A Visualization Tool to Analyse Usage of Web-Based Interventions: The Example of Positive Online Weight Reduction (POWeR)

Abstract

BackgroundAttrition is a significant problem in Web-based interventions. Consequently, this research aims to identify the relation between Web usage and benefit from such interventions. A visualization tool has been developed that enables researchers to more easily examine large datasets on intervention usage that can be difficult to make sense of using traditional descriptive or statistical techniques alone.ObjectiveThis paper demonstrates how the visualization tool was used to explore patterns in participants’ use of a Web-based weight management intervention, termed "positive online weight reduction (POWeR)." We also demonstrate how the visualization tool can be used to perform subsequent statistical analyses of the association between usage patterns, participant characteristics, and intervention outcome.MethodsThe visualization tool was used to analyze data from 132 participants who had accessed at least one session of the POWeR intervention.ResultsThere 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 reviews. The POWeR tools relating to the food diary and steps diary were reused 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 reuse the food and steps diaries. Reuse of the steps diary and the getting support tools was associated with greater weight loss.ConclusionsThe visualization 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 visualization techniques is recommended to (1) make sense of large datasets more quickly and efficiently; (2) determine the likely active ingredients in Web-based interventions, and thereby enhance the benefit they may provide; and (3) guide in designing (or redesigning) of future interventions to promote greater use and engagement by enabling users to easily access valued intervention content/tools.Trial RegistrationInternational Standard Randomized Controlled Trial Number (ISRCTN): 31685626; http://www.isrctn.com/ISRCTN31685626 (Archived by WebCite at http://www.webcitation.org/6YXYIw9vc).

Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis

“…Automatic tracking of use patterns, including number of logins, time spent online and the amount and type of content used during the intervention period, was the most commonly used measure of engagement in the behavioural science literature [11, 19, 20, 26, 44, 70–82]. Physiological measures including cardiac activity, respiratory depth [62] and electro-dermal activity [65], and psychophysical measures, such as eye tracking [51], have been used to measure engagement in the computer science and HCI literatures.…”

Randomised controlled trial and economic analysis of an internet-based weight management programme: POWeR+ (Positive Online Weight Reduction)

“…The development of the patient website is explained more fully elsewhere 21,25,46 but, in brief, it provided patients with a choice of two eating plans (low calorie or low carbohydrate) and two physical activity plans (a walking plan, with free pedometer, or a plan for any other physical activity). POWeR+ aimed to help patients to develop self-regulation skills by promoting weekly weighing, weekly reviews of eating and physical activity goals, as well as by providing cognitive-behavioural techniques in weekly online sessions.…”

A Framework for Analyzing and Measuring Usage and Engagement Data (AMUsED) in Digital Interventions: Viewpoint (Preprint)

“…movement through pages) requires alternative methods to identify and inform subsequent statistical analysis. Therefore, techniques such as visualisation and process mining may be more informative as they can reveal patterns of usage within the data, such as workflow through an intervention, clustering by participant groups, and temporal details [10,[21][22][23][24]. In applying stage 3 of the framework, the required data format for any analytical software should be considered.…”

“…The analytical tools available are SPSS for Windows (SPSS Inc., Chicago) and LifeGuide Visualisation Tool (LVT) [21,26] (stage 3; 2, Table 3). It is anticipated that there will be insufficient power for definitive hypothesis testing.…”

“…SPSS for Windows (SPSS Inc., Chicago) and LifeGuide Visualisation Tool (LVT) [21,26] will be used for data analysis (stage 3; 2, Table 3). There will be sufficient power to analyse average usage of the intervention (e.g.…”