Using Semiautomated WhatsApp Messages for Daily Stress Measurements: Integrated Usability and Feasibility Study

TL;DR

This study explores using WhatsApp messages to track work stress daily, finding that participants prefer text-based methods and report more stressors with this approach.

Contribution

The study introduces semiautomated WhatsApp messages as a novel method for collecting real-time work stress data with high adherence and detailed insights.

Findings

Participants showed a strong preference for multiple-choice and text messages over voice messages due to ease and convenience.

The MC-condition reported three times more stressors per week than the voice condition, despite similar average stress levels.

Voice messages yielded more words per response compared to text, but text and MC methods were more efficient for data collection.

Abstract

Stress is a key determinant of health outcomes and may influence work performance. Questionnaire-based assessments of stress are typically broad and retrospective. Daily stress measurements via smartphones offer more granular, real-time data but have adherence issues. Using an already established communication medium (WhatsApp) and a more conversational style assessment might improve adherence and help collect more detailed insights into (work) stress, underlying stressors, and countering energy sources.

This study focuses on the usability and feasibility of semiautomated voice- and text-messages (with and without emojis) via WhatsApp as a method to collect daily data on experienced work stress, stressors, and energy sources.

A sample of 210 workers was recruited via social media and participated in a 10-workday diary study using semiautomated WhatsApp messages to rate daily stress, stressors, and energy sources. Questions (with and without emojis) were presented by a chatbot as text messages with clickable buttons (multiple-choice questions; MC) or with instructions to answer with either a voice or a text message. The study used an experimental design with 4 groups: (1) week 1 voice, week 2 text/MC with emojis; (2) week 1 voice, week 2 text/MC without emojis; (3) week 1 text/MC, week 2 voice with emojis; (4) week 1 text/MC, week 2 voice without emojis. Pre- and poststudy web-based questionnaires assessed demographics, familiarity with voice messages, and usability, including participants’ preference for research studies. Open answers were coded using artificial intelligence, and the number of stressors or energy sources was compared across the 3 collection methods (MC, voice, and text messages) to determine if the amount and quality of information collected differ per method within participants.

A total of 158 workers completed at least 80% of scheduled conversations. The sample was predominantly women(170/210, 81%), highly educated (173/210, 82%), and a slight majority worked part-time (109/210, 52%). Mean adherence to the daily schedule was very high (mean of 95%). The postquestionnaire revealed a strong preference for MC and text over voice messages, mostly due to ease and convenience in a variety of situations. The number of stressors per week was approximately 3 times higher in the MC-condition than in the voice condition, even though average stress levels per week did not differ significantly within participants. The number of energy sources was comparable between open answers in the voice and text conditions, but voice messages consisted of more words.

Collecting (work) stress data via semiautomatic WhatsApp messages is a feasible method with low effort for participants. Usability ratings indicated a strong preference among participants for MC and text messages over voice messages. Future research should explore usability in more diverse samples and in direct comparison to traditional assessment methods.

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