Horizon CDT Research Highlights

Research Highlights

Embedding Digital Interventions into Everyday Life: Measuring Mood with Wearable Technology

  Marie Dilworth (2017 cohort)

It is widely reported that mental health disorders are on the rise, and that local and global health services are unable to keep up with demand [1, 2]. Compounding the issue is a lack of data about which treatment options work best, and consequently the provision of different kinds of treatments depends very much on local availability and trial and error [3].

With the increasing prevalence of wearable sensors in everyday life comes an opportunity to measure the physical indicators of mood and to quantify them for the purpose of mental disorder diagnosis, and for monitoring the effectiveness of treatments in a consistent and unobtrusive manner.

This PhD will seek to quantify a person’s mood through the use of wearable sensors and the gathering and interpretation of sensor data about physical motion and movement. For many years the most commonly used method for measuring mood and mental health has been through the use of self-assessment or clinician administered questionnaires. Wearable sensors now offer an opportunity to passively measure physical actions rather than relying on self-reported snapshots of mood.

Physical indicators frequently reported for mental disorders range through fatigue, sleep disturbance, psychomotor agitation and retardation, weight changes, social withdrawal, slumped posture, repetitive tics and restlessness and fidgeting. Wearable sensors offer an opportunity to measure some of these physical symptoms by analysing accelerometer data, GPS data, sleep pattern data and there is strong potential for identifying social interactions and emotional response from body movements and posture.

This potential new ability for the individual to measure mood fluctuations over time could provide the opportunity to understand which life events, activities, interventions and medications are having a positive or negative effect. This level of enhanced self-awareness, if accepted into everyday use, could revolutionise mood management.


  1. Independent Mental Health Taskforce, "The five year forward view for mental health," 2016.
  2. United Nations, Health - united nations sustainable development, (2017).
  3. A. Cipriani, T.A. Furukawa, G. Salanti, A. Chaimani, L.Z. Atkinson, Y. Ogawa, S. Leucht, H.G. Ruhe, E.H. Turner, J.P.T. Higgins, M. Egger, N. Takeshima, Y. Hayasaka, H. Imai, K. Shinohara, A. Tajika, J.P.A. Ioannidis and J.R. Geddes, Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: A systematic review and network meta-analysis, The Lancet.

This author is supported by the Horizon Centre for Doctoral Training at the University of Nottingham (RCUK Grant No. EP/L015463/1) and Nottingham Biomedical Research Centre (Mental Health Theme).