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We read with interest the article by De Vleeschhauwer et al1 about touchscreen skills in patients with Parkinson’s disease (PD). We acknowledge that this pioneer and exhaustive effort highlights precisely some of the main difficulties that these patients encounter while interacting with touchscreen devices. One of these examples is the prolonged duration of the sliding tasks performed by the patients. The article reveals that the patients’ accuracy and their intertap interval is similar to those shown by participants without PD. We achieved comparable results in previous research. In previous research, we designed a pilot study focusing on routine outpatient clinics by a trial with a prototype smartphone application (previously used by patients with essential tremor).2 A total of 18 patients with PD and 22 controls matched by age and sex (Table 1) were recruited. They performed 5 repetitions of 4 simple tests that were designed to be common touchscreen activities when using smartphones (further methodological details can be found in Supplementary Material S1). Despite the different methodologies and devices used, both studies shared small sample sizes and easy reaching, sliding, and tapping tasks. In both studies, patients with PD achieved similar accuracy and a similar intertap interval time (Table 1) to healthy participants. In contrast to De Vleeschhauwer et al, we did not find significant differences in the duration test performance between patients with PD and controls. This could be explained by the limited statistical power, more recent onset of the disease, the milder severity of our patients, and fewer repetitions of the tasks and the fact that the most complex sliding tasks were not performed. In addition, there were differences in the touchscreen interface settings3 concerning the type of device, software, button size, and the double-tapping act. We consider that the approach of De Vleeschhauwer et al dealing with more repetitions of the tasks makes the b

Wearable technology used in Parkinson's disease (PD) research has become an increasing focus of interest in this field. Our group assessed the feasibility, clinical correlation, reliability, and acceptance of smartwatches in order to quantify arm resting tremors in PD patients. An Android application on a smartwatch was used to obtain raw data from the smartwatch's gyroscopes. Twenty-two PD patients were consecutively recruited and followed for 1 year. Arm rest tremors were video filmed and scored by two independent raters using the motor subscale of the Unified Parkinson's Disease Rating Scale (UPDRS-III). The tremor intensity parameter was defined by the root mean square of the angular speed measured by the smartwatch at the wrist. Sixty-four smartwatch evaluations were completed. The Spearman coefficient among the mean of the resting tremor (UPDRS-III) scores and smartwatch measurements for tremor intensity was 0.81 (p < .001); smartwatch reliability to quantify tremors was checked by intraclass reliability coefficient with a resting tremor = 0.89, minimum detectable change = 59.03%. Good acceptance of the system was shown. Smartwatch use for PD tremor analysis is possible, reliable, well-correlated with clinical scores, and well-accepted by patients for clinical follow-up. The results from these experiments suggest that this commodity hardware has the potential to quantify PD patients' tremors objectively in a consulting-room.

Las nuevas tecnologías para la medición objetiva y precisa delos trastornos del movimiento, en general, y, del temblor esencial (TE) en particular, están revolucionando el manejo de estas enfermedades. Los principales campos tecnológicos emergentes en esta área son dos. El primero está representado por los dispositivos portátiles o wearables que utilizan sistemas de medida inercial (IMUs, de su acrónimo en inglés). Los IMUs integran acelerómetros y giróscopos que proporcionan medidas de aceleración lineal y angular, respectivamente, de las diferentes partes del cuerpo donde se colocan y sirven sobre todo para monitorizar o diagnosticar. El segundo aspecto tecnológico emergente son las aplicaciones y dispositivos diseñados ad hoc que pretenden facilitar la interacción con dispositivos electrónicos de los pacientes con temblor así como controlar físicamente el temblor. Sus utilidades van desdela monitorización o el registro objetivo de temblor, hasta la asistencia de los pacientes con TE a la hora de interactuar con pantallas, e incluso terapéuticas, contrarrestando mecánicamente el temblor..., New technologies for the objective measurement of movement disorders, particularly for essential tremor (ET), are disrupting the clinical management of these conditions. The main emerging fields are two. The first relates to wearable devices with embedded inertial measurement units (IMUs).These IMUs are composed by accelerometers and gyroscopes that register linear and angular accelerations, respectively, from the different body partsw here they are placed to monitor and diagnose a disease. The second field attains to the development of ad hoc applications and devices designed to helptremor patient access and interact with electronic devices as well as controlling physically tremor. These applications span from the continuous objective recording of tremor, to the assistance of ET patients to interact with touchscreen devices, and even the mechanical reduction of tremor...

[ES] El temblor esencial (ET) es una uno de los desórdenes motrices más prevalentes en adultos, con tratamientos poco efectivos en la mitad de los casos. En este documento, estudiamos si la estimulación mecánica aferente puede reducir el temblor en pacientes de ET al estimular selectivamente las vías aferentes. Para ello, hemos diseñado una plataforma que permite estimular la mano y el antebrazo del paciente utilizando actuadores piezoeléctricos mientras monitorizamos el temblor mediante sensores inerciales. Hemos analizado el efecto de cuatro estrategias de estimulación, incluyendo estimulación a frecuencia constante y variable, en 18 pacientes de ET. A pesar de que no existe una clara respuesta a la vibración ante las diferentes estrategias, en la mayoría de los casos (50-72%) la estimulación mecánica se asocia a un aumento en la amplitud del temblor. Por el contrario, el temblor se reduce entre el 5 y el 12% de los pacientes. Sin embargo, estos resultados son difíciles de interpretar debido a la variabilidad intrínseca del temblor: durante ensayos de la misma duración, el temblor varió significativamente en el 67% de los pacientes (aumentando en el 45% y disminuyendo en el 22%). Por tanto, la estimulación mecánica de la extremidad no tiene un efecto sistemático en el temblor propio del Temblor Esencial., [EN] In spite of being one of the major causes of disability, Essential Tremor (ET) is not effectively handled in 50% of cases. We have hypothesised that tremor in ET could be reduced by recruiting afferent pathways through mechanical stimulation. We have designed a platform to stimulate the forearm and hand of the patient with piezoelectric actuators while the tremor is measured using inertial sensors. We have studied the effect of four different stimulation strategies in 18 ET patients, each stimulation strategy was characterized by the frequency of the vibration. Although the response to stimulation was very heterogeneous across patients and strategies, the majority of patients increased their tremor (between 50% and 72%, depending on the strategy), while a very reduced proportion (between 5 and 22%) of patients decreased their tremor amplitude. However, we considered that these results were difficult to explain because the high variability of the tremor that we have assessed: during equally long trials without stimulation, tremor increased significantly in 45% of patients and decreased in 22% of them. Therefore, we claim that mechanical vibration does not have a consistent and homogenous effect on ET tremor.

Essential tremor (ET) is a major cause of disability and is not efectively managed in half of the patients. We investigated whether mechanical vibration could reduce tremor in ET by selectively recruiting aferent pathways. We used piezoelectric actuators to deliver vibratory stimuli to the hand and forearm during long trials (4min), while we monitored the tremor using inertial sensors. We analyzed the efect of four stimulation strategies, including diferent constant and variable vibration frequencies, in 18 ET patients. Although there was not a clear homogeneous response to vibration across patients and strategies, in most cases (50–72%) mechanical vibration was associated with an increase in the amplitude of their tremor. In contrast, the tremor was reduced in 5–22% of the patients, depending on the strategy. However, these results are hard to interpret given the intrinsic variability of the tremor: during equally long trials without vibration, the tremor changed signifcantly in 67% of the patients (increased in 45%; decreased in 22%). We conclude that mechanical vibration of the limb does not have a systematic efect on tremor in ET. Moreover, the observed intrinsic variability of the tremor should be taken into account when designing future experiments to assess tremor in ET and how it responds to any intervention.

Mobile technology is opening a wide range of opportunities for transforming the standard of care for chronic disorders. Using smartphones as tools for longitudinally tracking symptoms could enable personalization of drug regimens and improve patient monitoring. Parkinson's disease (PD) is an ideal candidate for these tools. At present, evaluation of PD signs requires trained experts to quantify motor impairment in the clinic, limiting the frequency and quality of the information available for understanding the status and progression of the disease. Mobile technology can help clinical decision making by completing the information of motor status between hospital visits. This paper presents an algorithm to detect PD by analyzing the typing activity on smartphones independently of the content of the typed text. We propose a set of touchscreen typing features based on a covariance, skewness, and kurtosis analysis of the timing information of the data to capture PD motor signs. We tested these features, both independently and in a multivariate framework, in a population of 21 PD and 23 control subjects, achieving a sensitivity/specificity of 0.81/0.81 for the best performing feature and 0.73/0.84 for the best multivariate method. The results of the alternating finger-tapping, an established motor test, measured in our cohort are 0.75/0.78. This paper contributes to the development of a home-based, high-compliance, and high-frequency PD motor test by analysis of routine typing on touchscreens.

[Resumen] Este documento describe una plataforma para analizar los efectos de la estimulación mecánica aferente en pacientes con Temblor Esencial (TE). El temblor esencial es una enfermedad caracterizada por provocar temblor bilateral, cinético y postural en antebrazos y manos del paciente. El propósito de la plataforma descrita es esclarecer el papel que juegan determinadas estructuras cerebrales en la génesis del temblor. Para ello se utilizan estimuladores piezoeléctricos capaces de generar una vibración mecánica controlada que estimule los corpúsculos de Paccini del paciente, así como sensores inerciales encargados de monitorizar los efectos que tiene esta estimulación en el temblor de los pacientes. El protocolo propuesto para seguir en los ensayos experimentales, así como el análisis de datos posterior son descritos también en este documento.

[Abstract] This paper presents a classification model for the automatic quantification of tremor severity in patients with essential tremor (ET). The system is based on the signals measured by two commercial smartwatches that the patients wear on their wrist and ankle. The smartwatches register acceleration and angular velocity in these body segments. A set of nine tremor features were used to train the classification algorithm. The proposed algorithm is based on a C4.5 decision tree classifier. It is able to assess rest and kinetic (postural or action) tremor. The method was evaluated using data collected from thirty-four patients with ET. The algorithm classifies the severity of tremor in five levels 0-4 corresponding to those in the Fahn-Tolosa-Marin tremor rating scale with a 94% accuracy. The method can be implemented in a networked platform for the remote monitoring and assessment of movement disorders such as ET or Parkinson’s disease.