A visualisation tool to analyse usage of web-based interventions: The example of Positive Online Weight Reduction (POWeR).

Abstract

Background: Attrition is a significant problem in web-based interventions. Consequently, research aims to identify the relation between web usage and benefit from such interventions. We have developed a visualisation tool that enables researchers to more easily examine large data sets on intervention usage that can be difficult to make sense of using traditional descriptive or statistical techniques alone. Objectives: This paper demonstrates how the visualisation tool was used to explore patterns in participants’ use of a web-based weight management intervention (POWeR: Positive Online Weight Reduction). We also demonstrate how the visualisation tool can be used to inform subsequent statistical analyses of the association between usage patterns, participant characteristics, and intervention outcome. Methods: The visualisation tool was used to analyse data from 132 participants who had accessed at least one session of the POWeR intervention. Results: There was a drop in usage of optional sessions after participants had accessed the initial, core POWeR sessions, but many users nevertheless continued to complete goal and weight review. POWeR tools relating to the food diary and steps diary were re-used most often. Differences in participant characteristics and usage of other intervention components were identified between participants who did and did not choose to access optional POWeR sessions (in addition to the initial core sessions) or re-use the food and steps diary. Re-use of the steps diary and the getting support tools was associated with greater weight loss. Conclusions: The visualisation tool provided a quick and efficient method for exploring patterns of web usage, which enabled further analyses of whether different usage patterns were associated with participant characteristics or differences in intervention outcome. Further usage of visualisation techniques is recommended in order to 1) make sense of large data sets more quickly and efficiently, 2) determine the likely active ingredients in web-based interventions, and thereby enhance the benefit they may provide and 3) inform (re-)design of future interventions to promote greater use and engagement by enabling users to easily access valued intervention content/tools.