Purpose To systematically evaluate and quantify the effects of Tai Chi/Qigong (TCQ) on motor (UPDRS III, balance, falls, Timed-Up-and-Go, and 6-Minute Walk) and non-motor (depression and cognition) function, and quality of life (QOL) in patients with Parkinson’s disease (PD). Methods A systematic search on 7 electronic databases targeted clinical studies evaluating TCQ for individuals with PD published through August 2016. Meta-analysis was used to estimate effect sizes (Hedge’s g) and publication bias for randomized controlled trials (RCTs). Methodological bias in RCTs was assessed by two raters. Results Our search identified 21 studies, 15 of which were RCTs with a total of 755 subjects. For RCTs, comparison groups included no treatment (n=7, 47%) and active interventions (n=8, 53%). Duration of TCQ ranged from 2 to 6 months. Methodological bias was low in 6 studies, moderate in 7, and high in 2. Fixed-effect models showed that TCQ was associated with significant improvement on most motor outcomes (UPDRS III [ES=-0.444, p<.001], balance [ES=0.544, p<.001], Timed-Up-and-Go [ES=−0.341, p=.005], 6MW [ES=−0.293, p=.06]), falls [ES=−.403, p=.004], as well as depression [ES=−0.457, p=.008] and QOL [ES=−0.393, p<.001], but not cognition [ES= −0.225, p=.477]). I2 indicated limited heterogeneity. Funnel plots suggested some degree of publication bias. Conclusion Evidence to date supports a potential benefit of TCQ for improving motor function, depression and QOL for individuals with PD, and validates the need for additional large-scale trials.

Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked bỹ 35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

ObjectivesTo assess the feasibility and inform design features of a fully powered randomized controlled trial (RCT) evaluating the effects of Tai Chi (TC) in Parkinson’s disease (PD) and to select outcomes most responsive to TC assessed during off-medication states.DesignTwo-arm, wait-list controlled RCT.SettingsTertiary care hospital.SubjectsThirty-two subjects aged 40–75 diagnosed with idiopathic PD within 10 years.InterventionsSix-month TC intervention added to usual care (UC) versus UC alone.Outcome MeasuresPrimary outcomes were feasibility-related (recruitment rate, adherence, and compliance). Change in dual-task (DT) gait stride-time variability (STV) from baseline to 6 months was defined, a priori, as the clinical outcome measure of primary interest. Other outcomes included: PD motor symptom progression (Unified Parkinson’s Disease Rating Scale [UPDRS]), PD-related quality of life (PDQ-39), executive function (Trail Making Test), balance confidence (Activity-Specific Balance Confidence Scale, ABC), and Timed Up and Go test (TUG). All clinical assessments were made in the off-state for PD medications.ResultsThirty-two subjects were enrolled into 3 sequential cohorts over 417 days at an average rate of 0.08 subjects per day. Seventy-five percent (12/16) in the TC group vs 94% (15/16) in the UC group completed the primary 6-month follow-up assessment. Mean TC exposure hours overall: 52. No AEs occurred during or as a direct result of TC exercise. Statistically nonsignificant improvements were observed in the TC group at 6 months in DT gait STV (TC [20.1%] vs UC [−0.1%] group [effect size 0.49; P = .47]), ABC, TUG, and PDQ-39. UPDRS progression was modest and very similar in TC and UC groups.ConclusionsConducting an RCT of TC for PD is feasible, though measures to improve recruitment and adherence rates are needed. DT gait STV is a sensitive and logical outcome for evaluating the combined cognitive-motor effects of TC in PD.

The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed noninfected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for “precision rehabilitation”. Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients’ responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.

Purpose: To assess the feasibility, safety, and preliminary effectiveness of a 12-week multimodal Qigong Mind-Body Exercise (QMBE) program for breast cancer survivors with persistent post-surgical pain (PPSP). Methods: This was a single-arm mixed-methods pilot study. Primary outcome measures were feasibility (recruitment, adherence) and safety. Validated self-report questionnaires were used to evaluate a constellation of interdependent symptoms, including pain, fatigue, mood, exercise, interoceptive awareness, and health-related quality of life at baseline and 12 weeks. A subset of the instruments was administered 6 months postintervention. Shoulder range of motion and grip strength were objectively assessed at baseline and 12 weeks. Qualitative interviews were conducted at baseline and 12 weeks. Results: Twenty-one participants were enrolled; 18 and 17 participants, respectively, completed the 12-week and 6-month outcome assessment. No serious adverse events were reported. Statistically significant improvements were observed at 12 weeks in pain severity and interference, fatigue, anxiety, depression, perceived stress, self-esteem, pain catastrophizing, and several subdomains of quality of life, interoceptive awareness, and shoulder range of motion. Changes in pain, fatigue, pain catastrophizing, anxiety, depression, and quality of life were clinically meaningful. Postintervention effects were sustained at 6 months. Conclusions: QMBE is a safe and gentle multimodal intervention that shows promise in conferring a broad range of psychosocial and physical benefits for breast cancer survivors with PPSP. Results support the value of future studies evaluating the impact of QMBE on multiple outcomes relevant to breast cancer survivors with PPSP.

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