Background
Asthma is one of the most common chronic diseases in childhood and has a major impact on the quality of life [
1,
2]. Paediatric asthma is characterized by chronic airway inflammation and bronchial hyperresponsiveness to triggers such as allergens, exercise and viral infections. Symptoms include shortness of breath, wheeze and cough hampering sleep, play and sports [
3]. National and international respiratory associations recognize the scale and impact of this chronic lung disease [
4,
5].
The Dutch lung alliance states that regular follow-up of asthma control is needed to prevent disease deterioration and boost quality of life [
4]. However, scheduled outpatient-clinic evaluations at infrequent intervals do not always follow the fluctuating course of paediatric asthma symptoms. Moreover, this follow-up normally requires extensive evaluation in a hospital setting to accurately assess the asthma status of a child according to the guidelines of the Global Initiative for Asthma (GINA) (i.e. the assessment of asthma symptom control, monitoring risk factors (lung function, airway hyperresponsiveness and exacerbations) and assessing treatment factors (adherence/inhalation technique)) [
5]. Ambulant monitoring provides opportunities to objectively follow-up physiological parameters by longitudinal measurements in daily life, outside regular visits, and may provide healthcare professionals with complementary insights into the dynamics of the asthma status.
Asthma control questionnaires are used to assist in monitoring symptom severity in the home-situation [
6‐
8]. These questionnaires offer an easy low-cost option to follow-up symptom control on a regular basis. However, they are also prone to symptom misperception, individual interpretation of the questions, and recall bias [
9,
10]. Moreover, children quickly adapt their behaviour to pathophysiological decline in asthma control and consequently report no or subtle symptoms, while the decline might be serious [
11,
12]. Monitoring the questionnaire scores alone has yet not been able to improve symptom management or impact daily life [
13]. This stresses the urge for additional complementary objective methods to monitor children with asthma at home, providing real-time assessment of symptoms and physiological modulation [
14].
The most frequently investigated home-monitoring device dates back from the pre-technology-era and is the peak expiratory flow meter [
15]. Kotses et al. [
16] concluded that peak flow only gives a small increment in effectiveness beyond that afforded by symptom monitoring. In the last decade, literature also reveals increasing efforts in monitoring medication adherence at home to steer asthma management [
17]. Other home-monitoring studies involved measurements of physical activity [
18,
19], inflammation markers [
20], respiratory distress [
21]
, or coughing and wheezing [
22]. All individual parameters showed potential in monitoring asthma but were individually not strongly related to control of asthma in a broad paediatric population.
Despite considerable interest in asthma home-monitoring in children, there is a paucity of scientific evidence, especially on multi-parameter monitoring approaches [
13,
23]. We hypothesize that a holistic home-monitoring approach, combining the outcomes of multiple wearable devices signalling respiratory physiology, can provide quantitative relevant information on paediatric asthma control. Therefore, the objective of the WEARCON study is to investigate whether asthma control can be accurately assessed in the home-situation with a combination of measurements from respiratory physiology sensors.
Discussion
This study showed that data acquired from home-monitoring devices is strongly associated with the control of asthma, as assessed in the outpatient-clinic during an extensive evaluation including a bronchoprovocation test. The variation in lung function, the wake-up-time, the reliever usage and the recovery time of the respiratory rate after exercise did significantly distinguish between controlled and uncontrolled asthma in univariate analysis. Most striking is that the combination of these parameters can accurately identify 88.9% of all uncontrolled asthmatic children, suggesting a high potential of a holistic monitoring approach to assess paediatric asthma control at home.
To our knowledge, no studies are available using a multi-dimensional wearable monitoring approach in children with asthma to objectively assess asthma control, making WEARCON unique through its innovative approach of using state of the art technology. Honkoop et al. [
38] published their study protocol about the prediction of exacerbations and deterioration in asthma control in adults using mHealth. Their approach resembles the WEARCON protocol in measuring spirometry, respiratory rate, physical activity and medication adherence.
Univariate analysis showed a significant difference in the variation in FEV
1, which implies that uncontrolled asthmatic children show a wider range of pre-exercise FEV
1 (mean 18.0%). Results of Brouwer et al. [
39] are in line with our results. They found a mean FEV
1 variation of 5.7% and suggested a disease cut-off of 11.8%. In their follow up research in 2010 Brouwer et al. [
40] concluded that the contribution of FEV
1 variation in diagnosing asthma in children is limited. Their study however aimed to differentiate asthmatic from non-asthmatic children, which may explain the different findings as controlled and uncontrolled asthmatic children were merged in one group.
The uncontrolled asthmatic children woke up earlier compared to the controlled asthmatic children. This is compatible with the circadian rhythms of asthma mediators such as cortisol and histamine [
41]. Although previous studies found that children with uncontrolled asthma wake-up more often during night [
42,
43], the wake-up-time was not previously found to be altered in children with uncontrolled asthma. Van Maanen et al. [
44] found no differences in sleep parameters between children with frequent asthma symptoms and children without symptoms in the PIAMA birth cohort study, but no electronic sleep monitoring was used and they questioned whether their asthma questions on nocturnal asthma were sensitive enough to find an effect.
The GINA asthma strategy states that children with high use of short-acting bronchodilators are at risk for uncontrolled asthma [
5]. The results of the WEARCON study correspond with that statement as the odds ratio indicates that every additional inhalation over a two-week period increases the risk of uncontrolled asthma with 10.5%. This emphasizes the importance of assessing inhaler use objectively with smart inhaler technology. Moreover, the reliever use data shows a high variability among children within the two asthma groups. We do believe that the classification of asthma control based on the amount of reliever use should therefore be made with caution, and in combination with other objective parameters, as poor symptom perception may influence the reliever use behavior.
The respiratory rate recovery time after exercise was on average almost twice as long (40 s) in children with uncontrolled asthma compared to children with controlled asthma. This seems small, but hampers children’s typical frequent short bust of intense activity [
45]. No other studies investigated this parameter in asthmatic children. Post-exercise recovery in adolescents and adults is mediated by change in the RR and in the tidal volume. However, in children the RR recovery is the main contributor [
46]. In children with uncontrolled asthma, the recovery of respiratory rate after exercise may be increased as bronchoconstriction compromises ventilation. Therefore, we expect the RR recovery to be a reproducible measure, just depending on the bronchoconstriction severity and possibly the cardio respiratory fitness. This is important to explore in a validity and reproducibility study.
Several single parameters could significantly distinguish between controlled and uncontrolled asthma in univariate analysis, which may reveal a suggestion for the individual patient whether his/her asthma is controlled or not. However, as Fig.
3 reveals, there is quite some overlap between the controlled and uncontrolled group, so the parameters in isolation may not provide sufficient accuracy, as previously found in literature [
18‐
22]. This also holds true for the clinical practice as clinicians will not let them guide based on a single question/answer during a patient visit. Clinicians are trained to combine all the factors to come up with the right diagnosis. The multivariate model resembles this viewpoint and based on the results of this study do provide a more accurate classification of asthma control compared to the GINA questions alone.
A limitation of this study is that the non-asthmatic group was not matched to the asthma groups for gender. Prevalence of asthma is higher in boys than girls [
1]. This corresponds with the baseline characteristics of the asthma groups in this study. However, our non-asthmatic group is 50/50 divided, possibly confounding univariate comparison between the asthmatic groups and the non-asthmatic children for several home-monitoring parameters (e.g. the amount of vigorous activities [
47]). Nevertheless, the multivariate model was not affected by this limitation as the model was solely build on the data of the asthmatic children.
Although the results of this study emphasize the potential relevance of home-monitoring, further studies should validate the model of the WEARCON study. The model has been built on a training dataset of 60 asthmatic children, but has to be validated with a validation dataset of home-monitoring data in asthmatic children to determine the exact effect size.
The implication of the observations in our study is that a tool to reliably monitor asthma control at home seems attainable. Moreover, children were adherent to the home-measurements for the study period of 2 weeks. Children and parents embraced home-monitoring as was shown in the high participation rate and high adherency. However, for long-term asthma care the home-monitoring tool should be lean, non-obtrusive and proportional to the severity of the disease to maximize usability, engagement and minimize the burden to the child [
48]. Such a tool could be a stepping stone to better follow the fluctuations of the asthma status and timely anticipate on signalled changes in asthma control. This could improve the current clinical evaluation of asthma control, which is intermittent and subjective. Proper randomized controlled trials and longitudinal studies will be needed to establish the efficacy of home-monitoring on asthma control when implemented in the paediatric asthma care.
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