Implementing Digital Care Companions for Diabetes and Hypertension: An Interrelated System to facilitate Self-management and Clinician Supervision

Introduction:
Hypertension and type 2 diabetes require continuous monitoring and long-term management; however, most health systems lack integrated follow-up mechanisms, leading to suboptimal adherence and delayed detection of complications. Digital Care Companions (DCCs) have the potential to integrate self-management with real-time clinician supervision through remote monitoring and support. This study assesses the feasibility and preliminary clinical signals of a digitally connected ecosystem incorporating DCCs for chronic disease management.

Methods:
A 12-week prospective pilot study was conducted across two outpatient clinics and included 45 adult participants (≥18 years) diagnosed with hypertension or type 2 diabetes. Participants used a DCC platform integrated with Bluetooth-enabled blood pressure monitors and glucometers. Platform features included medication reminders, symptom logging, educational modules, and secure two-way messaging. Clinicians accessed real-time patient data through a centralized dashboard. Feasibility was evaluated based on the frequency of vital sign submissions and adherence to medication reminders. Preliminary clinical signals included observed changes in mean systolic blood pressure and fasting glucose variability among participants with ≥70% data completeness. All outcomes were summarized using descriptive statistics.

Results:
Among the 45 participants, 78% submitted vital sign measurements regularly, and 64% adhered to medication reminders. Active users demonstrated an observed mean reduction in systolic blood pressure of 6.5 mmHg and a 10-12% reduction in fasting glucose variability. Clinicians reported improved visualization of longitudinal trends, fewer unplanned visits, and real-time therapeutic adjustments in 26% of users.

Conclusions:
The digitally connected DCC ecosystem demonstrated feasibility and supported anticipatory clinical decision-making, indicating strong potential for scalable chronic disease management in resource-constrained settings.