ArticleInternational journal of methods in psychiatric research2016
Behavioral activities collected through smartphones and the association with illness activity in bipolar disorder.
Article in International journal of methods in psychiatric research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 5 of them syntheses that pooled it.
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The trial behind it
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Who cites it
57 citing papers in PubMed, 5 syntheses or guidelines pooled it, 143 citations in OpenAlex.
- Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Digital phenotyping for mental health conditions: a systematic review of implementation and application.Frontiers in digital health · 2026Pooled it
- Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review.Journal of medical Internet research · 2017Pooled it
- Smartphone-Based Self-Assessment of Stress in Healthy Adult Individuals: A Systematic Review.Journal of medical Internet research · 2017Pooled it
- Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial.JMIR mHealth and uHealth · 2024Trial
- Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach.JMIR mHealth and uHealth · 2020Trial
- Trial
- Smartphone-Based Digital Phenotyping Across Health Conditions: Scoping Review.Journal of medical Internet research · 2026Article
- A systematic exploration of digital biomarkers for the detection of depressive episodes in bipolar disorder.Npj mental health research · 2026Article
- Performance of active and passive ambulatory assessment measures and mood monitoring in bipolar disorder: a systematic review.International journal of bipolar disorders · 2026Review
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- Mood states recognition based on Mandarin speech and deep learning in patients with bipolar disorder.BMC psychiatry · 2025Article
- Passive sensing of anhedonia and amotivation in a transdiagnostic sample.Journal of psychopathology and clinical science · 2025Article
- Circadian rhythm parameters differentiate euthymic, manic and depressive mood states in bipolar disorders - an explorative pilot study.International journal of bipolar disorders · 2025Article
- Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study.JMIR medical informatics · 2025Article
- Use of a Wearable Self-Tracking Instrument by Refugees With Complex Posttraumatic Stress Disorder: A Qualitative Study of Psychotherapeutic Mediation and Engagement.JMIR formative research · 2025Article
- Screen time and manic symptoms in early adolescents: prospective findings from the Adolescent Brain Cognitive Development Study.Social psychiatry and psychiatric epidemiology · 2025Article
- MoodMon: novel optimization of bipolar disorder monitoring through patient-driven voice parameter submission and AI technology.Postepy psychiatrii neurologii · 2024Article
- Sleep and circadian disruption in bipolar disorders: From psychopathology to digital phenotyping in clinical practice.Psychiatry and clinical neurosciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smartphones are useful in symptom-monitoring in bipolar disorder (BD). Objective smartphone data reflecting illness activity could facilitate early treatment and act as outcome in efficacy trials. A total of 29 patients with BD presenting with moderate to severe levels of depressive and manic symptoms used a smartphone-based self-monitoring system during 12 weeks. Objective smartphone data on behavioral activities were collected. Symptoms were clinically assessed every second week using the Hamilton Depression Rating Scale and the Young Mania Rating Scale. Objective smartphone data correlated with symptom severity. The more severe the depressive symptoms (1) the longer the smartphone's screen was "on"/day, (2) more received incoming calls/day, (3) fewer outgoing calls/day were made, (4) less answered incoming calls/day, (5) the patients moved less between cell towers IDs/day. Conversely, the more severe the manic symptoms (1) more outgoing text messages/day sent, (2) the phone calls/day were longer, (3) the fewer number of characters in incoming text messages/day, (4) the lower duration of outgoing calls/day, (5) the patients moved more between cell towers IDs/day. Further, objective smartphone data were able to discriminate between affective states. Objective smartphone data reflect illness severity, discriminates between affective states in BD and may facilitate the cooperation between patient and clinician. Copyright © 2016 John Wiley & Sons, Ltd.
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