According to the Global Initiative for Asthma (GINA), asthma is a heterogeneous disease characterized by chronic airway inflammation [1
]. The hallmark features of asthma include reversible airflow obstruction, airway eosinophilia, and history of recurrent wheeze along with cough and breathlessness [1
]. The pathophysiology of asthma involves various cells and cellular elements, such as mast cells, eosinophils, T lymphocytes, macrophages, neutrophils, and epithelial cells. The inflammation not only causes recurrent episodes of cough, wheezing, shortness of breath, and chest tightness, but also results in an associated increase in the existing bronchial hyperresponsiveness to various stimuli [3
Globally, approximately 335 million people are being affected by asthma, and in 2015 only, about 383,000 deaths were attributed to asthma [4
]. Asthma is ranked 14th among the most serious disorders due to its negative impacts on the people experiencing the disease, and its economic burden on healthcare facilities and governments [6
]. Asthma has been observed in both children and adults. The incidence and prevalence of pediatric asthma appear to be higher, while morbidity and mortality are more common among adults. Children with asthma may have impaired airway development as well as reduced maximally attained lung function, which may persist into adulthood without further progressive loss. Meanwhile, in adults, asthma usually facilitates a decline in lung function and enhances the risk of fixed airflow obstructions, especially for asthmatics who smoke [7
]. Asthma is also associated with a number of respiratory comorbidities, namely rhinosinusitis, allergen rhinitis, sleep-disordered breathing in children, and chronic obstructive pulmonary disease (COPD) and chronic sinusitis for adults. Such comorbidities may not only somewhat enhance the asthma symptoms but also complicate clinical care in various ways [8
Since traditional measures of asthma outcome, such as pulmonary functions and respiratory symptoms, are insufficient to demonstrate the limitations that asthma causes to patients, subjective experience of health-related quality of life of patients plays a critical role in the evaluation of interventions’ effectiveness [11
]. A number of factors have been reported to have an association with poor quality of life among people suffering from asthma, including sociodemographic characteristics (higher age, female gender, lower education level and unemployment), clinical conditions (severity, hospitalization, high levels of immune markers), poor control and management, and associated comorbidities [14
]. Therefore, conceptualized healthcare beyond medical treatment is crucial for individuals living with asthma, who need to be able to deal with and manage the symptoms themselves. The evaluation of health-related quality of life among patients with asthma is beyond a mere measurement of their situation and healthcare needs, as it also makes a great contribution to the assessment of the effectiveness of clinical interventions.
In addition to pharmacological treatment, including the use of bronchodilator and inhaled corticosteroids (ICS), or biological therapy, such as omalizumab, mepolizumab, and reslizumab, interventions to improve quality of life of asthmatics involve a personalized and comprehensive approach [1
]. Self-management, namely training in proper use of inhaler for children as well as caregivers, family members, and teachers, and writing asthma action plans are among common interventions [1
]. Since incorrect practice of using inhaler is highly common and usually results in an increased risk of asthma attacks, inhaler training with physical demonstration does play a crucial role. Regularly repeated inhaler training has been proved to improve the level of asthma control in adult patients [16
]. Along with self-monitoring, the importance of a written asthma action plan that guides the patients and their caregivers on how to promptly recognize and make correct responses to asthma exacerbations is undeniable. Such education has helped to reduce up to two-thirds of urgent healthcare, work and school absenteeism, and even night waking [17
]. Other approaches target comorbidities and/or modifiable risk factors, such as reducing the use of aspirin or other non-steroidal anti-inflammatory drugs (NSAIDs) in patients with aspirin-exacerbated respiratory disease, and avoidance of exposure to tobacco smokes, occupational pollution, and mold or damp [1
This study aims at demonstrating the research trends worldwide and identifying the research gaps in interventions for improving quality of life of patients with asthma. In order to report the trend in available articles over time and measure the global research growth based on the existing literature, we applied a bibliometric approach and content analysis, which can objectively evaluate the productivity and research landscapes generated by researchers, health professionals and institutions in this field. By pointing out the current research patterns, we are able to examine the development as well as productivity, and identify research gaps of the literature on quality of life among people suffering from asthma, and thus better inform health professionals worldwide.
2. Materials and Methods
2.1. Search Strategy
The data were retrieved from the Web of Science (WoS) Core Collection, which covers a large number of scientific domains and technology fields. WoS provides publications from high-quality scientific journals, which have been accessed by the experts of literature review committees. WoS is also among the databases with widest coverage, with citation and bibliographic data going back until the 1900s [19
The search strategy can be described as follows:
Step 1: With the use of Boolean operators “OR”, the search query was developed to identify the number of published items included “quality of life” OR “QoL” OR “HRQoL” OR “well-being”. We downloaded papers in text format (txt.) and imported to STATA for further extraction.
Step 2: Among papers found in Step 1, we used STATA syntax to filter the papers with the following terms in titles or abstracts: (“asthma” OR “asthmatic*”) AND (“intervention*” OR “trial*”). Papers that did not mention “asthma” in their titles and abstracts were removed at this step.
Step 3: We screened the abstracts of the papers from Step 2 to figure out the papers with asthmatics as study participants and which mentioned quality of life as an outcome. The process was performed by two independent researchers. Any arisen disagreement was solved by discussing with the research team and senior researchers.
2.2. Data Download and Extraction
In addition to titles and abstracts, metadata of each paper and reports generated by WoS database were downloaded in text format and imported in Excel for analyzing, including:
Total number of publications
Authors’ names, their affiliations and the number of total papers, and total citations for each author
Most prolific countries and collaborations
Institutional affiliations and frequency of citation
The top cited articles with titles, authors, journal details, year of publication, total citations and citation per year.
Titles, abstracts and keywords
Selected papers were research articles written in English. Grey literature, conference proceedings, reviews and books/book chapters were excluded. As the search was performed in the middle of 2019, only papers published before and in the year 2018 were included in the analysis.
2.3. Data Analysis
We used STATA version 15.0 (STATACorp., College Station, TX, USA) to analyze the final data in step 4 using the following information on the articles: authors’ affiliations, titles, journals’ names, keywords, total number of citations, and abstracts.
Publications’ general characteristics, namely year of publication, the number of papers per year of each country, accumulative citations from published year to 2018, yearly mean citation rate, total usage in the last six months and the last five years, and mean use rate in the last six months and the last five years, were described. We used STATA to calculate the number of papers by country in abstracts.
A network graph showing the co-occurrence of terms in title and abstracts was established by VOSviewer (version 1.6.11, Center for Science and Technology, Leiden University, Leiden, The Netherlands). A co-occurrence network can be described as the collective interconnection of terms and is generated by connecting pairs of terms based on a set of criteria that defines co-occurrence. In this study, when term A and term B both appear in title and/or abstract of a particular publication, they are said to “co-occur”. Additionally, another paper may contain terms B and C, and so on. By linking the co-occurrence of the identified terms, we created a co-occurrence network of terms.
Latent Dirichlet Allocation (LDA) was applied to classify papers into corresponding topics. LDA is a common topic modeling algorithm for text mining to determine the relationships of text documents. In LDA, each term is regarded as a random vector with the probability of drawing the words/texts associated with that term, in other words, that vector. The probability of a paper is calculated based on the probability of the terms, and papers with similar probability will be classified into one group [20
]. Thus, by applying LDA, we would be able to recognize the interdisciplinary structure of research development as well as obtain an in-depth view of hidden themes of research on interventions in asthma [26
]. We reviewed and read through the most cited papers of each group and assign the labels for each topic manually. In addition, to analyze the relationship of the research area, we utilized coincidence analysis using the STATA command ‘precoin’ and presented the results in the form of a dendrogram. Dendrograms graphically illustrate the information concerning which observations are grouped together at various levels of similarity.
Analytical techniques for each data types are summarized below (Table 1