Self-assess Momentary Mood in Mobile Devices: a Case Study with Mature Female Participants

November 02, 2022 Β· Declared Dead Β· πŸ› International Conference on Speech Technology and Human-Computer Dialogue

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Authors Caterina Senette, Maria Claudia Buzzi, Maria Teresa Paratore arXiv ID 2211.01015 Category cs.HC: Human-Computer Interaction Citations 0 Venue International Conference on Speech Technology and Human-Computer Dialogue Last Checked 5 months ago
Abstract
Starting from the assumption that mood has a central role in domain-specific persuasion systems for well-being, the main goal of this study was to investigate the feasibility and acceptability of single-input methods to assess momentary mood as a medium for further interventions in health-related mobile apps destined for mature women. To this aim, we designed a very simple android App providing four user interfaces, each one showing one interactive widget to self-assess mood. Two widgets report a hint about the momentary mood they represent; the last two do not have the hints but were previously refined through questionnaires administered to 63 women (age 45-65) in order to reduce their expressive ambiguity. Next, fifteen women (age 45-65 years) were recruited to use the app for 15 days. Participants were polled about their mood four times a day and data were saved in a remote database. Moreover, users were asked to fill out a preliminary questionnaire, at the first access to the app, and a feedback questionnaire at the end of the testing period. Results appear to prove the feasibility and acceptability of this approach to self-assess momentary mood in the target population and provides some potential input methods to be used in this context.
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