Building an emotion regulation recommender algorithm for socially anxious individuals using contextual bandits.

Autor: Beltzer ML; Department of Psychology, University of Virginia, Charlottesville, Virginia, USA., Ameko MK; Department of Engineering Systems and Environment, University of Virginia, Charlottesville, Virginia, USA., Daniel KE; Department of Psychology, University of Virginia, Charlottesville, Virginia, USA., Daros AR; Department of Psychology, University of Virginia, Charlottesville, Virginia, USA., Boukhechba M; Department of Engineering Systems and Environment, University of Virginia, Charlottesville, Virginia, USA., Barnes LE; Department of Engineering Systems and Environment, University of Virginia, Charlottesville, Virginia, USA., Teachman BA; Department of Psychology, University of Virginia, Charlottesville, Virginia, USA.
Jazyk: angličtina
Zdroj: The British journal of clinical psychology [Br J Clin Psychol] 2022 Jan; Vol. 61 Suppl 1, pp. 51-72. Date of Electronic Publication: 2021 Feb 14.
DOI: 10.1111/bjc.12282
Abstrakt: Objectives: Poor emotion regulation (ER) has been implicated in many mental illnesses, including social anxiety disorder. To work towards a scalable, low-cost intervention for improving ER, we developed a novel contextual recommender algorithm for ER strategies.
Design: N = 114 socially anxious participants were prompted via a mobile app up to six times daily for five weeks to report their emotional state, use of 19 different ER strategies (or no strategy), physical location, and social context. Information from passive sensors was also collected.
Methods: Given the large number of ER strategies, we used two different approaches for variable reduction: (1) grouping ER strategies into categories based on a prior meta-analysis, and (2) considering only the ten most frequently used strategies. For each approach, an algorithm that recommends strategies based on one's current context was compared with an algorithm that recommends ER strategies randomly, an algorithm that always recommends cognitive reappraisal, and the person's observed ER strategy use. Contextual bandits were used to predict the effectiveness of the strategies recommended by each policy.
Results: When strategies were grouped into categories, the contextual algorithm was not the best performing policy. However, when the top ten strategies were considered individually, the contextual algorithm outperformed all other policies.
Conclusions: Grouping strategies into categories may obscure differences in their contextual effectiveness. Further, using strategies tailored to context is more effective than using cognitive reappraisal indiscriminately across all contexts. Future directions include deploying the contextual recommender algorithm as part of a just-in-time intervention to assess real-world efficacy.
Practitioner Points: Emotion regulation strategies vary in their effectiveness across different contexts. An algorithm that recommends emotion regulation strategies based on a person's current context may one day be used as an adjunct to treatment to help dysregulated individuals optimize their in-the-moment emotion regulation. Recommending flexible use of emotion regulation strategies across different contexts may be more effective than recommending cognitive reappraisal indiscriminately across all contexts.
(© 2021 The British Psychological Society.)
Databáze: MEDLINE
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