Interpretation of Heart and Lungs Sounds Acquired via Remote, Digital Auscultation Reached Fair-to-Substantial Levels of Consensus among Specialist Physicians.

BACKGROUND: Technological advancement may bridge gaps between long-practiced medical competencies and modern technologies. Such a domain is the application of digital stethoscopes used for physical examination in telemedicine. This study aimed to validate the level of consensus among physicians regarding the interpretation of remote, digital auscultation of heart and lung sounds. METHODS: Seven specialist physicians considered both the technical quality and clinical interpretation of auscultation findings of pre-recorded heart and lung sounds of patients hospitalized in their homes. TytoCareTM system was used as a remote, digital stethoscope. RESULTS: In total, 140 sounds (70 heart and 70 lungs) were presented to seven specialists. The level of agreement was measured using Fleiss' Kappa (FK) variable. Agreement relating to heart sounds reached low-to-moderate consensus: the overall technical quality (FK = 0.199), rhythm regularity (FK = 0.328), presence of murmurs (FK = 0.469), appreciation of sounds as remote (FK = 0.011), and an overall diagnosis as normal or pathologic (FK = 0.304). The interpretation of some of the lung sounds reached a higher consensus: the overall technical quality (FK = 0.169), crepitus (FK = 0.514), wheezing (FK = 0.704), bronchial sounds (FK = 0.034), and an overall diagnosis as normal or pathological (FK = 0.386). Most Fleiss' Kappa values were in the range of "fare consensus", while in the domains of diagnosing lung crepitus and wheezing, the values increased to the "substantial" level. CONCLUSIONS: Bio signals, as recorded auscultations of the heart and lung sounds serving the process of clinical assessment of remotely situated patients, do not achieve a high enough level of agreement between specialized physicians. These findings should serve as a catalyzer for improving the process of telemedicine-attained bio-signals and their clinical interpretation.

International Comparison of Six Basic eHealth Indicators Across 14 Countries: An eHealth Benchmarking Study.

BACKGROUND: Many countries adopt eHealth applications to support patient-centered care. Through information exchange, these eHealth applications may overcome institutional data silos and support holistic and ubiquitous (regional or national) information logistics. Available eHealth indicators mostly describe usage and acceptance of eHealth in a country. The eHealth indicators focusing on the cross-institutional availability of patient-related information for health care professionals, patients, and care givers are rare. OBJECTIVES: This study aims to present eHealth indicators on cross-institutional availability of relevant patient data for health care professionals, as well as for patients and their caregivers across 14 countries (Argentina, Australia, Austria, Finland, Germany, Hong Kong as a special administrative region of China, Israel, Japan, Jordan, Kenya, South Korea, Sweden, Turkey, and the United States) to compare our indicators and the resulting data for the examined countries with other eHealth benchmarks and to extend and explore changes to a comparable survey in 2017. We defined "availability of patient data" as the ability to access data in and to add data to the patient record in the respective country. METHODS: The invited experts from each of the 14 countries provided the indicator data for their country to reflect the situation on August 1, 2019, as date of reference. Overall, 60 items were aggregated to six eHealth indicators. RESULTS: Availability of patient-related information varies strongly by country. Health care professionals can access patients' most relevant cross-institutional health record data fully in only four countries. Patients and their caregivers can access their health record data fully in only two countries. Patients are able to fully add relevant data only in one country. Finland showed the best outcome of all eHealth indicators, followed by South Korea, Japan, and Sweden. CONCLUSION: Advancement in eHealth depends on contextual factors such as health care organization, national health politics, privacy laws, and health care financing. Improvements in eHealth indicators are thus often slow. However, our survey shows that some countries were able to improve on at least some indicators between 2017 and 2019. We anticipate further improvements in the future.

Estimating dyskinesia severity in Parkinson's disease by using a waist-worn sensor: concurrent validity study.

Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson's disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. In this study, 13 Parkinson's patients, who were symptomatic with dyskinesias, were monitored with the device at home, for an average period of 30 minutes, while performing normal daily life activities. Each patient's activity was simultaneously video-recorded. A physician was in charge of reviewing the recorded videos and determining the severity of dyskinesia through the UDysRS for every patient. The sensor device yielded only one value for dyskinesia severity, which was calculated by averaging the recorded device readings. Correlation between the results of physician's assessment and the sensor output was analyzed with the Spearman's correlation coefficient. The correlation coefficient between the sensor output and UDysRS result was 0.70 (CI 95%: 0.33-0.88; p = 0.01). Since the sensor was located on the waist, the correlation between the sensor output and the results of the trunk and legs scale sub-items was calculated: 0.91 (CI 95% 0.76-0.97: p < 0.001). The conclusion is that the magnitude of dyskinesia, as measured by the tested device, presented good correlation with that observed by a physician.

Personalized Health Systems-Past, Present, and Future of Research Development and Implementation in Real-Life Environment.

Personal health systems (PHS) are designed to provide the individual with tailored care while enabling the healthcare system to deliver high-quality care to large populations and maintain a sustainable system. Solutions using electronic health records (EHRs) that include predictive models for the risk of disease onset and deterioration enable the care provider to better identify and treat patients with chronic disease and provide personalized prevention. These tools are well-accepted by doctors and have been proven to improve health outcomes and reduce costs. Integrated telecare programs were implemented for comorbid patients showing improved clinical outcomes self-management and quality of life (QoL). However, different patient populations benefit in different ways from these care plans, and thus, continuous evaluation, service adaptation in a real-life environment set with clear outcome measures, is required for best results. The challenge of the PHS today is to acquire patient-generated data (PGD) and behavioral and patient-reported outcomes (PROs) for PHS development that can be combined with existing clinical data. Some initiatives of healthcare organizations [health maintenance organizations (HMOs)] in Israel demonstrate how this goal can be achieved with relatively small efforts by using a stepwise and agile approach to service implementation that improve service by enabling adoption and adaptation of the service in the short term while collecting data for advanced PHS development in the long term. This approach, combined with programs and incentive payments at the national level, creates an environment and infrastructure for collaboration between healthcare, academia, and industry for research, development, and implementation of future PHS. This article presents examples of PHS development and implementation from the Israeli healthcare system. We discuss the lessons learned and suggest new approaches for research, development implementation, and evaluation of PHS that will address the needs of future healthcare.

A "HOLTER" for Parkinson's disease: Validation of the ability to detect on-off states using the REMPARK system.

The treatment of Parkinson's disease (PD) with levodopa is very effective. However, over time, motor complications (MCs) appear, restricting the patient from leading a normal life. One of the most disabling MCs is ON-OFF fluctuations. Gathering accurate information about the clinical status of the patient is essential for planning treatment and assessing its effect. Systems such as the REMPARK system, capable of accurately and reliably monitoring ON-OFF fluctuations, are of great interest. OBJECTIVE: To analyze the ability of the REMPARK System to detect ON-OFF fluctuations. METHODS: Forty-one patients with moderate to severe idiopathic PD were recruited according to the UK Parkinson's Disease Society Brain Bank criteria. Patients with motor fluctuations, freezing of gait and/or dyskinesia and who were able to walk unassisted in the OFF phase, were included in the study. Patients wore the REMPARK System for 3days and completed a diary of their motor state once every hour. RESULTS: The record obtained by the REMPARK System, compared with patient-completed diaries, demonstrated 97% sensitivity in detecting OFF states and 88% specificity (i.e., accuracy in detecting ON states). CONCLUSION: The REMPARK System detects an accurate evaluation of ON-OFF fluctuations in PD; this technology paves the way for an optimisation of the symptomatic control of PD motor symptoms as well as an accurate assessment of medication efficacy.

Corrigendum: Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales.

[This corrects the article on p. 431 in vol. 8, PMID: 28919877.].

Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales.

BACKGROUND: Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson's (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III). METHOD: Seventy-five patients suffering from Parkinson's disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient's home. Convergence between the algorithm and the scale was evaluated by using the Spearman's correlation coefficient. RESULTS: Correlation with the UPDRS-III was moderate (rho -0.56; p < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho -0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: "axial function, balance, and gait." The correlation between the algorithm outputs and this factor of the UPDRS-III was -0.67 (p < 0.01). CONCLUSION: The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson's disease and motor fluctuations.

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer.

Among Parkinson's disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient's treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

Data-as-a-Service Platform for Delivering Healthy Lifestyle and Preventive Medicine: Concept and Structure of the DAPHNE Project.

BACKGROUND: Overweight and obesity is related to many health problems and diseases. The current obesity epidemic, which is a major health problem, is closely related to a lack of physical activity, high levels of sedentary behavior, and increased energy intake; with evidence to show increasing incidence of these issues in the younger population. Tackling obesity and its comorbid conditions requires a holistic approach encompassing attention on physical activity, healthy diet, and behavioral activation in order to enable and maintain meaningful and long-term weight loss and weight maintenance. OBJECTIVE: The objective of the Data-as-a-Service Platform for Healthy Lifestyle and Preventive Medicine (DAPHNE) project is to develop a breakthrough information communications technology (ICT) platform for tracking health, weight, physical activity, diet, lifestyle, and psychological components within health care systems, whereby the platform and clinical support is linked. METHODS: The DAPHNE platform aims to deliver personalized guidance services for lifestyle management to the citizen/patient by means of (1) advanced sensors and mobile phone apps to acquire and store continuous/real-time data on lifestyle aspects, behavior, and surrounding environment; (2) individual models to monitor their health and fitness status; (3) intelligent data processing for the recognition of behavioral trends; and (4) specific services for personalized guidance on healthy lifestyle and disease prevention. It is well known that weight loss and maintenance of weight loss are particularly difficult. This tool will address some of the issues found with conventional treatment/advice in that it will collect data in real time, thereby reducing reliability issues known with recalling events once they have passed and will also allow adjustment of behavior through timely support and recommendations sent through the platform without the necessity of formal one-to-one visits between patient and clinician. Patient motivation/compliance is a particular issue with conventional weight loss regimes; DAPHNE aims to increase the individuals' awareness of their own behavior and fosters their accountability. RESULTS: The project has been funded and the research work has started. Results for the validation of the different components is due imminently. CONCLUSIONS: In contrast with previous existing solutions, the DAPHNE project tackles the obesity problem from a clinical point of view, designing the different interfaces for its use by patients (adults and children), physicians, and caregivers. A specific design for children and adolescent patients treated for obesity has been followed, guided by pediatric physicians at hospitals in Europe. The final clinical validation of the DAPHNE platform will be carried out in different European hospitals, testing the platform in both adolescents and adults.