AIMC Topic: Ecological Momentary Assessment
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Assessing the affective quality of soundscape for individuals: Using third-party assessment combined with an artificial intelligence (TPA-AI) model.
The Science of the total environment
Sep 10, 2024
When investigating the relationship between the acoustic environment and human wellbeing, there is a potential problem resulting from data source self-correlation. To address this data source self-correlation problem, we proposed a third-party assess...
Machine learning models for temporally precise lapse prediction in alcohol use disorder.
Journal of psychopathology and clinical science
Aug 22, 2024
We developed three machine learning models that predict hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal precision (i.e., lapses in the next week, next day, and next hour). Model features were based on raw sco...
Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide.
Journal of affective disorders
Aug 12, 2024
BACKGROUND: Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideation that indicate marked suicide risk are critically needed to guide interventions and improve care during care transition periods. Some studies indica...
Temporal prediction of suicidal ideation in an ecological momentary assessment study with recurrent neural networks.
Journal of affective disorders
May 23, 2024
INTRODUCTION: Ecological Momentary Assessment (EMA) holds promise for providing insights into daily life experiences when studying mental health phenomena. However, commonly used mixed-effects linear statistical models do not fully utilize the richne...
Person-specific and pooled prediction models for binge eating, alcohol use and binge drinking in bulimia nervosa and alcohol use disorder.
Psychological medicine
May 22, 2024
BACKGROUND: Machine learning could predict binge behavior and help develop treatments for bulimia nervosa (BN) and alcohol use disorder (AUD). Therefore, this study evaluates person-specific and pooled prediction models for binge eating (BE), alcohol...
Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks.
Administration and policy in mental health
Jan 10, 2024
Social interactions are essential for well-being. Therefore, researchers increasingly attempt to capture an individual's social context to predict well-being, including mood. Different tools are used to measure various aspects of the social context. ...
Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment.
European archives of psychiatry and clinical neuroscience
Sep 16, 2023
Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can dist...
Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments.
Behaviour research and therapy
Dec 11, 2021
Smartphones are capable of passively capturing persons' social interactions, movement patterns, physiological activation, and physical environment. Nevertheless, little research has examined whether momentary anxiety symptoms can be accurately assess...
Personalized machine learning of depressed mood using wearables.
Translational psychiatry
Jun 9, 2021
Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use...
Predicting the first smoking lapse during a quit attempt: A machine learning approach.
Drug and alcohol dependence
Oct 11, 2020
BACKGROUND: Just-in-time adaptive interventions (JITAI) aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Effective smoking cessation JITAI rely on the development of accurate d...
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