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.
The experience sampling method (ESM) @cite_10 was born in 1970s with the advent of pagers. It is a validated technique used for capturing frequency, intensity, and overall patterns of one's emotional experience @cite_12 . ESM alleviates people's inability to provide accurate retrospective information on their daily behavior and experience by capturing such information in the moment @cite_6 .
@cite_10 @cite_6 @cite_12
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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. To understand the dynamics of mental health, it is essential to develop measures for the frequency and the patterning of mental processes in every-day-life situations. The Experience-Sampling Method (ESM) is an attempt to provide a valid instrument to describe variations in self-reports of mental processes. It can be used to obtain empirical data on the following types of variables: (a) frequency and patterning of daily activity, social interaction, and changes in location; (b) frequency, intensity, and patterning of psychological states, i.e., emotional, cognitive, and conative dimensions of experience; (c) frequency and patterning of thoughts, including quality and intensity of thought disturbance. The article reviews practical and methodological issues of the ESM and presents evidence for its short-and long-term reliability when used as an instrument for assessing the variables outlined above. The Day Reconstruction Method (DRM) assesses how people spend their time and how they experience the various activities and settings of their lives, combining features of time-budget measurement and experience sampling. Participants systematically reconstruct their activities and experiences of the preceding day with procedures designed to reduce recall biases. The DRM's utility is shown by documenting close correspondences between the DRM reports of 909 employed women and established results from experience sampling. An analysis of the hedonic treadmill shows the DRM's potential for well-being research. We should not have to look at the entire corpus (e.g., the Web) to know if two words are associated or not. A powerful sampling technique called Sketches was originally introduced to remove duplicate Web pages. We generalize sketches to estimate contingency tables and associations, using a maximum likelihood estimator to find the most likely contingency table given the sample, the margins (document frequencies) and the size of the collection. Not unsurprisingly, computational work and statistical accuracy (variance or errors) depend on sampling rate, as will be shown both theoretically and empirically. Sampling methods become more and more important with larger and larger collections. At Web scale, sampling rates as low as 10-4 may suffice.