Background: We aimed to assess the effects of safinamide on depression, motor symptoms, and the serotonin syndrome related to its co-administration with antidepressants in patients with Parkinson’s disease (PD). Methods: We retrospectively analyzed the data of patients at 1 and 3 months of follow-up compared to baseline. Results: n = 82 (safinamide 50 mg = 22, 100 mg = 60, with antidepressants = 44). First, we found improvement in depression (Hamilton Depression Rating Scale: −6 ± 5.10 at 1 month and −7.27 ± 5.10 at 3 months, p < 0.0001; Patient Global Impression of Improvement Scale: 60.3% and 69.5% of patients at 1 and 3 months reported some improvement). Second, safinamide improved the daily life activities and motor symptoms/motor complications (Unified Parkinson’s Disease Rating Scale (UPDRS-II): −2.51 ± 6.30 and −2.47 ± 6.11 at 1 and 3 months, p < 0.0001; III: −3.58 ± 8.68 and −4.03 ± 8.95 at 1 and 3 months, p < 0.0001; IV: −0.61 ± 2.61 and −0.8 ± 2.53 at 1 and 3 months, p < 0.0001). Third, 7.31% and 8.53% of patients developed non-severe adverse events related to safinamide at 1 and 3 months. Serotonin syndrome was not observed in the patients treated with antidepressants; some isolated serotonin syndrome symptoms were reported. Conclusions: Safinamide could be useful for treating depression in PD; it was effective for motor symptoms and motor complications and safe even when co-administered with antidepressants.

This article provides an overview of recent research in Child-Computer Interaction with mobile devices and describe our framework ChildCI intended for: i) overcoming the lack of large-scale publicly available databases in the area, ii) generating a better understanding of the cognitive and neuromotor development of children along time, contrary to most previous studies in the literature focused on a single-session acquisition, and iii) enabling new applications in e-Learning and e-Health through the acquisition of additional information such as the school grades and childrens disorders, among others. Our framework includes a new mobile application, specific data acquisition protocols, and a first release of the ChildCI dataset (ChildCIdb v1), which is planned to be extended yearly to enable longitudinal studies. In our framework children interact with a tablet device, using both a pen stylus and the finger, performing different tasks that require different levels of neuromotor and cognitive skills. ChildCIdb is the first database in the literature that comprises more than 400 children from 18 months to 8 years old. Also, and as a demonstration of the potential of the ChildCI framework, we include experimental results for one of the many applications enabled by ChildCIdb: children age detection based on device interaction.

Objective: We aimed to analyze the real-world use of COMT inhibitors associated with levodopa in patients with Parkinson’s disease (PD) who present early fluctuations and to explore whether early COMT inhibition optimizes treatment outcomes. Methods: REONPARK is an ongoing 2-year prospective observational study. We included patients diagnosed with PD who presented signs of end-of-dose motor fluctuations for <2 years and started COMT inhibitors according to clinical practice. Outcomes included the clinician and patient global impression of change (CGI-C, PGI-C), the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), the Parkinson’s Disease Questionnaire-8 (PDQ-8), Non-Motor Symptoms Scale (NMSS), 19-Symptom Wearing-off Questionnaire (WOQ-19), and safety. We present a pre-planned interim analysis (cut-off date 3 July 2023) of patients who completed the first 3 months of follow-up. Results: Seventy patients were analyzed (mean levodopa dose at inclusion 484.8 mg; duration of motor fluctuations 0.6 years). In all cases, COMT inhibition was initiated with opicapone, and 81% maintained a stable levodopa dose at 3 months. After 3 months of treatment with opicapone, 73.5% and 62.8% of patients improved on CGI-C and PGI-C, respectively. MDS-UPDRS scores improved significantly with a mean change from baseline of −3.3 ± 7.7 (p < 0.001) for Part III and −1.3 ± 1.7 (p < 0.001) for Part IV. The mean OFF time decreased from 3.7 ± 2.6 h at baseline to 2.2 ± 2.3 h, and 20.6% of patients no longer experienced OFF periods. Patients experiencing no impact of fluctuations increased from 10% to 45.6%. Conclusions: In PD patients with early fluctuations, three months of opicapone reduced the OFF time and improved functional outcomes, suggesting potential benefits in the early stages.

This article proposes a novel children–computer interaction (CCI) approach for the task of age group detection. This approach focuses on the automatic analysis of the time series generated from the interaction of the children with mobile devices. In particular, we extract a set of 25 time series related to spatial, pressure, and kinematic information of the children interaction while colouring a tree through a pen stylus tablet, a specific test from the large-scale public ChildCIdb database. A complete analysis of the proposed approach is carried out using different time series selection techniques to choose the most discriminative ones for the age group detection task: (i) a statistical analysis and (ii) an automatic algorithm called sequential forward search (SFS). In addition, different classification algorithms such as dynamic time warping barycenter averaging (DBA) and hidden Markov models (HMM) are studied. Accuracy results over 85% are achieved, outperforming previous approaches in the literature and in more challenging age group conditions. Finally, the approach presented in this study can benefit many children-related applications, for example, towards an age-appropriate environment with the technology.

This article provides a comprehensive overview of recent research in the area of Child-Computer Interaction (CCI). The main contributions of the present article are two-fold. First, we present a novel longitudinal CCI database named ChildCIdbLong ^a , which comprises over 600 children aged 18 months to 8 years old, acquired continuously over 4 academic years (2019-2023). As a result, ChildCIdbLong comprises over 12K test acquisitions over a tablet device. Different tests are considered in ChildCIdbLong, requiring different touch and stylus gestures, enabling evaluation of skills like hand-eye coordination, fine motor skills, planning, and visual tracking, among others. In addition to the ChildCIdbLong database, we propose a novel quantitative metric called Test Quality (Q), designed to measure the motor and cognitive development of children through their interaction with a tablet device. In order to provide a better comprehension of the proposed Q metric, popular percentile-based growth representations are introduced for each test, providing a two-dimensional space to compare children’s development with respect to the typical age skills of the population. The results achieved in the present article highlight the potential of the novel ChildCIdbLong database in conjunction with the proposed Q metric to measure the motor and cognitive development of children as they grow up. The proposed framework could be very useful as an automatic tool to support child experts (e.g., paediatricians, educators, or neurologists) for early detection of potential physical/cognitive impairments during children’s development.