Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study

Abstract

ObjectivesDevelopment of digital biomarkers to predict treatment response to a digital behavioural intervention.DesignMachine 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).SettingData generated through ad libitum use of a digital therapeutic in the USA.ParticipantsDeidentified data from 135 adults with a starting blood pressure ≥130/80, who tracked blood pressure for at least 7 weeks using the digital therapeutic.ResultsThe 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.ConclusionsMachine 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.

A Novel Mobile App (Heali) for Disease Treatment in Participants With Irritable Bowel Syndrome: Randomized Controlled Pilot Trial

“…This is significant considering that peer-reviewed mobile apps using AI are able to optimize support of chronic disease treatment of conditions such as diabetes [ 23 , 39 ] and hypertension [ 25 ]; however, these apps have not been tested on less prominent conditions like IBS. This app-based intervention is especially novel as few studies have explored the possibilities of AI to improve dietary adherence, especially to the LFD [ 23 , 26 , 39 ]. This cements the novelty of this study on two fronts: (1) the use of AI to improve quality of life in patients with IBS and (2) the use of AI to improve IBS disease outcomes.…”

“…Mobile apps using artificial intelligence (AI) in consort with a multidisciplinary team within a platform are gaining traction as useful tools for supporting the management of chronic conditions like diabetes and hypertension [ 23 - 26 ]. However, an app designed to treat IBS symptoms using AI has yet to be explored.…”

A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data

“…Digital biomarkers offer researchers and clinicians access to large volumes of objective data that can be captured relatively passively within the context of free-living “normal” conditions [3, 4]. As the algorithms and hardware of digital devices improve, so too does their potential to offer more nuanced and comprehensive insights into the characteristics of diseases [5].…”

“…Indeed, identifying information that is important to patients is a primary goal of future drug and treatment developments [6]. However, significant challenges remain, including: how best to use the data derived from these devices [2, 4], ensuring reliability and reproducibility of results [2, 3, 7], an imposed reliance on the proprietary algorithms of individual devices, and the selection of clinically meaningful endpoints. Thus, identifying specific digital biomarkers that are best able to differentiate between healthy and clinical cohorts is a critical first step in this process.…”

Value of digital biomarkers in precision medicine: implications in cancer, autoimmune diseases, and COVID-19

“…Analytics for large volumes of data are usually leveraged to delineate trends and infer patterns at individual and population levels. We stress that an extension of the definition of DB is needed [9][10][11]. While continuing with the measurement of physiological parameters, the point of leverage would be the inclusion of other types of digital information sources: examples include next-generation mobile sensors and detectors with integrated solutions from the Internet of Medical Things (IoMT) platforms.…”