Sook Kyoung Park,Ju-Hee Nho
Research Trends of Lifestyle Intervention for Women With Obesity: A Text Network Analysis
Lifestyle,Obesity,Women
Background While the number of lifestyle intervention studies for women with obesity continues to increase, the research trends related to this topic should be evaluated through text network analysis. Methods A total of 231 relevant studies published in international journals between 2011 and 2021 were identified. Semantic morphemes of the abstracts were refined, and a co-occurrence matrix with 117 keywords was generated using the text network analysis program NetMiner 4.3. Results The core keywords were determined as the top 25 in degree centrality, closeness centrality, or betweenness centrality. ‘Lifestyle’, ‘Intervention’, ‘Diet’, ‘Exercise’, ‘Diabetes mellitus’, ‘Body’, ‘Composition’, ‘Quality of life’, ‘Obesity’, ‘Gain’, ‘Diet’, and ‘Loss’ were the most frequently used keywords. Conclusion The findings of this study provide a general overview of the research trends in lifestyle interventions for women with obesity and may be used as reference for future research.
Research Trends of Lifestyle Intervention for Women With Obesity: A Text Network Analysis

While the number of lifestyle intervention studies for women with obesity continues to increase, the research trends related to this topic should be evaluated through text network analysis.

A total of 231 relevant studies published in international journals between 2011 and 2021 were identified. Semantic morphemes of the abstracts were refined, and a co-occurrence matrix with 117 keywords was generated using the text network analysis program NetMiner 4.3.

The core keywords were determined as the top 25 in degree centrality, closeness centrality, or betweenness centrality. ‘Lifestyle’, ‘Intervention’, ‘Diet’, ‘Exercise’, ‘Diabetes mellitus’, ‘Body’, ‘Composition’, ‘Quality of life’, ‘Obesity’, ‘Gain’, ‘Diet’, and ‘Loss’ were the most frequently used keywords.

The findings of this study provide a general overview of the research trends in lifestyle interventions for women with obesity and may be used as reference for future research.

Both obesity and being overweight are the result of abnormal or excessive fat accumulation that poses a health risk. Globally, 39% of the adult population is overweight and 13% is obese; the increasing numbers have led the issue to become an epidemic that kills more than 4 million people each year [1]. Being overweight and obese are important risk factors for type 2 diabetes, cardiovascular disease, early death [2], adverse pregnancy outcomes [3], and chronic diseases [4]. In particular, obesity is also associated with some cancers, such as endometrial cancer, breast cancer, and ovarian cancer [1], and the quality of life is also low [5], which suggests the importance of obesity management [4]. To lower the risk of corpulence, it is important to maintain a diet that reduces calories from fat and sugar, increases the daily intake of fruits, vegetables, soybeans, whole grains, and nuts, and that encourages one to be physically active [1]. Lifestyle interventions primarily include diet and physical activity [6], which are the most basic interventions that help in the reduction of lifestyle-related risk factors [4]. Such interventions are an effective and primary strategy for the prevention of gestational diabetes in overweight and obese women [7], and is recommended as a strategy to reduce cardiovascular metabolic risk [8], depression and anxiety [9]. A text network analysis accumulates a large number of related research papers over a long period. It can explore research trends from various aspects [10] and help us understand global trends. In this study, the research trends of lifestyle intervention programs provided to overweight and obese women were identified through text network analysis. Therefore, this study intends to suggest directions for follow-up studies on lifestyle intervention programs for overweight and obese women. This study aimed to compare and analyze core keywords and research topics of lifestyle interventions for research related to obese women through the utilization of text network analysis. Based on the network centrality index of keywords from the abstracts of lifestyle intervention studies for obese women, a search and comparison of core keywords was conducted, and the direction of follow-up studies discussed. The specific purposes of this study are as follows: 1) The core-keywords and keywords are extracted from the studies on lifestyle intervention for obese women according to the frequency of occurrence. 2) The network relationship between the extracted keywords was identified, and the order of degree centrality, closeness centrality, and betweenness centrality was identified through centrality analysis. 3) The network sociogram of lifestyle intervention for obese women that was composed of core-keywords was identified to structurally identify keywords.

This study is a quantitative content analysis study; a text network analysis that explores core keywords and research topics. It constructs a network based on the relationship between the simultaneous appearances of keywords extracted from the abstracts of overseas lifestyle intervention-related studies.

This study used the abstracts of papers published in overseas lifestyle intervention-related academic journals as data and tried to understand the relationship of keyword co-occurrence. This study used the thesis abstract as the research subject data boundary, based on evidence that suggests that it is important to set the data boundary to obtain accurate research results [11]. Additionally, only the keywords suggested by the author were used to analyze the research literature. However, there is a possibility of bias due to the indexer effect to reduce this [12], “Keywords” extracted from the abstract were used together. Among the extracted keywords, the top 30 keywords were named “Core keywords”. The research procedure was conducted in the following order: 1) thesis collection and abstract extraction, 2) keyword extraction and refining of abstracts, 3) co-occurrence matrix and network generation between keywords, and 4) network statistical analysis and visualization (Fig. 1).

Four databases (Google Scholar, PubMed, EBSCO, and CINAHL) were used, and the publication year of the eligible theses was limited to 11 years; from January 2011 to December 2021. The search terms were set as ‘Lifestyle’, ‘Intervention’, ‘Obesity’, ‘Women’, and ‘Program evaluation’, combined with ‘AND’ and ‘OR’. Theses from March to 21-23, 2022 were searched and abstracts were collected from them. The search terms were required to be included in the title and abstract of the paper, and the language setting of the database was set to English. There is a difference in search results if the language setting is limited to Korean in the Google Scholar database. Therefore, it was set separately for all the languages. However, unlike the other three databases, the accuracy of the search results was low on Google Scholar, so the researcher selected papers related to the search terms while looking at the search results. The titles and abstracts of the searched papers were reviewed, and conference presentations, papers in other fields such as biology, and papers without abstracts were excluded. The title, author, publication year, journal name, and abstract of the paper written in English were extracted from each database’s website. The number of papers searched was 990 on Google Scholar, 114 on PubMed, 89 on EBSCO, and 99 on CINAHL. Of these, 235 articles were retrieved; 164 articles that were not in the form of a thesis, 708 articles that were not intervention studies, and 185 articles that were duplicated, were excluded. Two researchers read and reviewed the abstracts of the selected articles. Excluding 4 studies, 231 studies were selected as the final research subjects.

After the IDentification number, publication year, and abstract were arranged in one row for each paper using the Microsoft Excel 2019 (Microsoft, Redmond, WA, USA), keywords in the abstract were extracted using the ‘Semantic network analysis’ module [13] of the NetMiner program (Version 4.3). During this process, morpheme analysis was performed at a primitive stage in which stopwords such as numbers and pronouns were excluded using the automatic filtering function within the program; thus, the entire morpheme list from the abstract to be analyzed was obtained. To perform morpheme refinement for meaningful text network analysis, two researchers developed dictionaries that included a thesaurus, exception lists, and defined words that require refinement while the entire morpheme list obtained was examined. First, to create a dictionary of synonyms, words with the same or a similar meaning but having different notations were collected, and one representative word was designated. For example, ‘Treatment’ and ‘Intervention’ are case-insensitive, and all were recognized as the representative word ‘Intervention’. In addition, when a noun phrase and an abbreviation were used at the same time, the abbreviation was selected as a representative word (e.g. ‘QOL’ is the representative word for ‘Quality of life’). This was done to prevent the noun phrase and the abbreviation from being separated or used repeatedly in the morphological analysis. Second, a dictionary of negative words was developed after the exclusion of the morphemes from the analysis. Representatively; these were 1) title words that demonstrated abstract form (e.g. ‘Background’, ‘Objectives’); 2) statistical terms that were irrelevant to the purpose of this study (e.g. ‘p-value’, ‘Regression’, ‘Participants’, ‘Parameter’, ‘Percent’); and 3) words with inconsistent or very general concepts (e.g. ‘Take’, ‘Become’, ‘Setting’) were excluded. Third, instead of one morpheme, two or more morphemes are grouped and read as units. For example, ‘Diabetes mellitus’ is not separated into the words ‘Diabetes’ and ‘Mellitus’ but is extracted as one word. As a result of the application of synonyms, negatives, and designators agreed between researchers to the NetMiner program, 2,108 keywords were identified, along with their frequency of occurrence. In the text network analysis, only keywords that appeared more times than at a certain frequency are included in the analysis; the keywords that appear repeatedly [14] are focused on. For example, keywords with a frequency of one occurrence were excluded from the network analysis. There is no standardization for the determination of the appropriate frequency of keywords [15]. In this study, the appropriate level was determined by the examination of cases in which the frequency of appearance, which corresponded to the number of words, was low. For example, the frequency of occurrence of overseas research keywords varied from 1 to 4,349, but the frequency of occurrence was 1,943 (92.2%) less than five times, and 94.5% less than 10 times. Thus, keywords that appeared fewer than 10 times were removed, and 117 frequent keywords were selected.

A matrix was created using the frequency of co-occurrence between previously selected keywords as a value. A keyword network in which the co-occurrence relationship was expressed as a connecting line was developed. The co-occurrence between keywords in this study denotes that the distance between two keywords within the sentence of an individual abstract is 3 or less. The minimum value of the distance between keywords is 2, which means that the case in which two keywords appear side by side is recognized as a simultaneous co-occurrence. If the distance between keywords increases, it may be difficult to grasp a specific situation, so the “Distance between keywords” was determined as 3 in this study. Accordingly, a network that consists of 4,342 co-occurrences among the 31 keywords was developed.

To identify the core topic of lifestyle intervention-related research, text network analysis was performed using a previously developed keyword co-occurrence matrix. (i) Analysis of the centrality of keywords in the network and selection of core keywords Centrality is an indicator of the degree to which keywords are centered in the network. Keywords with high centrality are considered core keywords after they are evaluated on the basis of relative ranking rather than absolute size [16]. Among the various centrality indicators, degree, closeness, and centrality are the most widely used [16]. Degree centrality is a measure of “how many connections the nodes of a network have” [5]; it is the value obtained after the number of neighboring nodes directly connected to the keyword (degree) is divided by the number of nodes excluding itself (n–1). Degree centrality is a measure of activity, which is the degree to which it co-occurs with other keywords. Keywords with high connection centrality are recognized important as they are located at the heart of the network, and because they become the center of discussion [17]. Closeness centrality is “Measured based on the distance between nodes to show how close one node is to another” for all nodes in the network [15]. The closeness centrality of a node uses the reciprocal of the sum of the connection distances of the keywords, such that the closer the centrality of the keyword, the smaller the sum of the connection distances. For comparison between nodes, a value normalized by the multiplication of the inverse of the sum of the connection distances by the number of nodes excluding itself (n–1), was used as the closeness centrality value. In this study, a keyword with high closeness centrality is located at the center of the network in the distance while reaching other keywords the fastest. Betweenness centrality is a measure of “How much a node acts as an intermediary or bridge in forming a network with other nodes” [15]. Because it indicates how often a keyword appears in the shortest path of other keywords; a keyword with high betweenness centrality forms a bridge between the keywords in the network and affects the overall discussion. In other words, when keywords with high betweenness centrality disappear, it becomes impossible to link from one topic to another; therefore, these centrality values are measured as relative values within the range of 0 to 1 and are used to discover core keywords and topics. (ii) Visualization of core keywords for lifestyle intervention-related research A word cloud plugin was used to identify relatively important keywords according to their importance.

There were 231 foreign studies related to lifestyle interventions that were analyzed in this study. After consideration of the number of papers by publication year, the average number of papers was eight; the number was five between 2011 to 2014, but it gradually increased to 22 in 2015. Since then, approximately 30 studies have been published each year.

Keyword rankings were identified based on 1) simple occurrence frequency, that is, the number of papers in which the keyword appeared; 2) degree centrality; 3) closeness centrality; and 4) betweenness centrality of keywords used in the thesis abstract of studies related to lifestyle intervention. The top 25 keywords according to the criteria are listed in Table 1. In studies related to lifestyle intervention, the frequency of simple occurrence was highest in the order of ‘Lifestyle’, ‘Intervention’, ‘Diet exercise’, and ‘Diabetes mellitus’. With regard to the three centralities; in the top 25, core-keywords such as ‘Intervention’, ‘Pregnancy and postpartum’, ‘Diet’, ‘Infertility’, ‘Lifestyle’, ‘Obesity’, ‘Diabetes mellitus’, ‘Gestational diabetes mellitus’, ‘Gestational weight gain’, and ‘Activity’ were in common (Table 1).

A sociogram is presented in Fig. 1 to examine the network between core keywords based on degree centrality. These keywords have the largest node size owing to their high degree centrality, and they are connected by thick lines because they have a high frequency of co-occurrence with other keywords (Fig. 1).

For the 231 papers collected for this study, the number of words extracted through presetting and filtering was 3,111. The word cloud for the top 500 words based on word frequency by the aggregation of the data is shown in Fig. 2. The word cloud that was executed based on the frequency is derived by the marking of the most frequently appearing words as important based on their size. The color or position of a word has nothing to do with the analysis. According to Word Cloud, ‘Intervention’, ‘Lifestyle’, ‘Woman’, ‘Weight’, and ‘Obesity’ appeared more frequently than other words. Thus, the tendencies of the studies can be inferred (Fig. 2).

This study attempted to suggest a direction for follow-up research on lifestyle intervention programs for overweight and obese women in the future by the identification of research trends related to the lifestyles of overweight and obese women globally. Based on the core keywords published in papers overseas from 2011 to 2021, the field of lifestyle intervention for overweight and obese women was objectively confirmed as a potential research area. As a result, the common keywords for frequency and degree centrality, closeness centrality, and betweenness centrality were ‘Lifestyle’, ‘Intervention’, ‘Obesity’, ‘Diet’, ‘Energy’, ‘Diabetes mellitus’, ‘Gestational diabetes mellitus’, ‘Pregnancy postpartum’, ‘Pregnancy complication’, ‘Infertility’, ‘Breast cancer’, ‘Knee’, ‘Quality of life’, and ‘Community’ (Table 1). It was found that most of the keywords with high centrality were confirmed with high frequency. Thus, this study intends to classify and discuss common keywords based on their frequency and centrality. The keywords that entered the top 10 by frequency were ‘Lifestyle’, ‘Intervention’, ‘Diet exercise’, ‘Diabetes mellitus’, ‘Body composition’, ‘Quality of life’, ‘Obesity’, ‘Gain’, ‘Diet’, and ‘Loss’, in that order (Table 1). The demonstration of the need for health promotion through lifestyle intervention resulted in studies on the lifestyle of overweight and obese women. It may be observed that it is common to provide lifestyle interventions, inclusive of nutrition and exercise, for changes in lifestyle for overweight and obese women. In overweight or obese pregnant women, lifestyle interventions were effective in the maintenance of stable weight gain and the control of fatigue and pregnancy stress [18], and were also effective in the reduction of weight and improvement of lipid levels [19]. In overweight and obese middle-aged women, it was effective in the increasing of health-promoting behaviors, reduction of psychological distress, weight loss, improvement of reproductive system symptoms [20], and increase in the pregnancy rate of obese and infertile women [21]. In addition, it was confirmed that it had a beneficial effect on physical and mental health as depression and anxiety in obese women of childbearing age [22] were consistently reduced. As such, lifestyle interventions for overweight and obese women improved the body composition indices of body weight, waist circumference, hip circumference, and body mass index. That is, it promotes weight loss [22]. Thus, it was confirmed that it had a positive effect on the health and quality of life of the target patient by the reduction of the onset of diabetes [23] and the reduction of systolic blood pressure [22]. Therefore, lifestyle interventions for overweight and obese women include nutrition and physical activity. Most studies confirmed a decrease in body composition index, a decrease in the incidence of obesity-related metabolic problems, and an improvement in the quality of life. The top 10 keywords for degree centrality were ‘Intervention’, ‘Pregnancy postpartum’, ‘Diet’, ‘Infertility’, ‘Lifestyle’, ‘Obesity’, ‘Diabetes mellitus’, ‘Gestational diabetes mellitus’, ‘Gestational weight gain’, and ‘Activity’, in that order (Table 1). Among disease-related terms, ‘Diabetes mellitus’ was not only high in frequency, but also high in centrality. Thus, it was found that there were many intervention studies conducted on diabetic or gestational diabetes patients with regard to lifestyle interventions for overweight and obese women, and it also confirmed the effects of the management of infertility and the improvement of quality of life. In addition, it could be inferred that lifestyle interventions for overweight and obese women are often conducted during pregnancy or the postpartum period. These interventions primarily included diet and exercise [6]. Therefore, it can be considered that lifestyle intervention was implemented by the reflection of major medical issues to improve diabetes [23], gestational diabetes [7], infertility management [21] and quality of life [22] for overweight and obese women. The top 10 keywords for closeness centrality were ‘Pregnancy postpartum’, ‘Pregnancy complication’, ‘Activity’, ‘Diet’, ‘Infertility’, ‘Intervention’, ‘Lifestyle’, ‘Obesity’, ‘Preconception’, and ‘Breast cancer’, in that order. Additionally, the top 10 keywords for betweenness centrality were ‘Intervention’, ‘Obesity’, ‘Diabetes mellitus’, ‘Lifestyle’, ‘Diet’, ‘Pregnancy complication’, ‘Activity’, ‘Gestational diabetes mellitus’, ‘Gestational weight gain’, and ‘Advice’ (Table 1). Breast cancer had a high degree centrality and betweenness centrality, which supported the study results [1] that suggested that breast cancer and obesity are closely related. In addition, ‘Knee’ and ‘Community’ were confirmed among the frequency of keywords and degree centrality, closeness centrality, and betweenness centrality of the main words. It can be inferred that overweight obese women are easily exposed to musculoskeletal disorders, including knee disorders, and that lifestyle interventions for overweight obese women are often implemented at the organizational level. In the future, the process of the development of research topics that include degree or betweenness keywords on the network will be further necessary [24]. In addition, lifestyle-related research trends in overweight and obese women have been confirmed. Many intervention programs include nutrition and exercise related to general weight loss. Overweight and obese women need emotional support from a professional, co-worker, friend, or family member who can facilitate lifestyle improvement for weight management [25]. Knowledge and intensive health education to promote health are also considered important [7]. Therefore, follow-up studies related to the psychological and cognitive properties of overweight and obese women are necessary.