Personal stories encoding health information are an effective tool for promoting health behavior change. As millions of stories about health are accumulated daily in blogs and in social networks online there is an opportunity to harvest and index a large database of health stories for interventions. We envision such a database increasing user education, engagement, motivation and rapport in our conversational agent-based health intervention systems. In this paper we propose a model of indexing health stories based on health behavior change theory, enhanced demographics and quality metrics.
Prevention, diagnosis, and treatment of vector-borne diseases such as malaria, leishmaniasis, and dengue fever is a complex problem. Major reductions in transmission require multiple different interventions, including both disease treatment and vector control. This is particularly crucial in the high-transmission areas where conditions are optimal, or when epidemics are triggered. However, insect-control interventions are becoming less effective; development, evaluation, and introduction of new interventions are slow; and there is limited understanding of just how important these interventions are. To sustain and optimize disease control efforts, it will be necessary to develop more informative models that can inform the timely introduction of new control interventions.
Behavior plays a key role in maintaining health, and in the prevention, management, and treatment of disease and disability. Activities such as smoking, alcohol misuse, physical inactivity, and certain dietary behaviors contribute to the global disease burden and often lead to premature death [1, 2]. There has been a steady global increase in diseases attributed to behavioral risk factors, with substantial associated losses in national income. The need for effective and cost-effective health-related behavior change interventions is acute. Despite rapid growth in behavioral intervention research, the effects of behavioral interventions continue to be typically small, variable, and not maintained long-term [3, 4].
Abeyruwan, Saminda (University of Miami) | Baral, Ramesh (Florida International University) | Yasavur, Ugan (Florida International University) | Lisetti, Christine (Florida International University) | Visser, Ubbo (University of Miami)
We combined a spoken dialog system that we developed to deliver brief health interventions with the fully autonomous humanoid robot (NAO). The dialog system is based on a framework facilitating Markov decision processes (MDP). It is optimized using reinforcement learning (RL) algorithms with data we collected from real user interactions. The system begins to learn optimal dialog strategies for initiative selection and for the type of confirmations that it uses during theinteraction. The health intervention, delivered by a 3D character instead of the NAO, has already been evaluated, with positive results in terms of task completion, ease of use, and future intention to use the system. The current spoken dialog system for the humanoid robot is a novelty and exists so far as a proof ofconcept.
Reporting guidelines are structured tools developed using explicit methodology that specify the minimum information required by researchers when reporting a study. The use of AI reporting guidelines that address potential sources of bias specific to studies involving AI interventions has the potential to improve the quality of AI studies, through improvements in their design and delivery, and the completeness and transparency of their reporting. With a number of guidance documents relating to AI studies emerging from different specialist societies, this Review article provides researchers with some key principles for selecting the most appropriate reporting guidelines for a study involving an AI intervention. As the main determinants of a high‐quality study are contained within the methodology of the study design rather than the intervention, researchers are recommended to use reporting guidelines that are specific to the study design, and then supplement them with AI‐specific guidance contained within available AI reporting guidelines.