Evidence map›Paper›PMID 42490571›Full record

ArticlePLOS digital health2026

Quantitative analysis of handwriting kinematics in primary and lower secondary school children through a sensorized ink pen: A cross-sectional population-based study.

Simone Toffoli, Linda Greta Dui, Stefania Fontolan, Francesca Lunardini, Milad Malavolti, Chiara Piazzalunga, Alice Donati, Cesare Cornoldi, Cristiano Termine, Simona Ferrante

Abstract read
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Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Simone ToffoliDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
Linda Greta DuiDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.ORCID https://orcid.org/0000-0002-6826-3419
Stefania FontolanDepartment of Medicine and Technological Innovation, Università degli Studi dell'Insubria, Varese, Italy.
Francesca LunardiniCenter for Clinical Neuroscience, Hospital los Madroños, Madrid, Spain.ORCID https://orcid.org/0000-0003-4705-6733
Milad MalavoltiDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
Chiara PiazzalungaDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
Alice DonatiDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
Cesare CornoldiDepartment of General Psychology, Univeristà di Padova, Padova, Italy.
Cristiano TermineDepartment of Medicine and Technological Innovation, Università degli Studi dell'Insubria, Varese, Italy.
Simona FerranteDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The assessment of handwriting is fundamental for identifying difficulties, which may have long-term negative consequences. However, standard evaluation typically focuses only on the final handwritten product. For this reason, Italian guidelines recommended supporting traditional evaluation with digital tools to also analyze the handwriting process. A sensorized ink pen used on paper was employed by over 700 students, ranging from first grade in Italian primary school to third grade in lower secondary school, to execute two tasks of the BVSCO-3, the gold standard for handwriting assessment. From sensorized ink pen data, handwriting indicators in the domains of Time, Force, Smoothness, Tilt, and Frequency were extracted. These indicators were then analyzed to examine their correlation with clinical scores, to model cross-sectional trends across grades, and to identify handwriting difficulties. The correlation analysis revealed significant relationships between the indicators and the clinical score, particularly for the Time domain. A cross-sectional statistical analysis showed that the indicators follow developmental trends compatible with handwriting learning curves reported in the literature: for many indicators, a performance plateau was reached in grade 3, from both motor and processing perspectives. Lastly, binary classification models successfully distinguished subjects with handwriting difficulties (based on BVSCO-3 results) from proficient writers. The sensorized ink pen allowed uncovering relevant characteristics of children's handwriting process, while guaranteeing ecological data acquisition conditions. Its use could pave the way for a prompt identification of handwriting difficulties in school settings, thus facilitating an efficient referral to clinical services.

Identifiers

PMID42490571
PMCPMC13395322

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.