Reliability of Cognitive Measures in Individuals With a Chronic Spinal Cord Injury.

BACKGROUND: Following spinal cord injury (SCI), up to 64% of individuals experience cognitive deficit. However, the reliability of commonly used neuropsychological tests is currently unknown in this population. OBJECTIVES: To evaluate the test-retest reliability of cognitive measures in individuals with SCI. DESIGN: Cross-sectional study. SETTING: Vancouver General Hospital. PARTICIPANTS: Individuals with a chronic (>2 years) SCI (n = 22). METHODS: Across three visits (separated by ~16 days), 22 participants with chronic SCI completed a neuropsychological battery evaluating memory (Rey Auditory-Verbal Learning Test [RAVLT]), attention/concentration/psychomotor speed (Digit Span Task, Stroop Test), and executive function (Trail Making Test A&B, Symbol Digit Modalities Test, Controlled Oral Word Association Test). Coefficients of variation (CVintra ) and intraclass correlation coefficients (ICCs) were calculated to determine the reliability of each test between visits. Linear regressions were performed to assess the associations between variability (CVintra ) and participant characteristics, such as age or highest education level attained. Repeated-measures, one-way analysis of variance (ANOVA) was conducted to determine any significant practice effects, and smallest real differences (SRDs) were calculated. MAIN OUTCOME MEASUREMENTS: Repeated scores on aforementioned neuropsychological tests. RESULTS: ICCs ranged from 0.77 to 0.93, with the exception of RAVLT recognition score (ICC = 0.27). Age showed a moderate association with CVintra in RAVLT interference recall scores (r = 0.43, P = .047), but was not a confounding factor for other measures. Education was not associated with CVintra . Significant practice effects were noted for most of the cognitive tests assessed. CONCLUSIONS: Other than the RAVLT recognition score, these cognitive measures demonstrated good-to-excellent reliability. Although this is encouraging, test-retest variability should be considered when interpreting the efficacy of various cognitive training strategies to mitigate cognitive decline in this population. Thus, the SRD values presented herein will allow researchers and clinicians to identify "true" changes in cognitive function with repeated testing. LEVEL OF EVIDENCE: III.

Influence of accelerometer type and placement on physical activity energy expenditure prediction in manual wheelchair users.

PURPOSE: To assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs). METHODS: Seventeen MWUs (36 ± 10 yrs, 72 ± 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr-1) and propulsion at 4km·hr-1 (with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis. RESULTS: Accelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W. CONCLUSION: The results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.

Predicting physical activity energy expenditure in manual wheelchair users.

PURPOSE: This study aimed to assess the influence of anatomical placement of an accelerometer on physical activity energy expenditure prediction in manual wheelchair users. METHODS: Ten accelerometer units (ActiGraph GT3X+) were attached to a multiaxis shaker table and subjected to a sinusoidal oscillation procedure to assess mechanical validity and reliability. Fifteen manual wheelchair users (mean ± SD: age, 36 ± 11 yr; body mass, 70 ± 12 kg) then completed five activities, including desk work and wheelchair propulsion (2, 4, 6, and 8 km·h). Expired gases were collected throughout. GT3X+ accelerometers were worn on the right wrist, upper arm, and waist. The relations between physical activity counts and metabolic rate were subsequently assessed, and bias ± 95% limits of agreement was calculated. RESULTS: During mechanical testing, coefficients of variation ranged from 0.2% to 4.7% (intraunit) and 0.9% to 5.2% (interunit) in all axes. During human exercise testing, physical activity counts at each anatomical location was significantly (P < 0.01) correlated with metabolic rate (wrist, r = 0.93; upper arm, r = 0.87; waist, r = 0.73). The SEE for each correlation were 3.34, 4.38, and 6.07 kJ·min for the wrist, upper arm, and waist, respectively. The absolute bias ± 95% limits of agreement values were 0.0 ± 6.5 kJ·min, 0.0 ± 8.5 kJ·min, and 0.0 ± 11.8 kJ·min for the wrist, upper arm, and waist, respectively. CONCLUSIONS: The ActiGraph GT3X+ is a reliable tool for determining mechanical movements within the physiological range of human movement. Of the three anatomical locations considered, a wrist-mounted accelerometer explains more of the variance and results in the lowest random error when predicting physical activity energy expenditure in manual wheelchair users.