Background Mental health status assessment is mostly limited to clinical or research settings, but recent technological advances provide new opportunities for measurement using more ecological approaches. Leveraging apps already in use by individuals on their smartphones, such as chatbots, could be a useful approach to capture subjective reports of mood in the moment. Objective This study aimed to describe the development and implementation of the Identifying Depression Early in Adolescence Chatbot (IDEABot), a WhatsApp-based tool designed for collecting intensive longitudinal data on adolescents’ mood. Methods The IDEABot was developed to collect data from Brazilian adolescents via WhatsApp as part of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study. It supports the administration and collection of self-reported structured items or questionnaires and audio responses. The development explored WhatsApp’s default features, such as emojis and recorded audio messages, and focused on scripting relevant and acceptable conversations. The IDEABot supports 5 types of interactions: textual and audio questions, administration of a version of the Short Mood and Feelings Questionnaire, unprompted interactions, and a snooze function. Six adolescents (n=4, 67% male participants and n=2, 33% female participants) aged 16 to 18 years tested the initial version of the IDEABot and were engaged to codevelop the final version of the app. The IDEABot was subsequently used for data collection in the second- and third-year follow-ups of the IDEA-RiSCo study. Results The adolescents assessed the initial version of the IDEABot as enjoyable and made suggestions for improvements that were subsequently implemented. The IDEABot’s final version follows a structured script with the choice of answer based on exact text matches throughout 15 days. The implementation of the IDEABot in 2 waves of the IDEA-RiSCo sample (140 and 132 eligible adolescents in the second- and third-year follow-ups, respectively) evidenced adequate engagement indicators, with good acceptance for using the tool (113/140, 80.7% and 122/132, 92.4% for second- and third-year follow-up use, respectively), low attrition (only 1/113, 0.9% and 1/122, 0.8%, respectively, failed to engage in the protocol after initial interaction), and high compliance in terms of the proportion of responses in relation to the total number of elicited prompts (12.8, SD 3.5; 91% out of 14 possible interactions and 10.57, SD 3.4; 76% out of 14 possible interactions, respectively). Conclusions The IDEABot is a frugal app that leverages an existing app already in daily use by our target population. It follows a simple rule-based approach that can be easily tested and implemented in diverse settings and possibly diminishes the burden of intensive data collection for participants by repurposing WhatsApp. In this context, the IDEABot appears as an acceptable and potentially scalable tool for gathering momentary information that can enhance our understanding of mood fluctuations and development.

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BACKGROUND Assessment of mental health status is mostly limited to clinical or research settings, but recent technological advances provide new opportunities for measurement using more ecological approaches. Leveraging apps already in use by individuals on their smartphones, such as chatbots, could represent useful tools to capture subjective reports of mood in the moment. OBJECTIVE To describe the development and implementation of the Identifying Depression Early in Adolescence Chatbot (IDEABot), a WhatsApp-based tool used for collection of intensive longitudinal data on mood. METHODS The IDEABot was developed to collect data from Brazilian adolescents via WhatsApp as part of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study. It was designed to support the administration and collection of self-reported structured items/questionnaires and audio responses. Development explored the default features available in WhatsApp, such as emojis and recorded audio messages, but also focused on scripting conversations in a manner that was relevant and acceptable to the target population. It supports five types of interactions, including textual and audio questions, administration of a version of the Short Mood and Feelings Questionnaire, unprompted interactions, and a “snooze” function. Six adolescents (4 boys, 2 girls: aged 16 to 18 years) tested the initial version of IDEABot, and were engaged to co-develop the final version of the application. The IDEABot was subsequently used for data collection in the second- and third-year follow-ups of the IDEA-RiSCo study. RESULTS Adolescents (n=6) assessed the initial version of IDEABot as enjoyable and made suggestions for improvements that were subsequently implemented. The final version of IDEABot follows a structured script with the choice of answer based on exact text matches throughout 15 days. Implementation of the IDEABot in the IDEA-RiSCo sample (n=140 and 132 for second- and third-year) evidenced adequate engagement indicators, with good acceptance to use the tool [80% and 92.4% for second- and third-year use], low attrition (failing to engage in the protocol after initial interaction) [0.8% on both waves] and high compliance in terms of proportion of responses in relation to the total elicited prompts [85.6% and 93%, respectively]. CONCLUSIONS The IDEABot is a frugal application that takes advantage of an existing app already in daily use by our target population. The IDEABot follows a simple, rule-based approach which can be easily tested and implemented in diverse settings, and possibly diminishes the burden of intensive data collection for participants by repurposing WhatsApp. In this context, the IDEABot appears to be an acceptable and potentially scalable tool to collect momentary information that can further our understanding of how mood fluctuates and develops over time.