Stress system concordance as a predictor of longitudinal patterns of resilience in adolescence

Abstract

Resilience promotes positive adaptation to challenges and may facilitate recovery for adolescents experiencing psychopathology. This work examined concordance across the experience, expression, and physiological response to stress as a protective factor that may predict longitudinal patterns of psychopathology and well-being that mark resilience. Adolescents aged 14–17 at recruitment (oversampled for histories of non-suicidal self-injury; NSSI) were part of a three-wave (T1, T2, T3) longitudinal study. Multi-trajectory modeling produced four distinct profiles of stress experience, expression, and physiology at T1 (High-High-High, Low-Low-Low, High-Low-Moderate, and High-High-Low, respectively). Linear mixed-effect regressions modeled whether the profiles predicted depressive symptoms, suicide ideation, NSSI engagement, positive affect, satisfaction with life, and self-worth over time. Broadly, concordant stress response profiles (Low-Low-Low, High-High-High) were associated with resilient-like patterns of psychopathology and well-being over time. Adolescents with a concordant High-High-High stress response profile showed a trend of greater reduction in depressive symptoms (B = 0.71, p = 0.052), as well as increased global self-worth (B = –0.88, p = 0.055), from T2 to T3 compared to the discordant High-High-Low profile. Concordance across multi-level stress responses may be protective and promote future resilience, whereas blunted physiological responses in the presence of high perceived and expressed stress may indicate poorer outcomes over time.

Emergence and evolution of developmental resilience science over half a century

“…Nonetheless, collecting empirical data suitable for these methods can be daunting. Additional signs of progress can be found in papers applying network analysis to capture patterns of coordinated resilience resources or processes (e.g., Höltge et al, 2021;Ungar et al, 2023) or linked patterns of stressors, symptoms, and resilience factors (e.g., Kalisch et al, 2019); multilevel latent class analysis to identify profiles of linked risks, assets, services, and/or adaptive behavior (e.g., Cutuli et al, 2023) or multiple trajectory models (e.g., Wiglesworth et al, 2023). Nevertheless, multisystem theory about resilience remains substantially ahead of the empirical evidence to date on the patterns and processes of integrated multisystem resilience.…”