Objectives: Late-life major depressive disorder (MDD) can be conceptualized as a complex dynamic system. However, it is not straightforward how to analyze the covarying depressive symptoms over... Show moreObjectives: Late-life major depressive disorder (MDD) can be conceptualized as a complex dynamic system. However, it is not straightforward how to analyze the covarying depressive symptoms over time in case of sparse panel data. Dynamic time warping (DTW) analysis may yield symptom networks and dimensions both at the patient and group level. Methods: In the Netherlands Study of Depression in Older People (NESDO) depressive symptoms were assessed every 6 months using the 30-item Inventory of Depressive Symptomatology (IDS) with up to 13 assessments per participant. Our sample consisted of 182 persons, aged >= 60 years, with an IDS total score of 26 or higher at baseline. Symptom networks dimensions, and centrality metrics were analyzed using DTW and Distatis analyses. Results: The mean age was 69.8 years (SD 7.1), with 69.0% females, and a mean IDS score of 38.0 (SD = 8.7). DTW enabled visualization of an idiographic symptom network in a single NESDO participant. In the group-level nomothetic approach, four depressive symptom dimensions were identified: "core symptoms", "lethargy/somatic", "sleep", and "appetite/atypical". Items of the "internalizing symptoms" dimension had the highest centrality, whose symptom changes over time were most similar to those changes of other symptoms. Conclusions: DTW revealed symptom networks and dimensions based on the within-person symptom changes in older MDD patients. Its centrality metrics signal the most influential symptoms, which may aid personalized care. Show less
In this thesis the heterogeneity of late-life depression is being examined. The first part of the thesis focuses on data-driven analyses as a way of identifying subtypes of late-life depression.... Show moreIn this thesis the heterogeneity of late-life depression is being examined. The first part of the thesis focuses on data-driven analyses as a way of identifying subtypes of late-life depression. Through latent class analysis, we have identified three subtypes: a severe melancholic subtype, an atypical subtype, and a moderately severe subtype. These subtypes had different sociodemographic and clinical characteristics, but no specific biological disturbances could be addressed to the different subtypes. This is probably because of the tangle of (patho)physiological processes in aging itself, muddling the results. The stability of these subtypes over a two-year follow-up was however high, strengthening the clinical relevance of found subtypes.The second part of this thesis examines psychomotor disturbances in melancholic depression as a possible predicting symptom of response in electroconvulsive therapy. We have found that psychomotor disturbances predict to a certain amount the response to electroconvulsive therapy, but this effect was overruled by the predictive value of psychotic symptoms in depression. Finally, we have examined the speed of response of different depressive symptoms on electroconvulsive therapy in older depressed persons, and have found that all ten symptoms show response in two weeks, underlining the safety and efficacy of electroconvulsive therapy in older persons. Show less
