Patterns of User Engagement with Mobile- and Web-Delivered Self-Care Interventions for Adults with T2DM: A Review of the Literature

Abstract

Technology-delivered interventions can improve the health behaviors and clinical outcomes of persons with diabetes, but only if end users engage with these interventions. To summarize the current knowledge on engagement with technology-based interventions, we conducted a review of recent mobile- and web-delivered intervention studies for adults with type 2 diabetes published from 2011–2015. Among 163 identified studies, 24 studies satisfied our inclusion criteria. There was substantial variation in how intervention engagement was reported across studies. Engagement rates were lower among interventions with a longer duration, and engagement decreased over time. In several studies, older age and lower health literacy were associated with less engagement, and more engagement was associated with intervention improvement in at least one outcome, including glycemic control. Future technology-based intervention studies should report on engagement, examine and report on associations between user characteristics and engagement, and aim to standardize how this is reported, particularly in longer trials.

mHealth Intervention Elements and User Characteristics Determine Utility: A Mixed-Methods Analysis

“…24 To improve engagement and outcomes, mHealth interventions must tailor content and design to user preferences. 30,31 There are several limitations to our study. We could not reach 20 participants at follow-up; therefore, not everyone who experienced the intervention provided feedback on their experience.…”

Sustaining eHealth engagement in a multi‐year weight gain prevention intervention

“…Offering periodic campaigns was effective in increasing the number of weights being reported but did not significantly increase the proportion of participants who reported compared with the previous month. Given the consistent prior finding that engagement declines over time and the results presented here, it is clear that the trend is for fewer participants to report over time. Thus, while the campaigns did not boost the percentage of participation, it perhaps sustained participation during a month when participation otherwise would have declined by a few percentage points.…”

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

“…The checklists provide the means to increase transparency and make findings easier to replicate, whilst discouraging unsystematic data dredging. The process will also encourage greater detail and consistency in the reporting of usage and engagement, making it easier to apply the findings to a wider context [5] and enabling comparison across different interventions and evaluation studies. The framework helps to operationalize and measure usage in ways that will better inform our understanding of engagement with a DBCI, encompassing broad measures of usage by the whole sample through to specific theory-based usage variables and usage by sub-groups based on personal characteristics.…”

“…For researchers, trialing a complex DBCI can yield large-scale, detailed datasets. Usage analyses frequently examine the extent to which an intervention is used by the whole sample, utilizing variables such as the number of times users logged in, total time spent on an intervention, or number of pages viewed [5]. These broad level analyses do not always take advantage of the detailed and comprehensive data available, and they frequently assume that greater amounts of usage are indicative of higher levels of interaction that lead to increased changes in target behavior 6 .…”