydrate (%kcal/day) | 63.1 ± 0.1 | 62.2 ± 0.2 | 64.0 ± 0.1 | < 0.001 | ||
| Protein (%kcal/day) | 15.3 ± 0.1 | 15.8 ± 0.1 | 14.8 ± 0.1 | < 0.001 | ||
| Fat (%kcal/day) | 21.6 ± 0.1 | 22.0 ± 0.1 | 21.2 ± 0.1 | < 0.001 | ||
| Disease history | ||||||
| Heart disease (%) | 1.6 ± 0.1 | 2.2 ± 0.2 | 1.1 ± 0.1 | < 0.001 | ||
| Stroke (%) | 1.2 ± 0.1 | 1.5 ± 0.1 | 0.9 ± 0.1 | 0.0002 | ||
| Cancer (%) | 3.9 ± 0.2 | 2.8 ± 0.2 | 5.1 ± 0.2 | < 0.001 | ||
| Hypertension (%) | 23.9 ± 0.4 | 27.3 ± 0.6 | 20.5 ± 0.5 | < 0.001 | ||
| Diabetes (%) | 9.6 ± 0.3 | 11.0 ± 0.4 | 8.2 ± 0.3 | < 0.001 | ||
| Dyslipidemia (%) | 25.6 ± 0.4 | 27.9 ± 0.5 | 23.2 ± 0.5 | < 0.001 | ||
| Metabolic syndrome (%) | 24.3 ± 0.4 | 28.3 ± 0.6 | 20.2 ± 0.5 | < 0.001 | ||
Values are presented as mean ± standard error (SE) for continuous variables and percentage ± SE for categorical variables.
KNHANES: Korea National Health and Nutrition Examination Survey, Q: quartile.
*p-values for differences by sex were obtained using the Student t-test for continuous variables and the Rao-Scott chi-square test for categorical variables. †Walking exercise was defined as walking > 5 days per week for ≥ 30 minutes per session. ‡Weight training was defined as weight training that lasts > 2 days per week.
Key Findings from Multiple Linear Regression Analyses
HGS demonstrated a significant decreasing trend over time, including among men and women (p < 0.001). Overall, a reduction of 2.28 kg in HGS was observed in 2019 relative to 2014, with men and women showing a decrease of 2.51 kg and 2.09 kg, respectively. Lower MAR (p < 0.001), lower METs (p < 0.001), presence of depressive symptoms (p = 0.001), older age (p < 0.001), female sex (p < 0.001), lower education level (p < 0.001), and lower household income (p < 0.001) were significantly associated with lower HGS. Sex (standardized coefficients, -0.72; p < 0.001) and age (standardized coefficients, -0.22 for men; -0.17 for women; all p < 0.001) were the most significant factors after adjusting for other variables, and age had a pronounced negative impact on HGS for both sexes. For men and women, the next most significant factors were low household income and lower education level, respectively. Poor nutritional quality similarly affected HGS negatively across sexes. Reduced physical activity had a notable negative impact, particularly for women. Depressive mood had a significant negative effect on HGS in men but not in women. BMI was positively correlated with HGS in both sexes, while non-smoking and non-drinking habits also showed positive effects, but with sex differences. Total energy intake was positively and significantly associated with HGS in men but not in women (Table 2).
Table 2. Factors Associated with Handgrip Strength among Korean Adults, KNHANES 2014–2019.
| Parameter | Level | Overall (n = 20,083) | Male (n = 8,517) | Female (n = 11,521) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Coefficient | Standardized coefficient | p-value | Coefficient | Standardized coefficient | p-value | Coefficient | Standardized coefficient | p-value | ||
| Survey year | 2015 | –1.00 | –0.03 | < 0.001 | –1.37 | –0.07 | 0.0001 | –0.59 | –0.05 | 0.002 |
| 2016 | –1.49 | –0.05 | < 0.001 | –1.53 | –0.08 | < 0.001 | –1.43 | –0.11 | < 0.001 | |
| 2017 | –2.24 | –0.08 | < 0.001 | –2.43 | –0.12 | < 0.001 | –2.06 | –0.17 | < 0.001 | |
| 2018 | –3.74 | –0.14 | < 0.001 | –4.12 | –0.21 | < 0.001 | –3.41 | –0.28 | < 0.001 | |
| 2019 | –2.28 | –0.08 | < 0.001 | –2.51 | –0.13 | < 0.001 | –2.09 | –0.17 | < 0.001 | |
| Poor nutritional quality (ref: high MAR) | 1 | –0.94 | –0.04 | < 0.001 | –1.31 | –0.08 | < 0.001 | –0.76 | –0.08 | < 0.001 |
| 2 | –0.39 | –0.02 | 0.002 | –0.52 | –0.03 | 0.013 | –0.29 | –0.03 | 0.024 | |
| Lower physical activity (ref: high METs/wk) | < 600 | –1.23 | –0.05 | < 0.001 | –1.28 | –0.07 | < 0.001 | –1.17 | –0.11 | < 0.001 |
| 600–< 3,000 | –0.80 | –0.04 | < 0.001 | –0.85 | –0.06 | 0.0001 | –0.70 | –0.07 | < 0.001 | |
| Depressive mood (ref: no depression) | –0.62 | –0.02 | 0.001 | –1.37 | –0.04 | 0.001 | –0.25 | –0.02 | 0.150 | |
| Age | –0.08 | –0.12 | < 0.001 | –0.10 | –0.22 | < 0.001 | –0.05 | –0.17 | < 0.001 | |
| Female sex | –15.05 | –0.72 | < 0.001 | |||||||
| Lower education level (ref: ≥ high school graduate) | –1.39 | –0.05 | < 0.001 | –1.68 | –0.08 | < 0.001 | –1.42 | –0.12 | < 0.001 | |
| Household income (ref: highest Q4 income) | Q1 | –1.83 | –0.06 | < 0.001 | –2.61 | –0.11 | < 0.001 | –1.03 | –0.07 | < 0.001 |
| Q2 | –0.50 | –0.02 | 0.001 | –0.74 | –0.04 | 0.003 | –0.06 | –0.01 | 0.654 | |
| Q3 | 0.06 | 0.00 | 0.677 | 0.22 | 0.01 | 0.327 | –0.002 | –0.0002 | 0.984 | |
| Current smoker (ref: non–smoker) | 1.13 | 0.04 | < 0.001 | 1.07 | 0.07 | < 0.001 | 0.94 | 0.04 | < 0.001 | |
| Current drinker (ref: non–drinker) | 0.69 | 0.03 | < 0.001 | 1.17 | 0.05 | < 0.001 | 0.61 | 0.06 | < 0.001 | |
| Total energy intake | 0.0003 | 0.03 | 0.0002 | 0.0004 | 0.05 | 0.0004 | –0.0001 | –0.01 | 0.301 | |
| BMI | 0.38 | 0.13 | < 0.001 | 0.46 | 0.21 | < 0.001 | 0.25 | 0.19 | < 0.001 | |
Multiple linear regression models were fitted to identify the factors associated with handgrip strength. Coefficients and standardized coefficients were obtained with handgrip strength (kg) as the outcome variable in the model. Survey year of 2014, better nutritional quality (MAR tertile 3), being physically active (METs/wk of ≥ 3,000), no depression, a higher education level (≥ high school graduate), the highest quartile of household income, non-smoker, and non-drinker were considered reference values. The R2 values for the overall, male, and female models were 0.71, 0.23, and 0.19, respectively.
KNHANES: Korea National Health and Nutrition Examination Survey, MAR: mean nutrient adequacy ratio, MET: metabolic equivalent of task, Q: quartile, BMI: body mass index.
Trends in Health and Lifestyle Factors (2014–2019)
Between 2014 and 2019, nutritional quality, as indicated by MAR, showed a significant decrease of 8.6%, highlighting a decline in dietary quality over the 5-year period (p for linear trend < 0.001). Concurrently, physical activity levels, quantified in METs, decreased by 28.9% (p for linear trend < 0.001). This decline was pronounced in both vigorous and moderate physical activities, which reduced by 46.8% and 49.5%, respectively (p for linear trend < 0.001 for both). Moreover, the proportion of individuals reporting depressive symptoms decreased by 19.8% (p for linear trend = 0.006) (Table 3, Supplementary Table 2).
Table 3. Trends in Selected Risk Factors Associated with Handgrip Strength by Survey Year, KNHANES 2014–2019.
| Survey year | % difference, 2019 vs. 2014 | p for linea trend* | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |||||
| Overall (n = 20,038) | ||||||||||
| Sample size | 2,872 | 3,006 | 3,296 | 3,516 | 3,661 | 3,687 | ||||
| Diet | ||||||||||
| MAR score | 0.81 (0.8–0.82) | 0.81 (0.8–0.82) | 0.76 (0.75–0.77) | 0.76 (0.75–0.78) | 0.76 (0.75–0.77) | 0.75 (0.74–0.76) | –8.6 | < 0.001 | ||
| Total energy intake (kcal/day) | 2,085 (2,044–2,127) | 2,107 (2,070–2,144) | 2,048 (2,011–2,085) | 1,980 (1,947–2,013) | 1,994 (1,959–2,028) | 1,940 (1,910–1,971) | –7.0 | < 0.001 | ||
| Physical activity (METs/wk) | ||||||||||
| Total | 2,298 (2,157–2,438) | 1,925 (1,830–2,020) | 1,736 (1,638–1,834) | 1,650 (1,573–1,728) | 1,587 (1,503–1,671) | 1,632 (1,558–1,706) | –28.9 | < 0.001 | ||
| Vigorous | 378 (310–446) | 246 (204–288) | 202 (156–248) | 206 (171–241) | 200 (158–242) | 201 (164–238) | –46.8 | < 0.001 | ||
| Moderate | 553 (491–615) | 406 (355–457) | 317 (271–363) | 278 (245–312) | 289 (254–324) | 280 (250–310) | –49.5 | < 0.001 | ||
| Walking | 1,366 (1,274–1,458) | 1,273 (1,217–1,329) | 1,217 (1,154–1,279) | 1,166 (1,117–1,215) | 1,097 (1,051–1,144) | 1,152 (1,109–1,194) | –15.7 | < 0.001 | ||
| Depressive mood | ||||||||||
| Yes to EuroQoL question | 8.1 (6.9–9.3) | 6.7 (5.6–7.7) | 7.2 (6.1–8.2) | 5.4 (4.5–6.2) | 6.2 (5.4–7.1) | 6.5 (5.6–7.4) | –19.8 | 0.006 | ||
| Male sex (n = 8,517) | - | |||||||||
| Sample size | 1,222 | 1,293 | 1,392 | 1,499 | 1,553 | 1,558 | ||||
| Diet | ||||||||||
| MAR score | 0.84 (0.83–0.84) | 0.83 (0.83–0.84) | 0.79 (0.78–0.79) | 0.79 (0.79–0.80) | 0.79 (0.79–0.80) | 0.77 (0.77–0.78) | –7.2 | < 0.001 | ||
| Total energy intake (kcal/day) | 2,366 (2,302–2,430) | 2,384 (2,326–2,442) | 2,347 (2,295–2,400) | 2,238 (2,185–2,290) | 2,264 (2,208–2,319) | 2,201 (2,154–2,248) | –7.0 | < 0.001 | ||
| Physical activity (METs/wk) | ||||||||||
| Total | 2,585 (2,363–2,807) | 2,175 (2,016–2,334) | 1,937 (1,790–2,084) | 1,869 (1,735–2,003) | 1,807 (1,667–1,946) | 1,898 (1,776–2,019) | –26.6 | < 0.001 | ||
| Vigorous | 559 (438–679) | 343 (274–412) | 297 (221–373) | 303 (239–367) | 304 (224–383) | 312 (244–379) | –44.2 | 0.001 | ||
| Moderate | 548 (472–624) | 480 (402–558) | 386 (315–458) | 327 (275–378) | 354 (299–409) | 352 (309–395) | –35.7 | < 0.001 | ||
| Walking | 1,478 (1,342–1,614) | 1,352 (1,262–1,442) | 1,253 (1,168–1,339) | 1,240 (1,161–1,318) | 1,150 (1,083–1,216) | 1,234 (1,173–1,296) | –16.5 | < 0.001 | ||
| Depressive mood | ||||||||||
| Yes to EuroQoL question | 5.8 (4–7.5) | 4.3 (3–5.6) | 4.9 (3.6–6.1) | 3.8 (2.8–4.9) | 4.2 (3.2–5.2) | 4.5 (3.4–5.5) | –22.6 | 0.136 | ||
| Female sex (n = 11,521) | - | |||||||||
| Sample size | 1,650 | 1,713 | 1,904 | 2,017 | 2,108 | 2,129 | ||||
| Diet | ||||||||||
| MAR score | 0.8 (0.8–0.81) | 0.79 (0.79–0.80) | 0.74 (0.73–0.75) | 0.75 (0.74–0.76) | 0.74 (0.73–0.75) | 0.73 (0.73–0.74) | –8.7 | < 0.001 | ||
| Total energy intake (kcal/day) | 1,777 (1,734–1,819) | 1,804 (1,762–1,847) | 1,720 (1,680–1,759) | 1,697 (1,661–1,732) | 1,697 (1,658–1,735) | 1,650 (1,612–1,688) | –7.1 | < 0.001 | ||
| Physical activity (METs/wk) | ||||||||||
| Total | 2,036 (1,871–2,201) | 1,704 (1,599–1,808) | 1,566 (1,462–1,670) | 1,458 (1,375–1,541) | 1,391 (1,301–1,482) | 1,393 (1,314–1,471) | –31.6 | < 0.001 | ||
| Vigorous | 208 (158–259) | 161 (125–197) | 119 (85–153) | 118 (86–149) | 104 (75–132) | 98 (71–124) | –53.3 | < 0.001 | ||
| Moderate | 565 (461–669) | 337 (281–393) | 251 (209–293) | 235 (188–282) | 229 (184–274) | 211 (171–251) | –62.6 | < 0.001 | ||
| Walking | 1,263 (1,159–1,367) | 1,206 (1,136–1,276) | 1,196 (1,115–1,278) | 1,106 (1,048–1,164) | 1,059 (996–1,122) | 1,084 (1,027–1,142) | –14.1 | < 0.001 | ||
| Depressive mood | ||||||||||
| Yes to EuroQoL question | 11.5 (9.6–13.3) | 10.1 (8.4–11.8) | 10.4 (8.6–12.2) | 7.8 (6.5–9.2) | 9.2 (7.5–10.8) | 9.4 (7.9–10.9) | –18.3 | 0.023 | ||
Estimated means and 95% CIs for diet and physical activity levels and prevalences and 95% CIs for depression were obtained using the linear regression model after adjusting for age, sex, proportions of low education and income, current smoking, and current drinking for analyses.
KNHANES: Korea National Health and Nutrition Examination Survey, MAR: mean nutrient adequacy ratio, MET: metabolic equivalent of task.
*p-values for linear trends were estimated with the survey cycles modelled as a continuous variable in the linear regression model.
Both men and women exhibited declines in physical activity and nutritional quality. Male participants showed a 26.6% decrease in total physical activity (p for linear trend < 0.001), with the most significant reduction in vigorous physical activity (-44.2%, p for linear trend = 0.001). Moderate physical activity and nutritional quality also decreased significantly among men by 35.7% and 7.2%, respectively (p for linear trend < 0.001). Total energy intake among men was similarly reduced by 7.0% (p for linear trend < 0.001). However, changes in depressive mood among male participants did not show a statistically significant trend.
Female participants showed a more marked decrease in total physical activity of 31.6% (p for linear trend < 0.001), with moderate physical activity reducing by 62.6% (p for linear trend < 0.001) and vigorous physical activity by 53.3% (p for linear trend < 0.001). Additionally, women faced a significant reduction in both nutritional quality and total energy intake by 8.7% and 7.1%, respectively (p for linear trend < 0.001). Depressive mood among women declined by 18.3% (p for linear trend = 0.023).
DISCUSSION
In this study, utilizing data from KNHANES between 2014 and 2019, HGS and lifestyle trends were analyzed, and factors that were significantly associated with HGS decline were identified. Our comprehensive analysis has revealed a significant decrease in HGS across both sexes, noting an overall reduction of 2.28 kg in HGS from 2014 to 2019. A concerning trend is the significant decline in nutritional quality and physical activity levels, which occurred along-side the temporal decrease in HGS. This correlation suggests that the deterioration of these lifestyle factors may have significantly contributed to the observed decline in HGS. The study findings point to a broader decline in the physical health status of the public, pointing towards an increasing public health challenge.
This study identified key factors associated with a decline in HGS, which include older age, female sex, lower education level, and lower household income. These findings align with those of previous studies that have established age and sex as the most influential determinants.7,8,9) Despite the correlation between low HGS and lower income and educational levels, these socio-economic factors are difficult to address within the realm of public health. However, aspects such as mental health, nutritional status, and physical activity can be improved through public health interventions. The gradual improvement in depressive mood observed in our study, contrasted with the deterioration in nutritional status and physical activity levels, emphasizes the need for targeted health policies. These policies should aim to enhance physical activity and nutritional status to address the challenges posed by the declining HGS.
Recent studies have substantiated the trend of diminishing physical activity among Koreans. Han et al.,13) utilizing KNHNAES 2014–2021 data, highlighted an increase in sedentary behavior and obesity rates. Similarly, Kim et al.14) observed a reduction in both moderate and vigorous physical activities among adults of all ages and sexes, according to KNHNAES data from 1998 to 2018. Additionally, a longitudinal study revealed a positive correlation between physical activity and HGS.6) Ahn et al.8) reported that individuals engaged in a high level of physical activity exhibited a lower likelihood of reduced HGS. Furthermore, Seong et al.15) revealed that the group with insufficient aerobic exercise (less than 600 MET-min/wk) had a considerably higher risk of low HGS compared to the more active group. These studies collectively emphasize the crucial role of active physical engagement in maintaining HGS. Our research identified a significant reduction in both vigorous and moderate physical activities, suggesting that these trends significantly contribute to the temporal decline in grip strength.
Nutritional quality emerges as another possible factor influencing HGS across both sexes. Our findings revealed a decline in both nutritional quality and total energy intake for both sexes (Table 3). However, when examining their associations with HGS, only nutritional quality remained statistically significant in female sex, while total energy intake did not (Table 2). This suggests that nutritional quality may have a greater influence on the reduction of grip strength than total energy intake. Previous studies reported an association between diet quality and HGS.16,17,18,19,20) Shin and Kim16) observed that a superior diet quality correlated with greater HGS among older Korean men. Similarly, recent studies found a negative correlation between poor nutritional quality and HGS.17,18) Lee19) found a prudent dietary pattern had a significant positive correlation with HGS, even after adjusting for potential confounding variables. Mishra et al.20) highlighted the importance of protein intake, suggesting that higher protein consumption, alongside strength exercise, could mitigate the risk of low HGS. Additionally, Shin21) identified dietary fiber as a crucial component for alleviating the risk of sarcopenia in older Korean women. Furthermore, Zhang et al.22) reported an association between a diet high in ultra-processed foods and a more rapid decline in grip strength among middle-aged and older adults. These findings collectively underscore the impact of nutritional quality on HGS and suggest that enhancing dietary quality could counteract the trend of declining HGS in the Korean population.
The trend of HGS is reported to vary across different countries. While Shields et al.23) and Dodds et al.6) reported HGS decline among older adults in Canada and the United Kingdom, Ahrenfeldt et al.24) observed mixed trends in Europe, and the United States showed stable HGS levels across demographics.25) Notably, Japan saw an increase in HGS attributed to greater physical activity and body size.26,27) However, despite the increase in BMI in Canada and the United Kingdom, HGS declined, particularly in the United Kingdom, where a decrease in physical activity level was also noted. This pattern suggests that while body size changes do not consistently predict HGS trends, physical activity levels do. This is consistent with our findings and highlights the crucial role of physical activity in influencing HGS, regardless of body size variations.
This study has some limitations. First, the cross-sectional design makes it challenging to establish causality of the trends. Future prospective studies are needed to ascertain the causes of the decline in HGS among Koreans. Second, among the various factors related to HGS, this research primarily examined the effect of diet, physical activity, and depressive mood, without delving into concurrent chronic diseases or socioeconomic factors. Nonetheless, we believe that adopting healthy lifestyle habits is paramount to improving national trends of HGS. Third, data on nutritional quality, physical activity level, and depressive symptoms were collected using self-reported questionnaires. Therefore, the association between these factors and low HGS might have been inaccurately estimated due to information bias. Lastly, as this study was based on the KNHANES data targeting Koreans, its generalizability to other countries is limited. However, considering that normative data for HGS vary by race, the HGS data from KNHANES, which reflects a single-ethnic characteristic, may offer advantages for trend analysis.
This study identified a significant decline in HGS among Korean adults between 2014 and 2019, in parallel with reductions in physical activity and nutritional quality. Efforts to reverse these trends are required for maintaining functional health at the population level. Monitoring HGS trends might support the assessment of national health status.
ACKNOWLEDGEMENTS
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (RS-2023-00274240).
Footnotes
CONFLICT OF INTEREST: No potential conflict of interest relevant to this article was reported.
SUPPLEMENTARY MATERIAL
Supplementary material is available in the electronic version of this paper at the CiOS website, www.ecios.org.
Sociodemographic, Lifestyle, and Disease History Characteristics
Assessment of Multicollinearity Using VIF
Trends in Risk Factors Associated with Handgrip Strength by Survey Year, KNHANES 2014–2019
References
- 1.Sayer AA, Kirkwood TB. Grip strength and mortality: a biomarker of ageing? Lancet. 2015;386(9990):226–227. doi: 10.1016/S0140-6736(14)62349-7. [DOI] [PubMed] [Google Scholar]
- 2.Mathiowetz V, Weber K, Volland G, Kashman N. Reliability and validity of grip and pinch strength evaluations. J Hand Surg Am. 1984;9(2):222–226. doi: 10.1016/s0363-5023(84)80146-x. [DOI] [PubMed] [Google Scholar]
- 3.Dufner TJ, Fitzgerald JS, Lang JJ, Tomkinson GR. Temporal trends in the handgrip strength of 2,592,714 adults from 14 countries between 1960 and 2017: a systematic analysis. Sports Med. 2020;50(12):2175–2191. doi: 10.1007/s40279-020-01339-z. [DOI] [PubMed] [Google Scholar]
- 4.Wang T, Feng W, Li S, Tan Q, Zhang D, Wu Y. Impact of obesity and physical inactivity on the long-term change in grip strength among middle-aged and older European adults. J Epidemiol Community Health. 2019;73(7):619–624. doi: 10.1136/jech-2018-211601. [DOI] [PubMed] [Google Scholar]
- 5.Steiber N. Population aging at cross-roads: diverging secular trends in average cognitive functioning and physical health in the older population of Germany. PLoS One. 2015;10(8):e0136583. doi: 10.1371/journal.pone.0136583. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Dodds RM, Pakpahan E, Granic A, Davies K, Sayer AA. The recent secular trend in grip strength among older adults: findings from the English longitudinal study of ageing. Eur Geriatr Med. 2019;10(3):395–401. doi: 10.1007/s41999-019-00174-4. [DOI] [PubMed] [Google Scholar]
- 7.Kang Y, Kim J, Kim S, Park S, Lim H, Koh H. Trends in measures of handgrip strength from 2014 to 2017 among Korean adolescents using the Korean National Health and Nutrition Examination Survey Data. BMC Res Notes. 2020;13(1):307. doi: 10.1186/s13104-020-05112-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Ahn H, Choi HY, Ki M. Association between levels of physical activity and low handgrip strength: Korea National Health and Nutrition Examination Survey 2014-2019. Epidemiol Health. 2022;44:e2022027. doi: 10.4178/epih.e2022027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Lee K. Relative handgrip strength in relation to depressive mood and suicidal ideation in Koreans using the 2015 KNHANES data. J Musculoskelet Neuronal Interact. 2018;18(3):333–338. [PMC free article] [PubMed] [Google Scholar]
- 10.Kim CR, Jeon YJ, Jeong T. Risk factors associated with low handgrip strength in the older Korean population. PLoS One. 2019;14(3):e0214612. doi: 10.1371/journal.pone.0214612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Kominiarek MA, Rajan P. Nutrition recommendations in pregnancy and lactation. Med Clin North Am. 2016;100(6):1199–1215. doi: 10.1016/j.mcna.2016.06.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Rural Development Administration, National Institute of Agricultural Sciences (KR) Standard food composition table 10th edition. Rural development Administration, National Institute of Agricultural Sciences; 2022. [Google Scholar]
- 13.Han Y, Sung H, Choi Y, Kim YS. Trends in obesity, leisure-time physical activity, and sedentary behavior in Korean adults: Korea national health and nutritional examinations survey from 2014 to 2021. PLoS One. 2024;19(1):e0296042. doi: 10.1371/journal.pone.0296042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kim S, Choi S, Kim J, et al. Trends in health behaviors over 20 years: findings from the 1998-2018 Korea National Health and Nutrition Examination Survey. Epidemiol Health. 2021;43:e2021026. doi: 10.4178/epih.e2021026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Seong JY, Ahn HY, Park Y, Shin S, Ha IH. Association between aerobic exercise and handgrip strength in adults: a cross-sectional study based on data from the Korean National Health and Nutrition Examination Survey (2014-2017) J Nutr Health Aging. 2020;24(6):619–626. doi: 10.1007/s12603-020-1372-x. [DOI] [PubMed] [Google Scholar]
- 16.Shin WY, Kim JH. Age-specific association between handgrip strength and nutritional quality in Korean men: a nationwide cross-sectional study. Am J Mens Health. 2021;15(6):15579883211063339. doi: 10.1177/15579883211063339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kim H, Kwon O. Higher diet quality is associated with lower odds of low hand grip strength in the Korean elderly population. Nutrients. 2019;11(7):1487. doi: 10.3390/nu11071487. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Mendes J, Afonso C, Moreira P, et al. Association of anthropometric and nutrition status indicators with hand grip strength and gait speed in older adults. JPEN J Parenter Enteral Nutr. 2019;43(3):347–356. doi: 10.1002/jpen.1424. [DOI] [PubMed] [Google Scholar]
- 19.Lee S. Associations between dietary patterns and handgrip strength: the Korea National Health and Nutrition Examination Survey 2014-2016. J Am Coll Nutr. 2020;39(6):488–494. doi: 10.1080/07315724.2019.1691955. [DOI] [PubMed] [Google Scholar]
- 20.Mishra S, Goldman JD, Sahyoun NR, Moshfegh AJ. Association between dietary protein intake and grip strength among adults aged 51 years and over: what we eat in America, National Health and Nutrition Examination Survey 2011-2014. PLoS One. 2018;13(1):e0191368. doi: 10.1371/journal.pone.0191368. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Shin S. Association between dietary fiber intake and low muscle strength among Korean adults. Clin Nutr Res. 2024;13(1):33–41. doi: 10.7762/cnr.2024.13.1.33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zhang S, Gu Y, Rayamajhi S, et al. Ultra-processed food intake is associated with grip strength decline in middle-aged and older adults: a prospective analysis of the TCLSIH study. Eur J Nutr. 2022;61(3):1331–1341. doi: 10.1007/s00394-021-02737-3. [DOI] [PubMed] [Google Scholar]
- 23.Shields M, Tremblay MS, Laviolette M, Craig CL, Janssen I, Connor Gorber S. Fitness of Canadian adults: results from the 2007-2009 Canadian Health Measures Survey. Health Rep. 2010;21(1):21–35. [PubMed] [Google Scholar]
- 24.Ahrenfeldt LJ, Lindahl-Jacobsen R, Rizzi S, Thinggaard M, Christensen K, Vaupel JW. Comparison of cognitive and physical functioning of Europeans in 2004-05 and 2013. Int J Epidemiol. 2018;47(5):1518–1528. doi: 10.1093/ije/dyy094. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Loprinzi PD. An initial assessment of secular trends in muscular strength among children, adolescents, and adults across the lifespan: national sample of Americans. Am J Health Promot. 2018;32(3):705–707. doi: 10.1177/0890117116684890. [DOI] [PubMed] [Google Scholar]
- 26.Tomkinson GR, Kidokoro T, Dufner T, Noi S, Fitzgerald JS, Mcgrath RP. Temporal trends in handgrip strength for older Japanese adults between 1998 and 2017. Age Ageing. 2020;49(4):634–639. doi: 10.1093/ageing/afaa021. [DOI] [PubMed] [Google Scholar]
- 27.Ministry of Education, Culture, Sports, Science and Technology. Report book on the survey of physical fitness and athletic ability. Ministry of Education, Culture, Sports, Science and Technology; 1999-2018. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Sociodemographic, Lifestyle, and Disease History Characteristics
Assessment of Multicollinearity Using VIF
Trends in Risk Factors Associated with Handgrip Strength by Survey Year, KNHANES 2014–2019