A natural conversational interface that allows longitudinal symptom tracking would be extremely valuable in health wellness applications. However, the task of designing emotionally-aware agents for behavior change is still poorly understood. In this paper, we present the design and evaluation of an emotion-aware chatbot that conducts experience sampling in an empathetic manner. We evaluate it through a human-subject experiment with N=39 participants over the course of a week. Our results show that extraverts preferred the emotion-aware chatbot significantly more than introverts. Also, participants reported a higher percentage of positive mood reports when interacting with the empathetic bot. Finally, we provide guidelines for the design of emotion-aware chatbots for potential use in mHealth contexts.
@cite_1: Background Previous research has shown that gratitude positively influences psychological wellbeing and physical health. Grateful people are reported to feel more optimistic and happy, to better mitigate aversive experiences, and to have stronger interpersonal bonds. Gratitude interventions have been shown to result in improved sleep, more frequent exercise and stronger cardiovascular and immune systems. These findings call for the development of technologies that would inspire gratitude. This paper presents a novel system designed toward this end.
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@cite_13: The experience-sampling (ESM) technique is a method in which recording of feelings and activities is done on-line at the moment, either at randomly selected moments or at predetermined times. This method has the advantage of being able to not only assess people’s general feelings, but to link feelings with situations, times of day, and other circumstances. Thus, ESM provides a powerful way of moving beyond simple questions about who is “happy” and who is not, to more intricate questions about when and why people experience positive and negative feelings. Compared to retrospective reports of feelings, ESM is less influenced by memory biases. ESM also allows researchers to analyze the patterning and relationships of feelings as they unfold over time. Despite the strengths and promise of this method, there are also limitations. For example, the heavy demand placed on research participants means that the sample might be biased toward highly conscientious individuals, and repeatedly reporting one’s moods might itself influence feelings. How to analyze ESM data is discussed, including the issue of how to aggregate momentary feelings into global measures of the average subjective well-being of individuals.
Despite the strengths of ESM, it also has limitations. This method puts heavy demand on research participants, which makes it a better fit for conscientious individuals rather than the general population @cite_13 . There have been multiple efforts in improving the average user's engagement and compliance. Some researchers have tried to reduce the burden on the user by incorporating ESM more seamlessly into their daily pipelines, for example, by placing it in the phone unlock screen @cite_1 . Other researchers have designed engaging games, making short questionnaires part of the game flow, and have validated ESM responses captured in the game in comparison to the traditional setting @cite_21 .