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.

Background Congestive heart failure (CHF) is a disease that requires complex management involving multiple medications, exercise, and lifestyle changes. It mainly affects older patients with depression and anxiety, who commonly find management difficult. Existing mobile apps supporting the self-management of CHF have limited features and are inadequately validated. Objective The HeartMan project aims to develop a personal health system that would comprehensively address CHF self-management by using sensing devices and artificial intelligence methods. This paper presents the design of the system and reports on the accuracy of its patient-monitoring methods, overall effectiveness, and patient perceptions. Methods A mobile app was developed as the core of the HeartMan system, and the app was connected to a custom wristband and cloud services. The system features machine learning methods for patient monitoring: continuous blood pressure (BP) estimation, physical activity monitoring, and psychological profile recognition. These methods feed a decision support system that provides recommendations on physical health and psychological support. The system was designed using a human-centered methodology involving the patients throughout development. It was evaluated in a proof-of-concept trial with 56 patients. Results Fairly high accuracy of the patient-monitoring methods was observed. The mean absolute error of BP estimation was 9.0 mm Hg for systolic BP and 7.0 mm Hg for diastolic BP. The accuracy of psychological profile detection was 88.6%. The F-measure for physical activity recognition was 71%. The proof-of-concept clinical trial in 56 patients showed that the HeartMan system significantly improved self-care behavior (P=.02), whereas depression and anxiety rates were significantly reduced (P<.001), as were perceived sexual problems (P=.01). According to the Unified Theory of Acceptance and Use of Technology questionnaire, a positive attitude toward HeartMan was seen among end users, resulting in increased awareness, self-monitoring, and empowerment. Conclusions The HeartMan project combined a range of advanced technologies with human-centered design to develop a complex system that was shown to help patients with CHF. More psychological than physical benefits were observed. Trial Registration ClinicalTrials.gov NCT03497871; https://clinicaltrials.gov/ct2/history/NCT03497871. International Registered Report Identifier (IRRID) RR2-10.1186/s12872-018-0921-2

This paper presents a study of a computer game designed for the elderly, allowing them to train their memory while playing the game. The game supports both a single-player and a multiplayer mode, in which the elderly can play with their friends or family using an embedded video chat application. The main question that is addressed in this paper is how the elderly gamers' experience is influenced by the possibility to communicate directly with the other players. The study presents a comparison of the game experience and appreciation of older users and their (grand)children playing the game together, with or without the video chat application. Most importantly, the study shows that the added value of video chat is not limited to social contact, but that it also provides opportunities for the younger generation to assist the elderly during the game. In conclusion, the paper points out some intergenerational game design implications, and some future research suggestions.

In this paper, we present the GLID method to integrate verbal, material and other co-design outcomes in a structured and coherent analysis. GLID aims to increase internal rigor and transparency in Participatory Design practices and wants to go beyond the surface level of ideas, by identifying participants' values embedded in co-design outcomes. We discuss GLID's theoretical groundings in multimodality and a values-led approach to Participatory Design, and present a case study with primary school children. This case study demonstrates how the different stages of the GLID method can be applied in practice. Based on the case study, we reflect on how GLID contributes to a holistic, situated and more empathic understanding in co-design practices.

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