Marco Falletti

Background/Objectives: Parkinson’s disease (PD) is clinically heterogeneous, and motor complications are often inadequately captured during routine outpatient assessments. In this cross-sectional observational study, we investigated whether prolonged home-based wearable monitoring could enable digital motor profiling by identifying clinically meaningful patient subgroups through an unsupervised analytical approach. Methods: Seventy-six patients with PD underwent standardized clinical evaluation followed by prolonged home-based monitoring using a single waist-worn inertial sensor. Wearable-derived measures of gait, mobility, and complex motor manifestations, including dyskinesia and freezing of gait (FOG), were analyzed using an unsupervised clustering approach based exclusively on sensor-derived variables. The resulting subgroups were subsequently characterized using clinical and digital outcomes, and cluster stability and interpretability were examined through bootstrap and complementary machine-learning analyses. Results: Unsupervised clustering identified two distinct digital motor profiles that differed primarily in the burden of motor complications rather than overall disease severity. Although disease duration, Hoehn and Yahr stage, and clinical motor phenotype did not differ significantly, patients in one subgroup exhibited significantly greater dyskinesia, motor fluctuations, and dopaminergic treatment requirements, accompanied by a distinct sensor-derived motor profile during daily life. Dyskinesia-related metrics emerged as the strongest contributors to profile discrimination, and the identified clusters were reproduced with high cross-validated agreement by the surrogate classification model. Conclusions: Prolonged home-based wearable monitoring may support clinically meaningful digital motor profiling in PD. Beyond conventional clinical staging, this approach can identify patients with different burdens of motor complications under real-world conditions. Following appropriate validation, objective digital biomarkers could support patient stratification, longitudinal monitoring, and treatment optimization.

Background/Objectives: Spatio-temporal gait parameters have been proposed as surrogate markers for objective, remote monitoring of global motor status in Parkinson’s disease (PD). Our observational, cross-sectional pilot study tested whether gait metrics, derived from wearable sensors, reflect dopaminergic responsiveness in both axial and appendicular functions. Methods: Twenty-two PD patients were evaluated both under and not under L-Dopa (ON and OFF states, respectively). Motor performance was assessed using wearable inertial sensors during standardized tasks involving gait and upper/lower limb movements. From the recorded kinematics, measures of movement amplitude, speed, rhythm, and consistency were extracted, and dopaminergic response was compared in appendicular and axial functions. Results: Treatment effects were more pronounced on the more affected body side. Improvements in appendicular amplitude, speed, and consistency closely matched those observed in spatio-temporal gait parameters. In contrast, rhythm measures displayed a divergent pattern, with reduced gait cadence but increased hand movement frequency, showing an inverse correlation. No significant correlations emerged between axial and appendicular domains for amplitude, velocity, or consistency, whereas improvements in step length and gait velocity were associated with MDS-UPDRS III motor scores. Conclusions: These findings overall suggest that specific gait metrics, particularly those reflecting amplitude and velocity, may provide reliable, sensor-based indicators of overall motor status in PD, supporting their use in remote monitoring.

Background: Parkinson's disease (PD) leads to handwriting abnormalities primarily characterized by micrographia. Whether micrographia manifests early in PD, worsens throughout the disease, and lastly responds to L‐Dopa is still under scientific debate. Objectives: We investigated the onset, progression and L‐Dopa responsiveness of micrographia in PD, by applying a non‐invasive and cheap tool of artificial intelligence‐ (AI)‐based pen‐and‐paper handwriting analysis. Methods: Fifty‐seven PD undergoing chronic L‐Dopa treatment were enrolled, including 30 early‐stage (H&Y ≤ 2) and 27 mid‐advanced stage (H&Y > 2) patients, alongside 25 age‐ and sex‐matched controls. Participants completed two standardized pen‐and‐paper handwriting tasks in an ecological scenario. Handwriting samples were examined through clinically‐based (ie, perceptual) and AI‐based (ie, automatic) procedures. Both consistent (ie, average stroke size) and progressive (ie, sequential changes in stroke size) micrographia were evaluated. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the convolutional neural network (CNN) in classifying handwriting in PD and controls. Results: Clinically‐ and AI‐based analysis revealed a general reduction in stroke size in PD supporting the concept of parkinsonian micrographia. Compared with perceptual analysis, AI‐based analysis clarified that micrographia manifests early during the disease, progressively worsens and poorly responds to L‐Dopa. The AI models achieved high accuracy in distinguishing PD patients from controls (91%), and moderate accuracy in differentiating early from mid‐advanced PD (77%). Lastly, the AI model was not able to detect patients in OFF and ON states. Conclusions: AI‐based handwriting analysis is a valuable non‐invasive and cheap tool for detecting and quantifying micrographia in PD, for telemedicine purposes.

Normal pressure hydrocephalus (NPH) is a recognized cause of reversible cognitive and motor decline, with gait and balance impairments often emerging early. Technologies providing gait and balance measures can aid in early detection, diagnosis, and prognosis of the disease. This systematic review comprehensively discusses previous studies on the instrumental assessment of gait and balance in NPH. A PubMed search following PRISMA guidelines identified studies published between 2000 and 2024 that used laboratory instruments to assess gait and balance in NPH. Studies underwent quality assessment for internal, statistical, and external validity. Methodological details such as motor tasks, instruments, analytical approaches, and main findings were summarized. Overall, this review includes 41 studies on gait and 17 on balance, most of which used observational, cross-sectional designs. These studies employed various tools, such as pressure-sensitive platforms, optoelectronic motion-capture systems, and wearable inertial sensors. Significant differences in kinematic measures of gait and balance have been found in NPH patients compared to healthy controls and individuals with other neurological conditions. Finally, this review explores potential pathophysiological mechanisms underlying the kinematic changes in gait and balance in NPH and emphasizes the absence of longitudinal data, which hinders drawing definitive conclusions for prognostic purposes.

Background: Quantitative evidence of levodopa‐induced beneficial effects on parkinsonian rigidity in Parkinson's disease (PD) is lacking. Recent research has demonstrated the velocity‐dependent nature of objective rigidity in PD and revealed its neural underpinning.Objective: The present study aimed to examine the effect of levodopa on objective rigidity in PD.Methods: Eighteen patients with PD underwent clinical and instrumental evaluations of muscle tone in the OFF and ON states. The clinical assessments focused on rigidity in the most affected upper limb. The biomechanical components of objective rigidity were assessed using robot‐assisted wrist extensions at seven angular velocities (5–280°/s). Surface electromyography of the flexor carpi radialis muscle enabled the concurrent evaluation of short‐ and long‐latency stretch reflexes (SLR and LLR).Results: Levodopa improved the clinical scores of rigidity. Biomechanical measurements showed that levodopa reduced the total and neural components of force but had no effect on viscoelastic components. Levodopa reduced the velocity dependence of the LLRs but did not affect the SLRs. Finally, we found significant clinical–instrumental correlations between levodopa‐induced changes and biomechanical and neurophysiological measures of objective rigidity in PD.Conclusions: Levodopa improved objective rigidity in PD by decreasing its biomechanical neural component as well as the size of LLRs. The beneficial effect of levodopa on biomechanical and neurophysiological features of objective rigidity was related to the specific angular velocity of wrist extensions; that is, the higher the angular velocity, the greater the beneficial impact of levodopa on objective rigidity. These findings allowed the description of a new pathophysiological model of rigidity in PD. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Although rigidity is a cardinal motor sign in patients with Parkinson’s disease (PD), the instrumental measurement of this clinical phenomenon is largely lacking, and its pathophysiological underpinning remains still unclear. Further advances in the field would require innovative methodological approaches able to measure parkinsonian rigidity objectively, discriminate the different biomechanical sources of muscle tone (neural or visco-elastic components), and finally clarify the contribution to objective rigidity exerted by neurophysiological responses which have been previously associated with this clinical sign (i.e., the long-latency stretch-induced reflex). Twenty patients with PD (67.3 ± 6.9 years) and 25 age- and sex-matched controls (66.9 ± 7.4 years) were recruited. Rigidity was measured clinically and through a robotic device. Participants underwent robot-assisted wrist extensions at 7 different angular velocities randomly applied, when ON therapy. For each value of angular velocity, several biomechanical (i.e., elastic, viscous and neural components) and neurophysiologic measures (i.e., short- and long-latency reflex and shortening reaction) were synchronously assessed and correlated with the clinical score of rigidity (i.e., Unified Parkinson’s Disease Rating Scale – part III subitems for the upper limb). The biomechanical investigation allowed us to measure objective rigidity in PD and estimate the neuronal source of this phenomenon. In patients, objective rigidity progressively increased along with the rise of angular velocities during robot-assisted wrist extensions. The neurophysiological examination disclosed increased long-latency reflexes, but not short-latency reflexes nor shortening reaction, in PD compared with controls. Long-latency reflexes progressively increased according to angular velocities only in patients with PD. Lastly, specific biomechanical and neurophysiological abnormalities correlated with the clinical score of rigidity. Objective rigidity in PD correlates with velocity-dependent abnormal neuronal activity. The observations overall (i.e., the velocity-dependent feature of biomechanical and neurophysiological measures of objective rigidity) would point to a putative subcortical network responsible for objective rigidity in PD which requires further investigation.