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Explainable Artificial Intelligence and Mobile Health for Treating Eating Disorders in Young Adults with Autism Spectrum Disorder Based on the Theory of Change: A Mixed Method Protocol

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Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

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Abstract

Autistic children often face difficulties eating well into their early adolescence, putting them at a greater risk of developing disordered eating habits during this developmental stage. Research suggests that mobile devices are easily accessible to young adults, and their widespread use can be leveraged to provide support and intervention for autistic young adults in preventing and self-managing eating disorders. By utilising Explainable Artificial Intelligence (XAI) and Machine Learning (ML) powered mobile devices, a progressive learning system can be developed that provides essential life skills for independent living and improved quality of life. In addition, XAI can enhance healthcare professionals’ decision-making abilities by utilising trained algorithms that can learn, providing a therapeutic benefit for preventing and mitigating the risk of eating disorders. This study will utilise the theory of change (ToC) approach to guide the investigation and analysis of the complex integration of autism, ED, XAI, ML, and mobile health. This approach will be complemented by user-centred design, Patient and Public Involvement and Engagement (PPIE) tasks, activities, and a mixed method approach to make the integration more rigorous, timely, and valuable. Ultimately, this study aims to provide essential life skills to autistic young adults to prevent and self-manage eating disorders using XAI-powered mobile devices.

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Author information

Authors and Affiliations

  1. Cardiff Metropolitan University, Llandaff, Cardiff, UK

    Omobolanle Omisade, Catherine Tryfona, Sandeep Singh Sengar & Amie-Louise Prior

  2. University of Portsmouth, Portsmouth, UK

    Alex Gegov, Alice Good, Taiwo Adedeji & Carrie Toptan

  3. University of Plymouth, Plymouth, UK

    Shang-Ming Zhou

  4. De Montfort University, Leicester, UK

    Bangli Liu

Authors
  1. Omobolanle Omisade
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  2. Alex Gegov
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  3. Shang-Ming Zhou
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  4. Alice Good
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  5. Catherine Tryfona
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  6. Sandeep Singh Sengar
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  7. Amie-Louise Prior
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  8. Bangli Liu
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  9. Taiwo Adedeji
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  10. Carrie Toptan
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Corresponding author

Correspondence to Omobolanle Omisade .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Omisade, O. et al. (2023). Explainable Artificial Intelligence and Mobile Health for Treating Eating Disorders in Young Adults with Autism Spectrum Disorder Based on the Theory of Change: A Mixed Method Protocol. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_3

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