Trajectories of Resilience and Depression and Associations with Hope Between Discharge and 12 Months Post-Discharge after Acquired Neurological Injury

The hospital-to-home transition is a heightened period of psychological vulnerability after acquired brain injury (ABI) and spinal cord injury (SCI), during which individuals draw on internal resources such as resilience to cope. However, the stability of resilience during the first 12 months post-discharge is largely unknown. This study aimed to examine trajectories of resilience and depressive symptoms and associations with levels of hope during the first 12 months post-discharge after ABI and SCI. Individuals with ABI and SCI were recruited between June 2023 and July 2024 from specialist inpatient rehabilitation units and were administered measures of resilience, depressive symptoms, and hope at discharge and at 3 and 12 months post-discharge. In total, 125 individuals with ABI ( n = 70) and SCI ( n = 55) were recruited at discharge, with longitudinal data available for 107 of these participants. Latent Class Growth Mixture Modeling identified four trajectories of resilience over 12 months post-discharge, including two stable (high-stable n = 27; moderate-stable n = 67) and two changing (stable-decreasing n = 6; decreasing-stable n = 7). Four depression trajectories were also identified, including low-stable ( n = 70), moderate-fluctuating ( n = 25), decreasing-increasing ( n = 7), and increasing-stable ( n = 5). In terms of group membership across trajectories, individuals with high-stable resilience typically had low-stable depression (85%), whereas other resilience trajectories had more variable membership across depression trajectories. Levels of hope were significantly higher and more stable for the high-stable resilience trajectory than for other resilience trajectories. However, levels of hope significantly decreased for the low-stable depression trajectory. Overall, patterns of resilience post-discharge after SCI and ABI were related to, but distinct from, depressive symptoms and hope. While resilience was largely stable over time, depressive symptoms increased for nearly one-third of individuals, and hope often decreased in the context of stable levels of resilience and depression, thus informing priorities for clinical management.

Authors

Institutions

Publication Details

Journal
Journal of Neurotrauma
Published
2026-10-15
DOI
https://doi.org/10.1177/08977151261497616
Primary Topic
Resilience and Mental Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Trajectories of Resilience and Depression and Associations with Hope Between Discharge and 12 Months Post-Discharge after Acquired Neurological Injury

Christy Hogan, Kylie Ferguson, Rachel Brough, Timothy J. Geraghty et al.
Journal of Neurotrauma
Resilience and Mental Health
article

Trajectories of Resilience and Depression and Associations with Hope Between Discharge and 12 Months Post-Discharge after Acquired Neurological Injury

Christy Hogan, Kylie Ferguson, Rachel Brough, Timothy J. Geraghty, Tamara Ownsworth, Delena I. Amsters, Jessie Mitchell, Areti Kennedy, Ryan Bell, Kerrin Watter, Rachel Jones, Jayke Bennett, Melissa Kendall, Emily Bray
article en

Abstract

The hospital-to-home transition is a heightened period of psychological vulnerability after acquired brain injury (ABI) and spinal cord injury (SCI), during which individuals draw on internal resources such as resilience to cope. However, the stability of resilience during the first 12 months post-discharge is largely unknown. This study aimed to examine trajectories of resilience and depressive symptoms and associations with levels of hope during the first 12 months post-discharge after ABI and SCI. Individuals with ABI and SCI were recruited between June 2023 and July 2024 from specialist inpatient rehabilitation units and were administered measures of resilience, depressive symptoms, and hope at discharge and at 3 and 12 months post-discharge. In total, 125 individuals with ABI ( n = 70) and SCI ( n = 55) were recruited at discharge, with longitudinal data available for 107 of these participants. Latent Class Growth Mixture Modeling identified four trajectories of resilience over 12 months post-discharge, including two stable (high-stable n = 27; moderate-stable n = 67) and two changing (stable-decreasing n = 6; decreasing-stable n = 7). Four depression trajectories were also identified, including low-stable ( n = 70), moderate-fluctuating ( n = 25), decreasing-increasing ( n = 7), and increasing-stable ( n = 5). In terms of group membership across trajectories, individuals with high-stable resilience typically had low-stable depression (85%), whereas other resilience trajectories had more variable membership across depression trajectories. Levels of hope were significantly higher and more stable for the high-stable resilience trajectory than for other resilience trajectories. However, levels of hope significantly decreased for the low-stable depression trajectory. Overall, patterns of resilience post-discharge after SCI and ABI were related to, but distinct from, depressive symptoms and hope. While resilience was largely stable over time, depressive symptoms increased for nearly one-third of individuals, and hope often decreased in the context of stable levels of resilience and depression, thus informing priorities for clinical management.

Journal of Neurotrauma
Griffith University (AU), Princess Alexandra Hospital (AU), Metro South Health (AU)
Good health and well-being
Openalex Percentile: Top 8%
Resilience and Mental Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.