Home > Research > Publications & Outputs > Patient acceptance of self-monitoring on smartw...

Electronic data


Text available via DOI:

View graph of relations

Patient acceptance of self-monitoring on smartwatch in a routine digital therapy: A mixed-methods study

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Camille Nadal
  • Caroline Earley
  • Angel Enrique
  • Corina Sas
  • Derek Richards
  • Gavin Doherty
Article number3
<mark>Journal publication date</mark>26/08/2023
<mark>Journal</mark>ACM Transactions on Computer-Human Interaction (TOCHI)
Issue number1
Pages (from-to)1-50
Publication StatusPublished
Early online date26/08/23
<mark>Original language</mark>English


Self-monitoring of mood and lifestyle habits is the cornerstone of many therapies, but it is still hindered by persistent issues including inaccurate records, gaps in the monitoring, patient burden, and perceived stigma. Smartwatches have potential to deliver enhanced self-reports, but their acceptance in clinical mental health settings is unexplored and rendered difficult by a complex theoretical landscape and need for a longitudinal perspective. We present the Mood Monitor smartwatch application for mood and lifestyle habits self-monitoring. We investigated patient acceptance of the app within a routine 8-week digital therapy. We recruited 35 patients of the UK’s National Health Service and evaluated their acceptance through three online questionnaires and a post-study interview. We assessed the clinical feasibility of the Mood Monitor by comparing clinical, usage, and acceptance metrics obtained from the 35 patients with smartwatch with those from an additional 34 patients without smartwatch (digital treatment as usual). Findings showed that the smartwatch app was highly accepted by patients, revealed which factors facilitated and impeded this acceptance, and supported clinical feasibility. We provide guidelines for the design of self-monitoring on smartwatch and reflect on the conduct of HCI research evaluating user acceptance of mental health technologies.