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Войти в исследования Journal of consulting and clinical psychology · 2026
What happens after being not-on-track? Patterns of change, profiles and prediction of at-risk patients.
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Moggia Danilo, Schwartz Brian, Lutz Wolfgang
Аннотация
OBJECTIVES: Clinicians often struggle to accurately identify patients at risk of treatment failure. Feedback-informed treatment offers real-time outcome monitoring to help detect and address potential treatment failures early. This study examined patterns of change among not-on-track (NOT) patients during feedback-informed treatment, explored how patients' characteristics related to these patterns, and assessed the feasibility of predicting them.
METHOD: Data from 573 NOT patients in a university outpatient psychotherapy clinic were analyzed. Growth mixture modeling identified distinct change trajectories, omnibus tests compared patients' characteristics across patterns, and multinomial logistic regression evaluated the predictive value of these characteristics.
RESULTS: Three distinct patterns of change emerged, differing in symptom severity at the point of being flagged as NOT and in subsequent treatment response: C1: severe-modest responders, C2: severe-fast improvers, and C3: moderate-improvers. The patterns were differentiated by key intake variables, including symptom severity, personality disorder traits, interpersonal problems, and emotion regulation difficulties. Two of the patterns were predicted with acceptable accuracy.
CONCLUSION: All patient subgroups demonstrated improvement. Nevertheless, they became NOT for different reasons, and their responses were associated with different clinical characteristics. These findings suggest that a NOT alert should not be interpreted as a homogeneous signal, and patients may require different adaptations. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
METHOD: Data from 573 NOT patients in a university outpatient psychotherapy clinic were analyzed. Growth mixture modeling identified distinct change trajectories, omnibus tests compared patients' characteristics across patterns, and multinomial logistic regression evaluated the predictive value of these characteristics.
RESULTS: Three distinct patterns of change emerged, differing in symptom severity at the point of being flagged as NOT and in subsequent treatment response: C1: severe-modest responders, C2: severe-fast improvers, and C3: moderate-improvers. The patterns were differentiated by key intake variables, including symptom severity, personality disorder traits, interpersonal problems, and emotion regulation difficulties. Two of the patterns were predicted with acceptable accuracy.
CONCLUSION: All patient subgroups demonstrated improvement. Nevertheless, they became NOT for different reasons, and their responses were associated with different clinical characteristics. These findings suggest that a NOT alert should not be interpreted as a homogeneous signal, and patients may require different adaptations. (PsycInfo Database Record (c) 2026 APA, all rights reserved).