Demographic changes, progress in medicine technology and regional problems in providing healthcare to low density populations are posing great challenges to our healthcare systems. Rapid progress in computer sciences and information technologies have a great impact on the way healthcare will be delivered in the near future. This article describes opportunities and challenges of eHealth and telemedicine in the framework of our health systems and, in particular, in the context of today’s cardiology services. The most promising applications of eHealth and telemedicine include: (a) prevention and lifestyle interventions; (b) chronic disease management including hypertension, diabetes and heart failure; (c) arrhythmia detection including early detection of atrial fibrillation and telemonitoring of devices such as pacemaker, internal cardioverter defibrillators and implantable rhythm monitoring devices; (d) telerehabilitation. Major obstacles to the integration of eHealth and telemedicine into daily clinical practice include limited large-scale evidence, in particular, for cost-effectiveness, as well as lack of interoperability, inadequate or fragmented legal frameworks and lack of reimbursement. An important challenge for those involved in these new technologies will be to keep the main focus on patient’s individual needs and to carefully evaluate the evidence behind the practice.

Background: Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. Objective: In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. Methods: We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Results: Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed—measured by the number toss-and-turn events—as the most predictive sleep parameter (t score=–0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection. Conclusions: Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options.

The availability of simple, accurate, and affordable cuffless blood pressure (BP) devices has the potential to greatly increase the compliance with measurement recommendations and the utilization of BP measurements for BP telemonitoring. The aim of this study is to evaluate the correlation between findings from routine BP measurements using a conventional sphygmomanometer with the results from a portable ECG monitor combined with photoplethysmography (PPG) for pulse wave registration in patients with arterial hypertension. Methods: The study included 500 patients aged 32–88 years (mean 64 ± 7.9 years). Mean values from three routine BP measurements by a sphygmomanometer with cuff were selected for comparison; within one minute after the last measurement, an electrocardiogram (ECG) was recorded for 3 min in the standard lead I using a smartphone-case based single-channel ECG monitor (CardioQVARK^®-limited responsibility company “L-CARD”, Moscow, Russia) simultaneously with a PPG pulse wave recording. Using a combination of the heart signal with the PPG, levels of systolic and diastolic BP were determined based on machine learning using a previously developed and validated algorithm and were compared with sphygmomanometer results. Results: According to the Bland–Altman analysis, SD for systolic BP was 3.63, and bias was 0.32 for systolic BP. SD was 2.95 and bias was 0.61 for diastolic BP. The correlation between the results from the sphygmomanometer and the cuffless method was 0.89 (p = 0.001) for systolic and 0.87 (p = 0.002) for diastolic BP. Conclusion: Blood pressure measurements on a smartphone-case without a cuff are encouraging. However, further research is needed to improve the accuracy and reliability of clinical use in the majority of patients.

During acute coronary syndromes patients perceive intense distress. We hypothesized that retrospective ratings of patients' MI-related fear of dying, helplessness, or pain, all assessed within the first year post-MI, are associated with poor cardiovascular outcome. We studied 304 patients (61 ± 11 years, 85% men) who after a median of 52 days (range 12-365 days) after index MI retrospectively rated the level of distress in the form of fear of dying, helplessness, or pain they had perceived at the time of MI on a numeric scale ranging from 0 ("no distress") to 10 ("extreme distress"). Non-fatal hospital readmissions due to cardiovascular disease (CVD) related events (i.e., recurrent MI, elective and non-elective stent implantation, bypass surgery, pacemaker implantation, cerebrovascular incidents) were assessed at follow-up. The relative CVD event risk was computed for a (clinically meaningful) 2-point increase of distress using Cox proportional hazard models. During a median follow-up of 32 months (range 16-45), 45 patients (14.8%) experienced a CVD-related event requiring hospital readmission. Greater fear of dying (HR 1.21, 95% CI 1.03-1.43), helplessness (HR 1.22, 95% CI 1.04-1.44), or pain (HR 1.27, 95% CI 1.02-1.58) were significantly associated with an increased CVD risk without adjustment for covariates. A similarly increased relative risk emerged in patients with an unscheduled CVD-related hospital readmission, i.e., when excluding patients with elective stenting (fear of dying: HR 1.26, 95% CI 1.05-1.51; helplessness: 1.26, 95% CI 1.05-1.52; pain: HR 1.30, 95% CI 1.01-1.66). In the fully-adjusted models controlling for age, the number of diseased coronary vessels, hypertension, and smoking, HRs were 1.24 (95% CI 1.04-1.46) for fear of dying, 1.26 (95% CI 1.06-1.50) for helplessness, and 1.26 (95% CI 1.01-1.57) for pain. Retrospectively perceived MI-related distress in the form of fear of dying, helplessness, or pain was associated with non-fatal cardiovascular outcome independent of other important prognostic factors.

Background: Home monitoring sensor systems are increasingly used to monitor seniors in their apartments for detection of emergency situations. The aim of this study was to deliver a proof-of-concept for the use of multimodal sensor systems with pervasive computing technology for the detection of clinically relevant health problems over longer time periods. Methods: Data was collected with a longitudinal home monitoring study in Switzerland (StrongAge Cohort Study) in a cohort of 24 old and oldest-old, community dwelling adults over a period of one to two years. Physical activity in the apartment, toilet visits, refrigerator use and entrance door openings were quantified using a commercially available passive infrared (PIR) motion sensing system (Domosafety S.A., Switzerland). Heart rate, respiration rate, and sleep quality were recorded with the commercially available EMFIT QS bed sensor device (Emfit Ltd, Finland). Vital signs and contextual data were collected using a wearable sensor on the upper arm (Everion, Biovotion, Switzerland). Sensor data was correlated with health-related data collected from weekly visits of the seniors by health professionals including information about physical, psychological, cognitive and behavior status, health problems, diseases, medication and medical diagnoses. Results: Twenty of the 24 recruited participants (age 88.9 ± 7.5 years, 79% females) completed the study, 2 participants had to stop study participation because of severe health deterioration, whereas 2 participants died during the course of the study. A history of chronic disease was present in 12/24 seniors including heart failure, heart rhythm disturbances, pulmonary embolism, severe insulin-dependent diabetes and Parkinson disease. In total, 242’232 person-hours were recorded. During the monitoring period, 963 health status records were reported and repeated clinical assessments of ageing relevant indicators and outcomes were performed. Several episodes of health deterioration including heart failure worsening and heart rhythm disturbances could be captured by sensor signals from different sources. Conclusions: Our results indicate that monitoring of seniors with a multimodal sensor and pervasive computing system over longer time periods is feasible and well accepted with a great potential for detection of health deterioration. Further studies are necessary to evaluate the full range of the clinical potential of these findings.