Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model.

Introduction: Accurately and objectively quantifying the clinical features of Parkinson's disease (PD) is crucial for assisting in diagnosis and guiding the formulation of treatment plans. Therefore, based on the data on multi-site motor features, this study aimed to develop an interpretable machine learning (ML) model for classifying the "OFF" and "ON" status of patients with PD, as well as to explore the motor features that are most associated with changes in clinical symptoms. Methods: We employed a support vector machine with a recursive feature elimination (SVM-RFE) algorithm to select promising motion features. Subsequently, 12 ML models were constructed based on these features, and we identified the model with the best classification performance. Then, we used the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model agnostic Explanations (LIME) methods to explain the model and rank the importance of those motor features. Results: A total of 96 patients were finally included in this study. The naive Bayes (NB) model had the highest classification performance (AUC = 0.956; sensitivity = 0.8947, 95% CI 0.6686-0.9870; accuracy = 0.8421, 95% CI 0.6875-0.9398). Based on the NB model, we analyzed the importance of eight motor features toward the classification results using the SHAP algorithm. The Gait: range of motion (RoM) Shank left (L) (degrees) [Mean] might be the most important motor feature for all classification horizons. Conclusion: The symptoms of PD could be objectively quantified. By utilizing suitable motor features to construct ML models, it became possible to intelligently identify whether patients with PD were in the "ON" or "OFF" status. The variations in these motor features were significantly correlated with improvement rates in patients' quality of life. In the future, they might act as objective digital biomarkers to elucidate the changes in symptoms observed in patients with PD and might be used to assist in the diagnosis and treatment of patients with PD.

Worsening of Parkinson's Disease After Termination of COVID-19 Quarantine Cannot Be Reversed Despite Resumption of Physiotherapy.

In a retrospective analysis, we recently reported findings on the detrimental motor effects of interrupted physiotherapy following the COVID-19 pandemic in parkinsonian patients. Using an extended follow-up period, we investigated the beneficial effect of reinstated physiotherapy on patients' disease severity and reversal of interruption-induced motor deterioration. Compared to before the COVID-19 outbreak, we observed persistence of motor disease worsening despite full resumption of state-of-the-art physical therapy suggesting that motor deterioration after discontinuation of physical therapy could not be compensated for. Therefore, and considering possible future crises, establishing means to safeguard continuation of physical therapy and to foster remote provision of care should be major goals.

Worsened Parkinson's Disease Progression: Impact of the COVID-19 Pandemic.

Whilst some studies investigated the impact of viral infection or reduced access to medication during the COVID-19 pandemic in patients with Parkinson's disease (PD), data on the effects of pandemic restrictions are still scarce. We retrospectively analyzed motor symptoms of longitudinally followed PD patients (n = 264) and compared motor disease progression before and during the COVID-19 pandemic. Additionally, we performed a trend analysis of the yearly evolution of motor symptoms in 755 patients from 2016 until 2021. We observed a worsening of motor symptoms and a significantly increased motor disease progression during pandemic-related restrictions as compared to before the COVID-19 outbreak.

Parkinson's Disease Multimodal Complex Treatment (PD-MCT): Analysis of Therapeutic Effects and Predictors for Improvement.

Parkinson's disease Multimodal Complex Treatment (PD-MCT) is a multidisciplinary inpatient treatment approach that has been demonstrated to improve motor function and quality of life in patients with Parkinson's disease (PD). In this study, we assessed the efficacy of PD-MCT and calculated predictors for improvement. We performed a prospective analysis in a non-randomized, open-label observational patient cohort. Study examinations were done at baseline (BL), at discharge after two-weeks of inpatient treatment (DC) and at a six-week follow-up examination (FU). Besides Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III as a primary outcome, motor performance was measured by the Timed Up-and-Go (TUG), the Berg Balance Scale (BBS) and the Perdue Pegboard Test (PPT). Until DC, motor performance improved significantly in several parameters and was largely maintained until FU (MDS-UPDRS III BL-to-DC: -4.7 ± 1.2 (SE) p = 0.0012, BL-to-FU: -6.1 ± 1.3 p = 0.0001; TUG BL-to-DC: -2.5 ± 0.9 p = 0.015, BL-to-FU: 2.4 ± 0.9 p = 0.027; BBS BL-to-DC: 2.4 ± 0.7 p = 0.003, BL-to-FU: 1.3 ± 0.7 p = 0.176, PPT BL-to-DC: 3.0 ± 0.5 p = 0.000004, BL-to-FU: 1.7 ± 0.7 p = 0.059). Overall, nontremor items were more therapy responsive than tremor items. Motor complications evaluated with MDS-UPDRS IV occurred significantly less frequent at DC (-1.8 ± 0.5 p = 0.002). Predictor analyses revealed an influence of initial motor impairment and disease severity on the treatment response in different motor aspects. In summary, we demonstrate a significant positive treatment effect of PD-MCT on motor function of PD patients which can be maintained in several parameters for an extended time period of six weeks and identify predictors for an improvement of motor function.