ReviewInternational journal of bipolar disorders2016
Electronic monitoring of self-reported mood: the return of the subjective?
Review in International journal of bipolar disorders, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Probiotics reduce negative mood over time: the value of daily self-reports in detecting effects.Npj mental health research · 2025Article
- Emotion Forecasting: A Transformer-Based Approach.Journal of medical Internet research · 2025Article
- KIOS: A smartphone app for self-monitoring for patients with bipolar disorder.Bipolar disorders · 2024Article
- Identifying Current Feelings of Mild and Moderate to High Depression in Young, Healthy Individuals Using Gait and Balance: An Exploratory Study.Sensors (Basel, Switzerland) · 2023Article
- Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models.International journal of bipolar disorders · 2023Article
- Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation.JMIR mHealth and uHealth · 2022Article
- iHealth: The ethics of artificial intelligence and big data in mental healthcare.Internet interventions · 2022Article
- Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users.Entropy (Basel, Switzerland) · 2021Article
- Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach.JMIR mHealth and uHealth · 2021Article
- Digital health developments and drawbacks: a review and analysis of top-returned apps for bipolar disorder.International journal of bipolar disorders · 2020Article
- Mobile apps for mood tracking: an analysis of features and user reviews.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.