The development, validation, and intervention results are reported below for each eHealth domain. No intervention studies were found for the domains of air quality, activity, sleep, and audiovisual.
Lung Function Domain
Seventy-four development/validation articles were found that monitored lung function at home, and all used spirometric measurements, such as PEF and forced expiratory volume in 1 second (FEV1). Older articles mainly used hand-held peak flow devices, whereas more recent articles more often used hand-held spirometers. Sixteen studies combined spirometry with other home-monitoring devices, such as cough, sleep, and air quality sensors [21,22,31,34,36,40-45,51,54-58].
A good correlation of portable devices with hospital-based measurements was reported for both the PEF and FEV1 measurements [52,59-61], where home measurements tended to be lower (mean difference PEF: 22-55 L/min; FEV1: 0.02-0.15 L) [52,60,62]. The frequently incorrect, invented, or missing data in written diaries, with discrepancy between measured and self-reported values up to 35-50% [63-68], emphasize the need for electronic monitoring to prevent reporting bias [63,67,69,70].
There was little evidence evaluating the quality of execution of home-measured lung function. The quality of spirometry at home seemed acceptable and reproducible [71-74], even when compared with in-office spirometry [73,74]. Gamification methods were developed, aiming to enhance the quality and adherence of lung function measurements [75-77].
From the 1990s onwards, multiple studies reported on the limited value of PEF monitoring [78-80], as it was not a sensitive or specific objective index of lung function [81,82] and it poorly reflected changes in asthma activity [64,66,83-85]. When comparing PEF monitoring and spirometry, Sly et al [86] showed a moderate correlation between these. Two other validation studies using FEV1 for home-monitoring purposes showed a poor concordance of FEV1 with disease activity [84] and only small differences in FEV1 between symptom days and symptom-free days [85]. On the contrary, van der Kamp et al [3] described that variation in FEV1 distinguished between controlled and uncontrolled asthma.
Intervention Studies
Thirty-eight intervention studies on lung function monitoring were found. Within these intervention studies, lung function was commonly measured on standard intervals, with few studies reporting reversibility at home [87-90] or additional measurement of lung function when experiencing an increase in asthma symptoms [89-91]. Lung function monitoring was combined with symptom monitoring with a diary, questionnaire, or online contact in several studies [92-98]. Eleven studies took the quality of execution of spirometric maneuvers into account by providing feedback on the technique at more than one moment during clinical visits or by video assessment [88-90,92,95,99-103].
Patients and parents reported high rates of satisfaction with PEF monitoring [87,92,93,102], for example, to detect poor symptom perception, to assess bronchodilator response, or for reassurance in case of normal PEF values [87]. Despite known technical and logistical errors with PEF monitoring, acceptable compliance and feasibility were shown when introduced in a motivated group [90,104]. Multiple studies used a decision-support tool to adjust treatment based on home-monitored PEF values [97,100,105-107]. For example, Savva et al [105] showed a decrease in short-acting beta-agonist (SABA) use when SABA use was based on personal PEF values.
Myers et al [108] described the use of PEF monitoring in their review and concluded that the debate on the added value of PEF monitoring for asthma outcomes remains inconclusive. The reported advantages of PEF monitoring are increased self-management [98,109,110], increased trigger identification [111], and decreased emergency department contacts [92,110] and hospital admissions [92]. However, other studies reported no reduction in morbidity [112], QoL [88,92], or health care use [88], or improvement in symptom perception [88] when using PEF monitoring [112]. Moreover, Kamps et al [112] and Brouwer et al [113] concluded that PEF monitoring is not recommended in children with asthma on a routine basis, but can be helpful in some patients and can help to identify asthma triggers.
For interventions that include home spirometry monitoring, the results are also inconclusive, as some studies showed an improved QoL [94] and increased asthma control [96,114], whereas others showed no improvement in QoL [102] or the number of exacerbations [102]. Spirometry and PEF measurements at home are still an ongoing research topic, as several protocol studies included home monitoring of lung function to study the effects on perception, QoL, adherence, and health care use [115-117].
Audiovisual Measures Domain
Twenty-seven studies were found, and all used sound recordings, focusing on the presence, quantity, or characterization of wheeze or cough (13 studies focused on wheeze, 8 on cough, and 5 on both), mainly in primary school children (20/27, 74%). None of the retrieved studies used video recordings. There were several ways that cough or wheeze were recorded at home, such as microphones placed in the room [22], close to the mouth [125-129], or at the chest or trachea [128-130]. Yu et al [131] used a soft stethoscope that reduced ambient sounds, and Satat et al [132] used an array of stethoscopes to allow localization of respiratory sounds. Electromyography measurements in addition to audio recordings of cough can be helpful to reduce misinterpretation of audio signals [22,133,134]. From 2015 onwards, smartphones were increasingly used as a recording device for respiratory sounds [135].
Already in 1985, Archer et al [136] reported that cough recordings were a feasible way of objectifying nocturnal cough symptoms at home, and that objective measurements did not correlate with diary card scores reported by parents [137]. Bentur et al [58] found a similar result regarding wheezing.
Many studies focused on the development of algorithms to accurately identify wheeze [128,129,135,138,139] or cough [118,126,140,141] by signal processing. Wheezing algorithms traditionally used short-time Fourier transformations to retrieve dominant sound components and extract identifying wheezing features [128,139]. New methods have been developed to increase its sensitivity for low-intensity wheezes [129] and to decrease the computation time for real-time application [138]. Cough algorithms often used both temporal and spectrum analysis techniques to retrieve relevant characteristics [126,140]. Machine learning approaches, such as hidden Markov models, could then be used to combine relevant characteristics in order to distinguish silence, background noise, and cough [141].
The first development and pilot studies on cough monitoring showed some significant differentiating characteristics between asthmatic and nonasthmatic cough; however, there was a considerable error rate, suggesting that multivariate analysis would be required for accurate discrimination [125-127].
The cough count was the primary parameter used for monitoring purposes. Several studies found that cough occurred more frequently in children with current wheezing compared to asymptomatic children (39% vs 19%) [137] and also in children with mild asthma compared to healthy children [133]. Rietveld et al [55] showed that explicitly during exacerbations, children with asthma coughed significantly more than children without asthma. When cough counts were monitored over time, low to moderate temporal correlations were found with conventional measures of asthma symptoms and symptom control [142].
Several studies investigated the correlation of the presence of wheezing and lung function parameters. The diagnostic sensitivity and specificity of wheezing for a reduction in PEF of >20% were 88% and 92%, respectively [56]. Patients with objective nocturnal wheezing were characterized by a low morning FEV1 (51%) and a larger diurnal variation in FEV1 [58], and the nocturnal wheeze rate corresponded well with changes in FEV1 and symptom scores [143].
Questionnaires Domain
Twenty-four studies were found, and they focused on both primary school children and adolescents. Digital questionnaires could be divided into asthma symptom questionnaires, such as the Childhood Asthma Control Test (ACT) or Asthma Control Questionnaire (ACQ), and ecological momentary assessment, which is the collection of data from individuals in their own environment close to the occurrence to capture momentary experiences, such as thoughts, behaviors, or symptoms, when they occur. This can provide context to gathered data and prevent recall bias [31,150-152].
When comparing electronic and paper questionnaires, equal symptom scores and missing answers [153,154] and good internal consistency [155] were reported. Vargas et al [156] described higher concordance of a nurse interview and an electronic questionnaire compared to a paper questionnaire. Patients and their parents preferred electronic questionnaires over paper questionnaires [155], and electronic questionnaires tended to be classified as feasible [155-157]. van Vliet et al [158] stated that combining electronic symptom questionnaires and spirometry provides a more realistic view on asthma control than retrospective assessment during consultations in the hospital; however, adherence to electronic questionnaires and spirometry monitoring was low [158].
Nkoy et al [159] adjusted the ACT so that it could be used as a weekly monitoring tool and included a color-coded visual representation of symptom severity, which was shown to be reliable, valid, and responsive to change over time. Digital questionnaires were also used to validate other home-monitoring devices, such as FeNO [50,53], air quality [38,44], and cough sensors [142].
Intervention Studies
Eleven intervention studies using digital questionnaires were found, and of these, 8 used the ACT [14,15,96,160-164], 1 used the ACQ [165], and 2 used a different type of symptom score questionnaire [98,166]. Questionnaires were used within the interventions to keep track of asthma symptoms [96,98,160,161] or to alert health care professionals (HCPs) when symptoms increased [15,163,166]. Questionnaires were sometimes combined with other asthma control parameters (eg, lung function [98,167]) or a digital action plan [96,167].
Rikkers-Mutsaert et al [167] found a significant increase in asthma control and QoL after 3 months of “internet-based self-management” monitoring using an algorithm based on the ACQ to adjust asthma treatment when compared to usual care. However, this effect was no longer seen after 12 months [167]. Additionally, van den Wijngaart et al [15,162] showed a larger increase in the ACT score and more symptom-free days after receiving care through the “Virtual Asthma Clinic” that uses ACT monitoring compared to usual care, despite a decrease in login frequency during the 16 months of use. Asthma monitoring by use of questionnaires was cost-effective in the studies by van den Wijngaart et al; however, in the study by Beerthuizen et al [161], cost-effectiveness was not significantly proven.
Medication Monitoring Domain
Our search identified 39 development/validation studies on medication monitoring, focusing on both younger children (25 studies) and adolescents (27 studies; 18 studies focused on children of both age groups). The adherence rate of maintenance medication is an important and often monitored parameter [28,168,169], and other measurements at home include the assessment of inhaler technique and the timing, frequency, and location of rescue medication use. HCPs reported that the most significant benefit of medication monitoring is the ability to obtain real-time intervisit data and set alert thresholds based on the frequency of rescue inhaler use or the proportion of rescue versus controller inhaler use [170]. Others pointed out that it can be helpful to determine the context in which inhaler medication was used to assess the reasons for nonadherence [150,171,172].
Several ways of monitoring adherence were described, including electronic dose counters [3,33,42,169,173-185], electronic self-report of adherence [118,186], ecological momentary assessment [150,151,172], a chatbot that informs about medication adherence [187], and monitoring of inhaler adherence or technique through video or daily phone diary [49,151]. Electronic dose counters usually have the ability to register the amount of puffs used and sometimes to remind when the canister is empty [188] or when a dose is missed [189].
Electronic dose counters have shown satisfactory feasibility [189] and reliability [174,180] to remotely monitor real-time medication use, with 98% of returned monitors fully functioning and only 3.5% data loss [174]. The most common failure was actuation underrecording, indicating the importance of quality control [180]. Reviews concluded that electronic monitors are accurate but more costly than other methods of medication monitoring [168,190,191]. Chen et al [173,176] therefore described the development of low-cost electronic dose counters. Limited studies investigated inhaler technique monitoring. Nichols et al [151] concluded that monitoring of inhaler technique by video capture was feasible, and Nikander et al [184] showed a stable inhaler technique over time, even with declining adherence.
Several studies compared the accuracy of electronic adherence monitoring and self-report [178,185,191,192]. Pearce et al [191] and Bender et al [185] reported that self-report is insufficient to provide a stand-alone measure of adherence, as adherence is generally overreported in interviews, and others concluded that electronic dose counters are more accurate than adherence self-report [192] and adequately reliable when validated to canister weight [31]. Butz et al [178] furthermore concluded that electronic medication monitoring is a more precise measure of long-term medication use than self-report on diary cards, but that diary cards seem to be a valid alternative for short-term monitoring of medication use.
Increased usage of reliever medication was correlated with worse asthma control [3,172]. Walders et al [177] however stated that monitoring controller medication adherence may be more predictive of long-term morbidity than rescue medication use.
Intervention Studies
Sixty-nine intervention studies were found, and of these, 23% (16/69) were review or opinion articles. Boutopoulou et al [193] emphasized the remarkable heterogeneity between adherence assessment tools, although electronic metered-dose inhaler counters were the most common type of medication monitoring tool used in the intervention studies (over two-thirds [36/53, 68%] of studies). Most (38/53, 72%) medication monitoring interventions exclusively used medication monitoring as a single monitoring domain. A total of 9 studies (9/69, 13%) reported the combined use of medication monitoring with lung function monitoring [34,89,92,101,103,108,194-197]. Only Bui et al [34] reported a pilot intervention of a multimodal monitoring intervention including medication monitoring.
Katwa et al [198] described that a device to monitor adherence ideally reminds patients about taking medication. Overall, 40% (21/53) of intervention studies used medication reminders as a tool to increase adherence. Two distinct ways of reminding were (1) standard reminders [199-202] and (2) reminders based on monitored use [203-207]. Some studies used automatic reminders (text message feedback [206], audiovisual reminders in the device [204], and app-based reminders [207]), whereas other studies had nurses [205,208] or pharmacists [78,200,209] providing feedback. Moreover, 2 studies used the pharmacy refill data to remind patients that their inhaled corticosteroids prescription would soon be overdue [210,211].
Moreover, several protocol articles on eHealth and medication monitoring were found [78,196,197,209,212-215], and all mentioned that electronic medication monitoring will be used. In most of these protocols, medication adherence will be monitored and reminders will be used to optimize adherence [129,196,197,212,214,215]. Sportel et al [196] will also measure the inhalation technique with a smart inhaler device that can measure inhalation flow, duration, and orientation based on accelerometer sensors in the add-on device.
Usability and Feasibility
User experiences with electronic medication monitors vary when used in intervention studies. Interviews with HCPs revealed nearly unanimous agreement on the importance of electronic medication monitoring in outpatient asthma management [216]. It provides patients with an opportunity to demonstrate the responsibility they have for their condition and allows them to experience a greater sense of independence [217]. Several studies showed satisfactory feasibility of electronic monitoring [218-220]. Children and caregivers prefer devices that provide objective measures of activation, inhalation, and technique that are accurate, require little effort, and are easy to use and fit into existing routines [221,222]. Howard et al [217] reported on adolescents’ perspectives with regard to the use of automatic reminders in electronic medication monitoring and found that despite appreciating the helpfulness and overall benefit, many participants indicate that reminders could be annoying.
Feasibility concerns with electronic monitors were data transmission failure or data loss [223-225], device loss [225], and misfit of the device and canister [224]. Implementation of medication monitoring in clinical practice also revealed the challenges of the integration of sensor data with electronic health records and the adequate education of clinical staff to work with the electronic medication monitors and their generated data [216].
Effects on Adherence
In several eHealth intervention studies, automatic dose counters [194,202,226,227], pharmacy refill data [208,228], or digital diaries [195,199,201] were only used to assess medication adherence as an outcome measure, whereas other studies intervened based on medication monitoring data [190,203,229]. All studies that used reminders based on electronic medication monitoring showed a positive effect of reminders on inhaled corticosteroid adherence [203-206,210,211,230]. Furthermore, Kosse et al [200,231] showed that increased activity in a digital chat with an HCP correlated with improved self-reported adherence, while other mobile app functionalities like therapy education, peer chat, and symptom questionnaires did not affect adherence.
Monitoring interventions (with or without reminders) yielded positive effects; however, effect sizes were variable [190,203,229]. In the pooled analysis of Jeminiwa et al [10], the comparison of an eHealth intervention and control indicated a small but significant effect on medication adherence (standard mean difference=0.41; 95% CI 0.02-0.79). This was especially true for mobile health studies including audiovisual and text message reminders. Moreover, some specifically investigated nonadherence rates among pediatric asthma patients showed a decline in nonadherence rates using electronic medication monitoring with feedback [229].
Data on the sustained effects of improvement in adherence following medication monitoring are limited. Behrooz et al [230] showed a decrease in electronically measured adherence over a 12-week period while the intervention continued, whereas Spaulding et al [232] showed a sustained effect of electronic monitoring and feedback on medication adherence in their small pilot study up until 30 days after they stopped feedback (5 patients).
Effects on Asthma Outcomes
Increased asthma control, and reduced night-time wheezing, emergency department visits, and oral corticosteroid use were reported with sensor-based medication monitoring [233], whereas others showed no significant improvement in asthma outcomes [210,234]. A systematic review by Adejumo et al [235] showed meaningful sustained improvement in asthma-related outcomes in 20% of the included studies.
Monitoring Reliever Use and Inhalation Technique
While most studies focused on monitoring controller inhalation therapy, 3 intervention studies focused on monitoring rescue inhalation therapy [236-238]. These studies showed that monitoring SABA use and providing feedback led to a reduction in SABA use and an increase in symptom-free days [236-238]. Moreover, Barrett et al [236] used electronic monitoring of SABA use in combination with air quality data to identify geographical “hot spots” triggering asthma, and the data were used for policy recommendations regarding improvement of air quality.
Although the inhalation technique is an important aspect to achieve good effects of inhalation therapy, only 9 studies reported on monitoring of the inhalation technique at home by use of eHealth [92,239,240]. In these articles, there were 2 distinct ways of monitoring the inhalation technique: remote observation of therapy [92,239,240] and electronic measurement of aspects of the inhalation technique [89,196,232]. It was demonstrated that audiovisual support regarding the inhalation technique is feasible [229], can help to improve effective medication use by providing feedback on the inhalation technique [92,239,240], and can lead to improved ACT scores [229]. Bynum et al [240] showed that education and feedback for the metered-dose inhaler technique by video consultation were more effective than written instructions.
Digital Environment Domain
Our search identified 83 development and validation studies. Digital interventions used different strategies to allow remote monitoring, such as diaries and communication tools. Education, digital action plans, and automatic reminders were other components of digital environments to support asthma monitoring and treatment.
Several development studies emphasized the importance of patient-centered design approaches to meet the needs and priorities of users [241-247]. Jácome et al [248] reported that two-thirds of asthma patients expressed interest in using an app to manage their asthma, which was similar to the finding in patients with other health conditions. Interviews with patients about user preferences revealed which features of hypothetical apps were deemed important, such as reminders [230,247,249,250], tracking [230,247,249,250], social interaction [247,250,251], educational content [230,246,247,252], emergency support [247,250], and expert access [247,250]. Furthermore, the preference of customization of app features according to preferences and schedules was mentioned in several studies [247,249,253-257]. Moreover, younger children and adolescents showed different needs and user patterns regarding asthma apps [162].
The facilitators for using digital environments for pediatric asthma management were enthusiastic initiators, tailoring of care to individual patients, and long-term profit and efficiency [164]. The main barriers for the use of apps were technical problems or loss of data due to software updates [224], loss of devices [224], concerns about privacy [258], integration with the electronic medical record [164], increased clinical workload [164], and lack of financial reimbursement for services outside the routine of HCPs [258]. Meischke et al [259] showed that demographics and computer-related variables were not related to the engagement of interactive web programs.
Digital environments are increasingly including options to acquire and share monitoring data from previously mentioned monitoring domains, including medication monitoring [34,151,189,224,231,236,256], lung function [34,151,189,224,231,236,256], air pollution [23,24,28,31,33-35,124,236,260,261], and sleep or activity [28,33,34,118]. Digital environments enable easy interpretation using simple visualization of monitoring data [21,24]. Real-time analytics through clinical decision algorithms could eventually provide medical recommendations based on actual monitoring data [24].
Monitoring Components
A digital diary was often included to monitor asthma features, such as a report of personal asthma symptoms, medication use, or activity [28,33,118,124,153,186,187,199,200,249, 252,262-266]. Furthermore, several studies used predefined methods of logging, such as recurrent online questionnaires [35,54,124,249,259,267-271] or ecological momentary assessment [28,31,151,183]. Simple diary visualization by using icons in a digital calendar can be comprehended by primary school children and is therefore a usable tool to report data [264]. Equivalent [153] or better [265] test-retest reliability was reported for eDiaries compared to paper-and-pencil diaries, with a higher compliance [265].
Over 25% (21/83) of studies on digital interventions described the use of some form of online communication with peers, physicians, nurses, pharmacists, or other HCPs within the intervention. The different forms of communication included chatting [187,264,272], text messaging [224,257,258,267,273,274], video consulting [275], and calling [208,276]. Several studies reported the use of automated chatbots, which revealed good usability and the ability to elicit daily responses [187,264,272]. Yoo et al [250] described that most online communication tools were task focused instead of socioemotional. Roberts et al [277] showed that the majority of adolescents believed that apps could enhance communication with their medical provider and give them more control in the patient-provider relationship. This was also recognized in a pilot study by Haze et al [267], where teenagers and HCPs perceived improved access and quick response times when using a telephone app to communicate.
Supportive Components
Many studies included some form of educational content [187,200,219,231,242,243,249,252,262,263,267,276,278-281]. Others included education by peer support as sharing experiences can help to learn practical skills such as managing asthma, seeking support, or self-advocacy [251,263]. Schneider et al [243] demonstrated that patients preferred education through concise text or short videos (<10 min). Moreover, over the past years, asthma action plans have been integrated into digital asthma care technology [28,33,267,268,271,278,282]. Odom et al [268] showed the positive feasibility of digital asthma action plans.
Gamification was sometimes used with the aim to improve asthma knowledge and management [249,261,278,283], or more specifically improve treatment adherence [199,203] or spirometric adherence and quality [75,76]. An example of a visual incentive to improve home spirometry is real-time feedback through a dragon that spits fire based on spirometric performance [75,76]. Another game element that was often included to achieve and maintain compliance was a reward system [199].
Furthermore, automatic reminders were integrated into many digital environments, for example, to send sensor data or take maintenance medication [35,67,118,187,199,200,203,231,249, 263,276,282,284].
Intervention Studies
Seventy-six intervention studies were found within the digital intervention domain. Digital interventions for optimal and personalized asthma management included a range of digital tools for self-monitoring of symptoms or disease control, and addressed self-management action plans and patient educational materials [285]. The interventions were mostly performed by a nurse or were app based, and sometimes a medical specialist, pharmacist, or other HCP performed the intervention. Overall, 74% (56/76) of these interventions were mobile based, especially in the last few years. Future studies will focus on a variety of digital interventions, often combining different monitoring tools, such as a diary, lung function measurements, medication adherence, and ACT scores [117,152,197,209,213,285,286], with a trend toward more automatization in remote monitoring [117,209,213].
Several reviews showed that digital interventions can be beneficial for adherence, asthma self-management, and asthma control [7,8,287-289]. The effects of digital interventions were categorized into the following outcome measures: symptoms, QoL, lung function, adherence, self-management, health care use, and school absence.
Symptoms
Twenty-two studies showed significant asthma-related symptom reduction [8,14,98,290], whereas 9 studies showed no effect of the intervention on asthma symptoms [52,194,202,291-296]. Larger effects on asthma symptoms were reported when children had uncontrolled asthma at baseline [189,211,297].
Asthma questionnaires, such as the ACT [14,65,93,94,96,98,163,195,202,237,290,291,298-300] and ACQ [208,301] were often used to report asthma symptoms. Other measures used to specify asthma symptoms were SABA use [236-238] and symptom-free days [236,237,294,302] or alternative quantitative or qualitative methods to assess asthma symptoms. Overall, 87% (20/23) of the studies that showed significant improvements in asthma symptoms used a quantitative measure (eg, ACT, ACQ, symptom-free days, or SABA use).
Although some studies within the development and validation phase focused on symptom perception [118,245], no intervention studies reported on symptom perception as an outcome measure.
QoL
Of the 19 studies that reported on the effect regarding QoL, 10 reported a positive effect [8,98,290,303] and the other 9 reported no difference [14,293,304,305] regarding the studied intervention. There was no clear difference in the types of interventions that had significant or no significant effect on QoL.
Lung Function
A limited number of studies used lung function as an outcome measure. Five studies showed an improvement in lung function [8,92,95,98,289], whereas 4 studies showed equal lung function after the intervention [52,94,195,302]. There was no clear difference in the types of interventions that had significant or no significant effect on lung function outcomes.
Adherence
Most studies (19/23, 83%) showed a positive effect of their digital intervention on controller medication adherence [96,98,304,305], whereas few studies showed no effect [208,297]. This corresponds with the review results of Ramsey et al [8], who showed that 87% of digital interventions improved adherence. Furthermore, Fiks et al [228] reported an improvement only in children with uncontrolled asthma at baseline, and Wiecha et al [194] only reported improvement when baseline adherence was poor. Factors that had a positive impact on adherence were related to active involvement with the intervention, such as regular use of an app [96] and the use of the provided chat function [231].
Self-management
Different measures were used to define self-management, such as asthma knowledge score, self-confidence, support-seeking, coping, and self-efficacy score. Five studies showed an improvement in self-management [98,103,290,300,306], whereas 2 studies showed no effect [93,208]. The review by Tan et al [307] scored different asthma apps on a list of 6 self-management principles and reported that many apps fell short on these self-management principles. The 2 main components of digital interventions that reported on the influence on asthma self-management were (1) online education [98,103,290,300,306] and (2) the use of a digital asthma action plan [93,300].
Health Care Use and Costs
Within the studies that reported on health care use and costs, 2 types of interventions could be identified. The first type of intervention tried to replace current outpatient visits with digital alternatives, such as teleconsultation [291,302,308] or digital symptom monitoring [14,15]. These studies all reported reductions in outpatient care [14,291,302,308]. The Cochrane review by Kew et al [291] concluded that current randomized evidence does not demonstrate any important differences between face-to-face and remote asthma check-ups in terms of exacerbations, asthma control, or QoL. The second type of digital intervention aimed to reduce total or urgent health care use by improving asthma management and asthma control. Within this category, 6 studies showed reduced health care use [163,289,298,305,309,310], while 4 studies showed no significant change in health care use [14,87,293,297,311]. It is remarkable that although many studies adopted urgent care use as an outcome measure (emergency visits and hospital admissions), few studies reported on health care costs or cost effectiveness [298,311].
School Absence
Four studies showed a positive effect of the intervention on school absence [8,163,290,298], whereas 8 studies showed no significant effect [14,194,289,294,296,297,305]. A common factor in successful interventions was active involvement or interaction with HCPs after the digital intervention in terms of personalized feedback [290], nurse-led management [298], or proactive contact when issues were identified [163].