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7 - Technological Advances in Clinical Assessment

Ambulatory Assessment

from Part I - General Issues in Clinical Assessment and Diagnosis

Published online by Cambridge University Press:  06 December 2019

By
Timothy J. Trull ,
Sarah A. Griffin  and
Ashley C. Helle
Edited by
Martin Sellbom  and
Julie A. Suhr
Martin Sellbom
Affiliation:
University of Otago, New Zealand
Julie A. Suhr
Affiliation:
Ohio University
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Summary

The advancement and spread of technology have improved options for clinical assessment. Specifically, ambulatory assessment (AA) methods have improved the ability to assess constructs with a particular focus on intra-individual and dynamic time processes, which are highly relevant to the assessment of mood and behavior. This chapter reviews current technologies, including applications of online platforms and devices, often utilized to collect data in an AA framework, and discusses their applications within research and clinical settings (e.g., assessment of mood instability). AA has a number of benefits, including limited or no reliance on retrospective recall as well as the ability to assess context and construct of interest in the “real world,” and allows for the ability to gather rich information regarding mood, behavior, and psychophysiology as part of the clinical assessment process. Much of the clinical application of AA is in the early stages. A number of important considerations and recommendations, including data security, accessibility, and future directions, are also reviewed within the context of AA methods.

Keywords

ambulatory assessmentecological momentary assessmentassessmentecological momentary interventionmobile technologytreatmentexperience sampling method
Type
Chapter
Information
The Cambridge Handbook of Clinical Assessment and Diagnosis , pp. 80 - 89
Publisher: Cambridge University Press
Print publication year: 2019

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References

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association. https://dsm.psychiatryonline.org/doi/full/10.1176/appi.books.9780890425596.dsm02Google Scholar
Barrett, L. F., Gross, J., Christensen, T. C., & Benvenuto, M. (2001). Knowing what you are feeling and knowing what to do about it: Mapping the relation between emotion differentiation and emotion regulation. Cognition and Emotion, 15, 713–724.CrossRefGoogle Scholar
Ben-Zeev, D., & Young, M. A. (2010). Accuracy of hospitalized depressed patients’ and healthy controls’ retrospective symptom reports: An experience sampling study. The Journal of Nervous and Mental Disease, 198(4), 280–285.Google Scholar
Buckner, J. D., Crosby, R. D., Wonderlich, S. A., & Schmidt, N. B. (2012). Social anxiety and cannabis use: An analysis from ecological momentary assessment. Journal of Anxiety Disorders, 26, 297–304.Google Scholar
Byambasuren, O., Sanders, S., Beller, E., & Glasziou, P. (2018). Prescribable mHealth apps identified from an overview of systematic reviews. npj Digital Medicine, 1(1), 12.CrossRefGoogle ScholarPubMed
Carpenter, R. W., Wycoff, A. M., & Trull, T. J. (2016). Ambulatory assessment: New adventures in characterizing dynamic processes. Assessment, 23, 414–424.Google Scholar
Cohn, A. M., Hunter‐Reel, D., Hagman, B. T., & Mitchell, J. (2011). Promoting behavior change from alcohol use through mobile technology: The future of ecological momentary assessment. Alcoholism: Clinical and Experimental Research, 35(12), 2209–2215.Google Scholar
Conner, T. S., & Lehman, B. J. (2012). Getting started: Launching a study in daily life. In Mehl, M. R. & Connor, T. S. (Eds.), Handbook of research methods for studying daily life (pp. 89–107). New York: Guilford Press.Google Scholar
Crooke, A. H., Reid, S. C., Kauer, S. D., McKenzie, D. P., Hearps, S. J., Khor, A. S., & Forbes, A. B. (2013). Temporal mood changes associated with different levels of adolescent drinking: Using mobile phones and experience sampling methods to explore motivations for adolescent alcohol use. Drug and Alcohol Review, 32, 262–268.Google Scholar
Delespaul, P. A., & DeVries, M. W. (1987). The daily life of ambulatory chronic mental patients. The Journal of Nervous and Mental Disease, 175, 537–544.Google Scholar
Dubad, M., Winsper, C., Meyer, C., Livanoud, M., & Marwaha, S. (2018). A systematic review of the psychometric properties, usability and clinical impacts of mobile mood-monitoring applications in young people. Psychological Medicine, 48, 208–228.Google Scholar
Ebner-Priemer, U. W., & Sawitzki, G. (2007). Ambulatory assessment of affective instability in borderline personality disorder: The effect of sampling frequency. European Journal of Psychological Assessment, 23, 238–247.CrossRefGoogle Scholar
Ebner-Priemer, U. W., & Trull, T. J. (2009). Ecological momentary assessment of mood disorders and mood dysregulation. Psychological Assessment, 21, 463–475.CrossRefGoogle ScholarPubMed
Fatseas, M., Serre, F., Alexandre, J. M., Debrabant, R., Auriacombe, M., & Swendsen, J. (2015). Craving and substance use among patients with alcohol, tobacco, cannabis or heroin addiction: A comparison of substance‐and person‐specific cues. Addiction, 110(6), 1035–1042.CrossRefGoogle ScholarPubMed
Fisher, C. D., & To, M. L. (2012). Using experience sampling methodology in organizational behavior. Journal of Organizational Behavior, 33(7), 865–877.Google Scholar
Fredrickson, B. L., & Kahneman, D. (1993). Duration neglect in retrospective evaluations of affective episodes. Journal of Personality and Social Psychology, 65, 45–55.Google Scholar
Gard, D. E., Kring, A. M., Gard, M. G., Horan, W. P., & Green, M. F. (2007). Anhedonia in schizophrenia: Distinctions between anticipatory and consummatory pleasure. Schizophrenia Research, 93(1–3), 253–260.Google Scholar
Glaser, J. P., van Os, J., Mengelers, R., Myin-Germeys, I. (2007). A momentary assessment study of the reputed emotional phenotype associated with borderline personality disorder. Psychological Medicine, 38, 1–9.Google ScholarPubMed
Hebert, E. T., Stevens, E. M., Frank, S. G., Kendzor, D. E., Wetter, D. W., Zvolensky, M. J. et al. (2018). An ecological momentary intervention for smoking cessation: The associations of just-in-time, tailored messages with lapse risk factors. Addictive Behaviors, 78, 30–35.Google Scholar
Heron, K. E., & Smyth, J. M. (2010). Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behaviour treatments. The British Journal of Health Psychology, 15, 1–39.Google Scholar
Horan, W. P., Green, M. F., Kring, A. M., & Nuechterlein, K. H. (2006). Does anhedonia in schizophrenia reflect faulty memory for subjectively experienced emotions? Journal of Abnormal Psychology, 115(3), 496.Google Scholar
Hsin, H., Fromer, M., Peterson, B., Walter, C., Fleck, M., Campbell, A. et al. (2018). Transforming psychiatry into data-driven medicine with digital measurement tools. npj Digital Medicine, 1(1), 37.Google Scholar
Intille, S. S. (2007). Technological innovations enabling automatic, context-sensitive ecological momentary assessment. In Stone, A. A. & Shiffman, S. (Eds.), The science of real-time data capture: Self-reports in health research (pp. 308–337). Oxford: Oxford University Press.Google Scholar
Kaplan, R. M., & Stone, A. A. (2013). Bringing the laboratory and clinic to the community: Mobile technologies for health promotion and disease prevention. Annual Review of Psychology, 64, 471–498.Google Scholar
Kring, A. M., & Moran, E. K. (2008). Emotional response deficits in schizophrenia: Insights from affective science. Schizophrenia Bulletin, 34(5), 819–834.CrossRefGoogle ScholarPubMed
Kuppens, P., Sheeber, L. B., Yap, M. B., Whittle, S., Simmons, J. G., & Allen, N. B. (2012). Emotional inertia prospectively predicts the onset of depressive disorder in adolescence. Emotion, 12, 283–289.CrossRefGoogle ScholarPubMed
Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011). mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Professional Psychology: Research and Practice, 42, 505–512.Google Scholar
Mehl, M. R., Pennebaker, J. W., Crow, D. M., Dabbs, J., & Price, J. H. (2001). The Electronically Activated Recorder (EAR): A device for sampling naturalistic daily activities and conversations. Behavior Research Methods, Instruments, and Computers, 33(4), 517–523.Google Scholar
Moore, T. M., Seavey, A., Ritter, K., McNulty, J. K., Gordon, K. C., & Stuart, G. L. (2014). Ecological momentary assessment of the effects of craving and affect on risk for relapse during substance abuse treatment. Psychology of Addictive Behaviors, 28(2), 619–624.Google Scholar
Myin-Germeys, I., Delespaul, P. A., & DeVries, M. W. (2000). Schizophrenia patients are more emotionally active than is assumed based on their behavior. Schizophrenia Bulletin, 26(4), 847–854.Google Scholar
Myin-Germeys, I., Peeters, F., Havermans, R., Nicolson, N. A., DeVries, M. W., Delespaul, P. et al. (2018). Experience sampling methodology in mental health research: New insights and technical developments. World Psychiatry, 17, 123–132.Google Scholar
Myin‐Germeys, I., Peeters, F. P. M. L., Havermans, R., Nicolson, N. A., DeVries, M. W., Delespaul, P. A. E. G., & Van Os, J. (2003). Emotional reactivity to daily life stress in psychosis and affective disorder: An experience sampling study. Acta Psychiatrica Scandinavica, 107(2), 124–131.Google Scholar
Nahum-Shani, I., Hekler, E. B., & Spruijt-Metz, D. (2015). Building health behavior models to guide the development of just-in-time interventions: A pragmatic framework. Health Psychology, 34, 1209–1219.Google Scholar
Oorschot, M., Kwapil, T.R., Delespaul, P., & Myin-Germeys, I. (2009). Momentary assessment research in psychosis. Psychological Assessment, 21, 498–505.Google Scholar
Oorschot, M., Lataster, T., Thewissen, V., Lardinois, M., Wichers, M., van Os, J. et al. (2013). Emotional experience in negative symptoms of schizophrenia: No evidence for a generalized hedonic deficit. Schizophrenia Bulletin, 39(1), 217–225.Google Scholar
Perna, G., Grassi, M., Caldi, D., & Nemeroff, C. B. (2018). The revolution of personality psychiatry: Will technology make it happen sooner? Psychological Medicine, 48, 705–713.Google Scholar
Pew Research Center. (2018). Mobile fact sheet. February 5. www.pewinternet.org/fact-sheet/mobile/Google Scholar
Pfaltz, M. C., Michael, T., Grossman, P., Margraf, J., & Wilhelm, F. H. (2010). Instability of physical anxiety symptoms in daily life of patients with panic disorder and patients with posttraumatic stress disorder. Journal of Anxiety Disorders, 24(7), 792–798.Google Scholar
Piasecki, T. M., Hufford, M. R., Solhan, M., & Trull, T. J. (2007). Assessing clients in their natural environments with electronic diaries: Rationale, benefits, limitations, and barriers. Psychological Assessment, 19, 25–43.CrossRefGoogle ScholarPubMed
Prentice, J. L., & Dobson, K. S. (2014). A review of the risk and benefits associated with mobile phone applications and psychological interventions. Canadian Psychology, 55, 282–290.Google Scholar
Ray, L. A., Miranda, R. Jr., Tidey, J. W., McGeary, J. E., MacKillop, J., Gwaltney, C. J. et al. (2010). Polymorphisms of the μ-opioid receptor and dopamine D4 receptor genes and subjective responses to alcohol in the natural environment. Journal of Abnormal Psychology, 119(1), 115–125.CrossRefGoogle ScholarPubMed
Schneekloth, T. D., Biernacka, J. M., Hall-Flavin, D. K., Karpyak, V. M., Frye, M. A., Loukianova, L. L. et al. (2012). Alcohol craving as a predictor of relapse. The American Journal on Addictions, 21, S20–S26.CrossRefGoogle ScholarPubMed
Schueller, S. M., Begale, M., Penedo, F. J., & Mohr, D. C. (2014). Purple: A modular system for developing and deploying behavioral intervention technologies. Journal of Medical Internet Research, 16, e181.CrossRefGoogle ScholarPubMed
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.CrossRefGoogle ScholarPubMed
Shrout, P. E., & Lane, S. P. (2011). Psychometrics. In Mehl, M. R. & Conner, T. S. (Eds.), Handbook of research methods for studying daily life (pp. 302–320). New York: Guilford Press.Google Scholar
Solhan, M. B., Trull, T. J., Jahng, S., & Wood, P. K. (2009). Clinical assessment of affective instability: Comparing EMA indices, questionnaire reports, and retrospective recall. Psychological Assessment, 21, 425–436.Google Scholar
Stiglmayr, C. E., Ebner-Priemer, U. W., Bretz, J., Behm, R., Mohse, M., Lammers, C. H. et al. (2008). Dissociative symptoms are positively related to stress in borderline personality disorder. Acta Psychiatrica Scandinavica, 117, 139–147.Google Scholar
Stone, A. A., & Shiffman, S. (1994). Ecological momentary assessment (EMA) in behavioral medicine. Annals of Behavioral Medicine, 16(3), 199–202.Google Scholar
Stone, A. A., Shiffman, S., Atienza, A. A., & Nebeling, L. (2007). Historical roots and rationale of ecological momentary assessment (EMA). In Stone, A. A. & Shiffman, S. (Eds.), The science of real-time data capture: Self-reports in health research (pp. 3–10). Oxford: Oxford University Press.CrossRefGoogle Scholar
Stone, A. A., Shiffman, S., Schwartz, J. E., Broderick, J. E., & Hufford, M. R. (2002). Patient non-compliance with paper diaries. BMJ, 324(7347), 1193–1194.Google Scholar
Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176.Google Scholar
Trull, T. J., Lane, S. P., Koval, P., & Ebner-Priemer, U. W. (2015). Affective dynamics in psychopathology. Emotion Review, 7(4), 355–361.Google Scholar
Trull, T. J., Solhan, M. B., Traggesser, S. L., Jahng, S., Wood, P. K., Piasecki, T. M., & Watson, D. (2008). Affective instability: Measuring a core feature of borderline personality disorder with ecological momentary assessment. Journal of Abnormal Psychology, 117, 647–661.Google Scholar
Van de Leemput, I. A., Wichers, M., Cramer, A. O., Borsboom, D., Tuerlinckx, F., Kuppens, P., et al. (2014). Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences, 111, 87–92.Google Scholar
Van Os, J., Delespaul, P., Wigman, J., Myin-Germeys, I., & Wichers, M. (2013). Beyond DSM and ICD: Introducing “precision diagnosis” for psychiatry using momentary assessment technology. World Psychiatry, 12, 113–117.CrossRefGoogle ScholarPubMed
Villar, R., Beltrame, T., & Hughson, R. L. (2015). Validation of the Hexoskin wearable vest during lying, sitting, standing, and walking activities. Applied Physiology, Nutrition, and Metabolism, 40(10), 1019–1024.Google Scholar
Walentynowicz, M., Schneider, S., & Stone, A. A. (2018). The effects of time frames on self-report. PloS ONE, 13(8), e0201655.Google Scholar
Walz, L. C., Nauta, M. H., & aan het Rot, M. (2014). Experience sampling and ecological momentary assessment for studying the daily lives of patients with anxiety disorders: A systematic review. Journal of Anxiety Disorders, 28(8), 925–937.CrossRefGoogle ScholarPubMed
Wilhelm, P., Perrez, M., & Pawlik, K. (2012). Conducting research in daily life: A historical review. In Mehl, M. R. & Connor, T. A. (Eds.), Handbook of research methods for studying daily life (pp. 62–86). New York: Guilford Press.Google Scholar
Wright, C., Dietze, P. M., Agius, P. A., Kuntzche, E., Livingston, M., Black, O. C. et al. (2018). Mobile phone-based ecological momentary intervention to reduce young adults’ alcohol use in the event: A three-armed randomized controlled trial. Journal of Medical Internet Research mHealth and uHealth, 6(7), e149.Google Scholar

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