%0 Journal Article
%@ 2561-1011
%I JMIR Publications
%V 3
%N 1
%P e13030
%T Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study
%A Guthrie,Nicole L
%A Berman,Mark A
%A Edwards,Katherine L
%A Appelbaum,Kevin J
%A Dey,Sourav
%A Carpenter,Jason
%A Eisenberg,David M
%A Katz,David L
%+ Better Therapeutics, 445 Bush Street, Suite 300, San Francsico, CA, 94108, United States, 1 617 877 0327, mark@bettertherapeutics.io
%K hypertension
%K mobile health
%K mHealth
%K lifestyle medicine
%K digital therapeutics
%K digital medicine
%K machine learning, behavioral therapy
%D 2019
%7 12.03.2019
%9 Original Paper
%J JMIR Cardio
%G English
%X Background: Behavioral therapies, such as electronic counseling and self-monitoring dispensed through mobile apps, have been shown to improve blood pressure, but the 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: The objectives of this retrospective analysis were to examine the effect of a novel digital therapeutic on blood pressure in adults with hypertension and to explore the ability of machine learning to predict participant completion of the intervention. Methods: Participants with hypertension, who engaged with the digital intervention for at least 2 weeks and had paired blood pressure values, were identified from the intervention database. 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 blood pressure values at will, and changes were calculated using averages of baseline and final values 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 cohort comprised 172 participants with hypertension, having paired blood pressure values, who were engaged with the intervention. Of the total, 86.1% participants were women, the mean age was 55.0 years (95% CI 53.7-56.2), baseline systolic blood pressure was 138.9 mmHg (95% CI 136.6-141.3), and diastolic was 86.2 mmHg (95% CI 84.8-87.7). Mean change was –11.5 mmHg for systolic blood pressure and –5.9 mmHg for diastolic blood pressure over a mean of 62.6 days (P<.001). Among participants with stage 2 hypertension, mean change was –17.6 mmHg for systolic blood pressure and –8.8 mmHg for diastolic blood pressure. Changes in blood pressure remained significant in a mixed-effects model accounting for the baseline systolic blood pressure, age, gender, and body mass index (P<.001). A total of 43% of the participants tracking their blood pressure at 12 weeks achieved the 2017 American College of Cardiology/American Heart Association definition of blood pressure control. The 7-day predictive model for intervention completion was trained on 427 participants, and the area under the receiver operating characteristic curve was .78. Conclusions: Reductions in blood pressure were observed in adults with hypertension who used the digital therapeutic. The degree of blood pressure reduction was clinically meaningful and achieved rapidly by a majority of the 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. 
%M 31758792
%R 10.2196/13030
%U http://cardio.jmir.org/2019/1/e13030/
%U https://doi.org/10.2196/13030
%U http://www.ncbi.nlm.nih.gov/pubmed/31758792