Key result
An mHealth-based care model (mGlide) was less costly (59.6% of iterations) and yielded better health outcomes (99.6%) vs enhanced usual care, with 10-year mortality ARR of 0.6-3.3%.
Why the study?
The cost-effectiveness of a mobile health technology-based care model to facilitate home blood pressure monitoring and remote hypertension management in patients with elevated cardiovascular risk required evaluation.
Does an mHealth-based care model (mGlide) improve cost-effectiveness and reduce 10-year mortality compared to Enhanced Usual Care and No-Intervention in patients with hypertension and elevated cardiovascular risk?
Does an mHealth-based care model (mGlide) improve cost-effectiveness and reduce 10-year mortality compared to Enhanced Usual Care and No-Intervention in patients with hypertension and elevated cardiovascular risk?
Effect estimate: ARR 0.6-3.3% (vs EUC); ARR 4.9-5.8% (vs No-Intervention)
A Markov microsimulation model demonstrates that an mHealth-based care model for remote hypertension management is highly likely to be cost-effective and reduce 10-year mortality compared to enhanced usual care in high-risk cardiovascular patients.
Supports mHealth hypertension care in high-risk patients; extends RCT data to cost-effectiveness and projected mortality reduction.
Background This study evaluates the cost-effectiveness of a mobile health technology (mHealth) based care model to facilitate home blood pressure (BP) monitoring and remote hypertension management. Methods We used a Markov microsimulation model to study health and cost outcomes for three hypertension control strategies: an mHealth-based care model (mGlide) vs. Enhanced Usual Care (EUC) vs. No-Intervention. We sampled patient characteristics from the mGlide randomized controlled trial (RCT) and randomly transitioned patients through three possible health states over a 10-year horizon with monthly cycles: 1) No Cardiovascular Event, 2) Cardiovascular Event, and 3) Deceased. We determined transition probabilities from literature, allowing the probabilities to vary by patient characteristics, including BP values. We evaluated health and cost outcomes across a range of plausible deterministic treatment effects (expected BP reductions) supported by the RCT. We performed probabilistic sensitivity analysis to assess cost-effectiveness while randomly varying the treatment effect magnitude. Results The mHealth-based care model was cost-effective compared to No-Intervention and EUC across all deterministic scenarios. 10-year mortality absolute risk reductions ranged from 4.9-5.8% (vs No-Intervention) and 0.6-3.3% (vs EUC) across scenarios. In probabilistic sensitivity analysis, mHealth-based care (vs EUC) was less costly in 59.6% of iterations and had better health outcomes in 99.6%. Comparing all care models, mHealth-based care had the greatest net monetary benefit in 95% of iterations at $26,000/QALY and in 99% of iterations at $72,000/QALY. Conclusions In the current study, we estimated that mHealth-based care models can be cost-effective approaches to improve hypertension control in patients with elevated cardiovascular risk.
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Caputo et al. (2026) studied Hypertension with elevated cardiovascular risk. mHealth-based care model (mGlide) vs. Enhanced Usual Care (EUC) and No-Intervention was evaluated on Cost-effectiveness (net monetary benefit) and 10-year mortality (ARR 0.6-3.3% (vs EUC); ARR 4.9-5.8% (vs No-Intervention)). An mHealth-based care model (mGlide) was less costly (59.6% of iterations) and yielded better health outcomes (99.6%) vs enhanced usual care, with 10-year mortality ARR of 0.6-3.3%.
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