Researchers have created a digital therapy platform to help people caring for family members with dementia. 

Family carers of those living with dementia are at higher risk of mental health problems - with around one in three carers experiencing anxiety or depression.

To address this, the UEA team created a digital therapy platform (to be used on a mobile device or computer) to help people handle difficult or troubling thoughts and feelings.

And after testing it on hundreds of volunteers, they found that users reported lower levels of anxiety and depression.

“The improvement wasn’t just statistically significant - it was meaningful in real life. People genuinely felt better," said lead researcher Dr Naoko Kishita.

It is now hoped that the platform will be rolled out nationally and internationally to support carers in their important work.

A new study led by researchers at the Johns Hopkins Bloomberg School of Public Health and the American Enterprise Institute found that insurance denials of first attempts to fill prescriptions for brand-name prescription drugs with no generic competitors increased more than two-thirds between 2018 and 2024.

Many companies rely on AI for recruitment. A new study shows that job applicants mostly trust the avatars conducting their interviews – but only until they receive a rejection. How fair this decision is perceived to be depends on the avatars’ identity characteristics. The test subjects who felt treated the most unfairly were not those who differed most from the avatar – but rather those who matched it in either gender or skin color.

Certifying a third-party repair service can help companies retain customers by signaling that there’s value, or “unused utility,” left in broken products, according to a team led by Penn State researchers.

A new statistical tool could help researchers and practitioners determine when personalized treatments, educational programs, or recommendations are truly worth the extra cost and complexity, according to a new study. The findings offer a rigorous tool to identify when tailoring interventions is likely to outperform more conventional one-size-fits-all approaches. Personalized interventions are increasingly used in fields such as medicine, education, marketing, economics, and computer science because individuals often respond differently to the same treatment or support. However, personalized interventions are more expensive, often require more data, and can be more difficult to implement than universal interventions. There are very few tools to evaluate and identify scenarios when the added cost and complexity of personalization are justified by meaningful improvements in outcomes. To address this, Zhaoqi Li and Emma Brunskill developed a statistical hypothesis test – the K-fold Personalization Test (KPT) – that analyzes existing datasets to evaluate whether a personalized intervention policy is expected to outperform the best universal intervention. Unlike previous methods, the test accommodates multiple interventions, many individual characteristics, and flexible machine learning models while maintaining rigorous statistical guarantees, including control of false-positive (Type I) error. Across four datasets spanning job training, depression treatment, education, and marketing systems, the authors found that the KPT approach consistently demonstrated broad applicability and outperformed previous statistical methods for evaluating personalization. Li and Brunskill note, however, that the test evaluates personalization only within a user-specified class of decision policies and does not identify the optimal personalized policy itself. Yet even with these limitations, the KPT provides a rigorous and broadly applicable framework for helping researchers and practitioners determine when personalization is likely to deliver meaningful benefits.