Journal: npj Digital Medicine

Abbreviation

npj Digit. Med.

Publisher

Nature

Journal Volumes

ISSN

2398-6352

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2018 - 201912020 - 202622
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Publications1 - 10 of 23
  • Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
    Item type: Journal Article
    Sieberts, Solveig K.; Schwab, Patrick; et al. (2021)
    npj Digital Medicine
    Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).
  • Evaluating reliability in wearable devices for sleep staging
    Item type: Review Article
    Birrer, Vera; Elgendi, Mohamed; Lambercy, Olivier; et al. (2024)
    npj Digital Medicine
    Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.
  • The role of face regions in remote photoplethysmography for contactless heart rate monitoring
    Item type: Journal Article
    Bondarenko, Maksym; Menon, Carlo; Elgendi, Mohamed (2025)
    npj Digital Medicine
    Heart rate (HR) estimation is crucial for early cardiovascular diagnosis, continuous monitoring, and various health applications. While electrocardiography (ECG) remains the gold standard, its discomfort and impracticality for continuous use have spurred the development of non-contact methods like remote photoplethysmography (rPPG). This systematic review (PROSPERO: CRD 42024592157) examines 70 studies to assess the impact of Region of Interest (ROI) selection on HR estimation accuracy. Most methods (36.8%) use the holistic face, while forehead and cheek areas (24.5% and 21.7%) show superior accuracy. Machine learning-based approaches outperform traditional methods under motion artifacts and poor lighting, achieving Mean Absolute Error and Root Mean Square Error below 1.0 for some datasets. Combining multiple patches improves performance, though increasing ROIs beyond 60 patches results in diminishing returns and higher computational complexity. These findings highlight the significance of ROI optimization for robust rPPG-based HR estimation.
  • Predicting individual differences in digital alcohol intervention effectiveness through multimodal data
    Item type: Journal Article
    Fuchs, Magdalena; Boyd, Zachary M.; Schwarze, Alice; et al. (2026)
    npj Digital Medicine
    Digital interventions can change behaviors like alcohol use, but effectiveness varies widely across individuals. Accurately identifying non-responders—i.e., those least (vs. most) likely to change their behavior—before intervention delivery is difficult. Individual intervention effectiveness predictions from prior studies perform only slightly above chance (e.g., AUC ≈0.60; balanced accuracy ≈0.60). We present a novel approach integrating multimodal data across theory-driven domains—including psychological assessments, social network data, and neural responses to alcohol cues—to make ex-ante predictions about the effectiveness of smartphone-delivered alcohol interventions targeting psychological distancing in young adults (Study 1: N = 67; Study 2: N = 114). Demonstrating the feasibility of this approach, random forest models predicted individual differences in intervention effectiveness (Study 1: balanced accuracy = 0.71, 95% CI: 0.69–0.73, p = .020; AUC = 0.87, 95% CI: 0.85–0.88, p = .020) and replicated in a an external test sample (Study 2, balanced accuracy = 0.68; AUC = 0.68, 95% CI: 0.54–0.82), meeting clinical-utility thresholds from prior digital health studies (balanced accuracy = 0.67; correctly classifying (non)responders 67% of the time). Interventions were most effective for participants who perceived their peers as moderate but frequent drinkers. Peer drinking perceptions may serve as a low-burden indicator to support early identification of non-responders in preventive alcohol interventions among young adults. Future work can apply and extend the multimodal approach developed here for adaptive tailoring of digital behavior change interventions in real-world settings.
  • Fostering inclusive co-creation in digital health
    Item type: Other Journal Item
    Blasimme, Alessandro; Landers, Constantin; Vayena, Effy (2025)
    npj Digital Medicine
    Research on responsible digital health innovation has typically focused on technical aspects such as the reliability and trustworthiness. More recently, work in responsible digital health innovation has started to recognize that, to address those concerns, stakeholder involvement is key. Aligning technological advancements with stakeholders’ needs requires deliberate and inclusive processes. Such processes must incorporate diverse perspectives, including those of the users of digital health technologies, such as healthcare practitionerss and patients.
  • Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap
    Item type: Review Article
    Sokol, Kacper; Fackler, James; Vogt, Julia E. (2025)
    npj Digital Medicine
    Artificial intelligence promises to revolutionise medicine, yet its impact remains limited because of the pervasive translational gap. We posit that the prevailing technology-centric approaches underpin this challenge, rendering such systems fundamentally incompatible with clinical practice, specifically diagnostic reasoning and decision making. Instead, we propose a novel sociotechnical conceptualisation of data-driven support tools designed to complement doctors' cognitive and epistemic activities. Crucially, it prioritises real-world impact over superhuman performance on inconsequential benchmarks.
  • Sync fast and solve things—best practices for responsible digital health
    Item type: Journal Article
    Landers, Constantin; Blasimme, Alessandro; Vayena, Effy (2024)
    npj Digital Medicine
    Digital health innovation is expected to transform healthcare, but it also generates ethical and societal concerns, such as privacy risks, and biases that can compound existing health inequalities. While such concerns are widely recognized, existing regulatory principles, oversight methods and ethical frameworks seem out of sync with digital health innovation. New governance and innovation best practices are thus needed to bring such principles to bear with the reality of business, innovation, and regulation. To grant practical insight into best practices for responsible digital health innovation, we conducted a qualitative study based on an interactive engagement methodology. We engaged key stakeholders (n = 46) operating at the translational frontier of digital health. This approach allowed us to identify three clusters of governance and innovation best practices in digital health innovation: i) inclusive co-creation, ii) responsive regulation, and iii) value-driven innovation. Our study shows that realizing responsible digital health requires diverse stakeholders’ commitment to adapt innovation and regulation practices, embracing co-creation as the default modus operandi for digital health development. We describe these collaborative practices and show how they can ensure that innovation is neither slowed by overregulation, nor leads to unethical outcomes.
  • RMS: a ML-based system for ICU respiratory monitoring and resource planning
    Item type: Journal Article
    Hüser, Matthias; Lyu, Xinrui; Faltys, Martin; et al. (2025)
    npj Digital Medicine
    Acute hypoxemic respiratory failure (RF) occurs frequently in critically ill patients and is associated with substantial morbidity, mortality and resource use. We developed a comprehensive machine-learning-based monitoring system to support ICU physicians in managing RF through early detection, continuous monitoring, assessment of extubation readiness, and prediction of extubation failure (EF). In study patients, the model predicted 80% of RF events with 45% precision, identifying 65% of events more than 10 hours before, significantly outperforming standard clinical monitoring based on oxygenation index. The model was successfully validated in an external ICU cohort. We also demonstrated how predicted EF risk could help prevent extubation failure and unnecessarily prolonged ventilation. Lastly, we illustrated how prediction of RF risk, along with ventilator need and extubation readiness, helped ICU resource planning for mechanical ventilation. Our model predicted ICU-level ventilator demand 8-16 hours ahead, with a mean absolute error of 0.4 ventilators per 10 patients.
  • Blood pressure measurement using only a smartphone
    Item type: Review Article
    Frey, Lorenz; Menon, Carlo; Elgendi, Mohamed (2022)
    npj Digital Medicine
    Hypertension is an immense challenge in public health. As one of the most prevalent medical conditions worldwide, it is a major cause of premature death. At present, the detection, diagnosis and monitoring of hypertension are subject to several limitations. In this review, we conducted a literature search on blood pressure measurement using only a smartphone, which has the potential to overcome current limitations and thus pave the way for long-term ambulatory blood pressure monitoring on a large scale. Among the 333 articles identified, we included 25 relevant articles over the past decade (November 2011–November 2021) and analyzed the described approaches to the types of underlying data recorded with smartphone sensors, the signal processing techniques applied to construct the desired signals, the features extracted from the constructed signals, and the algorithms used to estimate blood pressure. In addition, we analyzed the validation of the proposed methods against reference blood pressure measurements. We further examined and compared the effectiveness of the proposed approaches. Among the 25 articles, 23 propose an approach that requires direct contact between the sensor and the subject and two articles propose a contactless approach based on facial videos. The sample sizes in the identified articles range from three to 3000 subjects, where 8 articles used sample sizes of 85 or more subjects. Furthermore, 10 articles include hypertensive subjects in their participant pools. The methodologies applied for the evaluation of blood pressure measurement accuracy vary considerably among the analyzed articles. There is no consistency regarding the methods for blood pressure data collection and the reference blood pressure measurement and validation. Moreover, no established protocol is currently available for the validation of blood pressure measuring technologies using only a smartphone. We conclude the review with a discussion of the results and with recommendations for future research on the topic.
  • A large language model-based approach to quantifying the effects of social determinants in liver transplant decisions
    Item type: Journal Article
    Robitschek , Emily; Bastani , Asal; Horwath , Kathryn; et al. (2025)
    npj Digital Medicine
    Psychosocial risk factors and social determinants of health (SDOH) contribute to persistent disparities in liver transplantation access. We developed a large language model framework to extract and analyze how these factors influence care trajectories. Prevalence of key modifiable barriers varied by demographics: social support gaps (35.4%, disproportionately affecting females), recent substance use (14.2–22.7%), and mental health challenges (17.6%, with Hispanic/Latino treatment gaps). Each factor was associated with 5–14 percentage point reductions in listing probability, comparable to clinical metrics. Psychosocial risk and SDOH factors explained 42.6% of racial disparities in listing decisions for Asian patients, exceeding liver health metrics (36.8%) and contributing to 94.6% collective explanation of differences. Priority interventions should target caregiver support, substance use, mental health, and patient education. This framework for systematically analyzing patient circumstances could enhance understanding of care decisions and health disparities.
Publications1 - 10 of 23