Relational Cues and Tailoring of e-Coach Dialogues

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Deborah Richards	--	1727	2023-10-11 10:55:59	\|
2	layout	Camila Xu	Meta information modification	1727	2023-10-12 02:54:55	\|

This entry is adapted from the peer-reviewed paper 10.3390/mti7100093

Relational cues are extracts from actual verbal dialogues that help build the therapist–patient working alliance and stronger bond through the depiction of empathy, respect and openness. ECAs (Embodied conversational agents) are human-like virtual agents that exhibit verbal and non-verbal behaviours. In the digital health space, ECAs act as health coaches or experts.

embodied conversational agents e-Coach e-health relational cues

1. Introduction

Embodied Conversational Agents (ECAs), which have both a visual virtual representation, typically human-like, as well as the ability to engage in a text-based interactive conversation with a human user, are increasingly being used in health applications. ECAs require socialisation traits and verbal and non-verbal responses that will motivate the user to build long-term relationship with the agent. ECAs have been studied across a diverse range of health programs, such as relational agents for anti-psychotic medication adherence ^[1], avatar-based health intervention to modify unhealthy lifestyles ^[2], exercise advisors that interact with older adults ^[3] and ECAs that can help cancer patients to adopt a positive lifestyle after chemotherapy ^[4]. Research has suggested frameworks to identify useful verbal behaviours for virtual agents, such as relational cues, including empathy, social dialogues and continuity ^[5] or the Big Five model of personality traits ^[6].

Bickmore and Cassell ^[7] identified a set of relational dialogue cues for designing dialogues in ECAs. A key motivation for these dialogue cues was to develop a working alliance between the ECA and the user ^[5]. A working alliance involves the development of shared goals, agreed tasks to achieve those goals and a sense of caring and trust ^[8] and has been found to be the strongest predictor of adherence to treatment advice ^[9]^[10]. Rojas-Barahona, Tseng, Dai, Mansfield, Ramadan, Ultes, Crawford and Gasic ^[11] used conversational agents who learned through deep learning to help patients with mental disorders, citing Bickmore’s framework as the foundational rule-based dialogue system for machine learning that generated a more sophisticated dialogue corpus. Another ECA was proposed by Almohanna, Win and Meedya ^[12], who aimed to provide post-delivery support for women in breastfeeding newborns, mentioning the framework of dialogue cues by Bickmore and Cassell ^[7] as a comprehensive solution for building computer-aided conversational agents. These and many other health-related works used Bickmore’s relational cues to provide human-like empathic responses, making his work a sound and workable framework for dialogues. However, despite extensive exploration of relational cues ^[13], systematic inclusion or exclusion of specific relational cues and tailoring to certain users have rarely been explored.

2. Further Background to Verbal Relational Cues

Asking caring questions and comforting the client/patient is a generic approach required in all empathic human interactions; the variation in terms of the verbiage of several cues, including self-disclosure and meta-relational cues, is dependent on the user’s personality, current emotional state and context of interaction ^[14]. There are standard dialogue sets for greetings and farewells, and they follow the norms of a human–human interaction. Humour is not liked by many, and it has been considered irrelevant in some studies ^[15]. Bickmore and colleagues have identified the single most comprehensive set of relational cues for ECA dialogues, including Social Dialogue; Meta-Relational Dialogue; Empathic Feedback; Humour; Continuity behaviours; Self-Disclosure; Reference to mutual/sharing knowledge; Solidarity and rapport-mirroring; Politeness; and Inclusive pronouns ^[5]. These cues have been used in the design of numerous ECA dialogues. However, specific descriptions of when certain cues should or should not be included or how certain users have responded to specific cues have not been elaborated. An exception is a work by Ranjbartabar, Richards, Bilgin, Kutay and Mascarenhas ^[16], described in the introduction. That work has not explored the impact of specific cues on specific health behaviours or development of working alliance. For ECAs that seek to motivate the user to change their health behaviours a one size fits all approach that proposes use of such cues without specific understanding of what cues are useful to whom and when and for what purpose is unlikely to be appropriate or effective.

2.1. Empowerment and Health Coaching

According to Aloni ^[17], empowerment in healthcare is based on a philosophy of seeing the patient as an equal autonomous member of the healthcare team, in line with Freire’s pedagogical ideas (a famous and progressive educationist in the late twentieth century, who believed that real learning happens when the learner is empowered to actively engage with real-world content). However, empowerment can be described in various ways, depending on the level of analysis: individual, organisational or community ^[18]. The ideal of equal patients with the right to participate in decisions about their own health requires tight cooperation between the patient and health professionals. Gibson ^[19] claims that empowerment can be defined as a social process of recognising, promoting and enhancing people’s abilities to meet their own needs, solve their problems and mobilise the necessary resources to feel in control of their own lives. In a study by Tveiten and Knutsen ^[20], patient feedback was taken on their two sessions in which small talk followed by empowering dialogues were delivered to them. Some interesting feedback is mentioned below:

“Instead of only getting drugs, you get dialogue, understanding and empathy in addition to pain therapy…”
“They have to know everything to give the full benefit. If they don’t allow me to tell all about myself, then they have no basis for helping me.”

This shows the importance of understanding the user and targeting the dialogue with them.

2.2. Working Alliance

Researchers have concentrated on exploring the relationship between alliance and therapeutic outcome in a variety of contexts: different types of treatments, different populations and diagnostic categories, the effects of gender, and various factors related to the therapist ^[8]. Psychotherapy research has found that that alliance cannot be seen as a one-size-fits-all package. On the contrary, the nature of the alliance varies depending on the individual patient and therapist, a reality that has generated the proliferation of expressions such as “matching patients to therapies” ^[21] and “tailoring psychotherapies and therapists to patients” ^[22].

Results reported by Castonguay, Constantino and Holtforth ^[23] help identify important features of the therapeutic relationship/alliance from an empirically informed clinical point of view.

The importance of the working alliance on coaching outcomes was confirmed in a review by Graßmann, Schölmerich and Schermuly ^[9]. A review of ECAs for behaviour changes also found that working alliance was a key driver in the design of numerous ECAs ^[24]. That review observed that much of the work on ECA social dialogue, empathic agents and the use of Bickmore’s relational cues are driven by a primary focus on the bond aspect of the working alliance. The review further noted the importance of addressing shared goals and mutual planning, which are consistent with the goals of empowerment and affirmation cues.

2.3. Affirmation

Cameron, Mazer, DeLuca, Mohile and Epstein ^[25] recognise affirmation as ‘acknowledging something complex or otherwise emotionally challenging for the patient’ (p. 7). The complexity can be due to multiple reasons ranging from whether or not to take a medication to whether to follow a recommended treatment that is not fully understood. The main purpose of affirmation is to enable the patient to express their frustration openly. Letting the patient voice their concerns and respond in understandable utterances is the essence of affirmation.

Affirmation includes strategies such as encouragement and expression of empathy and understanding. Empathic ECAs have been designed and evaluated in multiple studies, but most studies have tended to focus on non-verbal expressions of empathy ^[26]^[27]^[28] via the ECA’s facial expressions and body gestures. Where there is an expression of both verbal and non-verbal empathy by ECAs, the approach often involves mirroring and letting the user know they were “heard’, as in the case of listening agents ^[29].

Affirmative dialogues have been associated with the intention of building a long-term working alliance in multiple clinical studies, including ^[30], where the challenges inherent in establishing and maintaining an alliance with suicidal patients from a psychodynamic point of view were addressed using the relevant concepts of validation, empathy, and genuine relatedness. This work stresses the importance of the patient’s experience of feeling understood and accepted despite the need for change. This tension between acceptance and change is central to the therapeutic alliance, and it can help the patient stay engaged in treatment despite the extreme affective intensity and interpersonal difficulties that may arise. In another study of psychodynamic therapy ^[31], Saunders examined the relationship bonding between the patient and the therapist through different alliance variables. Clients who felt motivated and invested in therapy and who rated the session as being affirmative were likely to rate the session as helpful and productive.

2.4. Social Dialogue Role in Motivational Coaching

Laver ^[32] defines social dialogues as language constructs that are mostly uttered during greetings and farewells. They build continuity in the conversation and contribute to ice-breaking during the conversation. They are specifically non-task-oriented and bring structure to the conversation with which humans psychologically become ready to open up and talk. In eHealth, dialogues that are about a patient’s current health status or previous medical history are called task-oriented. According to Laver ^[32], task-oriented dialogues can be divided into three phases of conversation: the opening, middle and closing phases. The purpose of the opening phase is to ease the transition from non-task-oriented communication to task-oriented communication and to increase the level of comfort during the conversation. This helps ‘break the ice’ before the task-oriented middle phase begins. Hence, the objective is to work around the reason for which the discussion is happening. In eHealth, the middle phase seeks to classify the level of the health issue and recommendation of the treatment. The closing phase again helps in transitioning from task-oriented communication to a comfortable finish. Social dialogues commonly play their role in the opening and ending phases.

According to Higashinaka, Dohsaka and Isozaki ^[33], conversational systems need to build trust or cultivate long-term relationships with users through social dialogues that include self-disclosure followed by empathic dialogues of agreement and disagreement with the disclosure. Social dialogues that exhibit self-disclosure and politeness are necessary for building trust. Self-disclosure is analysed in psychology, especially in the verbal and behavioural literature, for its ability to induce self-disclosure from the recipient, a phenomenon known as reciprocity ^[34], whereby self-disclosure by one participant in a two-way social dialogue results in self-disclosure from the other participant in response. Bothe ^[35] analysed theories of socio-linguistic cues used in conversational analysis, such as emotion, sentiment, and dialogue acts, where politeness was established as a fundamental cue. Politeness is also a vital relational cue used in conversations that incur trust ^[36].

References

Bickmore, T.W.; Puskar, K.; Schlenk, E.A.; Pfeifer, L.M.; Sereika, S.M. Maintaining reality: Relational agents for antipsychotic medication adherence. Interact. Comput. 2010, 22, 276–288.
Lisetti, C.L.; Yasavur, U.; De Leon, C.; Amini, R.; Visser, U.; Rishe, N. Building an On-Demand Avatar-Based Health Intervention for Behavior Change. In Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference, Marco Island, FL, USA, 23–25 May 2012; pp. 175–180.
Bickmore, T.W.; Caruso, L.; Clough-Gorr, K. Acceptance and usability of a relational agent interface by urban older adults. In Proceedings of the CHI’05 Extended Abstracts on Human Factors in Computing Systems, Portland, OR, USA, 2–7 April 2005; pp. 1212–1215.
Greer, S.; Ramo, D.; Chang, Y.-J.; Fu, M.; Moskowitz, J.; Haritatos, J. Use of the chatbot “Vivibot” to deliver positive psychology skills and promote well-being among young people after cancer treatment: Randomized controlled feasibility trial. JMIR mHealth uHealth 2019, 7, e15018.
Bickmore, T.; Gruber, A.; Picard, R. Establishing the computer–patient working alliance in automated health behavior change interventions. Patient Educ. Couns. 2005, 59, 21–30.
Neff, M.; Wang, Y.; Abbott, R.; Walker, M. Evaluating the Effect of Gesture and Language on Personality Perception in Conversational Agents. In Intelligent Virtual Agents; Springer: Berlin/Heidelberg, Germany, 2010; pp. 222–235.
Bickmore, T.; Cassell, J. Social dialongue with embodied conversational agents. In Advances in Natural Multimodal Dialogue Systems; Springer: Berlin/Heidelberg, Germany, 2005; pp. 23–54.
Horvath, A.O.; Greenberg, L.S. The Working Alliance: Theory, Research, and Practice; John Wiley & Sons: Hoboken, NJ, USA, 1994.
Graßmann, C.; Schölmerich, F.; Schermuly, C.C. The Relationship between Working Alliance and Client Outcomes in Coaching: A Meta-Analysis. Hum. Relat. 2020, 73, 35–58.
Bennett, J.K.; Fuertes, J.N.; Keitel, M.; Phillips, R. The role of patient attachment and working alliance on patient adherence, satisfaction, and health-related quality of life in lupus treatment. Patient Educ. Couns. 2011, 85, 53–59.
Rojas-Barahona, L.; Tseng, B.-H.; Dai, Y.; Mansfield, C.; Ramadan, O.; Ultes, S.; Crawford, M.; Gasic, M. Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy. arXiv 2018, arXiv:00640.
Almohanna, A.A.; Win, K.T.; Meedya, S. Effectiveness of Internet-Based Electronic Technology Interventions on Breastfeeding Outcomes: Systematic Review. J. Med. Internet Res. 2020, 22, e17361.
Bickmore, T.; Cassell, J. Relational agents: A model and implementation of building user trust. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Seattle, WA, USA, 31 March-5 April 2001; pp. 396–403.
DeVault, D.; Artstein, R.; Benn, G.; Dey, T.; Fast, E.; Gainer, A.; Georgila, K.; Gratch, J.; Hartholt, A.; Lhommet, M. SimSensei Kiosk: A virtual human interviewer for healthcare decision support. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, Paris, France, 5–9 May 2014; pp. 1061–1068.
Richards, D.; Bilgin, A.A.; Ranjbartabar, H. Users’ perceptions of empathic dialogue cues: A data-driven approach to provide tailored empathy. In Proceedings of the 18th International Conference on Intelligent Virtual Agents, Sydney, Australia, 5–8 November 2018; pp. 35–42.
Ranjbartabar, H.; Richards, D.; Bilgin, A.A.; Kutay, C.; Mascarenhas, S. User-Models to Drive an Adaptive Virtual Advisor. In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, Auckland, New Zealand, 9–13 May 2020; pp. 2117–2119.
Aloni, N. Empowering Dialogues in Humanistic Education. Educ. Philos. Theory 2013, 45, 1067–1081.
Wallerstein, N. Powerlessness, empowerment, and health: Implications for health promotion programs. Am. J. Health Promot. 1992, 6, 197–205.
Gibson, C.H. A concept analysis of empowerment. J. Adv. Nurs. 1991, 16, 354–361.
Tveiten, S.; Knutsen, I.R. Empowering dialogues–the patients’ perspective. Scand. J. Caring Sci. 2011, 25, 333–340.
Roth, T. Insomnia: Definition, prevalence, etiology, and consequences. J. Clin. Sleep Med. 2007, 3, S7–S10.
De Herder, W.; Krenning, E.; Van Eijck, C.; Lamberts, S. Considerations concerning a tailored, individualized therapeutic management of patients with (neuro) endocrine tumours of the gastrointestinal tract and pancreas. Endocr.-Relat. Cancer 2004, 11, 19–34.
Castonguay, L.G.; Constantino, M.J.; Holtforth, M.G. The working alliance: Where are we and where should we go? Psychother. Theory Res. Pract. Train. 2006, 43, 271.
Abdulrahman, A.; Richards, D. In Search of Embodied Conversational and Explainable Agents for Health Behaviour Change and Adherence. Multimodal Technol. Interact. 2021, 5, 56.
Cameron, R.A.; Mazer, B.L.; DeLuca, J.M.; Mohile, S.G.; Epstein, R.M. In search of compassion: A new taxonomy of compassionate physician behaviours. J. Health Expect. 2015, 18, 1672–1685.
Ochs, M.; Sadek, D.; Pelachaud, C. A formal model of emotions for an empathic rational dialog agent. Auton. Agents Multi-Agent Syst. 2012, 24, 410–440.
Pelachaud, C. Modelling multimodal expression of emotion in a virtual agent. Philos. Trans. R. Soc. B Biol. Sci. 2009, 364, 3539–3548.
Niewiadomski, R.; Ochs, M.; Pelachaud, C. Expressions of empathy in ECAs. In Proceedings of the International Workshop on Intelligent Virtual Agents, Philadelphia, PA, USA, 20–22 September 2008; pp. 37–44.
Bevacqua, E.; Mancini, M.; Pelachaud, C. A listening agent exhibiting variable behaviour. In Proceedings of the International Workshop on Intelligent Virtual Agents, Philadelphia, PA, USA, 20–22 September 2008; pp. 262–269.
Schechter, M.A.; Goldblatt, M.J. Psychodynamic therapy and the therapeutic alliance: Validation, empathy, and genuine relatedness. In Building a Therapeutic Alliance with the Suicidal Patient; American Psychological Association: Washington, DC, USA, 2011; pp. 93–107.
Saunders, S.M. Examining the relationship between the therapeutic bond and the phases of treatment outcome. Psychother. Theory Res. Pract. Train. 2000, 37, 206.
Laver, J. Communicative functions of phatic communion. Organ. Behav. Face–Face Interact. 1975, 215, 238.
Higashinaka, R.; Dohsaka, K.; Isozaki, H. Effects of self-disclosure and empathy in human-computer dialogue. In Proceedings of the 2008 IEEE Spoken Language Technology Workshop, Goa, India, 15–19 December 2008; pp. 109–112.
Ravichander, A.; Black, A.W. An empirical study of self-disclosure in spoken dialogue systems. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, Melbourne, Australia, 12–14 July 2018; pp. 253–263.
Bothe, C. Polite Emotional Dialogue Acts for Conversational Analysis in Dialy Dialog Data. arXiv 2021, arXiv:2112.13572.
Gupta, S.; Walker, M.A.; Romano, D.M. How rude are you?: Evaluating politeness and affect in interaction. In Proceedings of the International Conference on Affective Computing and Intelligent Interaction, Lisbon, Portugal, 12–14 September 2007; pp. 203–217.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Computer Science, Artificial Intelligence

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Sana Salman

Deborah Richards

Mark Dras

View Times: 280

Update Date: 12 Oct 2023

Table of Contents

Video Upload Options

Confirm