Abstract
Background
Digital handwriting distinguishes PD patients from healthy controls but is rarely assessed quantitatively.
Objectives
To evaluate digital handwriting as an objective tool to assess motor impairment, dopaminergic response, and progression in PD.
Methods
Participants (Healthy Controls, n = 52; De Novo PD Patients assessed at baseline and after 7 weeks, n = 50; PD Patients in OFF–ON states following inhaled levodopa, n = 26; PD Progression Patients, n = 60) wrote a standard sentence on a digitizing tablet.
Results
De Novo PD Patients showed reduced handwriting speed and increased in‐air time. Acute dopaminergic stimulation in OFF–ON states was associated with shorter in‐surface and in‐air time. Longer disease duration was associated with increased variability in speed and acceleration. Higher MDS‐UPDRS Part III scores were associated with lower speed and acceleration.
Conclusions
Digital handwriting is an objective digital biomarker for quantifying PD dysgraphia and its response to dopaminergic treatment.
Keywords: digital handwriting analysis, dysgraphia, graphic tablet, handwriting capture pressure
Handwriting impairment is a frequent early motor sign in Parkinson's disease (PD) which comprises micrographia and a broader motor disturbances spectrum, altogether called “PD dysgraphia.” 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 It often represents the primary motive for medical consultation. 5 , 6 Importantly, several studies showed that kinematic variables such as velocity and fluency discriminate PD patients from controls more effectively than micrographia. 9 , 10 , 11
In routine assessments, handwriting is rarely evaluated quantitatively and relies on clinical scales. 12 , 13 Alternatively, digital handwriting analysis allows measuring kinematic parameters, like velocity, acceleration, and in‐air movement, which are related to motor processes sensitive to striatal dysfunction. 14 , 15 , 16 , 17 , 18 , 19 These parameters reliably distinguish patients from controls even at early PD stages as showed in our previous work. 20 More recently, AI has been applied to the evaluation of micrographia, making handwriting analysis more accessible. 21
Dopaminergic therapy has heterogeneous effects on handwriting. It improves kinematics parameters in simple tasks but shows less benefit in complex tasks, like sentence copying, probably due to increased cognitive load. 18 , 22 , 23 Although the effects of oral levodopa on handwriting have been documented, the impact of on‐demand dopaminergic therapies such as inhaled levodopa remains largely unexplored.
With this background, the present study investigates whether digital handwriting analysis can provide objective and quantitative assessment of motor dysfunction in PD patients. Using a digitizing tablet to capture detailed kinematic handwriting signals, we evaluated whether quantitative handwriting parameters can estimate motor impairment, response to dopaminergic treatment across different clinical states, and disease progression. In addition, we examined the relationship between digital handwriting parameters and clinical motor severity measured by the MDS‐UPDRS Part III. We aim to advance the development of objective tools that complement current scale‐based assessments and improve clinical diagnosis, monitoring, and therapeutic evaluation.
Methods
Participants
188 participants, which were > 18 years and provided written informed consent, were included in four groups (Fig. S1; Table S1, S2): Healthy Controls (HC, n = 52), De Novo PD Patients (DN‐PD, n = 50), PD Patients evaluated in OFF–ON states (PD‐OFF/ON, n = 26), and PD Progression Patients (PD‐PP, n = 60), recruited from three Spanish centers: Hospital Universitario Fundación Jiménez Díaz (Madrid), Hospitales Universitarios San Roque (Canary Islands), and Hospital Universitari de Mataró (Barcelona). PD was diagnosed following the UK Parkinson's Disease Society Brain Bank criteria.
The HC group included healthy individuals without history of neurological or psychiatric disorders. Participants underwent a single clinical and handwriting assessment and were matched to DN‐PD Patients.
The DN‐PD group included newly diagnosed and untreated PD patients. Clinical and handwriting assessments were performed at the time of diagnosis and after 7 weeks of continuous treatment (rasagiline 1 mg/day or levodopa/carbidopa titrated to 300 mg/day).
The PD‐OFF/ON group included PD patients with levodopa chronic therapy. Clinical and handwriting assessments were evaluated under an acute pharmacological protocol: baseline assessment after 20 hours of medication withdrawal (OFF state) and reassessment 30 minutes after administration of inhaled levodopa (2 × 33 mg capsules; ON state).
The PD‐PP group included PD patients representing the clinical spectrum of disease progression. Participants underwent a single cross‐sectional clinical and handwriting assessment.
Clinical Assessment and Selection Criteria
All participants were right‐handed and completed a standardized neurological evaluation. Demographic data (age, sex, and educational level) and motor severity (MDS‐UPDRS Part III and the Hoehn and Yahr scale) were recorded. 12 , 24 Patients with more than mild tremor, severe cognitive impairment, major psychiatric disorders, atypical parkinsonian syndromes, or musculoskeletal condition affecting the upper limbs were excluded.
Handwriting Task, Digital Acquisition, and Kinematic Parameter Extraction
Participants were asked to write the standardized Spanish sentence “La casa de Pamplona es bonita” (Fig. 1). After three repetitions on A4 paper a fourth trial was recorded using a Wacom Intuos Pro tablet (200 Hz sampling rate) with an ink stylus and paper overlay to preserve tactile feedback. The three repetitions were meant to familiarize participants with the writing task, minimize the potential influence of stress, and reduce the novelty effect associated with first‐time exposure to digital handwriting equipment. Handwriting signals were acquired using Handwriting Capture Pressure software and processed with SVC Analytics to obtain .svc raw data files from time‐ordered samples of horizontal and vertical coordinates and pen pressure. 19 parameters categorized into temporal, pressure, kinematic, non‐linear, and fluency domains (Table S3) were extracted with MATLAB from .svc files. Detailed information on parameter extraction is provided in the Supporting Information (Section S1).
Figure 1.
Digital handwriting trajectories and OFF–ON changes in PD. (A) Healthy Control (HC). (B) De Novo PD Patient (DN‐PD). (C) PD patient in the OFF state. (D) Same patient in the ON state after inhaled levodopa. Black lines represent on‐surface writing and red lines indicate in‐air movements. (E–F) Paired intra‐subject comparison showing reduced in‐air time (t_up) and on‐surface time (t_down) in the ON state. Each point represents an individual patient.
Statistical Analysis
Normality for the 19 parameters data was assessed using Shapiro–Wilk and Kolmogorov–Smirnov tests. Multivariable linear regression models adjusted for age and sex were used to compare HC and DN‐PD at baseline and to examine the relationship between disease duration and handwriting in PD‐PP. Intra‐subject changes in DN‐PD (7‐week follow‐up) and PD‐OFF/ON (OFF vs. ON states) were evaluated using paired Student's t‐tests. Each parameter was predefined a priori to quantify a distinct component of handwriting and was analyzed as an independent hypothesis‐driven outcome rather than as part of an exploratory analysis; therefore, correction for multiple comparisons was not applied. Associations with motor severity (MDS‐UPDRS Part III) were assessed using Pearson (r) and Spearman's (ρ) correlation coefficients (Fig. S1). All tests were two‐tailed (P < 0.05) and performed using R software (version 4.5.2).
Results
The demographic and clinical characteristics of the participants are summarized in Table S1. Statistical analyses are summarized in Tables S4–S8. The statistically significant differences are summarized in Table 1.
TABLE 1.
Significant digital handwriting changes associated with Parkinson's disease, dopaminergic response, disease duration, and motor severity
| Parameter | DN‐PD vs HC | OFF–ON states | Disease duration | Motor severity |
|---|---|---|---|---|
| t_up (In‐air time, ms) | ↑ (P < 0.001) | ↓ (P = 0.028) | — | — |
| t_down (On‐surface time, ms) | — | ↓ (P = 0.002) | ↑ (P = 0.021) | — |
| p_max (Maximum pressure, a.u.) | ↓ (P = 0.015) | — | — | — |
| speed_mean (Mean speed, mm/s) | ↓ (P < 0.001) | — | — | ↓ (P = 0.024) |
| speed_max (Maximum speed, mm/s) | ↓ (P = 0.012) | — | — | — |
| speed_median (Median speed, mm/s) | ↓ (P < 0.001) | — | — | ↓ (P = 0.025) |
| speed_std (Speed variability, mm/s) | ↓ (P < 0.001) | — | ↑ (P = 0.012) | — |
| acceleration_mean (Mean acc., mm/s2) | ↓ (P < 0.001) | — | — | — |
| acceleration_max (Maximum acc., mm/s2) | ↓ (P = 0.014) | — | — | — |
| acceleration_median (Median acc., mm/s2) | ↓ (P < 0.001) | — | — | ↓ (P = 0.009) |
| acceleration_std (Acc. variability, mm/s2) | ↓ (P = 0.002) | — | ↑ (P = 0.021) | — |
| entropy_p (Pressure complexity, bit) | — | ↓ (P = 0.033) | — | — |
| entropy_x (Spatial complexity X, bit) | — | — | — | ↑ (P = 0.040) |
| entropy_y (Spatial complexity Y, bit) | — | — | — | ↑ (P = 0.034) |
Note: ↑ indicates higher values and ↓ indicates lower values.
Abbreviations: HC, Healthy Controls; DN‐PD, De Novo PD Patients; PD‐OFF/ON, PD Patients in OFF–ON states; PD‐PP, PD Progression Patients.
DN‐PD Patients Showed Reduced Handwriting Velocity, Reduced Acceleration, and Increased in‐Air Time
Cross‐sectional comparisons between HC and DN‐PD Patients at baseline revealed that 10 handwriting parameters showed statistically significant differences between groups (Table S4). Compared with HC (Fig. 1A), DN‐PD Patients (Fig. 1B) showed reduced speed and acceleration and increased in‐air time.
DN‐PD Patients Did Not Show Changes in Handwriting Parameters after 7 Weeks of Dopaminergic Treatment
Intra‐subject comparisons in DN‐PD Patients between baseline and after 7‐weeks post‐treatment (41 DN‐PD patients −82%‐ received rasagiline; 9 DN‐PD patients −18%‐ received levodopa/carbidopa) did not reveal significant changes in handwriting parameters (Table S5).
PD‐OFF/ON Patients Showed Reduced Handwriting Duration and Pressure Entropy after Acute Dopaminergic Stimulation
Intra‐subject comparisons in PD‐OFF/ON Patients revealed that three handwriting parameters had statistically significant differences between OFF and ON states (Table S6). Compared with the OFF state (Fig. 1C), patients in the ON state (Fig. 1D) showed lower in‐air time, on‐surface time, and pressure entropy (Fig. 1E,F).
Longer Disease Duration Is Associated with Increased Variability in Handwriting Speed and Acceleration
Regression analyses revealed that longer disease duration was associated with increased variability in speed and acceleration, whereas age was associated with increased in‐air time and on‐surface time (Table S7).
Motor Severity Is Associated with Reduced Handwriting Speed and Acceleration and Increased Movement Complexity
Associations between handwriting parameters and MDS‐UPDRS Part III scores revealed that eight parameters related to speed, acceleration, complexity, and fluency were significantly correlated with motor severity (Table S8).
Discussion
DN‐PD Patients showed reduced handwriting speed and acceleration and increased in‐air time compared with HC. No changes were observed after 7 weeks of treatment, but acute dopaminergic stimulation in PD‐OFF/ON Patients was associated with lower on‐surface time and in‐air time. Longer disease duration was associated with increased variability in speed and acceleration. Finally, motor severity was associated with reduced handwriting speed and acceleration.
To our knowledge, this is the first quantitative handwriting study integrating motor impairment, dopaminergic response, and disease progression in PD.
Reduced speed and acceleration and increased in‐air time in DN‐PD Patients compared to HC reflect impaired movement execution and motor planning, 19 , 25 which is consistent with previous studies showing that kinematic features are more sensitive than static measures, because in‐air metrics reflect increased cognitive load and reduced automaticity. 17 , 18 , 20 , 26
No significant changes in handwriting parameters were observed in DN‐PD Patients after 7 weeks of dopaminergic treatment, probably due to the limited dopaminergic effect of MAO‐B inhibitors. 27 , 28 Additionally, continuous motor planning for sentence writing may not normalize after 7 weeks of dopaminergic treatment. 18 , 29 Previous studies showed that specific features of PD dysgraphia, particularly the sequence effect, are relatively resistant to dopaminergic therapy 5 , 19 , 30 , 31 , 32 ; thus, longer treatment may be required to improve handwriting. 5 , 18 , 33 , 34
To our knowledge, this is the first study to assess the effects of inhaled levodopa on digital handwriting in PD. One study of on‐demand therapy with subcutaneous apomorphine showed improvements in global kinematic measures. 35 We found that inhaled levodopa reduced in‐air time and on‐surface time, indicating more efficient temporal organization of handwriting. 5 , 34 Our findings highlight the sensitivity of temporal handwriting parameters to detect rapid motor changes during OFF–ON transitions and support digital handwriting analysis as an objective tool to monitor on‐demand therapies in PD. 22 , 36
The correlation between disease duration and increased variability in speed and acceleration reflects impaired movement consistency and timing. 5 , 6 , 9 , 10 This supports a dissociation between aging and disease progression, with slowing mainly associated with age and variability to disease duration. 6 , 21 , 37 , 38 However, the modest correlations observed in our study between handwriting parameters and MDS‐UPDRS III could be improved with specific evaluation of the motor status of the writing hand. 39
The present study has some limitations. First, De Novo PD Patients were evaluated twice, but HC subjects were only evaluated at baseline; this excluded the evaluation of test–retest reliability. Second, formal cognitive assessment was not performed; thus, the potential contribution of cognition to handwriting impairment cannot be excluded. Third, we did not digitally record the first three sentence repetitions; therefore, we could not evaluate sequence effects in handwriting. Fourth, we focused on kinematic handwriting parameters because they are more sensitive than micrographia for detecting dysgraphia and treatment response in PD, 5 but future studies could integrate kinematic and spatial handwriting features to obtain a more complete panorama of PD dysgraphia. AI, which discriminates micrographia in PD patients from healthy controls, 21 will help. Also, the study of sentence slope and inter‐word/inter‐letter spacing in isolated REM sleep behavior disorder, 40 together with other parameters could illuminate the development of PD dysgraphia.
In conclusion, digital handwriting analysis provides objective and quantitative measures of motor function, dopaminergic response, and disease progression, that complement conventional motor scales in PD. Its non‐invasive, rapid, and repeatable assessment supports its role as an objective digital biomarker and makes it a promising tool for clinical research and neurological practice.
Author Roles
(1) Research project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the first draft, B. Review and Critique.
L.D.F.: 1A, 1B, 1C, 2A, 2B, 3A
M.F.Z.: 2B, 2C, 3B
A.G.: 1C,3B
A.S.R: 1C
P.S.C: 1C, 3B
J.A.G.: 1C, 3B
M.A.G.C.: 1A, 1B, 3A, 3B
P.G.R.: 1A, 1B, 3B
Disclosures
Ethical Compliance Statement: This study was approved by the Clinical Research Ethics Committee of the Consorci Sanitari del Maresme (CEIm 29/20). All participants in the three centers provided written informed consent prior to inclusion in the study. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.
Financial Disclosures and Conflicts of Interest: Author disclosures are available in the Supporting Information.
Supporting information
Figure S1. Study design, handwriting acquisition, and statistical analysis.
Section S1. Kinematic parameter extraction and analysis.
TABLE S1. Demographic and clinical characteristics of the participants
TABLE S2. Participant age by group and Patient disease duration
TABLE S3. Digital handwriting parameters analyzed
TABLE S4. Cross‐sectional comparison between Healthy Controls and De Novo PD Patients at baseline
TABLE S5. Intra‐subject comparison between De Novo PD Patients at baseline and after 7 weeks post treatment
TABLE S6. Intra‐subject comparison between PD Patients (OFF–ON states)
TABLE S7. Association between disease duration and digital handwriting parameters in PD Progression Patients
TABLE S8. Pearson correlation coefficients (r) between MDS‐UPDRS III total score and handwriting parameters
Data S1. COI_disclosure.
Acknowledgment
The authors gratefully acknowledge all participants for their contribution to this study.
Financial Disclosures and Conflicts of Interest: The authors declare no conflicts of interest relevant to this work.
LDF was supported by a grant from the Presidencia de la República Dominicana (Ministerio de la Juventud, Programa Internacional de Becas Idelisa Bonnelly 2024). This work was supported by Grant PID2023‐146644OB‐I00 funded by MICIU/AEI/10.13039/501100011033 and FEDER, EU (MFZ).
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1. Study design, handwriting acquisition, and statistical analysis.
Section S1. Kinematic parameter extraction and analysis.
TABLE S1. Demographic and clinical characteristics of the participants
TABLE S2. Participant age by group and Patient disease duration
TABLE S3. Digital handwriting parameters analyzed
TABLE S4. Cross‐sectional comparison between Healthy Controls and De Novo PD Patients at baseline
TABLE S5. Intra‐subject comparison between De Novo PD Patients at baseline and after 7 weeks post treatment
TABLE S6. Intra‐subject comparison between PD Patients (OFF–ON states)
TABLE S7. Association between disease duration and digital handwriting parameters in PD Progression Patients
TABLE S8. Pearson correlation coefficients (r) between MDS‐UPDRS III total score and handwriting parameters
Data S1. COI_disclosure.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.