Nonmotor symptoms (NMS) in Parkinson's disease (PD) can precede onset of motor symptoms. Relationship between premotor symptoms onset and motor features is limited. Our aim is to describe the presence and perceived onset of NMS in PD as well as their possible association with motor phenotype. Presence and onset of NMS were assessed by a custom-made questionnaire in 109 newly diagnosed untreated PD patients and 107 controls from 11 Spanish and Austrian centers. Seventeen of thirty-one NMS were more common in patients than controls (P < 0.05). They were usually mild and frequently reported to occur at different time-spans before motor symptoms. Anhedonia, apathy, memory complaints, and inattention occurred more frequently during the 2-year premotor period. Those reported more frequently in the 2- to 10-year premotor period were smell loss, mood disturbances, taste loss, excessive sweating, fatigue, and pain. Constipation, dream-enacting behavior, excessive daytime sleepiness, and postprandial fullness were frequently perceived more than 10 years before motor symptoms. No correlation between NMS burden and motor severity, age, or gender was observed. NMS associated in four clusters: rapid eye movement sleep behavior disorder symptoms-constipation, cognition-related, mood-related, and sensory clusters. No cluster was associated with a specific motor phenotype or severity. NMS are common in early unmedicated PD and frequently reported to occur in the premotor period. They are generally mild, but a patient subgroup showed high NMS burden mainly resulting from cognition-related symptoms. Certain NMS when present at the time of assessment or in the premotor stage, either alone or in combination, allowed discriminating PD from controls.

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

The aim of this study was to analyze the efficacy of a cognitive training program on cognitive performance and quality of life in nondemented Parkinson's disease patients. Participants who met UK Brain Bank diagnosis criteria for Parkinson's disease, with I-III Hoehn & Yahr, aged 50-80, and nondemented (Mini-Mental State Examination ≥ 23) were recruited. Patient's cognitive performance and functional and quality-of-life measures were assessed with standardized neuropsychological tests and scales at baseline and after 4 weeks. Subjects were randomly and blindly allocated by age and premorbid intelligence (Vocabulary, Wechsler Adult Intelligence Scale-III) into 2 groups: an experimental group and a control group. The experimental group received 4 weeks of 3 weekly 45-minute sessions using multimedia software and paper-and-pencil cognitive exercises, and the control group received speech therapy. A total of 28 patients were analyzed. Compared with the control group participants (n = 12), the experimental group participants (n = 16) demonstrated improved performance in tests of attention, information processing speed, memory, visuospatial and visuoconstructive abilities, semantic verbal fluency, and executive functions. There were no observable benefits in self-reported quality of life or cognitive difficulties in activities of daily living. We concluded that intensive cognitive training may be a useful tool in the management of cognitive functions in Parkinson's disease. © 2011 Movement Disorder Society.

Freezing of gait (FoG) is one of the most disturbing and incapacitating symptoms in Parkinson's disease. It is defined as a sudden block in effective stepping, provoking anxiety, stress and falls. FoG is usually evaluated by means of different questionnaires; however, this method has shown to be not reliable, since it is subjective due to its dependence on patients’ and caregivers’ judgment. Several authors have analyzed the usage of MEMS inertial systems to detect FoG with the aim of objectively evaluating it. So far, specific methods based on accelerometer's frequency response has been employed in many works; nonetheless, since they have been developed and tested in laboratory conditions, their performance is commonly poor when being used at patients’ home. Therefore, this work proposes a new set of features that aims to detect FoG in real environments by using accelerometers. This set of features is compared with three previously reported approaches to detect FoG. The different feature sets are trained by means of several machine learning classifiers; furthermore, different window sizes are also evaluated. In addition, a greedy subset selection process is performed to reduce the computational load of the method and to enable a real-time implementation. Results show that the proposed method detects FoG at patients’ home with 91.7% and 87.4% of sensitivity and specificity, respectively, enhancing the results of former methods between a 5% and 11% and providing a more balanced rate of true positives and true negatives.Peer ReviewedPostprint (published version

Freezing of gait (FOG) is a common motor symptom of Parkinson's Disease (PD), which presents itself as an inability to initiate or continue gait. This paper presents a method to monitor FOG episodes based only on acceleration measurements obtained from a waist-worn device. Three approximations of this method are tested. Initially, FOG is directly detected by a support vector machine (SVM). Then, classifier's outputs are aggregated over time to determine a confidence value, which is used for the final classification of freezing (i.e. second and third approach). All variations are trained with signals of 15 patients and evaluated with signals from another 5 patients. Using a linear SVM kernel, the third approach outperforms results in the literature with 98.7% accuracy and a geometric mean of 96.1%. Moreover, it is investigated whether frequency features are enough to reliably detect FOG. Results show that these features allow the method to detect FOG with accuracies above 90% and that frequency features enable a reliable monitoring of FOG by using simply a waist sensor.