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Implementing Physiology in Clinical Assessments of Adult Social Anxiety: A Method for Graphically Representing Physiological Arousal to Facilitate Clinical Decision-Making

  • Emily Justine Dunn
  • Amelia Aldao
  • Andres De Los Reyes
Article

Abstract

Low-cost methods exist for taking in vivo assessments of patients’ physiology in response to clinically relevant stimuli. Paradigms that allow assessors without a background in physiology to interpret physiological data might facilitate integrating physiology into clinical decision-making. Having assessors judge graphical depictions of physiological data may allow them to detect data patterns that might go unnoticed if such judgments were based on numerical depictions of physiological data. One method—Chernoff Faces—involves graphically representing data using features on the human face (eyes, nose, mouth, face width); a method that capitalizes on humans’ abilities to detect even subtle variations among facial features. Using adult heart rate (HR) norms and Chernoff Faces, we instructed three naïve coders to make judgments about 240 undergraduate participants’ HR in response to emotionally evocative stimuli (i.e., film clips of disgust vs. craving stimuli). We assessed participants’ arousal with wireless, wristwatch HR monitors, and using Chernoff Faces we graphically represented participants’ HR data as well as normative HR values. For each participant, coders compared features of two Chernoff Faces: (a) participant’s HR within laboratory contexts (resting baseline, film clip) and (b) gender-matched normative HR values. Coders reliably and accurately identified elevations in participants’ arousal relative to normative arousal data. Further, participants’ self-reported social anxiety interacted with Chernoff Face judgments, in that participants’ arousal decreased from baseline to film clip exposure, but only for those who self-reported relatively high social anxiety. This study has important implications for implementing physiology to improve decision-making when clinically assessing adult social anxiety.

Keywords

Chernoff Face Emotional reactivity Psychophysiology Social anxiety 

Notes

Acknowledgments

The authors would like to thank Tara M. Augenstein for her assistance in applying the Chernoff Face paradigm to the physiological data and stimuli used for this study.

Conflict of Interest

The authors have no conflicts of interest to report.

Experiment Participants

The study reported in this article involved human participants, and as such we obtained approval for administration of study protocols from the Internal Review Board of the large Midwest University at which we conducted the study. We obtained informed consent from all participants before administration of study protocols.

References

  1. Achenbach, T. M., Krukowski, R. A., Dumenci, L., & Ivanova, M. Y. (2005). Assessment of adult psychopathology: meta-analyses and implications of cross-informant correlations. Psychological Bulletin, 131, 361–382. doi: 10.1037/0033-2909.131.3.361.
  2. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park: Sage.Google Scholar
  3. Aldao, A. (2013). The future of emotion regulation research: capturing context. Perspectives on Psychological Science, 8(2), 155–172. doi: 10.1177/1745691612459518.CrossRefPubMedGoogle Scholar
  4. Aldao, A., & De Los Reyes, A. (2015). Commentary: a practical guide for translating basic research on affective science to implementing physiology in clinical child and adolescent assessments. Journal of Clinical Child and Adolescent Psychology, 44(2), 341–351. doi: 10.1080/15374416.2014.895942.
  5. Amir, N., Najmi, S., Bomyea, J., & Burns, M. (2010). Disgust and anger in social anxiety. International Journal of Cognitive Therapy, 3(1), 3–10. doi: 10.1521/ijct.2010.3.1.3.CrossRefGoogle Scholar
  6. Anderson, E. R., & Hope, D. A. (2009). The relationship among social phobia, objective and perceived physiological reactivity, and anxiety sensitivity in an adolescent population. Journal of Anxiety Disorders, 23(1), 18–26. doi: 10.1016/j.janxdis.2008.03.011.PubMedCentralCrossRefPubMedGoogle Scholar
  7. Bögels, S. M., Alden, L., Beidel, D. C., Clark, L. A., Pine, D. S., Stein, M. B., & Voncken, M. (2010). Social anxiety disorder: questions and answers for the DSM-V. Depression and Anxiety, 27(2), 168–189. doi: 10.1002/da.20670.CrossRefPubMedGoogle Scholar
  8. Bouma, E. M. C., Riese, H., Ormel, J., Verhulst, F. C., & Oldehinkel, A. J. (2009). Adolescents’ cortisol responses to awakening and social stress: effects of gender, menstrual phase and oral contraceptives. The TRAILS study. Psychoneuroendocrinology, 34(6), 884–893. doi: 10.1016/j.psyneuen.2009.01.003.CrossRefPubMedGoogle Scholar
  9. Britton, J. C., Taylor, S. F., Berridge, K. C., Mikels, J. A., & Liberzon, I. (2006). Differential subjective and psychophysiological responses to socially and nonsocially generated emotional stimuli. Emotion, 6(1), 150–155. doi: 10.1037/1528-3542.6.1.150.
  10. Chernoff, H. (1973). The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 68(342), 361–368. doi: 10.1080/01621459.1973.10482434.CrossRefGoogle Scholar
  11. Cleveland, W. S., & McGill, R. (1984). Graphical perception: theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531–554. doi: 10.2307/2288400.
  12. Craske, M. G., Kircanski, K., Zelikowsky, M., Mystkowski, J., Chowdhury, N., & Baker, A. (2008). Optimizing inhibitory learning during exposure therapy. Behaviour Research and Therapy, 46(1), 5–27. doi: 10.1016/j.brat.2007.10.003.CrossRefPubMedGoogle Scholar
  13. Davis, T. E., May, A., & Whiting, S. E. (2011). Evidence-based treatment of anxiety and phobia in children and adolescents: current status and effects on the emotional response. Clinical Psychology Review, 31(4), 592–602. doi: 10.1016/j.cpr.2011.01.001.CrossRefPubMedGoogle Scholar
  14. De Los Reyes, A. (2013). Strategic objectives for improving understanding of informant discrepancies in developmental psychopathology research. Development and Psychopathology, 25(3), 669–682. doi: 10.1017/S0954579413000096.CrossRefGoogle Scholar
  15. De Los Reyes, A., & Aldao, A. (2015). Introduction to the special issue. Toward implementing physiological measures in clinical child and adolescent assessments. Journal of Clinical Child and Adolescent Psychology, 44(2), 221–237. doi: 10.1080/15374416.2014.891227.CrossRefGoogle Scholar
  16. De Los Reyes, A., Aldao, A., Thomas, S. A., Daruwala, S., Swan, A. J., Van Wie, M., … Lechner, W. V. (2012). Adolescent self-reports of social anxiety: Can they disagree with objective psychophysiological measures and still be valid? Journal of Psychopathology and Behavioral Assessment, 34(3), 308–322. doi: 10.1007/s10862-012-9289-2.
  17. De Los Reyes, A., Bunnell, B. E., & Beidel, D. C. (2013a). Informant discrepancies in adult social anxiety disorder assessments: links with contextual variations in observed behavior. Journal of Abnormal Psychology, 122(2), 376–386. doi: 10.1037/a0031150.
  18. De Los Reyes, A., Thomas, S. A., Goodman, K. L., & Kundey, S. M. A. (2013b). Principles underlying the use of multiple informants’ reports. Annual Review of Clinical Psychology, 9, 123–149. doi: 10.1146/annurev-clinpsy-050212-185617.CrossRefGoogle Scholar
  19. De Los Reyes, A., Augenstein, T. M., Aldao, A., Thomas, S. A., Daruwala, S., Kline, K., & Regan, T. (2015a). Implementing psychophysiology in clinical assessments of adolescent social anxiety: use of rater judgments based on graphical representations of psychophysiology. Journal of Clinical Child and Adolescent Psychology, 44(2), 264–279. doi: 10.1080/15374416.2013.859080.
  20. De Los Reyes, A., Augenstein, T. M., Wang, M., Thomas, S. A., Drabick, D. A. G., Burgers, D., & Rabinowitz, J. (2015b). The validity of the multi-informant approach to assessing child and adolescent mental health. Psychological Bulletin, 141. Advance online publication. doi: 10.1037/a0038498.
  21. Ellard, K. K., Farchione, T. J., & Barlow, D. H. (2012). Relative effectiveness of emotion induction procedures and the role of personal relevance in a clinical sample: a comparison of film, images, and music. Journal of Psychopathology and Behavioral Assessment, 34(2), 232–243. doi: 10.1007/s10862-011-9271-4.CrossRefGoogle Scholar
  22. Fienberg, S. E. (1979). Graphical methods in statistics. The American Statistician, 33(4), 165–178. doi: 10.1080/00031305.1979.10482688.Google Scholar
  23. Fox, N. A., Schmidt, L. A., Henderson, H. A., & Marshall, P. J. (2007). Developmental psychophysiology: conceptual and methodological perspectives. In J. T. Cacioppo, L. G. Tassinary, & G. G. Bern (Eds.), Handbook of psychophysiology (3rd ed., pp. 453–481). New York: Cambridge University Press.Google Scholar
  24. Ghisletta, P., & Spini, D. (2004). An introduction to generalized estimating equations and an application to assess selectivity effects in a longitudinal study on very old individuals. Journal of Educational and Behavioral Statistics, 29(4), 421–437. doi: 10.3102/10769986029004421.CrossRefGoogle Scholar
  25. Hope, D. A. (2006). Managing social anxiety: a cognitive-behavioral therapy approach therapist guide (1st ed.). USA: Oxford University Press.Google Scholar
  26. Hunsley, J., & Mash, E. J. (2007). Evidence-based assessment. Annual Review of Clinical Psychology, 3, 29–51. doi: 10.1146/annurev.clinpsy.3.022806.091419.CrossRefPubMedGoogle Scholar
  27. Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., … Wang, P. (2010). Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. The American Journal of Psychiatry, 167(7), 748–751. doi: 10.1176/appi.ajp.2010.09091379.
  28. Jarvis, B. G. (2004). MediaLab (Version 2004). New York, NY: Empirisoft Corporation.Google Scholar
  29. Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: a review. Biological Psychology, 84(3), 394–421. doi: 10.1016/j.biopsycho.2010.03.010.CrossRefPubMedGoogle Scholar
  30. Lee, M. D., Butavicius, M. A., & Reilly, R. E. (2003). Visualizations of binary data: a comparative evaluation. International Journal of Human-Computer Studies, 59(5), 569–602. doi: 10.1016/S1071-5819(03)00082-X.
  31. Lipkus, I. M., & Hollands, J. G. (1999). The visual communication of risk. Journal of the National Cancer Institute Monographs, 1999(25), 149–163. Retrieved from http://jncimonographs.oxfordjournals.org/content/1999/25/149.full.pdf+html
  32. Mankus, A. M., Aldao, A., Kerns, C., Mayville, E. W., & Mennin, D. S. (2013). Mindfulness and heart rate variability in individuals with high and low generalized anxiety symptoms. Behaviour Research and Therapy, 51(7), 386–391. doi: 10.1016/j.brat.2013.03.005.CrossRefPubMedGoogle Scholar
  33. Mattick, R. P., & Clarke, J. C. (1998). Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behaviour Research and Therapy, 36(4), 455–470. doi: 10.1016/S0005-7967(97)10031-6
  34. Olatunji, B. O., Haidt, J., McKay, D., & David, B. (2008). Core, animal reminder, and contamination disgust: three kinds of disgust with distinct personality, behavioral, physiological, and clinical correlates. Journal of Research in Personality, 42(5), 1243–1259. doi: 10.1016/j.jrp.2008.03.009.CrossRefGoogle Scholar
  35. Ostchega, Y., Porter, K. S., Hughes, J., Dillon, C. F., & Nwankwo, T., (2011). Resting pulse rate reference data for children, adolescents, and adults: United States, 1999–2008. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. Retrieved from http://www.cdc.gov/nchs/data/nhsr/nhsr041.pdf.
  36. Rapee, R. M., & Heimberg, R. G. (1997). A cognitive-behavioral model of anxiety in social phobia. Behaviour Research and Therapy, 35(8), 741–756. doi: 10.1016/S0005-7967(97)00022-3.CrossRefPubMedGoogle Scholar
  37. Rodebaugh, T. L., Woods, C. M., & Heimberg, R. G. (2007). The reverse of social anxiety is not always the opposite: the reverse-scored items of the Social Interaction Anxiety Scale do not belong. Behavior Therapy, 38(2), 192–206. doi: 10.1016/j.beth.2006.08.001.
  38. RStudio. (2014). (Version 0.98.994). Boston, MA: RStudio. Retrieved from http://www.rstudio.org/.
  39. Thomas, S. A., Aldao, A., & De Los Reyes, A. (2012). Implementing clinically feasible psychophysiological measures in evidence-based assessments of adolescent social anxiety. Professional Psychology: Research and Practice, 43(5), 510–519. doi: 10.1037/a0029183.CrossRefGoogle Scholar
  40. Vasey, M. W., & Lonigan, C. J. (2000). Considering the clinical utility of performance-based measures of childhood anxiety. Journal of Clinical Child Psychology, 29(4), 493–508. doi: 10.1207/S15374424JCCP2904_4.CrossRefPubMedGoogle Scholar
  41. Wakschlag, L. S., Leventhal, B. L., Briggs-Gowan, M. J., Danis, B., Keenan, K., Hill, C., & Carter, A. S. (2005). Defining the “disruptive” in preschool behavior: what diagnostic observation can teach us. Clinical Child and Family Psychology Review, 8(3), 183–201. doi: 10.1007/s10567-005-6664-5.CrossRefPubMedGoogle Scholar
  42. Wakschlag, L. S., Hill, C., Carter, A. S., Danis, B., Egger, H. L., Keenan, K., … Briggs-Gowan, M. J. (2008). Observational assessment of preschool disruptive behavior, Part I: Reliability of the Disruptive Behavior Diagnostic Observation Schedule (DB-DOS). Journal of the American Academy of Child and Adolescent Psychiatry, 47(6), 622–631. doi: 10.1097/CHI.0b013e31816c5bdb.
  43. Wolf, P., & Bielefeld, U. (2013). aplpack: Another Plot PACKage: stem.leaf, bagplot, faces, spin3R, plotsummary, plothulls, and some slider functions (Version 1.2.9). Retrieved from http://cran.r-project.org/web/packages/aplpack/index.html.
  44. Woody, S. R., & Teachman, B. A. (2000). Intersection of disgust and fear: normative and pathological views. Clinical Psychology: Science and Practice, 7(3), 291–311. doi: 10.1093/clipsy.7.3.291.Google Scholar
  45. Youngstrom, E. A., & De Los Reyes, A. (2015). Commentary. Moving towards cost-effectiveness in using psychophysiological measures in clinical assessment: validity, decision-making, and adding value. Journal of Clinical Child and Adolescent Psychology, 44(2), 352–361. doi: 10.1080/15374416.2014.913252.
  46. Zeger, S. L., & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42(1), 121–130. doi: 10.2307/2531248.

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Emily Justine Dunn
    • 1
  • Amelia Aldao
    • 1
  • Andres De Los Reyes
    • 2
  1. 1.Psychopathology & Affective Sciences Lab, Psychology DepartmentThe Ohio State UniversityColumbusUSA
  2. 2.Comprehensive Assessment and Intervention Program, Department of PsychologyUniversity of Maryland at College ParkCollege ParkUSA

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