ArticleNature medicine2023
Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy.
Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it, 89 citations in OpenAlex.
- A Systematic Review of Wearable Sensors in Rett Syndrome-What Physiological Markers Are Informative for Monitoring Disease States?Sensors (Basel, Switzerland) · 2025Pooled it
- The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review.Orphanet journal of rare diseases · 2023Pooled it
- Artificial Intelligence in Neuromuscular Diseases: Opportunities for a Data-Scarce Field.Neurology and therapy · 2026Review
- Cocreation of Digital Outcome Measures for Dravet Syndrome: Multistage Co-Design Feasibility Study.Journal of participatory medicine · 2026Article
- From Reactive to Proactive Healthcare: Synergizing Wearable Biomarkers and Machine Learning in Digital Therapeutics.Bioengineering (Basel, Switzerland) · 2026Review
- Novel Musculoskeletal Hypotheses in the Armed Services Trauma and Rehabilitation Outcome (ADVANCE) Cohort: Development and Application of Sparse Group Factor Analysis Methodology.Journal of medical Internet research · 2026Article
- IMU-based workspace area as a promising complementary tool to assess upper limb function in Neuromuscular diseases: A one-year follow-up.Journal of neuromuscular diseases · 2026Article
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- Freezing of Gait in Parkinson's Disease: A Scoping Review on the Path Towards Real-Time Therapies.Sensors (Basel, Switzerland) · 2026Article
- Textile suit for anywhere full-body motion capture.Science advances · 2026Article
- Wearable and Implantable Devices for Continuous Monitoring of Muscle Physiological Activity: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Mechanomedicine.Nature reviews bioengineering · 2026Article
- Digital biomarkers in the diagnosis of social anxiety disorder: a scoping review.BMC psychiatry · 2026Article
- Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data.Journal of medical Internet research · 2026Article
- Emerging therapeutic strategies in muscular dystrophy: an updated review on pathogenesis and treatment advances.Molecular biology reports · 2026Review
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Digital outcome measures in Duchenne muscular dystrophy: Lessons learnt from clinical trials.Journal of neuromuscular diseases · 2026Review
- Fully textile passive wireless sensing for human movement monitoring with multiple sensors.Frontiers in bioengineering and biotechnology · 2026Article
- Case-Based Lessons on Remote Patient Monitoring in Neurology Using Consumer-Grade Wearables.Journal of central nervous system disease · 2026Article
- A Novel Fabric Strain Sensor Array with Hybrid Deep Learning for Accurate Knee Movement Recognition.Micromachines · 2025Article
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
Abstract
Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we collected a digital readout of whole-body movement behavior of patients with Duchenne muscular dystrophy (DMD) (n = 21) and age-matched controls (n = 17). Movement behavior was assessed while the participant engaged in everyday activities using a 17-sensor bodysuit during three clinical visits over the course of 12 months. We first defined new movement behavioral fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioral fingerprints to make cross-sectional and longitudinal disease course predictions, which outperformed predictions derived from currently used clinical assessments. Finally, using Bayesian optimization, we constructed a behavioral biomarker, termed the KineDMD ethomic biomarker, which is derived from daily-life behavioral data and whose value progresses with age in an S-shaped sigmoid curve form. The biomarker developed in this study, derived from digital readouts of daily-life movement behavior, can predict disease progression in patients with muscular dystrophy and can potentially track the response to therapy.
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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 OpenQuestion graph.