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A Conversational Interface for Self-screening for ADHD in Adults

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11551))

Abstract

Self-screening for mental health problems is commonly used to detect and assess symptoms, as a first step in diagnosing a problem, and to give recommendations for possible treatment. This study explores the potential of conversational interfaces in providing screening services for mental health care. A chatbot was developed to perform a screening for attention deficit/hyperactivity disorder (ADHD) in adults by including the items from the Adult ADHD Self-Report Scale (ASRS). We compared the conversational chatbot interface responses with reports on the standardised paper-based ASRS, and evaluated the user interaction with the chatbot. The results showed a match between the two modalities in the screening results. Based on interviews with participants and chatlogs we discuss the challenges and user experience of doing self-screening in a conversational interface.

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Acknowledgements

This research has been carried out through the INTROMAT project, project code 259293/O70, funded by NFR – the Norwegian Research Council. IBM is an industry partner to INTROMAT. The authors would like to express gratitude for the careful reading and suggestions for improvement from the four anonymous reviewers. We also thank the participants that tested ROB.

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Correspondence to Jo Dugstad Wake .

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Håvik, R., Wake, J.D., Flobak, E., Lundervold, A., Guribye, F. (2019). A Conversational Interface for Self-screening for ADHD in Adults. In: Bodrunova, S., et al. Internet Science. INSCI 2018. Lecture Notes in Computer Science(), vol 11551. Springer, Cham. https://doi.org/10.1007/978-3-030-17705-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-17705-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17704-1

  • Online ISBN: 978-3-030-17705-8

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