TY - JOUR T1 - Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study JF - BMJ Open JO - BMJ Open DO - 10.1136/bmjopen-2019-030710 VL - 9 IS - 7 SP - e030710 AU - Nicole L Guthrie AU - Jason Carpenter AU - Katherine L Edwards AU - Kevin J Appelbaum AU - Sourav Dey AU - David M Eisenberg AU - David L Katz AU - Mark A Berman Y1 - 2019/07/01 UR - http://bmjopen.bmj.com/content/9/7/e030710.abstract N2 - 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. ER -