Emotionally Responsive Virtual Counselor for Behavior-Change Health Interventions

Transcription

1 Emotionally Responsive Virtual Counselor for Behavior-Change Health Interventions Reza Amini, Christine Lisetti, and Ugan Yasavur School of Computing and Information Sciences, Florida International University, Miami, Florida Abstract. In this paper, we discuss a novel approach to design an emotionally responsive system in the context of virtual health interventions for behavior change. We describe the system s design with a focus on enabling a multimodal Embodied Conversational Agent (ECA) to deliver the interventions empathetically. This is done by adapting its verbal and non-verbal behavior, in real-time, to those of the clients. Our current approach is based on a successful existing patient-centered intervention for behavior change - the Drinker's Check-Up (DCU). Although, the DCU uses a text-only web interface, it has been reported to reduce alcohol consumption in problem drinkers. We discuss the results of users' evaluation of the DCU intervention compared to the same intervention delivered with empathic and non-empathic ECAs. Results show that, the empathic virtual counselor has better acceptance than the other two systems. 1 Introduction A substantial amount work is done in developing natural user interfaces using different input modalities such as text, mouse click, touch screen gesture, and speech. However, in the application domains that involve emotionally sensitive contents, discarding affective signals and not adapting the system s behavior to user s affective states is an obvious shortcoming. Specially, health behavior change is a highly sensitive domain. In this paper, we discuss our emotionally responsive system design in health behavior change. Computer-based approaches are being used in delivering health interventions. They were reported as effective ways of making people aware of their unhealthy life styles and motivating them to change these behaviors [1], [2]. Even though, the effectiveness of the computer based applications are supported by many studies, the attrition rates are still very high [3]. We believe that our emotionally responsive design approach has the potential to better engage the users by providing additional channels of emotional interaction. Better engagement in the computer health interventions can increase the strength of the user-system connection which itself leads to lower dropout rates and better outcomes consequently. Embodied Conversational Agents (ECAs) are being used for delivering health material and envisioned to be helpful in computer-based therapy [4]. In a recent comprehensive M.C. Tremblay et al. (Eds.): DESRIST 2014, LNCS 8463, pp , Springer International Publishing Switzerland 2014

2 434 R. Amini, C. Lisetti, and U. Yasavur literature review of active assistance technologies in health-related behavior change systems [5], dialogue systems and ECAs are identified as emerging technology themes in the behavior change systems field. For example, the MIT FitTrack [6] uses an ECA to investigate the ability to establish and maintain a long-term working alliance with users in a behavior-change context. It creates rapport using social and empathic dialogs, politeness, and nonverbal behaviors (e.g., smile). Comparing to an equivalent agent without any socialemotional, this agent was reported as more respected, liked, and trusted. Also, Schulman et al. [7] designed a conversational agent as a virtual counselor for health behavior change. They use techniques drawn from MI to enhance client motivation and confidence to change. Users reported satisfaction from using this system. Although these health systems have shown some promising results, they miss the empathizing ability which supports the clients emotionally and helps them overcome their negative affects [8]. Empathizing helps the counselor to adapt his/her behavior to the clients affective states, which itself engages the clients, motivates them to use the system in long-term, and motivates them to change their unhealthy behaviors. However, the systems mentioned above are not emotionally responsive, they operate using only a single input channel, which consists of button clicks, and discard the experienced emotions. Therefore, they cannot engage the clients enough to the interaction and motivate them to continue the interaction and attend the follow-up sessions [1], [2], instead of dropping out which is a significant intervention problem [3]. Among different unhealthy behaviors, we selected excessive drinking as our target behavior. and based our work on a computer-based intervention called the Drinker's Check Up (DCU) [9]. It uses a patient-centered counseling technique called Motivational Interviewing (MI) to motivate people to change their unhealthy alcohol consumption behaviors. The DCU is reported to be able to decrease alcohol consumption by an average of 50% in a 12 month follow-up. In this research, we have developed an Empathic On-Demand Virtual Health Counselor (Emp-ODVIHC) which delivers the computer-based Brief Motivational Interventions (BMIs) through an ECA (shown in Fig. 1). Fig. 1. Emo-ODVIHC Amy in her office

3 Emotionally Responsive Virtual Counselor for Behavior-Change Health Interventions Health Counselor System Architecture We have developed an emotionally responsive animated character. It perceives the client's facial expressions and utterances during the health interaction and provides both empathic non-verbal expressions (emotional facial expressions, head nods, eyebrow movements), and verbal reflections. Our system architecture is composed of 3D animated character, dialogue manager, and empathy model. The system architecture is described in detail in [10], [11]. In this article, our focus is on the Empathy Model, which consists of an Affective Module and a Cognitive Module. The Empathy model is the main component which enables the user-system emotionally responsive communication. The Empathy Model captures user s facial expressions and head movements in real-time to assess the user's most probable affective states, then combines it with affect related information elicited from utterances to decide about the counselor's empathic responses. The Affective Module is responsible for fast and reactive responses such as simple verbal reflection of user's answers and head posture mimicry (to create closeness and mutual gaze with the client). The Cognitive Module on the other hand, is responsible for feedbacks that need more thinking and decision making before expression, such as facial expressions, and head nods. 3 Experiment and Evaluation Clients attended the first session of an interview with our virtual counselor, which includes the AUDIT [12] psychometric instrument to assess their alcohol dependence. The default counselor was a Caucasian female (AMY). We have implemented three conditions for the experiment: 1. Text-only Drinker's Check-Up (DCU): during the session, the exact same content of the DCU [9] is delivered to the user using text-only web page frames. 2. Non-empathic counselor: during the interview, Amy shows a neutral facial expression, and does not empathize with the user at all. 3. Empathic counselor: during the counseling session, Amy expresses different emotional facial expressions (happy, sad, concerned, surprised, and neutral); head gesture (nod); big/subtle smile; head posture mimicry (pitch, yaw, roll); eyebrow movement; mutual gaze; and lip synchronized verbal reflections. We hypothesize that counselors with different delivery modalities (i.e., virtual character vs. text) and different levels of empathizing abilities have different effects on the quality of the interaction with users. Participants were recruited from volunteer university students through fliers and s. They were randomly assigned to one of the three conditions. From the total number of 81 subjects, 26 were assigned to the empathic counselor, 25 to the nonempathic counselor, and 30 to the text-only version. Based on the Heerink s model [13], we designed an online after-experiment questionnaire to evaluate the character's user's acceptance (including Attitude (ATT), Intention to Use (ITU), Perceived Enjoyment (PENJ), Perceived Ease of Use (PEOU), Perceived Sociability (PS), Perceived Usefulness (PU), Social Presence (SP), Trust (TRUST), Anxiety (ANX), and Social Influence (SI)).

4 436 R. Amini, C. Lisetti, and U. Yasavur 3.1 Results and Discussion The clients' answers are analyzed using the Mantel-Haenszel-Chi-Square statistical method (df = 1, and Bonferroni corrected alpha = 1.7%) with two null hypotheses: (1) text-only and non-empathic counselor have the same effects on the users; and (2) empathic and non-empathic counselors have the same effects on the users. Also, we compared the mean values of subjects responses in each category to show the possible system improvements/deteriorations. Results show that: 1. In all the categories except PEOU, ANX, and SI, when comparing the empathic condition with the other two conditions using Chi-Square, 0.017, which rejects the second null hypothesis. Therefore, adding the empathizing ability significantly affects the user acceptance and perceived character features. 2. In all categories except PS, when comparing the textual system and the nonempathic counselor, 0.017, which approves the first null hypothesis. So, adding a non-empathic character does not affect significantly the users perceptions. 3. As shown in Fig. 2, except in ANX and PEOU, in all the categories, empathic counselor outperformed both textual system and the non-empathic counselor. Positive anxiety mean values show that subjects did not feel anxious when using these systems. Positive PEOU values indicate that the clients perceived all conditions easy to use. However, the clients prefer a character interface rather than a pure textual intervention. 4. As shown in Fig. 2, mean values show that, in ATT, ITU, PEOU, SP, and LIKE, nonempathic counselor outperforms that textual system. Whereas, in PENJ, PS, PU, TRUST, SI, PI, and PS the textual system outperforms the non-empathic counselor. Fig. 2. User acceptance mean values. Percentages show improvement of empathic over textual. References [1] Vernon, M.: A review of computer-based alcohol problem services designed for the general public. J. Subst. Abuse Treat. 38(3), (2010) [2] Portnoy, D.B., Scott-Sheldon, L.A.J., Johnson, B.T., Carey, M.P.: Computer-delivered interventions for health promotion and behavioral risk reduction: A meta-analysis of 75 randomized controlled trials, Prev. Med. (Baltim) 47(1), 3 16 (2008)

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