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Societal Participation of People with Traumatic Brain Injury Before and During the COVID-19 Pandemic: A NIDILRR Traumatic Brain Injury Model Systems Study
Moss Rehabilitation Research Institute, Elkins Park, PA, & Department of Rehabilitation Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA
Moss Rehabilitation Research Institute, Elkins Park, PA, & Department of Rehabilitation Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA
Center for Traumatic Brain Injury Research, Kessler Foundation, East Hanover, Dept. of Physical Medicine and Rehabilitation, Rutgers – NJ Medical School, Newark, NJ
TIRR Memorial Hermann, Brain Injury Research Center, Department of Physical Medicine & Rehabilitation, UT Health Sciences Center at Houston, Houston, TX
Depts. of Psychology, Physical Medicine and Rehabilitation, Virginia Commonwealth University, &, Central Virginia Veterans Affairs Health Care System, Richmond, VA
Dept. of Physical Medicine and Rehabilitation, University of Texas Southwestern Medical Center, Dallas, TX, & Department of Physical Medicine and Rehabilitation, Carolinas Rehabilitation, Charlotte, NC
To examine the impact of the COVID-19 pandemic on societal participation in people with moderate-to-severe traumatic brain injury (TBI).
Design
Cross-sectional retrospective cohort.
Setting
National TBI Model Systems (TBIMS) centers, United States.
Participants
TBIMS enrollees (N=7,003), ages 16 and older and 1-30 years post-injury, interviewed either pre-pandemic (PP) or during the pandemic (DP). The sample was primarily male (72.4%) and White (69.5%), with motor vehicle accidents as the most common cause of injury (55.1%).
Interventions
Not applicable.
Main Outcome Measure
The 3 subscales of the Participation Assessment with Recombined Tools-Objective (PART-O): Out and About (community involvement), Productivity, and Social Relations.
Results
Out and About, but not Productivity or Social Relations, scores were appreciably lower among DP participants compared to PP participants (medium effect). Demographic and clinical characteristics showed similar patterns of association with participation domains across PP and DP. When their unique contributions were examined in regression models, age, self-identified race, education level, employment status, marital status, income level, disability severity, and life satisfaction were variably predictive of participation domains, though most effects were small or medium in size. Depression and anxiety symptom severities each showed small zero-order correlations with participation domains across PP and DP, but had negligible effects in regression analyses.
Conclusions
Consistent with the impact of COVID-19 on participation levels in the general population, people with TBI reported less community involvement during the pandemic, potentially compounding existing post-injury challenges to societal integration. The pandemic does not appear to have altered patterns of association between demographic/clinical characteristics and participation. Assessing and addressing barriers to community involvement should be a priority for TBI treatment providers. Longitudinal studies of TBI that consider pandemic-related effects on participation and other societally linked outcomes will help to elucidate the potential longer-term impact the pandemic has on behavioral health in this population.
There is emerging evidence that the Coronavirus Disease 2019 (COVID-19) pandemic has exacerbated negative impacts of traumatic brain injury (TBI) and complicated access to rehabilitative care.1–4 Neurological impairment following moderate-to-severe TBI often leads to chronic physical, cognitive, and behavioral limitations that severely limit individuals’ ability to participate in society—an outcome of critical importance for those living with TBI and their families.5,6 It is well established that people with disabilities such as TBI are already less participatory than those without disabilities.7 Determining the effects of the pandemic on societal participation of those living with chronic TBI is imperative in order for rehabilitation professionals to adapt treatment approaches to the evolving public health climate.
The World Health Organization (WHO) defines participation as “involvement in life situations.”8 Prior studies have demonstrated that more frequent participation among those with TBI is associated with better quality of life,9,10 greater life satisfaction,11 and better mental health outcomes.12–14 Although many factors are known to predict less frequent participation (e.g., functional impairment, inability to drive, fatigue, depression, and older age13,15–21), these explain only a portion of the variability in participation outcomes post-TBI. As posited by the WHO, disability arises from a dynamic interaction between health conditions, personal factors, and the environment. The pandemic has had cascading impacts on the landscape of environmental barriers and facilitators that may influence participation for those living with chronic moderate-to-severe TBI.
The impact of the pandemic on participation in the general population has been profound, including record levels of joblessness,22 reduced physical activity,23 and decreased participation in outdoor leisure activities.24 People with disabilities have been especially vulnerable to pandemic-related inactivity, social isolation, and loneliness.25 However, information about how the pandemic has influenced the lives of individuals with TBI remains limited. In a study based on U.S. data from May-June of 2020, people with TBI (N=47) reported less pandemic-related behavior change than controls (i.e., they made fewer adjustments to their daily routines and health behaviors) but also noted that coping with the pandemic was challenging, including increased mental health concerns.26 The study's small sample size and reliance on retrospective judgments of change preclude definitive conclusions, but its observations highlight the importance of investigating the pandemic's effects on TBI-related outcomes in larger, clinically informative datasets.
Dramatic changes induced by pandemic restrictions could threaten the psychosocial health of individuals with TBI and may indicate a need for targeted intervention. Widespread pandemic-related disruption to daily life also potentially complicates analyses of health outcomes in multi-year longitudinal studies such as the TBI Model Systems (TBIMS), which follows individuals for decades after acute rehabilitation to elucidate long-term outcome trajectories. Knowledge of how participation in the TBIMS may have changed on a study-wide level from before to during the pandemic would be instructive for future analyses.
The current study aimed to determine whether pandemic-related disruptions to daily activities in the general population were detectable as trends in the TBIMS database (i.e., between interviews completed before the pandemic and those completed during the pandemic). To address this question, we planned a cross-sectional analysis of demographically similar cohorts before and during the pandemic. We first aimed to describe demographic and clinical correlates of participation in people with TBI (PwTBI) assessed in the approximately 2.5 years prior to pandemic onset (PP) and those assessed during the pandemic (DP). Time periods were chosen to be consistent with the samples selected for a study on mental health changes related to the pandemic,27 thereby enabling cross-study interpretations. We predicted that participation in the DP period would be lower relative to the PP period, and that the largest discrepancy would be in the community involvement participation subdomain (e.g., shopping, restaurant dining, travel) due to lockdown restrictions. Given established relationships between reduced participation and demographic and clinical characteristics such as age and functional impairment, we examined whether these relationships were augmented from PP to DP, which may reveal factors that are especially important to participation DP.
METHODS
Participants
Participants were drawn from the TBIMS—a multicenter, longitudinal cohort study of individuals (ages 16+) who receive inpatient rehabilitation for moderate-to-severe TBI, defined according to established clinical criteria.28 We employed the following additional inclusion criteria: follow-up window open between 1/01/2017 and 06/30/2021, with PART-O data collected during that follow up. When a participant had complete data for multiple follow-up sessions during this period, only data for the first follow-up were included. Follow-up data collected between 3/1/2020 and 3/31/2020 were excluded to account for societal transition to pandemic life. Each center received approval for human subjects research from their respective Institutional Review Boards.
Measures
The primary outcome was societal participation measured by the Participation Assessment with Recombined Tools-Objective (PART-O) subscales29 (see 30 for full measure and scoring information). The PART-O was developed for use in disability populations bearing in mind that “participation” may have different meanings across people,31,32 It assesses various ways people can be productive members of society (work, school, homemaking; captured by the Productivity subscale), are socially integrated (interacting with family, friends, spouses; Social Relations subscale), and demonstrate community involvement (going shopping, attending church, dining out; Out and About subscale).20 The PART-O has established validity and test-retest reliability.29,33–36 Psychometric analysis of the instrument suggests that its items represent a unidimensional construct; however, examination of meaningful subcomponents (i.e., abovementioned subscales) may be relevant for addressing certain clinical or social questions.32,37 Each PART-O item is scored out of 5 points, and subscale scores are derived from the average of the subscale items. The PART-O quantifies participation level in a typical week or month (e.g., Productivity subscale: “In a typical week, how many hours do you spend in active homemaking, including cleaning, cooking and raising children?”; Social subscale: “In a typical week, how many times do you socialize with friends (excluding family members), in person or by phone?”; Out and About subscale: “In a typical month, how many times do you eat in a restaurant?”). Data collectors at each site were instructed to query individuals on their typical behavior currently (i.e., typical week in the pandemic for data collected during this time). This procedure was implemented to avoid participants retrospectively reporting on their participation when queried during the pandemic.
Predictor variables included demographic, injury-related, psychosocial, and functional variables collected by the TBIMS: participant age at follow up, sex as a biological variable, race/ethnicity, education level at follow-up, cause of injury, disability severity, employment status, marital status, household income, urbanicity, problematic substance use, depression symptom severity, and anxiety symptom severity. For more detailed information on these variables, see Supplementary Table 1.
Data Analysis
Analyses were conducted in SPSS Version 2838 and R Version 4.0.4.39 Comparisons between participants who were retained and excluded, and comparisons between PP and DP groups, were assessed using chi-squared tests for categorical variables and independent samples t-tests for continuous variables. Associations between demographic and clinical characteristics (injury-related, functional, psychosocial variables) and participation outcomes were evaluated using Pearson correlations, point-biserial correlations, and phi coefficients in the full sample, as well as separately by time period (PP and DP).
To evaluate the hypothesis that time period would be uniquely associated with participation, separate linear regression models were constructed with demographic and clinical characteristics, along with a binary indicator of time period (PP vs. DP), as predictors of the PART-O subscale scores. Employment status was removed from the prediction of Productivity, and marital status from the prediction of Social Relations, as these variables are part of the measurement of those subscales. By design, there were no missing data on any of the outcome variables or on the time period predictor. Missing data on covariates ranged from 0% to 8.2%, though only two covariates (household income and disability severity) were more than 2% missing. Covariate missingness was uncorrelated with values on participation outcomes or with other covariate values (rs<. For regression models, complete case analysis (n=5,755; 82.2%) was used. Participants with complete data did not systematically differ from those with incomplete data on any model variables (rs<.
Given our ability to detect very small associations at α<.01 in this large sample, we focused on effect size versus statistical significance. Effect sizes were evaluated using phi (φ; binary variables) and Cramer's V (categorical variables) for the chi-squared tests, Pearson correlations (r, continuous variables), and Cohen's d for t-tests. In regression analyses, effect sizes were evaluated using semi-partial correlations.
RESULTS
Sample
Figure 1 depicts the analysis inclusion/exclusion process. Table 1 contains descriptive information for included (N=7,003) and excluded individuals. Demographic and clinical characteristics of those eligible, but not included, resembled those of the analytic sample. Small-magnitude differences were noted for age, race/ethnicity, education level, employment status, income, depression symptom severity (only 8.8% of excluded with complete data), and life satisfaction (only 19.2% of excluded with complete data). Differences in disability severity and participation scores were likely due to the requirement that participants have self-reported PHQ-9 and GAD-7 data, thereby excluding participants who were too impaired or otherwise unavailable to complete these measures without assistance. The included sample represented data from the entire DP period; interviews completed during a given DP quarter ranged from 354 to 446.
FIGURE 1Flow Diagram for Analytic Sample. Note. PART-O = Participation Assessment with Recombined Tools-Objective.
Table 1Comparisons of Included & Excluded Participants
Included
Excluded
Effect Size
n = 7003
n = 2385
Demographic Characteristics
Sex, male (%)
5071 (72.4)
1800 (75.7)
.03
Age (%)
.10
Early adult (16 – 34 years)
1902 (27.2)
604 (25.4)
Early middle (35 – 44 years)
1502 (21.5)
439 (18.5)
Late middle (45 – 64 years)
2376 (34.0)
705 (29.7)
Late adulthood (> 64 years)
1214 (17.4)
629 (26.5)
Race and Ethnicity (%)
.11
American Indian
30 (0.4)
17 (0.7)
Asian
186 (2.7)
84 (3.5)
Black
1152 (16.5)
468 (19.7)
Hispanic
690 (9.9)
375 (15.8)
“Other” racial group
77 (1.1)
38 (1.6)
White
4863 (69.5)
1396 (58.7)
Education (%)
.15
Less than high school (%)
825 (11.8)
518 (22.9)
High school or GED (%)
2147 (30.7)
765 (33.8)
Greater than high school (%)
4019 (57.5)
980 (43.3)
Employment status (%)
.19
Employed
3046 (43.6)
510 (23.1)
Student
150 (2.1)
31 (1.4)
Other (i.e., homemaker, retired)
2906 (41.6)
1369 (62.1)
Unemployed
890 (12.7)
296 (13.4)
Marital status (%)
.07
Single, never married
2616 (37.4)
950 (41.7)
Married
2564 (36.7)
713 (31.3)
Divorced
1265 (18.1)
387 (17.0)
Separated
249 (3.6)
70 (3.1)
Widowed
293 (4.2)
157 (6.9)
Household income (%)
.15
<$25k
2111 (32.8)
865 (49.2)
$25k - <$50k
1535 (23.9)
390 (22.2)
$50k - <$100k
1580 (24.6)
326 (18.5)
$100k - <$150k
676 (10.5)
99 (5.6)
$150k - <$200k
252 (3.9)
33 (1.9)
>$200k
274 (4.3)
46 (2.6)
Urbanicity (%)
.06
Rural
1970 (28.6)
523 (23.5)
Urban
2698 (39.1)
1001 (44.9)
Suburban
2224 (32.3)
703 (31.6)
Injury-related Characteristics
Follow-up period (%)
.12
Year 1
1641 (23.4)
747 (31.3)
Year 2
514 (7.3)
261 (10.9)
Year 5
1381 (19.7)
473 (19.8)
Year 10
1476 (21.1)
404 (16.9)
Year 15
1169 (16.7)
296 (12.4)
Year 20
616 (8.8)
146 (6.1)
Year 25
137 (2.0)
42 (1.8)
Year 30
69 (1.0)
16 (0.7)
Cause of injury (%)
.09
Motor vehicle
3852 (55.1)
1062 (44.8)
Fall
1679 (24.0)
733 (30.9)
Violence
654 (9.3)
286 (12.1)
Other cause
811 (11.6)
288 (12.2)
Disability rating, m (sd)
5.37 (4.4)
2.1 (2.1)
-1.19
Problematic substance use, yes (%)
1755 (28.0)
624 (26.0)
-.02
Mental Health
Satisfaction with life, m (sd)
22.2 (8.2)
20.6 (8.4) +
.20
Depression symptom severity, m (sd)
4.9 (5.5)
5.5 (5.7)+
-.10
Anxiety symptom severity, m (sd)
3.9 (5.1)
4.2 (5.0)
-.07
Participation (PART-O) Scores
Out & About
1.6 (0.7)
1.1 (0.8)
.64
Productivity
1.5 (1.0)
0.7 (0.9)
.76
Social
2.5 (1.0)
1.8 (1.1)
.64
Note. Included participants have complete data for PART-O, PHQ-9, GAD-7, and date of interview (COVID period determination). Effect sizes are phi or Cramer's V for categorical variables and Cohen's d for continuous variables. +Data are missing for > 80% of participants.
Correlations Overall and by Time Period (PP and DP)
Demographic and clinical characteristics of the participants were similar between PP and DP groups (Table 2). Follow-up interview timing varied across participants in the PP and DP periods, with a greater proportion of earlier post-injury follow-ups among PP participants than DP participants. This difference was likely due to the selection strategy, which retained data from only a participant's first follow-up.
Table 2Sample Descriptive Statistics
Pre-COVID
During COVID
Effect Size
n = 4995
n = 2008
Demographic Characteristics
Sex, male (%)
3620 (72.5)
1451 (72.3)
-.00
Age at follow-up (%)
.07
Early adult (16 – 34 years)
1448 (29.0)
454 (22.6)
Early middle (35 – 44 years)
1003 (20.1)
499 (24.9)
Late middle (45 – 64 years)
1688 (33.8)
688 (34.3)
Late adulthood (> 64 years)
850 (17.0)
364 (18.2)
Race and Ethnicity (%)
.02
American Indian
21 (0.4)
9 (0.4)
Asian
128 (2.6)
58 (2.9)
Black
801 (16.0)
351 (17.5)
Hispanic
504 (10.1)
186 (9.3)
“Other” racial group
56 (1.1)
21 (1.0)
White
3483 (69.8)
1380 (68.8)
Education (%)
.01
Less than high school (%)
594 (11.9)
231 (11.5)
High school or GED (%)
1518 (30.4)
629 (31.4)
Greater than high school (%)
2874 (57.6)
1145 (57.1)
Employment status (%)
.03
Employed
2180 (43.7)
866 (43.2)
Student
115 (2.3)
35 (1.7)
Other (i.e., homemaker, retired)
2087 (41.9)
819 (40.8)
Unemployed
605 (12.1)
285 (14.2)
Marital status (%)
.02
Single, never married
1890 (37.9)
726 (36.3)
Married
1814 (36.4)
750 (37.4)
Divorced
901 (18.1)
364 (18.2)
Separated
169 (3.4)
80 (4.0)
Widowed
210 (4.2)
83 (4.1)
Household income (%)
.04
<$25k
1545 (33.5)
566 (31.2)
$25k - <$50k
1120 (24.3)
415 (22.9)
$50k - <$100k
1111 (24.1)
469 (25.9)
$100k - <$150k
478 (10.4)
198 (10.9)
$150k - <$200k
166 (3.6)
86 (4.7)
>$200k
195 (4.2)
79 (4.4)
Urbanicity (%)
.00
Rural
1403 (28.6)
567 (28.6)
Urban
1926 (39.2)
772 (39.1)
Suburban
1582 (32.2)
642 (32.4)
Injury-related Characteristics
Follow-up period (%)
.21
Year 1
1152 (23.1)
489 (24.4)
Year 2
484 (9.7)
30 (1.5)
Year 5
1120 (22.4)
261 (13.0)
Year 10
1010 (20.2)
466 (23.2)
Year 15
736 (14.7)
433 (21.6)
Year 20
370 (7.4)
246 (12.3)
Year 25
87 (1.7)
50 (2.5)
Year 30
36 (0.7)
33 (1.6)
Cause of injury (%)
.04
Motor vehicle
2698 (54.1)
1154 (57.5)
Fall
1249 (25.0)
430 (21.4)
Violence
457 (9.2)
197 (9.8)
Other cause
585 (11.7)
226 (11.3)
Disability rating, m (sd)
2.0 (2.1)
2.2 (2.1)
-.07
Problematic substance use, yes (%)
1514 (30.6)
549 (27.6)
-.03
Mental Health
Satisfaction with life, m (sd)
22.1 (8.2)
22.5 (8.0)
-.04
Depression symptom severity, m (sd)
5.0 (5.5)
4.8 (5.4)
.03
Anxiety symptom severity, m (sd)
3.9 (5.1)
3.9 (5.0)
.00
Participation (PART-O) Scores
Out & About
1.6 (0.7)
1.4 (0.7)
.31
Productivity
1.5 (1.0)
1.5 (1.0)
.00
Social
2.5 (1.0)
2.5 (1.1)
.03
Note. Effect sizes are phi or Cramer's V for categorical variables and Cohen's d for continuous variables.
PART-O subscale scores showed remarkable consistency throughout the PP period (Figure 2a-c). Out & About subscale scores were lower among DP participants than those interviewed PP, suggesting that the former group spent less time in the community during the pandemic; this difference was medium-sized (Figure 2a). PART-O Productivity and Social subscale scores did not vary by time period.
FIGURE .2PART-O Subscale Scores Across Time (October 2017 – June 2021). Note. PART-O = Participation Assessment with Recombined Tools-Objective. Shaded area represents data collected during COVID-19 pandemic.
In the full sample, effects were typically small and differed across PART-O subscales. Higher educational attainment, employment, and marriage were associated with higher Out & About scores, while greater anxiety and depression symptom severities were associated with lower Out & About scores (small effects). Higher household income, less disability, and higher life satisfaction ratings were also associated with higher Out & About scores (all medium effects).
Younger age, higher educational attainment, marriage, greater time since injury, problematic substance use, and higher life satisfaction were associated with higher Productivity scores in the full sample, while higher depression and anxiety symptom severities were associated with lower Productivity (all small effects). Black participants reported lower Productivity relative to all other groups, and White participants reported higher scores compared to all other groups (all small effects). Small effects were also seen for injury mechanism: Productivity was higher in those with motor vehicle accidents and lower in those with falls, as compared to other injury mechanisms. Higher household income and less disability were associated with higher Productivity (medium effects). Large correlations were observed between employment status and Productivity because employment status contributes to the calculation of this subscale.
In the full sample, there were small associations between race and ethnicity, educational attainment, household income, depression symptom severity, and anxiety symptom severity with Social Relations (direction of effects same as described above for Productivity). Being employed and having less disability were associated with lower Social Relations (small effects), while life satisfaction and being married were positively associated with Social Relations (medium effects). Notably, marital status is included in the calculation of the Social Relations subscale, with marriage conferring higher scores.
The magnitudes of correlations between the predictors and PART-O subscales did not differ by time period (Table 4).
Factors Contributing to Participation
Given no evidence of moderation by PP/DP time period in bivariate analyses, only main effects were included in regression analyses (Table 5). Controlling for all other predictors, time period was independently associated with Out & About scores, such that DP participants reported spending less time in the community than PP participants (small effect). Out & About scores were inversely associated with disability severity and positively associated with life satisfaction; both effects were small in magnitude. Time period was not uniquely related to Productivity or Social Relations. In the analysis predicting Productivity, younger age (early adult vs. late middle/late adult), having higher household income, and greater life satisfaction emerged as unique predictors. The negative relationship between disability severity and Productivity was medium-sized and the largest independent effect in this model. In the model predicting Social Relations, only two small, unique effects emerged— income and life satisfaction, which were positively predictive.
Note. rsp = semi-partial correlation. Employment status is excluded from Productivity analysis. Marital status is excluded from Social Relations analysis. Indicates small (.1 r <.3) effect; Indicates medium (.3 r < .5) effect.
Consistent with our hypothesis, community involvement—as measured by the PART-O Out and About subscale—was significantly lower in the DP period compared to PP. Notably, this subscale assesses frequency of attendance at community establishments that were specifically targeted by pandemic restrictions, including restaurants, stores, theaters, gyms, sporting events, and religious services. Other contributing factors to decreased community involvement may be related to the significant social and political unrest that was also present during the pandemic period in the United States. No remarkable differences between the PP and DP periods were found in the Productivity and Social Relations subscales, which was somewhat surprising given how profoundly disruptive the pandemic has been to society at large.
As noted earlier, societal participation is already reduced among PwTBI, and associated with certain demographic and clinical characteristics (these were also borne out by our correlational analyses, viz. education, income, marital status, disability severity, emotional symptoms, and life satisfaction). The current findings indicate that, at the aggregate level, the effects of these characteristics on participation have persisted with similar magnitude during the pandemic; therefore, any contributory effect of the pandemic experience may have been rendered trivial. That is, participation limitations among PwTBI present before the pandemic may have made productivity and social limitations induced by the pandemic appear small in comparison. This should not be interpreted as resilience against the pandemic experience or taken to mean that PwTBI were well integrated in their communities during the pandemic. Rather, pandemic-specific effects in PwTBI may have been overshadowed by existing and enduring problems related to sociodemographic and clinical factors. Specifically, disability severity and greater age (excluding 65+) independently and negatively contributed to Productivity, while greater household income and life satisfaction had positive independent effects on Productivity. Greater household income and life satisfaction were also uniquely predictive of increased Social Relations.
Community reintegration continues to be problematic for PwTBI long after injury;40 thus, these individuals may be particularly vulnerable to daily life disruptions induced by the pandemic. Moreover, the pandemic has challenged social norms and altered human communities and institutions for the foreseeable future. While the general population will gradually adjust to these societal changes, PwTBI—who may not be as cognitively or behaviorally flexible—are at risk of being left behind. Clinicians should be aware of potential pandemic-related health disparities in this population to identify opportunities for early intervention/prevention of psychosocial complications. Encouragingly, there exist opportunities for both health and community resource facilitation that were not in wide use prior to the pandemic, including telehealth and home-community based rehabilitation and outreach services as alternatives to face-to-face and institution/hospital-based programs.4,41,42 Additionally, mobile health technology is a burgeoning area that holds significant promise for delivery of rehabilitation interventions.43 We anticipate that these innovative health care approaches will be increasingly important in addressing long-term psychosocial effects of the pandemic on PwTBI and others with chronic disability.
Study Limitations and Future Directions
The current study had limitations that reveal opportunities for future research. Although the PART-O is a validated measure of overall societal participation after TBI, it is not an exhaustive index of home and community activity. It is possible that activity patterns among PwTBI changed qualitatively during the pandemic (e.g., from in-person to virtual activity), which would not have been captured by the measure. Moreover, the PART-O considers only participation level, such as frequency of an activity. Despite relative consistency in participation levels between PP to DP, there could have been important changes in the quality or personal meaningfulness of participation among PwTBI.44 Additionally, in the absence of a control group, we could not draw comparisons between participation in the TBI and general populations, for example, to determine whether lack of change in PART-O scores were due to high baseline participation limitations in TBI. Although individuals excluded from analyses were demographically and clinically similar to those included based on effect sizes, the collective impact of small observed differences on external validity is unknown. Additionally, our study excluded individuals who could not self-report data and thus tended to be more disabled; consequently, our analysis cannot be generalized to individuals with very severe injury-related functional limitations.
Recognizing that geographic region can influence participation, we included urbanicity of individuals’ residential communities, but data on nuanced place features such as access to transportation and characteristics of the built environment may be illuminating. Further, participation during the pandemic may have been influenced by local, time-varying governmental policies regarding public health behavior; continuous policy data at the state or municipal level, with linking to more precise participant geodata, are needed to investigate these potential effects. We also did not have subjective measures of pandemic literacy and health risk appraisal, factors that likely guide personal decisions to engage in or avoid community activity. A strength of the TBIMS is that it systematically characterizes TBI recovery by employing clearly specified inclusion criteria (e.g., admission to acute rehabilitation), but these preconditions introduce selection bias. Because certain demographic groups have poorer access to care, results may underestimate health disparities.
The current study was a cross-sectional analysis of demographically similar cohorts, and a future step would be to investigate whether and how individual participation trajectories differ using within-subject analytic techniques. We expect that the longitudinal TBIMS database will be ideally suited to answer these questions in the coming years. As COVID-19 becomes endemic, we will be able to evaluate longer-term, within-participant changes in psychosocial outcomes from before to during and after the pandemic. The elucidation of factors potentially moderating participation, such as individual resilience and family/care-partner support, may also prove useful in identifying appropriate interventions.
Conclusion
The current data support a negative impact of the pandemic on community involvement. The extent to which this reflects a compounding of existing difficulties in societal integration after TBI should be addressed by clinicians on an individual basis to identify immediate patient needs. Relatively stable productive and social participation levels before and during the pandemic provide favorable conditions for ongoing longitudinal research studies as well as established clinical practices. However, it remains to be determined whether current clinical practices will be able to adequately address all residual pandemic-related participation problems across individuals. Known demographic, injury-related, psychosocial, and functional contributors to participation before the pandemic appear to have been similarly influential during the pandemic. Nonetheless, ongoing longitudinal studies such as the TBIMS will need to account for pandemic-related effects on participation and other behaviors associated with societal integration when modeling TBI outcome trajectories. Critically, this approach will be necessary to capture longer-term impacts of the pandemic on behavioral health in those living with chronic TBI, as well as recovery trajectories of individuals undergoing acute rehabilitation during the pandemic.
References
1. Boldrini P, Garcea M, Brichetto G, et al. Living with a disability during the pandemic. ‘Instant paper from the field’ on rehabilitation answers to the COVID-19 emergency. Eur J Phys Rehabil Med. 2020;56:331–334.
2. Figueroa JM, Boddu J, Kader M, et al. The Effects of Lockdown During the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Pandemic on Neurotrauma-Related Hospital Admissions. World Neurosurg. 2021;146:e1–e5. doi:https://doi.org/10.1016/j.wneu.2020.08.083
3. Kim GJ, Kim H, Fletcher J, et al. Differential Impact of the COVID-19 Pandemic on Health Care Utilization Disruption for Community-Dwelling Individuals With and Without Acquired Brain Injury. Arch Rehabil Res Clin Transl. 2022;4(1):100176. doi:https://doi.org/10.1016/j.arrct.2021.100176
4. Lester A, Leach P, Zaben M. The Impact of the COVID-19 Pandemic on Traumatic Brain Injury Management: Lessons Learned Over the First Year. World Neurosurg. 2021;156:28-32. doi:https://doi.org/10.1016/j.wneu.2021.09.030
5. Schipper K, Visser-Meily J, Hendrikx A, Abma TA. Participation of people with acquired brain injury: Insiders perspectives. Brain Inj. 2011;25(9):832-843. doi:10.3109/02699052.2011.589796.
6. Magasi S, Hammel J, Heinemann A, Whiteneck G, Bogner J. Participation: a comparative analysis of multiple rehabilitation stakeholders’ perspectives. J Rehabil Med. 2009;41(11):936-944. doi:10.2340/16501977-0450
7. Bogner JA, Whiteneck GG, Corrigan JD, Lai JS, Dijkers MP, Heinemann AW. Comparison of scoring methods for the participation assessment with recombined toolsobjective. Arch Phys Med Rehabil. Published online 2011. doi:10.1016/j.apmr.2010.11.014
9. Huebner RA, Johnson K, Bennett C, Schneck C. Community Participation and Quality of Life Outcomes After Adult Traumatic Brain Injury. The American Journal of Occupational Therapy. 2003;57(2):177–85.
10. Steadman-Pare D, Colantonio A, Ratcliff G, Chase S, Vernich L. Factors Associated with Perceived Quality of Life Many Years after Traumatic Brain Injury. J Head Trauma Rehabil. 2001;16(4):330–42.
11. Juengst SB, Adams L, Bogner J, et al. Trajectories of Life Satisfaction after Traumatic Brain Injury: Influence of Life Roles, Age, Cognitive Disability. Rehabil Psychol. 2015;60(4):353-364.
12. Andelic N, Howe E, Hellstrøm T, et al. Disability and Quality of Life 20 Years after Traumatic Brain Injury. Brain Behav. 2018;8(7):e01018.
13. Erler KS, Whiteneck G, Juengst S, et al. Predicting the Trajectory of Participation After Traumatic Brain Injury: A Longitudinal Analysis. J Head Trauma Rehabil. 2018;33(4):257–65.
14. Rabinowitz AR, Chervoneva I, Hart T, O'Neil-Pirozzi TM, Juengst SB, Hoffman JM. Heterogeneity in Temporal Ordering of Depression and Participation After Traumatic Brain Injury. Arch Phys Med Rehabil. 2020;101(11):1973-1979. doi:https://doi.org/10.1016/j.apmr.2020.05.026
15. Malone C, Erler KS, Giacino JT, et al. Participation Following Inpatient Rehabilitation for Traumatic Disorders of Consciousness: A TBI Model Systems Study. Front Neurol. 2019;10. doi:10.3389/fneur.2019.01314
16. Erler KS, Juengst S, Whiteneck GG, et al. The Association of Rehospitalization With Participation 5 Years After Traumatic Brain Inj. Journal of Head Trauma Rehabilitation. 2018;33(6):E77-E84. doi:10.1097/HTR.0000000000000386
17. Juengst S, Osborne C, Erler K, Raina KD. Effects of Fatigue, Driving Status, Cognition, and Depression on Participation in a Chronic Sample of Adults with Traumatic Brain Injury. Physical Medicine and Rehabilitation. 2017;1(1):4.
18. Rabinowitz AR, Chervoneva I, Hart T, O'Neil-Pirozzi TM, Juengst SB, Hoffman JM. Heterogeneity in Temporal Ordering of Depression and Participation After Traumatic Brain Injury. Arch Phys Med Rehabil. 2020;101(11):1973-1979. doi:https://doi.org/10.1016/j.apmr.2020.05.026
19. Erler K, Juengst S, Smith D, et al. Examining Driving and Participation 5 Years After Traumatic Brain Injury. OTJR (Thorofare N J). 2018;38(3):143-150. doi:10.1177/1539449218757739
20. Whiteneck GG, Dijkers MP, Heinemann AW, et al. Development of the participation assessment with recombined tools-objective for use after traumatic brain injury. Arch Phys Med Rehabil. 2011;92(4):542-551. doi:10.1016/J.APMR.2010.08.002
21. Corrigan JD, Cuthbert JP, Harrison-Felix C, et al. US population estimates of health and social outcomes 5 years after rehabilitation for traumatic brain injury. J Head Trauma Rehabil. 2014;29(6):E1-E9. doi:10.1097/HTR.0000000000000020
22. Lund S, Ellingrud K, Hancock B, Manyika J. COVID-19 and Jobs: Monitoring the US Impact on People and Places. McKinsey Global Institute.; 2020.
23. Dunton GF, Wang SD, Do B, Courtney J. Early effects of the COVID-19 pandemic on physical activity locations and behaviors in adults living in the United States. Prev Med Rep. 2020;20:101241.
24. Rice W, Mateer T, Reigner N, Newman P, Lawhon B, Derrick B. Changes in recreational behaviors of outdoor enthusiasts during the COVID-19 pandemic: analysis across urban and rural communities. Journal of Urban Ecology. 2020;6(1):juaa020.
25. Jesus TS, Bhattacharjya S, Papadimitriou, C. Bogdanova Y, Bentley J, Arango-Lasprilla JC, Refugee Empowerment Task Force ING of the AC of RMedicine (2021). Lockdown-related disparities experienced by people with disabilities during the first wave of the COVID-19 pandemic: Scoping review. Int J Environ Res Public Health. 2021;18(12):6178.
26. Morrow E, Patel N, Duff MC. Disability and the COVID-19 Pandemic: A Survey of Individuals With Traumatic Brain Injury. Arch Phys Med Rehabilitation. 2021;30(S0003-9993(21)):00091-5. doi:10.1016/j.apmr.2021.01.064.
27. Katta-Charles S, Adams LM, Chiaravalloti ND, et al. Depression and Anxiety in People with Traumatic Brain Injury Before and During the COVID-19 Pandemic: A NIDILRR TBI Model Systems Study. Archives of Physical Medicine and Rehabilitation. in press.
28. Dijkers M, Harrison-Felix C, Marwitz JH. The traumatic brain injury model systems: history and contributions to clinical service and research. J Head Trauma Rehabil. 2010;25(2):81-91.
29. Bogner JA, Whiteneck GG, Corrigan JD, Lai JS, Dijkers MP, Heinemann AW. Comparison of scoring methods for the participation assessment with recombined toolsobjective. Arch Phys Med Rehabil. Published online 2011. doi:10.1016/j.apmr.2010.11.014
30. The Center for Outcome Measurement in Brain Injury (COMBI). Introduction to the Participation Assessment with Recombined Tools-Objective. http://www.tbims.org/combi/parto/.
31. Sander AM, Clark A, Pappadis MR. What is community integration anyway?: defining meaning following traumatic brain injury. J Head Trauma Rehabil. 2010;25(2):121-127. doi:10.1097/HTR.0B013E3181CD1635
32. Whiteneck G, Dijkers MP. Difficult to measure constructs: conceptual and methodological issues concerning participation and environmental factors. Arch Phys Med Rehabil. 2009;90(11):S22–S35.
33. Bogner JA, Whiteneck GG, MacDonald J, et al. Test-Retest Reliability of Traumatic Brain Injury Outcome Measures. Journal of Head Trauma Rehabilitation. 2017;43210:1. doi:10.1097/HTR.0000000000000291
34. Braden C, Cuthbert J, Brenner L, et al. Health and wellness characteristics of persons with traumatic brain injury. Brain Inj. 2012;26(11):1315-1327.
35. Hart T, Brenner L, Clark AN, et al. Major and Minor Depression After Traumatic Brain Injury. Arch Phys Med Rehabil. 2011;92(8):1211-1219. doi:https://doi.org/10.1016/j.apmr.2011.03.005
36. Sander AM, Maestas KL, Nick TG, et al. Predictors of Sexual Functioning and Satisfaction 1 Year Following Traumatic Brain Injury: A TBI Model Systems Multicenter Study. Journal of Head Trauma Rehabilitation. 2013;28:18V194.
37. Bogner JA, Whiteneck GG, Corrigan JD, Lai JS, Dijkers MP, Heinemann AW. Comparison of scoring methods for the participation assessment with recombined toolsobjective. Arch Phys Med Rehabil. Published online 2011. doi:10.1016/j.apmr.2010.11.014
38. IBM Corp. SPSS for Macbook,. Published online 2021.
39. Team RC. R A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2021. ttps://www.r-project.org/.
40. Sloan S, Sloan O, Winkler D, Callaway L. COMMUNITY INTEGRATION FOLLOWING SEVERE TRAUMATIC BRAIN INJURY. Brain Impairment. 2004;5(1):12–29.
41. Davis LC, Sander AM, Bogaards JA, Pappadis MR. Implementation of resource facilitation to assess referral needs and promote access to state vocational rehabilitation services in people with traumatic brain injury. Neuropsychol Rehabil. 2018;28:1145-1160.
42. Whiting DL, Chuah SL, Simpson GK, Deane FP, Reynolds J. Video-consulting to address mental health needs after traumatic brain injury: evaluation of a training workshop to build capacity among psychologists. Brain Inj. 2021;35(9):1065-1074. doi:10.1080/02699052.2021.1953594
43. Juengst SB, Terhorst L, Nabasny A, et al. Use of mhealth technology for patient-reported outcomes in community-dwelling adults with acquired brain injuries: A scoping review. Int J Environ Res Public Health. 2021;18(4):1-19. doi:10.3390/IJERPH18042173
44. McLean AM, Jarus T, Hubley AM, Jongbloed L. Associations between social participation and subjective quality of life for adults with moderate to severe traumatic brain injury. https://doi.org/103109/096382882013834986. 2014;36(17):1409-1418. doi:10.3109/09638288.2013.834986
Disclosures
The authors report no conflicts of interest.
Acknowledgements
The contents of this publication were developed under grants from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR) to: Moss Rehabilitation Research institute/MossRehab (#90DPTB0004), Indiana University School of Medicine/Rehabilitation Hospital of Indiana (#90DPTB0002), University of Washington (#90DPTB0008), Craig Hospital (#90DPTB0018), TIRR Memorial Hermann (#90DPTB0016), Kessler Foundation (#90DPTB0003), University of Texas Southwestern Medical Center/Baylor Scott & White Institute for Rehabilitation (#90DPTB0013), Rusk Rehabilitation (#90DPTB0010), Virginia Commonwealth University (#90DPTB0005), and Carolinas Rehabilitation (#90DP0084). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this publication do not necessarily represent the policy of NIDILRR, ACL, or HHS, and are not necessarily endorsed by the Federal Government.
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