Background: Atrial fibrillation (AF) is a common arrhythmia that adversely affects health-related quality of life (HRQoL). We conducted a pilot trial of individuals with AF using a smartphone to provide a relational agent as well as rhythm monitoring. We employed our pilot to measure acceptability and adherence and to assess its effectiveness in improving HRQoL and adherence. Objective: This study aims to measure acceptability and adherence and to assess its effectiveness to improve HRQoL and adherence. Methods: Participants were recruited from ambulatory clinics and randomized to a 30-day intervention or usual care. We collected baseline characteristics and conducted baseline and 30-day assessments of HRQoL using the Atrial Fibrillation Effect on Quality of Life (AFEQT) measure and self-reported adherence to anticoagulation. The intervention consisted of a smartphone-based relational agent, which simulates face-to-face counseling and delivered content on AF education, adherence, and symptom monitoring with prompted rhythm monitoring. We compared differences in AFEQT and adherence at 30 days, adjusted for baseline values. We quantified participants’ use and acceptability of the intervention. Results: A total of 120 participants were recruited and randomized (59 to control and 61 to intervention) to the pilot trial (mean age 72.1 years, SD 9.10; 62/120, 51.7% women). The control group had a 95% follow-up, and the intervention group had a 93% follow-up. The intervention group demonstrated significantly higher improvement in total AFEQT scores (adjusted mean difference 4.5; 95% CI 0.6-8.3; P=.03) and in daily activity (adjusted mean difference 7.1; 95% CI 1.8-12.4; P=.009) compared with the control between baseline and 30 days. The intervention group showed significantly improved self-reported adherence to anticoagulation therapy at 30 days (intervention 3.5%; control 23.2%; adjusted difference 16.6%; 95% CI 2.8%-30.4%; P<.001). Qualitative assessments of acceptability identified that participants found the relational agent useful, informative, and trustworthy. Conclusions: Individuals randomized to a 30-day smartphone intervention with a relational agent and rhythm monitoring showed significant improvement in HRQoL and adherence. Participants had favorable acceptability of the intervention with both objective use and qualitative assessments of acceptability.

The most successful examples of such representations, those learned by convolutional neural networks, are structured by the scale and translational symmetries of the underlying space (e.g. a two-dimensional Euclidean space for images). It has been suggested that in humans the ability to make rich inferences based on abstract reasoning is rooted in the same neural mechanisms underlying relational reasoning in space [16, 19, 6, 7] and more specifically that abstract reasoning is facilitated by the learning of structural representations which serve to organise other learned representations in the same way that space organises the representations that enable spatial navigation [68, 41]. This raises a natural question: are there any ideas from mathematics that might be useful in designing general inductive biases for learning such structural representations? As a motivating example we take the recent progress on natural language tasks based on the Transformer architecture [66] which simultaneously learns to represent both entities (typically words) and relations between entities (for instance the relation between "cat" and "he" in the sentence "There was a cat and he liked to sleep"). These representations of relations take the form of query and key vectors governing the passing of messages between entities; messages update entity representations over several rounds of computation until the final representations reflect not just the meaning of words but also their context in a sentence. There is some evidence that the geometry of these final representations serve 2 to organise word representations in a syntax tree, which could be seen as the appropriate analogue to two-dimensional space in the context of language [33].

Recent work demonstrated the ability of relational agents to establish and maintain relationships with people over a series of interactions. In this effort, the agent played the role of an exercise advisor designed to motivate users to exercise more. One hundered subjects participated in a six-week study longitudinal study (four week intervention and two week follow up) to determine the efficacy of this agent. Results indicate that the agent was successful at creating and maintaining a trusting, caring relationship with users and increasing their desire to continue interacting with it.

We summarize our past, present and future research related to human-robot dialogue, starting with its foundations in collaborative discourse theory, continuing to our current research on recognizing and generating engagement, and concluding with an outline of new work we are beginning on the modeling of long-term relationships between humans and robots.