M-Health Solutions to Support the National Health Service in the Diagnosis and Monitoring of Autism Spectrum Disorders in Young Children

  • Catherine TryfonaEmail author
  • Giles Oatley
  • Ana Calderon
  • Simon Thorne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9739)


With estimates of prevalence between 1 in 68 and 1 in 88 children [11], accurate and early identification of autism spectrum disorders (ASDs) in young children remains a pressing public health issue. In the absence of a single biomarker for ASD, however, a diagnosis is currently reached on the basis of a portfolio of evidence assembled by various health care professionals, parents and educational specialists. Studies have shown that early diagnosis and subsequent intervention are key to a favourable prognosis for children with autism. Many families, however, experience long periods of time between appointments with health care professionals, thus delaying the diagnosis and subsequent access to support and interventions. In this paper, we consider the potential role of m-health software solutions in supporting the diagnosis and ongoing monitoring of ASDs in young children. We consider their application particularly within the context of the UK’s National Health Service. This paper also presents a review of some of the current literature on user-behaviour analysis software on mobile computing devices such as tablet computers and smartphones, along with some of the emerging m-health solutions for supporting the diagnosis of ASD in children.


Autism spectrum disorder M-Health User behaviour analysis 


  1. 1.
    National Autistic Society (2015) (Accessed 28 February 2016)
  2. 2.
    Matson, J.L., Goldin, R.L.: Diagnosing young children with autism. Int. J. Develop. Neuroscience 39, 44–48 (2014). CrossRefGoogle Scholar
  3. 3.
    Happé, F., Ronald, A.: The “fractionable autism triad”: a review of evidence from behavioural, genetic, cognitive and neural research. Neuropsychol. Rev. 18, 287–304 (2008). CrossRefGoogle Scholar
  4. 4.
    Williams, D.M., Bowler, D.M.: Autism spectrum disorder: fractionable or coherent? Autism 18(1), 2–5 (2014). CrossRefGoogle Scholar
  5. 5.
    National Health Service. Asperger’s not in DSM-5 mental health manual (2012). (Accessed 25 April 2015)
  6. 6.
    Taylor, C.M., Vehorn, A., Noble, H., Weitlauf, A.S., Warren, Z.E.: Brief report: can metrics of reporting bias enhance early autism screening measures? J. Autism Dev. Disord. 44(9), 2375–2380 (2014). CrossRefGoogle Scholar
  7. 7.
    Ousley, O., Cermak, T.: Autism spectrum disorder: defining dimensions and subgroups. Current Dev. Disord. Rep. 1, 20–28 (2013). CrossRefGoogle Scholar
  8. 8.
    Bishop, S., Luyster, R., Richler, J., Lord, C.: Diagnostic assessments. In: Chawarska, K., Klin, A., Volkmar, F. (eds.) Autism Spectrum Disorders in Infants and Toddlers, pp. 23–43, NewYork (2008)Google Scholar
  9. 9.
    Corsello, C.: Diagnositic instruments in autistic spectrum disorders. Encycl. Autism Spectrum Disord. 919–926 (2013)Google Scholar
  10. 10.
    Valicenti-McDermott, M., Hottinger, K., Seijo, R., Shulman, L.: Age at diagnosis of autism spectrum disorders. J. Pediatrics 161(3), 554–556 (2012). CrossRefGoogle Scholar
  11. 11.
    Centers for Disease Control and Pre. Autism Spectrum Disorder (ASD) (2015). (Accessed on 25 April 2015)
  12. 12.
    Thummler, C.: Digital health. In: Fricker, S., Thummler, C., Gavras, A. (eds.) Requirements Engineering for Digital Health, 1st edn, pp. 1–22. (2015)Google Scholar
  13. 13.
    Alepis, E., Lambrinidis, C.: M-Health: supporting automated diagnosis and electonic health records. SpringerPlus 2(1), 103–111 (2013)CrossRefGoogle Scholar
  14. 14.
    Istepanian, R.S.H.: M-Health: a decade of evolution and impact on services and global health (2010)Google Scholar
  15. 15.
    OFCOM. Media Facts and Figures (2015). (Accessed on 22 April 2015)
  16. 16.
    Mintel. Tablet Computers UK Executive Summary - November 2014 (2015)Google Scholar
  17. 17.
    Begale, M., Duffecy, J., Kane, J.M., Mohr, D.C.: Strategies for mHealth research: lessons from 3 Mobile intervention studies, pp. 157–167 (2015).
  18. 18.
    Norris, A.C., Stockdale, R.S., Sharma, S.: A strategic approach to m-health. Health Inform. J. 15(3), 244–253 (2009). CrossRefGoogle Scholar
  19. 19.
    Anzulewicz, A.: HARIMATA-Embracing mobile devices for early diagnosis of autism spectrum disorders. In: ITASD 2014 Paris Conference, France (2014).
  20. 20.
    Bollinger, R., Chang, L., Jafari, R., O’Callaghan, T., Ngatia, P., Settle, D., Al Shorbaji, N.: Leveraging information technology to bridge the health workforce gap. Bull. World Health Organ. 91, 890–892 (2013). CrossRefGoogle Scholar
  21. 21.
    Chib, A.: The promise and peril of mHealth in developing countries. Mobile Media Commun. 1, 69–75 (2013). CrossRefGoogle Scholar
  22. 22.
    Benton, L., Johnson, H., Ashwin, E., Brosnan, M., Grawemeyer, B. Developing IDEAS: supporting children with autism within a participatory design team. In: CHI 2012, pp. 2599–2608, Austin (2012).
  23. 23.
    Boucenna, S., Narzisi, A., Tilmont, E., Muratori, F., Pioggia, G., Cohen, D., Chetouani, M.: Interactive technologies for autistic children: a review. Cogn. Comput. 6(4), 722–740 (2014)CrossRefGoogle Scholar
  24. 24.
    Antal, M., Bokor, Z., Szabó, L.Z.: Information revealed from scrolling interactions on mobile devices. Pattern Recogn. Lett. 56, 7–13 (2015). CrossRefGoogle Scholar
  25. 25.
    Aziz, M.Z.A., Abdullah, S.A.C., Adnan, S.F.S., Mazalan, L.: Educational app for children with autism spectrum disorders (ASDs). Procedia Comput. Sci. 42(c), 70–77 (2014). CrossRefGoogle Scholar
  26. 26.
    Bertou, E., Tilburg, A.B.: Low-fidelity prototyping tablet applications for children, pp. 257–260 (2014).
  27. 27.
    Chien, C.F., Lin, K.Y., Yu, A.P.I.: User-experience of tablet operating system: an experimental investigation of Windows 8, iOS 6 and Android 4.2. Comput. Ind. Eng. 73, 75–84 (2014). CrossRefGoogle Scholar
  28. 28.
    Zapata, B.C., Fernández-alemán, J.L., Idri, A., Toval, A.: Empirical studies on usability of mHealth Apps: a systematic literature review (2015).
  29. 29.
    Abowd, G.: Pilot evaluation of a novel telemedicine platform to support diagnostic assessment for autism spectrum disorder. In: ITASD 2014 Paris Conference, Paris (2014)Google Scholar
  30. 30.
    Billeci, L.: Eye-tracking technology to assess joint attention deficit in children with Autism spectrum disorders. In: ITASD 2014 Paris Conference, France (2014).
  31. 31.
    Brady, N.C., Anderson, C.J., Hahn, L.J., Obermeier, S.M., Kapa, L.L.: Eye tracking as a measure of receptive vocabulary in children with autism spectrum disorders. Augmentative Altern. Commun. 30, 147–159 (2014). CrossRefGoogle Scholar
  32. 32.
    Stanford University. Cell Phones, Sensors and You (2012). (Accessed on 25 April 2015)
  33. 33.
    Nazneen, N., Rozga, A., Smith, C., Oberleitner, R., Abowd, G., Arriaga, R.: A novel system for supporting autism diagnosis using home videos: iterative development and evaluation of system design, 3(2) (2015)Google Scholar
  34. 34.
    FUNF Open Sensing Framework (2015). (Accessed on 1 January 2016)
  35. 35.
    HARIMATA play care technology (2015). (Accessed on 1 April 2015)

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Catherine Tryfona
    • 1
    Email author
  • Giles Oatley
    • 1
  • Ana Calderon
    • 1
  • Simon Thorne
    • 1
  1. 1.Cardiff Metropolitan UniversityCardiffUK

Personalised recommendations