ArticleBMJ open2019
Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Digital Biomarker-Based Interventions: Systematic Review of Systematic Reviews.Journal of medical Internet research · 2022Pooled it
- A Novel Mobile App (Heali) for Disease Treatment in Participants With Irritable Bowel Syndrome: Randomized Controlled Pilot Trial.Journal of medical Internet research · 2021Trial
- Investigating feature-engineered predictors for systolic blood pressure changes in an mHealth-based disease management program.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- DELTA: Strengthening human biological resilience with an N=1 digital health and dynamic biomarker protocol.PloS one · 2026Article
- The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review.Frontiers in digital health · 2025Review
- Towards a comprehensive assessment of QSP models: what would it take?Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Evolution of Digital Health and Exploration of Patented Technologies (2017-2021): Bibliometric Analysis.Interactive journal of medical research · 2024Article
- The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers.JAMIA open · 2024Review
- Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies.JMIR formative research · 2023Review
- Survey and Evaluation of Hypertension Machine Learning Research.Journal of the American Heart Association · 2023Article
- Evaluation of Chinese healthcare organizations' innovative performance in the digital health era.Frontiers in public health · 2023Article
- Equity in AgeTech for Ageing Well in Technology-Driven Places: The Role of Social Determinants in Designing AI-based Assistive Technologies.Science and engineering ethics · 2022Article
- Digital Biomarker-Based Studies: Scoping Review of Systematic Reviews.JMIR mHealth and uHealth · 2022Article
- Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning.Journal of pharmacokinetics and pharmacodynamics · 2022Review
- Teaching computational systems biology with an eye on quantitative systems pharmacology at the undergraduate level: Why do it, who would take it, and what should we teach?Frontiers in systems biology · 2022Article
- Outcomes of Digital Biomarker-Based Interventions: Protocol for a Systematic Review of Systematic Reviews.JMIR research protocols · 2021Article
- Translational precision medicine: an industry perspective.Journal of translational medicine · 2021Review
- Discovering Composite Lifestyle Biomarkers With Artificial Intelligence From Clinical Studies to Enable Smart eHealth and Digital Therapeutic Services.Frontiers in digital health · 2021Article
- Future possibilities for artificial intelligence in the practical management of hypertension.Hypertension research : official journal of the Japanese Society of Hypertension · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.