19 May 2020
Remote symptom monitoring using wearable technology: the relationship between mood, cognition and physical activity in depression
Capturing daily fluctuations in mood and cognition can help to more accurately monitor treatment response - but is this feasible for patients with depression?
In research presented at CNS2020, we examined the feasibility of high-frequency cognitive assessments, and their relationship with daily measures of depressive symptoms and physical activity.
- Cognitive symptoms are often under-recognised in depression, but can severely impact patients’ clinical symptoms, quality of life and risk of relapse
- Fluctuations of cognitive ability and depressive symptoms are interrelated processes requiring a higher fidelity and frequency of measurement
- Here we demonstrate the feasibility of remotely collecting cognitive data in individuals living with depression, as well as the relationship of these high-frequency cognitive assessments with the remote monitoring of depressive symptoms and physical activity
Using an apple watch, thirty adults with mild-to-moderate depression conducted brief, daily assessments of:
- Cognition: N-back brief cognitive assessments (2-Back / ~30sec).
- Mood: Self-reported
- Activity: Daily total step count extracted
- Daily cognitive tests and mood questionnaires showed good correspondence with widely used and validated measures
- Adherence was excellent with participants completing mood questionnaires on 94% of days and cognitive assessments on 96% of days
Covariation between mood, cognition and activity
- We observed covariation of Daily Mood & d-prime (p=0.01) and covariation of Daily Mood & Step Count (p=0.0001)
- Lagged analysis showed that an increased step count was associated with better mood on the following day (p=0.05)
- These data support the feasibility of deploying remote symptom monitoring techniques via wearable technology in psychiatric populations, such as depression
- This work establishes methods for synthesizing high-frequency cognitive data, brief mood and biometric data in order to create sensitive digital profiles of an individual’s clinical symptoms