A social network analysis approach to group and individual perceptions of child physical activity

Identifying Social Network Characteristics Associated With Youth Physical Activity Skill Competency at a Summer Care Program

While literature suggests that youth physical activity (PA) behaviors are affected by network influences, less is known about network influences on perceived skill competency, a component of physical literacy and self-efficacy. This study aims to provide an understanding of potential network characteristics which are associated with youth PA skill competency. Youth (n = 158) between the ages of 8 and 12 years recruited from two summer care programs (i.e., Boys & Girls Clubs) participated in researcher-administered surveys. Youth self-reported their age, sex, involvement in team sports, weekly PA, and skill competency assessed using a version of the PLAYself measure. Youth were also asked to report up to five people in the summer program and five out of the program with whom they interacted the most. Linear regression was used to evaluate associations between skill competency and demographics, PA, and social network characteristics. Skill competency was significantly associated (R2 = .17) with age (β = -.06, p = .01), sex (β = -.06, p = .01), sports team involvement (β = .16, p < .001), and weekly PA (β = -.20, p < .001). Skill competency was also significantly associated with the number of connections with whom the youth played frequently (β = .09, p < .001), the number of connections that helped the youth to be active (β = .18, p < .001), and the heterogeneity of the type of relationships within the youth's network (β = .11, p < .001). Skill competency scores were significantly associated with both individual variables and social network composition. In addition, youth with networks comprising several types of relationships (heterogeneity) reported a significantly high skill competency. PA interventions can be most effective when considering the reinforcing aspects of networks and skill competency.

Conducting Physical Activity Research on Racially and Ethnically Diverse Adolescents Using Social Network Analysis: Case Studies for Practical Use

Adolescent physical activity (PA) is significantly impacted by peer behaviors through peer influence, peer selection, and popularity. However, the scales for these social constructs may not fully capture the detailed social networks and mechanisms responsible for PA behavior changes. This level of detail and granularity can be quantified and analyzed through social network analysis (SNA). To demonstrate the variety, utility, and efficacy of SNA in adolescent PA research, this article aims to provide four case studies on the collection of social network and PA data on ethnically and racially diverse adolescents. Through case studies, this article provides tangible ways in which SNA can be used to evaluate social influences on PA behaviors. Case studies are presented on: (1) Youth Engagement in Sport-an egocentric analysis of middle school youth participation in an experiential sport program with 3- and 6-month follow-ups; (2) Summer care program networks-an egocentric and whole network longitudinal study of adolescents at summer care programs; (3) The Convoy method-a qualitative egocentric discussion activity with adolescents from colonias on the Texas-Mexico border; and (4) A father-focused, family-centered health program-an egocentric experimental analysis of children participating in a health program. Data collection procedures are listed and example surveys are provided. Descriptive analyses are included, as are recommendations on further analysis techniques for each type of network data. Using SNA, researchers can understand social contexts in a more specific manner, better positioning interventions to alter such influences.

Sport Participation Associations With Child Friend Selection and Physical Activity While at Summer Care Programs

Child physical activity (PA) declines during summer reducing PA-related health benefits. Summer care programs (e.g., Boys & Girls Clubs) can promote active play while providing opportunities for friendships. Sport participation is a positive influence on PA and social development. Purpose: This article investigates the role of sport participation in PA attainment, perceived skill competency, and social connection at a summer care program. Methods: Children self-reported PA, sport participation, and the names of up to five peers whom they played with most at the program at the start (T1; n = 100; M age = 9.94 years; SD = 1.34; 47% male) and end (T2; n = 77; M age = 9.83 years; SD = 1.46; 51% male) of summer. Network autocorrelation models were used to determine if child sport participation was significantly associated with those they played with at the program. Results:Children who reported sport participation reported significantly more PA than those who did not at T1, but not T2. Child sport participation was significantly related to that of the peers they said they spent time with while at the program for T1 (β = 0.07, p < .01), but not T2 (β = 0.06, p = .10). Additionally, PA (β = 0.08, p < .01; β = 0.07, p = .01) and skill competency (β = 0.11, p = .02; β = 0.15, p < .01) were significantly associated with sport participation at both time points. While sports participation was a key factor in child friendships at the start of summer, the program allowed children, regardless of sport participation, to become friends outside of sports. Conclusions: These results suggest summer care programs promote PA through play and provide opportunities for children to facilitate friendships regardless of sport participation.

Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.