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.

BackgroundLittle is known about the achievement of low density lipoprotein cholesterol (LDL-C) targets in patients at cardiovascular risk receiving stable lipid-lowering therapy (LLT) in countries outside Western Europe.MethodsThis cross-sectional observational study was conducted in 452 centres (August 2015−August 2016) in 18 countries in Eastern Europe, Asia, Africa, the Middle East and Latin America. Patients (n = 9049) treated for ≥3 months with any LLT and in whom an LDL-C measurement on stable LLT was available within the previous 12 months were included.ResultsThe mean±SD age was 60.2 ± 11.7 years, 55.0% of patients were men and the mean ± SD LDL-C value on LLT was 2.6 ± 1.3 mmol/L (101.0 ± 49.2 mg/dL). At enrolment, 97.9% of patients were receiving a statin (25.3% on high intensity treatment). Only 32.1% of the very high risk patients versus 51.9% of the high risk and 55.7% of the moderate risk patients achieved their LDL-C goals. On multivariable analysis, factors independently associated with not achieving LDL-C goals were no (versus lower dose) statin therapy, a higher (versus lower) dose of statin, statin intolerance, overweight and obesity, female sex, neurocognitive disorders, level of cardiovascular risk, LDL-C value unknown at diagnosis, high blood pressure and current smoking. Diabetes was associated with a lower risk of not achieving LDL-C goals.ConclusionsThese observational data suggest that the achievement of LDL-C goals is suboptimal in selected countries outside Western Europe. Efforts are needed to improve the management of patients using combination therapy and/or more intensive LLTs.

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