Behind the Paper
The real stories behind the latest research papers, from conception to publication, the highs and the lows
Filtered by: Healthcare & Nursing
Wearable sensors can tell you more than just step count: measuring nighttime scratching and sleep
A more holistic picture of life with atopic dermatitis
Increasing engagement with smartphone apps for anxiety and depression
Smartphone apps can reduce symptoms of depression and anxiety, but real-world users rarely use them for more than a few days. Our review aimed to identify features that make these apps more engaging and effective, which is especially crucial amidst the stress and isolation of the COVID-19 crisis.
COVID-19 was a Catalyst for Digital Health – Now, What Lies Ahead?
The COVID-19 pandemic has disrupted many industries, and healthcare is no exception. Digital health solutions deployed globally to facilitate extensive re-organisation of healthcare services and de-centralised care in the first 6 months of COVID-19 are presented with an overview of the road ahead.
Internet Search Trends - Diving Deeper
Richer and more diverse data on Internet search patterns allow scientists to begin to study the actual nature and time course of disease.
Post-pandemic Implications for the Future of Telehealth
It’s no secret that telehealth’s explosion is credited to the advent of the COVID-19 pandemic. What role will telehealth play in care delivery in the post-pandemic future?
Deep-learning system detects fractures on X-rays across the musculoskeletal system
A deep-learning system detecting fractures across the musculoskeletal system can help reduce common diagnostic errors and improve clinical outcomes
Can Internet search data illuminate the gun policy debate?
Gun violence is a major cause of death in the United States, yet the data necessary for informing the gun policy debate is woefully incomplete. Internet search patterns have the potential to serve as a valuable complementary information source to existing approaches.
Harnessing consumer smartphone and wearable sensors for clinical cancer research
Growing evidence suggests that consumer wearable and smartphone sensor data are related to symptoms, quality of life, and risk for adverse outcomes in cancer. Larger studies that use sensor data to inform and personalize clinical care are needed.
Ensemble Learning Predicts Multiple Sclerosis Disease Course in the SUMMIT Study
The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients.
