Rate of Progression in Activity and Participation Outcomes in Exercisers with Parkinson's Disease: A Five-Year Prospective Longitudinal Study

Functional improvement of patients with Parkinson syndromes using a rehabilitation training software

Introduction

Individuals with Parkinsonian disorders often face limited access to specialized physiotherapy and movement training due to staff shortages and increasing disease incidence, resulting in a rapid decline in mobility and feelings of despair. Addressing these challenges requires allocating adequate resources and implementing specialized training programs to ensure comprehensive care and support. Regarding these problems, a computer software was invented that might serve as an additional home-based extension to conventional physiotherapy.

Methods

The trial took place in a rehabilitation center where every patient received equivalent treatment apart from the training program that was set up to be investigated over 3 weeks. Seventy four Patients were included and randomized between two intervention and one control group. Intervention group 1 (IG1) trained with the computer-based system two times a week while Intervention group 2 (IG2) received five training sessions a week. Using the markerless Microsoft Kinect® camera, participants controlled a digital avatar with their own body movements. UPDRS-III and Clinical measurements were performed before and after the three-week period.

Results

Patients in all groups improved in UPDRS-III pre and post intervention whereas reduction rates were higher for IG1 (-10.89%) and IG2 (-14.04%) than for CG (-7.74%). Differences between the groups were not significant (value of ps CG/IG1 0.225, CG/IG2 0.347). Growth rates for the arm abduction angle were significantly higher in IG1 (11.6%) and IG2 (9.97%) than in CG (1.87%) (value of ps CG/IG1 0.006 and CG/IG2 0.018), as was the 5-steps-distance (CG 10.86% vs. IG1 24.5% vs. UG2 26.22%, value of ps CG/IG1 0.011 and CG/IG2 0.031).

Discussion

The study shows the beneficial effects of computer-based training and substantiates the assumption of a similar impact in a home-based setting. The utilized software is feasible for such interventions and meets with the patient's approval. Group dynamics seem to have an additional supporting effect for the aspired objective of improving mobility and should be seen as an essential aspect of video games in therapy.

The impact of Covid-19 restrictions in Ireland on symptom severity in mild to moderate Parkinson's disease

Introduction

The COVID pandemic and public health restrictions significantly impacted those living with neurological conditions such as Parkinson's Disease due to the curtailment of therapies. Patients attending a single centre movement disorders clinic reported reduced physical activity and quality of life during the pandemic. This study aimed to assess the impact of pandemic restrictions on Parkinson's Disease symptom severity in people with mild to moderate Parkinson's Disease.

Method

A cross-sectional study design with a convenience sample of 20 people living with mild to moderate Parkinson's Disease was adopted. A telephone survey questionnaire was completed to measure changes in symptom severity on the 14 most common Parkinson's Disease symptoms. Data were analysed using descriptive statistics.

Results

Nineteen participants completed the survey. Participants frequently reported a decline in nine symptoms of Parkinson's Disease; bradykinesia, rigidity, walking, sleep, mood, memory, quality of life and fatigue. Nil changes in freezing were reported. No change was reported in the nonmotor symptoms of constipation, speech and pain in 75, 65 and 95% of participants, respectively.

Conclusion

Findings of this study acknowledge the negative impact of restrictions on the motor and nonmotor symptoms of Parkinson's Disease. Flexibility to access and delivery of service should be considered to mitigate any future potential restrictions.

Community-based high-intensity cycling improves disease symptoms in individuals with Parkinson's disease: A six-month pragmatic observational study

Participation in supervised, laboratory-based aerobic exercise protocols holds promise in slowing the progression of Parkinson's disease (PD). Gaps remain regarding exercise adherence and effectiveness of laboratory protocols translated to community-based programs. The aim of the project was to monitor exercise behaviour and evaluate its effect on disease progression over a 6 month period in people with PD participating in a community-based Pedalling for Parkinson's (PFP) cycling program. A pragmatic, observational study design was utilised to monitor exercise behaviour at five community sites. The Movement Disorders Society-Unified Parkinson's disease Rating Scale Motor III (MDS-UPDRS-III) and other motor and non-motor outcomes were gathered at enrollment and following 6 months of exercise. Attendance, heart rate, and cadence data were collected for each exercise session. On average, people with PD (N = 41) attended nearly 65% of the offered PFP classes. Average percent of age-estimated maximum heart rate was 69.3 ± 11.9%; average cadence was 74.9 ± 9.0 rpms. The MDS-UPDRS III significantly decreased over the 6-month exercise period (37.2 ± 11.7 to 33.8 ± 11.7, p = 0.001) and immediate recall significantly improved (42.3 ± 12.4 to 47.1 ± 12.7, p = 0.02). Other motor and non-motor metrics did not exhibit significant improvement. Participants who attended ~74% or more of available PFP classes experienced the greatest improvement in MDS-UPDRS III scores; of those who attended less than 74% of classes, cycling greater than or equal to 76 rpms lead to  improvement. Attendance and exercise intensity data indicated that a laboratory-based exercise protocol can be successfully translated to a community setting. Consistent attendance and pedalling at a relatively high cadence may be key variables to PD symptom mitigation. Improvement in clinical ratings coupled with lack of motor and non-motor symptom progression over 6 months provides rationale for further investigation of the real-world, disease-modifying potential of aerobic exercise for people with PD.

Longitudinal clustering analysis and prediction of Parkinson's disease progression using radiomics and hybrid machine learning

BACKGROUND

We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson's disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features.

METHODS

We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms.

RESULTS

We identified 3 distinct progression trajectories. Hotelling's t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively.

CONCLUSIONS

This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data.