Abstract
Objective
To demonstrate the application of second-order growth mixture modeling (SO-GMM) using
life satisfaction among adolescents and young adults with traumatic brain injury (TBI)
up to 15 years post injury.
Design
SO-GMM, a data-driven modeling approach that accounts for measurement errors, was
adopted to uncover distinct growth trajectories of life satisfaction during 15 years
post injury. Membership in growth trajectories was then linked with baseline characteristics
to understand the contributing factors to distinct growth over time.
Setting
Traumatic Brain Injury Model System National Database.
Participants
A total of 3756 adolescents and young adults (AYAs) with TBI aged 16-25 years (mean
age, 20.49±2.66 years; 27.24% female).
Interventions
Not applicable.
Main Outcome Measures
Satisfaction With Life Scale.
Results
Four quadratic growth trajectories were identified: low-stable (16.6%), which had low initial life satisfaction and remained low over time; high-stable (49.3%), which had high life satisfaction at the baseline and stayed high over time;
high-decreasing (15.8%), which started with high life satisfaction but decreased over time; and low-increasing (18.2%), which started with low life satisfaction but increased over time. Sex, race,
preinjury employment status, age, and FIM cognition were associated with group assignment.
Conclusions
This study applied SO-GMM to a national TBI database and identified 4 longitudinal
trajectories of life satisfaction among AYAs with TBI. Findings provided data-driven
evidence for development of future interventions that are tailored at both temporal
and personalized levels for improved health outcomes among AYAs with TBI.
Keywords
List of abbreviations:
AIC (Akaike information criterion), AYA (adolescent and young adult), BIC (Bayesian information criterion), CFI (comparative fit index), GMM (growth mixture modeling), LMR (Lo-Mendell-Rubin), SABIC (sample size–adjusted Bayesian information criterion), SO-GMM (second-order growth mixture modeling), TBI (traumatic brain injury), TBIMS-NDB (Traumatic Brain Injury Model System National Database)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: January 17, 2022
Accepted:
December 21,
2021
Received in revised form:
December 16,
2021
Received:
August 12,
2021
Footnotes
The Traumatic Brain Injury (TBI) Model Systems National Database is a multicenter study of the TBI Model Systems Centers Program and is supported by the National Institute on Disability, Independent Living and Rehabilitation Research (NIDILRR), a center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). However, these contents do not necessarily reflect the opinions or views of the TBI Model Systems Centers, NIDILRR, ACL or HHS. Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award no. R00HD093814. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Disclosures: none
Identification
Copyright
© 2022 by the American Congress of Rehabilitation Medicine.
