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Background: Unipolar mania is a putative subtype of bipolar disorder (BD) in which individuals experience recurrent manic but not major depressive episodes. Few studies of unipolar mania have been conducted in developed countries and none in the UK. This study aimed to identify and characterise people with unipolar mania in the UK and France. \ud \ud Methods: People with unipolar mania were ascertained using a South London UK electronic case register and a French BD case series. Each unipolar mania group was compared to a matched group of people with BD who have experienced depressive episodes. \ud \ud Results: 17 people with unipolar mania were identified in South London and 13 in France. The frequency of unipolar mania as a percentage of the BD clinical population was 1.2% for the South London cohort and 3.3% for the French cohort. In both cohorts, people with unipolar mania experienced more manic episodes than people with BD, and in the French cohort were more likely to experience a psychotic illness onset and more psychiatric admissions. Treatment characteristics of people with unipolar mania were similar to people with BD except that the unipolar mania group was less likely to be treated with antidepressants. \ud \ud Limitations: The relatively small number of people with unipolar mania identified by this study limits its power to detect differences in clinical variables. \ud \ud Conclusions: People with unipolar mania can be identified in France and the UK, and they may experience a higher frequency of manic episodes but have similar treatment characteristics to people with BD.

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.