Designing efficacious mobile technologies for anxiety self-regulation

Hashini Senaratne, Kirsten Ellis, Sharon Oviatt, Glenn Melvin

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

8 Citations (Scopus)


This paper presents a step-by-step process that was developed primarily to extract design pre-requisites for personalized mobile technologies assisting anxiety self-regulation. This process, which is recognized as a preliminary framework, was developed, refined, and tested based on a multidisciplinary literature review and an exploratory study conducted with mental health professionals who treat anxiety disorders. The step-by-step nature of this framework draws from multiple disciplinary and stakeholder perspectives, integrates knowledge about efficacious psychological interventions, considers individual differences and specific challenges faced by patients, and realizes contextual needs. It also includes incremental and iterative refinements based on multidisciplinary sources to foster more evidence-based interface designs. Once reached its maturity, this framework can potentially be applied for designing efficacious technologies for a range of mental health conditions. The expected future contributions and limitations of the framework are also discussed.

Original languageEnglish
Title of host publicationExtended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
EditorsAnna Cox, Vassilis Kostakos
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Electronic)9781450359719
Publication statusPublished - 2019
EventInternational Conference on Human Factors in Computing Systems 2019 - Glasgow, United Kingdom
Duration: 4 May 20199 May 2019
Conference number: 37th (Website) (Proceedings)


ConferenceInternational Conference on Human Factors in Computing Systems 2019
Abbreviated titleCHI 2019
Country/TerritoryUnited Kingdom
Internet address


  • Anxiety
  • Design Framework
  • Mobile Technologies
  • Personalized
  • Self-Regulation

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