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Digital health interventions are effective for hypertension self-management, but a comparison of the effectiveness and implementation of the different modes of interventions is not currently available. This study aimed to compare the effectiveness of SMS, smartphone application, and website interventions on improving blood pressure in adults with hypertension, and to report on their reach, uptake, and feasibility.

Digital health interventions have shown promising results for the management of type 2 diabetes, but a comparison of the effectiveness and implementation of the different modes is not currently available. Therefore, this study aimed to compare the effectiveness of SMS, smartphone application, and website-based interventions on improving glycaemia in adults with type 2 diabetes and report on their reach, uptake, and feasibility.

Pervasive computing has revolutionized how we collect data and interact with information. Research interest in pervasive computing has been growing exponentially over the years, demonstrating enormous potential in biomedical applications ranging from a research-fertile field to clinical translation and healthcare delivery [1]. The sophisticated capabilities of smartphones integrating diverse sensors along with wearable and non-wearable sensors provide the opportunity to collect longitudinal, multimodal data streams and facilitate near real-time monitoring, over and above standardized self-reports [1], [2], [3], [4]. Overall, digital technologies (e.g., smartphones and smartwatches) are becoming increasingly affordable and have already been embraced by many people including elders [2], [3], facilitating large scale investigations and clinical trials. Furthermore, ubiquitous devices such as standard telephones, for example, have been used to collect speech signals for healthcare assessments, enabling large studies (∼10000 people) within months, across multiple countries, with minimal cost [5]. Nation-wide studies reaching 100000+ people who contribute their data have become possible, such as the U.K. BioBank (https://www.ukbiobank.ac.uk/), which has enabled novel data explorations into incident cardiovascular disease at scale [6]. Many innovative solutions capitalizing on (large) data streams from different sources have been proposed, coupled with emerging advances in data science and machine learning which enable fast and advanced processing of the collected datasets [7].

The development of technology-based interventions for people with cardiovascular disease needs to be reported clearly throughout each development stage. This kind of reporting will allow future research to repeat best practice development and can aid future evaluation.Cardiovascular diseases (CVDs) are a leading cause of premature death worldwide. International guidelines recommend routine delivery of all phases of cardiac rehabilitation (CR). Uptake of traditional CR remains suboptimal, as attendance at formal hospital-based CR programs is low, with community-based CR rates and individual long-term exercise maintenance even lower. Home-based CR programs have been shown to be equally effective in clinical and health-related quality of life outcomes and yet are not readily available. The aim of the current study was to develop the PATHway intervention (physical activity toward health) for the self-management of CVD. Increasing physical activity in individuals with CVD was the primary behavior. The PATHway intervention was theoretically informed by the behavior change wheel and social cognitive theory. All relevant intervention functions, behavior change techniques, and policy categories were identified and translated into intervention content. Furthermore, a person-centered approach was adopted involving an iterative codesign process and extensive user testing. Education, enablement, modeling, persuasion, training, and social restructuring were selected as appropriate intervention functions. Twenty-two behavior change techniques, linked to the six intervention functions and three policy categories, were identified for inclusion and translated into PATHway intervention content. This paper details the use of the behavior change wheel and social cognitive theory to develop an eHealth intervention for the self-management of CVD. The systematic and transparent development of the PATHway intervention will facilitate the evaluation of intervention effectiveness and future replication.

•Social robots can promote healthy behaviors in child healthcare•Social robots should be considered in the design of psychological interventions•Rigorous studies with social robot-based interventions are needed

Aims Previous generations of home monitoring systems have had limited usability. We aimed to develop and evaluate a user-centred and adaptive system for health monitoring and self-management support in patients with heart failure. Methods and results Patients with heart failure were recruited from three UK centres and provided with Internet-enabled tablet computers that were wirelessly linked with sensor devices for blood pressure, heart rate, and weight monitoring. Patient observations, interviews, and concurrent analyses of the automatically collected data from their monitoring devices were used to increase the usability of the system. Of the 52 participants (median age 77 years, median follow-up 6 months [interquartile range, IQR, 3.6–9.2]), 24 (46%) had no, or very limited prior, experience with digital technologies. It took participants about 1.5 min to complete the daily monitoring tasks, and the rate of failed attempts in completing tasks was <5%. After 45 weeks of observation, participants still used the system on 4.5 days per week (confidence interval 3.2–5.7 days). Of the 46 patients who could complete the final survey, 93% considered the monitoring system as easy to use and 38% asked to keep the system for self-management support after the study was completed. Conclusion We developed a user-centred home monitoring system that enabled a wide range of heart failure patients, with differing degrees of IT literacy, to monitor their health status regularly. Despite no active medical intervention, patients felt that they benefited from the reassurance and sense of connectivity that the monitoring system provided.