Background: Asthma is one of the most common chronic diseases in childhood. Regular follow-up of physiological parameters in the home setting, in relation to asthma symptoms, can provide complementary quantitative insights into the dynamics of the asthma status. Despite considerable interest in asthma home-monitoring in children, there is a paucity of scientific evidence, especially on multi-parameter monitoring approaches. Therefore, the aim of this study is to investigate whether asthma control can be accurately assessed in the home situation by combining parameters from respiratory physiology sensors. Methods: Sixty asthmatic and thirty non-asthmatic children were enrolled in the observational WEARCON-study. Asthma control was assessed according to GINA guidelines by the paediatrician. All children were also evaluated during a 2-week home-monitoring period with wearable devices; a physical activity tracker, a handheld spirometer, smart inhalers, and an ambulatory electrocardiography device to monitor heart and respiratory rate. Multiple logistic regression analysis was used to determine which diagnostic measures were associated with asthma control. Results: 24 of the 27 uncontrolled asthmatic children and 29 of the 32 controlled asthmatic children could be accurately identified with this model. The final model showed that a larger variation in pre-exercise lung function (OR = 1.34 95%-CI 1.07-1.68), an earlier wake-up-time (OR = 1.05 95%-CI 1.01-1.10), more reliever use (OR = 1.11 95%-CI 1.03-1.19) and a longer respiratory rate recovery time (OR = 1.12 95%-CI 1.05-1.20) were significant contributors to the probability of having uncontrolled asthma. Conclusions: Home-monitoring of physiological parameters correlates with paediatrician assessed asthma control. The constructed multivariate model identifies 88.9% of all uncontrolled asthmatic children, indicating a high potential for monitoring of asthma control. This may allow healthcare professionals to assess asthma control at home.

We have built and implemented a set of metaphors for breathing games by involving children and experts. These games are made to facilitate prevention of asthma exacerbation via regular monitoring of children with asthma through spirometry at home. To instruct and trigger children to execute the (unsupervised) spirometry correctly, we have created interactive metaphors that respond in real-time to the child's inhalation and exhalation. Eleven metaphors have been developed in detail. Three metaphors have been fully implemented based on current guidelines for spirometry and were tested with 30 asthmatic children. Each includes multi-target incentives, responding to three different target values (inhalation, peak expiration, and complete exhalation). We postulate that the metaphors should use separate goals for these targets, have independent responses, and allow to also go beyond expected values for each of these targets. From the selected metaphors, most children preferred a dragon breathing fire and a soccer player kicking a ball into a goal as a metaphor; least liked were blowing seeds of a dandelion and applying lotion to a dog to grow its hair. Based on this project we discuss the potential and benefits of a suite-of-games approach: multiple games that each can be selected and adapted depending on personal capabilities and interests. CCS CONCEPTS• Human-centered computing → Empirical studies in ubiquitous and mobile computing; User studies.

Background Early detection of loss of asthma control can effectively reduce the burden of the disease. However, broad implementation in clinical practice has not been accomplished so far. We are in need of research investigating the operationalization of eHealth pediatric asthma care in practice, which can provide the most potential benefits in terms of adoption, efficiency, and effectiveness. Objective The aim of this study was to investigate the technical and clinical feasibility, including an exploration of the efficacy and cost-efficiency, of an eHealth program implemented in daily clinical pediatric asthma practice. Methods We designed an eHealth-supported pediatric asthma program facilitating early detection of loss of asthma control while increasing symptom awareness and self-management. In the 6-month program, asthma control was monitored by 4 health care professionals (HCPs) by using objective home measurements and the web-based Puffer app to allow timely medical anticipation and prevent treatment delay. Technical feasibility was assessed by technology use, system usability, and technology acceptance. Clinical feasibility was assessed by participation and patient-reported health and care outcomes and via a focus group with HCPs regarding their experiences of implementing eHealth in daily practice. The efficacy and cost-efficiency were explored by comparing pretest-posttest program differences in asthma outcomes (asthma control, lung function, and therapy adherence) and medical consumption. Results Of 41 children, 35 children with moderate-to-severe asthma volunteered for participation. With regard to technical feasibility, the Puffer app scored a good usability score of 78 on the System Usability Scale and a score of 70 for technology acceptance on a scale of 1 to 100. Approximately 75% (18/24) of the children indicated that eHealth helped them to control their asthma during the program. HCPs indicated that home measurements and real time communication enabled them to make safe and substantiated medical decisions during symptom manifestations. With an average time commitment of 15 minutes by patients, eHealth care led to a 80% gross reduction (from €71,784 to €14,018, US $1=€0.85) in health care utilization, 8.6% increase (from 18.6 to 20.2, P=.40) in asthma control, 25.0% increase (from 2.8 to 3.5, P=.04) in the self-management level, and 20.4% improved (from 71.2 to 76.8, P=.02) therapy adherence. Conclusions eHealth asthma care seems to be technically and clinically feasible, enables safe remote care, and seems to be beneficial for pediatric asthma care in terms of health outcomes and health care utilization. Follow-up research should focus on targeted effectiveness studies with the lessons learned, while also enabling individualization of eHealth for personalized health care.