A personal decision support system for heart failure management (HeartMan) : study protocol of the HeartMan randomized controlled trial

Background: Heart failure (HF) is a highly prevalent chronic disease, for which there is no cure available. Therefore, improving disease management is crucial, with mobile health (mHealth) being a promising technology. The aim of the HeartMan study is to evaluate the effect of a personal mHealth system on top of standard care on disease management and health-related quality of life (HRQoL) in HF. Methods: HeartMan is a randomized controlled 1:2 (control: intervention) proof-of-concept trial, which will enrol 120 stable ambulatory HF patients with reduced ejection fraction across two European countries. Participants in the intervention group are equipped with a multi-monitoring health platform with the HeartMan wristband sensor as the main component. HeartMan provides guidance through a decision support system on four domains of disease management (exercise, nutrition, medication adherence and mental support), adapted to the patient's medical and psychological profile. The primary endpoint of the study is improvement in self-care and HRQoL after a six-months intervention. Secondary endpoints are the effects of HeartMan on: behavioural outcomes, illness perception, clinical outcomes and mental state. Discussion: HeartMan is technologically the most innovative HF self-management support system to date. This trial will provide evidence whether modern mHealth technology, when used to its full extent, can improve HRQoL in HF

Proof-of-concept trial results of the HeartMan mobile personal health system for self-management in congestive heart failure

This study tested the effectiveness of HeartMan—a mobile personal health system offering decisional support for management of congestive heart failure (CHF)—on health-related quality of life (HRQoL), self-management, exercise capacity, illness perception, mental and sexual health. A randomized controlled proof-of-concept trial (1:2 ratio of control:intervention) was set up with ambulatory CHF patients in stable condition in Belgium and Italy. Data were collected by means of a 6-min walking test and a number of standardized questionnaire instruments. A total of 56 (34 intervention and 22 control group) participants completed the study (77% male; mean age 63 years, sd 10.5). All depression and anxiety dimensions decreased in the intervention group (p < 0.001), while the need for sexual counselling decreased in the control group (p < 0.05). Although the group differences were not significant, self-care increased (p < 0.05), and sexual problems decreased (p < 0.05) in the intervention group only. No significant intervention effects were observed for HRQoL, self-care confidence, illness perception and exercise capacity. Overall, results of this proof-of-concept trial suggest that the HeartMan personal health system significantly improved mental and sexual health and self-care behaviour in CHF patients. These observations were in contrast to the lack of intervention effects on HRQoL, illness perception and exercise capacity

Characterization of constrained continuous multiobjective optimization problems : ǂa ǂfeature space perspective

Despite the increasing interest in constrained multiobjective optimization in recent years, constrained multiobjective optimization problems (CMOPs) are still unsatisfactory understood and characterized. For this reason, the selection of appropriate CMOPs for benchmarking is difficult and lacks a formal background. We address this issue by extending landscape analysis to constrained multiobjective optimization. By employing four exploratory landscape analysis techniques, we propose 29 landscape features (of which 19 are novel) to characterize CMOPs. These landscape features are then used to compare eight frequently used artificial test suites against a recently proposed suite consisting of real-world problems based on physical models. The experimental results reveal that the artificial test problems fail to adequately represent some realistic characteristics, such as strong negative correlation between the objectives and constraints. Moreover, our findings show that all the studied artificial test suites have advantages and limitations, and that no "perfect" suite exists. Benchmark designers can use the obtained results to select or generate appropriate CMOP instances based on the characteristics they want to explore

HeartMan DSS : a decision support system for self-management of congestive heart failure

Congestive heart failure is a chronic medical condition that affects about 2 % of the adult population. Even though it cannot be cured, it can be relieved by a proper, long-term, complex and personalized disease management. In this paper we present the HeartMan Decision Support System (DSS), aimed at supporting individual patients in their uptake of well-established clinical guidelines (i.e., both medication and behaviour based) for disease management. The HeartMan DSS is a central component of the wider HeartMan mobile-health platform that employs mobile phones, wristband sensors and a web application for communication with patients, their physicians and caregivers. The DSS itself provides recommendations for (1) managing patient's physical health in terms of exercise, nutrition, medications and self-monitoring, (2) psychological support, and (3) managing environmental parameters. The DSS employs a variety of methods: rule-based decision models and adaptable workflows developed using literature and in collaboration with medical experts, classification models developed by machine learning from data, and optimization algorithms. Taken together, they provide a comprehensive, personalized and user-friendly disease management platform. The system was evaluated in a clinical proof-orconcept trial, involving 56 patients in four hospitals. The results confirmed that the system was successful in improving self-care behaviour, decreased patients' levels of depression and anxiety, and improved the overall predicted 1-year mortality risk

A Personal Decision Support System for Heart Failure Management (HeartMan): study protocol of the HeartMan randomized controlled trial

Background: Heart failure (HF) is a highly prevalent chronic disease, for which there is no cure available. Therefore, improving disease management is crucial, with mobile health (mHealth) being a promising technology. The aim of the HeartMan study is to evaluate the effect of a personal mHealth system on top of standard care on disease management and health-related quality of life (HRQoL) in HF. Methods: HeartMan is a randomized controlled 1:2 (control:intervention) proof-of-concept trial, which will enrol 120 stable ambulatory HF patients with reduced ejection fraction across two European countries. Participants in the intervention group are equipped with a multi-monitoring health platform with the HeartMan wristband sensor as the main component. HeartMan provides guidance through a decision support system on four domains of disease management (exercise, nutrition, medication adherence and mental support), adapted to the patient’s medical and psychological profile. The primary endpoint of the study is improvement in self-care and HRQoL after a six-months intervention. Secondary endpoints are the effects of HeartMan on: behavioural outcomes, illness perception, clinical outcomes and mental state. Discussion: HeartMan is technologically the most innovative HF self-management support system to date. This trial will provide evidence whether modern mHealth technology, when used to its full extent, can improve HRQoL in HF. Trial registration: This trial has been registered on https://clinicaltrials.gov/ct2/show/NCT03497871, on April 13 2018 with registration number NCT03497871. Keywords: mHealth, Disease management, Heart failure, Health-related quality of life, Decision support systemstatus: publishe