2018 · Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · paywalled
Black-box Model Identification of Physical Activity in Type-l Diabetes Patients
Faccioli et al.
The finding, in our words
In six type-1 diabetes patients monitored for five days, inclusion of wearable-measured physical activity improved a black-box linear model's three-hour glucose prediction accuracy, raising the coefficient of determination by a mean of 18.5 per cent. This demonstrates that activity data can enhance digital biomarkers for glycaemic forecasting.
A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.
Decentralised monitoring of people with type 2 diabetes using continuous glucose monitoring and a hybrid smartwatch was feasible, with high participant retention and successful collection of glucose, heart rate, activity, sleep and GPS data via a study app.
Ali et al., Journal of diabetes science and technology (paywalled) · source ↗
A fully remote decentralised clinical trial in Danish adults with type 2 diabetes demonstrated operational feasibility with rapid recruitment, high retention (87 percent), and excellent adherence to continuous glucose monitoring (95 percent achieving ≥70 percent data coverage) and telemedicine visits (97 percent). Activity tracker adherence was poor (12 percent) due to technical issues, but remote safety monitoring was effective with no unexpected adverse events, supporting the viability of remote diabetes management.
In progressive multiple sclerosis, higher serum neurofilament light chain levels were associated with reduced daily step counts from wearables, while higher step counts were linked to lower sNfL. A ten percent increase in steps corresponded to a 0.015 decrease in sNfL z-score, suggesting rising sNfL may predict subsequent mobility decline.
Joseph et al., Annals of clinical and translational neurology (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 ↗
This narrative review found that metabolomic markers enhance prediction of cardiometabolic risk beyond classic indicators, and that personalised nutrition plans based on microbiome and clinical characteristics improve glycaemic control measured by continuous glucose monitoring, HbA1c and triglycerides more than a standard Mediterranean diet. The integration of wearable-derived digital biomarkers with multiomic data could support patient-centric clinical applications for cardiometabolic disease management.
Çelik et al., Journal of translational medicine · source ↗