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Ecological momentary facial emotion recognition in psychotic disorders

Published online by Cambridge University Press:  12 January 2021

Colin A. Depp ,
Snigdha Kamarsu Open the ORCID record for Snigdha Kamarsu [Opens in a new window] ,
Tess F. Filip ,
Emma M. Parrish ,
Philip D. Harvey ,
Eric L. Granholm ,
Samantha Chalker ,
Raeanne C. Moore  and
Amy Pinkham
Colin A. Depp*
Affiliation:
University of California San Diego Department of Psychiatry, San Diego, California, USA Veterans Affairs San Diego Healthcare System, San Diego, California, USA
Snigdha Kamarsu
Affiliation:
University of California San Diego Department of Psychiatry, San Diego, California, USA
Tess F. Filip
Affiliation:
University of California San Diego Department of Psychiatry, San Diego, California, USA
Emma M. Parrish
Affiliation:
University of California San Diego Department of Psychiatry, San Diego, California, USA San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California, USA
Philip D. Harvey
Affiliation:
University of Miami Miller School of Medicine, Miami, FL, USA Research Service, Bruce W. Carter VA Medical Center, Miami, FL, USA
Eric L. Granholm
Affiliation:
University of California San Diego Department of Psychiatry, San Diego, California, USA Veterans Affairs San Diego Healthcare System, San Diego, California, USA
Samantha Chalker
Affiliation:
Veterans Affairs San Diego Healthcare System, San Diego, California, USA
Raeanne C. Moore
Affiliation:
University of California San Diego Department of Psychiatry, San Diego, California, USA
Amy Pinkham
Affiliation:
The University of Texas at Dallas, Dallas, TX, USA
*
Author for correspondence: Colin Depp, E-mail: [email protected]
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Abstract

Background

Cognitive tasks delivered during ecological momentary assessment (EMA) may elucidate the short-term dynamics and contextual influences on cognition and judgements of performance. This paper provides initial validation of a smartphone task of facial emotion recognition in serious mental illness.

Methods

A total of 86 participants with psychotic disorders (non-affective and affective psychosis), aged 19–65, were administered in-lab ‘gold standard’ affect recognition, neurocognition, and symptom assessments. They subsequently completed 10 days of the mobile facial emotion recognition task, assessing both accuracy and self-assessed performance, along with concurrent EMA of psychotic symptoms and mood. Validation focused on task adherence and predictors of adherence, gold standard convergent validity, and symptom and diagnostic group variation.

Results

The mean rate of adherence to the task was 79%; no demographic or clinical variables predicted adherence. Convergent validity was observed with in-lab measures of facial emotion recognition, and no practice effects were observed on the mobile facial emotion recognition task. EMA reports of more severe voices, sadness, and paranoia were associated with worse performance, whereas mood more strongly associated with self-assessed performance.

Conclusion

The mobile facial emotion recognition task was tolerated and demonstrated convergent validity with in-lab measures of the same construct. Social cognitive performance, and biased judgements previously shown to predict function, can be evaluated in real-time in naturalistic environments.

Keywords

Mobile technologypsychometric assessmentpsychosissocial cognitiontime series analysis
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 13 , October 2022 , pp. 2531 - 2539
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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