Background: Parkinson’s disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether balance and gait variables obtained with wearable inertial motion sensors can be utilized to differentiate between PD and ET using machine learning. Additionally, we compared classification performances of several machine learning models.Methods: This retrospective study included balance and gait variables collected during the instrumented stand and walk test from people with PD (n = 524) and with ET (n = 43). Performance of several machine learning techniques including neural networks, support vector machine, k-nearest neighbor, decision tree, random forest, and gradient boosting, were compared with a dummy model or logistic regression using F1-scores.Results: Machine learning models classified PD and ET based on balance and gait characteristics better than the dummy model (F1-score = 0.48) or logistic regression (F1-score = 0.53). The highest F1-score was 0.61 of neural network, followed by 0.59 of gradient boosting, 0.56 of random forest, 0.55 of support vector machine, 0.53 of decision tree, and 0.49 of k-nearest neighbor.Conclusions: This study demonstrated the utility of machine learning models to classify different movement disorders based on balance and gait characteristics collected from wearable sensors. Future studies using a well-balanced data set are needed to confirm the potential clinical utility of machine learning models to discern between PD and ET.

Some of the most debilitating symptoms of fibromyalgia (FM) include widespread chronic pain, sleep disturbances, chronic fatigue, anxiety, and depression. Yet, there is a lack of effective self-management exercise interventions capable of alleviating FM symptoms. The objective of this study is to examine the efficacy of a 10-week daily Qigong, a mind-body intervention program, on FM symptoms. 20 participants with FM were randomly assigned to Qigong (experimental) or sham-Qigong (control) groups, with participants blinded to the intervention allocation. The Qigong group practiced mild body movements synchronized with deep diaphragmatic breathing and meditation. The sham-Qigong group practiced only mild body movements. Both groups practiced the interventions two times per day at home, plus one weekly group practice session with a Qigong instructor. Primary outcomes were: pain changes measured by the Short-Form McGill Pain Questionnaire, a visual analog scale for pain, pressure pain threshold measured by a dolorimeter. Secondary outcomes were: the Revised Fibromyalgia Impact Questionnaire the Pittsburgh Sleep Quality Index, the Hospital Anxiety and Depression Scale and the Quality of Life Scale. The experimental group experienced greater clinical improvements when compared to the control group on the mean score differences of pain, sleep quality, chronic fatigue, anxiety, depression, and fibromyalgia impact, all being statistically significant at p < 0.05. Daily practice of Qigong appears to have a positive impact on the main fibromyalgia symptoms that is beyond group interaction. ClinicalTrials.gov NCT03441997.

Accurate quantification of the impact of visual, somatosensory, and vestibular systems on postural control may inform tailor-made balance intervention strategies. The aim of this proof-of-concept study was to determine the safety, sense of presence, system usability, and face validity of a newly developed Virtual Reality Comprehensive Balance Assessment and Training (VR-ComBAT) in healthy young individuals. The VR-ComBAT included six balance condition: (1) stable surface with fixed virtual reality (VR) surroundings; (2) stable surface with blacked out VR surroundings; (3) stable surface with VR visual conflict; (4) unstable surface with fixed VR surroundings; (5) unstable surface with blacked out VR surroundings; and (6) unstable surface with VR visual conflict. Safety was evaluated using the number of adverse events, including scores on the Simulator Sickness Questionnaire. Sense of presence was evaluated using the igroup Presence Questionnaire (iPQ). System usability was assessed using the Systems Usability Scale (SUS). Friedman analyses with post hoc Wilcoxon Signed Rank tests were employed to demonstrate face validity by quantifying center of pressure (COP) changes in mean distance, mean velocity, and mean frequency in the anteroposterior (AP) and mediolateral (ML) direction across the six conditions. Twenty-three participants (27.4 ± 8.0 years old; 13 women) reported no adverse events. Participants scores on average 44.9 ± 9.6 on the iPQ and 79.7 ± 9.9 on the SUS. Post hoc analyses showed significant changes in COP-based measures when compared to baseline. The mean frequency change of COP showed direction-dependence in which increased frequency change in AP was observed while decreased change in ML was noted. The VR-ComBAT provides a safe, feasible, and cost-effective VR environment that demonstrates consistent sensory re-weighting between visual, somatosensory, and vestibular systems. Future studies should investigate whether VR-ComBAT can be used to inform precision rehabilitation of balance and fall prevention in older adults without and with neurological conditions.

Background: Inflammatory cytokine levels are often elevated in people with Parkinson’s disease (PD). People with PD often experience sleep disturbances that significantly impact quality of life. Past studies suggest inflammatory cytokines may be associated with various symptoms of PD. Benefits of Qigong, a mind–body exercise, have been shown in different neurological conditions, but there is still a lack of clinical evidence in the PD population. Methods: Ten people with PD were recruited and randomly assigned into two groups receiving six weeks of Qigong (experimental group) or sham Qigong (control group) intervention. The levels of TNF-α, IL-1β, and IL-6 in subjects’ serum and sleep quality were measured before and after the intervention. Results: After the intervention, the serum level of TNF-α in the experimental group was significantly decreased in all subjects, while the level in the control group showed a trend to increase. Qigong exercise significantly improved sleep quality at night. There was a strong correlation between changes in the level of TNF-α and sleep quality. Conclusion: Qigong exercise decreased TNF-α level in people with PD and helped improve sleep quality. TNF-α may have a potential to influence the sleep quality in people with PD.