| 1. |
- Fisher, Hadar, et al.
(författare)
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Therapists' oxytocin response mediates the association between patients' negative emotions and psychotherapy outcomes
- 2023
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Ingår i: Journal of Affective Disorders. - : Elsevier. - 0165-0327 .- 1573-2517. ; 338, s. 163-170
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Tidskriftsartikel (refereegranskat)abstract
- Background: Existing literature suggests that patients' experiences of emotions, especially negative emotions, predict outcomes in psychotherapies for major depressive disorder. However, the specific mechanisms underlying this effect remain unclear. Based on studies pointing to the role of oxytocin (OT) in attachment relationships, we proposed and tested a mediation model where the therapists' hormonal responses, as represented by increases in their OT levels, mediates the association between negative emotions and symptomatic change. Method: OT saliva samples (pre- and post-session, N = 435) were collected on a fixed schedule over 16 sessions from the therapists of 62 patients receiving psychotherapy for major depression. The Hamilton Rating Scale for Depression was administered to the patients before the sessions, and the patients reported their in-session emotions after the sessions. Results: The findings support the proposed within-person mediation model: (a) higher levels of negative emotions in patients predicted greater increases in therapist OT levels pre- to post-session throughout treatment; (b) greater OT levels in therapists, in turn, predicted reduction in patients' depressive symptoms on the subsequent assessment; and (c) the therapists' OT levels significantly mediated the association between patients' negative emotions and reduction in their depressive symptoms. Limitations: This design precluded establishing a time sequence between patients' negative emotions and therapists' OT; thus, causality could not be inferred. Conclusion: These findings point to a possible biological mechanism underlying the effects of patients' experiences of negative emotions on treatment outcomes. The findings suggest that therapists' OT responses could potentially serve as a biomarker of an effective therapeutic processes.
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| 2. |
- Fluckiger, Christoph, et al.
(författare)
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The Reciprocal Relationship Between Alliance and Early Treatment Symptoms: A Two-Stage Individual Participant Data Meta-Analysis
- 2020
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Ingår i: Journal of Consulting and Clinical Psychology. - : AMER PSYCHOLOGICAL ASSOC. - 0022-006X .- 1939-2117. ; 88:9, s. 829-843
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Even though the early alliance has been shown to robustly predict posttreatment outcomes, the question whether alliance leads to symptom reduction or symptom reduction leads to a better alliance remains unresolved. To better understand the relation between alliance and symptoms early in therapy, we meta-analyzed the lagged session-by-session within-patient effects of alliance and symptoms from Sessions I to 7. Method: We applied a 2-stage individual participant data meta-analytic approach. Based on the data sets of 17 primary studies from 9 countries that comprised 5,350 participants, we first calculated standardized session-by-session within-patient coefficients. Second, we meta-analyzed these coefficients by using random-effects models to calculate omnibus effects across the studies. Results: In line with previous meta-analyses, we found that early alliance predicted posttreatment outcome. We identified significant reciprocal within-patient effects between alliance and symptoms within the first 7 sessions. Cross-level interactions indicated that higher alliances and lower symptoms positively impacted the relation between alliance and symptoms in the subsequent session. Conclusion: The findings provide empirical evidence that in the early phase of therapy. symptoms and alliance were reciprocally related to one other, often resulting in a positive upward spiral of higher alliance/lower symptoms that predicted higher alliances/lower symptoms in the subsequent sessions. Two-stage individual participant data meta-analyses have the potential to move the field forward by generating and interlinking well-replicable process-based knowledge.
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| 3. |
- Shani, Reut, et al.
(författare)
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Personalized cognitive training : Protocol for individual-level meta-analysis implementing machine learning methods
- 2021
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Ingår i: Journal of Psychiatric Research. - : Elsevier BV. - 0022-3956 .- 1879-1379. ; 138, s. 342-348
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Tidskriftsartikel (refereegranskat)abstract
- Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.
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| 4. |
- Uckelstam, Carl-Johan, 1983-
(författare)
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Looking into the Future : How to Use Advanced Statistical Methods for Predicting Psychotherapy Outcomes in Routine Care
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Psychotherapy research has shifted from mainly focusing on the average effects of different treatments to concentrating more on questions related to the individual patient. When research attention shifts, it can give rise to the implementation of new statistical methods that, in turn, can illuminate new challenges that must be addressed.The aim of the thesis was to study how traditional methods for predicting certain psychotherapy outcomes have been conducted in the past, and how more advanced statistical methods might be used to enhance knowledge of how to predict these outcomes today.Three studies were performed: Paper I focused on how Multi Level Modeling (MLM) can be used to study certain aspects of the relationship between working alliance and treatment outcome. In Paper II, Latent Profile Analysis (LPA) and item-level analysis were used to give nuance to the relationship between psychological distress at baseline and change rate during treatment. Finally, in Paper III, Machine Learning (ML) was used to detect dropout patients in the early phase of treatment by exploring complex patterns of symptom distress during the early phase of treatment.The thesis showed how different goals of scientific exploration can be studied in the context of routine care with the use of these statistical frameworks and discussed some of the challenges and opportunities worth noting when entering this line of research.
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