@Article{info:doi/10.2196/69329,
author="Sanjeewa, Ruvini
and Iyer, Ravi
and Apputhurai, Pragalathan
and Wickramasinghe, Nilmini
and Meyer, Denny",
title="Perception of Empathy in Mental Health Care Through Voice-Based Conversational Agent Prototypes: Experimental Study",
journal="JMIR Form Res",
year="2025",
month="May",
day="7",
volume="9",
pages="e69329",
keywords="perceived empathy; mental health care; helpline service; conversational agent prototypes; voice interactions; demographic information",
abstract="Background: Empathy is a critical component of effective mental health care communication. Positive perceptions of empathy in conversational agents (CAs) operating in the health care domain are therefore needed to enhance the quality of care provided by these emerging technologies. However, research on how users perceive empathy in CAs is limited, particularly in voice-based prototypes. Objective: The objective of this study is to identify to what extent perceptions of empathy in CA prototypes correspond with the engineered empathy levels for these voice-based prototypes. In addition, as a secondary aim, this study investigates how the demographic characteristics of participants affect their perception of empathy in a mental health helpline service context. Methods: Swinburne University first-year psychology students (N=306) were presented with 9 CA prototypes engineered to portray low, medium, or high empathy levels, and their perceptions of empathy were collected via an electronic survey. Perceptions of empathy were rated using the Perceived Emotional Intelligence (PEI) Scale and the Raters' Scale (RS10). Results: Most participants were female (233/306, 76{\%}) with a mean age of 30 (SD 10.69) years, while a majority (194/306, 63{\%}) were of Australian and New Zealand background. A strong positive correlation between the PEI and RS10 ratings was observed (r=0.829, P<.001). The empathy ratings across the 3 engineered empathy levels showed significant differences when using both PEI ($\chi$22=11.865, P=.003) and RS10 ($\chi$22=19.737, P<.001) measures. A linear mixed model for PEI showed significantly higher ratings for high rather than low engineered empathy levels (t8=−2.34, P=.048). RS10 ratings were also significantly higher for high rather than low engineered empathy levels (t8=−2.45, P=.04). However, no significant differences were detected between the CAs with engineered medium-level empathy and the CAs with low or high engineered empathy levels. The linear mixed model for PEI showed significantly higher ratings for participants of the Asian and Other ethnic categories compared to the Oceanic category (t285=2.54, P=.01 and t286=2.25, P=.03 respectively). The RS10 ratings were also significantly higher for the Other category rather than for the Oceanic category (t284=2.24, P=.03). Women showed significantly higher RS10 ratings than men (t283=1.94, P=.05). Conclusions: Recognizing empathy levels in CA prototypes proved challenging, highlighting possible complexities involved with voice-based empathy detection. The perception of empathy may also be affected by different ethnic and gender-based factors. The study findings emphasize the importance of personalized communications by CAs, with expressions of empathy tailored to key demographic characteristics of users. Future studies in a similar context would benefit from the inclusion of participants who are end users of a mental health care service with more balanced gender and age distributions. Multimodal interactions could also be considered for CA prototype development. ",
issn="2561-326X",
doi="10.2196/69329",
url="https://formative.jmir.org/2025/1/e69329",
url="https://doi.org/10.2196/69329"
}