JMIR Publications

ABSTRACT

Background:

mHealth has the potential to radically improve health behaviors and quality of life; however, there are still key gaps in understanding how to optimize mHealth engagement. Most engagement research reports only on system use without consideration of whether the user is reflecting on the content cognitively. Although interactions with mHealth are critical, cognitive investment may also be important for meaningful behavior change. Notably, content designed to request too much reflection, may lead users to disengage. Understanding how to strike the balance between response and reflection burden has critical implications for achieving effective engagement to impact intended outcomes.

Objective:

We sought to understand the interplay between response burden and reflection burden for impacting mHealth engagement in an observational study. Specifically, we explored how varying the response and reflection burden of mHealth content would impact users’ text message response rate as part of an mHealth intervention.

Methods:

We recruited support persons of people with diabetes for a randomized controlled trial (RCT) evaluating an mHealth intervention for diabetes management. Support person participants assigned to the intervention (N=148) completed a survey and received text messages for 9 months. During the two-year RCT, we sent four versions of a weekly two-way text message that varied in both reflection burden (level of cognitive reflection requested relative to other messages) and response burden (level of information requested in the response relative to other messages). We quantified engagement using participant-level response rate. We compared the odds of responding to each text and used Poisson regression to estimate associations between participant characteristics and response rate.

Results:

The texts requesting the most reflection had the lowest response rates (median 10-23%), regardless of response burden. Response rate was highest for the text requesting the least reflection (90%), yet still relatively high for the text with medium reflection (75%). Lower odds of responding were associated with higher reflection burden (P<.001). Participants with younger age or who had lower socioeconomic status had lower response rates to texts with more reflection burden relative to their counterparts (P<.01 for each).

Conclusions:

As reflection burden increased, engagement decreased, and we found more disparities in engagement by participants’ characteristics. Content encouraging moderate levels of reflection may be ideal for achieving both cognitive investment and system use. Our findings provide insights into mHealth design and optimizing both engagement and effectiveness. Clinical Trial: NCT04347291