JMIR Publications

ABSTRACT

Background:

Automated texting platforms have emerged as a tool to facilitate communication between patients and healthcare providers with variable effects on achieving target blood pressure. Understanding differences in the way patients interact with these communication platforms can inform their use and design for hypertension management.

Objective:

Our primary aim was to explore the unique phenotypes of patient interactions with an automated text messaging platform for blood pressure (BP) monitoring. Our secondary aim was to estimate associations between interaction phenotypes and BP control.

Methods:

This study was a secondary analysis of data from a randomized controlled trial for adults with poorly controlled hypertension. 201 patients with established primary care were assigned to the automated texting platform; messages exchanged throughout the 4-month program were analyzed. We used the k-means clustering algorithm to characterize two different interaction phenotypes: program conformity and engagement style. First, we identified unique clusters signifying differences in program conformity based on the frequency over time of error alerts, which were generated to patients when they deviated from the requested text message format (e.g. ###/## for BP). Second, we explored overall engagement styles, defined by error alerts as well as responsiveness to text prompts, unprompted messages, and word count averages. Finally, we applied the Chi-square test to identify associations between each interaction phenotype and achieving the target BP.

Results:

We observed three categories of program conformity based on their frequency of error alerts: those who immediately and consistently submitted texts without system errors (perfect user), those who did so after an initial learning period (adaptive user), and those who consistently submitted messages generating errors to the platform (non-adaptive user). Next, we observed three categories of engagement style: the enthusiast who tended to submit unprompted messages with high word counts; the student who inconsistently engaged, and the minimalist who engaged only when prompted. Of all six phenotypes, we observed a statistically significant association between patients demonstrating the minimalist communication style (high adherence, few unprompted messages, limited information sharing) and achieving target BP.

Conclusions:

We identified unique interaction phenotypes among patients engaging with an automated text message platform for remote BP monitoring. Only the minimalist communication style was associated with achieving target BP. Identifying and understanding interaction phenotypes may be useful for tailoring future automated texting interactions and designing future interventions to achieve better BP control.