TY - JOUR
T1 - Network structure of time-varying depressive symptoms through dynamic time warp analysis in late-life depression
AU - van Zelst, Denise C. R.
AU - Veltman, Eveline M.
AU - Rhebergen, Didi
AU - Naarding, Paul
AU - Kok, Almar A. L.
AU - Ottenheim, Nathaly Rius
AU - Giltay, Erik J.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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 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.
AB - 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 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.
UR - https://www.scopus.com/pages/publications/85135428385
U2 - 10.1002/gps.5787
DO - 10.1002/gps.5787
M3 - Article
SN - 0885-6230
VL - 37
JO - International Journal of Geriatric Psychiatry
JF - International Journal of Geriatric Psychiatry
IS - 9
ER -