Association of adjustability of grasping force in dominant and nondominant hands with physical and cognitive function in community-dwelling older adults: A cross-sectional study

Association of adjustability of grasping force in dominant and nondominant hands with physical and cognitive function in community-dwelling older adults: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of adjustability of grasping force in dominant and nondominant hands with physical and cognitive function in community-dwelling older adults: A cross-sectional study Jun Yabuki, Shoji Yabuki, Kazuaki Iokawa, Hiroshi Hayashi, Toshimasa Sone, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9466503/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Adjustability of grasping force (AGF) is essential for activities of daily living (ADL) and instrumental ADL. However, its association with physical and cognitive functions in older adults, and whether these associations differ by hand remain unclear. Therefore, we investigated these associations using a visual tracking task. Methods This cross-sectional study included 129 community-dwelling older adults from Kagamiishi Town. AGF was assessed as the error between target and measured forces (grasping error) in dominant and nondominant hands. Physical function measures included grip strength, pulp and key pinch strengths, and Purdue Pegboard Test (PPT) score. Cognitive function was assessed using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). We used age- and sex-adjusted generalized linear models for the dominant and nondominant hands. Functional indicators included the MoCA-J visuospatial/executive subscore. Results A total of 129 participants (mean age: 76.2 ± 4.69 years; 69.0% female) were analyzed. Grasping errors did not differ between hands (dominant hand: 0.13 ± 0.09 N, nondominant hand: 0.12 ± 0.07 N; p = 0.113). In the dominant hand, only the MoCA-J visuospatial/executive subscore was significantly associated with grasping error (β = -0.156, p = 0.008). In the nondominant hand, both MoCA-J visuospatial/executive subscore (β = -0.102, p = 0.036) and PPT score (β = -0.057, p = 0.014) were significantly associated with grasping error. Conclusion Visuospatial/executive function was associated with AGF in both hands. Dexterity was additionally associated with AGF in the nondominant hand. These findings may inform hand-specific assessment or intervention strategies for older adults. grasping force adjustability visuospatial function executive function hand dexterity hand dominance visuospatial tracking Figures Figure 1 Background Population aging is a global challenge, and the number of older adults aged 65 years and older is projected to increase from approximately 700 million in 2019 to 1.5 billion by 2050 [ 1 ]. Japan (total population: approximately 124 million) exhibits particularly pronounced aging, with an average life expectancy of approximately 84 years and 31% of its population aged 65 years and over, significantly exceeding the global average [ 2 , 3 ]. Maintaining activities of daily living (ADL) and quality of life among older adults is crucial for promoting well-being. Grasping objects is a fundamental movement required for performing ADL and requires coordinated hand function. Among the various aspects of hand dexterity, the ability to appropriately adjust the grasping force according to the shape, size, and weight of objects is defined as the adjustability of grasping force (AGF) [ 4 , 5 ]. AGF is evaluated using a visuomotor tracking task in which individuals adjust their grasping force to match a target grasping force displayed on a monitor [ 4 – 7 ]. This task requires recognizing the error between the target and measured grasping force and appropriately modifying movements based on concurrent visual feedback regarding task performance. Thus, it evaluates aspects different from tasks that assess movement speed, such as the finger-tapping task [ 8 ], or tasks that evaluate manual dexterity, such as the Purdue Pegboard Test (PPT) [ 9 , 10 ]. Recently, tool manipulation ability involving precision force control has been reported to be associated with ADL in older adults [ 11 , 12 ]. Therefore, AGF may serve as an important indicator for comprehensively evaluating ADL ability in this population. Hand function in older adults depends on both physical and cognitive functions. Physical function is influenced by aging [ 13 – 15 ], grip strength [ 16 ], upper limb muscle strength [ 17 ], and laterality [ 6 , 18 ]. Additionally, cognitive function involves executive [ 19 , 20 ] and visuospatial functions [ 8 ]. Previous studies investigating the relationship between upper limb function and cognitive function have employed finger-tapping tasks and the PPT, which evaluates motor speed and manual dexterity [ 10 ]. On the other hand, AGF differs from these tasks in that it is expected to require fine force control based on visual feedback; however, the relative contributions of physical and cognitive functions have not been sufficiently verified. Therefore, in this cross-sectional study, we aimed to clarify the factors associated with AGF in community-dwelling older adults. Methods Participants This cross-sectional study included 133 community-dwelling older adults aged 65 years and older who participated in a physical fitness measurement event conducted in Kagamiishi Town in 2024. Participants were recruited through the town’s public relations bulletin. The exclusion criteria were as follows: 1) individuals who did not respond to the questionnaire, 2) those with finger loss or difficulty in operating devices, and 3) individuals with a history of neurological disease. This study was approved by the Ethics Committee of the Fukushima Medical University (approval number REC2024-055) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all the participants. Measurements of participant characteristics All assessments were conducted on the same day. Assessment of AGF AGF was evaluated using a grasping force measurement device (iWakka; Nagoya Institute of Technology, Nagoya, Japan) (Fig. 1 ). This device has been used in previous studies involving younger and older adults, and individuals with stroke [ 5 , 21 , 22 ]. The task consisted of waveforms in three continuously changing contraction modes: concentric contraction (increasing the grasping force), eccentric contraction (decreasing the grasping force), and isometric contraction (maintaining the grasping force) (Fig. 1 ). The trial lasted 93 s, including an initial 3-s stabilization period that was excluded from the analysis, and the participants were asked to match their grasping force to the target line displayed on the monitor (1.5–4 N). The measurement environment was based on a previous study [ 22 ], and the measurements were conducted in a seated position on a chair. The grasping device was placed on an aluminum plate to reduce its tilt and friction resistance. Furthermore, we established two measurement stations to control for measurement sequence effects: at station A, the dominant hand was evaluated first, and at station B, the nondominant hand was evaluated first. The participants were randomly assigned to either station (station A, n = 66; station B, n = 67). Before the measurement, the operation of the device was explained, and after confirming that the participants understood the task, measurements were performed once each for the dominant and nondominant hands. The AGF indicator was calculated as the mean absolute error between the target and the measured grasping force (grasping error) based on a previous study [ 7 ]. A smaller grasping error indicated a better AGF. Physical and cognitive function Physical function assessment included body composition, maximum grip strength, and maximum pinch strength. Body composition was measured using a multifrequency bioelectrical impedance analysis device (InBody S10, InBody Co., Ltd., Tokyo, Japan). This device uses an 8-point contact electrode direct segmental multifrequency measurement method and can measure muscle mass with high precision by combining six frequencies (1, 5, 50, 250, 500, and 1000 kHz) [ 23 , 24 ]. Participants lay supine on a bed with electrodes attached to the thumbs and middle fingers of both hands and ankles, and the measurement time was approximately 100 s. Skeletal muscle mass index (SMI) was used as an analytical indicator. SMI is a muscle mass indicator that does not depend on body size and is calculated by dividing skeletal muscle mass (kg) by height (m) squared. Maximum grip strength was measured using a digital grip strength meter (GRIP-D; Takei Scientific Instruments Co., Ltd., Niigata, Japan). The participants stood with the elbow joint fully extended and the proximal interphalangeal joint of the index finger at 90° to perform maximum-effort gripping [ 25 ]. Two measurements were performed with the dominant hand, and the maximum value was recorded. If the grip strength meter touched the body during measurement, the measurement was repeated. Maximum pinch strength was measured using a pinch force sensor (Mobii Z; SAKAI Medical Co., Ltd., Tokyo, Japan). Participants sat in a chair with the shoulder joint adducted, elbow flexed at 90°, and forearm in the mid-pronation-supination position. Pulp pinch and key pinch were measured using the index finger and thumb [ 26 ]. Two measurements were performed on each side for each condition, and the maximum value was recorded. Hand function was evaluated using the PPT (SAKAI Medical Co., Ltd., Tokyo, Japan). The PPT is a validated assessment method for evaluating finger dexterity and visuomotor coordination [ 9 ]. Participants were asked to insert as many pegs as possible into the holes within 30 s, and the number of inserted pegs was scored (PPT score). Measurements were taken once each in the order of the dominant hand, followed by the nondominant hand. Cognitive function was assessed using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). The MoCA-J evaluates multiple domains of cognitive function: visuospatial/executive function (three tasks: figure copying, clock drawing, and trail making; 0–5 points), naming (three items; 0–3 points), attention (four tasks: forward digit span, backward digit span, vigilance and serial subtraction; 0–6 points), language (two tasks: sentence repetition and phonemic fluency; 0–3 points), abstraction (two items; 0–2 points), delayed recall (five items; 0–5 points), and orientation (six items; 0–6 points), for a potential total score of 30 points [ 27 ]. The MoCA-J was administered by well-trained therapists and/or assessors in accordance with the standardized administration manual. According to the standard MoCA-J scoring protocol, one point was added to the total score for participants with ≤ 12 years of education [ 28 ]. In this study, in addition to the MoCA-J total score, the MoCA-J visuospatial/executive subscore (0–5 points), which was expected to be the most relevant to the AGF task, was used in the analysis. General information General information collected included age, sex, body mass index, and handedness. Handedness was determined by self-report. Statistical analysis Comparison between dominant and nondominant hands The Shapiro–Wilk test was performed for all continuous variables. The results showed a non-normal distribution for all variables except age (p = 0.466) and SMI (p = 0.055). Therefore, the laterality between the dominant and nondominant hands was compared using the Wilcoxon signed-rank test for the grasping error, maximum pulp and key pinch strengths, and PPT score. Generalized linear model (GLM) analysis Multivariate analysis using a GLM was conducted to identify factors associated with AGF in the dominant and nondominant hands of community-dwelling older adults. Simple regression analyses identified candidate explanatory variables for inclusion in the model. Grasping errors of the dominant and nondominant hands were considered the dependent variables, with age, sex, maximum grip strength, maximum pulp and key pinch strengths, SMI, MoCA-J visuospatial/executive subscore, and PPT score as explanatory variables. Variables showing associations (p < 0.05) were considered candidates, and identical models were constructed for both the dominant and nondominant hands to allow for direct comparison. Model 1 was created based on the results of the regression analysis. In Model 1, the PPT score, an indicator of motor coordination, was included as an explanatory variable for AGF. The MoCA-J visuospatial/executive subscore was adopted as an indicator of cognitive function, as it has been reported to be an independent factor associated with physical function decline in community-dwelling older adults [ 8 ]. The explanatory variables in Model 1 were age and sex as covariates, along with MoCA-J visuospatial/executive subscore, maximum grip strength, and PPT score. Furthermore, a sensitivity analysis was conducted in Model 2 by replacing the PPT with maximum pulp pinch strength and key pinch, which are indicators of muscle strength, to evaluate the robustness of Model 1. In Model 2, maximum pulp and key pinch strengths, an indicator of muscle strength, were included as an explanatory variable instead of the PPT score. This allowed for the comparison of the associations between the AGF and different indicators, such as the PPT score and pinch strength. As an exploratory analysis, a model was constructed in which the MoCA-J visuospatial/executive subscore was replaced with the MoCA-J total score to assess the impact of differences in cognitive function indicators on the results. Because the dependent variable, grasping error of the dominant and nondominant hands, showed non-normal distributions, a GLM with a gamma distribution and a log link function was used. Multiple imputation by chained equations was performed to address missing data in some explanatory variables [ 29 ]. Missing data occurred for one participant in SMI owing to a contraindication to bioelectrical impedance analysis (pacemaker implantation). For pinch strength, one to two participants had missing values under specific conditions; however, measurements were successfully obtained in the contralateral hand for all affected participants, suggesting that bilateral structural limitations were unlikely. The missing pinch strength data were attributed to multiple incidental factors, including physical fatigue accumulated during the single-day multi-measurement protocol requiring repeated maximal force exertion, time constraints inherent to the community-based event setting, and potential recording errors during data collection. Therefore, missing data were assumed to be missing at random rather than not at random. Predictive mean matching was applied, and 20 imputations were performed. A GLM was applied to each imputed dataset, and the 20 estimation results obtained were integrated using Rubin’s rule. All statistical analyses were performed using R version 4.5.1 [ 30 ]. The statistical significance level was set at p < 0.05. Results Participants’ characteristics Table 1 presents the characteristics of the participants. A total of 133 participants underwent physical fitness measurements. Four participants were excluded (one with finger loss and three with a history of stroke); therefore, 129 participants were included in the final analysis. In total, there were 126 right-handed individuals and three left-handed individuals. In the comparison between the dominant and nondominant hands, the dominant hand showed significantly higher values for the maximum pulp pinch strength (p = 0.002), maximum key pinch strength (p = 0.001), and PPT score (p < 0.001). No significant difference in grasping error was observed between the two hands (p = 0.113). Table 1 Participant characteristics Variable Mean ± SD n p-value Grasping error 0.113 Dominant hand (N) 0.13 ± 0.09 129 Nondominant hand (N) 0.12 ± 0.07 129 General information Age (years) 76.22 ± 4.69 129 Sex (male/female) 40/89 129 BMI (kg/m 2 ) 23.56 ± 3.27 129 Handedness (right/left) 126/3 129 Physical function SMI (kg/m 2 ) † 6.75 ± 1.07 128 Maximum grip strength (kg) 25.75 ± 6.51 129 Maximum pulp pinch strength (kg)† 0.002 Dominant hand 5.35 ± 1.83 128 Nondominant hand 5.10 ± 1.75 129 Maximum key pinch strength (kg) † 0.001 Dominant hand 6.11 ± 2.15 127 Nondominant hand 5.88 ± 2.07 128 PPT score (points) < 0.001 Dominant hand 12.33 ± 2.07 129 Nondominant hand 11.26 ± 2.03 129 Cognitive function MoCA-J total score (0–30) 23.70 ± 3.84 129 Visuospatial/executive subscore (0–5) 4.38 ± 0.95 129 Note. AGF, adjustability of grasping force; BMI, body mass index; MoCA-J, Japanese version of the Montreal Cognitive Assessment; SMI, skeletal muscle mass index; PPT, Purdue Pegboard Test. p-values for dominant versus nondominant comparisons were calculated using the Wilcoxon signed-rank test. † Missing data were handled via multiple imputations using chained equations. Factors associated with AGF Exploratory univariate analysis Simple regression analyses were conducted to identify variables associated with grasping error (Table 2 ). For the dominant hand, significant associations were found for sex (β = -0.269, SE = 0.132, p = 0.044), the MoCA-J visuospatial/executive subscore (β = -0.162, SE = 0.059, p = 0.007), and maximum grip strength (β = -0.025, SE = 0.009, p = 0.005). Furthermore, for the nondominant hand, significant associations were found for the MoCA-J total score (β = -0.044, SE = 0.012, p < 0.001), MoCA-J visuospatial/executive subscore (β = -0.165, SE = 0.047, p < 0.001), maximum grip strength (β = -0.019, SE = 0.008, p = 0.027), and PPT score (β = -0.070, SE = 0.022, p = 0.002). These variables, along with age and sex as covariates, were used to construct the primary model (Model 1). Furthermore, as a sensitivity analysis, Model 2 was constructed by replacing the PPT score with the maximum pulp and key pinch strengths. No multicollinearity was observed across all models, as indicated by a variance inflation factor < 5 for all explanatory variables. Table 2 Exploratory univariate linear regression analyses with grasping error Variable Grasping error (Dominant) Grasping error (Nondominant) β SE p-value β SE p-value Age (years) 0.003 0.013 0.828 0.012 0.012 0.298 Sex (male) -0.269 0.132 0.044 -0.176 0.121 0.149 MoCA-J total score (0–30) -0.019 0.016 0.231 -0.044 0.012 < 0.001 MoCA-J visuospatial/executive subscore (0–5) -0.162 0.059 0.007 -0.165 0.047 < 0.001 Maximum grip strength (kg) -0.025 0.009 0.005 -0.019 0.008 0.027 Maximum pulp pinch strength (kg) -0.055 0.032 0.082 -0.026 0.031 0.410 Maximum key pinch strength (kg) -0.053 0.028 0.069 -0.043 0.026 0.098 SMI (kg/m 2 ) -0.100 0.057 0.081 -0.062 0.052 0.240 PPT score (points) -0.021 0.029 0.464 -0.070 0.022 0.002 Note. β, Unstandardized regression coefficients; SE, standard error; AGF, adjustability of grasping force; MoCA-J, Japanese version of the Montreal Cognitive Assessment; SMI, skeletal muscle mass index; PPT, Purdue Pegboard Test. Dominant hand In the dominant hand, only the MoCA-J visuospatial/executive subscore showed an independent negative association (β = -0.156, 95% confidence interval [CI]: [-0.270, -0.042], p = 0.008), whereas age (p = 0.428), sex (p = 0.773), maximum grip strength (p = 0.121), and PPT score (p = 0.877) showed no significant associations in Model 1. For the sensitivity analysis, similar to Model 1, only the MoCA-J visuospatial/executive subscore showed an independent negative association (β = -0.160, 95% CI: [-0.273, -0.047], p = 0.006) in Model 2 (Table 3 ). Table 3 Generalized linear model results for dominant hand grasping error with MoCA-J visuospatial/executive subscore Variable Model 1 Model 2 β 95% CI p-value β 95% CI p-value Age -0.010 [-0.035–0.015] 0.428 -0.009 [-0.034–0.015] 0.444 Sex (female = reference) -0.054 [-0.423–0.316] 0.773 -0.039 [-0.415–0.337] 0.838 MoCA-J visuospatial/executive subscore -0.156 [-0.270–-0.042] 0.008 -0.160 [-0.273–-0.047] 0.006 Maximum grip strength -0.021 [-0.047–0.006] 0.121 -0.019 [-0.048–0.010] 0.203 PPT score (dominant) -0.004 [-0.058–0.050] 0.877 Maximum pulp pinch strength (dominant) -0.014 [-0.111–0.083] 0.777 Maximum key pinch strength (dominant) -0.002 [-0.096–0.093] 0.970 Note. GLM, Generalized linear model with gamma family and log link function. Multiple imputations (m = 20) were performed for the missing data. AGF, adjustability of grasping force; MoCA-J, Japanese version of the Montreal Cognitive Assessment; SMI, skeletal muscle mass index; PPT, Purdue Pegboard Test; β, regression coefficients from GLM with log link function; SE, standard error; CI, confidence interval. Nondominant hand In the nondominant hand, both the MoCA-J visuospatial/executive subscore (β = -0.102, 95% CI: [-0.197, -0.007], p = 0.036) and the PPT score (β = -0.057, 95% CI: [-0.103, -0.012], p = 0.014) showed a significant negative association in Model 1. Conversely, in Model 2, only the MoCA-J visuospatial/executive subscore showed an independent negative association (β = -0.151, 95% CI: [-0.250, -0.052], p = 0.003) (Table 4 ). Table 4 Generalized linear model results for nondominant hand grasping error with MoCA-J visuospatial/executive subscore Variable Model 1 Model 2 β 95% CI p-value β 95% CI p-value Age 0.002 [-0.018–0.022] 0.852 0.004 [-0.017–0.025] 0.723 Sex (female = reference) -0.121 [-0.427–0.185] 0.436 -0.027 [-0.350–0.296] 0.870 MoCA-J visuospatial/executive subscore -0.102 [-0.197–-0.007] 0.036 -0.151 [-0.250–-0.052] 0.003 Maximum grip strength -0.011 [-0.032–0.011] 0.338 -0.012 [-0.038–0.013] 0.344 PPT score (nondominant) -0.057 [-0.103–-0.012] 0.014 Maximum pulp pinch strength (nondominant) 0.023 [-0.060–0.106] 0.590 Maximum key pinch strength (nondominant) -0.023 [-0.100–0.054] 0.552 Note. GLM, Generalized linear model with gamma family and log link function. Multiple imputations (m = 20) were performed for the missing data. AGF, adjustability of grasping force; MoCA-J, Japanese version of the Montreal Cognitive Assessment; SMI, skeletal muscle mass index; PPT, Purdue Pegboard Test; β, regression coefficients from GLM with log link function; SE, standard error; CI, confidence interval. Exploratory analysis with the MoCA-J total score As an exploratory analysis, a model in which the MoCA-J visuospatial/executive subscore was replaced with the MoCA-J total score was examined. In the dominant hand, no significant independent factors were identified in either Model 1 or 2 (Supplemental Table S1 ). In the nondominant hand, a pattern similar to that observed when using the MoCA-J visuospatial/executive subscore was observed. In Model 1, both the MoCA-J total score (β = -0.029, 95% CI: [-0.053, -0.004], p = 0.024) and PPT score (β = -0.052, 95% CI: [-0.100, -0.005], p = 0.030) showed significant negative associations. However, in Model 2, only the MoCA-J total score (β = -0.043, 95% CI: [-0.068, -0.018], p < 0.001) showed an independent negative association (Supplemental Table S2 ). Discussion This cross-sectional study identified factors associated with AGF in community-dwelling older adults. The results showed that the MoCA-J visuospatial/executive subscore was a common factor associated with AGF in both the dominant and nondominant hands. Conversely, hand dexterity was significantly associated with AGF only in the nondominant hand. Furthermore, exploratory analyses suggested that the cognitive resources recruited differed between dominant and nondominant hands. These findings suggest that AGF may not be fully explained by conventional muscle strength indicators and appears to be associated with cognitive function. The MoCA-J visuospatial/executive subscore consists of a cube copy, clock drawing test, and trail-making test, and evaluates visuospatial information processing ability, visuomotor coordination, and executive function [ 28 ]. The AGF task is a visual tracking task that requires participants to match their grasping force to a target value based on real-time visual feedback. It requires the ability to confirm the target and actual grasping forces, recognize the magnitude of the error (visuospatial function), and execute movement corrections (executive function). Therefore, the MoCA-J visuospatial/executive subscore was considered a significant associated factor because it matched the cognitive functions that form the basis for visuomotor integration and error corrections required for performing the AGF task. This finding is consistent with those of several previous studies. Tomita et al. [ 8 ] reported that the MoCA-J visuospatial/executive subscore was specifically associated with performance on a finger-tapping task in 102 community-dwelling older adults. Their study examined the usefulness of motor tasks as a screening tool for the early detection of cognitive decline. In contrast, the present study focused on identifying factors associated with AGF. Furthermore, functional magnetic resonance imaging studies have shown that the frontoparietal network is involved in visual tracking tasks using precision grip control [ 31 ]. This network is involved in visuospatial attention, motor planning, and error monitoring. These functions overlap with the cognitive functions and neural substrates reflected by the MoCA-J visuospatial/executive subscore. Therefore, our study results suggest that AGF may reflect the involvement of cognitive function. In the dominant hand, task-specific cognitive functions such as visuospatial/executive function were selectively associated with AGF, whereas global cognitive function (MoCA-J total score) showed no significant association. This finding is consistent with the interpretation that motor control of the dominant hand may be more automated than the nondominant hand and selectively relies on specific cognitive domains in the context of the present task. The dominant hand is characterized by expanded and strengthened neural representations in the motor cortex owing to its frequent use in daily life [ 32 ]. Accordingly, motor execution using the dominant hand can be performed relatively efficiently when specific cognitive domains responsible for error detection and motor correction planning function appropriately. In contrast, the nondominant hand showed a qualitatively different pattern: both the MoCA-J visuospatial/executive subscore and the MoCA-J total score were significantly associated with AGF, and hand dexterity was also associated with AGF. Specifically, the MoCA-J total score was significantly associated with AGF in the nondominant hand but not in the dominant hand, despite the visuospatial/executive subscore being a significant factor in both hands. This hand-specific pattern of the MoCA-J total score suggests that cognitive domains beyond visuospatial/executive function may additionally contribute to nondominant hand AGF; however, this interpretation warrants caution given that the MoCA-J total score incorporates the visuospatial/executive subscore. These findings suggest that fine motor control of the nondominant hand may involve a broader scope of cognitive engagement than that required for the dominant hand. The nondominant hand is used less frequently in daily life than the dominant hand and has weaker neural representations in the motor cortex, resulting in relatively lower automation of motor control [ 33 ]. Consequently, precise motor control of the nondominant hand based on sensory feedback is likely to require widespread brain activity involving the recruitment of neural networks spanning multiple brain regions. Consistent with these findings, functional connectivity between motor-related areas and frontoparietal regions has been reported to increase during nondominant hand movements compared with dominant hand movements [ 34 ]. In line with these neural substrates, movement execution in the nondominant hand potentially involves a more distributed cognitive network, which may contribute to the observed association between global cognitive function and nondominant hand AGF. Consistent with this interpretation, both the MoCA-J total score and hand dexterity showed independent negative associations with nondominant hand AGF in the present exploratory study. These results suggest that laterality is not reflect in the overall performance level of the task but rather in the structure of associated factors that determine performance, particularly in the way in which cognitive function contributes. Overall, these findings suggest that the AGF task used in this study differs from conventional indicators of muscle strength and dexterity. Instead, it may reflect cognitive-motor integration processes based on concurrent visual feedback, thereby suggesting a potentially novel perspective on functional assessment in older adults. Although causal inferences cannot be drawn from the present cross-sectional data, these findings support the hypothesis that task designs incorporating visuomotor feedback may be a relevant direction for future intervention studies targeting AGF in older adults. Longitudinal and experimental studies are needed to examine whether visuospatial and executive cognitive functions mediate the effects of force-tracking practice on AGF. This study had certain limitations. First, given its cross-sectional design, causal relationships could not be established. Second, because this study focused on community-dwelling older adults, caution is warranted when generalizing the findings to other populations. Third, visual function was not assessed. Given that the AGF task depends on real-time visual feedback to the target force, individual differences in visual function may have influenced the task. Additionally, the MoCA-J total score used in the exploratory analysis includes the visuospatial/executive subscore; therefore, the independent contribution of cognitive domains beyond visuospatial/executive function to nondominant hand AGF cannot be fully determined from the present data. Future studies should incorporate visual function assessment and examine the effects of visuospatial/executive function training on AGF using longitudinal designs and cognitive measures that isolate non-visuospatial domains to more rigorously characterize the scope of cognitive involvement in nondominant hand AGF. Conclusions This study clarified that, among community-dwelling older adults, visuospatial/executive function is a common factor associated with AGF in both the dominant and nondominant hands, whereas hand dexterity is an additional associated factor only in the nondominant hand. The findings further suggest that task-specific cognitive functions may be a primary correlate of dominant hand AGF, whereas exploratory analyses indicated that global cognitive function was additionally associated with nondominant hand AGF, possibly reflecting broader cognitive involvement that may be related to the relatively lower automation of nondominant hand motor control. Furthermore, our findings suggest that AGF may be associated with cognitive function beyond what can be explained by conventional muscle strength indicators, potentially capturing aspects that differ from existing measures, such as grip strength and pinch strength. Future research should examine whether AGF can serve as a functional indicator of cognitive-motor integration and clarify its clinical significance in the context of functional assessment in older adults. Abbreviations ADL: activities of daily living AGF: adjustability of grasping force GLM: generalized linear model MoCA-J: Japanese version of the Montreal Cognitive Assessment PPT: Purdue Pegboard Test Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the Fukushima Medical University (approval number REC2024-055) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all the participants. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available because of privacy and ethical considerations, but are available from the corresponding author upon reasonable request ( [email protected] ). Competing interests The authors declare that they have no competing interests. Funding This work was supported by JSPS KAKENHI Grant-in-Aid for Early-Career Scientists Number JP24K20476. Author contributions JY contributed to Writing – original draft, Writing – review & editing, Software, Project administration, Investigation, Methodology, Conceptualization, Data curation, and Formal analysis. SY contributed to Writing – review & editing, Project administration, Investigation, Methodology, Conceptualization. KI contributed to Writing – review & editing, Project administration, Investigation, Methodology, Conceptualization. HH contributed to Writing – review & editing, Project administration, Investigation, Methodology, Conceptualization. TSone contributed to Writing – review & editing, Project administration, Investigation, Methodology, Conceptualization. TSato contributed to Writing – review & editing, Project administration, Investigation, Methodology, Conceptualization, and Data curation. MO contributed to Writing – review & editing, Project administration, Investigation, Methodology, Conceptualization. YH contributed to Writing – review & editing, Project administration, Investigation, Methodology, Conceptualization. Masayuki Hoshi: Writing – review & editing, Project administration, Investigation, Methodology, Conceptualization. YS contributed to Writing – review & editing, Project administration, Investigation, Methodology, Conceptualization, Supervision. All authors read and approved the final manuscript. Acknowledgments The authors sincerely thank the Fukushima Medical University staff for their assistance with data collection and Kazunori Akizuki, Ryohei Yamamoto, Kazuto Yamaguchi, and Wataru Nakano for their assistance with writing the draft. The authors gratefully acknowledge Editage for their English editing services. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the authors used Genspark in order to assist in generating code for data analysis. After using this tool/service, the authors reviewed and edited the content as needed and took full responsibility for the content of the published article. References United Nations Department of Economic and Social Affairs Population Division. World population ageing 2019: highlights (ST/ESA/SER.A/430); 2019. https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2019-Highlights.pdf. Accessed December 18, 2025. United Nations Department of Economic and Social Affairs Population Division; 2024. World population prospects 2024 [Data set]. https://population.un.org/wpp/graphs?loc=392&type=Demographic. Accessed December 18, 2025. World Health Organization. Japan [country overview]; 2025. https://data.who.int/countries/392. Accessed December 18, 2025. Kaneno T, Ito M, Kiguchi N, Sato A, Akizuki K, Yabuki J, et al. A comparative study of adjustability of grasping force between young people and elderly individuals. 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Classification of mild cognitive impairment and Alzheimer’s disease using manual motor measures. Neurodegener Dis. 2024;24:54–70. https://doi.org/10.1159/000539800. Yabuki J, Akizuki K, Yamamoto R, Yamaguchi K, Ohashi Y. Effectiveness of adjusted bandwidth knowledge of results in motor learning. Cogent Psychol. 2022;9:2131039. https://doi.org/10.1080/23311908.2022.2131039. Yabuki J, Kaneno T, Yamamoto R, Yamaguchi K, Nakano W, Akizuki K. Effects of visual terminal feedback on hand dexterity in relation to visuospatial ability in subacute stroke: a preliminary study. Sci Rep. 2025;15:7368. https://doi.org/10.1038/s41598-025-91806-2. Duarte Martins A, Paulo Brito J, Fernandes O, Oliveira R, Gonçalves B, Batalha N. Effects of a 16-week high-speed resistance training program on body composition in community-dwelling independent older adults: a clinical trial. Clin Nutr ESPEN. 2024;63:84–91. https://doi.org/10.1016/j.clnesp.2024.06.010. Hioka A, Akazawa N, Okawa N, Nagahiro S. Influence of aging on extracellular water-to-total body water ratio in community-dwelling females. Clin Nutr ESPEN. 2024;60:73–8. https://doi.org/10.1016/j.clnesp.2024.01.007. Balogun JA, Akomolafe CT, Amusa LO. Grip strength: effects of testing posture and elbow position. Arch Phys Med Rehabil. 1991;72:280–3. Mathiowetz V, Kashman N, Volland G, Weber K, Dowe M, Rogers S. Grip and pinch strength: normative data for adults. Arch Phys Med Rehabil. 1985;66:69–74. Fujiwara Y, Suzuki H, Yasunaga M, Sugiyama M, Ijuin M, Sakuma N, et al. Brief screening tool for mild cognitive impairment in older Japanese: validation of the Japanese version of the Montreal Cognitive Assessment. Geriatr Gerontol Int. 2010;10:225–32. https://doi.org/10.1111/j.1447-0594.2010.00585.x. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53:695–9. https://doi.org/10.1111/j.1532-5415.2005.53221.x. Van Buuren S. Multivariate missing data. In: Flexible imputation of missing data 2nd ed. Chapman and Hall/CRC; 2018. p. 105–38. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2025. https://www.R-project.org/. Accessed December 18, 2025. Papadelis C, Arfeller C, Erla S, Nollo G, Cattaneo L, Braun C. Inferior frontal gyrus links visual and motor cortices during a visuomotor precision grip force task. Brain Res. 2016;1650:252–66. https://doi.org/10.1016/j.brainres.2016.09.011. Ejaz N, Hamada M, Diedrichsen J. Hand use predicts the structure of representations in sensorimotor cortex. Nat Neurosci. 2015;18:1034–40. https://doi.org/10.1038/nn.4038. Kang N, Shinohara M, Zatsiorsky VM, Latash ML. Learning multi-finger synergies: an uncontrolled manifold analysis. Exp Brain Res. 2004;157:336–50. https://doi.org/10.1007/s00221-004-1850-0. Tsurugizawa T, Taki A, Zalesky A, Kasahara K. Increased interhemispheric functional connectivity during non-dominant hand movement in right-handed subjects. iScience. 2023;26:107592. https://doi.org/10.1016/j.isci.2023.107592. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9466503","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634201043,"identity":"ad754e5d-510a-4e3e-9129-4a8374888525","order_by":0,"name":"Jun Yabuki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYFACHgbGBgM2Bn4g8wNcUIIYLZINzIwzGBKI1gKkDA4ga8EHDI6fPSY5o4Avz/j8+YMNjD9sEhvYDz9gsNyBR8uZvDTJDQZsxWY3koG2JaQlNvCkGTBInsGj5UCOmeQDA7bEbTeY2R8wJBxObGDIYWCQbMOj5fwbiJbN/YdBtvxPbOB/Q0DLDaAtQIclbmAAO+xAYoMEAVskb7xLtpwB1DLjRrJhQ0JasnGbxDODA/j8wnc+9+DNnj/HEvv7Dz5s+GBjJ9vPn/zwsSSeEFM4AKaOQXgJQMwGxIclG3BrkYfI1aCKMn7Eo2UUjIJRMApGHAAA1I1UVA6tmSMAAAAASUVORK5CYII=","orcid":"","institution":"Tokyo University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yabuki","suffix":""},{"id":634201044,"identity":"6f25de86-883b-4b8e-829d-91dc9da83376","order_by":1,"name":"Shoji Yabuki","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shoji","middleName":"","lastName":"Yabuki","suffix":""},{"id":634201045,"identity":"9dadee1d-5e96-436f-90a9-bdb3995610df","order_by":2,"name":"Kazuaki Iokawa","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kazuaki","middleName":"","lastName":"Iokawa","suffix":""},{"id":634201048,"identity":"833862a7-cbd6-48f9-b7a0-3891b653a83d","order_by":3,"name":"Hiroshi Hayashi","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Hayashi","suffix":""},{"id":634201054,"identity":"c830752d-169a-495f-8c54-1e0974fe8f82","order_by":4,"name":"Toshimasa Sone","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Toshimasa","middleName":"","lastName":"Sone","suffix":""},{"id":634201056,"identity":"ac09eb16-1833-43df-a0d9-cf0049754bf8","order_by":5,"name":"Toshimi Sato","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Toshimi","middleName":"","lastName":"Sato","suffix":""},{"id":634201058,"identity":"675aef26-afc6-44de-be34-67461a8ccc20","order_by":6,"name":"Maki Ogasawara","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Maki","middleName":"","lastName":"Ogasawara","suffix":""},{"id":634201059,"identity":"0009a559-4159-4bc7-908a-0491d419daee","order_by":7,"name":"Yuko Horikoshi","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuko","middleName":"","lastName":"Horikoshi","suffix":""},{"id":634201062,"identity":"b70bf7a4-cc3b-4ed5-abef-a0f0a8da5d86","order_by":8,"name":"Masayuki Hoshi","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Masayuki","middleName":"","lastName":"Hoshi","suffix":""},{"id":634201064,"identity":"2cf27b31-4aca-46e8-9f89-7edd1111b943","order_by":9,"name":"Yoshitaka Shiba","email":"","orcid":"","institution":"Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yoshitaka","middleName":"","lastName":"Shiba","suffix":""}],"badges":[],"createdAt":"2026-04-20 04:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9466503/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9466503/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109067726,"identity":"f82fdcef-9e09-4e63-b381-a97e432eceb7","added_by":"auto","created_at":"2026-05-12 10:00:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4186487,"visible":true,"origin":"","legend":"\u003cp\u003eGrasping device and the adjustability of grasping force task. (a) The device enables measurement of grasping forces up to 5 N. (b) Initial resting position of the device held in the hand. (c) Device during active grasping. (d) The blue line represents the target grasping force, and the red line represents the measured grasping force. This task comprises three different force patterns that change continuously.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9466503/v1/1dc37f921586c8622093133f.png"},{"id":109249612,"identity":"25eacd5e-0b95-428b-8b1d-67f03ecb1682","added_by":"auto","created_at":"2026-05-14 08:57:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4036712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9466503/v1/0bc3da92-9706-40d8-8774-295f154ec8f0.pdf"},{"id":109081184,"identity":"14c0405a-8cf5-4c75-9693-26194f8898af","added_by":"auto","created_at":"2026-05-12 12:04:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27798,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalmaterialsTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9466503/v1/a651157f03c38b327a56fcb7.docx"},{"id":109037808,"identity":"2750b5ec-8d30-4dc8-aa48-917bea066ceb","added_by":"auto","created_at":"2026-05-12 03:00:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27720,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalmaterialsTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9466503/v1/f68223e34206e5dd610505f3.docx"},{"id":109037811,"identity":"61d9d080-f97e-42cc-8f4f-a59defca8c37","added_by":"auto","created_at":"2026-05-12 03:00:05","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17146338,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-9466503/v1/639fe8af5df98c1be14f141f.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of adjustability of grasping force in dominant and nondominant hands with physical and cognitive function in community-dwelling older adults: A cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003ePopulation aging is a global challenge, and the number of older adults aged 65 years and older is projected to increase from approximately 700\u0026nbsp;million in 2019 to 1.5\u0026nbsp;billion by 2050 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Japan (total population: approximately 124\u0026nbsp;million) exhibits particularly pronounced aging, with an average life expectancy of approximately 84 years and 31% of its population aged 65 years and over, significantly exceeding the global average [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Maintaining activities of daily living (ADL) and quality of life among older adults is crucial for promoting well-being.\u003c/p\u003e \u003cp\u003eGrasping objects is a fundamental movement required for performing ADL and requires coordinated hand function. Among the various aspects of hand dexterity, the ability to appropriately adjust the grasping force according to the shape, size, and weight of objects is defined as the adjustability of grasping force (AGF) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. AGF is evaluated using a visuomotor tracking task in which individuals adjust their grasping force to match a target grasping force displayed on a monitor [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This task requires recognizing the error between the target and measured grasping force and appropriately modifying movements based on concurrent visual feedback regarding task performance. Thus, it evaluates aspects different from tasks that assess movement speed, such as the finger-tapping task [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], or tasks that evaluate manual dexterity, such as the Purdue Pegboard Test (PPT) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Recently, tool manipulation ability involving precision force control has been reported to be associated with ADL in older adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, AGF may serve as an important indicator for comprehensively evaluating ADL ability in this population.\u003c/p\u003e \u003cp\u003eHand function in older adults depends on both physical and cognitive functions. Physical function is influenced by aging [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], grip strength [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], upper limb muscle strength [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and laterality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, cognitive function involves executive [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and visuospatial functions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous studies investigating the relationship between upper limb function and cognitive function have employed finger-tapping tasks and the PPT, which evaluates motor speed and manual dexterity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. On the other hand, AGF differs from these tasks in that it is expected to require fine force control based on visual feedback; however, the relative contributions of physical and cognitive functions have not been sufficiently verified.\u003c/p\u003e \u003cp\u003eTherefore, in this cross-sectional study, we aimed to clarify the factors associated with AGF in community-dwelling older adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included 133 community-dwelling older adults aged 65 years and older who participated in a physical fitness measurement event conducted in Kagamiishi Town in 2024. Participants were recruited through the town\u0026rsquo;s public relations bulletin. The exclusion criteria were as follows: 1) individuals who did not respond to the questionnaire, 2) those with finger loss or difficulty in operating devices, and 3) individuals with a history of neurological disease. This study was approved by the Ethics Committee of the Fukushima Medical University (approval number REC2024-055) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all the participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements of participant characteristics\u003c/h3\u003e\n\u003cp\u003eAll assessments were conducted on the same day.\u003c/p\u003e\n\u003ch3\u003eAssessment of AGF\u003c/h3\u003e\n\u003cp\u003eAGF was evaluated using a grasping force measurement device (iWakka; Nagoya Institute of Technology, Nagoya, Japan) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This device has been used in previous studies involving younger and older adults, and individuals with stroke [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The task consisted of waveforms in three continuously changing contraction modes: concentric contraction (increasing the grasping force), eccentric contraction (decreasing the grasping force), and isometric contraction (maintaining the grasping force) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The trial lasted 93 s, including an initial 3-s stabilization period that was excluded from the analysis, and the participants were asked to match their grasping force to the target line displayed on the monitor (1.5\u0026ndash;4 N). The measurement environment was based on a previous study [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and the measurements were conducted in a seated position on a chair. The grasping device was placed on an aluminum plate to reduce its tilt and friction resistance. Furthermore, we established two measurement stations to control for measurement sequence effects: at station A, the dominant hand was evaluated first, and at station B, the nondominant hand was evaluated first. The participants were randomly assigned to either station (station A, n\u0026thinsp;=\u0026thinsp;66; station B, n\u0026thinsp;=\u0026thinsp;67). Before the measurement, the operation of the device was explained, and after confirming that the participants understood the task, measurements were performed once each for the dominant and nondominant hands. The AGF indicator was calculated as the mean absolute error between the target and the measured grasping force (grasping error) based on a previous study [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A smaller grasping error indicated a better AGF.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePhysical and cognitive function\u003c/h3\u003e\n\u003cp\u003ePhysical function assessment included body composition, maximum grip strength, and maximum pinch strength. Body composition was measured using a multifrequency bioelectrical impedance analysis device (InBody S10, InBody Co., Ltd., Tokyo, Japan). This device uses an 8-point contact electrode direct segmental multifrequency measurement method and can measure muscle mass with high precision by combining six frequencies (1, 5, 50, 250, 500, and 1000 kHz) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Participants lay supine on a bed with electrodes attached to the thumbs and middle fingers of both hands and ankles, and the measurement time was approximately 100 s. Skeletal muscle mass index (SMI) was used as an analytical indicator. SMI is a muscle mass indicator that does not depend on body size and is calculated by dividing skeletal muscle mass (kg) by height (m) squared. Maximum grip strength was measured using a digital grip strength meter (GRIP-D; Takei Scientific Instruments Co., Ltd., Niigata, Japan). The participants stood with the elbow joint fully extended and the proximal interphalangeal joint of the index finger at 90\u0026deg; to perform maximum-effort gripping [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Two measurements were performed with the dominant hand, and the maximum value was recorded. If the grip strength meter touched the body during measurement, the measurement was repeated. Maximum pinch strength was measured using a pinch force sensor (Mobii Z; SAKAI Medical Co., Ltd., Tokyo, Japan). Participants sat in a chair with the shoulder joint adducted, elbow flexed at 90\u0026deg;, and forearm in the mid-pronation-supination position. Pulp pinch and key pinch were measured using the index finger and thumb [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Two measurements were performed on each side for each condition, and the maximum value was recorded. Hand function was evaluated using the PPT (SAKAI Medical Co., Ltd., Tokyo, Japan). The PPT is a validated assessment method for evaluating finger dexterity and visuomotor coordination [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Participants were asked to insert as many pegs as possible into the holes within 30 s, and the number of inserted pegs was scored (PPT score). Measurements were taken once each in the order of the dominant hand, followed by the nondominant hand. Cognitive function was assessed using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). The MoCA-J evaluates multiple domains of cognitive function: visuospatial/executive function (three tasks: figure copying, clock drawing, and trail making; 0\u0026ndash;5 points), naming (three items; 0\u0026ndash;3 points), attention (four tasks: forward digit span, backward digit span, vigilance and serial subtraction; 0\u0026ndash;6 points), language (two tasks: sentence repetition and phonemic fluency; 0\u0026ndash;3 points), abstraction (two items; 0\u0026ndash;2 points), delayed recall (five items; 0\u0026ndash;5 points), and orientation (six items; 0\u0026ndash;6 points), for a potential total score of 30 points [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The MoCA-J was administered by well-trained therapists and/or assessors in accordance with the standardized administration manual. According to the standard MoCA-J scoring protocol, one point was added to the total score for participants with \u0026le;\u0026thinsp;12 years of education [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this study, in addition to the MoCA-J total score, the MoCA-J visuospatial/executive subscore (0\u0026ndash;5 points), which was expected to be the most relevant to the AGF task, was used in the analysis.\u003c/p\u003e\n\u003ch3\u003eGeneral information\u003c/h3\u003e\n\u003cp\u003eGeneral information collected included age, sex, body mass index, and handedness. Handedness was determined by self-report.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eComparison between dominant and nondominant hands\u003c/h2\u003e \u003cp\u003eThe Shapiro\u0026ndash;Wilk test was performed for all continuous variables. The results showed a non-normal distribution for all variables except age (p\u0026thinsp;=\u0026thinsp;0.466) and SMI (p\u0026thinsp;=\u0026thinsp;0.055). Therefore, the laterality between the dominant and nondominant hands was compared using the Wilcoxon signed-rank test for the grasping error, maximum pulp and key pinch strengths, and PPT score.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eGeneralized linear model (GLM) analysis\u003c/h3\u003e\n\u003cp\u003eMultivariate analysis using a GLM was conducted to identify factors associated with AGF in the dominant and nondominant hands of community-dwelling older adults. Simple regression analyses identified candidate explanatory variables for inclusion in the model. Grasping errors of the dominant and nondominant hands were considered the dependent variables, with age, sex, maximum grip strength, maximum pulp and key pinch strengths, SMI, MoCA-J visuospatial/executive subscore, and PPT score as explanatory variables. Variables showing associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were considered candidates, and identical models were constructed for both the dominant and nondominant hands to allow for direct comparison. Model 1 was created based on the results of the regression analysis. In Model 1, the PPT score, an indicator of motor coordination, was included as an explanatory variable for AGF. The MoCA-J visuospatial/executive subscore was adopted as an indicator of cognitive function, as it has been reported to be an independent factor associated with physical function decline in community-dwelling older adults [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The explanatory variables in Model 1 were age and sex as covariates, along with MoCA-J visuospatial/executive subscore, maximum grip strength, and PPT score. Furthermore, a sensitivity analysis was conducted in Model 2 by replacing the PPT with maximum pulp pinch strength and key pinch, which are indicators of muscle strength, to evaluate the robustness of Model 1. In Model 2, maximum pulp and key pinch strengths, an indicator of muscle strength, were included as an explanatory variable instead of the PPT score. This allowed for the comparison of the associations between the AGF and different indicators, such as the PPT score and pinch strength. As an exploratory analysis, a model was constructed in which the MoCA-J visuospatial/executive subscore was replaced with the MoCA-J total score to assess the impact of differences in cognitive function indicators on the results. Because the dependent variable, grasping error of the dominant and nondominant hands, showed non-normal distributions, a GLM with a gamma distribution and a log link function was used. Multiple imputation by chained equations was performed to address missing data in some explanatory variables [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Missing data occurred for one participant in SMI owing to a contraindication to bioelectrical impedance analysis (pacemaker implantation). For pinch strength, one to two participants had missing values under specific conditions; however, measurements were successfully obtained in the contralateral hand for all affected participants, suggesting that bilateral structural limitations were unlikely. The missing pinch strength data were attributed to multiple incidental factors, including physical fatigue accumulated during the single-day multi-measurement protocol requiring repeated maximal force exertion, time constraints inherent to the community-based event setting, and potential recording errors during data collection. Therefore, missing data were assumed to be missing at random rather than not at random. Predictive mean matching was applied, and 20 imputations were performed. A GLM was applied to each imputed dataset, and the 20 estimation results obtained were integrated using Rubin\u0026rsquo;s rule. All statistical analyses were performed using R version 4.5.1 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The statistical significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u0026rsquo; characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the characteristics of the participants. A total of 133 participants underwent physical fitness measurements. Four participants were excluded (one with finger loss and three with a history of stroke); therefore, 129 participants were included in the final analysis. In total, there were 126 right-handed individuals and three left-handed individuals. In the comparison between the dominant and nondominant hands, the dominant hand showed significantly higher values for the maximum pulp pinch strength (p\u0026thinsp;=\u0026thinsp;0.002), maximum key pinch strength (p\u0026thinsp;=\u0026thinsp;0.001), and PPT score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference in grasping error was observed between the two hands (p\u0026thinsp;=\u0026thinsp;0.113).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrasping error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant hand (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNondominant hand (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40/89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandedness (right/left)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (kg/m\u003csup\u003e2\u003c/sup\u003e) \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum grip strength (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.75\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum pulp pinch strength (kg)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant hand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNondominant hand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum key pinch strength (kg) \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant hand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNondominant hand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPT score (points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant hand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNondominant hand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA-J total score (0\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisuospatial/executive subscore (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote. AGF, adjustability of grasping force; BMI, body mass index; MoCA-J, Japanese version of the Montreal Cognitive Assessment; SMI, skeletal muscle mass index; PPT, Purdue Pegboard Test. p-values for dominant versus nondominant comparisons were calculated using the Wilcoxon signed-rank test. \u0026dagger; Missing data were handled via multiple imputations using chained equations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with AGF\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eExploratory univariate analysis\u003c/h2\u003e \u003cp\u003eSimple regression analyses were conducted to identify variables associated with grasping error (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For the dominant hand, significant associations were found for sex (β = -0.269, SE\u0026thinsp;=\u0026thinsp;0.132, p\u0026thinsp;=\u0026thinsp;0.044), the MoCA-J visuospatial/executive subscore (β = -0.162, SE\u0026thinsp;=\u0026thinsp;0.059, p\u0026thinsp;=\u0026thinsp;0.007), and maximum grip strength (β = -0.025, SE\u0026thinsp;=\u0026thinsp;0.009, p\u0026thinsp;=\u0026thinsp;0.005). Furthermore, for the nondominant hand, significant associations were found for the MoCA-J total score (β = -0.044, SE\u0026thinsp;=\u0026thinsp;0.012, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MoCA-J visuospatial/executive subscore (β = -0.165, SE\u0026thinsp;=\u0026thinsp;0.047, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), maximum grip strength (β = -0.019, SE\u0026thinsp;=\u0026thinsp;0.008, p\u0026thinsp;=\u0026thinsp;0.027), and PPT score (β = -0.070, SE\u0026thinsp;=\u0026thinsp;0.022, p\u0026thinsp;=\u0026thinsp;0.002). These variables, along with age and sex as covariates, were used to construct the primary model (Model 1). Furthermore, as a sensitivity analysis, Model 2 was constructed by replacing the PPT score with the maximum pulp and key pinch strengths. No multicollinearity was observed across all models, as indicated by a variance inflation factor\u0026thinsp;\u0026lt;\u0026thinsp;5 for all explanatory variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploratory univariate linear regression analyses with grasping error\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGrasping error (Dominant)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGrasping error (Nondominant)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA-J total score (0\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA-J visuospatial/executive subscore (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum grip strength (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum pulp pinch strength (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum key pinch strength (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPT score (points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. β, Unstandardized regression coefficients; SE, standard error; AGF, adjustability of grasping force; MoCA-J, Japanese version of the Montreal Cognitive Assessment; SMI, skeletal muscle mass index; PPT, Purdue Pegboard Test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDominant hand\u003c/h2\u003e \u003cp\u003eIn the dominant hand, only the MoCA-J visuospatial/executive subscore showed an independent negative association (β = -0.156, 95% confidence interval [CI]: [-0.270, -0.042], p\u0026thinsp;=\u0026thinsp;0.008), whereas age (p\u0026thinsp;=\u0026thinsp;0.428), sex (p\u0026thinsp;=\u0026thinsp;0.773), maximum grip strength (p\u0026thinsp;=\u0026thinsp;0.121), and PPT score (p\u0026thinsp;=\u0026thinsp;0.877) showed no significant associations in Model 1. For the sensitivity analysis, similar to Model 1, only the MoCA-J visuospatial/executive subscore showed an independent negative association (β = -0.160, 95% CI: [-0.273, -0.047], p\u0026thinsp;=\u0026thinsp;0.006) in Model 2 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear model results for dominant hand grasping error with MoCA-J visuospatial/executive subscore\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.035\u0026ndash;0.015]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.034\u0026ndash;0.015]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (female\u0026thinsp;=\u0026thinsp;reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.423\u0026ndash;0.316]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.415\u0026ndash;0.337]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA-J visuospatial/executive subscore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.270\u0026ndash;-0.042]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.273\u0026ndash;-0.047]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum grip strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.047\u0026ndash;0.006]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.048\u0026ndash;0.010]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPT score (dominant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.058\u0026ndash;0.050]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum pulp pinch strength (dominant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.111\u0026ndash;0.083]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum key pinch strength (dominant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.096\u0026ndash;0.093]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. GLM, Generalized linear model with gamma family and log link function. Multiple imputations (m\u0026thinsp;=\u0026thinsp;20) were performed for the missing data. AGF, adjustability of grasping force; MoCA-J, Japanese version of the Montreal Cognitive Assessment; SMI, skeletal muscle mass index; PPT, Purdue Pegboard Test; β, regression coefficients from GLM with log link function; SE, standard error; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNondominant hand\u003c/h2\u003e \u003cp\u003eIn the nondominant hand, both the MoCA-J visuospatial/executive subscore (β = -0.102, 95% CI: [-0.197, -0.007], p\u0026thinsp;=\u0026thinsp;0.036) and the PPT score (β = -0.057, 95% CI: [-0.103, -0.012], p\u0026thinsp;=\u0026thinsp;0.014) showed a significant negative association in Model 1. Conversely, in Model 2, only the MoCA-J visuospatial/executive subscore showed an independent negative association (β = -0.151, 95% CI: [-0.250, -0.052], p\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear model results for nondominant hand grasping error with MoCA-J visuospatial/executive subscore\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.018\u0026ndash;0.022]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.017\u0026ndash;0.025]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (female\u0026thinsp;=\u0026thinsp;reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.427\u0026ndash;0.185]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.350\u0026ndash;0.296]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA-J visuospatial/executive subscore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.197\u0026ndash;-0.007]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.250\u0026ndash;-0.052]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum grip strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.032\u0026ndash;0.011]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.038\u0026ndash;0.013]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPT score (nondominant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.103\u0026ndash;-0.012]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum pulp pinch strength (nondominant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.060\u0026ndash;0.106]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum key pinch strength (nondominant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e[-0.100\u0026ndash;0.054]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. GLM, Generalized linear model with gamma family and log link function. Multiple imputations (m\u0026thinsp;=\u0026thinsp;20) were performed for the missing data. AGF, adjustability of grasping force; MoCA-J, Japanese version of the Montreal Cognitive Assessment; SMI, skeletal muscle mass index; PPT, Purdue Pegboard Test; β, regression coefficients from GLM with log link function; SE, standard error; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eExploratory analysis with the MoCA-J total score\u003c/h2\u003e \u003cp\u003eAs an exploratory analysis, a model in which the MoCA-J visuospatial/executive subscore was replaced with the MoCA-J total score was examined. In the dominant hand, no significant independent factors were identified in either Model 1 or 2 (Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In the nondominant hand, a pattern similar to that observed when using the MoCA-J visuospatial/executive subscore was observed. In Model 1, both the MoCA-J total score (β = -0.029, 95% CI: [-0.053, -0.004], p\u0026thinsp;=\u0026thinsp;0.024) and PPT score (β = -0.052, 95% CI: [-0.100, -0.005], p\u0026thinsp;=\u0026thinsp;0.030) showed significant negative associations. However, in Model 2, only the MoCA-J total score (β = -0.043, 95% CI: [-0.068, -0.018], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed an independent negative association (Supplemental Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study identified factors associated with AGF in community-dwelling older adults. The results showed that the MoCA-J visuospatial/executive subscore was a common factor associated with AGF in both the dominant and nondominant hands. Conversely, hand dexterity was significantly associated with AGF only in the nondominant hand. Furthermore, exploratory analyses suggested that the cognitive resources recruited differed between dominant and nondominant hands. These findings suggest that AGF may not be fully explained by conventional muscle strength indicators and appears to be associated with cognitive function.\u003c/p\u003e \u003cp\u003eThe MoCA-J visuospatial/executive subscore consists of a cube copy, clock drawing test, and trail-making test, and evaluates visuospatial information processing ability, visuomotor coordination, and executive function [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The AGF task is a visual tracking task that requires participants to match their grasping force to a target value based on real-time visual feedback. It requires the ability to confirm the target and actual grasping forces, recognize the magnitude of the error (visuospatial function), and execute movement corrections (executive function). Therefore, the MoCA-J visuospatial/executive subscore was considered a significant associated factor because it matched the cognitive functions that form the basis for visuomotor integration and error corrections required for performing the AGF task. This finding is consistent with those of several previous studies. Tomita et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported that the MoCA-J visuospatial/executive subscore was specifically associated with performance on a finger-tapping task in 102 community-dwelling older adults. Their study examined the usefulness of motor tasks as a screening tool for the early detection of cognitive decline. In contrast, the present study focused on identifying factors associated with AGF. Furthermore, functional magnetic resonance imaging studies have shown that the frontoparietal network is involved in visual tracking tasks using precision grip control [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This network is involved in visuospatial attention, motor planning, and error monitoring. These functions overlap with the cognitive functions and neural substrates reflected by the MoCA-J visuospatial/executive subscore. Therefore, our study results suggest that AGF may reflect the involvement of cognitive function.\u003c/p\u003e \u003cp\u003eIn the dominant hand, task-specific cognitive functions such as visuospatial/executive function were selectively associated with AGF, whereas global cognitive function (MoCA-J total score) showed no significant association. This finding is consistent with the interpretation that motor control of the dominant hand may be more automated than the nondominant hand and selectively relies on specific cognitive domains in the context of the present task. The dominant hand is characterized by expanded and strengthened neural representations in the motor cortex owing to its frequent use in daily life [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Accordingly, motor execution using the dominant hand can be performed relatively efficiently when specific cognitive domains responsible for error detection and motor correction planning function appropriately. In contrast, the nondominant hand showed a qualitatively different pattern: both the MoCA-J visuospatial/executive subscore and the MoCA-J total score were significantly associated with AGF, and hand dexterity was also associated with AGF. Specifically, the MoCA-J total score was significantly associated with AGF in the nondominant hand but not in the dominant hand, despite the visuospatial/executive subscore being a significant factor in both hands. This hand-specific pattern of the MoCA-J total score suggests that cognitive domains beyond visuospatial/executive function may additionally contribute to nondominant hand AGF; however, this interpretation warrants caution given that the MoCA-J total score incorporates the visuospatial/executive subscore. These findings suggest that fine motor control of the nondominant hand may involve a broader scope of cognitive engagement than that required for the dominant hand. The nondominant hand is used less frequently in daily life than the dominant hand and has weaker neural representations in the motor cortex, resulting in relatively lower automation of motor control [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Consequently, precise motor control of the nondominant hand based on sensory feedback is likely to require widespread brain activity involving the recruitment of neural networks spanning multiple brain regions. Consistent with these findings, functional connectivity between motor-related areas and frontoparietal regions has been reported to increase during nondominant hand movements compared with dominant hand movements [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In line with these neural substrates, movement execution in the nondominant hand potentially involves a more distributed cognitive network, which may contribute to the observed association between global cognitive function and nondominant hand AGF. Consistent with this interpretation, both the MoCA-J total score and hand dexterity showed independent negative associations with nondominant hand AGF in the present exploratory study. These results suggest that laterality is not reflect in the overall performance level of the task but rather in the structure of associated factors that determine performance, particularly in the way in which cognitive function contributes. Overall, these findings suggest that the AGF task used in this study differs from conventional indicators of muscle strength and dexterity. Instead, it may reflect cognitive-motor integration processes based on concurrent visual feedback, thereby suggesting a potentially novel perspective on functional assessment in older adults. Although causal inferences cannot be drawn from the present cross-sectional data, these findings support the hypothesis that task designs incorporating visuomotor feedback may be a relevant direction for future intervention studies targeting AGF in older adults. Longitudinal and experimental studies are needed to examine whether visuospatial and executive cognitive functions mediate the effects of force-tracking practice on AGF.\u003c/p\u003e \u003cp\u003eThis study had certain limitations. First, given its cross-sectional design, causal relationships could not be established. Second, because this study focused on community-dwelling older adults, caution is warranted when generalizing the findings to other populations. Third, visual function was not assessed. Given that the AGF task depends on real-time visual feedback to the target force, individual differences in visual function may have influenced the task. Additionally, the MoCA-J total score used in the exploratory analysis includes the visuospatial/executive subscore; therefore, the independent contribution of cognitive domains beyond visuospatial/executive function to nondominant hand AGF cannot be fully determined from the present data. Future studies should incorporate visual function assessment and examine the effects of visuospatial/executive function training on AGF using longitudinal designs and cognitive measures that isolate non-visuospatial domains to more rigorously characterize the scope of cognitive involvement in nondominant hand AGF.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study clarified that, among community-dwelling older adults, visuospatial/executive function is a common factor associated with AGF in both the dominant and nondominant hands, whereas hand dexterity is an additional associated factor only in the nondominant hand. The findings further suggest that task-specific cognitive functions may be a primary correlate of dominant hand AGF, whereas exploratory analyses indicated that global cognitive function was additionally associated with nondominant hand AGF, possibly reflecting broader cognitive involvement that may be related to the relatively lower automation of nondominant hand motor control. Furthermore, our findings suggest that AGF may be associated with cognitive function beyond what can be explained by conventional muscle strength indicators, potentially capturing aspects that differ from existing measures, such as grip strength and pinch strength. Future research should examine whether AGF can serve as a functional indicator of cognitive-motor integration and clarify its clinical significance in the context of functional assessment in older adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADL: activities of daily living\u003c/p\u003e\n\u003cp\u003eAGF: adjustability of grasping force\u003c/p\u003e\n\u003cp\u003eGLM: generalized linear model\u003c/p\u003e\n\u003cp\u003eMoCA-J: Japanese version of the Montreal Cognitive Assessment\u003c/p\u003e\n\u003cp\u003ePPT: Purdue Pegboard Test\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Fukushima Medical University (approval number REC2024-055) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available because of privacy and ethical considerations, but are available from the corresponding author upon reasonable request ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by JSPS KAKENHI Grant-in-Aid for Early-Career Scientists Number JP24K20476.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJY contributed to Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Software, Project administration, Investigation, Methodology, Conceptualization, Data curation, and Formal analysis. SY contributed to Writing \u0026ndash; review \u0026amp; editing, Project administration, Investigation, Methodology, Conceptualization. KI contributed to Writing \u0026ndash; review \u0026amp; editing, Project administration, Investigation, Methodology, Conceptualization. HH contributed to Writing \u0026ndash; review \u0026amp; editing, Project administration, Investigation, Methodology, Conceptualization. TSone contributed to Writing \u0026ndash; review \u0026amp; editing, Project administration, Investigation, Methodology, Conceptualization. TSato contributed to Writing \u0026ndash; review \u0026amp; editing, Project administration, Investigation, Methodology, Conceptualization, and Data curation. MO contributed to Writing \u0026ndash; review \u0026amp; editing, Project administration, Investigation, Methodology, Conceptualization. YH contributed to Writing \u0026ndash; review \u0026amp; editing, Project administration, Investigation, Methodology, Conceptualization. Masayuki Hoshi: Writing \u0026ndash; review \u0026amp; editing, Project administration, Investigation, Methodology, Conceptualization. YS contributed to Writing \u0026ndash; review \u0026amp; editing, Project administration, Investigation, Methodology, Conceptualization, Supervision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the Fukushima Medical University staff for their assistance with data collection and Kazunori Akizuki, Ryohei Yamamoto, Kazuto Yamaguchi, and Wataru Nakano for their assistance with writing the draft. The authors gratefully acknowledge Editage for their English editing services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used Genspark in order to assist in generating code for data analysis. After using this tool/service, the authors reviewed and edited the content as needed and took full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUnited Nations Department of Economic and Social Affairs Population Division. World population ageing 2019: highlights (ST/ESA/SER.A/430); 2019. https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2019-Highlights.pdf. 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Cogent Psychol. 2022;9:2131039. https://doi.org/10.1080/23311908.2022.2131039.\u003c/li\u003e\n\u003cli\u003eYabuki J, Kaneno T, Yamamoto R, Yamaguchi K, Nakano W, Akizuki K. Effects of visual terminal feedback on hand dexterity in relation to visuospatial ability in subacute stroke: a preliminary study. Sci Rep. 2025;15:7368. https://doi.org/10.1038/s41598-025-91806-2.\u003c/li\u003e\n\u003cli\u003eDuarte Martins A, Paulo Brito J, Fernandes O, Oliveira R, Gon\u0026ccedil;alves B, Batalha N. Effects of a 16-week high-speed resistance training program on body composition in community-dwelling independent older adults: a clinical trial. Clin Nutr ESPEN. 2024;63:84\u0026ndash;91. https://doi.org/10.1016/j.clnesp.2024.06.010.\u003c/li\u003e\n\u003cli\u003eHioka A, Akazawa N, Okawa N, Nagahiro S. Influence of aging on extracellular water-to-total body water ratio in community-dwelling females. Clin Nutr ESPEN. 2024;60:73\u0026ndash;8. https://doi.org/10.1016/j.clnesp.2024.01.007.\u003c/li\u003e\n\u003cli\u003eBalogun JA, Akomolafe CT, Amusa LO. Grip strength: effects of testing posture and elbow position. Arch Phys Med Rehabil. 1991;72:280\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eMathiowetz V, Kashman N, Volland G, Weber K, Dowe M, Rogers S. Grip and pinch strength: normative data for adults. Arch Phys Med Rehabil. 1985;66:69\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eFujiwara Y, Suzuki H, Yasunaga M, Sugiyama M, Ijuin M, Sakuma N, et al. Brief screening tool for mild cognitive impairment in older Japanese: validation of the Japanese version of the Montreal Cognitive Assessment. Geriatr Gerontol Int. 2010;10:225\u0026ndash;32. https://doi.org/10.1111/j.1447-0594.2010.00585.x.\u003c/li\u003e\n\u003cli\u003eNasreddine ZS, Phillips NA, B\u0026eacute;dirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53:695\u0026ndash;9. https://doi.org/10.1111/j.1532-5415.2005.53221.x.\u003c/li\u003e\n\u003cli\u003eVan Buuren S. Multivariate missing data. In: Flexible imputation of missing data 2nd ed. Chapman and Hall/CRC; 2018. p. 105\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2025. https://www.R-project.org/. Accessed December 18, 2025.\u003c/li\u003e\n\u003cli\u003ePapadelis C, Arfeller C, Erla S, Nollo G, Cattaneo L, Braun C. Inferior frontal gyrus links visual and motor cortices during a visuomotor precision grip force task. Brain Res. 2016;1650:252\u0026ndash;66. https://doi.org/10.1016/j.brainres.2016.09.011.\u003c/li\u003e\n\u003cli\u003eEjaz N, Hamada M, Diedrichsen J. Hand use predicts the structure of representations in sensorimotor cortex. Nat Neurosci. 2015;18:1034\u0026ndash;40. https://doi.org/10.1038/nn.4038.\u003c/li\u003e\n\u003cli\u003eKang N, Shinohara M, Zatsiorsky VM, Latash ML. Learning multi-finger synergies: an uncontrolled manifold analysis. Exp Brain Res. 2004;157:336\u0026ndash;50. https://doi.org/10.1007/s00221-004-1850-0.\u003c/li\u003e\n\u003cli\u003eTsurugizawa T, Taki A, Zalesky A, Kasahara K. Increased interhemispheric functional connectivity during non-dominant hand movement in right-handed subjects. iScience. 2023;26:107592. https://doi.org/10.1016/j.isci.2023.107592.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"grasping force adjustability, visuospatial function, executive function, hand dexterity, hand dominance, visuospatial tracking","lastPublishedDoi":"10.21203/rs.3.rs-9466503/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9466503/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdjustability of grasping force (AGF) is essential for activities of daily living (ADL) and instrumental ADL. However, its association with physical and cognitive functions in older adults, and whether these associations differ by hand remain unclear. Therefore, we investigated these associations using a visual tracking task.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included 129 community-dwelling older adults from Kagamiishi Town. AGF was assessed as the error between target and measured forces (grasping error) in dominant and nondominant hands. Physical function measures included grip strength, pulp and key pinch strengths, and Purdue Pegboard Test (PPT) score. Cognitive function was assessed using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). We used age- and sex-adjusted generalized linear models for the dominant and nondominant hands. Functional indicators included the MoCA-J visuospatial/executive subscore.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 129 participants (mean age: 76.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69 years; 69.0% female) were analyzed. Grasping errors did not differ between hands (dominant hand: 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 N, nondominant hand: 0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 N; p\u0026thinsp;=\u0026thinsp;0.113). In the dominant hand, only the MoCA-J visuospatial/executive subscore was significantly associated with grasping error (β = -0.156, p\u0026thinsp;=\u0026thinsp;0.008). In the nondominant hand, both MoCA-J visuospatial/executive subscore (β = -0.102, p\u0026thinsp;=\u0026thinsp;0.036) and PPT score (β = -0.057, p\u0026thinsp;=\u0026thinsp;0.014) were significantly associated with grasping error.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eVisuospatial/executive function was associated with AGF in both hands. Dexterity was additionally associated with AGF in the nondominant hand. These findings may inform hand-specific assessment or intervention strategies for older adults.\u003c/p\u003e","manuscriptTitle":"Association of adjustability of grasping force in dominant and nondominant hands with physical and cognitive function in community-dwelling older adults: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 03:00:00","doi":"10.21203/rs.3.rs-9466503/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"66790144002111047283955343665948870065","date":"2026-05-17T11:17:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T14:17:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61328340019141755365425183337674569621","date":"2026-05-10T08:00:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T10:56:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261992187488980280392241330670912344583","date":"2026-05-08T08:30:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T13:51:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-23T11:58:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T07:11:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-22T07:10:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-04-20T03:55:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a73237e0-d083-4701-86e3-d2690bed7fc2","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"66790144002111047283955343665948870065","date":"2026-05-17T11:17:42+00:00","index":31,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T14:17:07+00:00","index":26,"fulltext":""},{"type":"reviewerAgreed","content":"61328340019141755365425183337674569621","date":"2026-05-10T08:00:47+00:00","index":25,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T10:56:35+00:00","index":24,"fulltext":""},{"type":"reviewerAgreed","content":"261992187488980280392241330670912344583","date":"2026-05-08T08:30:12+00:00","index":23,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T03:05:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 03:00:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9466503","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9466503","identity":"rs-9466503","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}