ArticleFrontiers in psychiatry2022
Dynamic Tracking of State Anxiety
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Wearable ECG and PPG for anxiety detection: a translational digital medicine perspective.NPJ digital medicine · 2026Article
- A multimodal spatiotemporal convolutional network with attention mechanism for athlete anxiety behavior recognition.Scientific reports · 2026Article
- Article
- A classification system based on improved global exploration and convergence to examine student psychological fitness.Scientific reports · 2024Article
- Manipulating reinforcement probability changes the temporal overestimation of anticipated aversive stimuli.Psychological research · 2024Article
- Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions.Frontiers in psychiatry · 2024Article
- Enhancing the conversational agent with an emotional support system for mental health digital therapeutics.Frontiers in psychiatry · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anxiety induction is widely used in the investigations of the mechanism and treatment of state anxiety. State anxiety is accompanied by immediate psychological and physiological responses. However, the existing state anxiety measurement, such as the commonly used state anxiety subscale of the State-Trait Anxiety Inventory, mainly relies on questionnaires with low temporal resolution. This study aims to develop a tracking model of state anxiety with high temporal resolution. To capture the dynamic changes of state anxiety levels, we induced the participants' state anxiety through exposure to aversive pictures or the risk of electric shocks and simultaneously recorded multi-modal data, including dimensional emotion ratings, electrocardiogram, and galvanic skin response. Using the paired self-reported state anxiety levels and multi-modal measures, we trained and validated machine learning models to predict state anxiety based on psychological and physiological features extracted from the multi-modal data. The prediction model achieved a high correlation between the predicted and self-reported state anxiety levels. This quantitative model provides fine-grained and sensitive measures of state anxiety levels for future affective brain-computer interaction and anxiety modulation studies.
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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 Socratic graph.