Insights on Designing the MINT (Micro-Interventions for Navigating Tension) Chatbot System for Stress Management: Collaborative Autoethnography
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Published on: December 23, 2025
Marco Bolpagni1,2, Sofia Asteria Piffer2, Marco Pozza1,2
1Department of General Psychology, University of Padua, Via Venezia, 8, Padua, Veneto, 35131, Italy, (+39) 049 827 6485.
Background:
Digital microinterventions are increasingly used to support stress self-management in everyday life. While previous research has examined their feasibility, adherence, and preliminary effectiveness, much less is known about how these interventions are experienced over extended periods of real-world use.
Objective:
This study used collaborative autoethnography (CAE) to examine the everyday experience of living with MINT (Micro-Interventions for Navigating Tension), a Telegram-based chatbot for stress management. Our goal was not to evaluate clinical outcomes but to understand how scheduled microinterventions fit into everyday life, when they were ignored, and when people relied on other ways of coping instead, and to identify implications for the design of future digital mental health tools.
Methods:
We conducted a 4-week CAE involving 5 digital mental health researchers, who used the MINT chatbot as both participants and researchers. The experience was documented through multiple qualitative and quantitative sources, including reflective diaries, ecological momentary assessments (EMAs), workshop discussions, and wearable data, and analyzed collaboratively using reflexive thematic analysis (RTA).
Results:
Six interconnected themes described how MINT was experienced in everyday life: (T1) the importance of timing and emotional receptivity, (T2) the need for reciprocity and relational continuity, (T3) the tendency to favor existing coping strategies over suggested exercises, (T4) the meaning of nonengagement during periods of high stress, (T5) the collapse of self-initiated use after reminders were removed, and (T6) how everyday experiences with consumer wearables and general-purpose large language models reshaped expectations of digital mental health systems.
Conclusions:
Our findings show that CAE complements conventional evaluations of digital mental health interventions by revealing aspects of everyday use that are difficult to capture through feasibility studies, large-scale trials, or looking at usage data. Beyond the specific case of MINT, they suggest that future stress-management systems should better account for users' receptivity, provide greater continuity across interactions, build on existing coping strategies, and help users move from externally prompted engagement to autonomous self-management.
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