Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks.
Individualized machine learning models
Random forest
Shapley values
Social behavior
Social interactions
Time scales
Journal
Administration and policy in mental health
ISSN: 1573-3289
Titre abrégé: Adm Policy Ment Health
Pays: United States
ID NLM: 8914574
Informations de publication
Date de publication:
10 Jan 2024
10 Jan 2024
Historique:
accepted:
22
11
2023
medline:
11
1
2024
pubmed:
11
1
2024
entrez:
10
1
2024
Statut:
aheadofprint
Résumé
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. Digital phenotyping is a commonly used technology to assess a person's social behavior objectively. The experience sampling method (ESM) can capture the subjective perception of specific interactions. Lastly, egocentric networks are often used to measure specific relationship characteristics. These different methods capture different aspects of the social context over different time scales that are related to well-being, and combining them may be necessary to improve the prediction of well-being. Yet, they have rarely been combined in previous research. To address this gap, our study investigates the predictive accuracy of mood based on the social context. We collected intensive within-person data from multiple passive and self-report sources over a 28-day period in a student sample (Participants: N = 11, ESM measures: N = 1313). We trained individualized random forest machine learning models, using different predictors included in each model summarized over different time scales. Our findings revealed that even when combining social interactions data using different methods, predictive accuracy of mood remained low. The average coefficient of determination over all participants was 0.06 for positive and negative affect and ranged from - 0.08 to 0.3, indicating a large amount of variance across people. Furthermore, the optimal set of predictors varied across participants; however, predicting mood using all predictors generally yielded the best predictions. While combining different predictors improved predictive accuracy of mood for most participants, our study highlights the need for further work using larger and more diverse samples to enhance the clinical utility of these predictive modeling approaches.
Identifiants
pubmed: 38200262
doi: 10.1007/s10488-023-01328-0
pii: 10.1007/s10488-023-01328-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ID : VI.Vidi.201.119
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ID : NWO-Veni 191G.037
Informations de copyright
© 2024. The Author(s).
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