1932

Abstract

Outcome measurement in the field of psychotherapy has developed considerably in the last decade. This review discusses key issues related to outcome measurement, modeling, and implementation of data-informed and measurement-based psychological therapy. First, an overview is provided, covering the rationale of outcome measurement by acknowledging some of the limitations of clinical judgment. Second, different models of outcome measurement are discussed, including pre–post, session-by-session, and higher-resolution intensive outcome assessments. Third, important concepts related to modeling patterns of change are addressed, including early response, dose–response, and nonlinear change. Furthermore, rational and empirical decision tools are discussed as the foundation for measurement-based therapy. Fourth, examples of clinical applications are presented, which show great promise to support the personalization of therapy and to prevent treatment failure. Finally, we build on continuous outcome measurement as the basis for a broader understanding of clinical concepts and data-driven clinical practice in the future.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-clinpsy-071720-014821
2022-05-09
2024-04-30
Loading full text...

Full text loading...

/deliver/fulltext/clinpsy/18/1/annurev-clinpsy-071720-014821.html?itemId=/content/journals/10.1146/annurev-clinpsy-071720-014821&mimeType=html&fmt=ahah

Literature Cited

  1. Aderka IM, Nickerson A, Bøe HJ, Hofmann SG. 2012. Sudden gains during psychological treatments of anxiety and depression: a meta-analysis. J. Consult. Clin. Psychol. 80:193–101
    [Google Scholar]
  2. Anderson T, McClintock AS, Himawan L, Song X, Patterson CL 2016. A prospective study of therapist facilitative interpersonal skills as a predictor of treatment outcome. J. Consult. Clin. Psychol. 84:157–66
    [Google Scholar]
  3. Atzil-Slonim D, Juravski D, Bar-Kalifa E, Gilboa-Schechtman E, Tuval-Mashiach R et al. 2021. Using topic models to identify clients’ functioning levels and alliance ruptures in psychotherapy. Psychotherapy 58:2324–39
    [Google Scholar]
  4. Baldwin SA, Berkeljon A, Atkins DC, Olsen JA, Nielsen SL. 2009. Rates of change in naturalistic psychotherapy: contrasting dose-effect and good-enough level models of change. J. Consult. Clin. Psychol. 77:2203–11
    [Google Scholar]
  5. Baldwin SA, Imel ZE. 2013. Therapist effects: findings and methods. Bergin and Garfield's Handbook of Psychotherapy and Behavior Change MJ Lambert 258–97 New York: Wiley. , 6th ed..
    [Google Scholar]
  6. Baldwin SA, Imel ZE. 2020. Studying specificity in psychotherapy with meta-analysis is hard. Psychother. Res. 30:3294–96
    [Google Scholar]
  7. Barkham M, Connell J, Stiles WB, Miles JNV, Margison F et al. 2006. Dose-effect relations and responsive regulation of treatment duration: the good enough level. J. Consult. Clin. Psychol. 74:1160–67
    [Google Scholar]
  8. Barkham M, Lambert MJ. 2021. The efficacy and effectiveness of psychological therapies. See Barkham et al. 2021129–86
    [Google Scholar]
  9. Barkham M, Lutz W, Castonguay LG, eds. 2021. Bergin and Garfield's Handbook of Psychotherapy and Behavior Change New York: Wiley. , 7th ed..
  10. Barkham M, Rees A, Stiles WB, Shapiro DA, Hardy GE, Reynolds S. 1996. Dose-effect relations in time-limited psychotherapy for depression. J. Consult. Clin. Psychol. 64:5927–35
    [Google Scholar]
  11. Baur T, Clausen S, Heimerl A, Lingenfelser F, Lutz W, André E 2020. NOVA: a tool for explanatory multimodal behavior analysis and its application to psychotherapy. Lecture Notes in Computer Science, Vol. 11962: MultiMedia Modeling: 26th International Conference, MMM 2020, Daejeon, South Korea, January 58, 2020: Proceedings W-H Cheng 577–88 Cham, Switz: Springer
    [Google Scholar]
  12. Beard JIL, Delgadillo J. 2019. Early response to psychological therapy as a predictor of depression and anxiety treatment outcomes: a systematic review and meta-analysis. Depress. Anxiety 36:9866–78
    [Google Scholar]
  13. Bickman L. 2020. Improving mental health services: a 50-year journey from randomized experiments to artificial intelligence and precision mental health. Adm. Policy Ment. Health 47:795–843
    [Google Scholar]
  14. Bone C, Delgadillo J, Barkham M. 2021a. A systematic review and meta-analysis of the good-enough level (GEL) literature. J. Couns. Psychol. 68:2219–31
    [Google Scholar]
  15. Bone C, Simmonds-Buckley M, Thwaites R, Sandford D, Merzhvynska M et al. 2021b. Dynamic prediction of psychological treatment outcomes: development and validation of a prediction model using routinely collected symptom data. Lancet Digit. Health 3:4e231–40
    [Google Scholar]
  16. Borsboom D. 2017. A network theory of mental disorders. World Psychiatry 16:15–13
    [Google Scholar]
  17. Boswell JF, Kraus DR, Miller SD, Lambert MJ. 2015. Implementing routine outcome monitoring in clinical practice: benefits, challenges, and solutions. Psychother. Res. 25:16–19
    [Google Scholar]
  18. Bringmann LF. 2021. Person-specific networks in psychopathology: past, present, and future. Curr. Opin. Psychol. 41:59–64
    [Google Scholar]
  19. Bringmann LF, van der Veen DC, Wichers M, Riese H, Stulp G 2021. ESMvis: a tool for visualizing individual experience sampling method (ESM) data. Qual. Life Res. 30:3179–88
    [Google Scholar]
  20. Castonguay LG, Barkham M, Lutz W, McAleavey A. 2013. Practice-oriented research: approaches and applications. Bergin and Garfield's Handbook of Psychotherapy and Behavior Change MJ Lambert 85–133 New York: Wiley. , 6th ed..
    [Google Scholar]
  21. Castonguay LG, Barkham M, Youn SJ, Page AC. 2021. Practice-based evidence: findings from routine clinical settings. See Barkham et al. 2021 187–220
  22. Chang PGRY, Delgadillo J, Waller G. 2021. Early response to psychological treatment for eating disorders: a systematic review and meta-analysis. Clin. Psychol. Rev. 86:1102032
    [Google Scholar]
  23. Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A et al. 2021. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 20:2154–70
    [Google Scholar]
  24. Clark DM. 2018. Realizing the mass public benefit of evidence-based psychological therapies: the IAPT program. Annu. Rev. Clin. Psychol. 14:159–83
    [Google Scholar]
  25. Cohen ZD, Delgadillo J, DeRubeis RJ. 2021. Personalized treatment approaches. See Barkham et al. 2021 667–700
  26. Constantino MJ, Boswell JF, Coyne AE, Swales TP, Kraus DR. 2021. Effect of matching therapists to patients vs assignment as usual on adult psychotherapy outcomes: a randomized clinical trial. JAMA Psychiatry 78:9960–69
    [Google Scholar]
  27. Contreras A, Nieto I, Valiente C, Espinosa R, Vazquez C. 2019. The study of psychopathology from the network analysis perspective: a systematic review. Psychother. Psychosom. 88:271–83
    [Google Scholar]
  28. Cristea IA, Vecchi T, Cuijpers P. 2021. Top-down and bottom-up pathways to developing psychological interventions. JAMA Psychiatry 78:6593–94
    [Google Scholar]
  29. Crits-Christoph P, Connolly Gibbons MB 2021. Psychotherapy process-outcome research: advances in understanding causal connections. See Barkham et al. 2021 259–92
  30. Crits-Christoph P, Ring-Kurtz S, Hamilton JL, Lambert MJ, Gallop R et al. 2012. A preliminary study of the effects of individual patient-level feedback in outpatient substance abuse treatment programs. J. Subst. Abuse Treat. 42:3301–9
    [Google Scholar]
  31. Cuijpers P, Reijnders M, Huibers MJH 2019. The role of common factors in psychotherapy outcomes. Annu. Rev. Clin. Psychol. 15:207–31
    [Google Scholar]
  32. Dakos V, Carpenter SR, Brock WA, Ellison AM, Guttal V et al. 2012. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLOS ONE 7:7e41010
    [Google Scholar]
  33. de Jong K. 2016. Challenges in the implementation of measurement feedback systems. Adm. Policy Ment. Health 43:3467–70
    [Google Scholar]
  34. de Jong K, Conijn JM, Gallagher RAV, Reshetnikova AS, Heij M, Lutz MC. 2021. Using progress feedback to improve outcomes and reduce drop-out, treatment duration, and deterioration: a multilevel meta-analysis. Clin. Psychol. Rev. 85:102002
    [Google Scholar]
  35. de Jong K, Timman R, Hakkaart-Van Roijen L, Vermeulen P, Kooiman K et al. 2014. The effect of outcome monitoring feedback to clinicians and patients in short and long-term psychotherapy: a randomized controlled trial. Psychother. Res. 24:6629–39
    [Google Scholar]
  36. Deisenhofer A-K, Delgadillo J, Rubel JA, Böhnke JR, Zimmermann D et al. 2018. Individual treatment selection for patients with posttraumatic stress disorder. Depress. Anxiety 35:6541–50
    [Google Scholar]
  37. Deisenhofer A-K, Rubel JA, Bennemann B, Aderka IM, Lutz W. 2021. Are some therapists better at facilitating and consolidating sudden gains than others?. Psychother. Res. https://doi.org/10.1080/10503307.2021.1921302
    [Crossref] [Google Scholar]
  38. Delgadillo J, Ali S, Fleck K, Agnew C, Southgate A et al. 2021. Stratified care vs stepped care for depression: a cluster randomized clinical trial. JAMA Psychiatry 79:21018
    [Google Scholar]
  39. Delgadillo J, Gonzalez Salas Duhne P. 2020. Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach. J. Consult. Clin. Psychol. 88:114–24
    [Google Scholar]
  40. Delgadillo J, Lutz W. 2020. A development pathway towards precision mental health care. JAMA Psychiatry 77:9889–90
    [Google Scholar]
  41. Delgadillo J, McMillan D, Lucock M, Leach C, Ali S, Gilbody S 2014. Early changes, attrition, and dose-response in low intensity psychological interventions. Br. J. Clin. Psychol. 53:1114–30
    [Google Scholar]
  42. Delgadillo J, Rubel J, Barkham M 2020. Towards personalized allocation of patients to therapists. J. Consult. Clin. Psychol. 88:9799–808
    [Google Scholar]
  43. DeRubeis RJ, Cohen ZD, Forand NR, Fournier JC, Gelfand LA, Lorenzo-Luaces L. 2014. The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration. PLOS ONE 9:1e83875
    [Google Scholar]
  44. Douglas S, Button S, Casey SE. 2016. Implementing for sustainability: promoting use of a measurement feedback system for innovation and quality improvement. Adm. Policy Ment. Health 43:3286–91
    [Google Scholar]
  45. Ebner-Priemer UW, Trull TJ. 2009. Ecological momentary assessment of mood disorders and mood dysregulation. Psychol. Assess. 21:4463–75
    [Google Scholar]
  46. Eisele G, Vachon H, Lafit G, Kuppens P, Houben M et al. 2022. The effects of sampling frequency and questionnaire length on perceived burden, compliance, and careless responding in experience sampling data in a student population. Assessment 29:213651
    [Google Scholar]
  47. Epskamp S. 2020. Psychometric network models from time-series and panel data. Psychometrika 85:1206–31
    [Google Scholar]
  48. Evans DL, Herbert JD, Nelson-Gray RO, Gaudiano BA. 2002. Determinants of diagnostic prototypicality judgments of the personality disorders. J. Pers. Disord. 16:195–106
    [Google Scholar]
  49. Falkenström F, Ekeblad A, Holmqvist R 2016. Improvement of the working alliance in one treatment session predicts improvement of depressive symptoms by the next session. J. Consult. Clin. Psychol. 84:8738–51
    [Google Scholar]
  50. Finch AE, Lambert MJ, Schaalje BG 2001. Psychotherapy quality control: the statistical generation of expected recovery curves for integration into an early warning system. Clin. Psychol. Psychother. 8:4231–42
    [Google Scholar]
  51. Fisher AJ, Bosley HG, Fernandez KC, Reeves JW, Soyster PD et al. 2019. Open trial of a personalized modular treatment for mood and anxiety. Behav. Res. Ther. 116:69–79
    [Google Scholar]
  52. Fisher AJ, Soyster P, Ashlock L. 2021. Machine learning algorithms for generating early warning signals in real time. Biol. Psychiatry 89:9S58–59
    [Google Scholar]
  53. Flood N, Page A, Hooke G 2019. A comparison between the clinical significance and growth mixture modelling early change methods at predicting negative outcomes. Psychother. Res. 29:7947–58
    [Google Scholar]
  54. Flückiger C, Rubel J, Del Re AC, Horvath AO, Wampold BE et al. 2020. The reciprocal relationship between alliance and early treatment symptoms: a two-stage individual participant data meta-analysis. J. Consult. Clin. Psychol. 88:9829–43
    [Google Scholar]
  55. Fried EI. 2017. The 52 symptoms of major depression: lack of content overlap among seven common depression scales. J. Affect. Disord. 208:191–97
    [Google Scholar]
  56. Frumkin MR, Piccirillo ML, Beck ED, Grossman JT, Rodebaugh TL. 2021. Feasibility and utility of idiographic models in the clinic: a pilot study. Psychother. Res. 31:4520–34
    [Google Scholar]
  57. Garb HN. 2005. Clinical judgment and decision making. Annu. Rev. Clin. Psychol. 1:67–89
    [Google Scholar]
  58. Gilbody SM, House AO, Sheldon TA. 2002. Psychiatrists in the UK do not use outcomes measures: national survey. Br. J. Psychiatry 180:2101–3
    [Google Scholar]
  59. Goldfried MR. 2019. Obtaining consensus in psychotherapy: What holds us back?. Am. Psychol. 74:4484–96
    [Google Scholar]
  60. Gómez Penedo JM, Schwartz B, Giesemann J, Rubel JA, Deisenhofer A-K, Lutz W 2022. For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization. Psychother. Res. 32:215164
    [Google Scholar]
  61. Grove WM, Meehl PE. 1996. Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: the clinical-statistical controversy. Psychol. Public Policy Law 2:2293–323
    [Google Scholar]
  62. Hamaker EL, Wichers M. 2017. No time like the present: discovering the hidden dynamics in intensive longitudinal data. Curr. Dir. Psychol. Sci. 26:110–15
    [Google Scholar]
  63. Hammond KR. 1978. Toward increasing competence of thought in public policy formation. Judgement and Decision in Public Policy Formation KR Hammond 11–32 New York: Routledge
    [Google Scholar]
  64. Han S, Shuen WH, Wang W-W, Nazim E, Toh HC 2020. Tailoring precision immunotherapy: coming to a clinic soon?. ESMO Open 5:Suppl. 1e000631
    [Google Scholar]
  65. Hannan C, Lambert MJ, Harmon C, Nielsen SL, Smart DW et al. 2005. A lab test and algorithms for identifying clients at risk for treatment failure. J. Clin. Psychol. 61:2155–63
    [Google Scholar]
  66. Hehlmann MI, Schwartz B, Lutz T, Gómez Penedo JM, Rubel JA, Lutz W 2021. The use of digitally assessed stress levels to model change processes in CBT—a feasibility study on seven case examples. Front. Psychiatry 12:613085
    [Google Scholar]
  67. Heinonen E, Nissen-Lie HA. 2020. The professional and personal characteristics of effective psychotherapists: a systematic review. Psychother. Res. 30:4417–32
    [Google Scholar]
  68. Heinzel S, Tominschek I, Schiepek G 2014. Dynamic patterns in psychotherapy—discontinuous changes and critical instabilities during the treatment of obsessive compulsive disorder. Nonlinear Dyn. Psychol. Life Sci. 18:2155–76
    [Google Scholar]
  69. Hofmann SG, Hayes SC. 2019. The future of intervention science: process-based therapy. Clin. Psychol. Sci. 7:137–50
    [Google Scholar]
  70. Howard KI, Kopta SM, Krause MS, Orlinsky DE. 1986. The dose-effect relationship in psychotherapy. Am. Psychol. 41:2159–64
    [Google Scholar]
  71. Howard KI, Moras K, Brill PL, Martinovich Z, Lutz W 1996. Evaluation of psychotherapy: efficacy, effectiveness, and patient progress. Am. Psychol. 51:101059–64
    [Google Scholar]
  72. Huibers MJH, Cohen ZD, Lemmens LH, Arntz A, Peeters FP et al. 2015. Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the Personalized Advantage Index approach. PLOS ONE 10:11e0140771
    [Google Scholar]
  73. Husen K, Rafaeli E, Rubel JA, Bar-Kalifa E, Lutz W. 2016. Daily affect dynamics predict early response in CBT: feasibility and predictive validity of EMA for outpatient psychotherapy. J. Affect. Disord. 206:305–14
    [Google Scholar]
  74. Imel ZE, Barco JS, Brown HJ, Baucom BR, Baer JS et al. 2014. The association of therapist empathy and synchrony in vocally encoded arousal. J. Couns. Psychol. 61:1146–53
    [Google Scholar]
  75. Jacobson NC, Weingarden H, Wilhelm S. 2019. Digital biomarkers of mood disorders and symptom change. NPJ Digit. Med. 2:3
    [Google Scholar]
  76. Jacobson NS, Truax P. 1991. Clinical significance: a statistical approach to defining meaningful change in psychotherapy research. J. Consult. Clin. Psychol. 59:112–19
    [Google Scholar]
  77. Kahneman D, Klein G. 2009. Conditions for intuitive expertise: a failure to disagree. Am. Psychol. 64:6515–26
    [Google Scholar]
  78. Kaiser T, Laireiter A-R. 2018. Process-symptom-bridges in psychotherapy: an idiographic network approach. J. Pers.-Oriented Res. 4:249–62
    [Google Scholar]
  79. Kazdin AE. 2018. Innovations in Psychosocial Interventions and Their Delivery: Leveraging Cutting-Edge Science to Improve the World's Mental Health New York: Oxford Univ. Press
  80. Kraemer HC, Stice E, Kazdin A, Offord D, Kupfer D. 2001. How do risk factors work together? Mediators, moderators, and independent, overlapping, and proxy risk factors. Am. J. Psychiatry 158:6848–56
    [Google Scholar]
  81. Krause MS. 2018. Associational versus correlational research study design and data analysis. Qual. Quant. 52:62691–707
    [Google Scholar]
  82. Krüger A, Ehring T, Priebe K, Dyer AS, Steil R, Bohus M. 2014. Sudden losses and sudden gains during a DBT-PTSD treatment for posttraumatic stress disorder following childhood sexual abuse. Eur. J. Psychotraumatol. 5:124470
    [Google Scholar]
  83. Kyron MJ, Hooke GR, Page AC. 2019. Assessing interpersonal and mood factors to predict trajectories of suicidal ideation within an inpatient setting. J. Affect. Disord. 252:315–24
    [Google Scholar]
  84. Lambert MJ. 2017. Maximizing psychotherapy outcome beyond evidence-based medicine. Psychother. Psychosom. 86:280–89
    [Google Scholar]
  85. Lambert MJ, Hansen NB, Finch AE. 2001. Patient-focused research: using patient outcome data to enhance treatment effects. J. Consult. Clin. Psychol. 69:2159–72
    [Google Scholar]
  86. Lambert MJ, Whipple JL, Bishop MJ, Vermeersch DA, Gray GV, Finch AE 2002. Comparison of empirically-derived and rationally-derived methods for identifying patients at risk for treatment failure. Clin. Psychol. Psychother. 9:3149–64
    [Google Scholar]
  87. Lambert MJ, Whipple JL, Kleinstäuber M. 2018. Collecting and delivering progress feedback: a meta-analysis of routine outcome monitoring. Psychotherapy 55:4520–37
    [Google Scholar]
  88. Lenton TM, Held H, Kriegler E, Hall JW, Lucht W et al. 2008. Tipping elements in the Earth's climate system. PNAS 105:61786–93
    [Google Scholar]
  89. Lilienfeld SO, Ritschel LA, Lynn SJ, Cautin RL, Latzman RD 2015. Science-practice gap. The Encyclopedia of Clinical Psychology RL Cautin, SO Lilienfeld 2548–55 Hoboken, NJ: Wiley
    [Google Scholar]
  90. Lorenzo-Luaces L, DeRubeis RJ. 2018. Miles to go before we sleep: advancing the understanding of psychotherapy by modeling complex processes. Cogn. Ther. Res. 42:2212–17
    [Google Scholar]
  91. Lorenzo-Luaces L, DeRubeis RJ, van Straten A, Tiemens B. 2017. A prognostic index (PI) as a moderator of outcomes in the treatment of depression: a proof of concept combining multiple variables to inform risk-stratified stepped care models. J. Affect. Disord. 213:78–85
    [Google Scholar]
  92. Lutz W, de Jong K, Rubel JA, Delgadillo J. 2021. Measuring, predicting and tracking change in psychotherapy. See Barkham et al. 2021 89–133
  93. Lutz W, Deisenhofer A-K, Rubel J, Bennemann B, Giesemann J et al. 2022. Prospective evaluation of a clinical decision support system in psychological therapy. J. Consult. Clin. Psychol. 90:190106
    [Google Scholar]
  94. Lutz W, Ehrlich T, Rubel J, Hallwachs N, Röttger M-A et al. 2013. The ups and downs of psychotherapy: sudden gains and sudden losses identified with session reports. Psychother. Res. 23:114–24
    [Google Scholar]
  95. Lutz W, Hofmann SG, Rubel J, Boswell JF, Shear MK et al. 2014. Patterns of early change and their relationship to outcome and early treatment termination in patients with panic disorder. J. Consult. Clin. Psychol. 82:2287–97
    [Google Scholar]
  96. Lutz W, Leach C, Barkham M, Lucock M, Stiles WB et al. 2005. Predicting change for individual psychotherapy clients on the basis of their nearest neighbors. J. Consult. Clin. Psychol. 73:5904–13
    [Google Scholar]
  97. Lutz W, Martinovich Z, Howard KI 1999. Patient profiling: an application of random coefficient regression models to depicting the response of a patient to outpatient psychotherapy. J. Consult. Clin. Psychol. 67:4571–77
    [Google Scholar]
  98. Lutz W, Rubel JA, Schwartz B, Schilling V, Deisenhofer A-K. 2019. Towards integrating personalized feedback research into clinical practice: development of the Trier Treatment Navigator (TTN). Behav. Res. Ther. 120:103438
    [Google Scholar]
  99. Lutz W, Saunders SM, Leon SC, Martinovich Z, Kosfelder J et al. 2006. Empirically and clinically useful decision making in psychotherapy: differential predictions with treatment response models. Psychol. Assess. 18:2133–41
    [Google Scholar]
  100. Lutz W, Schwartz B. 2021. Trans-theoretical clinical models and the implementation of precision mental health care. World Psychiatry 20:3380–81
    [Google Scholar]
  101. Lutz W, Schwartz B, Hofmann SG, Fisher AJ, Husen K, Rubel JA. 2018. Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study. Sci. Rep. 8:17819
    [Google Scholar]
  102. May RM, Levin SA, Sugihara G 2008. Complex systems: ecology for bankers. Nature 451:7181893–95
    [Google Scholar]
  103. McAleavey AA, Nordberg SS, Moltu C. 2021. Initial quantitative development of the Norse Feedback system: a novel clinical feedback system for routine mental healthcare. Qual. Life Res. 30:3097–115
    [Google Scholar]
  104. McClintock AS, Perlman MR, McCarrick SM, Anderson T, Himawan L 2017. Enhancing psychotherapy process with common factors feedback: a randomized, clinical trial. J. Couns. Psychol. 64:3247–60
    [Google Scholar]
  105. McKay D, Jensen-Doss A. 2021. Harmful treatments in psychotherapy. Clin. Psychol.: Sci. Pract. 28:12–4
    [Google Scholar]
  106. Meehl PE 1973. Why I do not attend case conferences. Psychodiagnosis: Selected Papers PE Meehl 225–302 Minneapolis: Univ. Minn. Press
    [Google Scholar]
  107. Meisel C, El Atrache R, Jackson M, Schubach S, Ufongene C, Loddenkemper T 2020. Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting. Epilepsia 61:122653–66
    [Google Scholar]
  108. Miller G. 2012. The smartphone psychology manifesto. Perspect. Psychol. Sci. 7:3221–37
    [Google Scholar]
  109. Miller PR, Dasher R, Collins R, Griffiths P, Brown F. 2001. Inpatient diagnostic assessments: 1. Accuracy of structured versus unstructured interviews. Psychiatry Res. 105:3255–64
    [Google Scholar]
  110. Miller SD, Duncan BL, Sorrell R, Brown GS. 2005. The partners for change outcome management system. J. Clin. Psychol. 61:2199–208
    [Google Scholar]
  111. Moggia D, Lutz W, Arndt A, Feixas G. 2020. Patterns of change and their relationship to outcome and follow-up in group and individual psychotherapy for depression. J. Consult. Clin. Psychol. 88:8757–73
    [Google Scholar]
  112. Mütze K, Witthöft M, Lutz W, Bräscher A-K. 2021. Matching research and practice: prediction of individual patient progress and dropout risk for basic routine outcome monitoring. Psychother. Res. https://doi.org/10.1080/10503307.2021.1930244
    [Crossref] [Google Scholar]
  113. Ng MY, Schleider JL, Horn RL, Weisz JR. 2021. Psychotherapy for children and adolescents: from efficacy to effectiveness, scaling, and personalizing. See Barkham et al. 2021621–66
    [Google Scholar]
  114. Nordmo M, Monsen JT, Høglend PA, Solbakken OA. 2020. Investigating the dose–response effect in open-ended psychotherapy. Psychother. Res. 31:7859–69
    [Google Scholar]
  115. Olthof M, Hasselman F, Strunk G, van Rooij M, Aas B et al. 2020. Critical fluctuations as an early-warning signal for sudden gains and losses in patients receiving psychotherapy for mood disorders. Clin. Psychol. Sci. 8:125–35
    [Google Scholar]
  116. Østergård OK, Randa H, Hougaard E 2020. The effect of using the Partners for Change Outcome Management System as feedback tool in psychotherapy—a systematic review and meta-analysis. Psychother. Res. 30:2195–212
    [Google Scholar]
  117. Owen J, Adelson J, Budge S, Wampold B, Kopta M et al. 2015. Trajectories of change in psychotherapy. J. Clin. Psychol. 71:9817–27
    [Google Scholar]
  118. Page AC, Camacho KS, Page JT. 2019. Delivering cognitive behaviour therapy informed by a contemporary framework of psychotherapy treatment selection and adaptation. Psychother. Res. 29:8971–73
    [Google Scholar]
  119. Paz A, Rafaeli E, Bar-Kalifa E, Gilboa-Schectman E, Gannot S et al. 2021. Intrapersonal and interpersonal vocal affect dynamics during psychotherapy. J. Consult. Clin. Psychol. 89:3227–39
    [Google Scholar]
  120. Probst T, Kleinstäuber M, Lambert MJ, Tritt K, Pieh C et al. 2020. Why are some cases not on track? An item analysis of the assessment for signal cases during inpatient psychotherapy. Clin. Psychol. Psychother. 27:4559–66
    [Google Scholar]
  121. Ramseyer F, Tschacher W. 2011. Nonverbal synchrony in psychotherapy: coordinated body movement reflects relationship quality and outcome. J. Consult. Clin. Psychol. 79:3284–95
    [Google Scholar]
  122. Raudenbush SW, Bryk AS. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods Thousand Oaks, CA: Sage
  123. Robinson L, Delgadillo J, Kellett S. 2020a. The dose-response effect in routinely delivered psychological therapies: a systematic review. Psychother. Res. 30:179–96
    [Google Scholar]
  124. Robinson L, Kellett S, Delgadillo J. 2020b. Dose-response patterns in low and high intensity cognitive behavioral therapy for common mental health problems. Depress. Anxiety 37:3285–94
    [Google Scholar]
  125. Rubel JA, Fisher AJ, Husen K, Lutz W. 2018. Translating person-specific network models into personalized treatments: development and demonstration of the Dynamic Assessment Treatment Algorithm for Individual Networks (DATA-IN). Psychother. Psychosom. 87:4249–51
    [Google Scholar]
  126. Rubel JA, Rosenbaum D, Lutz W. 2017. Patients’ in-session experiences and symptom change: session-to-session effects on a within- and between-patient level. Behav. Res. Ther. 90:58–66
    [Google Scholar]
  127. Rubel JA, Zilcha-Mano S, Giesemann J, Prinz J, Lutz W 2020. Predicting personalized process-outcome associations in psychotherapy using machine learning approaches—a demonstration. Psychother. Res. 30:3300–9
    [Google Scholar]
  128. Saunders R, Cape J, Fearon P, Pilling S 2016. Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients. J. Affect. Disord. 197:107–15
    [Google Scholar]
  129. Saxon D, Barkham M 2012. Patterns of therapist variability: therapist effects and the contribution of patient severity and risk. J. Consult. Clin. Psychol. 80:4535–46
    [Google Scholar]
  130. Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR et al. 2009. Early-warning signals for critical transitions. Nature 461:726053–59
    [Google Scholar]
  131. Schilling VNLS, Zimmermann D, Rubel JA, Boyle KS, Lutz W. 2021. Why do patients go off track? Examining potential influencing factors for being at risk of psychotherapy treatment failure. Qual. Life Res. 30:3287–98
    [Google Scholar]
  132. Schwartz B, Cohen ZD, Rubel JA, Zimmermann D, Wittmann WW, Lutz W. 2021. Personalized treatment selection in routine care: integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy. Psychother. Res. 31:133–51
    [Google Scholar]
  133. Shimokawa K, Lambert MJ, Smart DW 2010. Enhancing treatment outcome of patients at risk of treatment failure: meta-analytic and mega-analytic review of a psychotherapy quality assurance system. J. Consult. Clin. Psychol. 78:3298–311
    [Google Scholar]
  134. Stiles WB, Honos-Webb L, Surko M. 1998. Responsiveness in psychotherapy. Clin. Psychol.: Sci. Pract. 5:4439–58
    [Google Scholar]
  135. Stoker TB, Barker RA. 2020. Recent developments in the treatment of Parkinson's Disease. F1000Research 9:Faculty Rev.862
    [Google Scholar]
  136. Strunk DR, Cooper AA, Ryan ET, DeRubeis RJ, Hollon SD. 2012. The process of change in cognitive therapy for depression when combined with antidepressant medication: predictors of early intersession symptom gains. J. Consult. Clin. Psychol. 80:5730–38
    [Google Scholar]
  137. Stulz N, Lutz W, Kopta SM, Minami T, Saunders SM 2013. Dose-effect relationship in routine outpatient psychotherapy: Does treatment duration matter?. J. Couns. Psychol. 60:4593–600
    [Google Scholar]
  138. Suvarna V. 2010. Phase IV of drug development. Perspect. Clin. Res. 1:257–60
    [Google Scholar]
  139. Tang TZ, DeRubeis RJ. 1999. Sudden gains and critical sessions in cognitive-behavioral therapy for depression. J. Consult. Clin. Psychol. 67:6894–904
    [Google Scholar]
  140. Tversky A, Kahneman D. 1974. Judgment under uncertainty: heuristics and biases. Science 185:41571124–31
    [Google Scholar]
  141. van Borkulo C, Boschloo L, Borsboom D, Penninx BWJH, Waldorp LJ, Schoevers RA. 2015. Association of symptom network structure with the course of corrected depression. JAMA Psychiatry 72:121219–26
    [Google Scholar]
  142. van Bronswijk SC, Bruijniks SJE, Lorenzo-Luaces L, DeRubeis RJ, Lemmens LHJM et al. 2021. Cross-trial prediction in psychotherapy: external validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression. Psychother. Res. 31:178–91
    [Google Scholar]
  143. Walfish S, McAlister B, O'Donnell P, Lambert MJ. 2012. An investigation of self-assessment bias in mental health providers. Psychol. Rep. 110:2639–44
    [Google Scholar]
  144. Webb CA, Forgeard M, Israel ES, Lovell-Smith N, Beard C, Björgvinsson T 2022. Personalized prescriptions of therapeutic skills from patient characteristics: an ecological momentary assessment approach. J. Consult. Clin. Psychol. 90:15160
    [Google Scholar]
  145. Webb CA, Trivedi MH, Cohen ZD, Dillon DG, Fournier JC et al. 2019. Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study. Psychol. Med. 49:71118–27
    [Google Scholar]
  146. White MM, Lambert MJ, Ogles BM, Mclaughlin SB, Bailey RJ, Tingey KM. 2015. Using the Assessment for Signal Clients as a feedback tool for reducing treatment failure. Psychother. Res. 25:6724–34
    [Google Scholar]
  147. Wichers M, Groot PC, Psychosystems, ESM Group, EWS Group. 2016. Critical slowing down as a personalized early warning signal for depression. Psychother. Psychosom. 85:2114–16
    [Google Scholar]
  148. Wichers M, Peeters F, Rutten BPF, Jacobs N, Derom C et al. 2012. A time-lagged momentary assessment study on daily life physical activity and affect. Health Psychol 31:2135–44
    [Google Scholar]
  149. Wright AGC, Woods WC. 2020. Personalized models of psychopathology. Annu. Rev. Clin. Psychol. 16:49–74
    [Google Scholar]
  150. Zell E, Strickhouser JE, Sedikides C, Alicke MD. 2020. The better-than-average effect in comparative self-evaluation: a comprehensive review and meta-analysis. Psychol. Bull. 146:2118–49
    [Google Scholar]
  151. Zilcha-Mano S. 2017. Is the alliance really therapeutic? Revisiting this question in light of recent methodological advances. Am. Psychol. 72:4311–25
    [Google Scholar]
  152. Zilcha-Mano S. 2019. Major developments in methods addressing for whom psychotherapy may work and why. Psychother. Res. 29:6693–708
    [Google Scholar]
/content/journals/10.1146/annurev-clinpsy-071720-014821
Loading
/content/journals/10.1146/annurev-clinpsy-071720-014821
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error