This exploratory study had a quasi-experiment single arm pretest-posttest design to assess the feasibility of an eHealth program implemented in pediatric asthma care. To explore the efficacy and efficiency of the eHealth program compared to those of regular care, historical data were used for comparison.

In total, 41 children (age 4-18 years) with moderate-to-severe pediatrician-diagnosed asthma were asked to participate. They were recruited from the pediatric department of Medisch Spectrum Twente, Enschede, The Netherlands between July 2018 and May 2019 by using consecutive sampling. Children with comorbid chronic diseases or children/parents unable to understand or speak Dutch were not eligible to participate. Offline written informed consent from parents and children >12 years was obtained prior to enrolment. During the exploratory eHealth program, both the HCPs and children and parents could restore regular outpatient follow-up if desired or medically justified.

The eHealth program (Multimedia Appendix 1) was designed to detect the loss of asthma control in daily life timely and accurately [17], to increase awareness of the severity of asthma symptoms, and to improve the safety of care for both physicians and patients by using objective measurements as the basis for joint decision making. The development and content of the eHealth program was frozen during this study and consisted of the Puffer app and a set of 2 monitoring devices:

The web-based Puffer app (Figure 1) consists of the following functionalities:

The program was offered for 6 months. Before start, children and parents received the monitoring devices and app, instruction materials, and were instructed on site by the HCPs. A self-made animation (Multimedia Appendix 2) was shared with the participants to illustrate the purpose of the program. Participants were in contact from home with their HCPs (nurse practitioners, technical physician, and pediatric pulmonologist) via a web-based app (Puffer app). Within the 6-month program period, children and parents were free to use the Puffer app whenever they felt to but were encouraged to update the HCPs at least weekly. Moreover, children and parents were instructed to contact the HCPs as soon as possible when symptoms occur but were also explicitly instructed to not wait for web-based communication in case of emergency asthma exacerbations by pursuing the regular paths within the health care system. HCPs checked the Puffer app daily for new content and data and provided personalized advice via the chat based on the communication and monitored data. All data were visible to both the participants and HCPs. Moreover, once a week, the HCPs came together for a multidisciplinary consultation, in which the data trends and the communications of all patients were discussed.

Demographic characteristics (ie, age, gender, inhaled corticosteroid use, long-acting beta-agonist use, and inhalation allergy) were retrieved from the electronic patient record. The health care utilization of the patient was categorized (light ambulatory, middle ambulatory, and clinical) according to the Dutch healthcare registration system of the Dutch Health Care Authority.

This explorative study used a per-protocol analysis as the dropout rate was low, causing the analysis to better reflect the effects of eHealth when used adherently and without complications in the majority of the asthmatic children. Missing data of children who finished the eHealth program were handled by pairwise deletion. Descriptive statistics were used to examine all the continuous outcome measures and were expressed in mean (SD) for normally distributed variables and in median (IQR) for not normally distributed variables. Univariate analyses were performed on the pretest-posttest differences of the patient-reported outcomes, asthma outcomes, and care utilization with SPSS statistics (IBM Corp). The Shapiro-Wilk test was used to determine whether the variables were normally distributed. The variables that did not have a normal distribution were tested for paired differences with the Wilcoxon signed-rank test. Normally distributed variables were tested with a paired two-tailed t test. P values less than or equal to .05 were considered as significant.

Of the 35 children who participated, 30 children (mean age 11.1 [SD 4.1] years, 22 boys) finished the half-year eHealth care period. Table 1 shows an overview of the characteristics of the children. The majority (25/30) of these children had a high health care utilization.

With regard to technology use, on average, 103 (SD 71) chat messages were sent and received per patient within the half year eHealth care, which is approximately 2 messages per patient per week. The median time spent on using the Puffer app by the participants was 15 minutes per week (IQR 10-26.25 minutes per week). The spirometry adherence was on average 55.7% (SD 9.5%), with week 4 as their median first week for skipping the data upload and week 10 as their median time to have skipped 3 weeks of spirometry data. The system usability score was 78 (SD 17), which indicated good usability. The technology acceptance score was 70 (SD 18) on a scale from 0 to 100. The specific components that made up the technology acceptance showed the highest scores on intention to use (81 [SD 17%]) and ease of use (77 [SD 24%]) and lowest on control over the system (64 [SD 25%]). No critical issues were identified from the nondirective interview with children and parents. Six participants indicated the serious issue that “it would be useful if you were notified if there was a new message from the doctor,” 3 participants preferred a native app instead of a web-based app, and 2 participants indicated that they would like a graphical overview for inspection of their own data.

Participation rate was high, as 85% (35/41) of the eligible children were willing to participate in the eHealth program. Of these 35 children, 2 dropped out as they indicated to prefer regular care because the eHealth care required “too much effort” from their side. The other 3 excluded participants were prematurely excluded on indication of the HCPs as they felt that the responsibility of care came at stake owing to insufficient good quality home-monitoring data, repeated technology issues that could not be solved remotely, or late responses of the participants to symptoms. Table 2 shows the patient-reported health and care outcomes at the start and at the end of the eHealth program. It reveals that the self-management score and the associated self-management level (patient activation measure-13 item questionnaire) significantly increased through participation in the eHealth care program (P=.02). The quality of care (CSQ-8) was significantly lower at the end of the program (P=.03). The quality of life showed no significant paired differences. Prior to the start of the project, 77% (10/13) of the participants indicated that eHealth could help to control the disease compared to 75% (18/24) afterwards. Prior to the start of the project, 69% (9/13) of the participants indicated that eHealth could help to prevent admissions and emergency department visits compared to 92% (22/24) afterwards.

The focus group was attended by 4 HCPs (a pediatric pulmonologist, a technical physician, a nurse practitioner, and an asthma nurse), who were all part of the eHealth asthma team. Table 3 shows an overview of the barriers, facilitators, and recommendations mentioned, categorized into 3 main domains: technical innovations, eHealth asthma care, and implementation considerations. An elaborate written collection of the experiences of the HCPs can be found in Multimedia Appendix 3.

This eHealth program led to an improvement in asthma outcomes, as shown in Table 4. It is noticeable that lung function (+10%), self-interpreted dyspnea (VAS) (+8%), and therapy compliance (+20%) increased significantly. Moreover, an average increase of 1.7 points in the C-ACT score was noticeable after eHealth care, shifting the average from uncontrolled asthma (≤19) to controlled asthma (>20); however, this difference was not statistically significant.

The asthma care registration before and after eHealth care changed as follows: clinical T_start, n=10, T_end, n=2; middle ambulatory T_start, n=15, T_end, n=2; and light ambulatory T_start, n=5, T_end, n=26. Moreover, Table 5 shows that eHealth care resulted in a reduction of care utilization in all aspects, with 85% (from 13 to 2 admissions, 11/13) fewer hospital admissions, 81% (from 21 to 4 emergency visits, 17/21) fewer emergency department visits, and 83% (from 116 to 20 outpatient visits, 96/116) fewer outpatient visits. The reduction in care utilization ensured an average cost reduction per patient of €1925.52 (US $1=€0.85) per half year (80%). Of this, 38.2% (735.55/1925.52) is covered by outpatient savings and therefore 61.8% (1189.97/1925.52) by savings on clinical care. The average program cost was €1291.50 consisting of €420 for the monitoring devices and €871.50 for the additional hours of the HCPs. Therefore, the net cost reduction was 26.3% (634.02/1925.52).

This exploratory study revealed a high feasibility for the use of eHealth-supported pediatric asthma care to monitor and manage children with moderate-to-severe asthma. The exploratory findings showed an increase in self-management, lung function, and therapy adherence and a gross reduction in health care utilization of 80% compared to the historical medical utilization in the same patients. With regard to the technical feasibility, the eHealth system showed good usability and good technology acceptance. No critical issues were identified, but improvements were suggested by patients and HCPs to increase compatibility, enable reminders, and work toward a higher technology readiness level. The technology use of participants was sufficient but became less adherent over time and were mainly adherent to the instructed monitoring frequency in periods with increasing asthma symptoms, consistent with the “law of attraction” as previously described by Eysenbach [48] and comparable to other asthma telehealth tools [49]. Participants indicated that the lack of time and lack of pop-up reminders made them forget to share data/communications. Moreover, participants may grow into “e-attainers,” thereby receiving what was needed (eg, experienced symptom reduction) from this program, even if not from the HCPs, and they would become less adherent [13,50].

This study showed that eHealth-supported asthma care can be beneficial for patients, HCPs, and care organizations. With a participation rate of 85.4%, our eHealth program compared well to other pediatric eHealth initiatives, especially considering the half year time span of the study [51-53]. The high willingness to participate in eHealth care could be due to strengthened position of the children and parents by engaging in their own health care [54]. This provided an opportunity for them to express themselves, measure their symptoms, discuss their insecurities, and enabled them to participate in decision making [55]. Moreover, 75% of the participating children/parents were convinced that this type of eHealth care could help control the disease. In line with this, our study revealed a significant increase in self-management, which is in line with the meta-analytic review of Cushing and Steele [56] who stated, “eHealth interventions that incorporate behavioral methods (eg, self-monitoring, goal setting, immediate feedback, contingency management) produce larger effect sizes for health behaviors and their associated outcomes than interventions that rely solely on education.” Moreover, de Jongh et al [57] reported that mobile phone messaging may facilitate self-management of long-term illnesses, emphasizing the importance of direct communication between HCPs and patients. In particular, communication substantiated by self-monitoring data can build up the confidence of children and parents and enhance understanding and self-management of disease. The C-ACT score did not show a significant improvement. However, the average increase from 18.6 (which reflects uncontrolled asthma) to 20.2 (which is clinically interpreted as controlled asthma) indicates a fair margin of improvement in the moderate-to-severe asthma in the population included in this study [5].

This study showed a 80% gross reduction in health care utilization, which was also reflected by the beliefs of the participants themselves; 77% (23/30) of the parents at the start of the program and 92% (24/26) after participating in the program claimed that eHealth in children with asthma could help prevent admissions and emergency department visits. The eHealth care provided a platform for transparent knowledge transfer about the course of individual asthma symptoms and how to manage these. This may have undermined a common belief in patients that asthma is an acute rather than a chronic condition [58], leading to improved therapy compliance and better asthma outcomes in a majority of the children and parents.

Although most eHealth interventions report improved patient outcomes, there still is skepticism about the use of eHealth [59-61]. What is the balance between obtrusiveness of home measurements versus the relevance for disease monitoring? How does eHealth adapt to the individual needs of a patient? Does continuous data collection at home compete with privacy rights and how are data securely managed, processed, and stored? These aforementioned barriers combined with the contextual obstacles (such as workplace reorganization and changes in employment and work priorities) contribute to the conservative attitude of HCPs toward eHealth [59,60]. In contrast, this proof-of-concept study and the study of Simpson et al [62] show legitimate support of mHealth to assist with asthma self-management by both individuals with asthma and HCPs. The HCPs who participated in this study’s focus group were confident and enthusiastic about the potential of eHealth care and indicated that current barriers in the organizational and technological aspects are solvable. Specific future focus should be on the safety aspects of eHealth care so that noncompliance to eHealth cannot lead to missing crucial disease information. HCPs also expressed that the ever-expanding data-driven community combined with the increasing amount and quality of available eHealth technologies will slowly occupy a permanent place within the pediatric asthma care. Nonetheless, there is still a lot to gain in bringing these interventions to practice. Next to technological and contextual improvements, HCPs indicated that follow-up research should focus on investigating the adoption of eHealth within the medical guidelines, individualization of eHealth interventions, and protocolization of eHealth use for specific subissues (ie, poor adherence, at risk for exacerbations, and low self-management level). Although cost-effectiveness is particularly important in health care, only few eHealth systems have demonstrated economic advantage, which makes investments in technology by commissioners of services unlikely and implementation even harder [61]. This exploratory study investigated the economic effect and revealed a marked reduction in health care costs by secondary and tertiary prevention with the use of objective monitoring and direct communication. Taking into account the task reallocation of the HCPs and the additional costs of eHealth care resulted in an estimated net cost reduction of 26%, enabling further steps for financial coverage.

The eHealth program incorporated combined sensing technologies, which could be used to monitor and estimate the disease course, enabling HCPs to anticipate early by medical interventions. This has not been explored in the field of pediatric eHealth care effect studies before and builds upon existing evidence of improved asthma outcomes in eHealth studies using questionnaire-based asthma monitoring or digital self-management support [63-66]. Moreover, our program focused on the development of self-management by using quick, substantiated (by data), and personalized communication by the HCPs in periods of symptoms. Another strength of this program was that children with moderate-to-severe asthma were included and that the baseline characteristics reflect this population well. This population is eminently the group at risk for exacerbations and hospitalizations, and therefore, the first target group for eHealth interventions to enable more effective and efficient pediatric asthma care [67].

This study was limited by the amount of missing questionnaire data. Questionnaires that were not filled completely or incorrectly (multiple answers given in multiple choice) were excluded from the analysis to retain the validity of the questionnaire scores. Moreover, some participants returned the questionnaires by post, which additionally contributed to missing data. Web-based survey systems may help to overcome these issues [68]. The EuroQol-5D quality of life questionnaire showed to be prone to ceiling effects in the pediatric asthma population, as the questions do not really correspond to the burden of asthma on children. Therefore, specific quality-of-life questionnaires for pediatric asthma such as the pediatric asthma quality of life questionnaire are recommended [69,70].

Although this program strongly suggests that eHealth asthma care might enhance asthma outcomes with a reduction in hospital care utilization, this study was not designed as a randomized controlled trial. At the start of the program, the development of the Puffer app was frozen, but the knowledge, skills, and expertise of HCPs for applying eHealth technology in the pediatric asthma care was expected to progress, which made it better suited for a quality improvement methodology [13]. This study, therefore, demands replication and validation with a control group.

This study revealed a high feasibility for the use of ambulatory pediatric asthma care supported by combined sensing technology that monitors moderate-to-severe asthma and provides timely and substantiated medical anticipation. Future research should focus on investigating adoption of eHealth within the medical pediatric asthma guidelines and individualization of eHealth interventions to reach maximal adoption. These studies can contribute to the development and implementation of feasible ambulatory pediatric asthma interventions, which may help reducing the health burden by increasing long-term respiratory health outcomes.