A call to incorporate systems theory and human factors into the existing investigation of harm in clinical research involving healthcare products.

This is a joint statement from individual pharmacology and pharmaceutical professionals acting in their own capacity, including members of the Alliance for Clinical Research Excellence and Safety (ACRES) and the International Society of Pharmacovigilance (ISoP). By building on the extensive pharmacological and regulatory investigations that already take place, we are calling for a fuller and more robust systems-based approach to the independent investigation of clinical research when serious incidents of harm occur, starting with first-in-human clinical trials. To complement existing activities and regulations, we propose an additional approach blending evidence derived from both pharmacological and organizational science, which addresses human factors and transparency, to enhance organizational learning and continuous improvement. As happens with investigations in other sectors of society, such as the chemical and aviation sector, this systems approach should be seen as an additional way to understand how problems occur and how they might be prevented in the future. We believe that repetition of potentially preventable and adverse outcomes during clinical research, by failing to identify and act upon all systematic vulnerabilities, is a situation that needs urgent change. As we will discuss further on, approaches based on applying systems theory and human factors are much more likely to improve objectivity and transparency, leading to better system design.

Intelligent mobile support for therapy adherence and behavior change.

Mobile applications have proven to be promising tools for supporting people in adhering to their health goals. Although coaching and reminder apps abound, few of them are based on established theories of behavior change. In the present work, a behavior change support system is presented that uses a computational model based on multiple psychological theories of behavior change. The system determines the user's reason for non-adherence using a mobile phone app and an online lifestyle diary. The user automatically receives generated messages with persuasive, tailored content. The system was designed to support chronic patients with type 2 diabetes, HIV, and cardiovascular disease, but can be applied to many health and lifestyle domains. The main focus of this work is the development of the model and the underlying reasoning method. Furthermore, the implementation of the system and some preliminary results of its functioning will be discussed.

A computational model for designing energy behaviour change interventions.

The conflicting evidence in the literature on energy feedback as a driver for energy behaviour change has lead to the realization that it is a complex problem and that interventions must be proposed and evaluated in the context of a tangled web of individual and societal factors. We put forward an integrated agent-based computational model of energy consumption behaviour change interventions based on personal values and energy literacy, informed by research in persuasive technologies, environmental, educational and cognitive psychology, sociology, and energy education. Our objectives are: (i) to build a framework to accommodate a rich variety of models that might impact consumption decisions, (ii) to use the simulation as a means to evaluate persuasive technologies in-silico prior to deployment. The model novelty lies in its capacity to connect the determinants of energy related behaviour (values, energy literacy and social practices) and several generic design strategies proposed in the area of persuasive technologies within one framework. We validate the framework using survey data and personal value and energy consumption data extracted from a 2-year field study in Exeter, UK. The preliminary evaluation results demonstrate that the model can predict energy saving behaviour much better than a random model and can correctly estimate the effect of persuasive technologies. The model can be embedded into an adaptive decision-making system for energy behaviour change.