MyAirCoach: the use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study

Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review

BACKGROUND

Artificial intelligence is experiencing rapid growth, with continual innovation and advancements in the health care field.

OBJECTIVE

This study aims to evaluate the application of artificial intelligence technologies across various domains of respiratory care.

METHODS

We conducted a narrative review to examine the latest advancements in the use of artificial intelligence in the field of respiratory care. The search was independently conducted by respiratory care experts, each focusing on their respective scope of practice and area of interest.

RESULTS

This review illuminates the diverse applications of artificial intelligence, highlighting its use in areas associated with respiratory care. Artificial intelligence is harnessed across various areas in this field, including pulmonary diagnostics, respiratory care research, critical care or mechanical ventilation, pulmonary rehabilitation, telehealth, public health or health promotion, sleep clinics, home care, smoking or vaping behavior, and neonates and pediatrics. With its multifaceted utility, artificial intelligence can enhance the field of respiratory care, potentially leading to superior health outcomes for individuals under this extensive umbrella.

CONCLUSIONS

As artificial intelligence advances, elevating academic standards in the respiratory care profession becomes imperative, allowing practitioners to contribute to research and understand artificial intelligence's impact on respiratory care. The permanent integration of artificial intelligence into respiratory care creates the need for respiratory therapists to positively influence its progression. By participating in artificial intelligence development, respiratory therapists can augment their clinical capabilities, knowledge, and patient outcomes.

The Effect of Inhaled Beta-2 Agonists on Heart Rate in Patients With Asthma: Sensor-Based Observational Study

BACKGROUND

Beta-2 agonists play an important role in the management of asthma. Inhaled long-acting beta-2 agonists (LABAs) and short-acting beta-2 agonists (SABAs) cause bronchodilation by stimulating adrenoceptors. These receptors are also present in cardiac cells and, as a side effect, could also be stimulated by inhaled beta-2 agonists.

OBJECTIVE

This study aims to assess the effect of beta-2 agonists on heart rate (HR).

METHODS

The data were retrieved from an observational study, the myAirCoach Quantification Campaign. Beta-2 agonist use was registered by self-reported monthly questionnaires and by smart inhalers. HR was monitored continuously with the Fitbit Charge HR tracker (Fitbit Inc). Patients (aged 18 years and older) were recruited if they had uncontrolled asthma and used inhalation medication. Our primary outcome was the difference in HR between LABA and non-LABA users. Secondary outcomes were the difference in HR on days SABAs were used compared to days SABAs were not used and an assessment of the timing of inhaler use during the day.

RESULTS

Patients using LABA did not have a clinically relevant higher HR (average 0.8 beats per minute difference) during the day. Around the moment of SABA inhalation itself, the HR does increase steeply, and it takes 138 minutes before it returns to the normal range.

CONCLUSIONS

This study indicates that LABAs do not have a clinically relevant effect on HR. SABAs are instead associated with a short-term HR increase.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02774772; https://clinicaltrials.gov/study/NCT02774772.

Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device

Objectives: Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting. We conducted a validation of the automatic RR measurement capability of AcuPebble RE100 (Acurable, London, UK) against a gold-standard capnography system and a type-III cardiorespiratory polygraphy system in two independent prospective and retrospective studies. Methods: The experiment for the prospective study was conducted at Imperial College London. Data from AcuPebble RE100 (Acurable, London, UK) and the reference capnography system (Capnostream™35, Medtronic, Minneapolis, MN, USA) were collected simultaneously from healthy volunteers. The data from a previously published study were used in the retrospective study, where the patients were recruited consecutively from a standard Obstructive Sleep Apnea (OSA) diagnostic pathway in a UK hospital. Overnight data during sleep were collected using the AcuPebble SA100 (Acurable, London, UK) sensor and a type-III cardiorespiratory polygraphy system (Embletta MPR Sleep System, Natus Medical, Pleasanton, CA, USA) at the patients' homes. Data from 15 healthy volunteers were used in the prospective study. For the retrospective study, 150 consecutive patients had been referred for OSA diagnosis and successfully completed the study. Results: The RR output of AcuPebble RE100 (Acurable, London, UK) was compared against the reference device in terms of the Root Mean Squared Deviation (RMSD), mean error, and standard deviation (SD) of the difference between the paired measurements. In both the prospective and retrospective studies, the AcuPebble RE100 algorithms provided accurate RR measurements, well within the clinically relevant margin of error, typically used by FDA-approved respiratory rate monitoring devices, with the RMSD under three breaths per minute (BPM) and mean errors of 1.83 BPM and 1.4 BPM, respectively. Conclusions: The evaluation results provide evidence that AcuPebble RE100 (Acurable, London, UK) algorithms produce reliable results and are hence suitable for overnight monitoring of RR.

Enhancing Adult Asthma Management: A Review on the Utility of Remote Home Spirometry and Mobile Applications

Asthma is a prevalent chronic disease, contributing significantly to the global burden of disease and economic costs. Despite advances in treatment, inadequate disease management and reliance on reliever medications lead to preventable deaths. Telemedicine, defined as the use of information and communication technology to improve healthcare access, has gained global attention, especially during the COVID-19 pandemic. This systematic review examines the effectiveness of home monitoring systems in managing severe asthma. A systematic literature search was conducted in PubMed, Web of Science, Scopus, and Cochrane Library, focusing on studies from 2014 to 2024. Fourteen studies involving 9093 patients were analyzed. The results indicate that telemedicine, through tools such as mobile applications and portable spirometers, positively impacts asthma control, self-management, and quality of life. Home spirometry, in particular, shows strong agreement with clinic spirometry, offering a feasible alternative for continuous monitoring. Digital coaching and machine learning-based telemedicine applications also demonstrate significant potential in improving asthma outcomes. However, challenges such as technology accessibility, data privacy, and the need for standardized protocols remain. This review highlights the promise of telemedicine in asthma management and calls for further research to optimize its implementation and address existing barriers.

iPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control

Purpose

The iPREDICT program aimed to develop an integrated digital health solution capable of continuous data streaming, predicting changes in asthma control, and enabling early intervention.

Patients and Methods

As part of the iPREDICT program, asthma triggers were characterized by surveying 221 patients (aged ≥18 years) with self-reported asthma for a risk-benefit analysis of parameters predictive of changes in disease control. Seventeen healthy volunteers (age 25-65 years) tested 13 devices to measure these parameters and assessed their usability attributes.

Results

Patients identified irritants such as chemicals, allergens, weather changes, and physical activity as triggers that were the most relevant to deteriorating asthma control. Device testing in healthy volunteers revealed variable data formats/units and quality issues, such as missing data and low signal-to-noise ratio. Based on user preference and data capture validity, a spirometer, vital sign monitor, and sleep monitor formed the iPREDICT integrated system for continuous data streaming to develop a personalized/predictive algorithm for asthma control.

Conclusion

These findings emphasize the need to systematically compare devices based on several parameters, including usability and data quality, to develop integrated digital technology programs for asthma care.

Enhancing asthma research and improving health equity through decentralized clinical trials (DCTs) and mHealth technology

The COVID-19 pandemic led to widespread disruption and termination of clinical research and a prompt adoption of mobile health (mHealth) technologies in the healthcare space. As the United States' healthcare system has rapidly become reliant on remotely conducted activities, the implementation of decentralized methods using mHealth technology in research investigation has become a necessary alternative to traditional in-person cohort studies. The aim of this article is to: report successful and unsuccessful examples of remote asthma clinical studies, explore the benefits and potential drawbacks of virtual clinical investigation, discuss the potential impact on equity and representation in asthma research, and provide suggestions through which investigators can implement decentralized clinical trials. Enhanced study accessibility, participant diversity, safety measures, and research efficacy are some of the benefits identified with a focused discussion on the impact on equity that decentralized clinical trials renders. Furthermore, potential concerns regarding regulatory compliance, data privacy, and effective mHealth design and solutions are discussed. Despite the setbacks and interruptions faced by the study participants and investigators due to the pandemic, the transition to decentralized clinical studies using mHealth technology is a positive, feasible step toward innovation and equity in the allergy and immunology field.

Telemedicine with special focus on allergic diseases and asthma-Status 2022: An EAACI position paper

Efficacious, effective and efficient communication between healthcare professionals (HCP) and patients is essential to achieve a successful therapeutic alliance. Telemedicine (TM) has been used for decades but during the COVID-19 pandemic its use has become widespread. This position paper aims to describe the terminology and most important forms of TM among HCP and patients and review the existing studies on the uses of TM for asthma and allergy. Besides, the advantages and risks of TM are discussed, concluding that TM application reduces costs and time for both, HCP and patients, but cannot completely replace face-to-face visits for physical examinations and certain tests that are critical in asthma and allergy. From an ethical point of view, it is important to identify those involved in the TM process, ensure confidentiality and use communication channels that fully guarantee the security of the information. Unmet needs and directions for the future regarding implementation, data protection, privacy regulations, methodology and efficacy are described.

iPREDICT: proof-of-concept study to develop a predictive model of changes in asthma control

BACKGROUND

The individualized PREdiction of DIsease Control using digital sensor Technology (iPREDICT) program was developed for asthma management using digital technology. Devices were integrated into daily lives of patients to establish a predictive model of asthma control by measuring changes from baseline health status with minimal device burden.

OBJECTIVES

To establish baseline disease characteristics of the study participants, detect changes from baseline associated with asthma events, and evaluate algorithms capable of identifying triggers and predicting asthma control changes from baseline data. Patient experience and compliance with the devices were also explored.

DESIGN

This was a multicenter, observational, 24-week, proof-of-concept study conducted in the United States.

METHODS

Patients (⩾12 years) with severe, uncontrolled asthma engaged with a spirometer, vital sign monitor, sleep monitor, connected inhaler devices, and two mobile applications with embedded patient-reported outcome (PRO) questionnaires. Prospective data were linked to data from electronic health records and transmitted to a secure platform to develop predictive algorithms. The primary endpoint was an asthma event: symptom worsening logged by patients (PRO); peak expiratory flow (PEF) < 65% or forced expiratory volume in 1 s < 80%; increased short-acting β2-agonist (SABA) use (>8 puffs/24 h or >4 puffs/day/48 h). For each endpoint, predictive models were constructed at population, subgroup, and individual levels.

RESULTS

Overall, 108 patients were selected: 66 (61.1%) completed and 42 (38.9%) were excluded for failure to respond/missing data. Predictive accuracy depended on endpoint selection. Population-level models achieved low accuracy in predicting endpoints such as PEF < 65%. Subgroups related to specific allergies, asthma triggers, asthma types, and exacerbation treatments demonstrated high accuracy, with the most accurate, predictive endpoint being >4 SABA puffs/day/48 h. Individual models, constructed for patients with high endpoint overlap, exhibited significant predictive accuracy, especially for PEF < 65% and >4 SABA puffs/day/48 h.

CONCLUSION

This multidimensional dataset enabled population-, subgroup-, and individual-level analyses, providing proof-of-concept evidence for development of predictive models of fluctuating asthma control.

Prediction of Asthma Exacerbations in Children

Asthma exacerbations are common in asthmatic children, even among those with good disease control. Asthma attacks result in the children and their parents missing school and work days; limit the patient's social and physical activities; and lead to emergency department visits, hospital admissions, or even fatal events. Thus, the prompt identification of asthmatic children at risk for exacerbation is crucial, as it may allow for proactive measures that could prevent these episodes. Children prone to asthma exacerbation are a heterogeneous group; various demographic factors such as younger age, ethnic group, low family income, clinical parameters (history of an exacerbation in the past 12 months, poor asthma control, poor adherence to treatment, comorbidities), Th2 inflammation, and environmental exposures (pollutants, stress, viral and bacterial pathogens) determine the risk of a future exacerbation and should be carefully considered. This paper aims to review the existing evidence regarding the predictors of asthma exacerbations in children and offer practical monitoring guidance for promptly recognizing patients at risk.

Home monitoring in asthma: towards digital twins

PURPOSE OF REVIEW

We highlight the recent advances in home monitoring of patients with asthma, and show that these advances converge towards the implementation of digital twin systems.

RECENT FINDINGS

Connected devices for asthma are increasingly numerous, reliable and effective: new electronic monitoring devices extend to nebulizers and spacers, are able to assess the quality of the inhalation technique, and to identify asthma attack triggers when they include a geolocation function; environmental data can be acquired from databases and refined by wearable air quality sensors; smartwatches are better validated. Connected devices are increasingly integrated into global monitoring systems. At the same time, machine learning techniques open up the possibility of using the large amount of data collected to obtain a holistic assessment of asthma patients, and social robots and virtual assistants can help patients in the daily management of their asthma.

SUMMARY

Advances in the internet of things, machine learning techniques and digital patient support tools for asthma are paving the way for a new era of research on digital twins in asthma.

Home monitoring with connected mobile devices for asthma attack prediction with machine learning

Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma using three smart-monitoring devices (smart-peak-flow-meter/smart-inhaler/smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air-quality reports, we collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly available. Between June-2021 and June-2022, in the midst of UK's COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data.

Domiciliary Fractional Exhaled Nitric Oxide and Spirometry in Monitoring Asthma Control and Exacerbations

BACKGROUND

Domiciliary measurements of airflow obstruction and inflammation may assist healthcare teams and patients in determining asthma control and facilitate self-management.

OBJECTIVE

To evaluate parameters derived from domiciliary spirometry and fractional exhaled nitric oxide (Feno) in monitoring asthma exacerbations and control.

METHODS

Patients with asthma were provided with hand-held spirometry and Feno devices in addition to their usual asthma care. Patients were instructed to perform twice-daily measurements for 1 month. Daily symptoms and medication change were reported through a mobile health system. The Asthma Control Questionnaire was completed at the end of the monitoring period.

RESULTS

One hundred patients had spirometry, of which 60 were given additional Feno devices. Compliance rates for twice-daily measurements were poor (median [interquartile range], 43% [25%-62%] for spirometry; 30% [3%-48%] for Feno); at least 15% of patients took little or no spirometry measurements and 40% rarely measured Feno. The coefficient of variation (CV) values in FEV1 and Feno were higher, and the mean % personal best FEV1 lower in those who had major exacerbations compared with those without (P < .05). Feno CV and FEV1 CV were associated with asthma exacerbation during the monitoring period (area under the receiver-operating characteristic curve, 0.79 and 0.74, respectively). Higher Feno CV also predicted poorer asthma control (area under the receiver-operating characteristic curve, 0.71) at the end of the monitoring period.

CONCLUSIONS

Compliance with domiciliary spirometry and Feno varied widely among patients even in the setting of a research study. However, despite significant missing data, Feno and FEV1 were associated with asthma exacerbations and control, making these measurements potentially clinically valuable if used.

Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations

Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients). Two ML models (XGBoost, one class SVM) and a logistic regression model provided predictions based on peak expiratory flow and asthma symptoms. These models were compared with an asthma action plan rule. Severe exacerbations occurred in 0.2% of all daily measurements in the development (154/92,787 days) and validation cohorts (94/40,185 days). The AUC of the best performing XGBoost was 0.85 (0.82-0.87) and 0.88 (0.86-0.90) for logistic regression in the validation cohort. The XGBoost model provided overly extreme risk estimates, whereas the logistic regression underestimated predicted risks. Sensitivity and specificity were better overall for XGBoost and logistic regression compared to one class SVM and the clinical rule. We conclude that ML models did not beat logistic regression in predicting short-term severe asthma exacerbations based on home monitoring data. Clinical application remains challenging in settings with low event incidence and high false alarm rates with high sensitivity.

Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol

INTRODUCTION

Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices.

METHODS AND ANALYSIS

A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire.

ETHICS AND DISSEMINATION

Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.

mHealth and telemedicine utility in the monitoring of allergic diseases

This literature review discusses the use of mHealth technologies and telemedicine for monitoring various allergic diseases both in everyday life and in the context of COVID-19. Telemedicine, whose popularity, and demand has skyrocketed during the pandemic, rely on mHealth technologies, video calls and websites as a resource-saving and safe way of consulting patients. The incorporation of new mHealth technologies into telemedicine practice may not only be relevant in the context of pandemic restrictions but can also be applied in everyday medical practice as an effective method of patient counseling. The mobile healthcare applications include a wide range of mobile apps for patients' education, monitoring, and disease management. However, applications for the people with food allergies lack relevant information about allergies and, like most other applications, are developed without the contribution of healthcare specialists. During the COVID-19 pandemic, low-risk food-allergic patients were able to rely on telemedicine services where they could get the help, they needed without increasing risk of contracting COVID-19 while saving time. Meanwhile, some applications for allergic rhinitis and asthma patients are showing practical benefits in clinical trials by allowing an efficient assessment of treatment regimens and efficacy. The use of digital symptom diaries further facilitates the implementation of real-life studies. However, for respiratory allergic diseases, the often insufficient quality of pollen prediction needs to be taken into account. Even though studies have shown that asthma is better controlled with mHealth technologies, the quality of mobile apps for asthma patients varies widely, as many products provide information that has not been scientifically proven. Inhaler sensors - have been shown to improve the course of asthma and its monitoring, while push notifications prompting people to take their medication double the likelihood of treatment adherence. Teledermatology has a high level of patient satisfaction - as it is perceived as a more time-saving method of consultation. However, the diagnostic accuracy of contact consultations remains higher. mHealth technologies provide a patient's health data from his/her daily life, which enables insights into behavioral patterns. This closer look at the daily routine can have a significant impact on developing individualized treatment and care guidelines.

Digital tools in allergy and respiratory care

Patient care in the allergy and respiratory fields is advancing rapidly, offering the possibility of the inclusion of a variety of digital tools that aim to improve outcomes of care. Impaired access to several health care facilities during the COVID-19 pandemic has considerably increased the appetite and need for the inclusion of e-health tools amongst end-users. Consequently, a multitude of different e-health tools have been launched worldwide with various registration and access options, and with a wide range of offered benefits. From the perspective of both patients and healthcare providers (HCPs), as well as from a legal and device-related perspective, several features are important for the acceptance, effectiveness,and long-term use of e-health tools. Patients and physicians have different needs and expectations of how digital tools might be of help in the care pathway. There is a need for standardization by defining quality assurance criteria. Therefore, the Upper Airway Diseases Committee of the World Allergy Organization (WAO) has taken the initiative to define and propose criteria for quality, appeal, and applicability of e-health tools in the allergy and respiratory care fields from a patient, clinician, and academic perspective with the ultimate aim to improve patient health and outcomes of care.

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Background

Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap.

Methods

We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm.

Results

Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms.

Discussion

In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

The Current and Future Role of Technology in Respiratory Care

Over the past few decades, technology and improvements in artificial intelligence have dramatically changed major sectors of our day-to-day lives, including the field of healthcare. E-health includes a wide range of subdomains, such as wearables, smart-inhalers, portable electronic spirometers, digital stethoscopes, and clinical decision support systems. E-health has been consistently shown to enhance the quality of care, improve adherence to therapy, and allow early detection of worsening in chronic pulmonary diseases. The present review addresses the current and potential future role of major e-health tools and approaches in respiratory medicine, with the aim of providing readers with trustful and updated evidence to increase their awareness of the topic, and to allow them to optimally benefit from the latest innovation technology. Collected literature evidence shows that the potential of technology tools in respiratory medicine mainly relies on three fundamental interactions: between clinicians, between clinician and patient, and between patient and health technology. However, it would be desirable to establish widely agreed and adopted standards for conducting trials and reporting results in this area, as well as to take into proper consideration potentially relevant pitfalls related to privacy protection and compliance with regulatory procedures.

A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than three million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.

Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research

Chronic obstructive pulmonary disease (COPD) remains a leading cause of morbidity and mortality despite current treatment strategies which focus on smoking cessation, pulmonary rehabilitation, and symptomatic relief. A focus of COPD care is to encourage self-management, particularly during COVID-19, where much face-to-face care has been reduced or ceased. Digital health solutions may offer affordable and scalable solutions to support COPD patient education and self-management, such solutions could improve clinical outcomes and expand service reach for limited additional cost. However, optimal ways to deliver digital medicine are still in development, and there are a number of important considerations for clinicians, commissioners, and patients to ensure successful implementation of digitally augmented care. In this narrative review, we discuss advantages, pitfalls, and future prospects of digital healthcare, which offer a variety of tools including self-management plans, education videos, inhaler training videos, feedback to patients and healthcare professionals (HCPs), exacerbation monitoring, and pulmonary rehabilitation. We discuss the key issues with sustaining patient and HCP engagement and limiting attrition of use, interoperability with devices, integration into healthcare systems, and ensuring inclusivity and accessibility. We explore the essential areas of research beyond determining safety and efficacy to understand the acceptability of digital healthcare solutions to patients, clinicians, and healthcare systems, and hence ways to improve this and sustain engagement. Finally, we explore the regulatory challenges to ensure quality and engagement and effective integration into current healthcare systems and care pathways, while maintaining patients' autonomy and privacy. Understanding and addressing these issues and successful incorporation of an acceptable, simple, scalable, affordable, and future-proof digital solution into healthcare systems could help remodel global chronic disease management and fractured healthcare systems to provide best patient care and optimisation of healthcare resources to meet the global burden and unmet clinical need of COPD.

Individual-level interventions to reduce personal exposure to outdoor air pollution and their effects on people with long-term respiratory conditions

BACKGROUND

More than 90% of the global population lives in areas exceeding World Health Organization air quality limits. More than four million people each year are thought to die early due to air pollution, and poor air quality is thought to reduce an average European's life expectancy by one year. Individuals may be able to reduce health risks through interventions such as masks, behavioural changes and use of air quality alerts. To date, evidence is lacking about the efficacy and safety of such interventions for the general population and people with long-term respiratory conditions. This topic, and the review question relating to supporting evidence to avoid or lessen the effects of air pollution, emerged directly from a group of people with chronic obstructive pulmonary disease (COPD) in South London, UK.

OBJECTIVES

1. To assess the efficacy, safety and acceptability of individual-level interventions that aim to help people with or without chronic respiratory conditions to reduce their exposure to outdoor air pollution. 2. To assess the efficacy, safety and acceptability of individual-level interventions that aim to help people with chronic respiratory conditions reduce the personal impact of outdoor air pollution and improve health outcomes.

SEARCH METHODS

We identified studies from the Cochrane Airways Trials Register, Cochrane Central Register of Controlled Trials, and other major databases. We did not restrict our searches by date, language or publication type and included a search of the grey literature (e.g. unpublished information). We conducted the most recent search on 16 October 2020.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and non-randomised studies (NRS) that included a comparison treatment arm, in adults and children that investigated the effectiveness of an individual-level intervention to reduce risks of outdoor air pollution. We included studies in healthy individuals and those in people with long-term respiratory conditions. We excluded studies which focused on non-respiratory long-term conditions, such as cardiovascular disease. We did not restrict eligibility of studies based on outcomes.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Two review authors independently selected trials for inclusion, extracted study characteristics and outcome data, and assessed risk of bias using the Cochrane Risk of Bias tool for RCTs and the Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) as appropriate. One review author entered data into the review; this was spot-checked by a second author. We planned to meta-analyse results from RCTs and NRS separately, using a random-effects model. This was not possible, so we presented evidence narratively. We assessed certainty of the evidence using the GRADE approach. Primary outcomes were: measures of air pollution exposure; exacerbation of respiratory conditions; hospital admissions; quality of life; and serious adverse events.

MAIN RESULTS

We identified 11 studies (3372 participants) meeting our inclusion criteria (10 RCTs and one NRS). Participants' ages ranged from 18 to 74 years, and the duration of studies ranged from 24 hours to 104 weeks. Six cross-over studies recruited healthy adults and five parallel studies included either people with pre-existing conditions (three studies) or only pregnant women (two studies). Interventions included masks (e.g. an N95 mask designed to filter out airborne particles) (five studies), an alternative cycle route (one study), air quality alerts and education (five studies). Studies were set in Australia, China, Iran, the UK, and the USA. Due to the diversity of study designs, populations, interventions and outcomes, we did not perform any meta-analyses and instead summarised results narratively. We judged both RCTs and the NRS to be at risk of bias from lack of blinding and lack of clarity regarding selection methods. Many studies did not provide a prepublished protocol or trial registration. From five studies (184 participants), we found that masks or altered cycle routes may have little or no impact on physiological markers of air pollution exposure (e.g. blood pressure and heart rate variability), but we are very uncertain about this estimate using the GRADE approach. We found conflicting evidence regarding health care usage from three studies of air pollution alerts, with one non-randomised cross-over trial (35 participants) reporting an increase in emergency hospital attendances and admissions, but the other two randomised parallel trials (1553 participants) reporting little to no difference. We also gave the evidence for this outcome a very uncertain GRADE rating. None of our included trials reported respiratory exacerbations, quality of life or serious adverse events. Secondary outcomes were not well reported, but indicated inconsistent impacts of air quality alerts and education interventions on adherence, with some trials reporting improvements in the intervention groups and others reporting little or no difference. Symptoms were reported by three trials, with one randomised cross-over trial (15 participants) reporting a small increase in breathing difficulties associated with the mask intervention, one non-randomised cross-over trial (35 participants) reporting reduced throat and nasal irritation in the lower-pollution cycle route group (but no clear difference in other respiratory symptoms), and another randomised parallel trial (519 participants) reporting no clear difference in symptoms between those who received a smog warning and those who did not.

AUTHORS' CONCLUSIONS

The lack of evidence and study diversity has limited the conclusions of this review. Using a mask or a lower-pollution cycle route may mitigate some of the physiological impacts from air pollution, but evidence was very uncertain. We found conflicting results for other outcomes, including health care usage, symptoms and adherence/behaviour change. We did not find evidence for adverse events. Funders should consider commissioning larger, longer studies, using high-quality and well-described methods, recruiting participants with pre-existing respiratory conditions. Studies should report outcomes of importance to people with respiratory conditions, such as exacerbations, hospital admissions, quality of life and adverse events.

Mixed methods process evaluation of my breathing matters, a digital intervention to support self-management of asthma

This study aimed to explore user engagement with 'My Breathing Matters', a digital self-management intervention for asthma, and identify factors that may influence engagement. In a mixed methods design, adults with asthma allocated to the intervention arm of a feasibility trial (n = 44) participated in semi-structured interviews (n = 18) and a satisfaction questionnaire (n = 36) to explore their views and experiences of the intervention. Usage data highlighted that key intervention content was delivered to most users. The majority of questionnaire respondents (78%; n = 28) reported they would recommend the intervention to friends and family. Interviewees expressed positive views of the intervention and experienced several benefits, mainly improved asthma control, medication use, and breathing technique. Factors that may influence user engagement were identified, including perceptions of asthma control, current self-management practices, and appeal of the target behaviours and behaviour change techniques. Findings suggested My Breathing Matters was acceptable and engaging to participants, and it was used as intended.

[Medical rehabilitation of children with bronchial asthma]

Medical rehabilitation of children suffering from bronchial asthma is based on the complex use of natural therapeutic factors, drug, non-drug therapy and other methods in order to achieve control over the disease and long-term remission. The use of non-drug technologies is necessary taking into account the pathogenesis of the bronchial asthma development and is aimed at relieving bronchospasm, reducing the activity of allergic inflammation in the bronchi, improving their drainage function, strengthening the respiratory muscles and increasing exercise tolerance. When carrying out rehabilitation measures it is important to observe the basic principles of medical rehabilitation. An individual medical rehabilitation program is drawn up taking into account the assessment of the child's clinical condition at each stage with the diagnosis of the initial rehabilitation status, the establishment of a rehabilitation diagnosis and an assessment of the rehabilitation potential. The article presents the data of the analysis of scientific publications and our own research allowing to draw up an algorithm for the formation of an individual program of medical rehabilitation for children with bronchial asthma.

The emerging clinical role of wearables: factors for successful implementation in healthcare

Wearable technologies promise to redefine assessment of health behaviors, yet their clinical implementation remains a challenge. To address this gap, two of the NIH’s Big Data to Knowledge Centers of Excellence organized a workshop on potential clinical applications of wearables. A workgroup comprised of 14 stakeholders from diverse backgrounds (hospital administration, clinical medicine, academia, insurance, and the commercial device industry) discussed two successful digital health interventions that involve wearables to identify common features responsible for their success. Seven features were identified including: a clearly defined problem, integration into a system of healthcare delivery, technology support, personalized experience, focus on end-user experience, alignment with reimbursement models, and inclusion of clinician champions. Health providers and systems keen to establish new models of care inclusive of wearables may consider these features during program design. A better understanding of these features is necessary to guide future clinical applications of wearable technology.

New Concepts and Technological Resources in Patient Education and Asthma Self-Management

Asthma is a chronic disease that is associated with significant morbidity and mortality. In general, the use of technology resources or electronic health (e-health) has been shown to have beneficial effects on patients with asthma. E-health can impact a broad section of patients and can be cost-effective and associated with high patient satisfaction. E-health may enable remote delivery of care, as well as timely access to health care, which are some of the common challenges faced by patients with asthma. Web-based asthma self-management systems have been found to improve quality of life, self-reported asthma symptoms, lung function, reduction in asthma symptoms/exacerbations, and self-reported adherence for adults. Social media is commonly being used as a platform to disseminate information on asthma to increase public awareness. It can facilitate asthma self-management in a patient friendly manner and has shown to improve asthma control test scores as well as self-esteem. Text massages reminders can increase awareness regarding asthma treatment and control, thus potentially can improve adherence to medications and asthma outcome. Mobile health applications can support asthma self-management, improve a patient's quality of life, promote medication adherence, and potentially reduce the overall costs for asthma care. Inhaler trackers have shown to be beneficial to asthma outcome in various populations by improving adherence to asthma medications. Barriers such as physician financial reimbursement as well as licensing for rendering tele-healthcare services are important concerns. Other limitations of using technology resources in health care are related to liability, professionalism, and ethical issues such as breach of patient confidentiality and privacy. Additionally, there may be less face-to-face interaction and care of the patient when e-health is used.

Current Challenges of Digital Health Interventions in Pakistan: Mixed Methods Analysis

Background

Digital health is well-positioned in low and middle-income countries (LMICs) to revolutionize health care due, in part, to increasing mobile phone access and internet connectivity. This paper evaluates the underlying factors that can potentially facilitate or hinder the progress of digital health in Pakistan.

Objective

The objective of this study is to identify the current digital health projects and studies being carried out in Pakistan, as well as the key stakeholders involved in these initiatives. We aim to follow a mixed-methods strategy and to evaluate these projects and studies through a strengths, weaknesses, opportunities, and threats (SWOT) analysis to identify the internal and external factors that can potentially facilitate or hinder the progress of digital health in Pakistan.

Methods

This study aims to evaluate digital health projects carried out in the last 5 years in Pakistan with mixed methods. The qualitative and quantitative data obtained from field surveys were categorized according to the World Health Organization’s (WHO) recommended building blocks for health systems research, and the data were analyzed using a SWOT analysis strategy.

Results

Of the digital health projects carried out in the last 5 years in Pakistan, 51 are studied. Of these projects, 46% (23/51) used technology for conducting research, 30% (15/51) used technology for implementation, and 12% (6/51) used technology for app development. The health domains targeted were general health (23/51, 46%), immunization (13/51, 26%), and diagnostics (5/51, 10%). Smartphones and devices were used in 55% (28/51) of the interventions, and 59% (30/51) of projects included plans for scaling up. Artificial intelligence (AI) or machine learning (ML) was used in 31% (16/51) of projects, and 74% (38/51) of interventions were being evaluated. The barriers faced by developers during the implementation phase included the populations’ inability to use the technology or mobile phones in 21% (11/51) of projects, costs in 16% (8/51) of projects, and privacy concerns in 12% (6/51) of projects.

Conclusions

We conclude that while digital health has a promising future in Pakistan, it is still in its infancy at the time of this study. However, due to the coronavirus disease 2019 (COVID-19) pandemic, there is an increase in demand for digital health and implementation of health outcomes following global social distancing protocols, especially in LMICs. Hence, there is a need for active involvement by public and private organizations to regulate, mobilize, and expand the digital health sector for the improvement of health care systems in countries.

WEARCON: wearable home monitoring in children with asthma reveals a strong association with hospital based assessment of asthma control

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.

Trial registration

Netherlands Trail Register, NL6087. Registered 14 February 2017.

Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma

BACKGROUND

Self-management programs have beneficial effects on asthma control, but their implementation in clinical practice is poor. Mobile health (mHealth) could play an important role in enhancing self-management.

OBJECTIVE

To assess the clinical effectiveness and technology acceptance of myAirCoach-supported self-management on top of usual care in patients with asthma using inhalation medication.

METHODS

Patients were recruited in 2 separate studies. The myAirCoach system consisted of an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a fraction exhaled nitric oxide device, and an app. The primary outcome was asthma control; secondary outcomes were exacerbations, quality of life, and technology acceptance. In study 1, 30 participants were randomized to either usual care or myAirCoach support for 3 to 6 months; in study 2, 12 participants were provided with the myAirCoach system in a 3-month before-after study.

RESULTS

In study 1, asthma control improved in the intervention group compared with controls (Asthma Control Questionnaire difference, 0.70; P = .006). A total of 6 exacerbations occurred in the intervention group compared with 12 in the control group (hazard ratio, 0.31; P = .06). Asthma-related quality of life improved (mini Asthma-related Quality of Life Questionnaire difference, 0.53; P = .04), but forced expiratory volume in 1 second was unchanged. In study 2, asthma control improved by 0.86 compared with baseline (P = .007) and quality of life by 0.16 (P = .64). Participants reported positive attitudes toward the system.

DISCUSSION

Using the myAirCoach support system improves asthma control and quality of life, with a reduction in severe asthma exacerbations. Well-validated mHealth technologies should therefore be further studied.

Validation of online Asthma Control Questionnaire and Asthma Quality of Life Questionnaire

Objective

Several newly developed eHealth applications use online questionnaires to monitor asthma control. The Asthma Control Questionnaire (ACQ) and Asthma Quality of Life Questionnaire (AQLQ) are two such commonly used questionnaires. These questionnaires are validated for use on paper. This study aims to validate them by assessing the agreement between online and paper versions of the ACQ and AQLQ.

Methods

Patients (aged 18 years and older) from the Self-Management in Asthma Supported by Hospitals, ICT, Nurses and General Practitioners (SMASHING)-trial and Davos@home study were included in this study. Patients completed both the paper and online Dutch versions of the ACQ and AQLQ in a random order within a 2-week interval. Agreement between the different versions was assessed with paired t-tests, intraclass correlation coefficients and Bland–Altman plots.

Results

In total 44 patients were eligible for analysis. The mean difference between the paper and online versions of the ACQ was 0.04 (p=0.40) and for the AQLQ it was 0.08 (p=0.06). The intraclass correlation coefficient scores were 0.94 for the ACQ and 0.95 for the AQLQ.

Conclusion

The online versions of the ACQ and AQLQ show high levels of agreement with the paper versions and can therefore be safely used in eHealth applications to respectively monitor asthma control and quality of life.

The self-administered online versions of the Asthma Control Questionnaire and Asthma Quality of Life Questionnaire show good agreement with the paper versions, and are therefore, valid measurement tools in eHealth applicationshttp://bit.ly/2rGBbRf

Enhancing Asthma Patients' Self-Management through Smartphone-Based Application: Design, Usability Evaluation, and Educational Intervention

OBJECTIVE

The aim of this study was to design and evaluate a smartphone-based application for improving self-management in patients with asthma and evaluate its effects on their knowledge.

METHODS

In this applied research, based on the Clinical Practice Guideline and a systematic review, a questionnaire was designed to determine the application information content and functionality requirements by 15 pediatric and adult asthma and allergy specialist. Then the application was designed and developed using Adobe Air software on the Android operating system. Usability evaluation of the mobile application was performed using the standard questionnaire for user interaction satisfaction (QUIS), which completed by 30 patients with asthma, 8 information technology (IT) specialists, and 2 asthma and allergy specialists. Self-management knowledge of 30 asthma patients was measured using a researcher-made questionnaire before and after using the application.

RESULTS

The number of specialists in the both Delphi rounds was 15 and the mean work experiences were 17.6 years. The most important elements for asthma self-management were avoiding exposure to allergen and triggers (96%), drug treatment (94.6%), and how to use the therapeutic tools (92.4%), and the most important functionalities were alerting the patients when they did not control asthma (92%), setting reminders for timely drug use (85.4%) and therapeutic tools (82.6%), recording prescription drugs (82.6%), and peak flow meter values (82%). Usability evaluation showed that 30 patients with asthma, 8 IT specialists, and 2 physicians evaluated the application at a "good" level. The mean score of the patients' knowledge before intervention was 2.43 ± 0.95 which after intervention was significantly increased to 4.3 ± 0.56 (p < 0.001).

CONCLUSION

Considering the desirable outcomes of application evaluation and a positive impact of this educational intervention on asthma patients' knowledge, it is possible to use mobile-based self-management programs to help these patients to manage illness and gain knowledge and self-management skills.

Prioritizing research challenges and funding for allergy and asthma and the need for translational research-The European Strategic Forum on Allergic Diseases

The European Academy of Allergy and Clinical Immunology (EAACI) organized the first European Strategic Forum on Allergic Diseases and Asthma. The main aim was to bring together all relevant stakeholders and decision-makers in the field of allergy, asthma and clinical Immunology around an open debate on contemporary challenges and potential solutions for the next decade. The Strategic Forum was an upscaling of the EAACI White Paper aiming to integrate the Academy's output with the perspective offered by EAACI's partners. This collaboration is fundamental for adapting and integrating allergy and asthma care into the context of real-world problems. The Strategic Forum on Allergic Diseases brought together all partners who have the drive and the influence to make positive change: national and international societies, patients' organizations, regulatory bodies and industry representatives. An open debate with a special focus on drug development and biomedical engineering, big data and information technology and allergic diseases and asthma in the context of environmental health concluded that connecting science with the transformation of care and a joint agreement between all partners on priorities and needs are essential to ensure a better management of allergic diseases and asthma in the advent of precision medicine together with global access to innovative and affordable diagnostics and therapeutics.

ERS/EAACI statement on severe exacerbations in asthma in adults: facts, priorities and key research questions

Despite the use of effective medications to control asthma, severe exacerbations in asthma are still a major health risk and require urgent action on the part of the patient and physician to prevent serious outcomes such as hospitalisation or death. Moreover, severe exacerbations are associated with substantial healthcare costs and psychological burden, including anxiety and fear for patients and their families. The European Academy of Allergy and Clinical Immunology (EAACI) and the European Respiratory Society (ERS) set up a task force to search for a clear definition of severe exacerbations, and to also define research questions and priorities. The statement includes comments from patients who were members of the task force.

What Is the Impact of Innovative Electronic Health Interventions in Improving Treatment Adherence in Asthma? The Pediatric Perspective

Suboptimal adherence to treatment is a significant issue in the management of pediatric asthma and is a major cause of uncontrolled disease, life-threatening attacks, and increased use of health care resources. Electronic health solutions have the potential to positively impact asthma self-management in children and adolescents and their families, thereby improving treatment adherence and asthma outcomes. However, there is a lack of sufficient data to support widespread adoption of electronic health tools in pediatric asthma practice. A critical evaluation of the impact of these new interventions on treatment adherence in childhood asthma must consider unmet needs, heterogeneity of trials, safety and data security issues, long-term effects, and cost-effectiveness. This article explores the most relevant issues facing the role of electronic health and its subcategory-mobile health-in promoting treatment adherence in childhood asthma, focusing on current evidence gaps and limitations, and future research perspectives.

Mobile health tools for the management of chronic respiratory diseases

BACKGROUND

The market of mobile health (mHealth) technology is rapidly evolving, making new mobile technologies potentially available for healthcare systems. Patient empowerment through self-monitoring of symptoms, shared decision making with the physician, and easily accessible education are important features extending the reach of mHealth technology beyond traditional care.

METHODS

Two digital distribution platforms (Apple App Store and Google Play Store) were searched for currently available mobile applications (apps) for patients with chronic respiratory diseases (CRDs). A new index (score ranging from 0 to 10) was developed to assess the potential of apps as a tool to empower patients through mobile technology (based on self-monitoring, personalized feedback, and patient education app features).

RESULTS

One hundred and twelve apps were retained for analysis and could be classified in 5 categories: Asthma (n = 71), COPD (n = 15), Asthma and COPD (n = 15), Rhinitis and Asthma (n = 5), and Rhinosinusitis (n = 6). Eighty percent were developed by medical technology companies compared to 18% by medical doctors and 2% by pharmaceutical companies. Two-thirds of apps allow disease self-monitoring, whereas over half of apps provide patient feedback through graphs. Sixty percent of apps contain easily accessible patient education material. Only three percent of apps reach a score of ≥7 on the newly designed patient empowerment index.

CONCLUSIONS

A variety of apps are available for patients with CRDs of which only few were developed by or jointly with medical doctors. The majority of these apps include self-monitoring tools, but only few also provide personalized feedback, which is needed to adopt these apps into daily care.

An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning

Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significance of this disease. If not treated, it can turn into a serious public health problem. The best method to treat asthma is to control it. Physicians recommend continuous monitoring on asthma symptoms and offering treatment preventive plans based on the patient's control level. Therefore, successful detection of the disease control level plays a critical role in presenting treatment plans. In view of this objective, we collected the data of 96 asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. A new ensemble learning algorithm with combining physicians' knowledge in the form of a rule-based classifier and supervised learning algorithms is proposed to detect asthma control level in a multivariate dataset with multiclass response variable. The model outcome resulting from the balancing operations and feature selection on data yielded the accuracy of 91.66%. Our proposed model combines medical knowledge with machine learning algorithms to classify asthma control level more accurately. This model can be applied in electronic self-care systems to support the real-time decision and personalized warnings on possible deterioration of asthma control level. Such tools can centralize asthma treatment from the current reactive care models into a preventive approach in which the physician's therapeutic actions would be based on control level.

Asthma exacerbation prediction: recent insights

PURPOSE OF REVIEW

Asthma attacks are frequent in children with asthma and can lead to significant adverse outcomes including time off school, hospital admission and death. Identifying children at risk of an asthma attack affords the opportunity to prevent attacks and improve outcomes.

RECENT FINDINGS

Clinical features, patient behaviours and characteristics, physiological factors, environmental data and biomarkers are all associated with asthma attacks and can be used in asthma exacerbation prediction models. Recent studies have better characterized children at risk of an attack: history of a severe exacerbation in the previous 12 months, poor adherence and current poor control are important features which should alert healthcare professionals to the need for remedial action. There is increasing interest in the use of biomarkers. A number of novel biomarkers, including patterns of volatile organic compounds in exhaled breath, show promise. Biomarkers are likely to be of greatest utility if measured frequently and combined with other measures. To date, most prediction models are based on epidemiological data and population-based risk. The use of digital technology affords the opportunity to collect large amounts of real-time data, including clinical and physiological measurements and combine these with environmental data to develop personal risk scores. These developments need to be matched by changes in clinical guidelines away from a focus on current asthma control and stepwise escalation in drug therapy towards inclusion of personal risk scores and tailored management strategies including nonpharmacological approaches.

SUMMARY

There have been significant steps towards personalized prediction models of asthma attacks. The utility of such models needs to be tested in the ability not only to predict attacks but also to reduce them.

Research highlights from the 2018 European Respiratory Society International Congress: airway disease

The annual European Respiratory Society (ERS) International Congress (held in Paris in 2018) was once again a platform for discussion of the highest-quality scientific research, cutting-edge techniques and innovative new therapies within the respiratory field. This article discusses only some of the high-quality research studies presented at this year's Congress, with a particular focus on airway diseases including asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis and cough, as presented through Assembly 5 of the ERS (Airway Diseases: Asthma and COPD). The authors establish the key take-home messages of these studies, compare their findings and place them in the context of current understanding.

Vilnius Declaration on chronic respiratory diseases: multisectoral care pathways embedding guided self-management, mHealth and air pollution in chronic respiratory diseases

Background

Over 1 billion people suffer from chronic respiratory diseases such as asthma, COPD, rhinitis and rhinosinusitis. They cause an enormous burden and are considered as major non-communicable diseases. Many patients are still uncontrolled and the cost of inaction is unacceptable. A meeting was held in Vilnius, Lithuania (March 23, 2018) under the patronage of the Ministry of Health and several scientific societies to propose multisectoral care pathways embedding guided self-management, mHealth and air pollution in selected chronic respiratory diseases (rhinitis, chronic rhinosinusitis, asthma and COPD). The meeting resulted in the Vilnius Declaration that was developed by the participants of the EU Summit on chronic respiratory diseases under the leadership of Euforea.

Conclusion

The Vilnius Declaration represents an important step for the fight against air pollution in chronic respiratory diseases globally and has a clear strategic relevance with regard to the EU Health Strategy as it will bring added value to the existing public health knowledge.

Digital technologies and adherence in respiratory diseases: the road ahead

Outcomes for patients with chronic respiratory diseases remain poor despite the development of novel therapies. In part, this reflects the fact that adherence to therapy is low and clinicians lack accurate methods to assess this issue. Digital technologies hold promise to overcome these barriers to care. For example, algorithmic analysis of large amounts of information collected on health status and treatment use, along with other disease relevant information such as environmental data, can be used to help guide personalised interventions that may have a positive health impact, such as establishing habitual and correct inhaler use. Novel approaches to data analysis also offer the possibility of statistical algorithms that are better able to predict exacerbations, thereby creating opportunities for preventive interventions that may adapt therapy as disease activity changes. To realise these possibilities, digital approaches to disease management should be supported by strong evidence, have a solid infrastructure, be designed collaboratively as clinically effective and cost-effective systems, and reflect the needs of patients and healthcare providers. Regulatory standards for digital interventions and strategies to handle the large amounts of data generated are also needed. This review highlights the opportunities provided by digital technologies for managing patients with respiratory diseases.

Novel methods for device and adherence monitoring in asthma

PURPOSE OF REVIEW

The article aims to provide an updated and evidence-based review of the innovative electronic health interventions to monitor and improve inhaler technique and adherence to recommended therapy in asthma.

RECENT FINDINGS

Out of the 290 articles identified by the search strategy, 23 manuscripts fulfilled the review inclusion criteria. Included studies mainly addressed m-health, electronic reminders, telemedicine, and inhaler tracker interventions. Investigations were performed both in adults and children. Remarkably, the majority of studies were performed in the most recent years, showing a progressively increasing interest for this field. Existing findings appear to be of moderate-high quality. A significant number of papers, however, were published in scientific journals with a low impact factor (<2). Furthermore, extremely high heterogeneity was found in the considered study endpoints. Collected evidence supports a relevant role for e-health in monitoring and improving inhaler use and treatment adherence in asthma. The patients' acceptance and satisfaction towards assessed interventions were also found to be positive.

SUMMARY

E-health represents a highly valuable tool for achieving optimal and personalized asthma management. Unanimously agreed and adopted standards for conducting trials and reporting results on e-health in asthma are however needed to fully understand its real added value.

High-Definition Medicine

The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution-what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of medicine.

Development of a Modular Research Platform to Create Medical Observational Studies for Mobile Devices

Background

Observational studies have proven to be a valuable resource in medical research, especially when performed on a large scale. Recently, mobile device-based observational studies have been discovered by an increasing number of researchers as a promising new source of information. However, the development and deployment of app-based studies is not trivial and requires profound programming skills.

Objective

The aim of this project was to develop a modular online research platform that allows researchers to create medical studies for mobile devices without extensive programming skills.

Methods

The platform approach for a modular research platform consists of three major components. A Web-based platform forms the researchers’ main workplace. This platform communicates via a shared database with a platform independent mobile app. Furthermore, a separate Web-based login platform for physicians and other health care professionals is outlined and completes the concept.

Results

A prototype of the research platform has been developed and is currently in beta testing. Simple questionnaire studies can be created within minutes and published for testing purposes. Screenshots of an example study are provided, and the general working principle is displayed.

Conclusions

In this project, we have created a basis for a novel research platform. The necessity and implications of a modular approach were displayed and an outline for future development given. International researchers are invited and encouraged to participate in this ongoing project