Seol et al.
European Review of Aging and Physical Activity
https://doi.org/10.1186/s11556-023-00319-2(2023) 20:9

RESEARCH ARTICLE

European Review ofAging and Physical Activity

Open Access

Effects of home‑based manual dexterity
training on cognitive function among older
adults: a randomized controlled trial
Jaehoon Seol1,2,3*† , Namhoon Lim4†, Koki Nagata5 and Tomohiro Okura2,3,5Abstract
Background The relationship between manual dexterity and cognitive function among older adults is well known;
however, few studies have focused on manual dexterity training that confirms cognitive load of training in older
adults through functional near-infrared spectroscopy (fNIRS) and verifies the effect of training. This study examinedthe effects of home-based manual dexterity training on cognitive function in older adults using a digital trail-making
peg test device combining two conventional assessment tools namely, the peg and trail-making tests.
Methods For 12 weeks, 57 healthy older adults aged 65–88 years participated in a parallel-group, randomized
controlled trial, wherein home-based manual dexterity training was performed for approximately 20 min daily. To
quantify the cognitive load in different manual dexterity conditions, we assessed the cortical activation patterns of theprefrontal cortex via a wearable four-channel fNIRS device. Participants in the control group were asked to continue
their usual daily routines during the intervention period. Cognitive function was assessed using the Stroop Color andWord and Cognitive Impairment Tests. Manual dexterity was assessed using the Purdue Pegboard Test. All outcomes
were estimated before and after the intervention.
Results We observed significant differences in prefrontal cortical activation between the different manual dexterity
conditions. Only the intervention group showed a significant improvement in Stroop interference (169.0–108.9 ms,
p = 0.032) and an executive function and assembly task of the Purdue Pegboard Test (22.5–26.4 counts, p < 0.001).
Additionally, except the clock drawing task, cognitive function had a larger effect size (Cohen’s d) in the intervention
group (d = 0.26–0.45) than in the control group (d = 0.11–0.28).
Conclusions Home-based manual dexterity training can improve performance in a complex manual dexterity task
and executive functioning in older adults.
Trial registration UMIN-CTR Clinical Trial, UMIN000047203. Registered 17 March 2022 – Retrospectively registered,https://​cente​r6.​umin.​ac.​jp/​cgi-​open-​bin/​ctr/​ctr.​cgi?​funct​ion=​brows​&​action=​brows​&​recpt​no=​R0000​53844​&​type=​
summa​ry&​langu​age=E
Keywords Executive function, Stroop Color and Word Test, Purdue Pegboard Test, Peg-Amore, Digit training†

Jaehoon Seol and Namhoon Lim contributed equally to this work.

*Correspondence:
Jaehoon Seol
seol.jaehoon.ge@u.tsukuba.ac.jp
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Seol et al. European Review of Aging and Physical Activity(2023) 20:9

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Background
Among the many deleterious effects of aging, deterioration of manual dexterity is one that can lead to difficultyin performing instrumental activities of daily living, suchas writing, cooking, gardening, working on crafts, andopening a bottle [1–3]. These principal aging-associatedchanges are observed in both men and women, especiallythose older than 65 years [4–6]. Preserving manual dexterity is essential for daily living in older adults [2].
Previous studies have revealed that manual dexterity
is positively related to cognitive functions, particularly
the executive function, which includes inhibitory control, working memory, and cognitive flexibility [2, 4, 7,
8]. Manual dexterity is related to not only executive function but also muscle strength [2, 9]. Moreover, the performance of executive functions is related to the motor
control performance (i.e., manual dexterity) of both
hands, especially the non-dominant hand in older adults[3, 10, 11]. Manual dexterity is particularly important toensure independent living of older adults owing to its
association with the performance of instrumental activities of daily living [6].
Performing tasks that require manual dexterity activates the prefrontal cortex, primary motor cortex, andprimary sensorimotor cortex in older adults [1, 12, 13].
The primary motor cortex and corticomotoneuronal cells
that synapse directly onto spinal motor neurons, bypassing the spinal interneurons, are involved in manual dexterity; hand movement with visual information involves
the primary motor cortex and associated neurons [1].
This corticomotoneuronal pathway is critical for individual finger movements and for grasping behaviors inprimates, including humans [1]. Corresponding to this, across-sectional epidemiologic study indicated that hand
use performance assessments, such as handgrip strength[9] and manual dexterity [2], are useful tools not only inestimating the status of cognitive function but also inscreening for mild cognitive impairment status in earlystages [10, 14, 15].
Additionally, manual dexterity has a training effect inolder adults [6, 16]. Our previous experimental study
showed that participants who performed an acute boutof manual dexterity movement had higher executivefunction than sedentary controls, and the effect size wasgreater than that of step exercises and stretching performed while sitting [17]. Another intervention study
lasting 2 months reported the effects of manual dexterity training (i.e., involved finger extension, counting,rotation, rock-paper-scissors, and shape generation) on
manual dexterity and cognitive function. Compared tothe only dominant training group, the training groupusing both hands improved manual dexterity and demonstrated a more pronounced activation of the primary

motor cortex during a pegging task [12]. Based on previous studies [12, 17], it seems reasonable to hypothesize that manual dexterity training could have a positiveeffect on manual dexterity and executive function inolder adults. Interestingly, an acute experimental study
revealed that compared to the easier task (control, andtrail-making test (TMT) part A), the difficult task (TMT
part B) shows greater activation of the prefrontal cortex(PFC) in younger adults [18]. However, only a few intervention studies exist that confirmed the cognitive load oftraining in older adults through fNIRS and verified theeffect of training [3].
Based on the evidence provided by the abovementioned studies (e.g., [18]), we hypothesize that the PFC
can be stimulated by manual dexterity training [19], andthus, the 12-weeks training would improve manual dexterity and cognitive function in older adults.

Methods
Study design

A parallel-group, randomized controlled trial was conducted from September 2021 to February 2022 (Supplementary Fig. S1). PFC activation of (i.e., cognitiveload) was confirmed during the three-mode (P-, A-, andB-modes) digital trail-making peg test using fNIRS during baseline measurement before randomization (Supplementary Fig. S2). After randomization, manual dexterity
and cognitive function tests were performed by all participants before and after the intervention (Supplementary
Fig. S1), and the results were recorded.
Participants

All participants were recruited using a local newspaperthat was distributed throughout Ibaraki Prefecture, Japan.
Initially, 164 older adults aged 65–88 years volunteered
for the study. We included adults aged > 65 years who
were right-handed, had no color blindness, had no physician-imposed exercise restrictions, and were not involvedin any experimental research during the past year. Of theremaining 73 people who were eligible, 60 were randomly
selected by a blinded third researcher using Excel version
1902. After intervention, individuals who were ambidextrous (n = 1) or had considerable cognitive impairment
based on a Mini-Mental State Examination score of ≤ 26
(n = 2) were excluded from the analysis (Supplementary
Fig. S1) [20]. All participants provided signed informed
consent and no monetary compensation was provided.
All protocols were approved by the ethics committee ofthe University of Tsukuba (Ref. Tai 021–34; Tai 021–42).
The study protocol (UMIN000047203) was retrospectively registered with the University Hospital Medical
Information Network Center (http://​www.​umin.​ac.​jp/​engli​sh/).

Seol et al. European Review of Aging and Physical Activity(2023) 20:9

Sample size

The optimal sample size was determined using
G*Power version 3.1 based on a 0.25-point effect size,
α level of p < 0.05, and 95% power as analysis of variance (ANOVA) designs [21]. The results indicated that
24 participants were required for each group. Assuming a 15% dropout rate, the minimum sample size wasdetermined to be 56 participants.
Randomization

Baseline measurements were collected. After stratification by sex (female, n = 43; male, n = 17) [22], participants were randomized into either the intervention
group (n = 30) or control group (n = 30) using the Excelversion 1902 randomizer function.

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The control group was asked to perform their daily
activities as usual during the intervention period. For
ethical reasons, the control group received the same
training program after the intervention period was over.
Among all training modes, the A- and B-modes werebased on TMT-A and TMT-B (Supplementary Methods).
TMT is a widely used test of executive function, and thecalculated B-A time and B/A ratio are related to working
memory, processing speed, and general cognitive function [26, 27]. Our previous study confirmed the validity
and reliability of general cognitive function among older
adults in the A- and B-modes [28]. Thus, we adopted theB-A time and B/A ratio using A-mode (trail-making peg
test part A) and B-mode (trail-making peg test part B)
during the intervention period.

Measurements
fNIRS data acquisition and analysis

Interventions

The intervention consisted of a 12-week training program that included seven different elements withthe aid of a training device that recorded the results.
We developed a digital trail-making peg test device(Peg-Amore [41 cm × 35 cm × 5 cm]; NEWCOM Inc.,
Saitama, Japan) (Supplementary Fig. S3). This training device, which combines the elements of boththe twenty-five-hole peg test [23] and TMT [24], wasused to evaluate manual dexterity and cognitive status in occupational therapy areas in Japan (Japan Patent 2019–024,707) [25]. The device was provided to theintervention group.
The participants were taught how to conduct the training and record the results. The training schedule required
the participants to exercise their right hand on Monday,
Wednesday, and Friday and their left hand on Tuesday,Thursday, and Saturday; the non-dominant hand had to
be trained on Sunday. The daily training regimen would
take approximately 20 min daily. The training program
consisted of seven modes that were intended to stimulateelements of executive, attention, memory, and dynamic
vision functions, as described in Supplementary Methods. After a familiarization period, the participants completed their dexterity training for each mode.
The digital trail-making peg test device was able torecord and store the participants’ data. After the intervention period, all of the device’s data were cross-checkedwith the participants’ self-reported training diary andthe adherence rate calculated. The training device had a“guest” mode to distinguish the participants’ data fromthat of a family member, friend, or guest who may haveused the device. The intervention group received personal feedback and encouragement from the researcher
every week via telephone.

To confirm the cognitive load of the manual dexterity
training, blood oxyhemoglobin, deoxyhemoglobin, andtotal hemoglobin levels were measured using fNIRS. We
used the Hb-131S fNIRS device (Astem Corp., Fukuoka,
Japan) with four active channels (two colors of LEDs, λ1|2:
770|830 nm with average power 1 mW or less, and four
avalanche photodiode light detectors) sampled at 10 Hz.
Data were converted to concentration changes using themodified Beer–Lambert law [29, 30]. Fpz in the 10–20
electrode system corresponds to the center of the fNIRS
cap [31] to estimate the PFC. Moreover, we removed
the movement artifact using the three axes accelerometer data attached to the fNIRS and algorithms described
previously [32]. The concrete position and details of thedevice have been described previously [33].
The participants’ cognitive load changes were measured during the P-mode (simple peg moving training),
A-mode (the participants grasp 25 pegs, one at a timewith one hand, and place them in vertically aligned holes
as rapidly as possible in the numerical order displayed on
the peg board [i.e., 1 → 2 → 3, …, 24 → 25]), and, afterat least 5 s of rest, B-mode (i.e., using a combined number and Japanese characters Hiragana order, the participants grasp a peg and alternately move it to the correct
hole in the displayed order [1 → [a] → 2 → [i] → 3 → [u],…, [shi] → 13]). In this study, we used a block design
with a 5-s-long baseline period and 25 sub-trials of eachtest (P-mode, A-mode, and B-mode). The rest interval
between the task blocks was longer than 30s.
Owing to technical problems (i.e., data not calculated
for unknown reason), data were missing for four participants of fNIRS data; 37 channels of cumulative 636
channels were omitted due to bad signal quality. Overall, the data of 53 fNIRS procedures were analyzed. The
fNIRS data were registered to the Montreal NeurologicalSeol et al. European Review of Aging and Physical Activity(2023) 20:9

Institute space, and the channel location was measured
using the three-dimensional spatial position of the channels measured by a digitizer (FASTRAK, Polhemus Inc.,
Colchester, VT, USA).
Data processing was performed using the Open Platform of Transparent Analysis Tools for fNIRS (Open
PoTATo) for MATLAB R2020a. All channels were preprocessed with a low-pass filter using a cut-off frequencyof 0.2 Hz based on a similar previous study that measured fNIRS during the TMT [18]. We used a 5-s set zerolevel function for Open PoTATo. The calculated oxy-Hblevels for each task are shown in Supplementary Fig. S2.
The actual elapsed times varied among the participants;
thus, we separated the average baseline oxy-Hb (5 s) andaverage of each oxy-Hb quartile (25%, 50%, 75%, and100%). All the Oxy-Hb and deoxy-Hb data are presented
in Supplementary Table S1.
Cognitive functions

Executive function was assessed using the Stroop
Color and Word Test consisting of neutral, congruent, and incongruent trials [34]. Two rows of messages
appeared on the computer screen. The bottom row
consistently contained the words YELLOW, GREEN,
RED, or BLUE. In the neutral trial, the top row, displayed “XXXX” in one of four colors: red, blue, green,or yellow. The bottom row words were printed inblack. In the congruent trial, the top row contained thewords RED, GREEN, BLUE, or YELLOW printed in acolor congruent with the words in the bottom row. In
the incongruent trial, the top row contained the words
YELLOW, GREEN, RED or BLUE in a color incongruent with the text color of the bottom row words. In alltrials, participants were asked whether the name of thecolor that appeared in the bottom row was the sameas the color of what appeared in the top row. Each session consisted of 30 trials, including 10 neutral, 10
congruent, and 10 incongruent trials in random order[35]. The average reaction time was used for analysis.
Stroop interference was calculated as the difference
between the reaction times of incongruent and congruent tasks [34, 36].
Memory and judgment function were assessed using
the Cognitive Impairment Test, which is also one of thetests required for driver license renewal among older
adults in Japan [37]. We adopted this cognitive test
since it was easy to generalize the changes in the scoresof Japanese older adults. Details of the test and scoring
have been described previously [38]. This test consistsof three tasks: orientation to time (0–15 points), freeand cued recall (0–32 points), and clock drawing (0–7
points). The total score is calculated using the following
equation:

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Total score = (1.15 × orientation to time)+ (1.94 × free and cued recall)+ (2.97 × clock drawing)
Manualdexterity

The Purdue Pegboard Test (model 32,020; Lafayette
Instrument Co., Lafayette, IN, USA) was used to assessmanual dexterity. The Purdue Pegboard Test is widelyused in both clinical and research fields, and its methods, validity and reliability have been confirmed [39].
The number of pegs successfully pinned on the pegboard in three trials lasting 30 s using the left, right, andboth hands was counted. The assembly task involved
assembling pins, washers, and collars alternately using
both hands for 60 s.
Potential confounding factors

Demographic variables included age, sex, body massindex (kg/m2), medication history, smoking status (current or past/never), drinking habits (daily/1–6 times
per week/ < 1–3 times per month), years of education,and depressive symptoms (15-item Geriatric Depression Scale) [40]. Daily physical activity level, social
relationships, and sleep quality were assessed using
the Physical Activity Scale for the Elderly [41], Lubben
Social Network Scale [42], and Pittsburgh Sleep Quality
Index [43], respectively.
Statistical analysis

We intended to perform ANOVA; however, owing to
the presence of missing data (i.e., 37 channel data of
cumulative 636 channels data were omitted due to badsignal quality), we analyzed the fNIRS signals during
the P-, A-, and B-modes using mixed-model ANOVA
instead. Group differences at baseline were determinedusing unpaired t-tests and chi-square tests for continuous and categorical variables, respectively. Two-way
repeated-measures ANOVA was performed to evaluate
the differences in the effect between groups (intervention vs. control) and time (pre-test vs. post-test), and apost hoc test was conducted on variables showing significant group-by-time interaction. Missing data (eightparticipants dropped out) were filled in with baseline
observations from the exit date using intention-to-treat
analysis [44]. We confirmed the normal distribution ofdata using the Shapiro–Wilk test; however, certain outcomes (Purdue Pegboard Test and Cognitive Impairment Test) were not normally distributed. The averagevalue for each week was analyzed using mixed-model
ANOVA based on the recorded log-data of manualSeol et al. European Review of Aging and Physical Activity(2023) 20:9

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dexterity training tasks during the intervention period.
Bonferroni post hoc tests were used to correct for multiple comparisons.
Pearson’s correlation coefficients were calculatedusing the changes in the primary outcomes (Stroop
task, Cognitive Impairment Test, and Purdue Pegboard Test) and executive function (B-A time and B/Aratio). Specifically, change in the primary outcomes was
defined as the amount of change from post-intervention to pre-intervention, as determined by subtracting
the post-intervention results from the pre-intervention
results. The change in the executive function performance using the digital trail-making peg test device
was defined as the difference between the average scoreobtained during the 12th-week and the average scoreobtained during the 1st-week period (a negative number indicated functional improvement). The correlation
coefficient was classified as no correlation (r = 0–0.19),
low correlation (r = 0.20–0.39), moderate correlation(r = 0.40–0.59), moderate high correlation (r = 0.60–0.79), and high correlation (r ≥ 0.80) [45]. Effect size
was determined using Cohen’s d [46]. The effect size
was classified as small (d = 0.2), medium (d = 0.5), andlarge (d = 0.8) [46].

Statistical significance was set at p < 0.05 (two-tailed).
All analyses were performed using SPSS for Windows
version 28.0 (IBM Corp., Armonk, NY, USA).

Results
Participant characteristics

Baseline participant characteristics are presented inTable 1. Among 57 participants, 49 completed the study(dropout rate, 14.1%). Attrition was due to personal andenvironmental reasons (hospitalization [n = 1], coronavirus disease [n = 1], and fear of contracting coronavirus
disease [n = 6]); all factors were unrelated to the study(Supplementary Fig. S1). The completion rate of thistraining calculated based on log-data was 88.7 ± 13.9%
in the intervention group. Supplementary Figs. S4 and S5
show the improvement in each training mode during theintervention period (all p values for trend were < 0.001).
Assessment of fNIRS cognitive load data

The oxy-Hb concentration of all channels during tasks
showed significant effects of time (p < 0.001), group(p < 0.001), and interaction (p < 0.001) (Supplementary Table S1, and Fig. 1). Compared to the P-mode,
the A- and B-modes showed a significant increase
after 50–100%, and the post hoc analysis revealed that

Table 1 Participants’ characteristics at baseline
Total participants(n = 57)
Mean
Age, years
Female, n (%)

73.6
39

±

SD

±

6.1

±

3.6

(68.4)

Body mass index, kg/m2

22.8

Smoking habit, n (%)

1

(1.8)

Alcohol consumption (drinker), n (%)

20(35.1)

Educational level, years

14.1

±

PASE score, points

125.9

MMSE score, points

29.6

Medical history±±

Intervention group(n = 28)

Control group (n = 29)

Mean

Mean

72.9
19

2.8

23.1

±

SD

±

5.6

±

3.1

(67.9)

1

(3.6)

12

(42.9)

13.9

±

67.7

141.9

0.7

29.6

±

±

74.4

SD

±

6.5

20

(69.0)

22.5

±

4.0

0
2.3

±

8

(27.6)

14.6

±

85.8

110.5

0.7

29.6

±

±

Unpaired t test or
chi-squarep value

0.348
0.928
0.503
0.305
0.227

2.0

0.203

39.6

0.085

0.7

0.801

None, n (%)

26(45.6)

14

(50)

12

(41.4)

0.514

Hypertension, n (%)

16(23.5)

9(32.1)

7(24.1)

0.501

Hyperlipidemia, n (%)

10(17.5)

5(17.9)

5(17.9)

0.951

Diabetes, n (%)

5

(8.8)

2

(7.1)

3

(10.3)

0.669

LSNS score, points

16.8

Social isolation (< 12 points), n (%)

10

PSQI score, points

5.0

Poor sleeper (> 5.5 points), n (%)

21

GDS score, points

3.6

Depression (> 5 points), n (%)

18

±

5.4

±

2.4

±

2.6

(17.5)(36.8)(31.6)

17.2
5
4.9
9
3.7
9

±

6.0

±

2.6

±

2.5

(17.9)(32.1)(32.1)

16.3
5
5.1
12
3.5
9

±

4.7

±

2.3

±

2.6

(17.2)(41.4)(31.0)

0.563
0.951
0.829
0.470
0.814
0.928

SD Standard deviation, PASE, Physical Activity Scale for the Elderly, MMSE Mini-Mental State Examination, LSNS Lubben Social Network Scale, PSQI Pittsburgh Sleep
Quality Index, GDS Geriatric Depression Scale 15

Seol et al. European Review of Aging and Physical Activity(2023) 20:9

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Fig. 1 Distinct effect of frontal lobe activation during each mode. F-values are denoted according to the color bar. Y-axis unit indicates oxy-Hb
signal (mM · mm). Error bars indicate standard error. $, significant differences between P- and A-modes; *, significant differences between P- andB-modes; # significant differences between A- and B-modes

the A- and B-modes were significantly higher than the
P-mode at 75% and 100% points in channels 1, 3, and
4. Channel 2 showed that only the B-mode was significantly higher than the P-mode at the 75% point, and allmodes were significantly different at the 100% point (allp < 0.05) (Supplementary Table S1, and Fig. 1).

incongruent tasks (d = 0.46 and 0.19) was higher in theintervention group than in the control group (Table 2).
The Cognitive Impairment Test did not show significant
differences; however, the effect size of total score in theintervention group was higher than that in the control
group (d = 0.27 and 0.12) (Table 2).

Cognitive function

Manual dexterity

Stroop interference showed significant effects of interaction (p = 0.025). The post hoc test showed a significant
improvement after training in the intervention group(169.0 ± 109.2 to 108.9 ± 110.9 ms; p = 0.032). The reaction time was not significantly different between theneutral, congruent, and incongruent tasks; however,
the effect size of the neutral (d = 0.22 and 0.13) and

The assembly task showed significant effects of time(p < 0.001) and interaction (p = 0.006). The post hoc
test showed significant improvement after training in
the intervention group (22.5 ± 3.9 to 26.4 ± 5.6 counts;
p < 0.001). The effect size of manual dexterity variables in
the intervention group (d = 0.32–0.81) was higher thanthat in the control group (d = 0.17–0.36) (Table 2).

Seol et al. European Review of Aging and Physical Activity(2023) 20:9

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Table 2 Change in executive function, cognitive function, and manual dexterity during the intervention period
Intervention group (n = 28)
Mean

± SD

Control group (n = 29)

Effect size Mean
Cohen’s d± SDInteraction p Group effect p Time effect p

Effect size
Cohen’s dStroop Color and Word Test, ms
Neutral task
Congruent task
Incongruent task

Pre

1176.7 ± 153.7

0.22

1199.9 ± 138.2

0.16

1368.9 ± 169.8

0.46

169.0

± 109.2

0.55

± 0.9

0.45

± 5.4

0.26

± 0.3

< 0.01± 11.1

0.27

± 1.9

0.32

± 1.4

0.36

± 1.4

0.57

± 3.9

0.48

± 3.9

0.81

Post 1138.6 ± 192.6

Pre

Post 1175.6 ± 159.2

Pre

Post 1284.6 ± 196.6

Stroop interference task Pre

Post 108.9
Cognitive Impairment Test, points
Orientation to time

Pre

14.6

Post 14.9
Free and cued recall

Pre

24.4

Post 25.8
Clock drawing

Pre

6.9

Post 6.9
Total score

Pre

84.6

Post 87.5
Purdue Pegboard Test, countRight hand task

Pre

12.4

Post 13.0
Left hand task

Pre

12.5

Post 13.0
Both hand task

Pre

9.6

Post 10.4
Sum of right-, left-, andboth hand task
Assembly task

Pre

34.6

Post 36.4
Pre

22.5

Post 26.4*± 110.9*

± 0.3

± 5.3

± 0.4

± 1.4

± 1.4

± 3.6

0.630

0.596

0.067

1247.7 ± 169.2 0.33

0.229

0.360

0.031

1350.1 ± 155.2 0.19

0.349

0.418

0.020

102.4

± 138.6 -0.19

0.025

0.395

0.395

± 0.4

0.28

0.224

0.387

0.123

± 5.4

0.11

0.393

0.910

0.034

± 0.5

< 0.01

0.423

0.739

0.989

± 11.1

0.12

0.379

0.908

0.024

± 2.1

0.22

0.746

0.390

0.006

± 1.9

0.25

0.966

0.087

0.018

± 1.9

0.36

0.944

0.515

< 0.001

± 5.3

0.30

0.823

0.233

< 0.001

± 5.1

0.17

0.006

0.332

< 0.001

1191.9 ± 168.0

1321.9 ± 136.5

129.9
14.8
14.9
24.9
25.5
6.8
6.8

± 10.7± 1.91192.0 ± 168.6 0.13

1169.6 ± 176.4

85.7
87.0
12.0
12.5
11.8
12.3
9.5
10.1
33.3
34.9

± 5.6*

22.8
23.7± 155.2

± 0.3

± 5.2

± 0.4

± 10.2

± 2.4

± 2.1

± 1.4

± 5.4

± 5.7

Significant differences between pre- and post-test

Cohen’s d:0.2 = small; d:0.5 = moderate; d:0.8 = large

SD Standard deviation; pre, pre-test; post, post-test

Changes in performance for manual dexterity training
mode in the intervention group

In the intervention group, improved B-A time was positively related to the reaction time of neutral (r = 0.449,
p = 0.041), congruent (r = 0.497, p = 0.022), and incongruent trials (r = 0.621, p = 0.003), Stroop interference(r = 0.436, p = 0.048), and the total score of the Cognitive Impairment Test (r = 0.450, p = 0.041) (Fig. 2a–e).
The B/A ratio was favorably related to the reaction timeof neutral (r = 0.434, p = 0.049) and incongruent trials(r = 0.620, p = 0.003) and Stroop interference (r = 0.511,
p = 0.018) (Fig. 2f–h).

Discussion
The present study is one of the first intervention studies to examine the effects of home-based manual dexterity training (i.e., grasping and moving an object withvisual information) using a digital trail-making peg
test device on cognitive function and manual dexterity among older adults. Compared to a previous study
using manual dexterity training [12], the present intervention did not significantly enhance simple manual
dexterity (number of pegs on right, left, and both
hands) in the intervention group, although a moderate
effect size for the performance changes of both hands

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Fig. 2 Correlation between training-induced primary outcomes ([post-test] – [pre-test]) and changes in executive variables. Stroop interference
calculated as ([incongruent – congruent] of post-test session) – ([incongruent – congruent] of pre-test session); B-A time calculated as ([differencetime of B- and A-modes] of last-week intervention period) – ([difference time of B- and A-modes] of first-time intervention period); B/A ratio
calculated as ([ratio of B- and A-modes] of last-time intervention period) – ([ratio of B- and A-modes] of first-time intervention period)(Cohen’s d = 0.57) was observed. In contrast, the performance in the complex manual dexterity task (e.g.,assembly task) improved in the intervention group(Table 2). Visual guidance of manual dexterity involves
more widespread brain network than that of simple
hand movements [1]. Specifically, the grasping, peg
movement, and peg insertion tasks performed in thisstudy, all of which required attention to switch between

elements, help to activate not only the primary motor
area but also the pathways of the primary visual cortex for the parietal reach region, dorsal premotor area,
anterior interparietal area, and ventral premotor area [1,47]. Considering younger adults, an acute experimental
study investigating the effects of the different TMT conditions on activation patterns of the PFC demonstrated
that, compared with the control condition, both TMT-A

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and TMT-B conditions are associated with higher activation of the PFC; however, no between-condition differences between TMT-A and TMT-B were observed[18]. In contrast, the results of the present study suggest that task-related activation differences of the PFC
between different conditions of the used manual dexterity tasks exist (see Supplementary Table S1 and Fig. 1).
Practically, manual dexterity tasks that involve grasping,peg movement, and peg insertion may result in greater
stimulation of the PFC than the TMT.
Another experimental study revealed that manual dexterity training with visual neurofeedback activates theanterior PFC more than training with visual neurofeedback [13]. We confirmed that the manual dexterity movement prescribed in this study cumulatively increased thecognitive load on the PFC in the P-, A-, and B-modes(Supplementary Table S1 and Fig. 1). This result supported the increased cognitive load and activation in the
PFC [48].
We also found a moderate correlation between
improved B-A time and B/A ratio during Stroop task
training, which represents executive function (Fig. 2).
This study confirms the training effect on manual dexterity over 12 weeks, and this effect, in turn, partiallyconveys a learning effect regarding all modes (Supplementary Figs. S4 and S5) [49, 50]. However, because thecalculation is based on the same point performance during the intervention period, the time difference between
B- and A-modes (B-A time) and the ratio of B- andA-modes may indicate not only a learning effect but also
an improvement in executive functions [50, 51].
Executive functions include the ability to plan to control goal-directed behavior, directing and maintaining
attention, organization, regulation, task switching, andmotor control [2]. Our training mode requires the ability
to switch from tasks involving the order of numbers andwords to finding an answer (peg into a hole), memorization, maintaining attention, and motor control (grasping
the peg, identifying a hole, and inserting it into a hole)[28]. These training processes may enhance executivefunction in older adults. In addition, a separate experimental study demonstrated a high correlation between
the TMT-B and Stroop tasks, and that executive function
and working memory were mainly associated with speedof rotational hand movements and movement speed inyounger adults [4]. The present study also demonstrated
a positive relationship with improved manual dexterity
and executive function in older adults using manual dexterity training, which have cognitive load.
Except executive function, other cognitive functions (such as those associated with components of the
Cognitive Impairment Test) did not show significant
improvements following the intervention. The training

effect on executive function has been demonstrated in a
relatively shorter period (1–3 months) [52] than that on
general cognitive function [53]. Since certain evidence
in the literature suggests that a longer training durationand higher frequency of training sessions are associated with greater effects on measures of global cognitive
function among older adults [53], our relatively shorttraining duration might not be sufficient for measurable changes in global cognitive performance. Thus, it
is possible that long-term training will improve general
cognitive function among older adults. One previously
mentioned limitation was that the intervention period
was short. There are certain limitations that should beconsidered when interpreting the study findings. First,
we did not perform follow-up assessments and thus
could not derive conclusions regarding the time courseof intervention-related effects (e.g., reversibility of theeffects, delayed effects). Second, our fNIRS systems
allow only the quantification of cortical activity changes
within the PFC. Moreover, we have used only a low-passfilter based on a similar previous study [18]; thus, futurestudies should use variable filters to remove systemic
physiological artifacts as recommended by the fNIRS
guidelines [54]. Since a previous study using functional
magnetic resonance imaging (fMRI) noticed that several areas in the human brain exhibited changes in theactivation patterns following hand movement or dexterity training [12], we recommend that future studies
should assess and compare the pre- and post-training
activation patterns in other relevant brain areas (e.g.,
primary motor area, primary visual cortex for the parietal reach region, dorsal premotor area, anterior interparietal area, and ventral premotor area). Third, in thecurrent study, only healthy older adults were included,
which limits the generalizability of our findings. Basedon our promising results, further research should aim toassess the effects of home-based manual dexterity training in other cohorts (e.g., older adults with mild cognitive impairment). Finally, it was beyond the scope of
this study to examine sex differences [10], although we
stratified sex [22].

Conclusions
The findings of the current study suggest that homebased manual dexterity training can improve the performance in a complex manual dexterity task and executivefunctioning in older adults. Cognizant of the limitationsof the present study, future studies should consider sex
differences and investigate the effects of manual dexterityon cognitive function and manual dexterity among older
adults who are frail and have mild cognitive impairment,
dementia, or other impairments.

Seol et al. European Review of Aging and Physical Activity(2023) 20:9

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Received: 4 January 2023 Accepted: 6 April 2023
Abbreviations
ANOVA	Analysis of variance
fMRI	Functional magnetic resonance imaging
fNIRS	Functional near-infrared spectroscopy
PFC	Prefrontal cortex
TMT	Trail-making test

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s11556-​023-​00319-2.
Additional file 1: Table S1. Changes of Oxy-Hb and Deoxy-Hb during
each task. Fig. S1. Flow diagram from initial contact with participants tostudy completion. Fig. S2. Changes in oxy-Hb level in the frontal lobe
over time in each mode. Fig. S3. Description of the digital trail-making
peg test device. Fig. S4. Change in performance training mode of A-, B-,
B-A time, and B/A ratio. A lower time for the A- and B-modes indicates a
positive performance. A lower B-A time and B/A ratio indicate positive
executive function. Fig. S5. Change in performance training mode of C-,
F-, M-, average P-, and V-mode during the intervention. Lower values of F-,
average P-, and V-modes and higher values of C- and M-modes indicate
positive performance
Acknowledgements
The authors thank all the participants of this study for their participation during the COVID-19 pandemic.
Authors’ contributions
JS, NL, and TO designed the research. JS, NL, and KN contributed to data collection. JS and KN analyzed the NIRS data. JS performed the formal analysis.
JS and NL wrote the first draft of the manuscript. JS, NL, KN, and TO wrote themanuscript. JS and TO secured the funding. All authors read and approved thefinal manuscript.
Funding
This work was supported by a collaborative research grant from NEWCOME
Inc., KAITEKI Institute Inc., JSPS KAKENHI (grant number: 21F21110), COI
STREAM launched in 2013 by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), and COI-NEXT launched in 2020 by MEXT (grant
number: JPMJPF2017). The sponsors had no role in the preparation of thismanuscript.
Availability of data and materials
The datasets used and/or analyzed during the current study are available fromthe corresponding author on reasonable request.

Declarations
Ethics approval and consent to participate
All the protocols were approved by the ethics committee of the University ofTsukuba (Ref. Tai 021–34; Tai 021–42). The study protocol (UMIN000047203)
was retrospectively registered with the University Hospital Medical Information Network Center (http://​www.​umin.​ac.​jp/​engli​sh/). All the participants
provided signed informed consent and no monetary compensation wasprovided.
Author details
1
Research Center for Overwork‑Related Disorders, National Institute of Occupational Safety and Health, Japan (JNIOSH), Kawasaki, Japan. 2 International
Institute for Integrative Sleep Medicine (WPI‑IIIS), University of Tsukuba,
Tsukuba, Japan. 3 R&D Center for Tailor‑Made QOL, University of Tsukuba,
1‑2 Kasuga, Tsukuba, Ibaraki 305‑8550, Japan. 4 Doctoral Program in Physical
Education, Health and Sports Sciences, Graduate School of Comprehensive
Human Sciences, University of Tsukuba, Tsukuba, Japan. 5 Faculty of Healthand Sport Sciences, University of Tsukuba, Tsukuba, Japan.

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