An exploration of the knowledge structure in studies on old people physical activities in Journal of Exercise Rehabilitation: by semantic network analysis

Abstract

Physical activity, a key component of maintaining health, is becoming an essential alternative in reducing medical expenses for the old people. This research was intended to analyze 51 research papers published in the Journal of Exercise Rehabilitation (JER) through semantic network analysis. The subjects of the study were the keywords that the authors of each paper used in journal search sites from 2013 to 2019. The present researcher analyzed the frequency, density, and centrality of the keywords of the indicators through semantic network analysis and then visualized them on the basis of findings using UCINET6 and the NetDraw program. Also, the researcher classified the hidden clusters by CONCOR (Convergence of iterated Correlations) analysis, which is a kind of cluster analysis. As a result, it was found that the keyword with the highest frequency was "exercise," followed by "cognition, "physical-activity," "old-women," "Korean," "fall," and "training." It was also found that most of the high-frequency keywords, such as "exercise," "cognition," "old-women," "program" and "depression" had high centrality. These keywords were classified into four clusters: (a) mental health research, (b) physical health research, (c) social behavior research, and (d) leisure efficacy research. This suggests that the old people-related research papers published in the JER have derived effective methods of maintaining physical and mental health using scientific exercise programs, and especially address the effects of exercise intervention for old women.

Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry

“…The CONCOR analysis was conducted to visually derive and cluster the network connectivity and patterns of sustainable fashion-related texts in SNS. The CONCOR analysis is a method of clustering words based on the relationship of structural equivalence by analyzing Pearson correlation in a co-occurrence matrix [82]. The CONCOR analysis is a clustering method that considers the connectivity of nodes (words) in a similar structure across the entire network structure and organizes them into a single block or cluster (Group).…”

“…Words with similarities form a cluster and grouped text words gain insight into the occurrence of other words and represent meaningful social networks between words [72,81]. It is not just a frequency analysis of how much a particular concept has emerged, but also a structural analysis that identifies interrelationships with other concepts and specific patterns between words can identify joint meanings among concepts [82]. It is a useful way to identify the meaning between the nodes through the word clusters to which individual words belong, and to identify the main meaning that texts convey [82].…”

“…It is not just a frequency analysis of how much a particular concept has emerged, but also a structural analysis that identifies interrelationships with other concepts and specific patterns between words can identify joint meanings among concepts [82]. It is a useful way to identify the meaning between the nodes through the word clusters to which individual words belong, and to identify the main meaning that texts convey [82]. The CONCOR analysis will help establish marketing strategies for sustainable fashion products.…”

“…Second, the "betweenness centrality" was calculated to identify the degree to which a node is located between other points in the network. Keywords with high betweenness centrality are important in that even if they simply appear in the semantic network and the degree is low, it will be easier to communicate smoothly when they are excluded [82,88]. Third, closeness centrality, which indicates the distance between indirectly linked nodes in addition to directly linked nodes, is a measure of centrality on the basis of the distance between nodes [89].…”

Identification of Occupant Dissatisfaction Factors in Newly Constructed Apartments: Text Mining and Semantic Network Analysis

“…In this context, DC reflects the number of nodes that a given node is directly connected to. In other words, a high value of DC means that the node is related to a large number of other nodes in the network [59]. The DC of the kth node is calculated using Equation (2):…”