Predictors of users' adherence to a fully automated digital intervention to manage insomnia complaints.

OBJECTIVE: Fully automated digital interventions show promise for disseminating evidence-based strategies to manage insomnia complaints. However, an important concept often overlooked concerns the extent to which users adopt the recommendations provided in these programs into their daily lives. Our objectives were evaluating users' adherence to the behavioral recommendations provided by an app, and exploring whether users' perceptions of the app had an impact on their adherence behavior. MATERIAL AND METHODS: Case series study of individuals completing a fully automated insomnia management program, conducted by a virtual agent, during December 2020 to September 2022. Primary outcome was self-reported adherence to the behavioral recommendations provided. Perceptions of the app and of the virtual agent were measured with the Acceptability E-Scale and ECA-Trust Questionnaire. Insomnia was evaluated with the Insomnia Severity Index at baseline (phase 1), after 7 days of sleep monitoring (phase 2) and post-intervention (phase 3). RESULTS: A total of 824 users were included, 62.7% female, mean age 51.85 (±12.55) years. Of them, 32.7% reported having followed at least one recommendation. Users' trust in the virtual agent and acceptance of the app were related to a pre-intervention effect in insomnia severity (phase 2). In turn, larger pre-intervention improvements predicted better adherence. Mediational analyses showed that higher levels of trust in the virtual agent and better acceptance of the app exerted statistically significant positive effects on adherence (ß = 0.007, 95% CI, 0.001-0.017 and ß = 0.003, 95% CI 0.0004-0.008, respectively). DISCUSSION: Users' adherence is motivated by positive perceptions of the app's features and pre-intervention improvements. CONCLUSIONS: Determinants of adherence should be assessed, and targeted, to increase the impact of fully automated digital interventions.

From improved sleep regularity to reduced sleep complaints and mental health conditions: a population-based interventional study using a smartphone-based virtual agent.

STUDY OBJECTIVES: To explore the effect of sleep regularity on sleep complaints and mental health conditions (i.e. insomnia, fatigue, anxiety, and depressive symptoms) in a population-based interventional study using a smartphone-based virtual agent. METHODS: A populational cohort based on the Kanopée application, which provided interactions with a virtual companion to collect data on sleep and make personalized recommendations to improve sleep over 17 days. A pre-intervention sleep diary and interview were used for cross-sectional analysis (n = 2142), and a post-intervention sleep diary and interview were used for longitudinal analysis (n = 732). The intra-individual mean (IIM) and standard deviation (ISD) of total sleep time (TST) were calculated to measure sleep quantity and sleep regularity. RESULTS: The mean age at baseline was 49 years, 65% were female, 72% reported insomnia, 58% fatigue, 36% anxiety, and 17% depressive symptoms. Before the intervention, irregular and short sleep was associated with a higher likelihood of insomnia (Relative risk [RR] = 1.26 [1.21-1.30] for irregular TST and RR = 1.19 [1.15-1.23] for short TST), fatigue, anxiety, and depressive symptoms. After the intervention, the IIM of the TST increased while the ISD of the TST and sleep complaints and mental health conditions decreased. More regular TST was associated with reduced insomnia and depressive symptoms (RR = 1.33 [1.10-1.52] and RR = 1.55 [1.13-1.98], respectively). CONCLUSIONS: Our results reveal a longitudinal association between sleep regularity and sleep complaints and mental health conditions. Policymakers, health professionals, and the general population should be aware that, beyond its positive effect on sleep health, regular sleep could promote mental health.

Adapting conversational strategies in information-giving human-agent interaction.

In this work, we focus on human-agent interaction where the role of the socially interactive agent is to optimize the amount of information to give to a user. In particular, we developed a dialog manager able to adapt the agent's conversational strategies to the preferences of the user it is interacting with to maximize the user's engagement during the interaction. For this purpose, we train an agent in interaction with a user using the reinforcement learning approach. The engagement of the user is measured using their non-verbal behaviors and turn-taking status. This measured engagement is used in the reward function, which balances the task of the agent (giving information) and its social goal (maintaining the user highly engaged). Agent's dialog acts may have different impact on the user's engagement depending on several factors, such as their personality, interest in the discussion topic, and attitude toward the agent. A subjective study was conducted with 120 participants to measure how third-party observers can perceive the adaptation of our dialog model. The results show that adapting the agent's conversational strategies has an influence on the participants' perception.

Designing Persuasive Food Conversational Recommender Systems With Nudging and Socially-Aware Conversational Strategies.

Unhealthy eating behavior is a major public health issue with serious repercussions on an individual's health. One potential solution to overcome this problem, and help people change their eating behavior, is to develop conversational systems able to recommend healthy recipes. One challenge for such systems is to deliver personalized recommendations matching users' needs and preferences. Beyond the intrinsic quality of the recommendation itself, various factors might also influence users' perception of a recommendation. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users' eating habits and current preferences. Users can interact with Cora in two different ways. They can select pre-defined answers by clicking on buttons to talk to Cora or write text in natural language. Additionally, Cora can engage users through a social dialogue, or go straight to the point. Cora is also able to propose different alternatives and to justify its recipes recommendation by explaining the trade-off between them. We conduct two experiments. In the first one, we evaluate the impact of Cora's conversational skills and users' interaction mode on users' perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users' perception of the interaction as well as their perception of the system. In the second evaluation, we evaluate the influence of Cora's explanations and recommendation comparisons on users' perception. Our results show that explanations positively influence users' perception of a recommender system. However, comparing healthy recipes with a decoy is a double-edged sword. Although such comparison is perceived as significantly more useful compared to one single healthy recommendation, explaining the difference between the decoy and the healthy recipe would actually make people less likely to use the system.

Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface.

Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using functional near infrared spectroscopy (fNIRS) and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent's facial expressions, in which action units (AUs) are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent's responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment.