Accepted for/Published in: JMIR Diabetes
Date Submitted: Jul 16, 2022
Date Accepted: Sep 7, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Analyzing User Engagement within a patient-reported outcomes texting tool for diabetes management
ABSTRACT
Background:
Patient-reported outcomes (PROs) capture patients’ views about their health condition and its management. PRO assessments are increasingly being used in clinical trials, including those targeting type 2 diabetes (T2D). Mobile Health (mHealth) tools offer novel solutions for collecting PRO data in real-time in clinical trials. While the patient is at the center of any PRO-based intervention, few studies have examined user engagement with PRO mHealth tools.
Objective:
We aim to: (1) evaluate user engagement with a PRO mHealth tool for T2D management; (2) identify patterns of user engagement, and similarities and differences between the groups; and (3) identify characteristics of patients who are likely to drop-out or be less-engaged with a PRO mHealth tool
Methods:
We extracted user engagement data from an ongoing clinical trial that is testing the efficacy of a PRO mHealth tool designed to improve HbA1c among patients with uncontrolled T2D. To date, 61 patients have been randomized to the intervention, where they are sent six PRO text messages a day that are relevant to T2D self-management (e.g., healthy eating, medication adherence) over the 12-month study. To analyze user engagement, we first compared the response rate (RR) and response time (RT) between the 42 users who completed the 12-month intervention and the 19 who dropped out early. Next, we classified patients from the completers group into three subgroups using latent class trajectory modeling. The classification was based on similarity in longitudinal engagement data. Finally, we investigated the difference between the subgroups among completers from various cross-sections (time of the day, day of the week) and PRO question types. We also explored the patients’ demographics overall, and their distribution among the subgroups.
Results:
Patients who dropped out from the program early (i.e., non-completers) had a lower RR to PRO questions and took longer times to respond to PRO questions than the completers. Among the completers, analysis of the longitudinal RRs identified differences in engagement patterns over time. The completers with the lowest engagement showed their peak engagement during month 5, almost at the mid-stage of the program. Alternatively, the remaining groups showed peak engagement at the beginning of the intervention, followed by either steady decline or sustained high engagement. Comparisons of the demographic characteristics showed significant differences between the high- and low-engaged groups. The high-engaged completers were predominantly older, of Hispanic descent, bilingual, and had a graduate degree. In comparison, the low-engaged group was composed of mostly African American patients who reported the lowest annual income, with one out of every three patients earning less than $20,000 annually.
Conclusions:
There are discernible engagement phenotypes based on individual PRO responses and their patterns vary in the timing of peak engagement and also based on demographics. Future studies can use these findings to predict engagement categories and tailor interventions to promote longitudinal engagement. Clinical Trial: Clinicaltrials.gov NCT03652389
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