Literature search

Publications were retrieved by using computerized searches from Scopus, on 1 January 2022.

At the start of the study, we searched for all articles related to Chinese medicine for smoking cessation in Scopus from 2005 to 2021, to identify clear trends in changes that meet the requirements of bibliometric analysis.

The search strategy was as follows: [Title (smoking cessation)] AND [Title-ABS-Key (‘TCM’) OR Title-ABS-Key (random)] AND [limit-to (OA, ‘all’)] AND [limit-to (‘ar’, doctype)] AND [limit-to (‘medi’, subjarea)] AND [limit-to (‘human’, exactkeyword)] OR limit-to (‘smoking’, exactkeyword)]. After deduplication, 5042 articles were filtered by title, keywords and abstract by Excel 2011 and R software biblioshiny. Finally, 178 full-text papers were evaluated for eligibility (Figure 1).

Figure 1

Study selection of TCM therapy for smoking cessation

The visualizations and distribution diagrams were established using R software biblioshiny, a statistical package, including data analysis and data visualization, which are available on R-Studio that facilitates comprehensive bibliometric research. The software is used to retrieve relevant bibliometric qualitative and quantitative information, such as a Sankey diagram, word-cloud, network analysis, thematic maps, tree-map, networks etc.

Analysis methods

The methods of analysis not only included examining selected documents against the basic characteristics of the database (e.g. authors, journals, papers), but also included the identification of knowledge concepts and knowledge structures through visual methods (e.g. word-cloud, thematic maps, tree-map, three-field maps and network analysis). There was a geographical file generated by R biblioshiny, in order to analyze the geographical heatmap and collaboration network. The data were from Scopus, which is cleaned with Excel 2011 in the report. The total citation index (TC) and H-index were used to judge the dynamic development of the authors’ country. There were four stages of analysis from Q1 to Q4. The bibliometric tools used at each analysis stage and their purpose are listed in Table 1.

The overview of the information of sampled data

The analyzed sample included at least one in 178 scientific papers published in 1908 journal articles from 2005 to 2021. It revealed the size, growth trend and distribution of TCM therapy for smoking cessation over the period. The total number of articles had continued to increase over the past few years, with some fluctuations, peaking in 2020 with 31 articles published. Our bibliometric analysis of research showed that the total number of published papers on smoking cessation in traditional Chinese medicine had grown steadily from 2005 to 2021.

The overview information of articles about TCM therapy for smoking cessation literature is presented in Table 2. The sample data showed that most of the contribution came from mainland China (52.14%), USA (18.57%) and Japan (3.57%), according to the sample studied. Chinese scholars contributed significantly to the topic articles (73), and ranked first in terms of total citations (1368). Moreover, Canada had the highest average article citation (81.5).

The top two academic affiliations were Beijing University and Henan University of Traditional Chinese Medicine, presented in Table 3. The data showed that the majority of contributions were made by mainland China (77.96%), Hong Kong (10.17%), Singapore (4.24%), and India (4.24%), according to the sample studied.

The leading journals

The top 20 resources accounted for 27.4% of the scientific output in the field studied, with nearly half published in ‘top five’ journals (Table 4). The Scopus database showed that Evidence-Based Complementary and Alternative Medicines ranked first in respect of citations (57), and surpassed PLOS Medicine (54), the Chinese Journal of Integrative Medicine and Nicotine and Tobacco Research (25 each). The journal with the most articles or most cited articles was Evidence-Based Complementary and Alternative Medicines.

The leading journals, authors, publications

To analyze the leading scholars who contributed to the literature on TCM therapy for smoking cessation from 2005 to 2021, the number of published articles and the number of citations are presented in Table 5, in which the top 20 authors that contributed to the field (4.31 authors per paper, 1138 articles) are presented. Most papers (over 95%) were the product of co-authorship, and almost half were multi-author (48.1%). The number of single country publications (SCP) was 54 and the multiple country publications (MCP) was 26, the rate of MCP-Ratio was 18.58%, an indication of cooperation among scholars from different countries. In these countries, China and USA cooperated most frequently.

Leading scholars were mostly from China (Table 5). The top 3 productive authors, Wang Y (10), Li Y (8) and Li J (6), were from Shanghai, Beijing and Guangzhou, respectively. The three cities are first-tier cities in China, where the level of economic development and financial strength are at the forefront. Concerning the authors’ impact, Wang Y ranked first both in the total citation index and H-Index¹¹.

Finally, the research by top authors represented the breadth, quality, and other potential scholars from 2005 to 2021. The larger the circle area, the more output the author’s paper has. It revealed that there were only several authors who were interested in this topic in the beginning, but more and more authors participated over time, reaching the peak in 2020. The special emphasis was placed on their increasingly active role in promoting TCM treatment for smoking cessation.

Leading publications

The analysis of the top articles on TCM therapy for smoking cessation was based on annual citations in the medical field (Supplementary file Table 1). The most cited articles were published in STROKE¹², which was the most productive journal (the top total citations). Although the older papers were generally better known in the literature, the newer articles were more popular among the top five most cited papers. It confirmed that there was emerging research on TCM therapy for smoking cessation.

The main diseases caused by smoking in patients were stroke, diabetes, chest pains, and cancer¹³, while for healthy people, the diseases mainly included cardiovascular, cancer, diabetes, and hypertension. Smoking was also harmful to pregnant women and babies. Although there were many professional journals in STROKE (caused by cardiovascular disease), the most cited articles were published mainly in the fields involving Medicine –Clinical Neurology.

Moreover, highly cited papers published in the journal of THORAX covered respiratory system research topics. Most of these articles focused on patients’ Respiratory Asthma Guidelines such as Douglas (2008). Finally, highly cited papers published in the journal LANCET ONCOL, such as the one by Goss (2014).

Three-field plots

In order to visualize the connections among scientific fields, we used three-field plots¹⁴, which simultaneously analyze the main relations from the selected fields (journals, authors, papers’ keywords and cited journals) and interpret how they were related, based on the method of Sankey diagram for three fields, which represents different flow diversion situations and the amount of traffic in a specific state. It displays related index through different colored rectangles proportional to the relationship value.

The Sankey diagram was used to visualize data flow and volume among nodes. The wider the lines are, the more closely the nodes are linked. It revealed the relations of ‘keywords–authors–journals’ in representative scholars (Figure 2), by visualizing the relationship among paper keywords (left), authors (middle), and journals (right). The analysis revealed that, different scholars had paid different attention to the keywords about TCM therapy for smoking cessation such as COVID-19¹⁵, chronic¹⁶, type 2 diabetes¹⁷, and risk factor¹⁸. Besides, it showed that most of the scientific work was widely published in the journals: Evidence-Based Complementary and Alternative Medicines, European Journal of Clinical Pharmacology, PLOS Medicine, and Chinese Journal of Clinical Rehabilitation.

Figure 2

Three-field plots for ‘keywords–authors–journals’ and ‘keywords–country/region–journals’

It revealed the relations of ‘keywords–country/region–journals’ in representative scholars (Figure 2), by visualizing the relationship among papers’ keywords (left), countries (middle) and cited journals (right). The analysis demonstrated the links among keywords, authors’ countries/areas and cited journals.

The two major journals with the most articles published by Chinese scholars were the World Journal of Pediatrics and Evidence-Based Complementary and Alternative Medicine. The major themes that Chinese scholars paid attention to were treatment methods such as traditional Chinese medicine (TCM), Chinese herbal medicine, and acupuncture. The related diseases concerned by scholars were mainly ‘hypertension’, ‘type 2 diabetes’ and ‘chronic obstructive pulmonary disease’. Some scholars¹⁹ believed that TCM for smoking cessation was a complementary and alternative medicine.

Network analysis

Firstly, we analyzed the most common words and keywords during our research period, which allowed us to visualize the topics through word-cloud. Afterwards, we explored the treatment methods in healthy people and patients, respectively, via a tree-map and clustering analysis. Finally, we analyzed the 3C networks (co-occurrence; co-keywords; co-citation), and visualized a conceptual structure by a topic dendrogram.

Word-cloud

The most common words in the research literature were highlighted by the method of word-cloud, (Figure 3). It demonstrated the repetition frequency of words in the sample data. The more frequently the words were mentioned, the larger their sizes are in the selected sample.

Figure 3

Word-clouds by keywords and by title

The top keywords at the forefront in frequency such as ‘acupuncture’, ‘traditional Chinese medicine’, ‘colitis’, ‘hypertension’, ‘chronic obstructive pulmonary disease’, ‘risk factors’ and ‘alternative medicine’ were located in the center of map, largely and conspicuously. While for words mentioned in lower frequency such as ‘complementary medicine’, ‘treatment’, ‘type 2 diabetes’, ‘airway inflammation ‘ and ‘evidence-based medicine’, their sizes were smaller than the top words but still more prominent compared with the great majority.

The top keywords that occurred in titles were ‘patients’, ‘Chinese’, ‘medicine’, ‘treatment’, ‘clinical’, ‘disease’, ‘lung’, ‘cancer’, ‘therapy’, ‘acupuncture’, suggesting that acupuncture was one of the most commonly used words in titles. It illustrated that scholars mainly focus on smoking-related diseases therapies²⁰. Besides, words such as ‘factors’, ‘effect’, ‘chronic’, ‘risk’, ‘management’, ‘alternative’, ‘complementary’, ‘traditional’, ‘randomized’, ‘trial’ and ‘control’ also recurred in many papers. These titles focused on the core content, test methods, and statistical methods.

The top keywords in abstracts according to the frequency they occurred in articles were ‘patients’, ‘treatment’, ‘therapy’, ‘clinical’, ‘disease’, ‘results’, located in the center of map. Besides, keywords such as ‘control’, ‘factors’, ‘acupuncture’, ‘cancer’, ‘blood’, ‘chronic’ and ‘hypertension’ also recurred in papers. This image demonstrates that the literature focused on the core concepts, patients, treatment methods, type of disease and results of TCM therapy on smoking cessation. Figure 3 illustrates the changing trend of hot topics in literature about TCM therapy for smoking cessation during the period 2005–2021. In 2020, new hotspots emerged in this field, which could be summarized by three keywords (COVID-19²¹, trial²², cell²³). It revealed that in the post-epidemic era, scholars²⁴ paid more attention to TCM therapy for smoking cessation.

Tree-map – the healthy people

The tree-map explored the frequency and proportion of each word in the target topic, and illustrated the visual proportion in the size of the ‘rectangular’ area.

The diseases mentioned mainly cardiovascular, cancer, diabetes, and hypertension. The top words in the abstract were smokers (322), cardiovascular (191), cancer (175), diabetes (166), chronic (150), blood (141), pressure (96), hypertension (89), and pregnancy (84).

Treatment methods mainly included traditional Chinese medicine and acupuncture. The keywords were treatment (246), and acupuncture (100). People mentioned mainly participants (250), women (162), the public (158), and pregnancy (84). It revealed clearly that the top words were risk (500), intervention (232), control (219), evidence (141), clinical (105), significant (95) and random (80).

Tree-map – the patients

Smoking is one of the risk factors for many diseases such as lung cancer, chronic bronchitis, and pulmonary fibrosis. The diseases of the patients concerned mainly cancer, diabetes, hematopathy, stroke, cardiovascular, diabetes, lung disease, carcinoma, hypertension and renal disease. The top words of the abstracts were cancer (49), diabetes (49), blood (45), stroke (33), cardiovascular (29), diabetic (29), lung (28), carcinoma (20), hypertension (20), renal (19) and colitis (8). Treatment methods mainly included traditional Chinese medicine and acupuncture. The top words at the forefront in frequency were traditional (31), herbal (23), and tongue (19).

The main types of people were smoker participants, the aged, women^25,26, the public, adults, and pregnant, with the top word at the forefront in frequency being women (40). The research methods mainly included logistic regression models, meta-analysis methods, statistical significance tests, etc. The related top words were logistic (19), evidence (16), significant (31), and significantly (31).

Co-keywords analysis

In order to present research hotspots on TCM therapy for smoking cessation, we performed a co-occurrence network analysis through abstracts (Figure 4). The link strength among keywords represented the number of publications by co-occurrence analysis. We could identify relationships among different concepts in the selected sample by the method of co-occurrence analysis. Therefore, we could observe what was the structure of a scientific article on a particular topic. In terms of link strength, the higher the association strength, the greater the similarity between terms.

Figure 4

Co-occurrence network through the assessment of abstract terms

The close interrelationships were highlighted among the topics by the co-occurrence network of abstracts. The treatment methods of TCM therapy for smoking cessation included treatment methods, drug therapy, controlled study, and treatment model. Many scholars explored the research from the perspective of a systematic review of evidence-based medicine, and the common topics of TCM therapy focused on the acupuncture, research method (method, control), type of disease (disease, blood, chronic, hypertension, diabetes), and evaluation (significantly, significant). To better present the type of research topics by linked keywords of the cluster analysis (especially the hierarchical clustering), it was used as follows. These articles were divided into three categories in selected samples: Cluster 1, with 7 words, including the methods and outcomes of the trial intervention; Cluster 2, with 13 words, including the clinical features and research methods; and Cluster 3, with 40 words, including types of diseases, TCM therapy.

The advantage of cluster analysis methods is that the existing research was mainly divided into several major themes distinctly. There were three types of analysis represented by the topic dendrogram. It revealed the flow of literature focused on TCM therapy for smoking cessation, clinical features (clinical), research method (randomized controlled trial, multi-center, double-blind), types of disease (stroke, lung, hypertension, diabetes, disease, hemopathy), and treatment methods (traditional Chinese medicine, acupuncture).

The co-occurrence network analysis was further explored to identify themes within a certain field, the relationships and the internal structure among these themes and their concentration. Therefore, we could present different topics by applying a clustering algorithm on the keyword network, which was finally displayed in the map.

In order to present the growth of topics and their relevance across the scientific field, the horizontal axis represented centrality and the vertical axis density. Based on this, four research quadrants in the field of TCM therapy for smoking cessation were drawn. Quadrant 1 (upper right corner) included a sports theme, both important and well-developed; and Quadrant 2 (upper left corner) included a very professional/niche subject which was well developed, but not for the current TCM therapy for the smoking cessation field; Quadrant 3 (lower left corner) included a new or a disappearing theme, which was a marginal topic, a poorly developed place, and might have just appeared or might be about to disappear; and Quadrant 4 (bottom right) included a basic theme, generally, a basic concept, revealing that the heart field of TCM therapy for smoking cessation was important but not well developed.

Therefore, as expected, the recurring cross-cutting themes (lower right quadrant) were ‘Chinese’, ‘traditional’, ‘medicine’, ‘patients’, ‘China’, ‘clinical’ and ‘risk’, ‘factor’, ‘population’, which were the most relevant to the area of knowledge considered. It demonstrated the methods of randomized trials related to TCM therapy for smoking cessation by the words ‘risk’ and ‘factor’, etc. The first cluster (lower right) focused on the treatment of TCM for smoking cessation according to their centrality and density. The second cluster (upper right), which was the ‘motor theme’, focused on the concepts on TCM therapy for smoking cessation. As for ‘therapy’, ‘acupuncture’, ‘smoking’, ‘study’, ‘cancer’ and ‘lung’ (upper left), which were linked to TCM therapy for smoking cessation (‘treatment’, ‘diagnosis’, ‘chronic’), developed well, but they were not important at present. Moreover, it is worth mentioning that ‘alternative’ and ‘complementary’ are still emerging themes on TCM therapy for smoking cessation (lower left quadrant).

Collaboration network analysis

The influential authors and potential collaborations related to TCM therapy for smoking cessation were identified through a network analysis of publications, co-authors, and co-cited authors.

We could also have used the social network theory to find potential clusters of topics, clusters of researchers, and national collaborations among countries and regions. The size of the nodes is proportional to the number of papers published in the field in a certain country. The width of the lines is proportional to the number of collaboration articles. It was worth noting that the research on TCM therapy for smoking cessation mainly came from China (Supplementary file Figure 2). The wider the line between the two nodes, the closer the cooperation between the two authors in different institutions. The size of the node was proportional to the number of author’s articles. In addition, different colors indicated the year of the two authors’ collaborative papers. Besides, the different colors indicated the year of the collaborative paper between the two authors.

Limitations

This study has some limitations. The bibliometric analysis of this study did not consider the content of the articles. In view of the development status of TCM therapies for smoking cessation, studies should focus on the improvement of evidence-based, evidence-driven and methodological quality on TCM therapy for smoking cessation, such as new treatment techniques (physical therapy) and development efforts. Furthermore, researchers should focus on outcome indicators of TCM therapy for smoking cessation, taking into account smoking craving, nicotine dependence, and daily smoking indicators such as changes in smoking volume, mood and physical symptoms.