24
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found

      Nutzung von Conjoint-Analysen zur Messung von Therapiezielpräferenzen aus Patientenperspektive in der Behandlung psychischer Störungen : Eine systematische Literaturübersicht

      research-article

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Hintergrund: Im Zuge der wachsenden Bedeutung von Ansätzen zur Patientenorientierung und -partizipation in der Gesundheitsversorgung gewinnt die Bestimmung subjektiver Therapiezielpräferenzen unterschiedlicher Akteure (Patienten, Behandler, Angehörige) zunehmend an Forschungsinteresse. Stated-Preference-Methods ermöglichen die systematische Untersuchung speziell patientenorientierter Fragestellungen. Ziele der Studie: Identifikation und Beschreibung (nach formalen, methodischen und inhaltlichen Merkmalen) von Studien mit Stated-Preference-Methods (Conjoint Measurements, Conjoint Analysis, Discrete Choice Experiments) in der Versorgung von Patienten mit psychischen Störungen mit dem Ziel, eine Bewertung zur Anwendbarkeit der Methode (Potential, Nutzen, Grenzen) in zukünftiger patientenorienterter Forschung abzuleiten. Methode: Systematische Literaturrecherche mit folgenden Studieneinschlusskriterien: Participants: Interventionen zur Behandlung von Patienten mit psychischer Störung; Intervention: psychotherapeutische, psychiatrische, hausärztliche Behandlungen (stationär, teil-stationär, ambulant); Comparison: Studien mit keiner (Ein-Gruppen-Design) oder mindestens einer Kontrollgruppe; Outcomes: conjoint-spezifische Angaben zu Nutzenwerten. Ergebnisse: Conjoint-Analysen werden in unterschiedlichen Forschungsdesigns und unter heterogenen Rahmenbedingungen (Stichprobe, Störungsbild, Setting, Intervention, Zieldimension) zur Messung von Therapiezielpräferenzen eingesetzt. Die Erstellung des Conjoint-Designs erfolgt in der Regel reduziert (orthogonal), mithilfe von Softwarepaketen, die Erhebung als Fragebogen. Schlussfolgerungen: Conjoint-Analysen ermöglichen differenzierte Aussagen über Therapiepräferenzstrukturen auf Basis relationaler Beurteilungsszenarien und stellen damit eine fundiertere Basis zur Verbesserung der Patientenorientierung in der Gesundheitsversorgung zur Verfügung. Die Befundlage belegt, dass sich die Methode zur Untersuchung patientenorientierter Fragestellungen (mehrheitlich zu Pharmakotherapie und Kombinationsbehandlung) in der Versorgung psychischer Störungen (depressive Störungen, ADHS, Schizophrenie, bipolare Störungen, Tabak- und Alkoholabhängigkeit und chronische Schmerzen) eignet. Allerdings ist der erfolgreiche Einsatz der Methodik an einige Voraussetzungen geknüpft (u. a. Unabhängigkeit der betrachteten Therapiezielaspekte, Designkomplexität). Forschungsbedarf besteht u. a. im Hinblick auf bisher nicht untersuchte Störungsbilder (u. a. somatoforme, Angst-, Ess-, Persönlichkeitsstörungen) und Interventionen (u. a. reine Psychotherapie, störungsspezifische Behandlungen).

          Conjoint Analysis for Measuring Treatment Preferences of Patients With Psychiatric Disorders: A Systematic Literature Review

          Background: Due to the growing interest in patient participation and patient-centered health care, the determination of subjective treatment goals of the health systems various actors (patients, professionals, relatives) becomes more important. Stated preference methods allow for the systematic investigation of patient-centered objectives. Aims: Identification and description (according to formal, methodological, and contentual criteria) of studies which apply stated preference methods (conjoint measurements, conjoint

          analysis, discrete choice experiments) in the health care for patients suffering from psychiatric disorders, in order to evaluate the method’s applicability (potential, convenience, limitations) in future patient-centered research. Method: Systematic literature review applying the following inclusion criteria: Participants: interventions treating patients with psychiatric disorders; Intervention: psychotherapeutic, psychiatric, general practice (in-patient, day-patient, out-patient); Comparison: studies with no (one-group-design) or at least one control group; and Outcomes: conjoint-specific information on utility values. Results: Conjoint analysis is applied to various study designs under heterogeneous conditions (sample, disorder, setting, treatment, goals) to access preferences about treatment goals. Conjoint designs are usually applied in fractional orthogonal design, using software packages, data is collected with questionnaires. Conclusions: Conjoint analysis allows sophisticated conclusions about patients preferences for treatment goals based on relational judgments and therefore offers a sound basis for improving patient-centeredness in health care. Findings show that conjoint analysis can be applied to investigate patient-centered objectives (mostly pharmacotherapy and combined therapy) in health care of psychiatric disorders like depression, ADHD, schizophrenia, bipolar disorders, tobacco and alcohol related disorders, or chronic pain. However, the successful use requires certain conditions (e. g., independence of the considered treatment goals, complexity of design). There is need for further research including not yet investigated disorders (e. g., somatic related, anxiety, eating, personality disorders) and interventions (e. g., only psychotherapy, disorder specific treatments).

          Related collections

          Most cited references51

          • Record: found
          • Abstract: found
          • Article: not found

          Do increases in patient activation result in improved self-management behaviors?

          The purpose of this study is to determine whether patient activation is a changing or changeable characteristic and to assess whether changes in activation also are accompanied by changes in health behavior. To obtain variability in activation and self-management behavior, a controlled trial with chronic disease patients randomized into either intervention or control conditions was employed. In addition, changes in activation that occurred in the total sample were also examined for the study period. Using Mplus growth models, activation latent growth classes were identified and used in the analysis to predict changes in health behaviors and health outcomes. Survey data from the 479 participants were collected at baseline, 6 weeks, and 6 months. Positive change in activation is related to positive change in a variety of self-management behaviors. This is true even when the behavior in question is not being performed at baseline. When the behavior is already being performed at baseline, an increase in activation is related to maintaining a relatively high level of the behavior over time. The impact of the intervention, however, was less clear, as the increase in activation in the intervention group was matched by nearly equal increases in the control group. Results suggest that if activation is increased, a variety of improved behaviors will follow. The question still remains, however, as to what interventions will improve activation.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Conjoint Measurement for Quantifying Judgmental Data

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              The Importance of Utility Balance in Efficient Choice Designs

                Bookmark

                Author and article information

                Journal
                zkp
                Zeitschrift für Klinische Psychologie und Psychotherapie
                Forschung und Praxis
                Hogrefe Verlag, Göttingen
                1616-3443
                2190-6297
                Januar 2015
                : 44
                : 1
                : 1-16
                Affiliations
                [ 1 ] Institut für Psychologie, Professur für Klinische Psychologie, TU Chemnitz
                Author notes
                Dipl.-Psych. Frederik Haarig, Prof. Dr. Stephan Mühlig, Technische Universität Chemnitz, Fakultät für Human- und Sozialwissenschaften, Institut für Psychologie, 09107 Chemnitz, E-Mail: frederik.haarig@ 123456psychologie.tu-chemnitz.de , E-Mail: stephan.muehlig@ 123456psychologie.tu-chemnitz.de
                Article
                zkp_44_1_1
                10.1026/1616-3443/a000287
                f47f2c50-eafd-4219-81ee-bdaad556b11c
                Copyright @ 2015
                History
                Categories
                Originalia

                Psychology,Clinical Psychology & Psychiatry
                Patientenorientierung,stated preference methods,conjoint analysis,treatment preferences,mental disorders,patient orientation,Stated-Preference-Methoden,psychische Störungen,Behandlungspräferenzen,Conjoint Analyse

                Comments

                Comment on this article