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Background: Falls have high socioeconomic costs. Information and communication technologies may support provision and monitoring of multisensory (MSR) physiotherapy programmes. The HOLOBalance platform used augmented reality holograms to provide patient-centred, individualised MSR., Objectives: To determine the platform's safety, acceptability and feasibility, investigate functional gait and dynamic balance benefits and provide data for a definitive trial., Design and Setting: Single-blinded pilot randomised controlled feasibility study. Interventions were conducted at clinical sites or participants' homes in three European countries., Participants: Community-dwelling older adults (median age 73 years; 64.2% female) at risk of falls were enrolled (May 2020-August 2021)., Methods: Participants were randomised to an 8-week clinic or home-based telerehabilitation MSR or OTAGO (control group) programme. Compliance, satisfaction, and adverse events determined feasibility. Clinical outcomes, assessed (blinded) within one-week prior to and post-intervention, included functional gait assessment (FGA), Mini BESTest and cognitive function., Results: Randomisation to completion rate was 76.15% with 109 participants recruited (n = 289 screened). Drop-out rate was similar between groups. Adverse events were reported (n = 3) in the control group. Sixty-nine percent would recommend the HOLOBalance intervention. Findings were similar for the home and clinic-based arms of each intervention; data was combined for analysis. FGA (95%CI [1.63, 4.19]) and Mini-BESTest (95%CI [1.46, 3.93]) showed greater improvement in the HOLOBalance group with a clinically meaningful change of 4/30 noted for the FGA., Conclusions: HOLObalance was feasible to implement and acceptable to older adults at risk of falls, with FGA and Mini-BEST improvements exceeding those for the OTAGO programme. A definitive trial is warranted., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society.)

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., (©Boris Schmitz, Svenja Wirtz, Manuela Sestayo-Fernández, Hendrik Schäfer, Emma R Douma, Marta Alonso Vazquez, Violeta González-Salvado, Mirela Habibovic, Dimitris Gatsios, Willem Johan Kop, Carlos Peña-Gil, Frank Mooren. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.02.2024.)

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., (©Vassilios Tsakanikas, Dimitris Gatsios, Athanasios Pardalis, Kostas M Tsiouris, Eleni Georga, Doris-Eva Bamiou, Marousa Pavlou, Christos Nikitas, Dimitrios Kikidis, Isabelle Walz, Christoph Maurer, Dimitrios Fotiadis. Originally published in JMIR Rehabilitation and Assistive Technology (https://rehab.jmir.org), 31.08.2022.)

Emotional computing has been previously applied to assess physiological behavior in a wide variety of tasks and activities. This study extends for the first time the use of emotional computing in the field of balance rehabilitation training. A proof-of-concept study was conducted to assess arousal and pleasure response to a range of physical exercises from the OTAGO and HOLOBALANCE balance rehabilitation programs with varying levels of difficulty and physical demand. Eleven participants were enrolled and performed a set of exercises wearing an ECG sensor, reporting arousal and pleasure at the end of each session. A dataset of 264 unique sessions was collected and used to extract heart rate variability (HRV) features from the measured RR intervals and automatically assess user arousal and pleasure, evaluating different classification algorithms. The results suggested that assessment of both emotions is feasible, reaching an accuracy of 72% and 74% for arousal and pleasure estimation, resnectively. Clinical Relevance- Arousal and pleasure are clinically useful indicators of patient's experience and engagement while performing balance rehabilitation exercises with novel sensing technologies and monitoring platforms.

Falls are a major health concern. The HOLOBALANCE tele-rehabilitation system was developed to deliver an evidence based, multi-sensory balance rehabilitation programme, to the elderly at risk of falls. The system delivers a series of balance physiotherapy exercises and cognitive and auditory training tasks prescribed by an expert balance physiotherapist following an initial balance assessment. The HOLOBALANCE system uses augmented reality (AR) to deliver exercises and games, and records task performance via a combination of body worn sensors and a depth camera. The HOLOBALANCE tele-rehabilitation system provides feedback to the supervising clinical team regarding task performance, participant usage and user feedback. Herewith we present the findings from the first 25 study participants regarding the feasibility and acceptability of the proposed system. The results of the clinical study indicate that the system is acceptable by the end users and also feasible for using in hospital and home environments.

Palliative care for Parkinson's disease is characterized by inconsistency and varies from country to country. Although some countries have taken significant steps to include palliative care in their health programs, others, such as Greece, are still at an early stage. One step towards the widespread adoption of palliative care is the education of all stakeholders, especially clinicians. This paper presents a preliminary version of a curriculum toolkit for Palliative Care education in Parkinson's disease. Also, we explore Greek neurologists' knowledge of Palliative care based on a questionnaire and present their feedback on the topics included in this toolkit.Clinical Relevance-The toolkit aims to benefit patients in need of palliative care through promoting health literacy and further educating healthcare providers. The proposed toolkit provides all the necessary information to become sufficient knowledge and ultimately translate into clinical practice skills.

Background: Palliative care education among all stakeholders involved in the care of patients with late-stage Parkinson's disease is not adequate. In fact, there are many unmet educational and training needs as confirmed with a targeted, narrative literature review., Methods: To address these needs we have developed the "Best Care for People with Late-Stage Parkinson's Disease" curriculum toolkit. The toolkit is based on recommendations and guidelines for training clinicians and other healthcare professionals involved in palliative care, educational material developed in recent research efforts for patients and caregivers with PD and consensus meetings of leading experts in the field. The final version of the proposed toolkit was drafted after an evaluation by external experts with an online survey, the feedback of which was statistically analysed with the chi-square test of independence to assess experts' views on the relevance and importance of the topics. A sentiment analysis was also done to complement statistics and assess the experts positive and negative sentiments for the curriculum topics based on their free text feedback., Results: The toolkit is compliant with Kern's foundational framework for curriculum development, recently adapted to online learning. The statistical analysis of the online survey, aiming at toolkit evaluation from external experts (27 in total), confirms that all but one (nutrition in advanced Parkinson's disease) topics included, as well as their objectives and content, are highly relevant and useful., Conclusions: In this paper, the methods for the development of the toolkit, its stepwise evolution, as well as the toolkit implementation as a Massive Open Online Course (MOOC), are presented. The "Best Care for People with Late-Stage Parkinson' s disease" curriculum toolkit can provide high-quality and equitable education, delivered by an interdisciplinary team of educators. The toolkit can improve communication about palliative care in neurological conditions at international and multidisciplinary level. It can also offer continuing medical education for healthcare providers., (© 2021. The Author(s).)

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

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.

A proposed microsurgical training program is presented that includes all the existing training methods, such as simulation in nonliving models, virtual reality simulation system and exercise in living models. Our experience in microsurgery training over the last decades indicates the need of evolution in training programs. This can be achieved with the introduction of new technologies into education and training. The first primary results of the described training program are promising, however this system needs to be assessed by training greater number of microsurgeons. Furthermore, more complex scenarios (such as whole operations) should be inserted into the virtual reality simulation system to create a more interactive experience., Competing Interests: Declaration of Competing Interest All authors declare no conflict of interests., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Rehabilitation programs play an important role in improving the quality of life of patients with balance disorders. Such programs are usually executed in a home environment, due to lack of resources. This procedure usually results in poorly performed exercises or even complete drop outs from the programs, as the patients lack guidance and motivation. This paper introduces a novel system for managing balance disorders in a home environment using a virtual coach for guidance, instruction, and inducement. The proposed system comprises sensing devices, augmented reality technology, and intelligent inference agents, which capture, recognize, and evaluate a patient's performance during the execution of exercises. More specifically, this work presents a home-based motion capture and assessment module, which utilizes a sensory platform to recognize an exercise performed by a patient and assess it. The sensory platform comprises IMU sensors (Mbientlab MMR © 9axis), pressure insoles (Moticon © ), and a depth RGB camera (Intel D415 © ). This module is designed to deliver messages both during the performance of the exercise, delivering personalized notifications and alerts to the patient, and after the end of the exercise, scoring the overall performance of the patient. A set of proof of concept validation studies has been deployed, aiming to assess the accuracy of the different components for the sub-modules of the motion capture and assessment module. More specifically, Euler angle calculation algorithm in 2D ( R 2 = 0.99) and in 3D ( R 2 = 0.82 in yaw plane and R 2 = 0.91 for the pitch plane), as well as head turns speed ( R 2 = 0.96), showed good correlation between the calculated and ground truth values provided by experts' annotations. The posture assessment algorithm resulted to accuracy = 0.83, while the gait metrics were validated against two well-established gait analysis systems ( R 2 = 0.78 for double support, R 2 = 0.71 for single support, R 2 = 0.80 for step time, R 2 = 0.75 for stride time (WinTrack © ), R 2 = 0.82 for cadence, and R 2 = 0.79 for stride time (RehaGait © ). Validation results provided evidence that the proposed system can accurately capture and assess a physiotherapy exercise within the balance disorders context, thus providing a robust basis for the virtual coaching ecosystem and thereby improve a patient's commitment to rehabilitation programs while enhancing the quality of the performed exercises. In summary, virtual coaching can improve the quality of the home-based rehabilitation programs as long as it is combined with accurate motion capture and assessment modules, which provides to the virtual coach the capacity to tailor the interaction with the patient and deliver personalized experience., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Tsakanikas, Gatsios, Dimopoulos, Pardalis, Pavlou, Liston and Fotiadis.)

Background and Objective: Parkinson's disease (PD) is a degenerative disorder of the central nervous system for which currently there is no cure. Its treatment requires long-term, interdisciplinary disease management, and usage of typical medications, including levodopa, dopamine agonists, and enzymes, such as MAO-B inhibitors. The key goal of disease management is to prolong patients' independence and keep their quality of life. Due to the different combinations of motor and non-motor symptoms from which PD patients suffer, in addition to existing comorbidities, the change of medications and their combinations is difficult and patient-specific. To help physicians, we developed two decision support models for PD management, which suggest how to change the medication treatment., Methods: The models were developed using DEX methodology, which integrates the qualitative multi-criteria decision modelling with rule-based expert systems. The two DEX models differ in the way the decision rules were defined. In the first model, the decision rules are based on the interviews with neurologists (DEX expert model), and in the second model, they are formed from a database of past medication change decisions (DEX data model). We assessed both models on the Parkinson's Progression Markers Initiative (PPMI) and on a questionnaire answered by 17 neurologists from 4 European countries using accuracy measure and the Jaccard index., Results: Both models include 15 sub-models that address possible medication treatment changes based on the given patients' current state. In particular, the models incorporate current state changes in patients' motor symptoms (dyskinesia intensity, dyskinesia duration, OFF duration), mental problems (impulsivity, cognition, hallucinations and paranoia), epidemiologic data (patient's age, activity level) and comorbidities (cardiovascular problems, hypertension and low blood pressure). The highest accuracy of the developed sub-models for 15 medication treatment changes ranges from 69.31 to 99.06 %., Conclusions: Results show that the DEX expert model is superior to the DEX data model. The results indicate that the constructed models are sufficiently adequate and thus fit for the purpose of making "second-opinion" suggestions to decision support users., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

This review focuses on virtual coaching systems that were designed to enhance healthcare interventions, combining the available sensing and system-user interaction technologies. In total, more than 1,200 research papers have been retrieved and evaluated for the purposes of this review, which were obtained from three online databases (i.e.,PubMed, Scopus and IEEE Xplore) using an extensive set of search keywords. After applying exclusion criteria, the remaining 41 research papers were used to evaluate the status of virtual coaching systems over the past 10 years and assess current and future trends in this field. The results suggest that in home coaching systems were mainly focused in promoting physical activity and a healthier lifestyle, while a wider range of medical domains was considered in systems that were evaluated in lab environment. In home patient monitoring with IoT devices and sensors was mostly limited to activity trackers, pedometers and heart rate monitoring. Real-time evaluations and personalized patient feedback was also found to be rather lacking in home coaching systems and this is the most alarming find of this analysis. Feasibility studies in controlled environment and an ongoing active research on Horizon 2020 funded projects, show that the future trends in this field are aiming to close the loop with automated patient monitoring, real-time evaluations and more precise interventions., (Copyright © 2020 Tsiouris, Tsakanikas, Gatsios and Fotiadis.)

Absence seizures are expressed with distinctive spike-and-wave complexes in the electroencephalogram (EEG), which can be used to automatically distinguish them from other types of seizures and interictal activity. Considering the chaotic nature of the EEG signal, it is very unlikely that such continuous, repetitive patterns with strict periodic behavior would occur naturally under normal conditions. Searching for spectral activity in the range of 2.5-4.5 Hz and assessing the presence of synchronous, repeated patterns across multiple EEG channels in an unsupervised manner, the proposed methodology provides high absence seizure detection sensitivity of 93.94% with a low false detection rate of 0.168 FD/h using the open TUSZ dataset.

In this study, we propose a dynamic Bayesian network (DBN)-based approach to behavioral modelling of community dwelling older adults at risk for falls during the daily sessions of a hologram-enabled vestibular rehabilitation therapy programme. The component of human behavior being modelled is the level of frustration experienced by the user at each exercise, as it is assessed by the NASA Task Load Index. Herein, we present the topology of the DBN and test its inference performance on real-patient data.Clinical Relevance- Precise behavioral modelling will provide an indicator for tailoring the rehabilitation programme to each individual's personal psychological needs.

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., (©Dimitris Gatsios, Angelo Antonini, Giovanni Gentile, Andrea Marcante, Clelia Pellicano, Lucia Macchiusi, Francesca Assogna, Gianfranco Spalletta, Heather Gage, Morro Touray, Lada Timotijevic, Charo Hodgkins, Maria Chondrogiorgi, George Rigas, Dimitrios I Fotiadis, Spyridon Konitsiotis. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 29.06.2020.)

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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The rate of Parkinson's Disease (PD) progression in the initial post-diagnosis years can vary significantly. In this work, a methodology for the extraction of the most informative features for predicting rapid progression of the disease is proposed, using public data from the Parkinson's Progression Markers Initiative (PPMI) and machine learning techniques. The aim is to determine if a patient is at risk of expressing rapid progression of PD symptoms from the baseline evaluation and as close to diagnosis as possible. By examining the records of 409 patients from the PPMI dataset, the features with the best predictive value at baseline patient evaluation are found to be sleep problems, daytime sleepiness and fatigue, motor symptoms at legs, cognition impairment, early axial and facial symptoms and in the most rapidly advanced cases speech issues, loss of smell and affected leg muscle reflexes.

PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patient's symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes.

Parkinson's disease (PD) is a complex, chronic disease that many patients live with for many years. In this work we propose a mHealth approach based on a set of unobtrusive, simple-in-use, off-the-self, co-operative, mobile devices that will be used for motor and non-motor symptoms monitoring and evaluation, as well as for the detection of fluctuations along with their duration through a waking day. Ideally, a multidisciplinary and integrated care approach involving several professionals working together (neurologists, physiotherapists, psychologists and nutritionists) could provide a holistic management of the disease increasing the patient's independence and Quality of Life (QoL). To address these needs we describe also an ecosystem for the management of both motor and non-motor symptoms on PD facilitating the collaboration of health professionals and empowering the patients to self-manage their condition. This would allow not only a better monitoring of PD patients but also a better understanding of the disease progression.

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.

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., (© 2014 American Heart Association, Inc.)

In an epidemiologic study in 17 villages from different areas of Greece, 2898 persons were examined in order to find possible relations between HBSAg prevalence, altitude and endemic goitre morbidity. A lower prevalence with a significant difference was found in areas with a high altitude as compared with those with a low one, in endemic goitre than non-endemic areas, and in low altitude endemic than in low altitude non-endemic areas. Goitrous subjects had a lower HBSAg prevalence than non-goitrous subjects, but the difference was of borderline significance. The results can probably be explained by the presence of an environmental factor associated with altitude acting together with a host factor predisposing to endemic goitre and favouring the immunity to HBV infection.