Trajectories and determinants of functional limitations in late-life depression: A 2-year prospective cohort study
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Background: In mental health research, functional recovery is increasingly valued as an important outcome in addition to symptomatic remission. Methods: Course types of functional limitations among depressed older patients and its relation with symptomatic remission were explored in a naturalistic cohort study (Netherlands Study of Depression in Older persons). 378 depressed older patients (≥60 years) and 132 non-depressed persons were included. Depressive disorders were assessed with Composite International Diagnostic Interview at baseline and two-year follow-up. Functional limitations were assessed every 6 months with the World Health Organization Disability Assessment II. Results: Depressed patients had more functional limitations compared to their non-depressed counterparts. Growth Mixture Modeling among depressed patients identified two trajectories of functional limitations, both starting at a high disability level. The largest subgroup (81.2%) was characterized by a course of high disability levels over time. The smaller subgroup (18.8%) had an improving course (functional recovery). After two years, the main predictor of functional recovery was the remission of depression. Among symptomatic remitted patients, female sex, higher level of education, higher gait speed, and less severe depression were associated with no functional recovery. Non-remitted patients without functional recovery were characterized by the presence of more chronic somatic diseases, a lower sense of mastery, and a higher level of anxiety. Conclusions: 1 in 5 depressed older patients have a course with functional recovery. Combining functional and symptomatic recovery points to a subgroup of older patients that might profit from more rigorous psychiatric treatment targeted at psychiatric comorbidity and a group of frail depressed older patients that might profit from integrated geriatric rehabilitation.
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ML and AI techniques, such as deep learning and longitudinal modeling, can analyze large, complex datasets spanning multiple life stages to identify patterns and trajectories of health and disease over time.
“ML and AI techniques, such as deep learning and longitudinal modeling, can analyze large, complex datasets spanning multiple life stages to identify patterns and trajectories of health and disease over time [30, 31].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [31]
Unsupervised learning techniques, such as clustering and latent class analysis, can uncover distinct subgroups of individuals with similar developmental trajectories, which may inform the timing of interventions.
“Unsupervised learning techniques, such as clustering and latent class analysis, can uncover distinct subgroups of individuals with similar developmental trajectories, which may inform the timing of interventions [30, 31].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [31]
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References 48 · Cited by 22
- Authors
- Wassink-Vossen, S., Collard, R. M., Wardenaar, K. J., Verhaak, P. F. M., Rhebergen, D., Naarding, P., & Voshaar, R. C. O.
- Affiliations
- Radboud University Medical Center (NL)
- University Medical Center Groningen (NL)
- Amsterdam Public Health (NL)
- GGNet (NL)
- ORCID
- Venue
- European Psychiatry
- Publisher
- Cambridge University Press (CUP)
- Published
- 2019-10
- Volume
- 62
- Pages
- 90-96
- ISSN
- 0924-9338 (ISSN-L)
- 1778-3585
- OpenAlex
- W2973426911
- Cited by
- 22
- References
- 48
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 6 · 2022: 3 · 2021: 2 · 2020: 1
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Alliance for Research on Schizophrenia and Depression
- Universiteit Leiden
- Radboud Universiteit
- Vrije Universiteit Amsterdam
- Fonds NutsOhra
- GGZ inGeest
- Rivierduinen
- Leids Universitair Medisch Centrum
- Universitair Medisch Centrum Groningen
- Radboud Universitair Medisch Centrum
- Stichting tot Steun Vereniging tot Christelijke Verzorging van Geestes- en Zenuwzieken
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Aging, Health, and Disability
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
- Keywords
- geriatric depression, functional limitations, functional recovery, disability trajectories, depression remission, older adults, walking speed, chronic somatic diseases, sense of mastery, anxiety, geriatric rehabilitation, psychiatric comorbidity
- MeSH terms
- Aged, Aged, 80 and over, Depression, Depressive Disorder, Disability Evaluation, Educational Status, Female, Humans, Male, Middle Aged, Netherlands, Prospective Studies, Sex Factors
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