- Email: [email protected]
Poster 13 Concordance of Actigraphy with Overnight Polysomnography: A Pilot Study
2011 ACRM-ASNR ANNUAL CONFERENCE ABSTRACTS
Main Outcome Measures: In addition to the AMAT-9, upper extremity Fugl-Meyer Assessment (UE-FMA), Wolf Motor Function Test (WMFT), Action Research Arm Test (ARAT), and Stroke Impact Scale – Physical Subscale (SIS) were completed at baseline and after the 12 week treatment. We calculated the standardized response mean (SRM) for each measure. Results: 29 subjects completed the protocol with mean age of 56.0⫾12.4 years, mean time post-stroke of 3.5⫾2.3 years, mean baseline AMAT-9 score of 1.2⫾0.6 (lower range of the scale), and a mean National Institutes of Health Stroke Score of 4.1⫾2.1. Fifty percent of the group had right-sided hemiparesis. SRM for the AMAT-9, UE-FMA, WMFT, ARAT, and SIS were 0.97, 1.26, 0.84, 0.89 and 1.07, respectively. Conclusions: These data would suggest that responsiveness of the AMAT-9, as a standardized measure of upper extremity function, is in an intermediate range when compared to other more impairment or participation-based measures over a 12 week treatment period. It remains unclear whether the decreased responsiveness relative to the FMA and SIS is acceptable in order to gain an assessment of activity level performance. Further research should examine other attributes of change over time and in a larger sample. Key Words: Stroke; Responsiveness; Arm motor ability test; Rehabilitation. Poster 12 Addressing Selection Bias in Observational Rehabilitation Research Using Large Datasets. Amol Karmarkar (University of Texas Medical Branch, Galveston, TX), James Graham, Kenneth Ottenbacher. Disclosure: None disclosed. Objective: Demonstrate the use of propensity-score matching to decrease selection bias in observational research using large dataset. Data: Centers for Medicare and Medicaid Services (CMS) administrative medical records from Medicare fee-for-service beneficiaries who received inpatient rehabilitation services for lower-extremity joint replacement in 2008. The final sample included 49,210 patients. We stratified the sample based on reason for Medicare eligibility: agerelated entitlement (n⫽40,074) versus disability (n⫽9,136). Data Analysis: Comparing inpatient rehabilitation outcomes from dissimilar groups, such as Medicare entitlement and Medicare disability patients, can be problematic for several reasons. First, substantial differences in sample sizes (40,074 versus 9,136) can skew the results and tests of significance. Second, fundamental demographic differences (e.g. age, race/ethnicity, number or severity of comorbidities, etc.) can confound the comparisons. We used propensity-score matching to account for these potential problems and match cases (Medicare disability) to an equal number of controls (Medicare entitlement). We used age and race/ethnicity (white versus non-white) to compute propensity scores via the psmatch2 and pstest commands in the Stata software package. Results: Stratification of the original sample resulted in significant differences between the Medicare entitlement and Medicare disability groups in mean age (76.9 versus 64.1 years) and race/ethnicity proportions (12% versus 27% non-white). Propensityscore matching reduced bias in the age and race/ethnicity variables by 98% and 60%, respectively. Conclusions: Selection bias is an important yet understudied phenomenon in rehabilitation research. There are several established techniques for addressing selection bias. The choice of one method over other should be based on study design, sample size, independent variables, and outcomes of interest. Key Words: Rehabilitation. Poster 13 Concordance of Actigraphy with Overnight Polysomnography: A Pilot Study. Tracy S. Kretzmer (Department of Mental Health and Behavioral Sciences, James A. Haley Veterans Hospital, Tampa, FL, University of South Florida, Department of Psychology, Tampa, FL), Bradley J. Daniels, Praveen K. Gootam, Marissa McCarthy, Bryan Merritt, William M. Anderson, Risa NakaseRichardson.
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Disclosure: None disclosed. Objective: With limited data regarding validity of actigraphy (ACG) to monitor sleep in TBI patients, the purpose of this study was to examine the concordance of ACG with Polysomnography (PSG), the clinical gold standard for evaluating sleep. Design: Sleep indices were recorded during overnight PSG (Somnostar 9.1/Viasys) with simultaneous ACG placement using Respironics Actiwatch-2. Sleep indices were compared during the lights-off through lights-on period of PSG. Setting: Veterans polytrauma inpatient rehabilitation unit. Participants: Eight participants with a history of TBI (63% mild; 37% severe) were included. All individuals were male, primarily Caucasian (87%), with an average age of 35.1 years at evaluation. Subjects were a median of 64 months (Range ⫽ 13-222 months) from initial injury. Interventions: Not Applicable. Main Outcome Measures: PSGACG Total Sleep Time (TST), PSG-ACG Sleep Efficiency (SE), and PSG-ACG Wake After Sleep Onset (WASO). Results: Pearson correlations revealed high agreement between PSG-ACG TST (r⫽.80, p⬍.05) and SE (r⫽.75, p⬍.05). In contrast, this was not demonstrated across PSG-ACG WASO. Conclusions: Sleep disturbance is a common problem during both acute and chronic phases of TBI recovery with potential for significant morbidity. Given the high cost and limited recording interval of PSG, alternative mechanisms for screening and monitoring sleep sequelae are needed. Self-report of sleep is often inaccurately reported among healthy adults. Individuals with TBI are likely to have similar accuracy challenges. The use of an objective technology may provide more accurate respresentation of the sleepwake cycle for TBI patients. These preliminary findings suggest ACG measurements of TST and SE yield comparable results to PSG indices in TBI patients. Further research is still needed to demonstrate the concordance of ACG with PSG in a broader range of patient severity. Key Words: Polysomnography (PSG); Actigraphy (ACG); Traumatic brain injury (TBI); Sleep; Rehabilitation. Poster 14 Advanced Longitudinal Data Analysis. Christopher Pretz (Craig Hospital, Englewood, CO), Scott Kreider. Disclosure: None disclosed. As the necessity for analyzing data longitudinally becomes more prevalent either through clinical trials or large observational databases such as the Traumatic Brain Injury and the Spinal Cord Injury Model Systems National Databases, so does the need for advanced statistical methodology in interpreting this type of data. As a proxy for longitudinal methodology, data are often interpreted via a combined series of cross-sectional analyses. These and similar approaches are problematic as they do not explicitly model time nor do they account for the inner correlations between measurements extracted temporally. A set of three modern statistical techniques designed specifically to analyze longitudinal data will be presented. The first approach utilizes mean response curves which allow the researcher to make comparisons across groups over time. The second method is a form of hierarchical linear modeling known as individual or latent growth curves. This technique has the advantage of not only being able to compare individual trajectories over time with an “overall” trajectory but also allows for covariate assessment. The final approach will employ generalized estimating equations and general linear mixed effects modeling and are somewhat analogous to mean response and individual growth curves respectively; the substantive difference being the later methodology are applicable when data are dichotomous or frequency based. Each method will be explicated through use of examples based on data from the Rocky Mountain Regional Brain Injury and Spinal Injury Systems at Craig Hospital. Interpretation of results will be highlighted. Key Words: Longitudinal analysis; Hierarchical linear models; Generalized estimating equations; General linear mixed model; Generalized linear mixed model; General linear model; Generalized linear model; Rehabilitation. Arch Phys Med Rehabil Vol 92, October 2011
Main Outcome Measures: In addition to the AMAT-9, upper extremity Fugl-Meyer Assessment (UE-FMA), Wolf Motor Function Test (WMFT), Action Research Arm Test (ARAT), and Stroke Impact Scale – Physical Subscale (SIS) were completed at baseline and after the 12 week treatment. We calculated the standardized response mean (SRM) for each measure. Results: 29 subjects completed the protocol with mean age of 56.0⫾12.4 years, mean time post-stroke of 3.5⫾2.3 years, mean baseline AMAT-9 score of 1.2⫾0.6 (lower range of the scale), and a mean National Institutes of Health Stroke Score of 4.1⫾2.1. Fifty percent of the group had right-sided hemiparesis. SRM for the AMAT-9, UE-FMA, WMFT, ARAT, and SIS were 0.97, 1.26, 0.84, 0.89 and 1.07, respectively. Conclusions: These data would suggest that responsiveness of the AMAT-9, as a standardized measure of upper extremity function, is in an intermediate range when compared to other more impairment or participation-based measures over a 12 week treatment period. It remains unclear whether the decreased responsiveness relative to the FMA and SIS is acceptable in order to gain an assessment of activity level performance. Further research should examine other attributes of change over time and in a larger sample. Key Words: Stroke; Responsiveness; Arm motor ability test; Rehabilitation. Poster 12 Addressing Selection Bias in Observational Rehabilitation Research Using Large Datasets. Amol Karmarkar (University of Texas Medical Branch, Galveston, TX), James Graham, Kenneth Ottenbacher. Disclosure: None disclosed. Objective: Demonstrate the use of propensity-score matching to decrease selection bias in observational research using large dataset. Data: Centers for Medicare and Medicaid Services (CMS) administrative medical records from Medicare fee-for-service beneficiaries who received inpatient rehabilitation services for lower-extremity joint replacement in 2008. The final sample included 49,210 patients. We stratified the sample based on reason for Medicare eligibility: agerelated entitlement (n⫽40,074) versus disability (n⫽9,136). Data Analysis: Comparing inpatient rehabilitation outcomes from dissimilar groups, such as Medicare entitlement and Medicare disability patients, can be problematic for several reasons. First, substantial differences in sample sizes (40,074 versus 9,136) can skew the results and tests of significance. Second, fundamental demographic differences (e.g. age, race/ethnicity, number or severity of comorbidities, etc.) can confound the comparisons. We used propensity-score matching to account for these potential problems and match cases (Medicare disability) to an equal number of controls (Medicare entitlement). We used age and race/ethnicity (white versus non-white) to compute propensity scores via the psmatch2 and pstest commands in the Stata software package. Results: Stratification of the original sample resulted in significant differences between the Medicare entitlement and Medicare disability groups in mean age (76.9 versus 64.1 years) and race/ethnicity proportions (12% versus 27% non-white). Propensityscore matching reduced bias in the age and race/ethnicity variables by 98% and 60%, respectively. Conclusions: Selection bias is an important yet understudied phenomenon in rehabilitation research. There are several established techniques for addressing selection bias. The choice of one method over other should be based on study design, sample size, independent variables, and outcomes of interest. Key Words: Rehabilitation. Poster 13 Concordance of Actigraphy with Overnight Polysomnography: A Pilot Study. Tracy S. Kretzmer (Department of Mental Health and Behavioral Sciences, James A. Haley Veterans Hospital, Tampa, FL, University of South Florida, Department of Psychology, Tampa, FL), Bradley J. Daniels, Praveen K. Gootam, Marissa McCarthy, Bryan Merritt, William M. Anderson, Risa NakaseRichardson.
1697
Disclosure: None disclosed. Objective: With limited data regarding validity of actigraphy (ACG) to monitor sleep in TBI patients, the purpose of this study was to examine the concordance of ACG with Polysomnography (PSG), the clinical gold standard for evaluating sleep. Design: Sleep indices were recorded during overnight PSG (Somnostar 9.1/Viasys) with simultaneous ACG placement using Respironics Actiwatch-2. Sleep indices were compared during the lights-off through lights-on period of PSG. Setting: Veterans polytrauma inpatient rehabilitation unit. Participants: Eight participants with a history of TBI (63% mild; 37% severe) were included. All individuals were male, primarily Caucasian (87%), with an average age of 35.1 years at evaluation. Subjects were a median of 64 months (Range ⫽ 13-222 months) from initial injury. Interventions: Not Applicable. Main Outcome Measures: PSGACG Total Sleep Time (TST), PSG-ACG Sleep Efficiency (SE), and PSG-ACG Wake After Sleep Onset (WASO). Results: Pearson correlations revealed high agreement between PSG-ACG TST (r⫽.80, p⬍.05) and SE (r⫽.75, p⬍.05). In contrast, this was not demonstrated across PSG-ACG WASO. Conclusions: Sleep disturbance is a common problem during both acute and chronic phases of TBI recovery with potential for significant morbidity. Given the high cost and limited recording interval of PSG, alternative mechanisms for screening and monitoring sleep sequelae are needed. Self-report of sleep is often inaccurately reported among healthy adults. Individuals with TBI are likely to have similar accuracy challenges. The use of an objective technology may provide more accurate respresentation of the sleepwake cycle for TBI patients. These preliminary findings suggest ACG measurements of TST and SE yield comparable results to PSG indices in TBI patients. Further research is still needed to demonstrate the concordance of ACG with PSG in a broader range of patient severity. Key Words: Polysomnography (PSG); Actigraphy (ACG); Traumatic brain injury (TBI); Sleep; Rehabilitation. Poster 14 Advanced Longitudinal Data Analysis. Christopher Pretz (Craig Hospital, Englewood, CO), Scott Kreider. Disclosure: None disclosed. As the necessity for analyzing data longitudinally becomes more prevalent either through clinical trials or large observational databases such as the Traumatic Brain Injury and the Spinal Cord Injury Model Systems National Databases, so does the need for advanced statistical methodology in interpreting this type of data. As a proxy for longitudinal methodology, data are often interpreted via a combined series of cross-sectional analyses. These and similar approaches are problematic as they do not explicitly model time nor do they account for the inner correlations between measurements extracted temporally. A set of three modern statistical techniques designed specifically to analyze longitudinal data will be presented. The first approach utilizes mean response curves which allow the researcher to make comparisons across groups over time. The second method is a form of hierarchical linear modeling known as individual or latent growth curves. This technique has the advantage of not only being able to compare individual trajectories over time with an “overall” trajectory but also allows for covariate assessment. The final approach will employ generalized estimating equations and general linear mixed effects modeling and are somewhat analogous to mean response and individual growth curves respectively; the substantive difference being the later methodology are applicable when data are dichotomous or frequency based. Each method will be explicated through use of examples based on data from the Rocky Mountain Regional Brain Injury and Spinal Injury Systems at Craig Hospital. Interpretation of results will be highlighted. Key Words: Longitudinal analysis; Hierarchical linear models; Generalized estimating equations; General linear mixed model; Generalized linear mixed model; General linear model; Generalized linear model; Rehabilitation. Arch Phys Med Rehabil Vol 92, October 2011