Facing Change: Using Automated Facial Expression Analysis to Examine Emotional Flexibility in the Treatment of Depression.
Computerized measures
Emotional flexibility
Facial expression
Process-outcome research
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:
25 Oct 2023
25 Oct 2023
Historique:
accepted:
02
10
2023
medline:
26
10
2023
pubmed:
26
10
2023
entrez:
25
10
2023
Statut:
aheadofprint
Résumé
Depression involves deficits in emotional flexibility. To date, the varied and dynamic nature of emotional processes during therapy has mostly been measured at discrete time intervals using clients' subjective reports. Because emotions tend to fluctuate and change from moment to moment, the understanding of emotional processes in the treatment of depression depends to a great extent on the existence of sensitive, continuous, and objectively codified measures of emotional expression. In this observational study, we used computerized measures to analyze high-resolution time-series facial expression data as well as self-reports to examine the association between emotional flexibility and depressive symptoms at the client as well as at the session levels. Video recordings from 283 therapy sessions of 58 clients who underwent 16 sessions of manualized psychodynamic psychotherapy for depression were analyzed. Data was collected as part of routine practice in a university clinic that provides treatments to the community. Emotional flexibility was measured in each session using an automated facial expression emotion recognition system. The clients' depression level was assessed at the beginning of each session using the Beck Depression Inventory-II (Beck et al., 1996). Higher emotional flexibility was associated with lower depressive symptoms at the treatment as well as at the session levels. These findings highlight the centrality of emotional flexibility both as a trait-like as well as a state-like characteristic of depression. The results also demonstrate the usefulness of computerized measures to capture key emotional processes in the treatment of depression at a high scale and specificity.
Identifiants
pubmed: 37880472
doi: 10.1007/s10488-023-01310-w
pii: 10.1007/s10488-023-01310-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Israel Science Foundation
ID : ISF #2466/21
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Baldwin, S. A., Imel, Z. E., Braithwaite, S. R., & Atkins, D. C. (2014). Analyzing multiple outcomes in clinical research using multivariate multilevel models. Journal of Consulting and Clinical Psychology, 82(5), 920.
doi: 10.1037/a0035628
pubmed: 24491071
pmcid: 4119868
Bar-Kalifa, E., & Atzil-Slonim, D. (2020). Intrapersonal and interpersonal emotional networks and their associations with treatment outcome. Journal of Counseling Psychology, 67(5), 580.
doi: 10.1037/cou0000415
pubmed: 32039610
Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck depression inventory.
Bonanno, G. A., Papa, A., Lalande, K., Westphal, M., & Coifman, K. (2004). The importance of being flexible: The ability to both enhance and suppress emotional expression predicts long-term adjustment. Psychological Science, 15(7), 482–487.
doi: 10.1111/j.0956-7976.2004.00705.x
pubmed: 15200633
Brose, A., Schmiedek, F., Koval, P., & Kuppens, P. (2015). Emotional inertia contributes to depressive symptoms beyond perseverative thinking. Cognition and Emotion, 29(3), 527–538.
doi: 10.1080/02699931.2014.916252
pubmed: 24820350
Cohn, J. F., Ambadar, Z., & Ekman, P. (2007). Observer-based measurement of facial expression with the Facial Action Coding System. Handbook of emotion elicitation and assessment (pp. 203–221). Oxford University Press.
Cohn, J. F., & De la Torre, F. (2015). Automated face analysis for affective computing
Coifman, K. G., & Summers, C. B. (2019). Understanding emotion inflexibility in risk for affective disease: Integrating current research and finding a path forward. Frontiers in Psychology, 10, 392. https://doi.org/10.3389/fpsyg.2019.00392 .
Ekman, P., & Friesen, W. V. (1976). Measuring facial movement. Environmental Psychology and Nonverbal Behavior, 1(1), 56–75. https://doi.org/10.1007/BF01115465 .
doi: 10.1007/BF01115465
Fosha, D. (2001). The dyadic regulation of affect. Journal of Clinical Psychology, 57(2), 227–242.
doi: 10.1002/1097-4679(200102)57:2<227::AID-JCLP8>3.0.CO;2-1
pubmed: 11180149
Freitas-Magalhães, A. (2012). Microexpression and macroexpression. Encyclopedia of Human Behavior, 2, 173–183.
doi: 10.1016/B978-0-12-375000-6.00387-6
Gavrilescu, M., & Vizireanu, N. (2019). Predicting depression, anxiety, and stress levels from videos using the facial action coding system. Sensors (Basel, Switzerland), 19(17), 3693.
doi: 10.3390/s19173693
pubmed: 31450687
Girard, J. M., Cohn, J. F., Mahoor, M. H., Mavadati, S., & Rosenwald, D. P. (2013). Social risk and depression: Evidence from manual and automatic facial expression analysis. 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 1–8. https://doi.org/10.1109/FG.2013.6553748 .
Greenberg, L. S. (2012). Emotions, the great captains of our lives: Their role in the process of change in psychotherapy. American Psychologist, 67(8), 697.
doi: 10.1037/a0029858
pubmed: 23163464
Gupta, S., & Bonanno, G. A. (2011). Complicated grief and deficits in emotional expressive flexibility. Journal of Abnormal Psychology, 120(3), 635.
doi: 10.1037/a0023541
pubmed: 21766973
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology Neurosurgery and Psychiatry, 23(1), 56.
doi: 10.1136/jnnp.23.1.56
pubmed: 14399272
pmcid: 495331
Harati, S., Crowell, A., Huang, Y., Mayberg, H., & Nemati, S. (2019). Classifying depression severity in recovery from major depressive disorder via dynamic facial features. IEEE Journal of Biomedical and Health Informatics, 24(3), 815–824.
doi: 10.1109/JBHI.2019.2930604
pubmed: 31352356
Herrmann, I. R., Greenberg, L. S., & Auszra, L. (2016). Emotion categories and patterns of change in experiential therapy for depression. Psychotherapy Research, 26(2), 178–195.
doi: 10.1080/10503307.2014.958597
pubmed: 25265453
Höfling, T. T. A., Gerdes, A. B. M., Föhl, U., & Alpers, G. W. (2020). Read my face: Automatic facial coding versus psychophysiological indicators of emotional valence and arousal. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.01388 . https://www.frontiersin.org/article/
Imel, Z. E., Caperton, D. D., Tanana, M., & Atkins, D. C. (2017). Technology-enhanced human interaction in psychotherapy. Journal of Counseling Psychology, 64(4), 385.
doi: 10.1037/cou0000213
pubmed: 28318277
pmcid: 5491349
Kashdan, T. B., & Rottenberg, J. (2010). Psychological flexibility as a fundamental aspect of health. Clinical Psychology Review, 30(7), 865–878. https://doi.org/10.1016/j.cpr.2010.03.001 .
doi: 10.1016/j.cpr.2010.03.001
pubmed: 21151705
pmcid: 2998793
Koval, P., Kuppens, P., Allen, N. B., & Sheeber, L. (2012). Getting stuck in depression: The roles of rumination and emotional inertia. Cognition and Emotion, 26(8), 1412–1427. https://doi.org/10.1080/02699931.2012.667392 .
doi: 10.1080/02699931.2012.667392
pubmed: 22671768
Kramer, U., Pascual-Leone, A., Despland, J. N., & de Roten, Y. (2015). One minute of grief: Emotional processing in short-term dynamic psychotherapy for adjustment disorder. Journal of Consulting and Clinical Psychology, 83(1), 187. https://doi.org/10.1037/a0037979 .
Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010). Emotional inertia and psychological maladjustment. Psychological Science, 21(7), 984–991. https://doi.org/10.1177/0956797610372634 .
doi: 10.1177/0956797610372634
pubmed: 20501521
Lewinski, P., Uyl, T., & Butler, C. (2014). Automated facial coding: Validation of Basic Emotions and FACS AUs in FaceReader. Journal of Neuroscience Psychology and Economics, 7, 227–236. https://doi.org/10.1037/npe0000028 .
doi: 10.1037/npe0000028
Luborsky, L., & Mark, D. (1991). Short-term supportive-expressive psychoanalytic psychotherapy. In Handbook of Short-Term Dynamic Psychotherapy, 110–136.
Luborsky, L., Mark, D., Hole, A. V., Popp, C., Goldsmith, B., & Cacciola, J. (1995). Supportive-expressive dynamic psychotherapy of depression: A time-limited version.
McCullough, L., & Magill, M. (2009). Affect-focused short-term dynamic therapy. In Handbook of evidence-based psychodynamic psychotherapy (pp. 249–277). Springer.
Nasser, S. A., Hashim, I. A., & Ali, W. H. (2020). A review on depression detection and diagnoses based on visual facial cues. 2020 3rd International Conference on Engineering Technology and Its Applications (IICETA), 35–40.
Noldus (2022). Facial expression analysis—FaceReader. Facial Expression Analysis - FaceReader. https://www.noldus.com/facereader/facial-expression-analysis .
Ogbaselase, F. A., Mancini, K. J., & Luebbe, A. M. (2020). Indirect effect of family climate on adolescent depression through emotion regulatory processes. Emotion.
Pampouchidou, A., Simos, P. G., Marias, K., Meriaudeau, F., Yang, F., Pediaditis, M., & Tsiknakis, M. (2017). Automatic assessment of depression based on visual cues: A systematic review. IEEE Transactions on Affective Computing, 10(4), 445–470.
doi: 10.1109/TAFFC.2017.2724035
Pascual-Leone, A., & Greenberg, L. S. (2007). Emotional processing in experiential therapy: Why the only way out is through. Journal of Consulting and Clinical Psychology, 75(6), 875.
doi: 10.1037/0022-006X.75.6.875
pubmed: 18085905
Pe, M. L., Kircanski, K., Thompson, R. J., Bringmann, L. F., Tuerlinckx, F., Mestdagh, M., Mata, J., Jaeggi, S. M., Buschkuehl, M., & Jonides, J. (2015). Emotion-network density in major depressive disorder. Clinical Psychological Science, 3(2), 292–300.
doi: 10.1177/2167702614540645
Pos, A. E., Greenberg, L. S., & Warwar, S. H. (2009). Testing a model of change in the experiential treatment of depression. Journal of Consulting and Clinical Psychology, 77(6), 1055.
doi: 10.1037/a0017059
pubmed: 19968382
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). sage.
Rottenberg, J. (2017). Emotions in depression: What do we really know. Annual Review of Clinical Psychology, 13(1), 241–263.
doi: 10.1146/annurev-clinpsy-032816-045252
pubmed: 28375721
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161.
Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., Hergueta, T., Baker, R., & Dunbar, G. C. (1998). The mini-international neuropsychiatric interview (MINI): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59(20), 22–33.
pubmed: 9881538
Skiendziel, T., Rösch, A. G., & Schultheiss, O. C. (2019). Assessing the convergent validity between the automated emotion recognition software Noldus FaceReader 7 and Facial Action Coding System Scoring. PloS One, 14(10), e0223905.
Subica, A. M., Fowler, J. C., Elhai, J. D., Frueh, B. C., Sharp, C., Kelly, E. L., & Allen, J. G. (2014). Factor structure and diagnostic validity of the Beck Depression Inventory–II with adult clinical inpatients: Comparison to a gold-standard diagnostic interview. Psychological Assessment, 26(4), 1106.
doi: 10.1037/a0036998
pubmed: 24932646
Terhürne, P., Schwartz, B., Baur, T., Schiller, D., Eberhardt, S. T., André, E., & Lutz, W. (2022). Validation and application of the non-verbal behavior analyzer: An automated tool to assess non-verbal emotional expressions in psychotherapy. Frontiers in Psychiatry, 13, 1026015. https://doi.org/10.3389/fpsyt.2022.1026015
doi: 10.3389/fpsyt.2022.1026015
pubmed: 36386975
pmcid: 9650367
Wang, Q., Yang, H., & Yu, Y. (2018). Facial expression video analysis for depression detection in chinese patients. Journal of Visual Communication and Image Representation, 57, 228–233. https://doi.org/10.1016/j.jvcir.2018.11.003 .
doi: 10.1016/j.jvcir.2018.11.003
Wichers, M., Wigman, J. T. W., & Myin-Germeys, I. (2015). Micro-level affect dynamics in psychopathology viewed from complex dynamical system theory. Emotion Review, 7(4), 362–367.
doi: 10.1177/1754073915590623
Williams, J. B. (1988). A structured interview guide for the Hamilton Depression Rating Scale. Archives of General Psychiatry, 45(8), 742–747.
doi: 10.1001/archpsyc.1988.01800320058007
pubmed: 3395203
Zilcha-Mano, S. (2021). Toward personalized psychotherapy: The importance of the trait-like/state-like distinction for understanding therapeutic change. American Psychologist, 76(3), 516.
doi: 10.1037/amp0000629
pubmed: 32658495