Search

Introduction Searching the internet for health-related information is a complex and dynamic goal-oriented process that ostensibly places demands on executive functions, which are higher-order cognitive abilities that can deteriorate with older age. This study examined the effects of older age on electronic health (eHealth) search behavior and the potential mediating influence of executive functions. Method Fifty younger adults (≤ 35 years) and 41 older adults (≥50 years) completed naturalistic eHealth search tasks involving fact-finding (Fact Search) and symptom determination (Symptom Search), a neurocognitive battery, and a series of self-report questionnaires. Results Multiple regression models controlling for potentially confounding psychiatric symptoms, health conditions, literacy, and demographic variables revealed that older adults were slower and less accurate than younger adults on the eHealth Fact Search task, but not on the eHealth Symptom Search task. Executive functions mediated the relationship between age and Fact and Symptom Search accuracy, independent of basic processing speed and attention. Parallel mediation models showed that episodic memory was not an independent mediator of age and search accuracy for either eHealth task once speed/attention and executive functions were included. Conclusions Older adults can experience difficulty searching the internet for some health-related information, which is at least partly attributable to executive dysfunction. Future studies are needed to determine the benefits of training in the organizational and strategic aspects of internet search for older adults and whether these findings are applicable to clinical populations with executive dysfunction.

Background Sleep problems and disorders are prevalent in patients with traumatic brain injury (TBI) and are associated with negative outcomes. Incidence varies because of challenges including differences in assessment methods, particularly in the acute stages of recovery when patients are cognitively impaired and unable to complete traditional self-report methods. Actigraphy (ACG) recently has been validated in the acute TBI rehabilitation setting and may serve as a superior method of assessing sleep-wake patterns at this stage of recovery. Although a few studies with small sample sizes have described the use of ACG, none have described feasibility and implementation protocols. Objective To describe the feasibility and implementation protocol of ACG to evaluate sleep-wake patterns and white-light exposure data in patients with acute TBI during inpatient rehabilitation. Sleep-wake patterns and light exposure data are presented to characterize the sample using these methods to inform future research. Design Retrospective study. Setting Acute inpatient rehabilitation unit at a Veterans' Affairs Polytrauma Rehabilitation Center. Participants Veterans (age ≥18 years) admitted to inpatient rehabilitation and enrolled in the Traumatic Brain Injury Model Systems study who were admitted and discharged in the calendar year 2013. Methods Veterans underwent actigraph watch placement as soon as possible after admission. Records from the calendar year 2013 were reviewed to determine the number of admissions that met study criteria and what percentage of those patients had 3 days of continuous ACG data collected. The barriers to successful watch placement in this population were reviewed. Average sleep, light, and wake data from available records were collected for the study sample. Main Outcome Measurements Percentage of patients who met study criteria and who had 72 hours of continuous ACG data collected. The barriers to successful watch placement in this population were reviewed. Average sleep, light, and wake data from available records were collected. Results Of 22 eligible Traumatic Brain Injury Model Systems admissions, 3 consecutive nights of ACG data were successfully obtained for 86% (n = 19) of the sample. Barriers to data collection included patient access due to abbreviated lengths of stay, staff availability for ACG placement, and data collection protocols to prevent loss of data in Veterans' Affairs computing systems. Conclusions ACG is feasible for collecting data about sleep, wake, and light exposure in patients who are in acute TBI inpatient rehabilitation settings. Level of Evidence III

The purpose of this study was to determine how well scores from the Rey Complex Figure Test (RCFT) could serve as embedded measures of performance validity in a large, heterogeneous clinical sample at an urban-based Veterans' Affairs hospital. Participants were divided into credible performance (n = 244) and noncredible performance (n = 87) groups based on common performance validity tests during their respective clinical evaluations. We evaluated how well preselected RCFT scores could discriminate between the 2 groups using cut scores from single indexes as well as multivariate logistic regression prediction models. Additionally, we evaluated how well memory error patterns (MEPs) could discriminate between the 2 groups. Optimal discrimination occurred when indexes from the Copy and Recognition trials were simultaneous predictors in logistic regression models, with 91% specificity and at least 53% sensitivity. Logistic regression yielded superior discrimination compared with individual indexes and compared with the use of MEPs. Specific scores on the RCFT, including the Copy and Recognition trials, can serve as adequate indexes of performance validity, when using both cut scores and logistic regression prediction models. We provide logistic regression equations that can be applied in similar clinical settings to assist in determining performance validity.

OBJECTIVE To prospectively examine the incidence and risk factors for sleep apnea in consecutive brain injury rehabilitation admissions. SETTING Inpatient neurorehabilitation hospital. PARTICIPANTS Participants (n = 86) were consecutive neurorehabilitation admissions. DESIGN Retrospective analysis of prospectively collected data. MAIN MEASURES Polysomnography. RESULTS Half (49%) of the sample was diagnosed with sleep apnea. For the full sample, univariate logistic regression revealed age (odds ratio: 1.08; 95% confidence interval: 1.04-1.11) and hypertension (odds ratio: 7.77; 95% confidence interval: 2.81-21.47) as significant predictors of sleep apnea diagnosis. Results of logistic regression conducted within the traumatic brain injury group revealed age (odds ratio: 1.07; 95% confidence interval: 1.02-1.13) as the only significant predictor of apnea diagnosis after adjustment for other variables. Hierarchical generalized linear regression models for the prediction of apnea severity (ie, apnea-hypopnea index found that Functional Independence Measure Cognition Score (P = .01) and age (P < .01) were significant predictors. Following adjustment for all other terms, only age (P < .01) remained significant. CONCLUSION Sleep apnea is prevalent in acute neurorehabilitation admissions and traditional risk profiles for sleep apnea may not effectively screen for the disorder. Given the progressive nature of obstructive sleep apnea and morbidity associated with even mild obstructive sleep apnea, early identification and intervention may address comorbidities influencing acute and long-term outcome.

Following traumatic brain injury, both sleep dysfunction and cognitive impairment are common. Unfortunately, little is known regarding the potential associations between these 2 symptoms during acute recovery. This study sought to prospectively examine the relationship between ratings of sleep dysfunction and serial cognitive assessments among traumatic brain injury acute neurorehabilitation admissions.Participants were consecutive admissions to a free-standing rehabilitation hospital following moderate to severe traumatic brain injury (Median Emergency Department Glasgow Coma Scale = 7). Participants were assessed for sleep-wake cycle disturbance (SWCD) and cognitive functioning at admission and with subsequent weekly examinations. Participants were grouped on the basis of presence (SWCD+) or absence (SWCD-) of sleep dysfunction for each examination; groups were equivalent on demographic and injury variables. Individual Growth Curve modeling was used to examine course of Cognitive Test for Delirium performance across examinations.Individual Growth Curve modeling revealed a significant interaction between examination number (ie, time) and SWCD group (β = -4.03, P.001) on total Cognitive Test for Delirium score. The SWCD+ ratings on later examinations were predicted to result in lower Cognitive Test for Delirium scores and greater cognitive impairment over time.This study has implications for improving neurorehabilitation treatment, as targeting sleep dysfunction for early intervention may facilitate cognitive recovery.