%0 Journal Article %A Nicole L Guthrie %A Jason Carpenter %A Katherine L Edwards %A Kevin J Appelbaum %A Sourav Dey %A David M Eisenberg %A David L Katz %A Mark A Berman %T Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study %D 2019 %R 10.1136/bmjopen-2019-030710 %J BMJ Open %P e030710 %V 9 %N 7 %X Objectives Development of digital biomarkers to predict treatment response to a digital behavioural intervention.Design Machine learning using random forest classifiers on data generated through the use of a digital therapeutic which delivers behavioural therapy to treat cardiometabolic disease. Data from 13 explanatory variables (biometric and engagement in nature) generated in the first 28 days of a 12-week intervention were used to train models. Two levels of response to treatment were predicted: (1) systolic change ≥10 mm Hg (SC model), and (2) shift down to a blood pressure category of elevated or better (ER model). Models were validated using leave-one-out cross validation and evaluated using area under the curve receiver operating characteristics (AUROC) and specificity- sensitivity. Ability to predict treatment response with a subset of nine variables, including app use and baseline blood pressure, was also tested (models SC-APP and ER-APP).Setting Data generated through ad libitum use of a digital therapeutic in the USA.Participants Deidentified data from 135 adults with a starting blood pressure ≥130/80, who tracked blood pressure for at least 7 weeks using the digital therapeutic.Results The SC model had an AUROC of 0.82 and a sensitivity of 58% at a specificity of 90%. The ER model had an AUROC of 0.69 and a sensitivity of 32% at a specificity at 91%. Dropping explanatory variables related to blood pressure resulted in an AUROC of 0.72 with a sensitivity of 42% at a specificity of 90% for the SC-APP model and an AUROC of 0.53 for the ER-APP model.Conclusions Machine learning was used to transform data from a digital therapeutic into digital biomarkers that predicted treatment response in individual participants. Digital biomarkers have potential to improve treatment outcomes in a digital behavioural intervention. %U https://bmjopen.bmj.com/content/bmjopen/9/7/e030710.full.pdf