The Identifying Depression Early in Adolescence Chatbot (IDEABot): Protocol for development and implementation of a frugal innovation tool for mood assessment

Anna Viduani; Victor Cosenza; Helen Fisher; Claudia Buchweitz; Jadar Piccin; Rivka Pereira; Brandon A Kohrt; Valeria Mondelli; Alastair van Heerden; Ricardo Matsumura Araújo; Christian Kieling

doi:10.2196/44388

The Identifying Depression Early in Adolescence Chatbot (IDEABot): Protocol for development and implementation of a frugal innovation tool for mood assessment

Anna Viduani, Victor Cosenza, Helen Fisher, Claudia Buchweitz, Jadar Piccin, Rivka Pereira, Brandon A Kohrt, Valeria Mondelli, Alastair van Heerden, Ricardo Matsumura Araújo, Christian Kieling

Research output: Contribution to journal › Article › peer-review

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Abstract

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.

Original language	English
Article number	e44388
Journal	JMIR Human Factors
Volume	10
Early online date	7 Aug 2023
DOIs	https://doi.org/10.2196/44388
Publication status	Published - 2023

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10.2196/44388Licence: CC BY

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Viduani, A., Cosenza, V., Fisher, H., Buchweitz, C., Piccin, J., Pereira, R., Kohrt, B. A., Mondelli, V., van Heerden, A., Araújo, R. M., & Kieling, C. (2023). The Identifying Depression Early in Adolescence Chatbot (IDEABot): Protocol for development and implementation of a frugal innovation tool for mood assessment. JMIR Human Factors, 10, Article e44388. https://doi.org/10.2196/44388

@article{4b2f5a817d7c49bfaf0ff8c30a510330,

title = "The Identifying Depression Early in Adolescence Chatbot (IDEABot): Protocol for development and implementation of a frugal innovation tool for mood assessment",

abstract = "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{\textquoteright} 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{\textquoteright}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{\textquoteright}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.",

author = "Anna Viduani and Victor Cosenza and Helen Fisher and Claudia Buchweitz and Jadar Piccin and Rivka Pereira and Kohrt, {Brandon A} and Valeria Mondelli and {van Heerden}, Alastair and Ara{\'u}jo, {Ricardo Matsumura} and Christian Kieling",

note = "Funding Information: The authors are extremely grateful to the schools and individuals who participated in this study and to all members of the Identifying Depression Early in Adolescence (IDEA) team for their dedication, hard work, and insights. This project was supported by the Royal Academy of Engineering under the Frontiers Follow-On Funding scheme (FF\1920\1\61). The original IDEA project was funded by an MQ Brighter Futures grant (MQBF/1 IDEA). Additional support was provided by the UK Medical Research Council (MC_PC_MR/R019460/1) and the Academy of Medical Sciences (GCRFNG_100281) under the Global Challenges Research Fund. This work was also supported by research grants from Brazilian public funding agencies Conselho Nacional de Desenvolvimento Cient{\'i}fico e Tecnol{\'o}gico (477129/2012-9 and 445828/2014-5), Coordena{\c c}{\~a}o de Aperfei{\c c}oamento de Pessoal de N{\'i}vel Superior (62/2014), and Funda{\c c}{\~a}o de Amparo {\`a} Pesquisa do Estado do Rio Grande do Sul (17/2551-0001009-4). CK is a Conselho Nacional de Desenvolvimento Cient{\'i}fico e Tecnol{\'o}gico researcher and an Academy of Medical Sciences Newton Advanced Fellow. CK and BAK are supported by the US National Institute of Mental Health (R21MH124072). HLF was partly supported by the Economic and Social Research Council Centre for Society and Mental Health at King{\textquoteright}s College London (ES/S012567/1). VM was supported by the National Institute for Health and Care Research Maudsley Biomedical Research Centre hosted by South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London and MQ funding (MQBF/4). The views expressed are those of the authors and not necessarily those of the funders, the National Health Service, the National Institute for Health and Care Research, the Department of Health and Social Care, the Economic and Social Research Council, or King{\textquoteright}s College London. Publisher Copyright: {\textcopyright} Anna Viduani, Victor Cosenza, Helen L Fisher, Claudia Buchweitz, Jader Piccin, Rivka Pereira, Brandon A Kohrt, Valeria Mondelli, Alastair van Heerden, Ricardo Matsumura Ara{\'u}jo, Christian Kieling.",

year = "2023",

doi = "10.2196/44388",

language = "English",

volume = "10",

journal = "JMIR Human Factors",

issn = "2292-9495",

publisher = "JMIR Publications",

}

Viduani, A, Cosenza, V, Fisher, H, Buchweitz, C, Piccin, J, Pereira, R, Kohrt, BA, Mondelli, V, van Heerden, A, Araújo, RM & Kieling, C 2023, 'The Identifying Depression Early in Adolescence Chatbot (IDEABot): Protocol for development and implementation of a frugal innovation tool for mood assessment', JMIR Human Factors, vol. 10, e44388. https://doi.org/10.2196/44388

TY - JOUR

T1 - The Identifying Depression Early in Adolescence Chatbot (IDEABot): Protocol for development and implementation of a frugal innovation tool for mood assessment

AU - Viduani, Anna

AU - Cosenza, Victor

AU - Fisher, Helen

AU - Buchweitz, Claudia

AU - Piccin, Jadar

AU - Pereira, Rivka

AU - Kohrt, Brandon A

AU - Mondelli, Valeria

AU - van Heerden, Alastair

AU - Araújo, Ricardo Matsumura

AU - Kieling, Christian

N1 - Funding Information: The authors are extremely grateful to the schools and individuals who participated in this study and to all members of the Identifying Depression Early in Adolescence (IDEA) team for their dedication, hard work, and insights. This project was supported by the Royal Academy of Engineering under the Frontiers Follow-On Funding scheme (FF\1920\1\61). The original IDEA project was funded by an MQ Brighter Futures grant (MQBF/1 IDEA). Additional support was provided by the UK Medical Research Council (MC_PC_MR/R019460/1) and the Academy of Medical Sciences (GCRFNG_100281) under the Global Challenges Research Fund. This work was also supported by research grants from Brazilian public funding agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (477129/2012-9 and 445828/2014-5), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (62/2014), and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (17/2551-0001009-4). CK is a Conselho Nacional de Desenvolvimento Científico e Tecnológico researcher and an Academy of Medical Sciences Newton Advanced Fellow. CK and BAK are supported by the US National Institute of Mental Health (R21MH124072). HLF was partly supported by the Economic and Social Research Council Centre for Society and Mental Health at King’s College London (ES/S012567/1). VM was supported by the National Institute for Health and Care Research Maudsley Biomedical Research Centre hosted by South London and Maudsley NHS Foundation Trust and King’s College London and MQ funding (MQBF/4). The views expressed are those of the authors and not necessarily those of the funders, the National Health Service, the National Institute for Health and Care Research, the Department of Health and Social Care, the Economic and Social Research Council, or King’s College London. Publisher Copyright: © 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.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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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DO - 10.2196/44388

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SN - 2292-9495

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JO - JMIR Human Factors

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The Identifying Depression Early in Adolescence Chatbot (IDEABot): Protocol for development and implementation of a frugal innovation tool for mood assessment

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