Predicting the Healing of Lower Extremity Fractures Using Wearable Ground Reaction Force Sensors and Machine Learning
North et al.
The finding, in our words
In a retrospective study of 25 patients with lower extremity fractures, a gait monitoring insole combined with machine learning predicted healing time within a 30-day window with approximately 76% accuracy, showing that underfoot loading patterns can serve as digital biomarkers for fracture healing. This demonstrates how wearable sensors can enable objective, data-driven monitoring and early complication detection in decentralised rehabilitation settings.
A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.
A direct-to-participant, multichannel recruitment strategy enrolled 34,244 older adults into a virtual trial testing an Apple Watch based heart health programme with electrocardiogram and irregular rhythm notification features, achieving broad geographic and gender diversity but under-representing non-White ethnic groups.
Analysis of six studies revealed that decentralised clinical trial elements enhanced data completeness by enabling direct access to medical records, although this introduced a significant burden for data abstraction. The results suggest that decentralised approaches are not a universal solution but provide specific metrics that can help design fit-for-purpose trials.
Wearable-derived digital biomarkers have been accepted by regulatory agencies as endpoints in clinical trials for Duchenne muscular dystrophy, showing their potential to support decentralised diagnostics through patient-centric monitoring. This matters because it enables more sensitive and continuous assessment of disease progression outside clinical settings.
Ma et al., Neurology and therapy (paywalled) · source ↗
The High-Definition Oncology feasibility study in 30 women with metastatic cancer demonstrated high adherence to biospecimen collection (97.4% plasma, 80.7% stool) and wearable monitoring (70–95% of days captured for activity, heart rate, sleep, and oxygen saturation). This establishes a viable framework for decentralised, multimodal data collection in oncology to support individualised treatment models.
Garma et al., JCO precision oncology (paywalled) · source ↗
Smartwatch photoplethysmography demonstrated high accuracy for continuous atrial fibrillation burden monitoring in 728 patients when validated against patch electrocardiography, achieving 98.7% sensitivity and 99.6% specificity at interval level with near-perfect correlation (r=0.999). This establishes wearables as a viable tool for long-term cardiac rhythm surveillance outside hospital, enabling patient-centric remote monitoring.
Zuo et al., JACC. Clinical electrophysiology (paywalled) · source ↗