Preinjury Predictors of Life Satisfaction at 1 Year After Traumatic Brain Injury

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Preinjury Predictors of Life Satisfaction at 1 Year After Traumatic Brain Injury Lynne C. Davis, PhD, Mark Sherer, PhD, Angelle M. Sander, PhD, Jennifer A. Bogner, PhD, John D. Corrigan, PhD, Marcel P. Dijkers, PhD, Robin A. Hanks, PhD, Thomas F. Bergquist, PhD, Ronald T. Seel, PhD ABSTRACT. Davis LC, Sherer M, Sander AM, Bogner JA, Corrigan JD, Dijkers MP, Hanks RA, Bergquist TF, Seel RT. Preinjury predictors of life satisfaction at 1 year after traumatic brain injury. Arch Phys Med Rehabil 2012;93:1324-30. Objective: To investigate the predictive value of preinjury factors for satisfaction with life (SWL) at 1-year posttraumatic brain injury (TBI). Design: Secondary analysis of prospective, longitudinal registry using data collected during inpatient rehabilitation and at 1-year post-TBI. Setting: Fifteen specialized brain injury units providing acute rehabilitation care as part of the Traumatic Brain Injury Model Systems (TBIMS) program. Participants: Community-dwelling persons (N⫽444) with moderate to severe TBI aged 16 to 64 years enrolled in the TBIMS program between October 2007 and October 2008 with 1-year follow-up data. Interventions: Not applicable. Main Outcome Measure: Satisfaction With Life Scale (SWLS). Results: Hierarchical stepwise linear regression revealed that injury-related and demographic variables did not contribute significantly to the explained variance in SWLS scores. In contrast, the preinjury functioning (education, productivity/ employment) and preinjury condition (psychiatric and substance use problems, severe sensory dysfunction, learning problems, prior TBI) blocks each contributed significantly to the explained variance in SWLS scores. Preinjury functioning accounted for 2.9% of the variance and preinjury conditions for 3.8%. Conclusions: Although their contributions are small, preinjury functioning and preinjury conditions are important to consider in the prediction of SWL post-TBI. Educational level and history of psychiatric and other premorbid difficulties are particularly important for clinicians to consider when imple-

From the Brain Injury Research Center, TIRR Memorial Hermann, Houston, TX (Davis, Sherer, Sander); Department of Physical Medicine and Rehabilitation, Baylor College of Medicine/Harris County Hospital District, Houston, TX (Sander); Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH (Bogner, Corrigan); Department of Rehabilitation Medicine, Mount Sinai School of Medicine, New York, NY (Dijkers); Department of Physical Medicine and Rehabilitation, Wayne State University School of Medicine, Detroit, MI (Hanks); Mayo Clinic College of Medicine, Rochester, MN (Bergquist); and the Crawford Research Institute, Shepherd Center, Atlanta, GA (Seel). Supported by the National Institute on Disability and Rehabilitation Research, U.S. Department of Education (grant nos. H133A080044, H133B090023, H133A070029, and H133A070033). No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit on the authors or on any organization with which the authors are associated. Correspondence to Lynne C. Davis, PhD, Brain Injury Research Center, TIRR Memorial Hermann, 2323 S Shepherd, Ste 907, Houston, TX 77019, e-mail: [email protected]. Reprints are not available from the author. 0003-9993/12/9308-00065$36.00/0 http://dx.doi.org/10.1016/j.apmr.2012.02.036

Arch Phys Med Rehabil Vol 93, August 2012

menting or developing interventions for persons with moderate to severe TBI. Key Words: Brain injuries; Outcome assessment (health care); Personal satisfaction; Quality of life; Rehabilitation; Treatment outcome. © 2012 by the American Congress of Rehabilitation Medicine N THE TRAUMATIC brain injury (TBI) literature, research Icomes, has centered on investigating predictors of objective outsuch as employment and functional independence, whereas subjective outcomes, including satisfaction with life (SWL), have received relatively less attention. As noted by Corrigan and Bogner,1 subjective states and objective measures of life situations are often uncorrelated and may represent different underlying constructs. Thus, it is important to obtain both objective and subjective indices of SWL to comprehensively evaluate rehabilitation outcomes. SWL can be viewed as a subjective, cognitively based appraisal of one’s life conditions relative to expectations.2 Individuals with TBI report decreased SWL relative to persons without disabilities, both early after injury and over the long term.3-7 Corrigan et al8 reported that SWL scores decreased from early after injury to 2 years postinjury. TBI often causes substantial physical, cognitive, emotional, and interpersonal/ social difficulties, as well as life role changes and significantly greater declines in life quality for individuals with lower levels of cognitive and motor functional independence.9 Elucidation of the preinjury, demographic, and injury-related predictors of SWL may help shape the design of postinjury clinical interventions and improve psychosocial outcomes. Among previous SWL investigations, most have focused on identifying demographic, injury-related, and postinjury predictors. Age at injury,7,10-15 education,15 marital status,7,16-18 income,19 social integration,2,10,13,20 employment/ productivity,2,7,10,16,20-24 family satisfaction,16,25,26 and social communication skills27,28 have been shown to predict SWL among persons with moderate to severe TBI. An association between impaired self-awareness and greater SWL has also been identified.29 In contrast, findings about the predictive value of injury severity, race, sex,9,14,30 and cognitive impairment to SWL have been mixed.18,20,22,31 The use of different outcome assessment intervals likely contributes to these variable findings, as does the use of

List of Abbreviations SWL SWLS TBI TBIMS VIF

satisfaction with life Satisfaction With Life Scale traumatic brain injury Traumatic Brain Injury Model Systems variance inflation factor

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PREDICTORS OF LIFE SATISFACTION, Davis

different instruments to measure nondemographic variables. Additionally, predictors of SWL after TBI may change over time.2,9 Postinjury emotional functioning has shown an association with SWL in persons with TBI. Several studies have noted an association between SWL and depressed mood.2,12,24,29,32-35 Personality factors, self-efficacy, and coping style have also been found to be predictive of SWL in persons with TBI.22,36 Sense of coherence, a general feeling of confidence that life events are comprehensible and that personal resources exist to meet life’s challenges, has shown a strong association with SWL over the long term after TBI.37 Social support has frequently been studied as a correlate of SWL. Persons with high levels of functional independence and satisfaction with family relationships have been shown to experience increases in SWL over time.25 Others have also reported that family satisfaction is predictive of SWL in individuals with TBI.16 Additionally, perceived social support among caregivers has been shown to predict SWL in injured persons.26 Environmental, economic, and social characteristics of an individual’s neighborhood have shown mixed results in the prediction of SWL. One study using a sample from Ohio found that by including characteristics of one’s neighborhood in a model with individual-based characteristics, the model accounted for an additional 7% of the variance in SWL at 2 and 5 years postinjury.38 However, a more recent study of SWL in a national sample 1, 2, and 5 years postinjury did not find that neighborhood characteristics significantly contributed to the model.39 Other studies have found that unmet service needs (eg, medical, rehabilitative, financial, psychosocial, job-related, educational/information) are associated with decreased SWL in individuals with TBI.40,41 The potential role of preinjury factors in determining SWL after TBI has received little attention. In the general population, personality factors impacting SWL before a substantial life change remain influential after the life change.42-44 Furthermore, psychological factors may contribute more substantially to SWL than actual life events.45,46 Corrigan et al2 reported that a preinjury history of substance abuse was associated with decreased SWL at 1 and 2 years postinjury. These findings are consistent with those of Bogner et al.47,48 To our knowledge, no studies examining the predictive ability of other nondemographic preinjury variables, such as psychiatric history, prior TBI, or history of learning difficulties or sensory dysfunction, have been conducted. The purpose of the current study was to examine the predictive value of preinjury factors for SWL at 1-year post-TBI, after accounting for the contribution of demographic and injury-related factors. All injury-related factors were measured at the time of acute rehabilitation discharge. We hypothesized that: (1) demographic factors predict SWL, after accounting for injury-related factors; (2) preinjury functioning, including education and employment/productivity, predict SWL, after accounting for injury-related and demographic factors; and (3) preinjury conditions, including psychiatric and substance use problems, severe sensory dysfunction, learning problems, and prior TBI, predict SWL after accounting for all of the factors above. While factors such as emotional functioning, social support, and environment have been shown to be related to SWL in persons with TBI, these are concurrent factors, measured at the same time as SWL, and are not known at the time of rehabilitation. The current article focuses on variables that are known at the time of rehabilitation, because these can be used to target at-risk individuals for treatment. With the exception of substance use problems, the predictive associations between the preinjury condition variables and SWL have not

been examined in prior studies. Thus, this study has the potential to add to the prediction of SWL after TBI and therefore impact intervention designs. We selected the specific preinjury condition variables on the basis of their availability in the national database used for this study. METHODS Participants The study sample consisted of persons aged 16 to 64 years with medically documented TBI who were enrolled in the Traumatic Brain Injury Model Systems (TBIMS) program between October 2007 and October 2008. Criteria for enrollment in the TBIMS include being at least 16 years of age, admission to a system hospital for acute care within 72 hours of injury for moderate to severe TBI, and admission to comprehensive inpatient rehabilitation at a TBIMS hospital. Demographic, preinjury, and injury-related data were collected during inpatient rehabilitation stay, and SWL data were collected at 1-year post-TBI in the context of a comprehensive follow-up interview. During the study period, 946 persons were enrolled in the TBIMS program, of whom 149 were excluded on the basis of being older than 64 years. For the remaining 797 persons, 1-year follow-up data were collected for 594, gathered either from the person with TBI or a significant other. Reasons for missing data included: withdrawal of authorization/refusal (n⫽26), expired (n⫽53), and lost to follow-up (n⫽124). Among persons with 1-year follow-up data, 492 provided information by self-report. The 102 persons who did not were excluded from the study sample because SWL data for the person with injury were not collected from significant others. Reasons for missing self-report data included: physically/ cognitively unable to respond (n⫽63), language barrier (n⫽4), and unavailable for interview (n⫽35). There were 6 individuals who provided follow-up data by self-report, but who lacked SWL data. An additional 26 persons were excluded from the study sample because they belonged to racial/ethnic groups other than white, black, or Hispanic and were too few in number to constitute an additional group (15 Asian/Pacific Islander, 7 Native American, 4 other). Missing data ranged from a minimum of 0 and a maximum of 6 across study variables, resulting in 16 participants with incomplete data. Only those with complete data were included in the analyses, yielding a final study sample of 444 individuals. Three hundred and two participants were white (68%), 83 were black (18.7%), and 59 were Hispanic (13.3%). Educational level varied from 1 to 20 years (mean ⫾ SD, 12.43⫾2.70y). Consistent with reports in the TBI literature, most participants were men (72%). Sample characteristics appear in table 1. Individuals who were included in the study sample were compared with those who were excluded (n⫽337) on demographic, preinjury, and injury-related characteristics. Individuals included in the study sample had higher FIM cognitive (t⫽–5.55, P⬍.001) and FIM motor (t⫽–7.24, P⬍.001) scores at discharge. They were less likely to be married/ cohabitating (␹2⫽ 6.12, P⬍.01), unemployed/nonproductive (␹2⫽73.78, P⬍.001), and to have premorbid conditions (␹2⫽10.53, P⬍.001). However, persons included in the study sample were more likely to have a substance use problem (␹2⫽12.95, P⬍.001). Procedure Study procedures, including informed consent, were approved by the institutional review boards at each of the 15 TBIMS sites that contributed data. Participants were recruited Arch Phys Med Rehabil Vol 93, August 2012

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PREDICTORS OF LIFE SATISFACTION, Davis Table 1: Sample Characteristics Characteristics

Mean age ⫾ SD (y) Mean days ⫾ SD to follow commands Mean education ⫾ SD (y) Sex (%) Male Female Race/ethnicity (%)† White Black Hispanic Other Marital status (% married/cohabitating) Employed/productive (%) Substance use problem (%) Psychiatric problem (%) Premorbid conditions (%) Mean FIM motor score ⫾ SD Mean FIM cognitive score ⫾ SD Mean SWLS score ⫾ SD

Study Sample (N⫽444)

Excluded Persons* (n⫽337)

35.5⫾14.2 8.9⫾13.2

52.4⫾22.7 7.0⫾12.2

12.4⫾2.7

12.2⫾3.3

72.0 28.0

69.0 31.0

68.0 18.7 13.3 0.0 31.3

65.1 12.8 12.1 10.0 40.0

83.0 46.5 18.7 21.7 68.2⫾16.7 24.4⫾6.1 21.3⫾8.3

54.8 34.0 20.2 32.1 57.9⫾21.7 21.7⫾7.6 NA

Abbreviation: NA, not applicable. *Missing data values for the excluded persons group ranged from a minimum of 3 to a maximum of 18 across the variables above. † Twenty-six individuals were excluded from the study sample because they belonged to racial/ethnic groups other than white, black, or Hispanic.

shortly after admission to inpatient rehabilitation. At that time, caregivers provided demographic and other preinjury information when persons with injury were unable to provide such information themselves. Follow-up interviewers were research assistants who had completed specialized training and certification through the TBIMS. The structured interview was conducted following policies and procedures set forth in the TBIMS syllabus. Injury-related information was abstracted from medical records. All persons with injury completed the Satisfaction With Life Scale (SWLS)49 at approximately 1 year after injury as part of the standard interview follow-up conducted by the TBIMS (mean ⫾ SD, 373⫾38d; 25th percentile⫽341d; 75th percentile⫽408d). Measures Structured interview for preinjury and demographic information. At the time of enrollment, a structured interview was administered to persons with injury and/or their caregivers to collect demographic information and preinjury medical and psychiatric difficulties. Information gathered included date of birth, race/ethnicity, language preference, marital status, living situation, educational history, and employment status. Questions were also asked to assess: (1) preinjury blindness, deafness, or severe vision or hearing impairment; (2) presence of another condition substantially limiting 1 or more basic physical activities (ie, walking, climbing stairs, reaching, lifting, carrying); (3) prior hospitalization for TBI; (4) special education classification; and (5) persistent preinjury problems with learning, remembering, or concentrating; dressing, bathing, or getting around at home; leaving home to go shopping or go to a doctor’s office; or working. The following psychiatric issues were inquired about: lifetime history of treatment for mental Arch Phys Med Rehabil Vol 93, August 2012

health problems, hospitalizations for psychiatric problems, suicide attempts, and history of drug and alcohol use. Substance use history was coded as positive if any of the following conditions were met: a history of illicit drug use at any point during the previous year, 1 or more alcohol binges in the month before injury (defined as consumption of ⱖ5 drinks during a single day), or heavy alcohol consumption during the month before injury (defined as ⱖ14 drinks per week for men or ⱖ7 drinks per week for women). Psychiatric history was coded as positive if there was a lifetime history of treatment for mental health issues, psychiatric hospitalization, or suicide attempt. The premorbid conditions variable was coded as positive if there was a history of blindness, deafness, or severe vision or hearing impairment, another condition substantially limiting 1 or more basic physical activities, prior hospitalization for TBI, or classification as a special education student. The number of days from injury to successful execution of simple motor commands was used as an index of brain injury severity.50,51 There was 1 participant who did not acquire the ability to follow commands prior to discharge from inpatient rehabilitation. For this case, the number of days from injury to inpatient rehabilitation discharge was used. Because of the highly positively skewed distribution for this variable (range, 0.5–90d), the values were recoded into quartiles (0.5d; 1–2d; 3–10d; ⬎10d). FIM. The FIM52-55 was administered at inpatient rehabilitation discharge to assess degree of functional independence. The FIM is an 18-item measure administered by FIM-certified examiners that assesses cognitive and motor functioning across the following areas: self-care, sphincter control, transfers, locomotion, communication, and social cognition. Each item is scored on a 1 (complete dependence) to 7 (complete independence) scale. The cognitive and motor subscales were used for this study. Satisfaction With Life Scale. Individuals with TBI were administered the SWLS as part of the follow-up interview. The SWLS49 is a 5-item scale assessing global SWL circumstances. Each item is rated on a 7-point Likert scale that ranges from 1 (strongly disagree) to 7 (strongly agree). Higher scores indicate greater SWL. Scale authors have reported moderate 2-month test-retest stability (r⫽.82), good internal consistency (coefficient ␣⫽.87), and a unidimensional factor structure.49 The SWLS has shown convergence with other measures of life satisfaction and subjective well-being, and lower scores have been obtained by groups such as abused women and prisoners, thus offering evidence for its construct validity.56 The mean total SWLS score ⫾ SD for persons with TBI who had undergone rehabilitation was reported as 20.3⫾8.1 at 1 year postinjury,2 which compares to a mean score ⫾ SD of 23.5⫾6.4 among healthy university students.49 Data Analyses For the purpose of analyses, subjects were divided into 3 race/ethnicity groups: non-Hispanic whites, blacks, and Hispanics. Age, education, FIM cognitive, FIM motor, and SWLS scores were used as continuous variables. Preinjury marital status was coded dichotomously as: (1) married/cohabitating or (2) single/divorced/separated/widowed/other. Preinjury employment/ productivity was coded dichotomously as: (1) competitively employed/student/homemaker or (2) retired/unemployed/employed with supports/on leave/other. The preinjury substance use problem variable was coded dichotomously as positive or negative, as were the psychiatric problem and premorbid conditions variables. A hierarchical stepwise linear regression model was constructed to determine the contributions of preinjury func-

PREDICTORS OF LIFE SATISFACTION, Davis Table 2: Predictors of SWLS Scores Block No.

1

2

3

4

Adjusted R2 F R2 Change Change Change

Variables Entered

Days to follow commands FIM motor FIM cognitive Age at injury Sex Marital status Race/ethnicity Employment/ productivity Education Substance use problem Psychiatric problem Premorbid conditions

df

P

.002

–.004

0.354 3,440 .786

.017

.008

1.900 4,436 .109

.029

.025

6.590 2,434 .002*

.038

.032

6.050 3,431 .001*

*P⬍.01.

tioning and preinjury conditions to the explained variance in SWLS scores, after accounting for injury-related and demographic variables. The injury-related variables entered in block 1 were: days to follow commands, FIM cognitive at rehabilitation discharge, and FIM motor at discharge. Next, the demographic predictors were entered as block 2: age, sex, race/ ethnicity (coded with dummy variables), and marital status. The preinjury education and employment/productivity variables were then entered as block 3. Finally, preinjury conditions, including substance use, psychiatric, and premorbid conditions were entered as block 4. Changes in the proportion of variance accounted for were determined at each of the 4 steps in the model. Variance inflation factor (VIF) values were low (minimum⫽1.05, maximum⫽1.45), indicating that higher order collinearity was not problematic in this study. RESULTS SWLS Scores A mean ⫾ SD of 21.3⫾8.3 was obtained at 1 year postinjury on the SWLS (minimum score⫽5; maximum score⫽35; 25th percentile⫽15; 75th percentile⫽28; median⫽22). Prediction of SWLS Scores Results appear in table 2, and include both unadjusted and adjusted R2 change values. Adjusted values were included to account for possible overestimation of variance represented in unadjusted values. The injury-related variables (days to follow commands, FIM motor, FIM cognitive) did not explain SWLS variability. The demographic variables (age, sex, marital status, race/ethnicity) entered in the second block also did not contribute significantly to the explained variance in SWLS scores. In contrast, the preinjury functioning block (no. 3) contributed significantly to the explained variance, but its contribution was small (R2 change⫽.029, P⬍.002). Examination of the obtained beta weights indicated that participants with more years of education reported greater SWL (standardized ␤ coefficient⫽.15, t⫽2.9, P⬍.004). Preinjury conditions (block no. 4) also contributed significantly to the variability in SWLS scores (R2 change⫽.038, P⬍.001), after accounting for the

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above variables. Evaluation of the corresponding beta weights revealed that individuals with a self-reported lifetime history of psychiatric difficulties reported lower SWL (standardized ␤ coefficient⫽⫺.15, t⫽⫺3.09, P⬍.002). Additionally, those with premorbid conditions reported lower SWL (standardized ␤ coefficient⫽⫺.10, t⫽⫺1.99, P⬍.05). With all injury-related, demographic, preinjury functioning, and preinjury condition variables entered into the overall model, age at injury contributed significantly to the variability in SWLS scores (standardized ␤ coefficient⫽⫺.108, t⫽⫺1.97, P⬍.05), although the demographic block (no. 2), as a whole, did not contribute significantly. The total R2 change for the model was .087. DISCUSSION The study findings provided partial support for our hypotheses. Consistent with some prior studies, injury severity and functional status early after TBI were not predictive of SWL at 1 year postinjury. Contrary to our hypothesis, demographic variables such as age, sex, marital status, and race/ethnicity did not predict SWL in this sample. Consistent with our hypothesis, indicators of preinjury functioning were predictive of SWL. Additional analysis showed that the primary effect of preinjury functioning on postinjury SWL was because of persons with higher levels of preinjury education reporting greater SWL at 1 year postinjury. Finally, also consistent with our hypothesis, preinjury conditions accounted for significant variability in postinjury SWL. Persons with preinjury psychiatric histories reported poorer postinjury SWL, as did persons with premorbid conditions, such as a reported history of blindness, deafness, or severe vision or hearing impairment, a condition substantially limiting 1 or more basic physical activities, prior hospitalization for TBI, or classification as a special education student. While we found that at 1 year postinjury, preinjury level of functioning and premorbid conditions, such as psychiatric history and functional limitations, were predictive of SWL for persons with TBI, the amount of variance accounted for was quite low. The full model accounted for only 8.7% of variance in SWL, with preinjury functioning accounting for less than 3% and preinjury conditions for less than 4% after adjustment for other predictors. The amount of variance accounted for in this study is somewhat less than that found in other studies examining life satisfaction 1 year after moderate-severe TBI.2,20 The literature in general, however, shows weak predictive models for this time frame and population. Our study of persons with moderate to severe TBI found levels of SWL similar to other studies of persons with moderate and severe TBI at 1 year postinjury,8,57 and that these levels are not markedly different from the reports of healthy university students. While patients’ perception of their level of impairment after injury has been found to be related to mood, this relationship is complicated by the presence of impaired selfawareness during the first 1 to 2 years after onset of injury.58 Arguably, impaired self-awareness may complicate patient rating of SWL during this same time frame. Unfortunately, there is no measure of impaired self-awareness in the TBIMS dataset to test for this possible confound. Conversely, some persons with brain injury may experience a response shift in quality of life by 1 year postinjury, in which their life expectations have adjusted to adapt to their current levels of impairment.59 Factors occurring postdischarge may exert a stronger influence than preinjury and injury-related factors. Other studies using models limited to variables obtained during rehabilitation have shown less or no predictive value than the Arch Phys Med Rehabil Vol 93, August 2012

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PREDICTORS OF LIFE SATISFACTION, Davis

current model,14,60 while studies that have incorporated concurrent factors have somewhat stronger models. Corrigan et al2 predicted 14% of the variance using a model that included preinjury substance abuse, FIM motor at discharge from rehabilitation, and current employment, while Pierce and Hanks20 predicted 17% of the variance with a model that incorporated physical exam variables during rehabilitation with measures of demographics, activity limitations, and participation obtained concurrently with the measure of SWL. Notably, both of these studies included concurrent variables in the model, and the sole variable obtained during rehabilitation in the Pierce and Hanks20 study was not a significant contributor to the model. In contrast to the current study, studies that have examined SWL over a longer time frame postinjury and that have included concurrently measured variables have yielded stronger predictive models (though a large amount of variance also remains to be explained even in these studies).2,22,38,48,61 Employment and other aspects of community participation,2,22 activity limitations,25 mood,32 and self-efficacy22 are withinperson factors that have been associated with SWL years postinjury. Environmental factors, such as social support, family functioning, perceived environmental barriers,38 unmet service needs,40,41 and objective measures of neighborhood characteristics38 have also been found to be predictive of SWL in persons who are several years post-TBI. Unfortunately, these variables are not known at the times of inpatient rehabilitation. While the preinjury variables in the current study showed limited ability to predict later SWL, their availability at the time of rehabilitation yields potential for targeting those at risk for developing poor SWL after discharge. With the exception of studies that have investigated the role of preinjury substance use problems in predicting SWL after TBI, this is the first study, to our knowledge, to identify the contributions of preinjury condition variables. Future studies should investigate the relative contribution of preinjury factors, while also accounting for concurrent factors. Study Limitations The study sample differed from persons excluded from the study in several ways. Included persons had higher levels of functional independence and higher rates of employment/ productivity, and had lower rates of premorbid conditions relative to those who were excluded. Included individuals were also less likely to be married/cohabitating and showed higher rates of substance use problems. Additionally, even though the TBIMS has implemented many mechanisms to minimize attrition, the number of potential subjects who were not followed up with was rather large (26 withdrawals, 124 lost to follow-up, 35 not available), constituting 25% of the 744 subjects under age 65 who had not died by the time a follow-up interview was sought. Level and predictors of SWL remain unknown for this group. Even if attrition were random, individuals admitted to TBIMS hospitals are not necessarily a random sample of all persons with significant brain injury due to external forces. These issues of attrition and differences between the included versus excluded groups suggest that the study sample may have differed from the general population of persons with TBI. Findings from this study may not represent SWL in those with very mild injuries or those too severe to be admitted to inpatient rehabilitation. Even though a recent analysis indicated that on most demographic and functional independence factors TBIMS patients did not differ from those admitted to inpatient rehabilitation facilities nationwide,62 no data were available that allowed a comparison on the preinjury factors that are of interest in the present analysis as determinants of postinjury SWL. Arch Phys Med Rehabil Vol 93, August 2012

CONCLUSIONS The results from this study indicate that preinjury factors play a small, but significant role in SWL after TBI. While the role may be small, these factors are known at the time of rehabilitation and can assist with targeting individuals who may be at risk for poor SWL. Preinjury education may be a strength that enables persons with TBI to cope better with the impact of TBI. In contrast, preinjury limitations, such as physical disability, mental health difficulties, and learning disabilities, serve as risk factors for poor SWL after TBI. People with a history of these problems may require specialized services after TBI, such as psychoeducation about the interaction between TBI and preexisting disabilities or psychotherapy aimed at facilitating coping with multiple disabilities. References 1. Corrigan JD, Bogner J. Latent factors in measures of rehabilitation outcomes after traumatic brain injury. J Head Trauma Rehabil 2004;19:445-58. 2. Corrigan JD, Bogner JA, Mysiw WJ, Clinchot D, Fugate L. Life satisfaction after traumatic brain injury. J Head Trauma Rehabil 2001;16:543-55. 3. Dijkers MP. Quality of life after traumatic brain injury: a review of research approaches and findings. Arch Phys Med Rehabil 2004;85(4 Suppl 2):S21-35. 4. Kreuter M, Dahllof AG, Gudjonsson G, Sullivan M, Siosteen A. Sexual adjustment and its predictors after traumatic brain injury. Brain Inj 1998;12:349-68. 5. Dawson DR, Levine B, Schwartz M, Stuss DT. Quality of life following traumatic brain injury: a prospective study. Brain Cognition 2000;44:35-9. 6. Wood RL, Rutterford NA. Psychosocial adjustment 17 years after severe brain injury. J Neurol Neurosurg Psychiatry 2006;77:71-3. 7. Jacobsson LJ, Westerberg M, Lexell J. Health-related quality-oflife and life satisfaction 6-15 years after traumatic brain injuries in northern Sweden. Brain Inj 2010;24:1075-86. 8. Corrigan JD, Smith-Knapp K, Granger CV. Outcomes in the first 5 years after traumatic brain injury. Arch Phys Med Rehabil 1998;79:298-305. 9. Resch JA, Villarreal V, Johnson CL, et al. Trajectories of life satisfaction in the first 5 years following traumatic brain injury. Rehabil Psychol 2009;54:51-9. 10. Heinemann AW, Whiteneck GG. Relationships among impairment, disability, handicap and life satisfaction in persons with neurotrauma. J Head Trauma Rehabil 1995;10:54-63. 11. Willer BS, Linn R, Allen K. Community integration and barriers to integration for individuals with brain injury. In: Finlayson MAJ, Garner S, editors. Brain injury rehabilitation: clinical considerations. Baltimore: Williams & Wilkins; 1993. p 355-75. 12. Corrigan JD, Smith-Knapp K, Granger CV. Validity of the functional independence measure for persons with traumatic brain injury. Arch Phys Med Rehabil 1997;78:828-34. 13. Burleigh SA, Farber RS, Gillard M. Community integration and life satisfaction after traumatic brain injury: long-term findings. Am J Occup Ther 1998;52:45-52. 14. Saban KL, Smith BM, Collins EG, Pape TL. Sex differences in perceived life satisfaction and functional status one year after severe traumatic brain injury. J Womens Health (Larchmt) 2011; 20:179-86. 15. Smith JL, Magill-Evans J, Brintnell S. Life satisfaction following traumatic brain injury. Can J Rehabil 1998;11:131-40. 16. Warren L, Wrigley JM, Yoels WC, Fine PR. Factors associated with life satisfaction among a sample of persons with neurotrauma. J Rehabil Res Dev 1996;33:404-8.

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17. Arango-Lasprilla JC, Ketchum JM, Dezfulian T, et al. Predictors of marital stability 2 years following traumatic brain injury. Brain Inj 2008;22:565-74. 18. Hicken BL, Putzke JD, Novack T, Sherer M, Richards JS. Life satisfaction following spinal cord and traumatic brain injury: a comparative study. J Rehabil Res Dev 2002;39:359-65. 19. Tsaousides T, Warshowsky A, Ashman TA, Cantor JB, Spielman L, Gordon WA. The relationship between employment-related self-efficacy and quality of life following traumatic brain injury. Rehabil Psychol 2009;54:299-305. 20. Pierce CA, Hanks RA. Life satisfaction after traumatic brain injury and the World Health Organization model of disability. Am J Phys Med Rehab 2006;85:889-98. 21. Harker WF, Dawson DR, Boschen KA, Stuss DT. A comparison of independent living outcomes following traumatic brain injury and spinal cord injury. Int J Rehabil Res 2002;25:93-102. 22. Cicerone KD, Azulay J. Perceived self-efficacy and life satisfaction after traumatic brain injury. J Head Trauma Rehabil 2007; 22:257-66. 23. Kreuter M, Sullivan M, Dahllof A, Siosteen A. Partner relationship, functioning, mood and global quality of life in persons with spinal cord injury and traumatic brain injury. Spinal Cord 1998; 36:252-61. 24. Mailhan L, Azouvi P, Dazord A. Life satisfaction and disability after severe traumatic brain injury. Brain Inj 2005;19:227-38. 25. Johnson CL, Resch JA, Elliott TR, et al. Family satisfaction predicts life satisfaction trajectories over the first 5 years after traumatic brain injury. Rehabil Psychol 2010;55:180-7. 26. Vangel SJ Jr, Rapport LJ, Hanks RA. Effects of family and caregiver psychosocial functioning on outcomes in persons with traumatic brain injury. J Head Trauma Rehabil 2011;26:20-9. 27. Dahlberg C, Hawley L, Morey C, Newman J, Cusick CP, Harrison-Felix C. Social communication skills in persons with postacute traumatic brain injury: three perspectives. Brain Inj 2006; 20:425-35. 28. Dahlberg CA, Cusick CP, Hawley LA, et al. Treatment efficacy of social communication skills training after traumatic brain injury: a randomized treatment and deferred treatment controlled trial. Arch Phys Med Rehabil 2007;88:1561-73. 29. Evans CC, Sherer M, Nick TG, Nakase-Richardson R, Yablon SA. Early impaired self-awareness, depression, and subjective well-being following traumatic brain injury. J Head Trauma Rehabil 2005;20:488-500. 30. Seibert PS, Reedy DP, Hash J, et al. Brain injury: quality of life’s greater challenge. Brain Inj 2002;16:837-48. 31. Arango-Lasprilla JC, Kreutzer JS. Racial and ethnic disparities in functional, psychosocial, and neurobehavioral outcomes after brain injury. J Head Trauma Rehabil 2010;25:128-36. 32. Hart T, Brenner L, Clark AN, et al. Major and minor depression after traumatic brain injury. Arch Phys Med Rehabil 2011;92: 1211-9. 33. 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;16:330-42. 34. Brown M, Dijkers MP, Gordon WA, Ashman T, Charatz H, Cheng Z. Participation objective, participation subjective: a measure of participation combining outsider and insider perspectives. J Head Trauma Rehabil 2004;19:459-81. 35. Underhill AT, Lobello SG, Stroud TP, Terry KS, Devivo MJ, Fine PR. Depression and life satisfaction in patients with traumatic brain injury: a longitudinal study. Brain Inj 2003;17:973-82. 36. Rutterford NA, Wood RL. Evaluating a theory of stress and adjustment when predicting long-term psychosocial outcome after brain injury. J Int Neuropsychol Soc 2006;12:359-67. 37. Jacobsson LJ, Westerberg M, Malec JF, Lexell J. Sense of coherence and disability and the relationship with life satisfaction 6-15

38.

39.

40.

41.

42. 43.

44.

45.

46.

47.

48.

49. 50.

51.

52.

53.

54.

55.

56.

1329

years after traumatic brain injury in northern Sweden. Neuropsychol Rehabil 2011;21:383-400. Corrigan JD, Bogner JA. Neighborhood characteristics and outcomes after traumatic brain injury. Arch Phys Med Rehabil 2008; 89:912-21. Corrigan JD, Bogner J, Pretz C, et al. Use of neighborhood characteristics to improve prediction of psychosocial outcomes: a Traumatic Brain Injury Model Systems investigation. Arch Phys Med Rehabil 2012;93:1350-8. Heinemann AW, Sokol K, Garvin L, Bode RK. Measuring unmet needs and services among persons with traumatic brain injury. Arch Phys Med Rehabil 2002;83:1052-9. Pickelsimer EE, Selassie AW, Sample PL, Heinemann AW, Gu JK, Veldheer LC. Unmet service needs of persons with traumatic brain injury. J Head Trauma Rehabil 2007;22:1-13. Myers DG. The funds, friends, and faith of happy people. Am Psychol 2000;55:56-67. Argyle M. Causes and correlates of happiness. In: Kahneman D, Diener E, Schwarz N, editors. Subjective well-being: the foundations of hedonic psychology. New York: Russell Sage Foundation; 1999. p 353-73. Diener E, Lucas RE. Personality and subjective well-being. In: Kahneman D, Diener E, Schwarz N, editors. Subjective wellbeing: the foundations of hedonic psychology. New York: Russell Sage Foundation; 1999. p 213-29. Diener E, Lucas RE, Scollon CN. Beyond the hedonic treadmill: revising the adaptation theory of well-being. Am Psychol 2006; 61:305-14. Frederick S, Loewenstein G. Hedonic adaptation. In: Kahneman D, Diener D, Schwarz N, editors. Subjective well-being: the foundations of hedonic psychology. New York: Russell Sage Foundation; 1999. p 302-29. Bogner JA, Corrigan JD, Spafford DE, Lamb-Hart GL. Integrating substance abuse treatment and vocational rehabilitation following traumatic brain injury. J Head Trauma Rehabil 1997; 12:57-71. Bogner JA, Corrigan JD, Mysiw WJ, Clinchot D, Fugate L. A comparison of substance abuse and violence in the prediction of long-term rehabilitation outcomes after traumatic brain injury. Arch Phys Med Rehabil 2001;82:571-7. Diener E, Emmons R, Larsen J, Griffin S. The Satisfaction With Life Scale. J Pers Assess 1985;49:71-5. Dikmen SS, Ross BL, Machamer JE, Temkin NR. One year psychosocial outcome in head injury. J Int Neuropsychol Soc 1995;1:67-77. Dikmen SS, Machamer JE, Powell JM, Temkin NR. Outcome 3 to 5 years after moderate to severe traumatic brain injury. Arch Phys Med Rehabil 2003;84:1449-57. Uniform Data System for Medical Rehabilitation. The Guide for the Uniform Data Set for Medical Rehabilitation (including the FIM instrument) version 5.1. Buffalo: UDSMR; 1997. Linacre JM, Heinemann AW, Wright BD, Granger CV, Hamilton BB. The structure and stability of the Functional Independence Measure. Arch Phys Med Rehabil 1994;75:127-32. Dodds TA, Martin DP, Stolov WC, Deyo RA. A validation of the Functional Independence Measure and its performance among rehabilitation inpatients. Arch Phys Med Rehabil 1993; 74:531-6. Granger CV, Cotter AS, Hamilton BB, Fieldler RC, Hens MM. Functional assessment scales: a study of persons with multiple sclerosis. Arch Phys Medicine Rehabil 1990;71:870-5. Pavot W, Diener E. Review of the Satisfaction With Life Scale. Psychol Assess 1993;5:164-72. Arch Phys Med Rehabil Vol 93, August 2012

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PREDICTORS OF LIFE SATISFACTION, Davis

57. Brown M, Vandergoot D. Quality of life for individuals with traumatic brain injury: comparison with others living in the community. J Head Trauma Rehabil 1998;13:1-23. 58. Malec JF, Testa JA, Rush BK, Brown AW, Moessner AM. Selfassessment of impairment, impaired self-awareness, and depression after traumatic brain injury. J Head Trauma Rehabil 2007; 22:156-66. 59. Barclay-Goddard R, Lix LM, Tate R, Weinberg L, Mayo NE. Health-related quality of life after stroke: does response shift occur in self-perceived physical function? Arch Phys Med Rehabil 2011;92:1762-9.

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60. Hanks RA, Millis SR, Ricker JH, et al. The predictive validity of a brief inpatient neuropsychologic battery for persons with traumatic brain injury. Arch Phys Med Rehabil 2008;89: 950-7. 61. Waldron-Perrine B, Rapport LJ, Hanks RA, Lumley M, Meachen SJ, Hubbarth P. Religion and spirituality in rehabilitation outcomes among individuals with traumatic brain injury. Rehabil Psychol 2011;56:107-16. 62. Corrigan JD, Cuthbert JP, Whiteneck GG, et al. Representativeness of the Traumatic Brain Injury Model Systems National Database. J Head Trauma Rehabil; 2011 Sep 2. [Epub ahead of print].