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Article

Comparative Analysis of Physical Activity and Neuromuscular Characteristics in Middle-Aged and Young Men

by
Byungkwan Kim
1,
Kihong Kim
2,
Sanghyun Lee
3,
Jaeheon Son
4 and
Hwanjong Jeong
3,*
1
Sports Science Institute, College of Sports Science, Dankook University, Cheonan 31116, Republic of Korea
2
Department of Recreation and Leisure Sports, College of Sports Science, Dankook University, Cheonan 31116, Republic of Korea
3
Department of Sports Healthcare, Graduate School, Dankook University, Cheonan 31116, Republic of Korea
4
Department of Sports and Health Management, Division of Art and Physical Education, Daejeon Institute of Science and Technology, Daejeon 35408, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(18), 9952; https://doi.org/10.3390/app15189952
Submission received: 26 August 2025 / Revised: 8 September 2025 / Accepted: 10 September 2025 / Published: 11 September 2025
(This article belongs to the Special Issue Recent Advances in AI Convergence: Innovations at the Crossroads of Disciplines)
Versions Notes

Abstract

This study investigated the associations among age, physical activity level, knee muscle function, and neuromuscular junction (NMJ) biomarkers in a cohort of 80 middle-aged and young men residing in the Republic of Korea. Despite comparable levels of physical activity between the groups, the middle-aged participants exhibited significantly higher body fat percentage, elevated levels of the neurodegeneration marker neurofilament light chain (NfL), and a marked decline in lower-extremity muscle function compared to their younger counterparts. Advancing age was negatively associated with knee extensor peak torque, body weight–normalized torque (BW/PT), and the rate of torque development at 0.18 s (RTD@0.18s). In contrast, higher physical activity levels were positively correlated with certain indicators of muscle function and were associated with lower circulating concentrations of the NMJ degeneration marker, C-terminal agrin fragment (CAF). These findings suggest that neuromuscular decline and muscle function deterioration may begin as early as middle age. The results underscore the importance of implementing tailored exercise regimens and lifestyle interventions to preserve neuromuscular health and prevent early-onset muscle loss.

1. Introduction

Since 2017, the Republic of Korea has officially entered an aged society, with over 14% of its population aged 65 years or older, and it currently has the lowest fertility rate in the world [1]. Consequently, attention is needed to mitigate social issues such as increased medical expenditures, slowed economic growth, and weakened national defense capabilities resulting from population aging [2,3]. Therefore, it is crucial to develop intervention strategies to prevent sarcopenia before old age and to provide opportunities to maintain skeletal muscle health [4].
Middle age marks a transitional period during which biological aging, lifestyle factors (e.g., sedentary behavior, poor dietary habits), occupational demands (e.g., career development), and family responsibilities (e.g., child-rearing) contribute to gradual physical decline. Time constraints also limit the opportunity to engage in regular exercise during this life stage [5]. In many countries, middle-aged adults are significantly less physically active than younger adults and frequently fall short of recommended physical activity levels for maintaining health [6,7].
Age-related changes in body composition have been well-documented [5]. Skeletal muscle mass begins to decline around the age of 50, with estimated losses of approximately 1–2% per year and a cumulative loss of 12–15% per decade [8]. In a study of adults aged 18 to 88 years, Janssen et al. [9] reported that total skeletal muscle mass declined by about 1.9 kg per year after the age of 50. In parallel, age-related reductions in basal metabolic rate due to decreased energy efficiency contribute to increased fat accumulation [10]. However, because muscle mass loss and fat gain often occur simultaneously, these changes may not be reflected in total body weight, and therefore, may not be captured accurately by BMI [11].
Muscle strength a key determinant of physical function typically begins to decline after the age of 40 [12] and decreases more rapidly than muscle mass itself [13,14,15,16]. This can lead to a greater risk of falls [17] and the loss of mobility and independence [18]. Studies utilizing isokinetic dynamometers, the gold standard for measuring muscle strength, have shown that knee extensor strength declines annually by 1.1–1.4% in adults aged 40–59 years and by 0.9–1.0% in adults aged 20–39 years over a 10-year period [19].
From a healthy aging perspective, neuromuscular denervation and neuromuscular junction (NMJ) degradation have recently been identified as key mechanisms contributing to age-related sarcopenia and functional decline [20,21]. The C-terminal agrin fragment (CAF), a 22 kDa peptide released upon the cleavage of agrin by neurotrypsin, regulates the assembly of pre- and postsynaptic molecules and clustering of acetylcholine receptors [22], and serves as a biomarker of NMJ disruption. Likewise, neurofilament light chain (NfL), released during axonal damage or neuronal degeneration, is considered a marker of neurodegeneration [23]. Sarto et al. [24] reported that adults over 70 years had significantly higher circulating levels of CAF and NfL than younger adults aged 18–35. Furthermore, Qaisar et al. [25] found that CAF levels increased with age and were associated with muscle weakness and slower gait speed.
Aging-related physiological changes can begin as early as young adulthood and extend throughout the adult lifespan. Although middle-aged adults may have similar levels of muscle mass as younger adults, they tend to exhibit reduced muscle strength and power, along with decreased neuromuscular efficiency [26]. While middle-aged individuals are clearly affected by the aging process [8], most aging research has focused on older adults [27,28], often excluding the middle-aged population [29]. This is likely due to the perception that adults aged 20–59 years are relatively healthy; however, this research gap limits our understanding of the physiological transitions that occur during middle age—a critical window for the early detection and prevention of neuromuscular decline [30,31].
Given the growing proportion of the global population entering middle and older age, and the rapid rise in adults aged 60 and older, it is increasingly important to identify age-related differences in physical activity, body composition, muscle strength, and NMJ status. Investigating how these variables change across adulthood can provide crucial insights for maintaining muscle health and functional independence prior to old age.
Therefore, the primary objective of this study was to identify differences in physical activity, body composition, knee muscle function, and NMJ biomarkers between young and middle-aged men, and to evaluate the effects of age and physical activity level on these parameters.

2. Materials and Methods

2.1. Participants

The participants in this study were recruited from the Sports Science Institute at the Cheonan Campus of Dankook University, Republic of Korea. The study was conducted from November 2023 to February 2024. All participants were males in their 20s to 50s residing in Cheonan-si, Chungcheongnam-do, and were classified as non-athletic individuals engaging primarily in sedentary lifestyles or recreational physical activity.
Exclusion criteria included being under 20 or over 59 years of age, a history of unstable cardiovascular disease, malignant tumors or infectious diseases, musculoskeletal disorders, and current medication use. Although major pathological conditions were addressed through exclusion criteria, non-pathological variables such as dietary intake, sleep habits, supplement use, smoking status, and comorbidities not specified in the exclusion criteria were not assessed in this study. Therefore, the potential influence of these factors on muscle strength and NMJ biomarkers was not controlled.
Initially, a total of 82 participants were recruited. However, based on dropout and duplication criteria, a final sample of 80 participants was included in the analysis. The study protocol was approved by the Institutional Review Board of Dankook University (approval number: DKU-IRB No. 2023-09-019-002), and written informed consent was obtained from all participants prior to their enrollment.

2.2. Body Composition

Bioelectrical impedance analysis (BIA) instrument (Inbody 470, Inbody Co., Ltd., Seoul, Republic of Korea) was used to measure body composition (weight, body fat, BMI). The subjects were asked to fast for at least 8 h before testing. When participants arrived at the laboratory, they were instructed to remove shoes, socks, and metal ornaments before the measurement. Then, they stood up on the footboard of the measuring instrument. During the measurement, the subjects were instructed not to move or talk with the handle.

2.3. International Physical Activity Questionnaire Scale

To assess physical activity levels, all participants were asked to complete the Korean version of the International Physical Activity Questionnaire (IPAQ) [32] using a self-reported format. Weekly total physical activity was calculated as the sum of walking (3.3 METs × minutes × days), moderate-intensity activity (4.0 METs × minutes × days), and vigorous-intensity activity (8.0 METs × minutes × days), and expressed as MET-minutes per week (see Table 1). Activities lasting less than 10 min were not included in the calculation.

2.4. Blood Sampling

All participants fasted for at least eight hours prior to testing. A qualified nurse collected 6 mL of venous blood using an EDTA tube (BD Vacutainer®, Becton, Dickinson and Company, Franklin Lakes, NJ, USA). The collected blood samples were centrifuged at 3000 RPM for 10 min at 4 °C. Following centrifugation, 1 mL of serum was separated via pipetting and immediately stored at −70 °C until analysis. The samples were subsequently submitted to a certified analytical laboratory for testing. Serum concentrations of NfL and CAF, biomarkers of neurodegeneration, were quantified using ELISA kits (NBP2-81184, Novus, Centennial, CO, USA; ab216945, Abcam, Cambridge, UK).

2.5. Isokinetic Strength

Isokinetic muscle function was measured using biodex system 4 (biodex medical systems, Inc., Shirley, NY, USA). The subjects were asked to sit on an isokinetic equipment chair, which had a seat setting where the hip joint was bent 90°. To minimize external force intervention on the body other than the lower extremity during knee flexion and bending exercises, the upper body, pelvis, and thigh were secured in place using a belt. The lateral epicondyle of the femur was matched with the rotation axis of the dynamometer, and the lateral malleolus was fixed with a velcro strap.
The maximum extension of the knee joint was set to 0°, and the range of knee joint flexion was set to 90°. Gravity correction was performed at 25° to exclude the weight of the lower leg from being added or subtracted from the measured value during extension and flexion. Three preliminary exercises were performed at the predetermined load speed before the measurement, and sufficient rest was taken. Then, knee flexion and extension were performed 5 times at 60°/s. When measuring, the investigators encouraged the participants to exert their maximum force. Peak Torque (PT), Peak Toqure/Body weight (PT/BW), Torque at 0.18 s (RTD@0.18s) were used for the analysis.

2.6. Statistical Analysis

The collected data were analyzed using IBM SPSS Statistics version 22.0 (IBM Co., Armonk, NY, USA). An independent samples t-test was conducted to compare differences between middle-aged men (40–59 years) and young men (20–39 years). Furthermore, multiple regression analysis was performed to examine the causal relationships between age, IPAQ scores, and the dependent evaluation variables.

3. Results

3.1. Participant Characteristics According to Variables

The results of the independent samples t-test comparing the characteristics of the middle-aged and young male groups are presented in Table 2. The independent samples t-test showed that middle-aged men had significantly higher body fat and NfL levels, whereas young men demonstrated significantly greater isokinetic muscle function (PT, BW/PT) and RTD@0.18s.

3.2. Multiple Regression Analysis Assessing the Relationship Between Age, Physical Activity, and Body Composition Variables

As shown in Table 3, the results of the regression analysis for weight indicated a statistically significant trend (F = 3.345, p = 0.040). The model explained 8.0% of the variance in weight (R2 = 0.080, adjusted R2 = 0.056), and among the predictors, IPAQ score emerged as a significant predictor (β = −0.241, p = 0.031).
The regression analysis for body fat did not yield statistically significant results (F = 2.777, p = 0.068). The model explained 6.7% of the variance in body fat (R2 = 0.067, adjusted R2 = 0.043), and age was identified as a significant predictor (β = 0.256, p = 0.023).
Similarly, the regression model for BMI was also not statistically significant (F = 1.788, p = 0.174). Neither age (β = −0.077, p = 0.489) nor IPAQ score (β = −0.199, p = 0.078) emerged as significant predictors.

3.3. Multiple Regression Analysis of Age, Physical Activity, and Neuromuscular Biomarkers on Isokinetic Strength

As presented in Table 4, the multiple regression analysis for PT revealed statistically significant results (F = 19.542, p < 0.01). The model accounted for 51.0% of the variance in peak torque (R2 = 0.510, adjusted R2 = 0.484), with age (β = −0.701, p < 0.001), IPAQ score (β = 0.446, p < 0.01), and NfL (β = 0.701, p = 0.042) emerging as significant predictors.
Similarly, the regression model for BW/PT was statistically significant (F = 14.035, p < 0.01), explaining 42.8% of the variance (R2 = 0.428, adjusted R2 = 0.398). Both age (β = −0.542, p < 0.01) and IPAQ score (β = 0.525, p < 0.01) were identified as significant predictors.
The regression analysis for RTD@0.18s also yielded statistically significant results (F = 14.028, p < 0.01), accounting for 42.8% of the variance (R2 = 0.428, adjusted R2 = 0.397). Among the predictors, age was the only variable that significantly predicted RTD@0.18s (β = −0.624, p < 0.001).

3.4. Multiple Regression Analysis of Age, Physical Activity, and Isokinetic Strength on Neuromuscular Biomarkers

As shown in Table 5, the results of the regression analysis for CAF demonstrated statistical significance (F = 6.595, p < 0.01). The model explained 30.8% of the variance in CAF (R2 = 0.308, adjusted R2 = 0.261), and among the predictors, IPAQ score was identified as a significant predictor (β = −0.574, p < 0.001).
Similarly, the results for NfL also showed statistical significance (F = 8.089, p < 0.01). The model accounted for 35.3% of the variance in NfL (R2 = 0.353, adjusted R2 = 0.310), with age emerging as a significant predictor (β = 0.623, p < 0.001).

3.5. Correlation Analysis of Isokinetic Strength with Age, Physical Activity, Neuromuscular Biomarkers

As shown in Table 6, Pearson’s correlation analysis revealed significant associations between isokinetic strength variables and age, physical activity, and neuromuscular biomarkers.
Age demonstrated significant negative correlations with PT (r = −0.591, p < 0.001), BW/PT (r = −0.442, p < 0.001), and RTD@0.18s (r = −0.651, p < 0.001). Conversely, IPAQ scores were positively correlated with PT (r = 0.376, p < 0.001) and BW/PT (r = 0.466, p < 0.001).
With respect to neuromuscular biomarkers, CAF showed a negative correlation with BW/PT (r = −0.244, p = 0.029), while NfL exhibited negative correlations with PT (r = −0.231, p = 0.040) and RTD@0.18s (r = −0.384, p < 0.001).

4. Discussion

This study aimed to examine the associations among age, physical activity level, knee muscle function, and the NMJ in middle-aged and young adult males in the Republic of Korea. The primary findings indicated that, despite comparable levels of physical activity as assessed by the IPAQ, the middle-aged group exhibited poorer neuromuscular health. These results suggest that physiological adaptations to physical activity may vary depending on the degree of aging [12], highlighting the need for individualized training variables tailored to different stages of the life cycle. Furthermore, the observed differences in NfL levels may reflect age-related denervation processes occurring at the NMJ [33], with the loss of motor units likely contributing to the decline in muscular strength.
Grip strength is widely utilized as a representative indicator for assessing age-related declines in muscle strength; however, it does not adequately capture unexpected events such as falls or muscle injuries that can limit mobility [34]. Moreover, muscle loss due to aging is more pronounced in the lower extremities than in the upper extremities [35,36]. In particular, the knee extensors are known to be among the first muscle groups to exhibit strength reductions due to physical inactivity, making them useful for the early detection of age-related muscle loss [37,38]. In this study, an isokinetic dynamometer recognized as the gold standard for muscle strength assessment was employed [39]. The results indicated that increasing age had a negative effect on PT and BW/PT, whereas higher IPAQ scores were positively associated with both variables. These findings are consistent with those of Harbo et al. [40], who reported similar results in a population of white individuals aged 15 to 83 years. Additionally, Alcazar et al. [41] and Marcell et al. [42] have reported that significant muscle loss already occurs in middle age, with an annual decline in muscle mass of approximately 1.5–5% among individuals aged 40 to 50 years and older. Therefore, the present findings suggest that interventions aimed at preventing age-related frailty and sarcopenia should be initiated as early as middle age.
RTD@0.18s, an indicator associated with the rate of force development (RFD) in muscle, is influenced by factors such as changes in maximal muscle strength or muscle mass [43], structural characteristics of the muscle [44], muscle fiber type distribution [45], and muscle-tendon stiffness [46]. In the present study, RTD@0.18s was negatively correlated with age and NfL, suggesting an age-related decline. Previous studies support these findings; for instance, Thompson et al. [47] reported significant differences in knee extensor RTD between men in their 20s and those in their 50s, while Bemben et al. [48] demonstrated that the RFD peak in middle-aged men was approximately 26% lower than that of young men, indicating clear age-related changes. RTD in the knee extensors may decline more markedly than maximal strength [49], and this reduction has been associated with slower walking speeds and functional deterioration. Prior research has shown that the maximum lean angle recoverable by middle-aged adults was 22% lower than that of younger adults [50]. Furthermore, Hill et al. [51] through the Star Excursion Balance Test, reported significant differences between middle-aged and younger adults in the reach distance of the contralateral leg during a single-leg stance. These findings suggest that age-related reductions in RTD may impair dynamic balance recovery, obstacle avoidance, agile movements, stair negotiation, and other mobility-related functions [52]. Therefore, the decline in RTD may serve as a contributing factor that accelerates age-related physical deterioration.
CAF, a marker of NMJ stability, did not show significant differences between the groups. According to previous studies, elevated CAF levels have been observed in individuals with low physical activity [53], older adults [54], and populations with poor health status [55]. Additionally, a short-term (10-day) bed rest study conducted in young men reported an approximately 19% increase in CAF concentrations, with muscle biopsies revealing initial and partial signs of neurodegeneration [56]. In the present study, regression and correlation analyses revealed negative associations between CAF and both IPAQ scores (physical activity level) and BW/PT, suggesting that higher levels of physical activity may delay neuromuscular degeneration and provide a foundation for the maintenance of maximal strength. Notably, maximal strength is closely associated with high motor unit discharge rates during tasks involving heavy resistance or physical labor. These findings underscore the importance of implementing resistance training programs aimed at preventing neuromuscular aging in middle-aged populations.
NfL showed a significant positive association with age, and group comparisons also revealed notable differences. This finding is consistent with previous studies reporting that NfL levels are particularly elevated in individuals over the age of 60 [57]. Elevated circulating NfL concentrations have been linked to aging, damage to the central and peripheral nervous systems, cardiovascular risk factors, and pregnancy [58]. Furthermore, even among individuals under the age of 60, increases in BMI, blood volume, reduced renal function, and advancing age have been associated with higher NfL levels [59]. In the present study, the significantly higher age and body fat percentage observed in the middle-aged group compared to the younger group may have contributed to increased NfL concentrations. Moreover, the observed negative correlations between NfL and both PT and RTD@0.18s in the correlation analysis suggest that NfL may be linked to functional neuromuscular aging.
Our findings underscore the importance of initiating interventions aimed at addressing age-related neuromuscular changes beginning in middle age. To preserve high rates of force development, maximal strength, and the integrity of the NMJ, the implementation of high-velocity power training which induces rapid muscle contractions [60] and high-intensity resistance training designed to enhance motor unit recruitment and discharge rates [61] is recommended.
Meanwhile, although this study provides novel insights based on a relatively healthy and young population, several limitations should be acknowledged. First, due to the cross-sectional design, causal relationships between variables cannot be determined. Second, the exclusive use of a male participant group limits the generalizability of the findings to female populations. Third, since physical activity was assessed using the self-reported IPAQ, the data may be subject to recall bias. Additionally, the study was based on a relatively small sample size, and participants were recruited from a single geographic location, which may further limit the generalizability of the results. Lastly, non-pathological variables such as dietary intake, sleep habits, supplement use, smoking status, and comorbidities not specified in the exclusion criteria were not controlled for. Future research should aim to address these limitations by utilizing larger and more diverse samples, incorporating objective measures, and controlling for a broader range of potential confounders to improve the validity and applicability of the findings.

5. Conclusions

This study analyzed the associations among age, physical activity, lower-limb muscle function, and NMJ biomarkers in middle-aged and young men. Despite comparable levels of physical activity, the middle-aged group exhibited reduced lower-limb muscle function, increased body fat percentage, and elevated NfL concentrations compared to the younger group. These findings suggest that neuromuscular degeneration and functional decline may become apparent as early as middle age.
Regression analyses revealed that age had a negative effect on peak torque, BW/PT, and RTD@0.18s, whereas physical activity was positively associated with muscle strength indices and negatively associated with CAF concentrations. Furthermore, bivariate correlation analyses showed that CAF was negatively correlated with BW/PT, while NfL was negatively correlated with PT and RTD@0.18s. These results suggest that physical activity may serve as a protective factor against early neuromuscular degeneration and may help delay sarcopenia-related changes.
The observation of NMJ impairment in middle-aged individuals underscores the importance of early intervention. The negative relationships observed between NMJ-related biomarkers and muscle strength variables suggest that fitness programs targeting middle-aged adults should not solely focus on increasing muscle mass but must also emphasize functional enhancements, such as improvements in maximal strength and rate of force development (RFD). Adequate training intensity is required to promote these adaptations, which may in turn contribute to the prevention of NMJ aging.
Future research should include larger and more diverse populations and adopt longitudinal designs to examine the effects of exercise type and intensity on NMJ biomarkers. Such efforts will be instrumental in developing effective strategies for preventing sarcopenia and age-related functional decline.

Author Contributions

Conceptualization, B.K.; methodology, B.K. and K.K.; validation, K.K., J.S. and H.J.; formal analysis, B.K. and H.J.; investigation, S.L., J.S. and H.J.; resources, B.K., K.K. and S.L.; data curation, B.K. and J.S.; writing—original draft preparation, B.K.; writing—review and editing, H.J. and J.S.; visualization, S.L. and J.S.; supervision, K.K. and H.J.; project administration, S.L. and J.S.; funding acquisition, B.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2023S1A5B5A16079659).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Dankook University (DKU-IRB No. 2023-09-019-002, approval date 1 November 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. IPAQ continuous score.
Table 1. IPAQ continuous score.
Physical Activity LevelCalculate
Walking MET3.3 (MET level) × Walking time (min) × Day
Moderate activity MET4.0 (MET level) × Moderate intensity activity time (min) × Day
Vigorous activity MET8.0 (MET level) × Vigorous intensity activity time (min) × Day
Table 2. Results of independent t-tests for participant characteristics by variable.
Table 2. Results of independent t-tests for participant characteristics by variable.
VariableMiddle-Age (n = 40)Young-Age (n = 40)p
Behavioral variable
IPAQ score2428.95 ± 873.712413.78 ± 998.380.943
Anthropometric characteristics
Age (years)50.35 ± 4.4829.05 ± 3.08<0.001
Height (cm)172.33 ± 6.85173.63 ± 5.100.339
Weight (kg)74.61 ± 8.3577.51 ± 12.730.243
BMI (kg/m2)25.22 ± 3.1025.14 ± 4.310.535
Body fat (%)23.95 ± 4.8421.47 ± 6.090.047
Isokinetic strength
PT (Nm)178.04 ± 32.46222.55 ± 46.45<0.001
BW/PT (%)240.56 ± 46.45294.33 ± 72.33<0.001
RTD@0.18s (Nm)95.02 ± 30.11143.42 ± 30.18<0.001
Neuromuscular junctions
CAF (pg/mL)3354.54 ± 627.093138.30 ± 536.840.102
NfL (pg/mL)10.91 ± 4.806.50 ± 3.16<0.001
BMI: Body mass index, PT: Peak torque, BW/PT: Body weight/Peak torque, RTD@0.18s: Torque at 0.18 s, CAF: C-terminal agrin fragment, NfL: Neurofilament light chain.
Table 3. Results of multiple regression analysis on body composition variables.
Table 3. Results of multiple regression analysis on body composition variables.
Dependent VariableIndependent VariableBS.E.βtpVIF
Weight (a)Age−0.1500.104−0.158−1.4480.1521.002
IPAQ score−0.0030.001−0.241−2.204 *0.0311.002
Body Fat (b)Age0.1260.0540.2562.328 *0.0231.002
IPAQ score0.0000.001−0.029−0.2620.7941.002
BMI (c)Age−0.0250.037−0.077−0.6950.4891.002
IPAQ score−0.0010.000−0.199−1.7880.0781.002
* p < 0.05, BMI: Body mass index, (a) Weight regression model: F = 3.345 (p = 0.040), R2 = 0.080, adjR2 = 0.056, Durbin-Watson = 1.776, (b) Body fat regression model: F = 2.777 (p = 0.068), R2 = 0.067, adjR2 = 0.043, Durbin-Watson = 2.215, (c) BMI regression model: F = 1.788 (p = 0.174), R2 = 0.044, adjR2 = 0.020, Durbin-Watson = 1.914.
Table 4. Results of multiple regression analysis on Isokinetic strength variables.
Table 4. Results of multiple regression analysis on Isokinetic strength variables.
Dependent VariableIndependent VariableBS.E.βtpVIF
PT (a)Age−2.5740.357−0.701−7.211 ***<0.0011.448
IPAQ score0.0200.0040.4464.647 ***<0.0011.414
CAF0.0090.0070.1301.3480.1821.417
NfL1.8210.8800.2012.069 *0.0421.444
BW/PT (b)Age−3.1530.611−0.542−5.163 ***<0.0011.448
IPAQ score0.0370.0070.5255.054 ***<0.0011.414
CAF0.0110.0120.0960.9200.3601.417
NfL2.8551.5060.1991.8960.0621.444
RTD@0.18s (c)Age−2.1180.357−0.624−5.941 ***<0.0011.448
IPAQ score0.0020.0040.0370.3550.7241.414
CAF−0.0010.007−0.016−0.1500.8811.417
NfL−0.3530.879−0.042−0.4020.6891.444
*** p < 0.001, * p < 0.05, PT: Peak torque, BW/PT: Body weight/Peak torque, RTD@0.18s: Torque at 0.18 s, CAF: C-terminal agrin fragment, NfL: Neurofilament light chain, (a) PT regression model: F = 19.542 (p < 0.001), R2 = 0.510, adjR2 = 0.484, Durbin-Watson = 1.434, (b) BW/PT regression model: F = 14.035 (p < 0.001), R2 = 0.428, adjR2 = 0.398, Durbin-Watson = 0.991, (c) RTD@0.18s regression model: F = 14.028 (p < 0.001), R2 = 0.428, adjR2 = 0.397, Durbin-Watson = 2.221.
Table 5. Results of multiple regression analysis on neuromuscular junction variables.
Table 5. Results of multiple regression analysis on neuromuscular junction variables.
Dependent VariableIndependent VariableBS.E.βtpVIF
CAF (a)Age11.1517.2780.2151.5320.1302.106
IPAQ score−0.3630.071−0.574−5.092 ***<0.0011.360
PT2.3902.4550.1690.9740.3333.230
BW/PT0.0931.4180.0100.0660.9482.702
RTD@0.18s−0.8812.012−0.058−0.4380.6631.854
NfL (b)Age0.2520.0550.6234.593 ***<0.0012.106
IPAQ score−0.0010.001−0.262−2.407 *0.0191.360
PT0.0230.0190.2071.2290.2233.230
BW/PT0.0080.0110.1180.7670.4462.702
RTD@0.18s−0.0140.015−0.114−0.8960.3731.854
*** p < 0.001, * p < 0.05, CAF: C-terminal agrin fragment, NfL: Neurofilament light chain, PT: Peak torque, BW/PT: Body weight/Peak torque, RTD@0.18s: Torque at 0.18 s, (a) CAF regression model: F = 6.595 (p < 0.001), R2 = 0.308, adjR2 = 0.261, Durbin-Watson = 2.055, (b) NfL regression model: F = 8.089 (p < 0.001), R2 = 0.353, adjR2 = 0.310, Durbin-Watson = 2.115.
Table 6. Correlation Analysis Between Isokinetic Strength and Age, Physical Activity, and Neuromuscular Biomarkers.
Table 6. Correlation Analysis Between Isokinetic Strength and Age, Physical Activity, and Neuromuscular Biomarkers.
VariableAgeIPAQ ScoreCAFNfL
PT−0.591 ***0.376 ***−0.197−0.231 *
BW/PT−0.442 ***0.466 ***−0.244 *−0.165
RTD@0.18s−0.651 ***0.079−0.148−0.384 **
*** p < 0.001, ** p < 0.01, * p < 0.05, PT: Peak torque, BW/PT: Body weight/Peak torque, RTD@0.18s: Torque at 0.18 s, CAF: C-terminal agrin fragment, NfL: Neurofilament light chain.
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Kim, B.; Kim, K.; Lee, S.; Son, J.; Jeong, H. Comparative Analysis of Physical Activity and Neuromuscular Characteristics in Middle-Aged and Young Men. Appl. Sci. 2025, 15, 9952. https://doi.org/10.3390/app15189952

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Kim B, Kim K, Lee S, Son J, Jeong H. Comparative Analysis of Physical Activity and Neuromuscular Characteristics in Middle-Aged and Young Men. Applied Sciences. 2025; 15(18):9952. https://doi.org/10.3390/app15189952

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Kim, Byungkwan, Kihong Kim, Sanghyun Lee, Jaeheon Son, and Hwanjong Jeong. 2025. "Comparative Analysis of Physical Activity and Neuromuscular Characteristics in Middle-Aged and Young Men" Applied Sciences 15, no. 18: 9952. https://doi.org/10.3390/app15189952

APA Style

Kim, B., Kim, K., Lee, S., Son, J., & Jeong, H. (2025). Comparative Analysis of Physical Activity and Neuromuscular Characteristics in Middle-Aged and Young Men. Applied Sciences, 15(18), 9952. https://doi.org/10.3390/app15189952

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