Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Latent Change Score Modeling as a Method for Analyzing the Antidepressant Effect of a Psychosocial Intervention in Alzheimer's Disease
Stockholm University, Faculty of Social Sciences, Aging Research Center (ARC), (together with KI). Humboldt Universität zu Berlin, Germany.
2015 (English)In: Psychotherapy and Psychosomatics, ISSN 0033-3190, E-ISSN 1423-0348, Vol. 84, no 3Article in journal (Refereed) Published
Abstract [en]

Background: Developing and evaluating interventions for patients with age-associated disorders is a rising field in psychotherapy research. Its methodological challenges include the high between-subject variability and the wealth of influencing factors associated with longer lifetime. Latent change score modeling (LCSM), a technique based on structural equation modeling, may be well suited to analyzing longitudinal data sets obtained in clinical trials. Here, we used LCSM to evaluate the antidepressant effect of a combined cognitive behavioral/cognitive rehabilitation (CB/CR) intervention in Alzheimer's disease (AD). Methods: LCSM was applied to predict the change in depressive symptoms from baseline as an outcome of the CORDIAL study, a randomized controlled trial involving 201 patients with mild AD. The participants underwent either the CORDIAL CB/CR program or standard treatment. Using LCSM, the model best predicting changes in Geriatric Depression Scale scores was determined based on this data set. Results: The best fit was achieved by a model predicting a decline in depressive symptoms between before and after testing. Assignment to the intervention group as well as female gender revealed significant effects in model fit indices, which remained stable at 6-and 12-month follow-up examinations. The pre-post effect was pronounced for patients with clinically relevant depressive symptoms at baseline. Conclusions: LCSM confirmed the antidepressant effect of the CORDIAL therapy program, which was limited to women. The effect was pronounced in patients with clinically relevant depressive symptoms at baseline. Methodologically, LCSM appears well suited to analyzing longitudinal data from clinical trials in aged populations, by accounting for the high between-subject variability and providing information on the differential indication of the probed intervention.

Place, publisher, year, edition, pages
2015. Vol. 84, no 3
Keyword [en]
Structural equation modeling, Dementia, Depression
National Category
Psychiatry Psychology
Identifiers
URN: urn:nbn:se:su:diva-117056DOI: 10.1159/000376583ISI: 000352379800003OAI: oai:DiVA.org:su-117056DiVA: diva2:811191
Note

AuthorCount:4;

Available from: 2015-05-11 Created: 2015-05-06 Last updated: 2017-12-04Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text
By organisation
Aging Research Center (ARC), (together with KI)
In the same journal
Psychotherapy and Psychosomatics
PsychiatryPsychology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 49 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf