Social support and networks, the structural properties that characterize a set of relationships (Turner & Marino, 1994; Umberson et al., 1996), play a critical role in determining people's health behaviors including physical activity participation. Social support and network data, however, have often been analyzed by conventional statistical methods, which ignore the internal associations and rich information within network data. The purpose of this presentation is to introduce social network analysis (SNA) for studying complex social network data. First applied to social support in the 1980s, SNA explicitly defines networks and their characteristics with a focus on understanding relationships and the structure of a network, which could include family, friends, colleagues and community “actors”. Two major emphases of SNA are to determine how actors are located or "embedded" in the overall network and how the whole pattern of individual choices gives rise to more holistic patterns. Social networks are graphically and numerically represented by nodes (actors or individuals) and edges (relations). Networks can be differentiated by their scopes: Ego-centric networks encompass the ties surrounding a single focal individual while whole networks include all ties among members of the network. Ties within networks are usually dichotomous, directional or nondirectional, between two actors. Ties can be represented at several levels, e.g., the individual, dyadic, and triadic level. Each level shows different measures such as outdegree and indegree (individual level), mutuality (dyadic level), and transitivity and cyclicity (triadic level). Subgroup analysis focuses on the existence of cohesive collections of actors who have many relational ties to one another. At the highest level, global measures represent features of the whole network. Often using questionnaires, network data collection is often based on the “name generator” and “name interpreter” sequence. The name generator elicits the names of network members and the name interpreter consists of additional questions which obtains information about these named individuals. A number of application software has been developed for SNA and a few examples are UCINET, Pajek, StOCNET, NetMiner, and NetDraw. Using UCINET6 (Borgatti et al., 2002), an application of SNA to physical activity support will be illustrated in the presentation.Keyword(s): active participation, interdisciplinary, research