Heterogeneous associations of a mobile health-based disease management program on uncontrolled hypertension: A target trial emulation study

Abstract

Long-term effectiveness of digital health interventions for hypertension remains unclear, particularly regarding individual variability in treatment response. This study examined the association of a mobile health-based disease management program for uncontrolled hypertension and assessed treatment effect heterogeneity using a target trial emulation framework. We analyzed health checkup data of employees June 2021–December 2023. Individuals with hypertension, diabetes, or dyslipidemia were invited to participate in a six-month mobile health-based disease management program incorporating lifestyle tracking via a mobile application and remote behavioral coaching. We compared the following two treatments using a target trial emulation framework: mobile health-based disease management program combined with conventional treatment, versus conventional treatment alone. The primary outcome was uncontrolled hypertension at the one-year follow-up (systolic ≥140 mmHg or diastolic ≥90 mmHg). We estimated average and individual treatment effects using outcome regression based on the G-formula with ensemble machine learning methods for model specification. Clustering analysis was used to identify heterogeneous subgroups and potential effect modifiers. Mobile health-based disease management program was associated with a 5.2% (95% confidence interval: 4.4% to 6.0%) lower prevalence of uncontrolled hypertension compared with conventional treatment. Treatment response varied, with greater benefits observed in individuals with a strong intention to improve lifestyle habits, higher diastolic blood pressure, and more favorable behavioral and metabolic characteristics. Age was associated with benefit, though it had relatively lower importance. Participation in a mobile health-based disease management program was associated with better blood pressure control over one year. The substantial variation in treatment effectiveness highlights the need for personalized digital health strategies.