Beyond "one-size-fits-all": A person-centered analysis of psychological profiles and engagement with self-tracking technologies.

Abstract

Self-tracking technologies are widely adopted, yet user engagement with them varies considerably. This thesis investigates whether distinct psychological profiles emerge from combinations of health consciousness, goal orientation, and self-efficacy among active self-tracking users, and whether these profiles differ in their engagement patterns. Adopting a person-centered approach, this study used Fuzzy C-Means cluster analysis on survey data from 130 active self-tracking users, comparing the resulting clusters across affective, cognitive, and behavioral engagement via one-way ANOVA. Four profiles emerged: the competitive user, the disengaged user, the mastery tracker, and the health-conscious user. Total engagement did not differ significantly across clusters, but each individual engagement dimension did, indicating that the profiles differ not in how much users engage, but in how, a distinction that aggregate engagement scores don’t reveal. These findings support person-centered methods as a tool for surfacing heterogeneity that variable-centered analyses obscure, and suggest that platforms should tailor engagement strategies and onboarding to users' psychological profiles rather than applying them uniformly.