Electronic monitoring of self-reported mood: the return of the subjective?

Abstract

This narrative review describes recent developments in the use of technology for utilizing the self-monitoring of mood, provides some relevant background, and suggests some promising directions. Subjective experience of mood is one of the valuable sources of information about the state of an integrated mind/brain system. During the past century, psychiatry and psychology moved away from subjectivity, emphasizing external observation, precise measurement, and laboratory techniques. This shift, however, provided only a limited improvement in the understanding of mood disorders, and it appears that self-monitoring of mood has the potential to enrich our knowledge, particularly when combined with the advances in technology. Modern technology, with its ability to transfer information from the individual directly to the researcher via electronic applications, enables us now to study mood regulation more thoroughly. Frequent subjective ratings can be helpful in identifying individualized treatment with effective mood stabilizers and recognizing subtypes of mood disorders. The variability of subjective ratings may also help us estimate the increased risk of recurrence and guide a tailored treatment.

Large-scale roll out of electronic longitudinal mood-monitoring for research in affective disorders: Report from the UK bipolar disorder research network

“…Such methods pose a significant risk of unreliable and biased recall and are thus limited in their ability to accurately identify long-term patterns of mood variability. The emergence of new technologies has led to vast improvements in the methods used for capturing real-time mood data and a number of studies have reported on the feasibility of technology-driven mood monitoring in clinical samples of individuals with affective disorders (Ortiz and Grof, 2016).…”

iHealth: The ethics of artificial intelligence and big data in mental healthcare

“…active collection of data by the user ( Bartels et al, 2019 ), is an established method in mental healthcare. It provides data that allows to study mood regulation, predict the onset or episodes of a disorder, or select the best-suited mood stabilizer for the patient ( Ortiz and Grof, 2016 ). A consistent, valid, and timely self-monitoring is also considered crucial for effective self-management, self-insight, and initiation of behavior change ( Bakker and Rickard, 2018 ; Bartels et al, 2019 ; van Os et al, 2017 ).…”

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

“…However, responsiveness to EMAs decreased after the first 2 weeks. Studies have shown that user-friendly interfaces and directly useful features such as allowing the data to be viewable to participants and increasing their self-awareness and tracking their progress can increase engagement with EMAs [ 56 , 57 ]. Creating a sustainable system that incorporates the collection of perceived mental health status will require directly providing more value to users.…”