Early Development of a Virtual Coach for Healthy Coping Interventions in Type 2 Diabetes Mellitus: Validation Study.

BACKGROUND: Mobile health solutions aimed at monitoring tasks among people with diabetes mellitus (DM) have been broadly applied. However, virtual coaches (VCs), embedded or not in mobile health, are considered valuable means of improving patients' health-related quality of life and ensuring adherence to self-care recommendations in diabetes management. Despite the growing need for effective, healthy coping digital interventions to support patients' self-care and self-management, the design of psychological digital interventions that are acceptable, usable, and engaging for the target users still represents the main challenge, especially from a psychosocial perspective. OBJECTIVE: This study primarily aims to test VC interventions based on psychoeducational and counseling approaches to support and promote healthy coping behaviors in adults with DM. As a preliminary study, university students have participated in it and have played the standardized patients' (SPs) role with the aim of improving the quality of the intervention protocol in terms of user acceptability, experience, and engagement. The accuracy of users' role-playing is further analyzed. METHODS: This preliminary study is based on the Obesity-Related Behavioral Intervention Trial model, with a specific focus on its early phases. The healthy coping intervention protocol was initially designed together with a team of psychologists following the main guidelines and recommendations for psychoeducational interventions for healthy coping in the context of DM. The protocol was refined with the support of 3 experts in the design of behavioral intervention technologies for mental health and well-being, who role-played 3 SPs' profiles receiving the virtual coaching intervention in a Wizard of Oz setting via WhatsApp. A refined version of the healthy coping protocol was then iteratively tested with a sample of 18 university students (mean age 23.61, SD 1.975 years) in a slightly different Wizard of Oz evaluation setting. Participants provided quantitative and qualitative postintervention feedback by reporting their experiences with the VC. Clustering techniques on the logged interactions and dialogs between the VC and users were collected and analyzed to identify additional refinements for future VC development. RESULTS: Both quantitative and qualitative analyses showed that the digital healthy coping intervention was perceived as supportive, motivating, and able to trigger self-reflection on coping strategies. Analyses of the logged dialogs showed that most of the participants accurately played the SPs' profile assigned, confirming the validity and usefulness of this testing approach in preliminary assessments of behavioral digital interventions and protocols. CONCLUSIONS: This study outlined an original approach to the early development and iterative testing of digital healthy coping interventions for type 2 DM. Indeed, the intervention was well-accepted and proved its effectiveness in the definition and refinement of the initial protocol and of the user experience with a VC before directly involving real patients in its subsequent use and testing.

Explainable AI meets persuasiveness: Translating reasoning results into behavioral change advice.

Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. After receiving the input of the intake food, the system performs reasoning to understand whether the users follow an unhealthy behavior. Successively, the system has to communicate the results in a clear and effective way, that is, the output message has to persuade users to follow the right dietary rules. In this paper, we address the main challenges to build such systems: (i) the Natural Language Generation of messages that explain the reasoner inconsistency; and, (ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users' behaviors.

New Perspectives in the Adaptive Assessment of Depression: The ATS-PD Version of the QuEDS.

Measurement is a crucial issue in psychological assessment. In this paper a contribution to this task is provided by means of the implementation of an adaptive algorithm for the assessment of depression. More specifically, the Adaptive Testing System for Psychological Disorders (ATS-PD) version of the Qualitative-Quantitative Evaluation of Depressive Symptomatology questionnaire (QuEDS) is introduced. Such implementation refers to the theoretical background of Formal Psychological Assessment (FPA) with respect to both its deterministic and probabilistic issues. Three models (one for each sub-scale of the QuEDS) are fitted on a sample of 383 individuals. The obtained estimates are then used to calibrate the adaptive procedure whose performance is tested in terms of both efficiency and accuracy by means of a simulation study. Results indicate that the ATS-PD version of the QuEDS allows for both obtaining an accurate description of the patient in terms of symptomatology, and reducing the number of items asked by 40%. Further developments of the adaptive procedure are then discussed.

ATS-PD: An Adaptive Testing System for Psychological Disorders.

The clinical assessment of mental disorders can be a time-consuming and error-prone procedure, consisting of a sequence of diagnostic hypothesis formulation and testing aimed at restricting the set of plausible diagnoses for the patient. In this article, we propose a novel computerized system for the adaptive testing of psychological disorders. The proposed system combines a mathematical representation of psychological disorders, known as the "formal psychological assessment," with an algorithm designed for the adaptive assessment of an individual's knowledge. The assessment algorithm is extended and adapted to the new application domain. Testing the system on a real sample of 4,324 healthy individuals, screened for obsessive-compulsive disorder, we demonstrate the system's ability to support clinical testing, both by identifying the correct critical areas for each individual and by reducing the number of posed questions with respect to a standard written questionnaire.