Fitting the Message to the Moment: Designing Calendar-Aware Stress Messaging with Large Language Models

TL;DR

This paper explores how large language models can utilize digital calendar data to deliver timely, personalized stress management messages, emphasizing the importance of event prioritization and tone calibration for effectiveness.

Contribution

It demonstrates a novel approach to integrating calendar data with LLMs for adaptive stress support, highlighting design considerations for relevance and trust.

Findings

Participants valued stress interventions focusing on stressful events.

Concise, colloquial tone increased message relevance and trust.

Structured questioning improved message appropriateness.

Abstract

Existing stress-management tools fail to account for the timing and contextual specificity of students' daily lives, often providing static or misaligned support. Digital calendars contain rich, personal indicators of upcoming responsibilities, yet this data is rarely leveraged for adaptive wellbeing interventions. In this short paper, we explore how large language models (LLMs) might use digital calendar data to deliver timely and personalized stress support. We conducted a one-week study with eight university students using a functional technology probe that generated daily stress-management messages based on participants' calendar events. Through semi-structured interviews and thematic analysis, we found that participants valued interventions that prioritized stressful events and adopted a concise, but colloquial tone. These findings reveal key design implications for LLM-based stress-management tools, including the need for structured questioning and tone calibration to foster relevance and trust.