ArticleNPJ digital medicine2024
Personalized mood prediction from patterns of behavior collected with smartphones.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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Who cites it
19 citing papers in PubMed, 22 citations in OpenAlex.
- Individual-Level Modeling of Depressive Symptom Severity Using Smartphone and Wearable Data: Time-Aware 1-Year Study With Feature-Group Contributions.JMIR formative research · 2026Article
- Fine-tuning large language models for detecting negative self-referential language in smartphone text among youth with depression.Journal of psychopathology and clinical science · 2026Article
- Comparing training window selection methods for prediction in non-stationary time series.The British journal of mathematical and statistical psychology · 2026Article
- Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.Pharmacopsychiatry · 2026Review
- Interpretable depressive symptoms screening via statistical reasoning-augmented large language models using wearable and environmental data.Scientific reports · 2026Article
- Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping.Scientific reports · 2026Article
- Can Psychiatric Genetics Advance Without Incorporating a Life Course Perspective?Biological psychiatry · 2026Review
- Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study.JMIR mental health · 2026Article
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Comparing personalized and population-based models for predicting momentary negative affect in internalizing disorders: A digital phenotyping study.Neuroscience applied · 2026Article
- From Biomarkers to Behavior: Mapping the Neuroimmune Web of Pain, Mood, and Memory.Biomedicines · 2025Article
- Practical AI application in psychiatry: historical review and future directions.Molecular psychiatry · 2025Review
- Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recognition: Protocol for a Prospective Cohort Study.JMIR research protocols · 2025Article
- Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research.Molecular psychiatry · 2025Review
- Comparing self reported and physiological sleep quality from consumer devices to depression and neurocognitive performance.NPJ digital medicine · 2025Article
- Developing interactive VR-based digital therapeutics for Acceptance and Commitment Therapy (ACT): a structured framework for the digital transformation integrating gamification and multimodal arts.Frontiers in psychiatry · 2025Article
- The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review.Frontiers in digital health · 2025Review
- Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.Journal of medical Internet research · 2024Article
- No prediction without prevention: A global qualitative study of attitudes toward using a prediction tool for risk of developing depression during adolescence.Global mental health (Cambridge, England) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 2 institutions in 1 country.
Funding
Abstract
Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R
Identifiers
What OpenQuestion holds
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.