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Across the country there are many areas in which follow-up service provision for people diagnosed with functional neurological disorder (FND) is lacking. To address the absence of follow-up service provision in our own locality, we established an interdisciplinary psychoeducational group for people with FND. Here, we describe the intervention and present a preliminary analysis of outcome measures and feedback forms. Results of this analysis show promise, but further investigation is needed to better understand the group’s effectiveness.

OBJECTIVE: We recently demonstrated that 998 features derived from a simple 7-minute smartphone test could distinguish between controls, people with Parkinson's and people with idiopathic Rapid Eye Movement sleep behavior disorder, with mean sensitivity/specificity values of 84.6-91.9%. Here, we investigate whether the same smartphone features can be used to predict future clinically relevant outcomes in early Parkinson's. METHODS: A total of 237 participants with Parkinson's (mean (SD) disease duration 3.5 (2.2) years) in the Oxford Discovery cohort performed smartphone tests in clinic and at home. Each test assessed voice, balance, gait, reaction time, dexterity, rest, and postural tremor. In addition, standard motor, cognitive and functional assessments and questionnaires were administered in clinic. Machine learning algorithms were trained to predict the onset of clinical outcomes provided at the next 18-month follow-up visit using baseline smartphone recordings alone. The accuracy of model predictions was assessed using 10-fold and subject-wise cross validation schemes. RESULTS: Baseline smartphone tests predicted the new onset of falls, freezing, postural instability, cognitive impairment, and functional impairment at 18 months. For all outcome predictions AUC values were greater than 0.90 for 10-fold cross validation using all smartphone features. Using only the 30 most salient features, AUC values greater than 0.75 were obtained. INTERPRETATION: We demonstrate the ability to predict key future clinical outcomes using a simple smartphone test. This work has the potential to introduce individualized predictions to routine care, helping to target interventions to those most likely to benefit, with the aim of improving their outcome. ispartof: ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY vol:6 issue:8 pages:1498-1509 ispartof: location:United States status: published

Objective: We recently demonstrated that 998 features derived from a simple 7-minute smartphone test could distinguish between controls, people with Parkinson’s and people with idiopathic Rapid Eye Movement sleep behaviour disorder, with mean sensitivity/specificity values of 84.6-91.9%. Here we investigate whether the same smartphone features can be used to predict future clinically relevant outcomes in early Parkinson’s.Methods: 237 participants with Parkinson’s (mean (SD) disease duration 3.5 (2.2) years) in the Oxford Discovery cohort performed smartphone tests in clinic and at home. Each test assessed voice, balance, gait, reaction time, dexterity, rest and postural tremor. In addition, standard motor, cognitive and functional assessments and questionnaires were administered in clinic. Machine learning algorithms were trained to predict the onset of clinical outcomes provided at the next 18-month follow up visit using baseline smartphone recordings alone. The accuracy of model predictions was assessed using 10-fold and subject-wise cross validation schemes. Results: Baseline smartphone tests predicted the new onset of falls, freezing, postural instability, cognitive impairment and functional impairment at 18 months. For all outcome predictions AUC values were greater than 0.90 for 10-fold cross validation using all smartphone features. Using only the 30 most salient features, AUC values greater than 0.75 were obtained.Interpretation: We demonstrate the ability to predict key future clinical outcomes using a simple smartphone test. This work has the potential to introduce individualised predictions to routine care, helping to target interventions to those most likely to benefit, with the aim of improving their outcome.

ObjectivesRapid eye movement (REM) sleep behavior disorder (RBD) is the most specific marker of prodromal alpha-synucleinopathies. We sought to delineate the baseline clinical characteristics of RBD and evaluate risk stratification models.MethodsClinical assessments were performed in 171 RBD, 296 control, and 119 untreated Parkinson’s (PD) participants. Putative risk measures were assessed as predictors of prodromal neurodegeneration, and Movement Disorders Society (MDS) criteria for prodromal PD were applied. Participants were screened for common leucine-rich repeat kinase 2 (LRRK2)/glucocerebrosidase gene (GBA) gene mutations.ResultsCompared to controls, participants with RBD had higher rates of solvent exposure, head injury, smoking, obesity, and antidepressant use. GBA mutations were more common in RBD, but no LRRK2 mutations were found. RBD participants performed significantly worse than controls on Unified Parkinson’s Disease Rating Scale (UPDRS)-III, timed “get-up-and-go”, Flamingo test, Sniffin Sticks, and cognitive tests and had worse measures of constipation, quality of life (QOL), and orthostatic hypotension. For all these measures except UPDRS-III, RBD and PD participants were equally impaired. Depression, anxiety, and apathy were worse in RBD compared to PD participants. Stratification of people with RBD according to antidepressant use, obesity, and age altered the odds ratio (OR) of hyposmia compared to controls from 3.4 to 45.5. 74% (95% confidence interval [CI] 66%, 80%) of RBD participants met the MDS criteria for probable prodromal Parkinson’s compared to 0.3% (95% CI 0.009%, 2%) of controls.ConclusionsRBD are impaired across a range of clinical measures consistent with prodromal PD and suggestive of a more severe nonmotor subtype. Clinical risk stratification has the potential to select higher risk patients for neuroprotective interventions.

Background/Aims: Large epidemiological prospective studies represent an important opportunity for investigating risk factors for rare diseases such as Parkinson's disease (PD). Here we describe the procedures we used for ascertaining PD cases in the EPIC (European Prospective Investigation into Cancer and Nutrition) study. Methods: The following three-phase procedure was used: (1) elaboration of a NeuroEPIC4PD template for clinical data collection, (2) identification of all potential PD cases via record linkage and (3) validation of the diagnosis through clinical record revision, in a population of 220,494 subjects recruited in 7 European countries. All cases were labelled with the NeuroEPIC4PD diagnoses of ‘definite', ‘very likely', ‘probable', or ‘possible' PD. Results: A total of 881 PD cases were identified, with over 2,741,780 person-years of follow-up (199 definite, 275 very likely, 146 probable, and 261 possible). Of these, 734 were incident cases. The mean age at diagnosis was 67.9 years (SD 9.2) and 458 patients (52.0%) were men. Bradykinesia was the most frequent presenting motor sign (76.5%). Tremor-dominant and akinetic rigid forms of PD were the most common types of PD. A total of 289 patients (32.8%) were dead at the time of the last follow-up. Conclusions: This exercise proved that it is feasible to ascertain PD in large population-based cohort studies and offers a potential framework to be replicated in similar studies.