Characteristics and Factors Linked to Declined Manual Dexterity in Frailty in Community-Dwelling Older Women

Abstract

Finger dexterity declines in frail older adults, but confounding by age and cognition limits inference. This study aimed to isolate the association between frailty and dexterity in community-dwelling older women. We assessed 119 women on the Kihon Checklist (KCL), cognition, Nine-Hole Peg Test (NHPT), grip strength, upper-limb muscle mass, and attention. NHPT was performed with the dominant hand under normal and “inhibit” conditions (concurrent isometric pinch with the non-dominant hand). Participants were classified as robust or frail by KCL. After propensity score matching based on age and cognitive function, NHPT completion time was verified using a two-factor mixed ANOVA, while grip strength, muscle mass, and attention were compared between groups. The association between NHPT and muscle mass was also examined. The frail group showed longer NHPT completion times than robust peers under both conditions and lower grip strength. In frail participants, NHPT time under the inhibit condition correlated positively with right and left upperlimb muscle mass. After controlling for age and cognition, frailty was associated with impaired finger dexterity in older women. However, since participants were recruited from only one region and were limited to women, these findings should be interpreted with caution when generalizing to other populations.

Keywords

frailty motor control and learning experimental geriatrics/gerontology functional assessment rehabilitation

Introduction

The global population of older adults is increasing at a rapid rate, with the phenomenon of “super-aging” societies becoming increasingly prevalent, particularly in developed countries (Zastavetska et al., 2024). Considering this aging trend, it is imperative to identify effective strategies that promote independence in older adults and enhance their overall health. To this end, maintaining optimal cognitive and physical function is essential. In particular, manual dexterity has garnered significant attention as a pivotal skill for activities of daily living (ADL; Dollar, 2014; Kilbreath & Heard, 2005; Lee & Jung, 2015). A decline in finger dexterity with age has been documented among older adults (Olafsdottir et al., 2007).

Frailty comprehensively describes the physical, psychological, and social vulnerability of older adults. Previous studies have reported that older adults with frailty exhibit decreases in grip strength, finger movement, and overall cognitive function (Fujikawa et al., 2025; Robinson et al., 2022; Stringa et al., 2023). Consequently, the decline in finger dexterity observed in frail elderly individuals is likely attributable to these multifaceted factors. Many of these factors also overlap with the well-documented physiological changes associated with aging, and numerous previous studies have not adequately controlled for confounding variables such as age and cognitive function. As a result, the independent effect of frailty itself on finger dexterity remains unclear.

Given this background, controlling for potential confounding factors such as age and cognitive function is necessary to clarify the characteristics of finger dexterity decline associated specifically with frailty. Therefore, in this study, we aimed to elucidate the association between manual dexterity and frailty in community-dwelling older women. We compared manual dexterity between frail and robust groups and examined how frailty was associated with manual dexterity under conditions that controlled for age and cognitive function.

Methods

Study Design

The present study was a cross-sectional study. The evaluation encompassed the assessment of finger dexterity, physical function, and cognitive function in elderly women residing within the community. A comparative analysis was conducted between robust elderly women and frail elderly women.

Participants

The subjects were 191 community-dwelling older women who participated in a physical fitness test event held in Yasu City, Shiga Prefecture, between September 2 and 6, 2024. Gender disparities in the deterioration of physical function associated with aging are evident, particularly in the decline of grip strength and the progression of sarcopenia, which are more pronounced in women (Kato et al., 2022; Kwak et al., 2020). Additionally, research has indicated gender disparities in upper limb motor function performance (Noble et al., 2011). Consequently, in this study, we excluded gender-related confounding factors and focused exclusively on women to elucidate the characteristics of finger dexterity in a more homogeneous group.

The exclusion criteria for subjects were as follows: (i) subjects under 65 years of age, (ii) subjects with impaired finger dexterity due to musculoskeletal or central nervous system disorders, (iii) left-handed individuals, (iv) subjects with missing measurement data, (v) subjects who did not consent to participate in this study, and (vi) subjects who were determined to be prefrail based on the Kihon checklist (KCL). According to the criteria, the final analysis encompassed 119 participants (Figure 1).

Figure 1.

Flowchart of participants classification.

The present study was conducted in accordance with the Declaration of Helsinki, and ethical considerations were thoroughly addressed. Prior to participation, study participants were provided with comprehensive explanations of the study’s purpose and content in both written and verbal formats. Their consent to participate in this study was obtained. The present study was conducted with the approval of the institutional research ethics review committee (approval number 22-61).

Measures

The age of the participants was recorded as a basic attribute.

Physical Assessment

The physical function of the subjects was assessed by measuring several variables, including finger dexterity, grip strength, and upper limb muscle mass. The finger dexterity of the participants was assessed using the Nine-Hole Peg Task (NHPT), as described by Naito et al. (2020). The time required to complete the task was measured. The NHPT involves the use of a pegboard with nine holes, the removal of the pegs from the pegboard with the right hand, flipping them over, and the reinsertion of them into the holes. A shorter task completion time is indicative of higher finger dexterity. In this study, participants were instructed to flip the nine pegs in a specified order and measure the time required to complete the task (Figure 2a). The study established two conditions: a normal condition and an inhibit condition. In the normal condition, participants were instructed to position their left hand on the table and perform the task as expeditiously as possible with their right hand. In the inhibit condition, participants were instructed to perform an isometric pinch with their left hand while performing the task with their right hand (Figure 2b). For the isometric contraction of the left hand, a Pinch Exerciser (Rolyan Graded Pinch Exerciser; Performance Health Holdings, Inc., Warrenville, IL, USA) was utilized, and subjects were instructed to grasp the device with their left thumb, index finger, and middle finger while maintaining an open position. This required the participants to perform isometric contraction of the left finger opposition muscles against a resistance of 1.8 kg. The task was to be executed as expeditiously as possible with the right hand under normal conditions. In both conditions, the examiner demonstrated the task, and after two practice sessions, the task was performed in a randomized order for each participant. The participants were provided with sufficient rest between tasks to ensure that they did not become fatigued. They were also verbally confirmed to be free of fatigue.

Figure 2.

Experimental setup of the Nine-Hole Peg Task (NHPT). (a) Trial order for the NHPT. (b) NHPT set up with inhibit conditions. In the sitting position, the left side kept the Pinch exerciser open using the thumb, index, and middle finger, while the right side performed the NHPT.

Grip strength was measured using a digital grip strength meter (TKK 5401; Takei Scientific Instruments Co., Ltd., Niigata, Japan). The grip was adjusted to ensure that the second joint of the index finger was positioned at a right angle when grasping the grip strength meter. The measurement position was standing, with both upper limbs naturally hanging at the sides of the body. The grip strength of the right hand was measured on two separate occasions, and the maximum value obtained was adopted for analysis (Abe et al., 2016).

The measurement of upper limb muscle mass was conducted on both sides of the body using a body composition analyzer (InBody470; InBody Japan Inc., Tokyo, Japan; Nonaka et al., 2018).

Cognitive Function Assessment

The cognitive function of the participants was assessed using two standard assessments: the Mini-Mental State Examination (MMSE) and the Trail Making Test Part A (TMT-A). The MMSE is the most widely used test internationally for assessing general cognitive function. The test comprises 11 questions, encompassing orientation, memory, attention/calculation, language, and constructional ability. A score of less than 24 is indicative of suspected cognitive decline (Folstein et al., 1975). The TMT-A is a method primarily used to assess the selectivity and sustainability of attention, and its reliability and validity have been confirmed. Participants are instructed to arrange the numbers 1 to 25, which are written on paper, in an ascending sequence as expeditiously as possible. The duration required to complete the task is measured using a digital stopwatch. A prolonged completion time is indicative of impaired attention function (Heilbronner et al., 1991).

Assessment of Frailty

The KCL was utilized to evaluate frailty (Arai & Satake, 2015; Satake et al., 2017). The KCL is a self-administered questionnaire consisting of 25 questions in seven areas: five items related to activities of daily living, five items related to musculoskeletal function, two items related to malnutrition, three items related to cognitive function, and five items related to depressive mood. The respondents are instructed to respond to each question with a “yes” or “no.” The higher the score, the greater the assessment of problems with daily functioning. Although initially developed in Japan as a tool to identify older adults at high risk of requiring nursing care, the KCL has recently been utilized as a method for assessing frailty. It is recommended in international clinical guidelines on frailty as a valid indicator (Dent et al., 2017; Sentandreu-Mañó et al., 2021). In accordance with the findings of preceding studies, a score of 3 or less was classified as robust, a score of 4 to 7 as pre-frail, and a score of 8 or more as frail (Satake et al., 2016; Sentandreu-Mañó et al., 2021). In this study, the pre-frail group was excluded, and participants were classified into robust and frail groups (Figure 1).

Statistical Analysis

In this study, propensity score matching was employed to mitigate selection bias and to account for factors that influence manual dexterity. Initially, age and MMSE were utilized as covariates, and propensity scores were calculated using binary logistic regression analysis. The caliper width was set to 0.2 times the standard deviation, and matching was performed using the nearest neighbor method at a 1:1 ratio (Austin, 2011). Additionally, covariate balance between the frail and robust groups was evaluated using standardized mean differences (SMD) before and after matching. Subsequently, the Shapiro-Wilk test was employed to ascertain the normality of the data. To ascertain whether the effects of age and cognitive function were controlled by propensity score matching, a Mann-Whitney U test was performed to compare the groups based on age and MMSE. The comparison of NHPT completion times was performed using a two-factor mixed design analysis of variance based on group factors (robust group vs. frail group) and condition factors (normal condition vs. inhibit condition). The assumption of equal variances was confirmed using Levene’s test. For intergroup comparisons, an unpaired t-test was employed to assess grip strength, upper limb muscle mass, and TMT. Furthermore, the association between NHPT completion time and upper limb muscle mass was analyzed using Pearson’s correlation analysis. Correlations were assessed using the Holm method and confirmed by adjusted p-values. Statistical analyses were conducted using SPSS Version 29.0 (IBM Corp., Armonk, NY, USA), with a statistical significance level of 5%.

Results

As a result of propensity score matching, 76 participants were excluded from the robust group and 3 from the frail group, with 20 matched from each group. No missing values existed for age and MMSE used in the matching. The SMDs for age and MMSE before matching were 0.84 and 0.67, respectively, indicating a significant imbalance between the frail and healthy groups. After matching, the SMD for age decreased to 0.15 and the SMD for MMSE decreased to 0.11, indicating improved covariate balance (Figure 3). Supplementary materials for these results are shown in Figure S1.

Figure 3.

Standardized mean differences for covariates pre- and post-propensity score matching. Shows changes in standardized mean differences before and after propensity score matching for age and MMSE.

The results of the between-group comparisons conducted after propensity score matching are shown in Table 1. There was no significant difference between the robust group and the frail group in terms of age and MMSE, which were used as covariates in propensity score matching (p > .05; Table 1). A statistically significant difference was observed in grip strength between the frail and robust groups (p < .05). Conversely, there were no statistically significant differences observed between the groups in terms of upper limb muscle mass and TMT-A (p > .05; Table 1).

Table 1.

Comparison Between the Robust and Frail Groups After Propensity Score Matching.

Variables		Robust (n = 20)	Frail (n = 20)	p-Value	Mean diff [95% CI]	Hedges’ g [95% CI]
Age (years)^a		75.5 (71.5–79.5)	78.0 (74.5–81.5)	.60	-	-
MMSE (points)^a		27.5 (26.0–29.0)	28.0 (26.0–30.0)	.72	-	-
Grip strength (kg)^b		23.08 ± 3.69	18.78 ± 4.35	<.01*	−3.93 [−6.40 to −1.45]	3.95 [−1.64 to −0.34]
Skeletal muscle mass of upper limbs (kg)^b	Left	1.61 ± 0.25	1.52 ± 0.42	.31	0.35 [−0.86 to 0.36]	−0.09 [−0.31 to 0.13]
Skeletal muscle mass of upper limbs (kg)^b	Right	1.63 ± 0.25	1.52 ± 0.42	.42	0.35 [−0.93 to 0.29]	−0.11 [−0.33 to 0.11]
TMT-A (s)^b		107.80 ± 35.40	113.45 ± 29.63	.59	5.66 [−15.14 to 26.44]	33.13 [−0.44 to 0.78]

Note. Mean values ± standard deviations, Median (First Quartile-Third Quartile), Mean diff = Mean/Median group differences [95% confidence interval (CI)], Hedges’ g [95% confidence interval (CI)]; MMSE = Mini-Mental State Examination; TMT = Trail-making test part A.

Mann-Whitney U test.

Unpaired t-test.

p < .05.

The mixed-design two-factor ANOVA revealed significant main effects of group (F = 8.66, p < .05, partial η² = 0.19) and condition (F = 5.27, p < .05, partial η² = 0.12), but no significant interaction between the two factors (F = 0.17, p > .05, partial η² < 0.01; Table 2). Supplementary materials for these results are shown in Figure S2.

Table 2.

Comparison of Nine-Hole Peg Task (NHPT) Time for Groups and Conditions After Propensity Score Matching.

Group / Effect	Statistic	Normal condition (s)	Inhibit condition (s)
Robust (n = 20)		11.47 ± 2.31	12.08 ± 2.02
Frail (n = 20)		13.35 ± 1.97	13.77 ± 1.86
Group	F	8.66
	p	<.01*
	partial η² [95% CI]	0.19 [0.04–0.36]
Condition	F	5.27
	p	0.03*
	partial η² [95% CI]	0.12 [0.00–0.32]
Group × Condition	F	0.17
	p	.68
	partial η² [95% CI]	0.004 [0.00–0.12]
Levene’s test	p	.62	.93

Note. Mean values ± standard deviations, Partial η²[95% confidence interval (CI)].

p < .05.

A post hoc power analysis was conducted for the mixed-design two-factor analysis using G*Power 3.1 (Heinrich-Heine-University Düsseldorf, Germany). The observed effect sizes (Cohen’s f calculated from partial η ² ) were 0.48 for the group effect, 0.37 for the condition effect, and 0.063 for the interaction effect. Assuming a significance level of α = .05, a total sample size of N = 40 (with two groups and two conditions), a correlation among repeated measures of r = .80, and a nonsphericity correction of ε = 1.00, the analysis yielded power (1 – β) values of .88 for the group effect, 1.00 for the condition effect, and .23 for the interaction effect. In addition, a sensitivity analysis (Minimum Detectable Effect, MDE) under the present design (α = .05, target power = 0.80, N = 40; two groups and two conditions; r = 0.80; ε = 1.00) indicated that the smallest detectable effect sizes were f = 0.443 (partial η² ≈ .164; Cohen’s d ≈ 0.886) for the group main effect and f = 0.449 (partial η² ≈ .168; Cohen’s d_z ≈ 0.443) for the condition main effect.

The results of the correlation analysis between NHPT time and upper limb muscle mass demonstrated no significant correlation in either the normal or inhibit conditions in the robust group (p > .05). Conversely, within the frail cohort, no substantial correlation was detected under normal conditions. However, under inhibit conditions, a significant positive association was observed between NHPT time and upper limb muscle mass on both sides (left r = .54, p < .05; right: r = .55, p < .05; Table 3). Supplementary materials for these results are shown in Figure S3 and Table S1.

Table 3.

Correlation between NHPT time and skeletal muscle mass of left and right upper limbs by group and condition.

Group	Condition of NHPT	Skeletal muscle mass of upper limb	Pearson’s r [95% CI]	p-Value
Robust	Normal	Left	0.02 [−0.44 to 0.47]	.93
	Normal	Right	0.07 [−0.39 to 0.51]	.93
	Inhibit	Left	0.21 [−0.27 to 0.61]	.93
	Inhibit	Right	0.25 [−0.23 to 0.64]	.87
Frail	Normal	Left	0.24 [−0.24 to 0.63]	.46
	Normal	Right	0.24 [−0.24 to 0.63]	.46
	Inhibit	Left	0.54 [0.11 to 0.78]	.04*
	Inhibit	Right	0.55 [0.13 to 0.79]	.04*

Note. NHPT: Nine-Hole Peg Task, Pearson’s r [95% confidence interval (CI)], Left: Muscle mass of the left upper limb, Right: Muscle mass of the right upper limb.

The p-values were adjusted for multiple comparisons using the Holm method.

p < .05.

Discussion

The present study examined the association between frailty and finger dexterity while controlling for age and cognitive function. It also compared finger dexterity, grip strength, upper limb muscle mass, and attention function in frail and robust older adults. The objective was to clarify the factors associated with the decline in finger dexterity among frail older adults.

In this study, propensity score matching was employed to adjust for the effects of age and cognitive function. Consequently, no substantial disparities were identified between the robust and frail cohorts in terms of age and cognitive function, thereby confirming that the matching procedure effectively reduced confounding by age and cognition in assessing the association between frailty and finger dexterity.

The findings of this study indicated that, even after controlling for age and cognitive function, finger dexterity in frail older adults was notably lower compared to that in robust older adults. Prior studies had not adequately controlled for confounding factors such as age and cognitive function, leaving the independent effect of frailty on finger dexterity unclear. In this study, we have suggested that frailty itself may be specifically associated with impaired finger dexterity after adjusting for age and cognitive function.

Additionally, the study revealed a significant decrease in grip strength among the frail group compared to the robust group. However, no significant difference was observed between the groups in the TMT-A, suggesting that attention function remained relatively intact. A multitude of studies have previously reported a relationship between the decline of finger dexterity and both grip strength and attention function (Kobayashi-Cuya et al., 2018; Liu et al., 2017; Martin et al., 2015; Rodríguez-Aranda et al., 2016). Furthermore, the coexistence of frailty with sarcopenia has been documented (Davies et al., 2018; Nishiguchi et al., 2015), and in this study, the decline in grip strength associated with reduced skeletal muscle mass may be related to lower finger dexterity. Conversely, no substantial disparities in attention function were identified between the groups in this study. In the context of the relationship between finger dexterity and visual attention function, it has been documented that age-related delays in visual information processing may contribute to impaired finger dexterity (Baweja et al., 2015). In the present study, the impact of age-related decline in attention function was potentially mitigated by controlling for age and cognitive function, resulting in no significant differences observed between the groups. These results suggest that a decline in grip strength, rather than attention function, was associated with lower finger dexterity in the present cross-sectional analysis.

Furthermore, in the NHPT conducted under inhibit conditions, both groups demonstrated a significant increase in the time required compared to normal conditions. Specifically, the frail group demonstrated the longest time required under inhibit conditions. Moreover, a significant positive correlation was observed between upper limb muscle mass and NHPT time only under inhibit conditions. Conversely, this correlation was not observed under normal conditions or in the robust group. This suggests that even elderly individuals with substantial upper limb muscle mass may exhibit suboptimal finger dexterity, rather than the expected enhancement. Conventionally, an increase in muscle mass is accompanied by an increase in muscle strength, which should result in a decrease in NHPT time. Consequently, the positive correlation between upper limb muscle mass and NHPT time observed in this study is a paradoxical result. One possible explanation for this finding is that overactivity in the right hemisphere and reduced interhemispheric inhibition could contribute to inefficient motor coordination under the inhibit condition. Previous studies have reported that greater activation of the right hemisphere during unilateral movements may occur when motor coordination efficiency declines in older adults (Fling & Seidler, 2012; Fujiyama et al., 2016; Morishita et al., 2022; Talelli et al., 2008). However, since this study did not include any direct neurophysiological measurements, this interpretation remains speculative and should be considered as a working hypothesis. Future studies incorporating neurophysiological assessments are warranted to verify whether altered interhemispheric interaction indeed contributes to reduced finger dexterity in frail older adults.

This study has several limitations. First, the statistical power to detect interaction effects was low (0.23), suggesting that small effects may not have been adequately detected. Future studies should use larger sample sizes to examine group-by-condition interactions more robustly. Second, because brain activity under inhibit conditions was not directly measured, a decrease in interhemispheric interaction under these conditions could not be confirmed. Third, upper-limb muscle activity was not recorded, preventing confirmation of compensatory increases in muscle activation that might occur in response to reduced grip strength under inhibit conditions. Fourth, as this study was conducted in a single region and included only older women, the results should be interpreted with caution when generalizing to other populations. Future studies should include participants from multiple regions and both genders and incorporate measurements of brain activity under inhibit conditions to clarify the mechanisms underlying the reduced finger dexterity observed in frail older adults. Finally, because this was a cross-sectional study, the observed associations cannot establish causality between frailty and finger dexterity. Therefore, the present findings should be interpreted as statistical associations rather than causal effects.

Conclusion

The findings of this study indicated that, when controlled for age and cognitive function, the decline in finger dexterity in frail older adults was significantly more advanced than in robust older adults. Specifically, the decline in grip strength exhibited a robust correlation with finger dexterity. In contrast, the decline in attention function did not demonstrate a discernible impact within the confines of the study. These findings are expected to provide new perspectives for the development of strategies for frailty prevention and rehabilitation in older adults.

Supplemental Material

sj-docx-1-ggm-10.1177_30495334251404261 – Supplemental material for Characteristics and Factors Linked to Declined Manual Dexterity in Frailty in Community-Dwelling Older Women

Supplemental material, sj-docx-1-ggm-10.1177_30495334251404261 for Characteristics and Factors Linked to Declined Manual Dexterity in Frailty in Community-Dwelling Older Women by Takato Nishida, Shin Murata, Shun Sawai, Shoya Fujikawa, Ryosuke Yamamoto, Yusuke Shizuka, Naoki Shimizu and Hideki Nakano in Sage Open Aging

Footnotes

Acknowledgements

We would like to thank all the volunteers who participated in this study.

ORCID iD

Hideki Nakano

Ethical Considerations

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Kyoto Tachibana University (approval number: 22-61).

Consent to Participate

Informed consent was obtained from all participants.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by JSPS KAKENHI Grant Numbers JP20K11173 and JP23K21578.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available because they contain information that could compromise the privacy of research participants.

Supplemental Material

Supplemental material for this article is available online.

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