Search Results (133)

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder that impairs mobility. Treadmill training (TT) is a common rehabilitation strategy for improving gait parameters in individuals with PD. This systematic review evaluated the effectiveness of TT in improving motor function, walking ability, and overall functional mobility in PD patients. Methods: We compared TT to other forms of gait and motor rehabilitation, including conventional and robotic gait training. Trials that compared a treadmill training group with a non-intervention group were excluded from this review. We searched multiple databases for RCTs involving Parkinson’s patients until January 2025. The primary outcomes were motor function (UPDRS-III) and walking ability (6 MWT and TUG test). Results: We identified 285 articles; 199 were excluded after screening. We assessed the full text of 86 articles for eligibility, and 13 RCTs met the inclusion criteria. Some of them were included in the meta-analysis. The TT group showed a significant improvement in UPDRS-III scores [mean difference (MD): −1.36 (95% CI: −2.60 to −0.11)] and greater improvement in TUG performance [MD, −1.75 (95% CI: −2.69 to −0.81)]. No significant difference in walking capacity as assessed through the 6 MWT was observed [MD: 26.03 (95% CI: −6.72 to 58.77). Conclusions: The current study suggests that TT is effective in improving the motor symptoms and functional mobility associated with PD. Further studies are needed to develop protocols that consider the patients’ clinical characteristics, disease stage, exercise tolerance, and respiratory function. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder commonly marked by upper limb tremors that interfere with daily activities. Wearable devices, such as smartwatches, represent a promising solution for continuous and objective monitoring in PD. This study aimed to develop and validate a tremor-detection algorithm using smartwatch sensors. Data were collected from 21 individuals with PD and 27 healthy controls using both a commercial inertial measurement unit (G-Sensor, BTS Bioengineering, Italy) and a smartwatch (Apple Watch Series 3). Participants performed standardized arm movements while sensor signals were synchronized and processed to extract relevant features. Statistical analyses assessed discriminant and concurrent validity, reliability, and accuracy. The algorithm demonstrated moderate to strong correlations between smartwatch and commercial IMU data, effectively distinguishing individuals with PD from healthy controls showing associations with clinical measures, such as the MDS-UPDRS III. Reliability analysis demonstrated agreement between repeated measurements, although a proportional bias was noted. Power spectral density (PSD) analysis of accelerometer and gyroscope data along the x-axis successfully detected the presence of tremors. These findings support the use of smartwatches as a tool for detecting tremors in PD. However, further studies involving larger and more clinically impaired samples are needed to confirm the robustness and generalizability of these results. Full article

Background: Metabolic syndrome and Parkinson’s disease have common pathophysiological denominators. This study aimed to investigate how metabolic syndrome contributes to Parkinson’s disease progression, as well as the genetic traits shared by PD and MetS. Methods: Four hundred and twenty-three newly diagnosed drug-naïve PD patients were analyzed from the Parkinson’s Progression Markers Initiative (PPMI) database. We compared longitudinal changes in the total and subscale scores of the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) between PD patients with and without metabolic syndrome over a five-year follow-up. We assessed the frequency of PD-associated genetic variants in both groups. Results: At baseline, Parkinson’s patients with MetS were typically men (p < 0.01) and older (p = 0.04), with a higher Hoehn and Yahr score (p = 0.01) compared with their counterparts without MetS. They showed higher Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) total scores at baseline and in follow-up years 2, 3, 4, and 5 (all p-values < 0.05) as analyzed by the Generalized Estimating Equation model. These differences were primarily driven by elevated motor scores (MDS-UPDRS Part III) (p < 0.01). MetS was associated with a higher frequency of the ZNF646.KAT8.BCKDK_rs14235 variant and a lower frequency of the NUCKS1_rs823118 and CTSB_rs1293298 variants. Conclusions: PD patients with MetS had worse motor symptomatology. Both conditions appear to share genetic susceptibility, involving genes related to lipid metabolism (BCKDK), autophagy and inflammation (CTSB), and chromatin regulation (NUCKS1). Full article

The quantitative assessment of Parkinson’s disease (PD) is critical for guiding diagnosis, treatment, and rehabilitation. Conventional clinical evaluations—heavily dependent on manual rating scales such as the Unified Parkinson’s Disease Rating Scale (UPDRS)—are time-consuming and prone to inter-rater variability. In this study, we propose a fully automated UPDRS gait-scoring framework. Our method combines (a) surface electromyography (EMG) signals and (b) inertial measurement unit (IMU) data into a single deep learning model. Our end-to-end network comprises three specialized branches—a diagnosis head, an evaluation head, and a balance head—whose outputs are integrated via a customized fusion-detection module to emulate the multidimensional assessments performed by clinicians. We validated our system on 21 PD patients and healthy controls performing a simple walking task while wearing a four-channel EMG array on the lower limbs and 2 shank-mounted IMUs. It achieved a mean classification accuracy of 92.8% across UPDRS levels 0–2. This approach requires minimal subject effort and sensor setup, significantly cutting clinician workload associated with traditional UPDRS evaluations while improving objectivity. The results demonstrate the potential of wearable sensor-driven deep learning methods to deliver rapid, reliable PD gait assessment in both clinical and home settings. Full article

Background: One of the major contributors to homeostasis at the level of the central nervous system, specifically the brain, is the glymphatic system, which is described as an exchange occurring at the level of and between the interstitial fluid and cerebrospinal fluid that has been linked to neurodegenerative processes. Methods: Fourteen studies were included after PROSPERO registration and a literature search. Screening, reviewing, and data extraction were performed by two reviewers. Quality assessment scales were used. General continuous and subgroup analysis, heterogeneity tests, and random effect models were run using SPSS. Forest plots were constructed based on subgroup analysis. Results: Significant correlations (p < 0.05) were detected between MRI indices and outcomes quantifying neurodegenerative diseases. Studies on Alzheimer’s disease showed a positive correlation between diffusivity indices and cognitive scores. Studies on Parkinson’s disease showed negative correlations between diffusivity indices and disease severity, progression, and motor function (p < 0.05). As for other conditions, the conclusions remain uncertain, yet positive results were detected (p < 0.05). Conclusions: Positive significant correlations were deduced between the ALPS index and cognitive scores, indicating that low cognition is correlated with a low ALPS index and enlarged PVSs. Negative significant correlations were deduced between ALPS indices and UPDRS scores, indicating motor dysfunction is correlated with lower ALPS indices and enlarged PVSs. Finally, MRI parameters may help to deduce disease progression across subgroups. Despite the presence of heterogeneity between studies, significant correlations with moderate to large effect sizes were detected. Glymphatic dysfunction measured through MRI indices is correlated with neurodegenerative changes across various neurological conditions. Full article

Background/Objective: Parkinson’s disease (PD) is characterized by motor and cognitive impairments that significantly affect quality of life. Robotic-assisted therapies, such as the AMADEO^® system, have shown potential in rehabilitating upper limb function but are underexplored in PD. This study aimed to assess the effects of Robotic-Assisted Therapy (RAT) compared to Conventional Physical Therapy (CPT) on cognitive, motor, and functional outcomes in PD patients. Methods: A single-blind, randomized controlled trial was conducted with PD patients allocated to RAT or CPT. Participants were assessed at baseline (T0) and post-intervention (T1) using measures including MoCA, FAB, UPDRS-III, 9-Hole Peg Test, FMA-UE, FIM, and PDQ-39. Statistical analyses included ANCOVA and regression models. Results: RAT led to significant improvements in global cognition (MoCA, p < 0.001) and executive functioning (FAB, p = 0.0002) compared to CPT. Motor function improved, particularly in wrist and hand control (FMA-UE), whereas changes in fine motor dexterity (9-Hole Peg Test) were less consistent and did not reach robust significance. No significant improvements were observed in broader quality of life domains, depressive symptoms, or memory-related cognitive measures. However, quality of life improved significantly in the stigma subdomain of the PDQ-39 (p = 0.0075). Regression analyses showed that baseline motor impairment predicted cognitive outcomes. Conclusions: RAT demonstrated superior cognitive and motor benefits in PD patients compared to CPT. These results support the integration of robotic rehabilitation into PD management. Further studies with larger sample sizes and long-term follow-up are needed to validate these findings and assess their sustainability. Full article

Parkinson’s disease (PD) associated with GBA1 mutations—recently termed Sidransky syndrome—differs from idiopathic PD (iPD) by earlier onset, more rapid progression, and higher rates of non-motor symptoms. Our objective was to assess whether GBA1 mutations contribute to olfactory dysfunction in PD and in asymptomatic carriers of the mutation. We compared olfactory and motor functions in 119 participants: Sidransky syndrome (n = 18), iPD (n = 30), GBA1 variant carriers without PD (n = 21), Gaucher disease patients (n = 20), and healthy controls (n = 30). All were evaluated with the Brief Smell Identification Test (BSIT^®) and the motor part of the Movement Disorders Society Unified PD Rating Scale (MDS-mUPDRS). Mean age was 59.2 ± 11.7 years. Mean disease duration was 2.5 ± 2.2 years in Sidransky syndrome and 5.4 ± 4.9 years in iPD. We found that both PD groups had significantly lower BSIT^® scores than non-PD groups (p < 0.001), particularly for leather, smoke, natural gas, pineapple, clove, rose, and lemon. Sidransky syndrome patients scored lower than iPD patients (p = 0.04). No significant olfactory deficits were observed in GBA1 carriers or Gaucher patients without PD. We conclude that hyposmia is more pronounced in Sidransky syndrome than in iPD. However, normal olfaction in non-parkinsonian GBA1 carriers suggests that GBA1 variants alone do not account for olfactory loss in PD. Hyposmia likely reflects broader PD pathology rather than a direct effect of the GBA1 mutation. Full article

Parkinson’s disease (PD) is the second-most common neurodegenerative disease. With more than 20,000 new diagnosed cases each year, PD affects millions of individuals worldwide and is most prevalent in the elderly population. The current clinical methods for the diagnosis and severity assessment of PD rely on the visual and physical examination of subjects and identifying key disease motor signs and symptoms such as bradykinesia, rigidity, tremor, and postural instability. In the present study, we developed a method for the diagnosis and severity assessment of PD using Gramian Angular Fields (GAFs) in combination with deep Convolutional Neural Networks (CNNs). Our model was applied to PD gait signals captured using pressure sensors embedded into insoles. Our results indicated an accuracy of 98.6%, a true positive rate (TPR) of 99.2%, and a true negative rate (TNR) of 98.5%, showcasing superior classification performance for PD diagnosis compared to the methods used in recent studies in the literature. The estimation of disease severity scores using gait signals showed a high accuracy for the Hoehn and Yahr score as well as the Timed Up and Go (TUG) test score (R² > 0.8), while we achieved a lower prediction performance for the Unified Parkinson’s Disease Rating Scale (UPDRS) and its motor component (UPDRSM) scores (R² < 0.2). These results were achieved using gait signals recorded in time windows as small as 10 s, which may pave the way for shorter, more accessible assessment tools for diagnosis and severity assessment of PD. Full article

Background: Parkinson Disease (PD) is a progressive neurodegenerative disorder. Current therapeutic trials investigate treatments that can potentially modify the disease course. Testing their efficiency requires outcome assessments that are relevant to patients’ daily lives, which include gait and balance. Home-based examinations may enhance patient compliance and, in addition, produce more reliable results by assessing patients more regularly in their familiar surroundings. Objective: The objective of this pilot study was to assess the feasibility of a home-based outcome assessment designed to video record the Timed up and Go (vTUG) test via a study-specific smartphone app for patients with PD. Methods: 28 patients were recruited and asked to perform at home each week a set of three consecutive vTUG tests, over a period of 12 weeks using an app. The videos were subjected to a manual review to ascertain the durations of the individual vTUG phases, as well as to identify any errors or deviations in the setup that might have influenced the result. To evaluate the usability and user-friendliness of the vTUG and app, the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) were administered to patients at the study end. Results: 19 patients completed the 12-week study, 17 of which recorded 10 videos or more. A total of 706 vTUGs with complete timings were recorded. Random Forest Regression yielded “time to walk up” as the most important segment of the vTUG for predicting the total time. Variance of vTUG total time was significantly higher between weeks than it was between the three consecutive vTUGs at one time point [F(254,23) = 6.50, p < 0.001]. The correlation between vTUG total time and UPDRS III total score was weak (r = 0.24). The correlation between vTUG and a derived gait subscore (UPDRS III items 9–13) was moderate (r = 0.59). A linear mixed-effects model revealed a significant effect of patient-reported motion status on vTUG total time. Including additional variables such as UPDRS III gait subscore, footwear and chairs used further improved the model fit. Conclusions: Assessment of gait and balance by home-based vTUG is feasible. Factors influencing the read-out were identified and could be better controlled for future use and longitudinal trials. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms, with tremors being one of the most prominent. Traditional assessment methods, such as the Unified Parkinson’s Disease Rating Scale (UPDRS), rely on subjective, intermittent evaluations, which can miss symptom fluctuations. This study presents the development and validation of the ELENA system, an IoT-based wearable device designed for the continuous monitoring of tremors in PD patients and medication tracking in PD patients. Named in honor of a 67-year-old woman who has lived with Parkinson’s since 2011 and inspired the project, the ELENA system integrates an MPU6050 accelerometer, an ESP32 microcontroller, and cloud-based data analysis and MATLAB. The ELENA system was calibrated and validated against an Apple Watch, demonstrating high accuracy with frequency deviations under 0.5% and an average percentage error of −0.37%. Unlike commercial devices, ELENA offers a clinical-grade solution with customizable data access and visualization tailored for healthcare providers. Participants, including PD patients and a non-PD control group, completed a series of clinical tasks to evaluate tremor monitoring capabilities. The results showed that the system effectively captured tremor frequency and amplitude, enabling the analysis of resting, action, and postural tremors. This study highlights the ELENA system’s potential to enhance PD management by providing real-time, remote monitoring of tremors. The scalable, cost-effective solution supports healthcare professionals in tracking disease progression and optimizing treatment plans, paving the way for improved patient outcomes. Full article

Background/Objectives: Cognitive impairment (CI) in Parkinson’s disease (PD) is a major burden and significantly affects patients’ quality of life. Previous studies found that older age at onset and presence of the akinetic–rigid (AR) subtype are associated with an increased likelihood of CI in PD. The present study aimed to assess factors that are related to the development of CI in PD. Methods: Eighty-three PD patients were consecutively recruited. Demographic information, clinical details, Montreal cognitive assessment (MoCA), Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), walking speed, and instrumental activity of daily living (IADL) were assessed. Resting motor threshold (rMT), was also assessed for subgroup of patients with versus without cognitive impairment. Results: According to the MoCA cut-off score of 26, 45 had PD without CI (PD-NCI) (54.22%) and 38 cases (45.78%) had PD with CI (PD-CI). The age and age at onset were significantly older in the PD-CI group (p = 0.006 and 0.018, respectively). The patients were reclassified into AR and tremor-dominant (TR) phenotype. PD-CI patients were more likely to have the AR (81.6%). Walking speed, MDS-UPDRS score, and IADL scores were significantly worse in PD-CI than in PD-NCI. Stepwise linear regression analysis of risk factors associated CI revealed that higher MDS-UPDRS scores, later age of onset, and higher rMT values were considered risk factors for developing CI. Conclusions: Higher UPDRS score, later age of onset, and higher rMT values were considered as risk factors associated CI in PD patients and provide valuable insights for further investigation and potential clinical considerations. Full article

The clinical diagnosis of Parkinson’s disease (PD) primarily relies on clinician-administered observational assessment tools, such as the Unified Parkinson’s Disease Rating Scale (UPDRS). However, these approaches are significantly influenced by subjectivity and exhibit insufficient sensitivity for early-stage symptom detection. The introduction of deep learning techniques has opened new avenues for the early diagnosis of PD. In contrast to traditional methods, deep learning models are capable of processing large-scale, high-dimensional, and complex datasets to automatically learn latent feature relationships, making them particularly suitable for scenarios involving multimodal data fusion. The multimodal diagnosis of PD is confronted with two enduring challenges: (1) the dependence on pre-existing knowledge of established genetic risk loci, and (2) the low efficiency and limited interpretability in handling interactions among cross-modal features. To address these challenges, this study introduces an innovative multimodal deep learning framework with two primary contributions: (1) a Genome-Wide Association Study (GWAS)-Transformer architecture that autonomously selects single nucleotide polymorphism (SNP) features through GWAS and utilizes a multi-head attention mechanism to model potential associations between non-risk loci, thereby eliminating the reliance on known susceptibility genes; (2) a Residual Attention Contrastive Fusion (RACF) module that tackles the heterogeneity of cross-modal features by dynamically allocating attention weights and applying contrastive loss constraints. Evaluation results on the Parkinson’s Progression Markers Initiative (PPMI) dataset demonstrate that our model achieves a classification accuracy of 91.2% and an AUC of 0.94, and predicts nine potential novel risk loci. This work presents a novel paradigm for the discovery of new risk loci based on deep learning and offers valuable insights from a multi-omics perspective for advancing PD research. Full article

Physical rehabilitation complements the treatment of Parkinson’s disease (PD). The applied physical exercises are effective in PD by promoting activity-dependent neuroplasticity. The main aim of this study was to assess the effect of a 16-week moderate-intensity functional physical rehabilitation program (FPR) on the concentration of mature brain-derived neurotrophic factor (BDNF) and its precursor (proBDNF) in blood serum and the severity of symptoms and quality of life in people with PD. People with PD (Hoehn and Yahr stage 3) were randomly assigned to the experimental (FPR) and control (CG) groups. FPR participated in movement training to improve functional mobility, motor coordination, and balance. Pre- and post-intervention assessments included serum levels of proBDNF, mature BDNF, MDS-UPDRS sub-scales, and the PDQ-39 quality of life measured. In the FPR group, a statistically significant increase in serum proBDNF levels by 39.42% (p = 0.006) was observed, as well as an improvement in motor and non-motor aspects of daily functioning, motor complications, and overall quality of life. No statistically significant changes in BDNF levels were observed. The results indicate that moderately intensive FPR enhances neurotrophic mechanisms, primarily through regulating proBDNF and improving motor functions and quality of life in patients with PD. The results underline the potential of targeted rehabilitation programs to increase neuroplasticity and improve clinical outcomes in PD. Full article

Background and Objectives: Robotic exoskeletons show potential in PD gait rehabilitation. But the optimal assistive force and its equivalence to clinical gold standard assessments are unclear. This study aims to explore the clinical equivalence of the lower limb exoskeleton in evaluating PD patients’ gait disorders and find the best assistive force for clinical use. Methods: In this prospective controlled trial, 60 PD patients (Hoehn and Yahr stages 2–4) and 60 age-matched controls underwent quantitative gait analysis using a portable exoskeleton (Relink-ANK-1BM) at four assistive force levels (0 N, 40 N, 80 N, 120 N). Data from 57 patients and 57 controls were analyzed with GraphPad Prism 10. Different statistical tests were used based on data distribution. Results: ROC analysis showed that exoskeleton-measured velocity had the strongest power to distinguish PD patients from controls (AUC = 0.9198, p < 0.001). Other parameters also had high reliability and validity. There was a strong positive correlation between UPDRS-III lower extremity sub-score changes and gait velocity changes in PD patients (r = 0.8564, p < 0.001). The 80 N assistive force led to the best gait rehabilitation, with a 58% increase in gait velocity compared to unassisted walking (p < 0.001). Conclusions: 80 N is the optimal assistive threshold for PD gait rehabilitation. The wearable lower limb exoskeleton can be an objective alternative biomarker to UPDRS-III, enabling personalized home-based rehabilitation. Full article

Background/Objectives: Parkinson’s disease (PD) and hypertension are often coexistent conditions that interact in entwined ways at various levels. Cardiovascular autonomic dysfunction (CAD), a non-motor feature of PD occurring across all stages, alters blood pressure (BP) regulation. Methods: We conducted a cross-sectional study enrolling patients with PD and primary hypertension, without diabetes mellitus or other causes of secondary CAD, aiming to characterize BP profiles/patterns by ambulatory BP monitoring. We also sought associations between different CAD phenotypes and PD characteristics, disability, and cardiovascular comorbidities. Results: We included 47 patients with a median age of 71 years, PD duration of 9 years, and Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III score of 40. Diurnal and nocturnal BP values were within the reference range, but BP load was excessive. Almost one-third had neurogenic orthostatic hypotension (OH) and 80% were non-dippers. The overall burden of non-motor symptoms was significant in these phenotypes. Patients with neurogenic OH were more prone to constipation, anxiety, and urinary problems, whereas gustatory dysfunction, loss of libido, and erectile dysfunction were more frequently reported by non-dippers. No significant differences with regard to cognitive decline were identified in subjects with and without neurogenic OH. Neurogenic OH was symptomatic in 78% of the cases, whereas 56% of those with orthostatic symptoms did not have OH at repeated measurements. Conclusions: Neurogenic OH is an independent predictor of disability in patients with PD and hypertension, after adjusting for PD duration, Hoehn and Yahr stage, levodopa equivalent daily dose (LEDD), and Montreal Cognitive Assessment (MoCA) score. Full article