The Mystar mHealth program reduced mean morning systolic blood pressure by 3.5 mmHg over 24 weeks with prediction models achieving a correlation of approximately 0.85 at week 22.
Observational (n=2,318)
Does incorporating feature-engineered variables improve the prediction of systolic blood pressure changes in patients participating in a 24-week mHealth-based disease management program?
Feature engineering improved the correlation of individual predictors with systolic blood pressure changes in the early phase of an mHealth program, but overall prediction model performance remained largely unchanged compared to using conventional predictors.
Effect estimate: Correlation coefficient r up to 0.85 at 22 weeks predicting SBP change
Abstract Mobile health (mHealth)-based disease management programs enable continuous monitoring of blood pressure (BP) and related health behaviors. Feature engineering may help to extract informative predictors from longitudinal data, potentially improving BP change prediction. This study aimed to evaluate whether feature-engineered predictors can improve the prediction of systolic BP (SBP) changes using an mHealth-based disease management program. We analyzed data from participants with hypertension, dyslipidemia, or diabetes mellitus who completed the 24-week Mystar program, which combined phone-based coaching, remote monitoring, and app-based logging of BP and behavioral data. The primary outcome was the change in morning SBP from baseline to the end of the program. Prediction models for SBP changes were developed using ElasticNet regression at weeks 4, 8, 12, and 22 by comparing models with and without feature-engineered variables generated by feature tools. In total, 2318 participants were included in the analysis. At week 4, the top feature after feature engineering showed a stronger correlation with SBP change (r = 0.561) than the best original predictor (r = 0.455), although the model-level performance was similar (r = 0.561 vs. 0.559). By week 22, both models achieved a high correlation of approximately 0.85 with no substantial difference in performance. Feature engineering increased the correlation between individual predictors and SBP change in the early phase; however, the overall prediction performance of the ElasticNet model remained largely unchanged. Further studies are required to confirm these findings and examine their applicability in broader clinical and implementation contexts.
Kanai et al. (Tue,) conducted a observational in Adults with hypertension, dyslipidemia, or diabetes mellitus enrolled in an mHealth-based disease management program (n=2,318). Mystar mHealth-based disease management program combining phone-based coaching, remote monitoring, app-based logging of BP and behavioral data vs. No comparator (observational pre-post study) was evaluated on Change in morning systolic blood pressure (SBP) from baseline to program end (Correlation coefficient r up to 0.85 at 22 weeks predicting SBP change). The Mystar mHealth program reduced mean morning systolic blood pressure by 3.5 mmHg over 24 weeks with prediction models achieving a correlation of approximately 0.85 at week 22.