Assessing Mood With the Identifying Depression Early in Adolescence Chatbot (IDEABot): Development and Implementation Study.

Autor:	Viduani A; Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.; Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil., Cosenza V; Center for Technological Advancement, Universidade Federal de Pelotas, Pelotas, Brazil., Fisher HL; Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.; Economic and Social Research Council Centre for Society and Mental Health, King's College London, London, United Kingdom., Buchweitz C; Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.; Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil., Piccin J; Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.; Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil., Pereira R; Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.; Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil., Kohrt BA; Division of Global Mental Health, Department of Psychiatry, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States., Mondelli V; Department of Psychological Medicine, Institute of Psychiatry, Psychology, King's College London, London, United Kingdom., van Heerden A; Centre for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa., Araújo RM; Center for Technological Advancement, Universidade Federal de Pelotas, Pelotas, Brazil., Kieling C; Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.; Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Jazyk:	angličtina
Zdroj:	JMIR human factors [JMIR Hum Factors] 2023 Aug 07; Vol. 10, pp. e44388. Date of Electronic Publication: 2023 Aug 07.
DOI:	10.2196/44388
Abstrakt:	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. (©Anna Viduani, Victor Cosenza, Helen L Fisher, Claudia Buchweitz, Jader Piccin, Rivka Pereira, Brandon A Kohrt, Valeria Mondelli, Alastair van Heerden, Ricardo Matsumura Araújo, Christian Kieling. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 07.08.2023.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu