The cornerstone of obesity treatment is behavioural weight management, resulting in significant improvements in cardio-metabolic and psychosocial health. However, there is ongoing concern that dietary interventions used for weight management may precipitate the development of eating disorders. Systematic reviews demonstrate that, while for most participants medically supervised obesity treatment improves risk scores related to eating disorders, a subset of people who undergo obesity treatment may have poor outcomes for eating disorders. This review summarises the background and rationale for the formation of the Eating Disorders In weight-related Therapy (EDIT) Collaboration. The EDIT Collaboration will explore the complex risk factor interactions that precede changes to eating disorder risk following weight management. In this review, we also outline the programme of work and design of studies for the EDIT Collaboration, including expected knowledge gains. The EDIT studies explore risk factors and the interactions between them using individual-level data from international weight management trials. Combining all available data on eating disorder risk from weight management trials will allow sufficient sample size to interrogate our hypothesis: that individuals undertaking weight management interventions will vary in their eating disorder risk profile, on the basis of personal characteristics and intervention strategies available to them. The collaboration includes the integration of health consumers in project development and translation. An important knowledge gain from this project is a comprehensive understanding of the impact of weight management interventions on eating disorder risk.

Population-based structural health monitoring (PBSHM) provides a means of accounting for inter-turbine correlations when solving the problem of wind farm anomaly detection. Across a wind farm, where a group of structures (turbines) is placed in close vicinity to each other, the environmental conditions and, thus, structural behavior vary in a spatiotemporal manner. Spatiotemporal trends are often overlooked in the existing data-based wind farm anomaly detection methods, because most current methods are designed for individual structures, that is, detecting anomalous behavior of a turbine based on the past behavior of the same turbine. In contrast, the idea of PBSHM involves sharing data across a population of structures and capturing the interactions between structures. This paper proposes a population-based anomaly detection method, specifically for a localized population of structures, which accounts for the spatiotemporal correlations in structural behavior. A case study from an offshore wind farm is given to demonstrate the potential of the proposed method as a wind farm performance indicator. It is concluded that the method has the potential to indicate operational anomalies caused by a range of factors across a wind farm. The method may also be useful for other tasks such as wind power and turbine load modeling.