Living Lab Data of Patient Needs and Expectations for eHealth-Based Cardiac Rehabilitation in Germany and Spain From the TIMELY Study: Cross-Sectional Analysis.

BACKGROUND: The use of eHealth technology in cardiac rehabilitation (CR) is a promising approach to enhance patient outcomes since adherence to healthy lifestyles and risk factor management during phase III CR maintenance is often poorly supported. However, patients' needs and expectations have not been extensively analyzed to inform the design of such eHealth solutions. OBJECTIVE: The goal of this study was to provide a detailed patient perspective on the most important functionalities to include in an eHealth solution to assist them in phase III CR maintenance. METHODS: A guided survey as part of a Living Lab approach was conducted in Germany (n=49) and Spain (n=30) involving women (16/79, 20%) and men (63/79, 80%) with coronary artery disease (mean age 57 years, SD 9 years) participating in a structured center-based CR program. The survey covered patients' perceived importance of different CR components in general, current usage of technology/technical devices, and helpfulness of the potential features of eHealth in CR. Questionnaires were used to identify personality traits (psychological flexibility, optimism/pessimism, positive/negative affect), potentially predisposing patients to acceptance of an app/monitoring devices. RESULTS: All the patients in this study owned a smartphone, while 30%-40% used smartwatches and fitness trackers. Patients expressed the need for an eHealth platform that is user-friendly, personalized, and easily accessible, and 71% (56/79) of the patients believed that technology could help them to maintain health goals after CR. Among the offered components, support for regular physical exercise, including updated schedules and progress documentation, was rated the highest. In addition, patients rated the availability of information on diagnosis, current medication, test results, and risk scores as (very) useful. Of note, for each item, except smoking cessation, 35%-50% of the patients indicated a high need for support to achieve their long-term health goals, suggesting the need for individualized care. No major differences were detected between Spanish and German patients (all P>.05) and only younger age (P=.03) but not sex, education level, or personality traits (all P>.05) were associated with the acceptance of eHealth components. CONCLUSIONS: The patient perspectives collected in this study indicate high acceptance of personalized user-friendly eHealth platforms with remote monitoring to improve adherence to healthy lifestyles among patients with coronary artery disease during phase III CR maintenance. The identified patient needs comprise support in physical exercise, including regular updates on personalized training recommendations. Availability of diagnoses, laboratory results, and medications, as part of a mobile electronic health record were also rated as very useful. TRIAL REGISTRATION: ClinicalTrials.gov NCT05461729; https://clinicaltrials.gov/study/NCT05461729.

Automated Assessment of Balance Rehabilitation Exercises With a Data-Driven Scoring Model: Algorithm Development and Validation Study.

BACKGROUND: Balance rehabilitation programs represent the most common treatments for balance disorders. Nonetheless, lack of resources and lack of highly expert physiotherapists are barriers for patients to undergo individualized rehabilitation sessions. Therefore, balance rehabilitation programs are often transferred to the home environment, with a considerable risk of the patient misperforming the exercises or failing to follow the program at all. Holobalance is a persuasive coaching system with the capacity to offer full-scale rehabilitation services at home. Holobalance involves several modules, from rehabilitation program management to augmented reality coach presentation. OBJECTIVE: The aim of this study was to design, implement, test, and evaluate a scoring model for the accurate assessment of balance rehabilitation exercises, based on data-driven techniques. METHODS: The data-driven scoring module is based on an extensive data set (approximately 1300 rehabilitation exercise sessions) collected during the Holobalance pilot study. It can be used as a training and testing data set for training machine learning (ML) models, which can infer the scoring components of all physical rehabilitation exercises. In that direction, for creating the data set, 2 independent experts monitored (in the clinic) 19 patients performing 1313 balance rehabilitation exercises and scored their performance based on a predefined scoring rubric. On the collected data, preprocessing, data cleansing, and normalization techniques were applied before deploying feature selection techniques. Finally, a wide set of ML algorithms, like random forests and neural networks, were used to identify the most suitable model for each scoring component. RESULTS: The results of the trained model improved the performance of the scoring module in terms of more accurate assessment of a performed exercise, when compared with a rule-based scoring model deployed at an early phase of the system (k-statistic value of 15.9% for sitting exercises, 20.8% for standing exercises, and 26.8% for walking exercises). Finally, the resulting performance of the model resembled the threshold of the interobserver variability, enabling trustworthy usage of the scoring module in the closed-loop chain of the Holobalance coaching system. CONCLUSIONS: The proposed set of ML models can effectively score the balance rehabilitation exercises of the Holobalance system. The models had similar accuracy in terms of Cohen kappa analysis, with interobserver variability, enabling the scoring module to infer the score of an exercise based on the collected signals from sensing devices. More specifically, for sitting exercises, the scoring model had high classification accuracy, ranging from 0.86 to 0.90. Similarly, for standing exercises, the classification accuracy ranged from 0.85 to 0.92, while for walking exercises, it ranged from 0.81 to 0.90. TRIAL REGISTRATION: ClinicalTrials.gov NCT04053829; https://clinicaltrials.gov/ct2/show/NCT04053829.

Investigating the feasibility and acceptability of the HOLOBalance system compared with standard care in older adults at risk for falls: study protocol for an assessor blinded pilot randomised controlled study.

INTRODUCTION: Approximately one in three of all older adults fall each year, with wide ranging physical, psychosocial and healthcare-related consequences. Exercise-based interventions are the cornerstone for falls prevention programmes, yet these are not consistently provided, do not routinely address all components of the balance system and are often not well attended. The HOLOBalance system provides an evidence-based balance training programme delivered to patients in their home environment using a novel technological approach including an augmented reality virtual physiotherapist, exergames and a remote monitoring system. The aims of this proof-of-concept study are to (1) determine the safety, acceptability and feasibility of providing HOLOBalance to community dwelling older adults at risk for falls and (2) provide data to support sample size estimates for a future trial. METHODS: A single (assessor) blinded pilot randomised controlled proof of concept study. 120 participants will be randomised to receive an 8-week home exercise programme consisting of either: (1) HOLOBalance or (2) The OTAGO Home Exercise Programme. Participants will be required to complete their exercise programme independently under the supervision of a physiotherapist. Participants will have weekly telephone contact with their physiotherapist, and will receive home visits at weeks 0, 3 and 6. Outcome measures of safety, acceptability and feasibility, clinical measures of balance function, disability, balance confidence and cognitive function will be assessed before and immediately after the 8 week intervention. Acceptability and feasibility will be explored using descriptive statistics, and trends for effectiveness will be explored using general linear model analysis of variance. ETHICS AND DISSEMINATION: This study has received institutional ethical approvals in Germany (reference: 265/19), Greece (reference: 9769/24-6-2019) and the UK (reference: 19/LO/1908). Findings from this study will be submitted for peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT04053829. PROTOCOL VERSION: V.2, 20 January 2020.

Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson's Disease.

Freezing of Gait (FoG) is a common symptom in Parkinson's Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial.

BACKGROUND: Mobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely. OBJECTIVE: We explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities. METHODS: PD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson's Disease Rating Scale estimations by clinicians were explored. RESULTS: Of the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers' burden. Motor Aspects of Experiences of Daily Living and patients' self-rated health status also influence the system's use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. CONCLUSIONS: We found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care. TRIAL REGISTRATION: ISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-018-2767-4.

Acceptability to patients, carers and clinicians of an mHealth platform for the management of Parkinson's disease (PD_Manager): study protocol for a pilot randomised controlled trial.

BACKGROUND: Parkinson's disease is a degenerative neurological condition causing multiple motor and non-motor symptoms that have a serious adverse effect on quality of life. Management is problematic due to the variable and fluctuating nature of symptoms, often hourly and daily. The PD_Manager mHealth platform aims to provide a continuous feed of data on symptoms to improve clinical understanding of the status of any individual patient and inform care planning. The objectives of this trial are to (1) assess patient (and family carer) perspectives of PD_Manager regarding comfort, acceptability and ease of use; (2) assess clinician views about the utility of the data generated by PD_Manager for clinical decision making and the acceptability of the system in clinical practice. METHODS/DESIGN: This trial is an unblinded, parallel, two-group, randomised controlled pilot study. A total of 200 persons with Parkinson's disease (Hoehn and Yahr stage 3, experiencing motor fluctuations at least 2 h per day), with primary family carers, in three countries (110 Rome, 50 Venice, Italy; 20 each in Ioannina, Greece and Surrey, England) will be recruited. Following informed consent, baseline information will be gathered, including the following: age, gender, education, attitudes to technology (patient and carer); time since Parkinson's diagnosis, symptom status and comorbidities (patient only). Randomisation will assign participants (1:1 in each country), to PD_Manager vs control, stratifying by age (1 ≤ 70 : 1 > 70) and gender (60% M: 40% F). The PD_Manager system captures continuous data on motor symptoms, sleep, activity, speech quality and emotional state using wearable devices (wristband, insoles) and a smartphone (with apps) for storing and transmitting the information. Control group participants will be asked to keep a symptom diary covering the same elements as PD_Manager records. After a minimum of two weeks, each participant will attend a consultation with a specialist doctor for review of the data gathered (by either means), and changes to management will be initiated as indicated. Patients, carers and clinicians will be asked for feedback on the acceptability and utility of the data collection methods. The PD_Manager intervention, compared to a symptom diary, will be evaluated in a cost-consequences framework. DISCUSSION: Information gathered will inform further development of the PD_Manager system and a larger effectiveness trial. TRIAL REGISTRATION: ISRCTN Registry, ISRCTN17396879 . Registered on 15 March 2017.

Symptoms and medications change patterns for Parkinson's disease patients stratification.

Quality of life of patients with Parkinson's disease degrades significantly with disease progression. This paper presents a step towards personalized management of Parkinson's disease patients, based on discovering groups of similar patients. Similarity is based on patients' medical conditions and changes in the prescribed therapy when the medical conditions change. We present two novel approaches. The first algorithm discovers symptoms' impact on Parkinson's disease progression. Experiments on the Parkinson Progression Markers Initiative (PPMI) data reveal a subset of symptoms influencing disease progression which are already established in Parkinson's disease literature, as well as symptoms that are considered only recently as possible indicators of disease progression by clinicians. The second novelty is a methodology for detecting patterns of medications dosage changes based on the patient status. The methodology combines multitask learning using predictive clustering trees and short time series analysis to better understand when a change in medications is required. The experiments on PPMI data demonstrate that, using the proposed methodology, we can identify some clinically confirmed patients' symptoms suggesting medications change. In terms of predictive performance, our multitask predictive clustering tree approach is mostly comparable to the random forest multitask model, but has the advantage of model interpretability.

Tremor UPDRS estimation in home environment.

In this paper, a method for the assessment of the Unified Parkinson Disease Rating scale (UPDRS) related to tremor is presented. The method described consists of hand resting and posture state detection, tremor detection and tremor quantification based on accelerometer and gyroscope readings from a wrist worn sensor. The initial results on PD patient recordings on home environment indicate the feasibility of the proposed method in monitoring UPDRS tremor in patient home environment.

Identification of genetic markers for treatment success in heart failure patients: insight from cardiac resynchronization therapy.

BACKGROUND: Cardiac resynchronization therapy (CRT) can improve ventricular size, shape, and mass and reduce mitral regurgitation by reverse remodeling of the failing ventricle. About 30% of patients do not respond to this therapy for unknown reasons. In this study, we aimed at the identification and classification of CRT responder by the use of genetic variants and clinical parameters. METHODS AND RESULTS: Of 1421 CRT patients, 207 subjects were consecutively selected, and CRT responder and nonresponder were matched for their baseline parameters before CRT. Treatment success of CRT was defined as a decrease in left ventricular end-systolic volume >15% at follow-up echocardiography compared with left ventricular end-systolic volume at baseline. All other changes classified the patient as CRT nonresponder. A genetic association study was performed, which identified 4 genetic variants to be associated with the CRT responder phenotype at the allelic (P<0.035) and genotypic (P<0.031) level: rs3766031 (ATPIB1), rs5443 (GNB3), rs5522 (NR3C2), and rs7325635 (TNFSF11). Machine learning algorithms were used for the classification of CRT patients into responder and nonresponder status, including combinations of the identified genetic variants and clinical parameters. CONCLUSIONS: We demonstrated that rule induction algorithms can successfully be applied for the classification of heart failure patients in CRT responder and nonresponder status using clinical and genetic parameters. Our analysis included information on alleles and genotypes of 4 genetic loci, rs3766031 (ATPIB1), rs5443 (GNB3), rs5522 (NR3C2), and rs7325635 (TNFSF11), pathophysiologically associated with remodeling of the failing ventricle.