Abstract
Objective
To explore trajectories of functional recovery that occur during the first 2 years
after spinal cord injury (SCI).
Design
Observational cohort study.
Setting
Eight SCI Model System sites.
Participants
A total of 479 adults with SCI completed 4 Spinal Cord Injury–Functional Index (SCI-FI)
item banks within 4 months of injury and again at 2 weeks, 3, 6, 12, and 24 months
after baseline assessment (N=479).
Intervention
None.
Main Outcome Measures
SCI-FI Basic Mobility/Capacity (C), Fine Motor Function/C, Self-care/C, and Wheelchair
Mobility/Assistive Technology (AT) item banks.
Results
Growth mixture modeling was used to identify groups with similar trajectory patterns.
For the Basic Mobility/C and Wheelchair Mobility/AT domains, models specifying 2 trajectory
groups were selected. For both domains, a majority class exhibited average functional
levels and gradual improvement, primarily in the first 6 months. A smaller group of
individuals made gradual improvements but had greater initial functional limitations.
The Self Care/C domain exhibited a similar pattern; however, a third, small class
emerged that exhibited substantial improvement in the first 6 months. Finally, for
individuals with tetraplegia, trajectories of Fine Motor Function/C scores followed
2 patterns, with individuals reporting generally low initial scores and then making
either modest or large improvements. In individual growth curve models, injury/demographic
factors predicted initial functional levels but less so regarding rates of recovery.
Conclusions
Trajectories of functional recovery followed a small number of change patterns, although
variation around these patterns emerged. During the first 2 years after initial hospitalization,
SCI-FI scores showed modest improvements; however, substantial improvements were noted
for a small number of individuals with severe limitations in fine motor and self-care
function. Future studies should further explore the personal, medical, and environmental
characteristics that influence functional trajectories during these first 2 years
and beyond.
Keywords
List of abbreviations:
AIC (Akaike's information criterion), AT (Assistive Technology (as descriptor for SCI-FI Wheelchair Mobility item bank)), C (Capacity (as descriptor for SCI-FI item banks)), GMM (growth mixture modeling), SABIC (sample-size adjusted Bayesian information criterion), SCI (spinal cord injury), SCI-FI (Spinal Cord Injury–Functional Index)To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Archives of Physical Medicine and RehabilitationAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Using rasch motor FIM individual growth curves to inform clinical decisions for persons with paraplegia.Spinal Cord. 2014; 52: 671-676
- An introduction to applying individual growth curve models to evaluate change in rehabilitation: a National Institute on Disability and Rehabilitation Research Traumatic Brain Injury Model Systems report.Arch Phys Med Rehabil. 2013; 94: 589-596
- Spinal cord injury rehabilitation outcome: the impact of age.J Clin Epidemiol. 1988; 41: 173-177
- Relation between inpatient and postdischarge services and outcomes 1 year postinjury in people with traumatic spinal cord injury.Arch Phys Med Rehabil. 2013; 94: S165-S174
- Association of various comorbidity measures with spinal cord injury rehabilitation outcomes.Arch Phys Med Rehabil. 2013; 94: S75-S86
- Using individual growth curve models to predict recovery and activities of daily living after spinal cord injury: an SCIRehab project study.Arch Phys Med Rehabil. 2013; 94: S154-S164
- Hierarchical linear modeling of FIM instrument growth curve characteristics after spinal cord injury.Arch Phys Med Rehabil. 2001; 82: 329-334
- Methods and measures: growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups.Int J Behav Dev. 2009; 33: 565-576
- Developing a contemporary functional outcome measure for spinal cord injury research.Spinal Cord. 2010; 48: 262-267
- Spinal cord injury-functional index: item banks to measure physical functioning in individuals with spinal cord injury.Arch Phys Med Rehabil. 2012; 93: 1722-1732
- Development and initial evaluation of the spinal cord injury-functional index.Arch Phys Med Rehabil. 2012; 93: 1733-1750
- American Spinal Injury Association. International standards for neurological and functional classification of spinal cord injury. Chicago: American Spinal Injury Association.International Spinal Cord Society. 2002;
- Development and initial evaluation of the SCI-FI/AT.J Spinal Cord Med. 2015; 38: 409-418
Tulsky D, Kisala P, et al. SCI-FI structure and recent advances. Arch Phys Med Rehabil; in press.
- The development of a clinical outcomes survey research application: assessment center.Qual Life Res. 2010; 19: 677-685
Muthén LK, Muthén BO. Mplus user's guide. 8th ed. Los Angeles: Muthén & Muthén; 1998-2017.
- Structural equations with latent variables.Wiley, New York1989
- Latent growth curves within developmental structural equation models.Child Dev. 1987; 58: 110-133
- Latent class analysis and finite mixture modeling.(editor)in: Little TD The Oxford handbook of quantiative methods: volume 2 statistical analysis. Oxford University Press, New York2013: 551-611 (p)
- An entropy criterion for assessing the number of clusters in a mixture model.J Classif. 1996; 13: 195-212
- Statistical power to detect the correct number of classes in latent profile analysis.Struct Equ Modeling. 2013; 20: 640-657
- Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes.Alcohol Clin Exp Res. 2000; 24: 882-891
- Fitting linear mixed-effects models using lme4.J Stat Softw. 2015; 67: 1-48
- A warning on separation in multinomial logistic models.Res Polit. 2018; 52053168018769510
- Late neurologic recovery after traumatic spinal cord injury1.Arch Phys Med Rehabil. 2004; 85: 1811-1817
Article info
Publication history
Published online: October 19, 2021
Accepted:
September 20,
2021
Received in revised form:
August 21,
2021
Received:
January 16,
2019
Footnotes
Supported by grant no. G2015-26 from the Rick Hansen Institute and grant nos. H133N110002, H133N110007, H133N110006, H133N110020, H133N110019, H133N110014, H133N110005, and H133N110003 from the National Institute on Disability, Independent Living, and Rehabilitation Research.
SCI-FI Basic Mobility/Capacity (C), Fine Motor Function/C, Self-care/C, and Wheelchair Mobility/Assistive Technology (AT) item banks Items, Parameters, and Data are copyright © 2013–2021 David Tulsky, Kessler Foundation, and Boston University. All rights reserved. All items are freely available to the public via [email protected], and Dr Tulsky does not receive royalties from use of these instruments.
Identification
Copyright
© 2021 Published by Elsevier Inc. on behalf of The American Congress of Rehabilitation Medicine.
