Adherence Patterns of Patients Using Remote Patient Management after Myocardial Infarction: A Mixed-Methods Persona Approach
ABSTRACT
Background:
Background:
Remote patient management (RPM) utilizing smartphone-enabled health monitoring devices (SHMDs) can be an effective, value-added part of cardiovascular care. However, cardiac patients’ adherence to RPM is variable. Personas can be used to understand the needs of the patient group and guide tailoring towards more personalized and effective eHealth intervention.
Objective:
Objective:
The aim of this study was to develop data-driven personas for myocardial infarction (MI) patients based on both quantitative and qualitative results.
Methods:
Methods:
This study employed a mixed methods design involving (1) database analysis of MI patient (N=261) SMHD usage data (blood pressure (BP), weight, step count) over the course of a one-year care track and (2) semi-structured interviews with MI patients (N=16) currently using SHMDs. Overall, 12-month adherence rates were calculated based on the number of weeks patients managed to perform the prescribed home measurements.
Results:
Results:
A cluster analysis was conducted on the self-monitoring data resulting in four distinctive usage-patterns labelled as: stiff starting (non-adherent in first week(s), 13% of users), temporary persisting (decreasing adherence, 24%), loyally persisting (continuously adherent, 26%), and negligent quitting (non-adherent, 37%). Health outcomes were analyzed based on these patterns. More adherent usage-patterns show better controlled BP when compared to less adherent usage-patterns, suggesting that adherence is associated with health outcomes. Patient experiences relating to each of the four distinctive usage-patterns were uncovered by means of semi-structured interviews, providing insight into user experiences and adherence factors most relevant for each of the groups. Thus, four distinct personas were developed by data collection, persona segmentation, and persona creation.
Conclusions:
Conclusion: This study identified four personas regarding usage and experiences of patients within an RPM care-track. Adherent patterns were characterized by improved BP and step count. These personas can guide future tailoring of eHealth interventions to maximize patient adherence.
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