JMIR Publications

ABSTRACT

Background:

Behavioral therapies, such as e-counseling and self-monitoring dispensed through mobile apps, have been shown to improve blood pressure but results vary and long-term engagement is a challenge. Machine learning is a rapidly advancing discipline that can be used to generate predictive and responsive models for the management and treatment of chronic conditions and shows potential for meaningfully improving outcomes.

Objective:

This retrospective analysis of data from the digital therapeutic, Better, examines its effect on blood pressure in hypertensive adults and explores the use of machine learning methods to predict intervention completion.

Methods:

The Better database was queried for participants with hypertension, who engaged with the intervention for at least 2 weeks and had paired blood pressure (BP) values. Participants were required to be ≥ 18 years old, reside in the United States and own a smartphone. The digital intervention offers personalized behavior therapy, including goal setting, skill building and self-monitoring. Participants reported BP at will. Changes in BP were calculated using averages of baseline and ending BP for each participant. Machine learning was used to generate a model of participants who would complete the intervention. Random forest models were trained at days 1, 3 and 7 of the intervention and the generalizability of the models was assessed using leave-one-out cross validation.

Results:

The primary analysis cohort was comprised of 172 hypertensive participants with paired BP values who were engaged with the intervention. Participants were 86.1% female, with a mean age of 55.0 years (95% CI 53.7, 56.2), baseline systolic blood pressure (SBP) of 138.9 mmHg (95% CI 136.6, 141.3) and diastolic (DBP) of 86.2 mmHg (95% CI 84.8, 87.7). Mean BP change was -11.5 mmHg for SBP and -5.9 mmHg for DBP over a mean of 62.6 days (P < .001). Amongst participants with Stage 2 Hypertension, mean BP change was -17.6 mmHg for SBP and -8.8 mmHg for DBP. Changes in BP remained significant in a mixed effects model accounting for baseline SBP, age, gender and BMI (P < .001). 43% of participants tracking BP at 12 weeks achieved the 2017 ACC/AHA definition of blood pressure control. The 7-day predictive model for intervention completion was trained on 429 participants and the AUC of the receiver operating characteristic was .72 with a false positive rate of 25%.

Conclusions:

The Better digital therapeutic was effective in lowering blood pressure among hypertensive adults. The degree of BP reduction was clinically meaningful and achieved rapidly by a majority of studied participants. Greater improvement was observed in participants with more severe hypertension at baseline. A successful proof of concept for using machine learning to predict intervention completion was presented