Research Article
Issue Date: January/February 2021
Published Online: December 07, 2020
Updated: June 16, 2021
Development of the CAT–FER: A Computerized Adaptive Test of Facial Emotion Recognition for Adults With Schizophrenia
Author Affiliations
  • Shih-Chieh Lee, PhD, is Postdoctoral Researcher, School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.
  • Gong-Hong Lin, PhD, is Assistant Professor, Master Program in Long-Term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan. At the time this article was submitted, Lin was Postdoctoral Researcher, School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.
  • Chen-Chung Liu, MD, PhD, is Psychiatrist, Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan, and Associate Professor, Department of Psychiatry, College of Medicine, National Taiwan University, Taipei, Taiwan.
  • En-Chi Chiu, PhD, is Associate Professor, Department of Long-Term Care, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan; enchichiu@ntunhs.edu.tw
  • Ching-Lin Hsieh, PhD, is Professor, School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan; Occupational Therapist, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan; and Adjunct Professor, Department of Occupational Therapy, College of Medical and Health Science, Asia University, Taichung, Taiwan; clhsieh@ntu.edu.tw
Article Information
Mental Health / Research Articles
Research Article   |   December 07, 2020
Development of the CAT–FER: A Computerized Adaptive Test of Facial Emotion Recognition for Adults With Schizophrenia
American Journal of Occupational Therapy, December 2020, Vol. 75, 7501205140. https://doi.org/10.5014/ajot.2020.043463
American Journal of Occupational Therapy, December 2020, Vol. 75, 7501205140. https://doi.org/10.5014/ajot.2020.043463
Abstract

Importance: The most frequently used measures of facial emotion recognition (FER) are insufficiently comprehensive, reliable, valid, and efficient; moreover, the impact of gender on scoring has not been controlled.

Objective: To develop a computerized adaptive test of FER for adults with schizophrenia.

Design: First, we selected photographs from a published database. Second, items that fitted well to a Rasch model were used to form the item bank. Third and last, we determined the best administration mode for prospective users to achieve both high reliability and efficiency.

Setting: Psychiatric hospitals and the community.

Participants: Adults living with schizophrenia (n = 351) and adults without diagnosed mental illness (n = 101).

Results: After removal of misfit items (infit or outfit ≥1.4), the remaining 165 items were selected to form an item bank. Among them, 39 showed severe gender bias, so the item difficulties were adjusted accordingly. On the basis of the item bank, two administration modes were recommended for prospective users. The reliable mode required approximately 128 items (nearly 20 min) to achieve reliability (.72–.81), similar to that of the entire item bank. The efficient mode required approximately 73 items (approximate 11 min) to provide acceptable reliability (.69–.73) for the seven domain scores.

Conclusions and Relevance: Our newly developed measure provides comprehensive, valid, and unbiased (to examinees’ gender) assessments of FER in adults living with schizophrenia. In addition, the administration modes can be flexibly changed to optimize the reliability or efficiency for prospective users.

What This Article Adds: This newly developed FER measure can help occupational therapists identify deficits in recognizing specific basic emotions and plan corresponding interventions to manage the impact on their clients’ social functions.