Qigong Training Positively Impacts Both Posture and Mood in Breast Cancer Survivors With Persistent Post-surgical Pain: Support for an Embodied Cognition Paradigm.

Theories of embodied cognition hypothesize interdependencies between psychological well-being and physical posture. The purpose of this study was to assess the feasibility of objectively measuring posture, and to explore the relationship between posture and affect and other patient centered outcomes in breast cancer survivors (BCS) with persistent postsurgical pain (PPSP) over a 12-week course of therapeutic Qigong mind-body training. Twenty-one BCS with PPSP attended group Qigong training. Clinical outcomes were pain, fatigue, self-esteem, anxiety, depression, stress and exercise self-efficacy. Posture outcomes were vertical spine and vertical head angles in the sagittal plane, measured with a 3D motion capture system in three conditions: eyes open (EO), eyes open relaxed (EOR) and eyes closed (EC). Assessments were made before and after the Qigong training. The association between categorical variables (angle and mood) was measured by Cramer's V. In the EO condition, most participants who improved in fatigue and anxiety scales also had better vertical head values. For the EOR condition, a moderate correlation was observed between changes in vertical head angle and changes in fatigue scale. In the EC condition, most of the participants who improved in measures of fatigue also improved vertical head angle. Additionally, pain severity decreased while vertical spine angle improved. These preliminary findings support that emotion and other patient centered outcomes should be considered within an embodied framework, and that Qigong may be a promising intervention for addressing biopsychosocially complex interventions such as PPSP in BCSs.

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).

Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder associated with motor and non-motor symptoms. Current treatments primarily focus on managing motor symptom severity such as tremor, bradykinesia, and rigidity. However, as the disease progresses, treatment side-effects can emerge such as on/off periods and dyskinesia. The objective of the Levodopa Response Study was to identify whether wearable sensor data can be used to objectively quantify symptom severity in individuals with PD exhibiting motor fluctuations. Thirty-one subjects with PD were recruited from 2 sites to participate in a 4-day study. Data was collected using 2 wrist-worn accelerometers and a waist-worn smartphone. During Days 1 and 4, a portion of the data was collected in the laboratory while subjects performed a battery of motor tasks as clinicians rated symptom severity. The remaining of the recordings were performed in the home and community settings. To our knowledge, this is the first dataset collected using wearable accelerometers with specific focus on individuals with PD experiencing motor fluctuations that is made available via an open data repository.

Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors.

OBJECTIVE: Rehabilitation specialists have shown considerable interest for the development of models, based on clinical data, to predict the response to rehabilitation interventions in stroke and traumatic brain injury survivors. However, accurate predictions are difficult to obtain due to the variability in patients' response to rehabilitation interventions. This study aimed to investigate the use of wearable technology in combination with clinical data to predict and monitor the recovery process and assess the responsiveness to treatment on an individual basis. METHODS: Gaussian Process Regression-based algorithms were developed to estimate rehabilitation outcomes (i.e., Functional Ability Scale scores) using either clinical or wearable sensor data or a combination of the two. RESULTS: The algorithm based on clinical data predicted rehabilitation outcomes with a Pearson's correlation of 0.79 compared to actual clinical scores provided by clinicians but failed to model the variability in responsiveness to the intervention observed across individuals. In contrast, the algorithm based on wearable sensor data generated rehabilitation outcome estimates with a Pearson's correlation of 0.91 and modeled the individual responses to rehabilitation more accurately. Furthermore, we developed a novel approach to combine estimates derived from the clinical data and the sensor data using a constrained linear model. This approach resulted in a Pearson's correlation of 0.94 between estimated and clinician-provided scores. CONCLUSION: This algorithm could enable the design of patient-specific interventions based on predictions of rehabilitation outcomes relying on clinical and wearable sensor data. SIGNIFICANCE: This is important in the context of developing precision rehabilitation interventions.

Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery.

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.

"Making Peace with Our Bodies": A Qualitative Analysis of Breast Cancer Survivors' Experiences with Qigong Mind-Body Exercise.

Objectives: Breast cancer treatment leaves breast cancer survivors (BCS) with an array of lasting side effects, including persistent postsurgical pain (PPSP). In this study, we explored the perceptions of BCS with PPSP as they learned Qigong mind-body exercise (QMBE), a multimodal practice rooted in Traditional Chinese Medicine. Methods: Participants included 18 female BCS treated for stage 0-III breast cancer and experiencing PPSP. Participants were taught QMBE over 12 weeks. Semi-structured interviews were conducted before and after the intervention. Results: BCS disclosed a disconnect between mind and body that emerged during treatment. They perceived QMBE as moving meditation, which enabled them to reconnect mind and body, lessen their pain, and make peace with their bodies. Conclusion: These women's experiences both inform the promise of integrating QMBE and related mind-body exercise into PPSP clinical practice guidelines and suggest new areas of research regarding the role of multimodal interventions for holistic healing in BCS.

Sensorimotor conflict tests in an immersive virtual environment reveal subclinical impairments in mild traumatic brain injury.

Current clinical tests lack the sensitivity needed for detecting subtle balance impairments associated with mild traumatic brain injury (mTBI). Patient-reported symptoms can be significant and have a huge impact on daily life, but impairments may remain undetected or poorly quantified using clinical measures. Our central hypothesis was that provocative sensorimotor perturbations, delivered in a highly instrumented, immersive virtual environment, would challenge sensory subsystems recruited for balance through conflicting multi-sensory evidence, and therefore reveal that not all subsystems are performing optimally. The results show that, as compared to standard clinical tests, the provocative perturbations illuminate balance impairments in subjects who have had mild traumatic brain injuries. Perturbations delivered while subjects were walking provided greater discriminability (average accuracy ≈ 0.90) than those delivered during standing (average accuracy ≈ 0.65) between mTBI subjects and healthy controls. Of the categories of features extracted to characterize balance, the lower limb accelerometry-based metrics proved to be most informative. Further, in response to perturbations, subjects with an mTBI utilized hip strategies more than ankle strategies to prevent loss of balance and also showed less variability in gait patterns. We have shown that sensorimotor conflicts illuminate otherwise-hidden balance impairments, which can be used to increase the sensitivity of current clinical procedures. This augmentation is vital in order to robustly detect the presence of balance impairments after mTBI and potentially define a phenotype of balance dysfunction that enhances risk of injury.

Qigong Mind-Body Exercise as a Biopsychosocial Therapy for Persistent Post-Surgical Pain in Breast Cancer: A Pilot Study.

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.

mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease.

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 by ~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.

Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?

Goal: 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 non-infected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

Motor skill acquisition during a balance task as a process of optimization of motor primitives.

It has been argued that the central nervous system relies on combining simple movement elements (i.e. motor primitives) to generate complex motor outputs. However, how movement elements are generated and combined during the acquisition of new motor skills is still a source of debate. Herein, we present results providing new insights into the role of movement elements in the acquisition of motor skills that we obtained by analysing kinematic data collected while healthy subjects learned a new motor task. The task consisted of playing an interactive game using a platform with embedded sensors whose aggregate output was used to control a virtual object in the game. Subjects learned the task over multiple blocks. The analysis of the kinematic data was carried out using a recently developed technique referred to as "movement element decomposition." The technique entails the decomposition of complex multi-dimensional movements in one-dimensional elements marked by a bell-shaped velocity profile. We computed the number of movement elements during each block and measured how closely they matched a theoretical velocity profile derived by minimizing a cost function accounting for the smoothness of movement and the cost of time. The results showed that, in the early stage of motor skill acquisition, two mechanisms underlie the improvement in motor performance: 1) a decrease in the number of movement elements composing the motor output and 2) a gradual change in the movement elements that resulted in a shape matching the velocity profile derived by using the above-mentioned theoretical model.

Rehabilitation in Africa.

In Africa, rehabilitation services are insufficient and marred with inadequate political commitments and collaborations of stakeholders. Infrastructures and expertise for rehabilitation are scarce and poorly coordinated. Community-based rehabilitation programs are fragmented and fractured and lack working partnership with rehabilitation services in health care systems. Locally responsive policy frameworks, service delivery models, and health governance practices are prerequisites for meeting rehabilitation needs of the ever-increasing number of persons with chronic disabling conditions. Concerted global efforts are required for equitable and accessible coordinated continuum of rehabilitation care at various levels of health services and the community in most Sub-Saharan African countries.

Effects of Ultrasound-Guided Administration of Botulinum Toxin (IncobotulinumtoxinA) in Patients with Lateral Epicondylitis.

How effective and safe are incobotulinumtoxinA injections in adult patients with lateral epicondylitis refractory to other treatments? In this experimental study, ultrasound-guided incobotulinumtoxinA 10⁻30 U/muscle was injected into extensor carpi ulnaris, extensor digiti minimi, extensor digitorum longus and extensor carpi radialis brevis muscles. Pain (visual analogue scale [VAS], 0 to 10 [no pain to severe pain]) and upper-limb functionality (QuickDASH scale, 0 to 100 [best to worst]), assessed at baseline, 1, 3 and 6 months post-treatment, were analysed using repeated-measures analysis of variance (ANOVA) and Tukey post-hoc tests. Secondary analyses stratifying patient population by sex and baseline VAS were performed. Adverse events were reported. Twenty-four patients (mean [standard deviation] age 46.8 years) were included. Compared with baseline, mean VAS and QuickDASH scores improved at all follow-ups (p < 0.001 and p = 0.001, respectively; repeated-measures ANOVA). Secondary analyses revealed significant differences between baseline and all follow-ups in the group with baseline VAS ≥ 6 and in males and females (all p < 0.05, Tukey post-hoc test). No adverse events, except for the expected third finger weakness, were reported. In conclusion, ultrasound-guided incobotulinumtoxinA injections may be an effective treatment for lateral epicondylitis in the appropriate patient population.

Complex Upper-Limb Movements Are Generated by Combining Motor Primitives that Scale with the Movement Size.

The hand trajectory of motion during the performance of one-dimensional point-to-point movements has been shown to be marked by motor primitives with a bell-shaped velocity profile. Researchers have investigated if motor primitives with the same shape mark also complex upper-limb movements. They have done so by analyzing the magnitude of the hand trajectory velocity vector. This approach has failed to identify motor primitives with a bell-shaped velocity profile as the basic elements underlying the generation of complex upper-limb movements. In this study, we examined upper-limb movements by analyzing instead the movement components defined according to a Cartesian coordinate system with axes oriented in the medio-lateral, antero-posterior, and vertical directions. To our surprise, we found out that a broad set of complex upper-limb movements can be modeled as a combination of motor primitives with a bell-shaped velocity profile defined according to the axes of the above-defined coordinate system. Most notably, we discovered that these motor primitives scale with the size of movement according to a power law. These results provide a novel key to the interpretation of brain and muscle synergy studies suggesting that human subjects use a scale-invariant encoding of movement patterns when performing upper-limb movements.

Tai Chi for Reducing Dual-task Gait Variability, a Potential Mediator of Fall Risk in Parkinson's Disease: A Pilot Randomized Controlled Trial.

OBJECTIVES: To 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. DESIGN: Two-arm, wait-list controlled RCT. SETTINGS: Tertiary care hospital. SUBJECTS: Thirty-two subjects aged 40-75 diagnosed with idiopathic PD within 10 years. INTERVENTIONS: Six-month TC intervention added to usual care (UC) versus UC alone. OUTCOME MEASURES: Primary 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. RESULTS: Thirty-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. CONCLUSIONS: Conducting 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.

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

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.