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ORIGINAL ARTICLE
Fine motor skills and motor control networking in
developmental age
Danilo Bondi
1
| Claudio Robazza
2
| Christiane Lange-Küttner
3,4
|
Tiziana Pietrangelo
1
1
Department of Neuroscience, Imaging
and Clinical Sciences, University “G.
d'Annunzio”of Chieti-Pescara, Chieti,
Italy
2
Department of Medicine and Aging
Sciences, University “G. d'Annunzio”of
Chieti-Pescara, Chieti, Italy
3
Department of Psychology, University of
Greifswald, Germany
4
Department of Psychology, University of
Bremen, Bremen, Land Bremen, Germany
Correspondence
Danilo Bondi, Department of
Neuroscience, Imaging and Clinical
Sciences, University “G. d'Annunzio”of
Chieti-Pescara, Via dei Vestini, 31, Chieti,
66100, Italy.
Email: danilo.bondi@unich.it
Funding information
Italian Ministry of Education, University
and Research - Departments of Excellence
2018-2022; University “G. d'Annunzio”of
Chieti - Pescara grant
Abstract
Objectives: We investigated the relationships between fine motor skills, fit-
ness, anthropometrics, gender and perceived motor performance in school
beginners. The aim of our study was to delineate whether and to what extent
fine motor control would show meaningful synchrony with other motor vari-
ables in the age of onset of handwriting in school.
Methods: A sample of N=239 of 6-to-8-year-old children were tested with an
array of tasks measuring fine motor (i.e., dexterity and speed) and grapho-
motor performance (tracing on a tablet screen), anthropometric indexes, and
fitness (shuttle run) measures. A subset of 95 children was also tested for per-
ceived motor competence.
Results: In spite of an overall poor anthropometric condition, our participants
were relatively fit. As expected, older children performed better in both, fine
motor tasks and the shuttle test. The girls were better in fine motor skills, and
an original speed-quality trade-off in the drawing was found. However, the
magnitude of difference by grade was greater for boys' fine motor skills than
those of girls'. A network analysis revealed three specific clusters, (1) perceived
competencies, (2) fitness, and (3) fine motor skills.
Conclusions: Given the relative independence of these areas of physical per-
formance, we suggest focusing on these three clusters as distinct areas of physi-
cal education. Fine motor skills deserve further consideration, especially at an
early school age. We have demonstrated that network analysis and technology
devices used to evaluate motor development are useful and meaningful tools.
1|INTRODUCTION
We have considerable knowledge about how gross motor
skills develop in children (Ghassabian et al., 2016;
Smith & Thelen, 2003) and how to assess them (Griffiths
et al., 2018). Thus, such results in gross motor skills
become included in educational settings. In contrast,
although some effort has been devoted to research on
fine motor control development and interventions in
babies and pre-school children (Strooband et al., 2020),
less is known about fine motor skills in school age chil-
dren. The aim of our study was to delineate whether and
Received: 13 October 2021 Revised: 30 April 2022 Accepted: 4 May 2022
DOI: 10.1002/ajhb.23758
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2022 The Authors. American Journal of Human Biology published by Wiley Periodicals LLC.
Am J Hum Biol. 2022;e23758. wileyonlinelibrary.com/journal/ajhb 1of15
https://doi.org/10.1002/ajhb.23758
to what extent fine motor control would show meaning-
ful synchrony with other motor variables because of the
onset of handwriting in school.
The astonishing use of hands is a fundamental
achievement of humankind: a wide-ranging sort of hand-
based interaction with the environment led to a vital loop
increasing our manual skills. In particular, the morphol-
ogy and function of the thumb in manipulation and com-
plex grasping is considered a cornerstone in functional
evolutionary development (Marzke, 1992). Phenotypical
features in postural and functional kinematics lead to the
genesis of grapho-motor gestures, and the pattern of
those gestures may reflect the developmental trajectories,
allowing to assess grapho-motor levels (Vaivre-Douret
et al., 2021). Increasing complexity and meaning in the
use of tools was instrumental in the evolution of human
intelligence (Lockman & Kahrs, 2017). Gender differ-
ences exist in performing grapho-motor tasks during
developmental ages, with girls having an early advantage
in performing fine motor tasks (Reikerås et al., 2017) and
learning novel tasks (Flatters et al., 2014), while boys out-
perform girls in tasks requiring object control (such as
throwing, Escolano-Pérez et al., 2021). The reasons lie
both in biological factors—such as the differential
involvement of visuo-motor integration (Giammarco
et al., 2016)—and environmental factors, such as gender-
differentiated expectations made by parents about suit-
able and proper motor tasks (Dinkel & Snyder, 2020).
Developing object manipulation skills in childhood
promotes future physical activity (Barnett et al., 2009).
Especially the advanced use of graphic tools has been
playing a fundamental role in society: the complex use of
such devices in drawing and handwriting was conducive
to nearly every culture in this world (Milbrath &
Council, 2003). This argument has been extended to digi-
tal technologies as they are integrated into school settings
and online learning from home as innovative (and
socially distanced) learning tools during the COVID pan-
demic (see also Scaradozzi et al., 2021).
The current study investigates how motor skills are
related in children who have just begun school. This cru-
cial transition point in child development when they are
learning to write changes drawing from basic shapes and
minimal detail to visually realistic drawing that captures
modulated figure contours (Lange-Küttner et al., 2002;
Toomela, 2002).
1.1 |Motor networks in development
From a kinesiological point of view, we can consider
grapho-motor skills as special tool-related fine motor
skills. Drawing, tracing, and writing result from an
integrated input of visual and motor modalities (eye-hand
coordination) during precise, small-scale movements.
Defining fine motor skills, beyond grapho-motricity,
other clusters involved are dexterity, manipulation of
small objects, and speed-dominated skills in simple and
very likely repetitive movements (Martzog et al., 2019).
Fine motor skills in school children consist of an
interplay of spatio-temporal processing, visuo-motor
integration, strength control, perceptual, and cognitive
skills (Feder & Majnemer, 2007; Lange-Küttner, 1998;
Tabatabaey-Mashadi et al., 2015). These related skills are
a great example of networked neuromotor development.
Different grapho-motor tasks such as writing and draw-
ing lie on both common and distinct underlying neural
circuits in the motor and posterior parietal. Drawing acti-
vates a ventral pathway responsible for overseeing and
orienting in spatial fields, while writing activates a dorsal
pathway that controls letter features (Raimo et al., 2021;
Yuan & Brown, 2015). Moreover, delays and disturbances
in the fine motor network are present in children with
special needs such as ADHD and autism (Lange-
Küttner & Kochhar, 2020) and are greatly correlated with
low IQ, math and reading (Mayes et al., 2018; see also
Suggate et al., 2018). Gaul and Issartel (2016) claimed
that children's fine motor skill proficiency was not pro-
gressing at the expected rate given by normative data,
over early school ages.
Thus, researchers have been using digital tablets and
pen to acquire and evaluate the parameters pressure,
velocity, and time in children's drawing and writing
(Lange-Küttner, 1998; Tabatabaey-Mashadi et al., 2015).
There is an emerging field of Artificial Intelligence in
Education (AIED) for motor skills learning (Santos, 2016)
that needs a clear characterization of the motor tasks in
school children to set interactive technologies and foster
advanced learning experiences. The multidisciplinary
field of “graphonomics”needs to be linked within a
broad spectrum of other motor skills and behavior (Van
Gemmert & Contreras-Vidal, 2015). Recently, it has been
suggested that grapho-motor performances can provide a
paradigm to investigate motor skills that may deviate
from the expected developmental trajectories (Bondi, Di
Sano, Verratti, et al., 2020). While grapho-motor tasks, as
other fine motor tasks, share common underlying path-
ways with gross-motor tasks rely on conscious and inten-
tional motor strategy and control development, they
develop differently in the way of how these shared roots
emerge. Various factors such as the set of task con-
straints, goal-oriented decisions, motor synergies, optimal
feedback, previous learning experience, and the adapta-
tion processes influence general motor control
(Todorov & Jordan, 2002), feeding into an aggregate per-
formance in motor coordination tests. Scordella
2of15 BONDI ET AL.
et al. (2015) showed a functional common basis of gross-
motor and grapho-motor skills based on visual–spatial
processes.
Such performances may be also differently influenced
by the fitness and anthropometric status as relevance can
be assumed in all coordinative motor skills (Augustijn
et al., 2018). Overweight children perform poorly on
motor tests and report greater body dissatisfaction and
lower self-efficacy than their nonoverweight peers
(Colella et al., 2009). Matarma et al. (2018) cogently dem-
onstrated that, in young children, gross-motor coordina-
tion was related to anthropometric status, while fine
motor coordination was not influenced by body weight.
However, D'Hondt et al. (2008) reported childhood obe-
sity to have a negative impact on fine motor perfor-
mances, both with or without a simultaneous mechanical
postural demand, suggesting a likely suffering from
underlying perceptual-motor coordination difficulties in
obese preschool children.
Among several determinant factors, Zaqout
et al. (2016) highlighted BMI, age and sex as the strongest
determinants of children's physical fitness, independent
of physical activity. Boys often overestimate and girls
underestimate their own competence regarding sports
practice; this mismatch between objective competence
and perceived competence may negatively influence
health outcomes and motor skills in particular (Pesce
et al., 2018). The perceptual judgment of sports rules is
related to actual play experience, and gender difference
influence the amount of sports experience (Lange-
Küttner & Bosco, 2016).
1.2 |The current study
Because of the onset of handwriting, fine motor skills
gained at preschool age are expected to change during
the first scholastic years, ensuring a new proactive con-
trol of manual tasks (Meulenbroek & Van Galen, 1988).
Thus, we explored the grapho-motor performance of
early school-age children.
Given that the expression of fine motor skills and
their development occur in different contexts and are
thus task-dependent, our analysis included fine and gross
motor tests as well as ancillary variables to assess the
different underlying motor pathways. We analyzed the
ability clusters of school children's graphonomics perfor-
mance in relation to fitness and anthropometric status, as
well as perceived competence. We also considered gender
and age in early school-age children from 6 to 8 years of
age. We hypothesized that the different domains of fine
motor skills, including those related to graphonomics,
were differently related to each other across age and
gender, as the extent and time course of fine motor skills
may have different development for boys and girls. We
hypothesized a link between physical characteristics and
performance with fine motor skills.
2|METHODS
2.1 |Participants
The original sample consisted of 239 children of the first
(all children of this grade were 6-to-7 years old) and sec-
ond (all children of this grade were 7-to-8 years old)
grade of First-Level School, living in the Abruzzo region,
Italy. No child was years ahead or behind. All children
had a similar preschool experience at local public
schools. All schools were following similar curricula, that
is, teaching of handwriting during the first and second
grades. A total of 13 school classes were involved,
according to school availability. All children were
enrolled in the classrooms with their age peers chrono-
logically matched. All children were invited to take part,
with neither inclusion nor exclusion criteria. The
number of boys and girls did not differ significantly,
χ
2
(1) =0.235, p=.628 (Table 1).
Participants' median weight and height were 25.9 kg
and 123 cm, respectively (24.9 kg and 121 cm in first
grade, 26.9 kg and 126 cm in second grade). Manual
dominance was evaluated as a self-report by asking chil-
dren to write their names. Splitting by manual domi-
nance, 88.1% of children were right-handed (RH) and
11.9% left-handed (LH). In particular, the ratio of LH
children was 14.4% in first grade and 8.9% in second
grade, 10.6% in girls and 13.1% in boys. Because we
focused on healthy motor behavior, the results of the six
children with a certificate of disability were excluded
from the data analysis. In this way, we were considering
the setting of an inclusive experience as they were
allowed to be part of the testing experience. In the
remaining sample there were no children with graphic or
writing problems.
We randomly selected a subgroup of seven school
classes to administer the Pictorial Scale of Perceived
TABLE 1 Boys and girls distribution across school grades
Grade
Gender First Second Total
Girls 60 52 112
Boys 72 55 127
Total 132 107 239
BONDI ET AL.3of15
Movement Skill Competence-2 (PMSC-2; Barnett,
Robinson, et al., 2015). This was due to time and logistic
constraints, Thus, only a subsample of n=95 children
(42% of the first grade and 58% of the second grade, 49%
boys and 51% girls, 88% RH and 12% LH) completed
this test.
2.2 |Materials and apparatus
The grapho-motor test was performed on a tablet PC, car-
ried out following an established procedure (Giammarco
et al., 2016) which involved a blank page with a square
and a diamond represented in four gray segments shown
on a tablet screen (Samsung Galaxy note, 10.1 in.,
1280 800). The software was developed by the authors
of the cited study (Giammarco et al., 2016) and got by
them, who also positively tested the retest reliability with
Primary school children. The size of the square and dia-
mond were large enough to cover the entire screen of the
tablet (see the original article for a deeper description
and images). Children were supposed to redraw the
shapes with a digital pen.
The Pictorial Scale of Perceived Movement Skill
Competence-2 (PMSC-2; Barnett, Robinson, et al., 2015)
was administered on printed A4 sheets. It is a widely
used pictorial scale used to assess perceived competence
on object control—hand competence skills (C), locomo-
tor—dynamic leg competence skills (L), and other skills
involved in free time, active play activities (A). Anthropo-
metric testing required a measuring tape and a portable
scale. Reliability of this scale was demonstrated for the
age group of the present study (internal consistency of
0.73 and test–retest reliability of 0.83 for children aged 5–
7 years; Barnett, Ridgers, et al., 2015; internal consistency
of 0.72 and test–retest reliability of 0.73 for children aged
5–8 years: Barnett, Robinson, et al., 2015).
Fine motor coordination was assessed with two tests:
a transitive (tool-related) action test (Floppy) and an
intransitive (non-tool related) action test (Thumb). These
tests required a box of floppy disks and a stopwatch; they
have been already used for testing children of similar
ages (Bondi, Robazza, & Pietrangelo, 2020; Bondi,
Robazza, Russo, et al., 2020). For procedural details, see
the following sections.
2.3 |Procedure
The cross-sectional design encompassed physiological,
anthropometric, and motor measurements. The time
frame of our study consisted of 4–6 months after the
onset of the scholastic season. The study ethics was
approved by the Italian Olympic Committee (CONI) and
by the regional Board of Education of the Italian Ministry
of Education, University and Research (MIUR), Abruzzo
region, Italy. Permission was obtained by the school
administration and informed consent was signed by the
parents of the children. The study conformed to the Dec-
laration of Helsinki.
Children's motor skills were tested individually. Chil-
dren were assessed at their school in the presence of the
teachers, during regular physical education time. The
assessment of the subsample of N=95 children who
completed the PMSC-2 assessment procedure took
around 10–15 min per child.
In the first data collection session, after the PMSC-2
was administered, we collected anthropometric mea-
sures and conducted the fitness test (Shuttle test, see
below). In the following data collection session, the
grapho-motor tablet task and the fine motor tasks were
given in random order. The test–retest reliability of
Floppy and Thumb tests was assessed by the same
observer in three consecutive trials during this data col-
lection session: the Intraclass Correlation Coefficients
(ICCs) were good-to-excellent (Koo & Li, 2016), see
Table 2. Therefore, the data of first repetition were used
for further analyses. The total testing time of the second
data collection session was about 15 min per child, plus
5 min for retest.
Of the entire sample, we collected height, weight,
and waist circumference as anthropometric data, then
we calculated body mass index (BMI) and waist-to-
height ratio (WtHR; Mamen & Fredriksen, 2018). We
assessed speed and agility through the 4 10 m shuttle
run test (Shuttle; Ruiz et al., 2011), that is a running
and turning test at greatest speed: briefly, two parallel
lines are drawn on the floor 10 m apart; the child runs
from the starting to the other line and returns with a
sponge; the sponge is changed by another sponge, then
the child goes back running to the opposite line and
changes the sponge by another one and finally runs
back to the start. The time to complete the task was
the outcome variable.
The grapho-motor test started with a familiarization
phase and thereafter consisted of two trials: the first trial
TABLE 2 Reliability coefficients of floppy and thumb
tests (N=233)
Test Grade
First Second Overall
Floppy 0.95 0.76 0.88
Thumb 0.96 0.90 0.93
4of15 BONDI ET AL.
required to trace as accurate as possible (Best); the second
trial required to trace as accurate as possible with a
higher velocity (Fast). All lines drawn with the pen on
the tablet screen were stored as strokes. The pressure and
the speed exerted on the pen were calculated as scores of
the average over the length of lines. For both trials, we
acquired the measures of the number of Strokes, the Pres-
sure, the Speed as well as the mean oscillation (quadratic
deviation of the points) from the graphic line (Quality).
Recording data were processed whenever the digital pen
exerted a pressure on the screen. Using this tool
permitted us to simulate the effective functional parame-
ters of drawing and to measure the required objective
parameters.
The Floppy test was used to assess manual dexterity
(Martzog et al., 2019). It required participants to insert
12 floppy disks one at a time in the floppy box, as fast as
possible, holding the box while inserting the disks
(to provide a different role for each hand). The rationale
for using this test was based on manual dexterity assess-
ment and, with respect to similar tests—like the coins
task of M-ABC 2 battery—the present test was not
affected by motor experience of common tasks (Zoia
et al., 2018). This test required a strategy to better carry
out the task, with increased difficulty during execution
(as the disks are placed into the box, the empty space is
reduced).
The Thumb testwasusedtoassessupperlimb
coordination, as well as speed-dominated skills
(Martzog et al., 2019). Participants were required to
touch each finger of one hand with the thumb of the
same hand in an alternating pattern (5th, 4th, 3rd, and
then 2nd finger, and reverse), as fast as possible. The
rationale for using this test was based on the visual-
motor control assessment of the BOT-2 battery
(Bruininks & Bruininks, 2005) which is a common task
in clinical assessment (finger–thumb test) and motor
rehabilitation. As for the Floppy test, the outcome vari-
able was time of completing as measured with a
stopwatch.
2.4 |Data generation
We conducted an Exploratory Factor Analysis (EFA) on
four tablet test parameters, see Table 3, with a Maximum
Likelihood rotation. We obtained only one fine motor skills
factor, based on an eigenvalue >1 and on the scree plot.
We then conducted a Confirmatory Structural
Equation Modeling (SEM), considering the expected cor-
relations, and putting into the model the factor from the
EFA in Best condition, the same factor in Fast condition,
the hypothesized relationships between Best and Fast
conditions. We tested age and gender in the model, eval-
uating fit measures to choose the best model. Such mea-
sures suggested including gender and the correlation
between Speed and Quality in Best condition. With more
than two predictors, it is possible to obtain values larger
than +1, as the value of the path between TPB and
Speed-B. Error calculation method: Robust; estimator:
Diagonally Weighted Least Squares (DWLS). Fit mea-
sures: RMSEA =.042, SRMR =.050, TLI =.987,
CFI =.995, GFI =.989; χ
2
(9, 239) =11.56, p=.239.
TPB: Tablet Performance Best; TPF: Tablet Performance
Fast; B: Best; F: Fast. Four equations were produced:
•Girls TPB:Quality +(38.28 Speed)(11.62
Strokes)
•Girls TPF:Quality +(65.10 Speed)(2.76
Strokes)
•Boys TPB:Quality +(55.66 Speed)(22.78
Strokes)
•Boys TPF:Quality +(70.04 Speed)(2.22
Strokes)
We evaluated the fit measures for different models,
concluding that the best model was the one shown in
Figure 1.
We applied the assumption of normality and multi-
variate normality, and subsequently a nonparanormal
transformation (Liu et al., 2009) before conducting corre-
lations. The coefficients in Figure 1were used to build
unweighted undirected graphs. The analysis revealed that
gender had to be taken into account when defining the
structural model of tracing performance. Finally, the four
equations were used to calculate TPB and TPF, separately
for both boys and girls.
2.5 |Data analysis
Statistical analyses were carried out using GraphPad
Prism Software, version 9 (GraphPad Software, La Jolla,
USA), R-based open-source software Jamovi (https://
www.jamovi.org) and Jasp (https://jasp-stats.org). Data
TABLE 3 Exploratory factor analysis for four tablet test
parameters (N=233)
Factor
1
Strokes 0.749
Pressure /
Speed 0.997
Quality 0.572
Note: Root mean square error of approximation (RMSEA) =.102; Tucker-
Lewis index (TLI) =.947; χ
2
(2, 239) =5.97, p=.050.
BONDI ET AL.5of15
distributions were analyzed using Shapiro–Wilk and
D'Agostino-Pearson methods. Analysis of outliers was
conducted with the ROUT method, implemented in
Prism (Motulsky & Brown, 2006). An alpha value of .05
was considered as statistically significant.
Considering the assumption check, robust ANOVA
and robust t-test were chosen to run the comparison
tests. In particular, a series of robust ANOVAs
(median method) were carried out to test the gender
age effect on TPB,TPF,Shuttle,Floppy,Thumb,
BMI,WtHR,Control,Locomotor,Active play.Incase
of a significant interaction effect, post-hoc analyses
with Tukey correction for multiple comparisons were
conducted. Robust t-tests (Yuen's test with 0.1
trimmed proportion and median method), in addition
to the nonparametric Mann–Whitney Utest, were car-
ried out for the left versus right-side manual domi-
nance effect on TPB,TPF,Shuttle,Floppy,Thumb,
BMI,andWtHR.
The links between graphonomic outcomes, other fine
motor skills, fitness, and anthropometric status, as well
as perceived competence, were addressed with network
models, using correlations as estimators. Further network
analyses were conducted with TPB,TPF,Shuttle,Floppy,
Thumb,BMI and WtHR, splitting by age and gender.
PMSC-2 parameters were excluded from the split
networks because of the smaller sample sizes of the sub-
groups. For the details of network analysis, refer to the
figure legends.
3|RESULTS
The BMI values of participants were compared to the
normative values of providedbytheWorldHealth
Organization (WHO, 2007). Our P50 values ranged
between normative P50 and P75 matching for the
same gender and age: BMI of our participants was
therefore higher than this reference. We further classi-
fied the children according to the obesity cut-off
norms of P97 from the WHO reference (Valerio
et al., 2018). As a result, 24.76% of children in our
sample had obesity.
The WtHR values of our participants were compared
to the recent values reported by Santomauro et al. (2017)
on Italian children from Tuscany, a region in central
Italy, north of Abruzzo region. Our P50 values ranged
between their P50 and P75 matching for the same gender
and age. Therefore, WtHR values of our participants were
higher than this reference. We further characterized the
participants according to the health hazard cut-off of 0.50
(Maffeis et al., 2008). We found 14.35% of children to be
at metabolic risk.
Shuttle performances of participants were compared
to international norms for this task (Kolimechkov
et al., 2019). Median of boys in first grade were between
P30 and P40 of that reference, girls in first grade were
between P50 and P60; boys in second grade were between
P50 and P60, girls in second grade were between P60 and
P70. Shuttle performance was therefore overall higher
than this reference, except for boys in the school entrance
class.
PMSC-2 values of participants were compared to two
different samples of school children data reported by
Barnett, Robinson, et al. (2015). Australian children rated
on average 19.9 in object control subscale (C) and 20.7 in
locomotor subscale (L); American children rated 20.5 in
Cand 20.7 in L; present study, children rated 20.4 in
Cand 22.2 in L. Participants of our study therefore rated
higher on locomotor subscale of PMSC-2 than these ref-
erence values.
3.1 |Comparison by gender and grade
Group means are shown in Tables 4and 5. Results were
analyzed through a series of robust ANOVAs with the
median method. All the pvalues of statistical compari-
sons are shown in Tables S1 and S2, Supporting
FIGURE 1 Confirmatory structural equation modeling (SEM)
of tablet test parameters. P: Percentile; TPB: Tablet Performance
Best; TPF: Tablet Performance Fast; BMI: Body mass index; the
showed numbers are the standardized coefficient (standardizing
both label and observed variables)
6of15 BONDI ET AL.
Information. Box and whiskers plots of the results split
by gender and grade are shown in Figure 2
Overall, the higher the speed of the pen on the screen,
the lower the quality of drawing. Regarding the tablet
test, we found strong differences by gender in both the
TPB and TPF (Q
1,188
=23.50, p< .001, partial η
2
=.239;
Q
1,165
=59.52, p< .001, partial η
2
=.376). The gender
age interaction was not significant (p=.127 and
p=.323, respectively).
Task-specific analyses showed that older children
(Mdn =14.31 s) performed better than younger ones
(Mdn =15.78 s) in the Shuttle test which measures the
fitness domains of speed and agility (Q
1,204
=27.17, p<
.001, partial η
2
=.139). Older children (Q
1,178
=15.33,
p=.040, partial η
2
=.042) and girls (Q
1,178
=4.22,
p< .001, partial η
2
=.042) performed better in the Floppy
test which measures fine coordination on a tool-related
(namely transitive) action. Both boys and girls performed
better with age in the Thumb test (Q
1,191
=df1 =1,
df2 =191, p< .001, partial η
2
=.053) which measures
fine coordination of fingers on one hand without holding
a tool (namely intransitive action).
There was a main effect of gender on the WtHR (girls
showed a higher WtHR than boys, Q
1,200
=4.74,
p=.029, partial η
2
=.024), and a significant two-way
interaction (Q
1,198
=8.17, p=.004, partial η
2
=.037);
post-hoc analysis revealed no significant difference
between boys and girls in the first grade, whereas boys
showed higher WtHR values (Mdn =0.48) than girls
(Mdn =0.45) in the second grade (p
Tukey
=.005, Cohen's
d=0.699).
There was a difference between boys and girls by sex
in the BMI (gender age comparison: Q
1,199
=6.49,
p=.011, partial η
2
=.035); post-hoc analysis revealed no
significant difference in the first grade, but a tendency for
girls to show lower BMI values (Mdn =16.5 kg/m
2
) than
boys (Mdn =18.0 kg/m
2
) in the second grade
(p
Tukey
=.082, Cohen's d=0.496).
For what concerns the PMSC-2 test, boys perceived
their object control skills to be higher than girls
TABLE 4 Results of the comparisons by grade and gender, N=239
Graphonomics Fine motor skills Anthropometrics Fitness
Grade TPB TPF Floppy time (s) Thumb time (s) BMI (kg/m
2
) WtHR Shuttle time (s)
Q1 Girls First 979 1794 19.9 8.62 15.7 0.443 15.2
Second 832 1638 19.2 7.53 15.5 0.429 13.6
Boys First 413 906 21.1 8.58 15.9 0.447 14.5
Second 348 978 19.6 7.89 16.2 0.465 13.1
Median Girls First 1292 2424 22.8 10.00 17.2 0.468 15.8
Second 1291 2080 20.0 8.29 16.5 0.448 14.4
Boys First 649 1191 24.2 10.6 16.7 0.464 15.6
Second 848 1115 21.5 9.29 18.0 0.478 13.9
Q3 Girls First 1740 2908 25.4 11.6 19.4 0.492 16.6
Second 1643 2590 22.7 9.81 18.9 0.468 15.8
Boys First 1011 1711 27.5 12.4 17.9 0.486 16.9
Second 1066 1489 24.3 10.8 20.4 0.499 15.2
Median (whole sample) 983 1643 22.4 9.4 16.9 0.466 15.1
Abbreviations: BMI, body mass index; Q, quartile; TPB, Tablet Performance Best; TPF, Tablet Performance Fast; WtHR, waist-to-height ratio.
TABLE 5 Results of the comparisons in PMSC questionnaire
by grade and gender, N=95
Grade CLA
Q1 Girls First 16.0 20.5 17.0
Second 19.0 21.0 17.0
Boys First 20.0 20.0 15.0
Second 20.5 22.5 16.0
Mdn Girls First 19.0 23.0 19.0
Second 20.5 23.0 20.0
Boys First 21.0 23.0 18.5
Second 22.0 23.0 19.0
Q3 Girls First 21.5 24.0 22.0
Second 21.0 24.0 23.0
Boys First 23.0 24.0 22.8
Second 23.0 24.0 21.5
Mdn (whole sample) 21.0 23.0 19.0
Abbreviations:A, active play; C, object control; L, locomotor; Mdn, median;
Q, quartile.
BONDI ET AL.7of15
(Mdn =22 vs. 20; Q
1,93
=4.45, p=.035, partial
η
2
=.093), see Table 5, while no significant differences
were found in Locomotor or Active play subscales.
3.2 |Network analysis
To analyze the synchronous relationships between vari-
ables, we conducted a network analysis. In Figures 2
and 3, the task components that refer to one test are
depicted in the same color. Network clusters are mar-
ked by the connections between these components,
with thicker lines showing stronger connections. The
analysis of the sample of N=95 revealed three funda-
mental clusters. One is the PMSC-2 cluster, with the
three subscales strongly linked to each other with the
following weights: Locomotor and Control =.851,
Locomotor and Active play =.894, Control and Active
play =.879. The second is the Fitness cluster,withthe
anthropometric measures and the Shuttle test showing
moderate links of the Shuttle test with both BMI
(weight =.396) and WtHR (weight =.399), and a
strong link between WtHR and BMI (weight =.902).
Finally, the third fundamental cluster is the Fine Motor
Control cluster, with moderate links between the differ-
ent tablet and floppy parameters.
In particular, the following weights were observed:
Floppy and Thumb =.589, Floppy and TPB =.547,
Floppy and TPF =.368, Thumb and TPB =.522, Thumb
and TPF =.510, TPB and TPF =.433. The centrality
measures revealed Floppy to be the most central,
although weakly, node of the network (see Table 6).
FIGURE 2 Box and whiskers plots of anthropometric and motor skills results. Each of the three panels represent min-to-max lines and
median with IQR boxes. For each dataset, girls (white boxes) and boys (gray boxes) results are shown split by school grade
FIGURE 3 Network plot of the 10 parameters. The bolder the
line, the higher the weight of the correlation between nodes. Nodes
of the same colors belong to the same group, according to
clustering measures of the analysis. Nodes are positioned using the
Fruchterman-Reingold algorithm, based on the strength of
connections. TPB: Tablet Performance Best; TPF: Tablet
Performance Fast; BMI: Body mass index; WtHR: Waist-to-height
ratio
TABLE 6 Centrality measures per variable in overall network
analysis
Closeness Strength
Active play 0.091 0.907
BMI 0.859 0.410
Control 0.180 1.031
Floppy 1.407 0.019
Locomotor 0.788 1.414
Shuttle 0.604 0.997
TPB 0.729 0.831
TPF 2.121 1.736
Thumb 0.365 0.428
WtHR 0.305 0.250
Note:“Closeness”refers to the inverse of the “peripherality”(the sum of the
shortest paths starting from the node of interest), “strength”refers to the
centrality “degree”(sum of absolute weights) of the node of interest.
Abbreviations: BMI, body mass index; TPB, Tablet Performance Best; TPF,
Tablet Performance Fast; WtHR, waist-to-height ratio.
8of15 BONDI ET AL.
We then ran the networks split by gender and age
which revealed different models for boys and girls, see
Figure 4. Considering the size of subsamples, we run this
model without the aim to infer results, but to provide
pilot evidence to be possibly confirmed by ad hoc
designed studies.
The Shuttle, WtHR, and BMI were strongly linked
in girls of both grades, but only to some degree in the
younger group of boys. Thumb and Floppy were more
linked in second than in first grade in girls showing a
growing network of fine motor skills, but in boys,
thumb and floppy were linked in both grades. More-
over, in boys, the tablet parameters became linked with
thumbs and floppy task showing a generalization of
fine motor expertise extending to computers. In con-
trast, in the older grade of girls, fast tablet motions
were not networked anymore.
4|DISCUSSION
A structured analysis of various motor tasks provides a
better understanding of the motor tasks. In this regard,
network analyses allow going beyond the typical correla-
tion analyses, thanks to centrality measures and cluster-
ing. We examined the important transition into school
when fine motor skills become essential for learning how
to write. We addressed the structured links between
grapho-motor performances and other fine motor skills,
speed and agility, anthropometrics, and self-perception of
motor skills in early school-age children. Our result dem-
onstrated that, first, grapho-motor skills varied between
boys and girls and exhibited gender-related differences,
second, that three clusters (namely, perceived compe-
tence, fitness, and fine motor skills) emerged from the
network model, and third, that the interconnectedness of
FIGURE 4 Network plots split by gender and school grade. The drawings reflect the strength, the closeness, and the clustering of the
variables. The bolder the line, the higher the weight of the correlation between nodes. Nodes of the same colors belong to the same group,
according to clustering measures of the analysis. Nodes are positioned in space using the Fruchterman-Reingold algorithm, based on the
strength of connections. TPB: Tablet Performance Best; TPF: Tablet Performance Fast; BMI: Body mass index; WtHR: Waist-to-height ratio
BONDI ET AL.9of15
fine motor skills with other domains may change with
age and gender during early school age.
4.1 |Fine motor skills, cognitive
functions, and tasks
Our results showed that when the strategy to carry out
the grapho-motor task was focused on accuracy, this
reduced the speed of the graphical task which confirms
the finding by Giammarco et al. (2016) (see also Lange-
Küttner, 1998) who observed that the slower the speed,
the better is the quality. The speed accuracy trade-off has
been studied widely, with evidence also in graphic
tasks for school age children (e.g., Smits-Engelsman
et al., 2006). Looking at the underlying factors of
increased accuracy, we refer to Simpson et al. (2017),
who suggested that inhibition is the key ability that
enables children to draw recognizable objects. Inhibition
may be evoked, for instance, when stopping drawing a
line of overlapping figures (hidden line elimination)
(e.g., Chen & Holman, 1989; Lange-Küttner, 2000). This
possible inhibition requirement was here particularly evi-
dent in boys, although further research should confirm
this finding with a study designed to discriminate slow vs
fast tasks. The reason can be found in the gender-based
differences in visuo-motor integration. For instance, con-
trolling oscillations in tracing performance varies in boys
but not in girls (Giammarco et al., 2016; Tabatabaey-
Mashadi et al., 2015). Drawing development also differs
between boys and girls in the near-far conceptualization
of pictorial space as boys drew silhouettes as they would
be visible from afar, while girls focused on ornaments
that could be seen close-up (Lange-Küttner, 2011).
We could prove that the development of fine motor
skills varies in boys and girls showing gender and age-
related clusters that added further learning-related evi-
dence to the field of graphonomics and grapho-motor
performances. Beyond the strong individual motivation
in the aim of accomplishing the motor task, both
strategy and adaptation to the velocity constraint were
gender-related. Fine motor skills seemed to be already
networked in girls at the beginning of school but became
linked and generalized in boys in the second year
supported the view that sex differences; this result should
be considered to understand performance strategies and
underlying motor, cognitive, and neuronal patterns. This
showed how a task can be solved by the two genders with
different strategies and may shed new understanding on
development of competence (Lange-Küttner, 2017).
Indeed, also in the current study, strategy and perfor-
mance on fine motor control tests were task-related. The
link between the two tablet parameters and the two fine
coordinative tests were only small and changed with
regards to age and gender, as shown in the networks.
This dynamic perspective was also shown by Snapp-
Childs and colleagues who reported specific training
effects to eliminate drawing performance differences
between ages (7–8vs10–12-year old children) and differ-
ent coordination skill levels (Snapp-Childs et al., 2015).
4.2 |Observed and perceived physical
performances
Our results of Shuttle test were comparable to findings of
the study of Kolimechkov et al. (2019). Instead, our par-
ticipants' data were slightly worse on anthropometric
indexes (Santomauro et al., 2017; WHO, 2007). In partic-
ular, the high percentage of children over the limits of
overweight and health hazard may highlight an alarming
situation for public health, with the need for better inter-
ventions. However, we found that even if our participants
performed quite well on the Shuttle test, they could nev-
ertheless have a poor anthropometric status. The moder-
ate positive weights revealed by network analysis of
Shuttle with both BMI and WtHR nevertheless support
the call for policy approaches to improve anthropometric
status as a modifiable factor to enhance physical fitness
(Zaqout et al., 2016).
Notwithstanding the anthropometric status, our par-
ticipants reported high values of perceived movement
skills competence. Fine and gross motor skills, fitness
performance and morpho-functional parameters repre-
sent necessary modules to be evaluated objectively in
order to define developmental trajectories (Bondi, Di
Sano, Verratti, et al., 2020). Assessment of perceived com-
petence is a measure that integrates such evaluations
from a subjective point of view and allows to focus physi-
cal education on the possible mismatch between objec-
tive and perceived skills. In our study, boys scored higher
than girls on the object control sub-scale; this result
agrees with the findings of Estevan et al. (2018) on Span-
ish children. However, this higher self-evaluation is not
accompanied by better objective results: ours and other
results (Flatters et al., 2014) showed that young girls per-
form better than young boys on manual skills. Further
studies should therefore increase the knowledge about
gender differences in the relationship between objective
and subjective motor skills, the developmental course of
these linkages, sports and social consequences and the
related educational interventions. A careful examination
of the alignment of the objective skills and children's
subjective ratings will help to strengthen the knowledge
on these topics. Beyond the subjective measures of
perceived skills, further studies should address also some
10 of 15 BONDI ET AL.
parameters of the biopsychosocial framework (Bortoli
et al., 2018) and some parameters of personal feeling such
as enjoyment in physical activity experiences (Carraro
et al., 2008).
4.3 |Fine motor skills in children's
development
We consider fine motor control as an emergent issue in
the understanding of motor behavior for auxological
(auxology is the science of human growth), educational
and kinesiological studies. The wide range of expressions
of motor control suggests considering all the related sub-
domains. The current study supports the argument of
addressing the several fine motor skills, in addition to
gross motor skills, for children.
Improvement of fine motor skills in children is spe-
cific to practice of fine motor skills, even in an education-
oriented household (Suggate et al., 2017). However, fine
motor coordination was demonstrated in two studies to
be of more importance for math than for literacy because
of the visual–spatial imagery required in both pictorial
and mathematical space (Pitchford et al., 2016). Thus,
one could predict that stimulation of fine motor skills
may even be relevant for the improvement of higher cog-
nitive skills in the prepubertal period. More attention
should be directed toward this field, providing a wide
range of different motor experiences. Although being a
fundamental learning experience in the beginning of
school when learning to write. Fine motor skills repre-
sent a fundamental learning experience in the beginning
of school when learning to write. However, children
rarely choose to use these skills in other learning contexts
such as free play, independently of whether their fine
motor skills are well, or less well developed (Marr
et al., 2004). We should also take advantage by acquiring
longitudinal track records of fine motor coordination
data, in the same way as already reported for gross-motor
coordination (Reikerås et al., 2017; Vandorpe
et al., 2012). Thus, both higher attention on fine-
coordinative skills in early education and implementa-
tion of gross-coordinative activities and evaluation need
more articulated planning and organization of physical
education, addressing motor control and learning as a
whole.
Motor control also reflects gender differences in fine
motor coordination through the developmental process.
Reikerås et al. (2017) showed girls to have a more pro-
nounced and anticipatory fine motor coordination than
boys. Also Flatters et al. (2014) reported superior man-
ual control abilities in prepubescent girls performing
novel tasks. We contributed to the field showing that
the relationship between fine motor coordination and
other domains is different by age and gender in early
school age. Both the clustering of nodes of fine motor
control and the clustering of physical fitness with
anthropometric measures were likely influenced by gen-
der, as shown in the differential networks by the differ-
ences in both the nodes positioning and the weight of
edges. This topic is worth further investigating with
longitudinal studies and we thus suggest that the rela-
tionships between motor tasks themselves and other
physical outcomes might follow gender-based develop-
ment trajectories. Also training studies (van Abswoude
et al., 2019) may be interesting to the emerging field of
research in fine motor skills.
Cognition and motor skills are interrelated at the
level of brain structure, supported by similar cortical neu-
ral substrates (Pangelinan et al., 2011). Therefore, further
studies are needed to better characterize the relationships
of morphological and neural connections with different
coordinative domains, and the advantages of optimizing
sensory–motor integration during precisely controlled
hand movements to foster the learning of fine motor
skills (Ose Askvik et al., 2020; van der Meer & van der
Weel, 2017). Interesting findings may result from studies
focused on evaluating differences based on handedness
and asymmetries, as they have been shown to be
related to the type of manual task exerted (Bondi,
Prete, Malatesta, & Robazza, 2020; Bondi, Robazza, &
Pietrangelo, 2020).
4.4 |Limitations
The study did not come without limitations, such as the
absence of data about social background and academic
performance. One other gap in the assessment may have
been that we did not measure previous use of digital
devices that might have affected fine motor skills
(Petrigna et al., 2021). Thus, the results of the study rec-
ommend not only including fine motor skills in early
school age research, but also studying them as a special
cluster.
5|CONCLUSIONS
Our network analysis revealed three specific clusters:
perceived competence, fitness, and fine motor skills.
Given the relative independence of these physical per-
formance areas, we suggest focusing on these three
clusters as separate areas in physical education. Fine
motor skills deserve further consideration in early
school age, since these skills are important in a variety
BONDI ET AL.11 of 15
of tasks like sorting objects, handwriting, and handling
digital media. The latter has become even more urgent
during distant learning in the Covid lockdowns. Digital
devices represent fashionable and effective tools to be
used under an educational perspective; in remote
teaching times, teaching and evaluation of academic
homework inevitably require digital devices for moni-
toring and developing children's fine motor skills.
Whether learning to write and thus increased fine
motor control influences manual dominance is still an
open question and further studies should provide clear
insights.
Motor development can be interpreted in the para-
digm of Dynamic System Theory (Smith & Thelen,
2003): person, task, and environment interact dynami-
cally and critical changes in one or more subsystems can
produce improvements in the resulting performance.
From this perspective, the human movement system is a
complex network of subsystems consisting of a plethora
of interacting components (Glazier et al., 2003). The con-
straint of repetition in task-specific practice greatly
enhances related skills and has more or less potential to
be transferred to other skills. Even a slight change in
one component of the dynamic system can lead to reor-
ganization of structure and developmental change
(Smith & Thelen, 2003). Therefore, both interventions
and evaluations need to be carefully and finely tuned.
Specific evidence of several motor tests can lead to a
proposal of “sentinels”for early identification of various
neural and motor deficits. Network research provides an
effective framework for discussing fine and gross motor
skills during developmental age; network models can aid
better modeling of motor tasks and design of smart class-
rooms, while providing insights into novel interactive
technologies.
AUTHOR CONTRIBUTIONS
Danilo Bondi: Conceptualization (lead); formal analysis
(lead); investigation (lead); methodology (lead); resources
(supporting); visualization (lead); writing –original draft
(lead); writing –review and editing (equal). Claudio
Robazza: Conceptualization (supporting); methodology
(supporting); project administration (equal); resources
(lead); supervision (lead); writing –original draft
(supporting); writing –review and editing (equal).
Christiane Lange-Küttner: Supervision (supporting);
writing –original draft (equal); writing –review and
editing (lead). Tiziana Pietrangelo: Conceptualization
(equal); funding acquisition (lead); investigation
(supporting); methodology (supporting); project adminis-
tration (equal); resources (supporting); supervision (lead);
writing –original draft (supporting); writing –review
and editing (supporting).
ACKNOWLEDGMENTS
The authors would like to thank PE experts Sara Ienni
and Maristella Melfi who helped with tests and logistics.
The authors would also like to thank teachers and both
scholastic and sports administrations. Special thanks go
to all the children involved in the study. This work was
supported by a ‘G. d'Annunzio’University grant, by
2012N8YJC3_003 PRIN National Grant to Tiziana
Pietrangelo and by the “Departments of Excellence
2018-2022”initiative of the Italian Ministry of Education,
University and Research for the Department of Neurosci-
ence, Imaging and Clinical Sciences (DNISC) of the Uni-
versity of Chieti-Pescara. Open Access Funding provided
by Universita degli Studi Gabriele d'Annunzio Chieti
Pescara within the CRUI-CARE Agreement.
CONFLICT OF INTEREST
The authors declare no competing interests.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are avail-
able from the corresponding author upon reasonable
request.
ORCID
Danilo Bondi https://orcid.org/0000-0003-1911-3606
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How to cite this article: Bondi, D., Robazza, C.,
Lange-Küttner, C., & Pietrangelo, T. (2022). Fine
motor skills and motor control networking in
developmental age. American Journal of Human
Biology, e23758. https://doi.org/10.1002/ajhb.23758
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