Objective While colleges have implemented brief, tailored interventions for health-risk areas such as alcohol prevention, theoretically-guided digital learning offerings for weight gain prevention have lagged behind in programming and implementation. Thus, the objective was to design and usability test a weight gain prevention digital learning platform for college students with modules targeting key nutrition and physical activity behaviors. Methods Development occurred in iterative phases: formative research, descriptive normative data collection, prototype development, and usability testing. Formative research consisted of background work and survey administration to incoming and current freshmen. Prototype development was guided by theories of behavior change and cognitive processing, and consisted of brief assessment and feedback using written text, graphs, and videos. Iterative usability testing was conducted. Results Current freshmen reported eating more quick order meals per week than incoming freshman, but fewer high-fat snacks and fewer sugary beverages. Current freshmen reported more sedentary time than incoming freshmen. Based on iterative testing results, eight behavioral targets were established: breakfast, high-fat snacks, fried foods, sugary beverages, fruit/vegetables, physical activity, pizza intake, and sedentary behavior. Initial usability testers indicated the modules were easy to understand, held their attention, and were somewhat novel. Analysis of qualitative feedback revealed themes related to content, layout, structure and suggested refinements to the modules. Conclusions A gap exists for evidence-based obesity prevention programs targeted to adolescents as they transition into adulthood. Brief, tailored digital learning interventions show promise towards addressing key behavioral nutrition and physical activity targets among students during the transition to college.
Weight and Shape Concern Impacts Weight Gain Prevention in the SNAP Trial: Implications for Tailoring Intervention Delivery
