Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data
Shapiro et al.
The finding, in our words
Analysis of person-generated health data showed that wearable-derived deviations in daily steps and resting heart rate were similar in magnitude for individuals with COVID-19 and influenza. These results demonstrate that wearable-based detection algorithms require influenza comparator arms to ensure diagnostic specificity.
A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.
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 ↗
Patients with metastatic cancer who recorded ≤3013 steps/day during the 14‑day post‑discharge period had substantially higher rates of 90‑day unplanned readmission (73% versus 13%) and mortality (47% versus 9%) than more active patients. Wearable‑derived mobility is a promising decentralised digital biomarker for risk stratification after hospital discharge, but requires external validation before guiding supportive care.
Akdogan et al., Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer (paywalled) · source ↗
This study found that a decentralised approach using home-based sleep apnoea screening, heart rhythm monitoring and activity tracking in patients with atrial fibrillation is feasible, with high data completeness and low drop-out rates. The results support the use of patient-centric digital tools in decentralised trials for chronic cardiac conditions.
Hashiba et al., Danish medical journal (paywalled) · source ↗
In 50 cancer patients monitored at home with an Apple Watch for 28 days, a method that classified missing step data by gap duration and context produced the most reliable daily step estimates. Daily step counts on days with at least ten hours of wear time predicted time to first clinical event (p=0.068) and identified participants at lower hazard of mortality, with 83.3% accuracy for clinical event prediction, demonstrating that processed consumer wearable data can support remote monitoring in decentralised oncology trials.
Oakley-Girvan et al., Digital biomarkers · source ↗
In this decentralised trial of 899 adults with asthma, participants using a digital self-management programme with wearable device integration showed a 4.6-point gain on the Asthma Control Test at 12 months relative to usual care. This provides evidence that remote digital interventions can achieve clinically significant improvements in asthma control without in-person visits.