An Online Group-based Self-tracking Program to Increase Fruit and Vegetable Consumption: The Effects of Demographic Similarity, Social Modeling and Performance Discrepancy
Date Submitted: Aug 23, 2016
Open Peer Review Period: Aug 24, 2016 - Oct 19, 2016
Background: Self-tracking allows people to reflect on their health behaviors and make improvements accordingly to achieve a health goal. Web-based interventions with a self-tracking component have been found to be effective in promoting adults’ fruit and vegetable consumptions (FVC). However, these interventions primarily focus on individual- rather than group-based self-tracking. The rise of social media technologies enables sharing and comparing self-tracking records in a group context. Therefore, we develop an online group-based self-tracking program to promote FVC in early young adults, who are at an important stage of developing food patterns that will affect their future. Objective: This study aims to examine (1) the effectiveness of online group-based self-tracking on FVC, and (2) composition characteristics of online self-tracking groups that make the group more effective in promoting FVC in early young adults. Methods: During a 4-week web-based experiment, 113 college students self-tracked their FVC either individually (i.e., the control group) or in an online group characterized by a 2 (demographic similarity: demographically similar vs. demographically diverse) × 2 (social modeling: incremental-change vs. ideal-change) experimental design. Each online group consisted of one focal participant and three confederates as group members whose demographics and FVC were manipulated to create the four treatment groups. Self-reported FVC were assessed using the food frequency questionnaire at baseline and after the 4-week experiment, and were recorded using participants’ self-tracking messages during the 4-week experiment. Results: Participants who self-tracked their FVC collectively with other group members consumed more FV than participants who self-tracked their FVC individually, P = .02, η2 = .08, controlling for demographics, BMI, baseline FVC and meal plan enrollment. The results did not show significant main effects of demographic similarity (P = .47) or types of social modeling (P = .54) in making self-tracking groups more effective in promoting FVC. However, additional analyses revealed the main effect of performance discrepancy (i.e., difference in FVC between a focal participant and his/her group members during the 4-week experiment), such that participants who had a low performance discrepancy from other group members consumed greater FVC than participants who had a high performance discrepancy from other group members, P = .003, η2 = .16. A mediation test showed that low performance discrepancy led to greater downward contrast (b = -0.78, CI = [-2.44, -0.15]), which in turn, led to greater FVC. Conclusions: Online self-tracking groups with models consistently increasing their FVC were more effective than self-tracking alone in promoting FVC for early young adults. Low performance discrepancy from models would lead to downward contrast, which in turn, increased participants’ FVC over time. The study highlighted social comparison processes in online groups that allow for sharing personal health information.