Objective. The main hypothesis, and the objective of the study, was to test if the participants allocated to the treatment group would show a larger reduction in depressive symptoms than those in the control group.Methods. This study was a randomized nine week trial of an Internet-administered treatment based on guided physical exercise for Major Depressive Disorder (MDD). A total of 48 participants with mild to moderate depression, diagnosed using the Structured Clinical Interview for DSM-IV Axis I Disorders, were randomized either to a treatment intervention or to a waiting-list control group. The main outcome measure for depression was the Beck Depression Inventory-II (BDI-II), and physical activity level was measured using the International Physical Activity Questionnaire (IPAQ). The treatment program consisted of nine text modules, and included therapist guidance on a weekly basis.Results. The results showed significant reductions of depressive symptoms in the treatment group compared to the control group, with a moderate between-group effect size (Cohen’s d = 0.67; 95% confidence interval: 0.09–1.25). No difference was found between the groups with regards to increase of physical activity level. For the treatment group, the reduction in depressive symptoms persisted at six months follow-up.Conclusions. Physical activity as a treatment for depression can be delivered in the form of guided Internet-based self-help.Trial Registration. The trial was registered at ClinicalTrials.gov (NCT01573130).

The results indicated a substantial dyadic reciprocity in alliance ratings. Within-therapist variation in alliance was a better predictor of treatment outcome than between-therapist variation in alliance ratings.

Understanding how different groups of patients change at different rates is important for treatment selection, planning and evaluation. This study aimed to assess whether an approach to classifying patients on the basis of initial symptom distress profiles (ISDPs) derived from a self-rated questionnaire measuring psychological distress may be useful in predicting treatment response. The Clinical Outcome in Routine Evaluation-Outcome Measure were collected from 1,095 first line mental health service patients (M [SD] age ϭ 37.3 [14.3] years; 74% female) prior to every session. Latent profile analysis was performed on the questionnaires from the first session to classify participants into subtypes, which were then used to predict change rates. The best-fitting model identified 4 ISDP subtypes with significantly different treatment responses. Profile 1 predicted very slow change rate and indicated low initial distress coupled with low deviations among problem areas. Profile 2 predicted slow change rate with average initial distress and low emphasis on questions relating to risk of harming oneself and/or others. Profile 3 predicted fast improvement rate and showed high initial distress combined with low emphasis on the risk area. Profile 4 predicted moderate change rate and displayed very high initial distress accompanied with more emphasis on the risk area. Findings support the potential utility of ISDP subtypes to predict treatment response, suggesting that intake data that is easily collected by the clinician contain reliable information about treatment prognosis. The study is exploratory and needs to be replicated before stable conclusions can be drawn. Public Significance StatementThis study demonstrates the importance of considering specific compositions of patient rated psychological distress at the first treatment session to predict recovery rate. Better understanding of how baseline factors relate to outcome in psychotherapy enables guidance for clinicians and can facilitate treatment planning as well as evaluation of treatment response in ongoing treatments (treatment monitoring).

Background: Psychotherapy research has shifted from mainly focusing on the average effects of different treatments to concentrating more on questions related to the individual patient. This aligns with the goals of precision medicine and patient-focused research and might include studying predictors to forecast important patient behaviors, such as premature termination or expected change rates.When research attention shifts, it often gives rise to the implementation of new statistical methods that, in turn, can illuminate new challenges that must be addressed. For example, the fields of classical statistics and data science have merged in interesting ways in recent times, which has led to an expansion of the methodological toolbox available for psychotherapy research. However, some strengths and limitations of these new approaches should be recognized as they come from slightly different research traditions. For instance, the broader goals of scientific exploration, such as description, prediction, and explanation, might be addressed and given different importance under these approaches.Studying these matters in the context of routine care leads to specific considerations that must be reflected upon, such as how to define relevant outcomes and draw sound conclusions from observational data collected in a naturalistic setting.Aim: The aim of the thesis was to concretize these matters by contrasting how traditional methods for predicting certain psychotherapy outcomes have been studied in the past, and how more advanced statistical methods might be used to enhance knowledge of how to predict these outcomes today.Studies/Results: Three studies were performed: Paper I focused on how Multi Level Modeling (MLM) can be used to study aspects of the relationship between working alliance and treatment outcome that has been overlooked by most earlier studies. The classical way of studying this question has been by using simple correlation analysis to explore the association between patient-rated alliance and treatment outcome. However, with the help of MLM and ratings from both parties of the therapeutic dyad, it is possible to analyze how these ratings relate to each other at different levels in the data (i.e., from patient to patient within the same therapist, denoted within therapist herein, and from therapist to therapist, denoted between therapists herein). MLM also makes it possible to explore the degree of correlation between working alliance and treatment outcome when studied between compared to within therapists, which might have importance for how to direct further studies of this association.