To examine the ability of walking performance during dual-task walking at hospital discharge post stroke to predict daily walking activity at 3-months post hospital discharge.
We compared single and dual-task performance on each independent variable (gait speed, stride time, stride time variability, stride length, cadence, and correct response rate) using a paired-
were not normally distributed, the Wilcoxon-signed Rank test was used. The individual and group mean for the relative DTE on gait speed and cognitive performance (WPM) were plotted in order to identify patterns of dual-task interference. Demographic and stroke characteristics, cognition, comorbidity, self-efficacy for balance and walking, depression, walking endurance, and balance and lower extremity motor control were assessed using a one-way ANOVA to determine which characteristics were associated with distinct interference patterns. Walking activity measures extracted from the physical activity monitor were summarized for the all participants and then by functional ambulation category.
Our original analysis plan included linear and multilinear modeling for this aim but due to non-normally distributed data for the exposure, dual-task gait speed, and the outcome, total number of steps taken per day (SPD), we decided to use a binary logistic model instead. Attempts to normalize the data via transformation were not successful for the outcome variable (SPD). Based on the average total daily SPD at 3 months post hospital discharge, the participants we classified into one of the four functional ambulation categories based on the total number of SPD as established by Fulk et al (2017).27 In order to have a minimum of 10 participants per group, we collapsed the community ambulator categories (most limited, limited, and unlimited) into one group. The participants were then dichotomized into 2 groups, household ambulators for those walking ≤ 2499 SPD or community ambulators for those walking ≥ 2500 SPD.
An analysis of group differences for the outcome of functional ambulation status (household or community) and bivariate associations for each explanatory variable were completed. For the analysis of group differences, chi-square tests were used for categorical variables, one-way ANOVA for ordinal variables or continuous variables that met normality assumptions, and Fischer’s Exact Test for those not meeting assumptions. Alpha levels were not
adjusted for multiple independent variables based on recommendations by Savitz and Olsan.169 This was done to maintain statistical power, test specific associations of interest, and inform analysis for predictors of walking activity. For the bivariate associations, an emphasis was placed on the odds ratio (OR) and confidence intervals (e.g., width) as measures of effect and precision. Bivariate linear regression models were used to examine the relationship between explanatory variables and main exposure dual-task gait speed at hospital discharge. Spearman correlation matrices were used to determine the relationship between the continuous explanatory variables (covariates).
Next, multiple unadjusted and adjusted binary logistic regressions were examined. Using identical methods as with AIM I a potential confounder for these analysis was considered a variable that: (1) was significantly different for those classified as household versus community ambulators, (2) had bivariate associations with the outcome, functional ambulation status, and (3) had a linear relationship with the exposure, dual-task gait speed. The correlation matrices were grouped by common assessment domains to assist with determining the covariates appropriate for use in the logistic regression models and to ensure a parsimonious model.
Pre-stroke physical activity level as measured by the IPAQ and age were assessed for inclusion in the model based on known associations with daily walking activity (Table 3.7).91 According to previous research,91 being active 1 year after stroke (n = 117) could be predicted by pre-stroke physical activity (OR 7.46, 95% CI 1.51, 36.82). Further, low levels of physical activity at 1 year after stroke (n = 117) could be predicted by age at stroke onset (OR 1.13, 95% CI 1.06, 1.21).91
Next, binary logistic regression models using a backward deletion method with Wald p- values > 0.05 to be removed from the model, was used to examine the association between dual-
task gait speed and functional ambulation status with those covariates found to have: (1) significant differences for those classified as community versus household ambulator (exposure) and (2) bivariate associations with the exposure, dual-task gait speed, and the primary outcome, functional ambulation status. Satisfying both an association with the outcome and main exposure would indicate that this covariate was a potential confounder. Model fit and outliers were assessed as described for Aim 1. In this analysis, we also examined other patient-specific, mobility, and self- report variables using the same criteria above. In addition, we considered including variables even if they were not found to be significant in our model based on previous research findings.In order to ensure that the multivariable model provided a robust description of the association between the predictor variables and the outcome without overfitting, an event-per-variable ratio of 10 (patients who are community ambulators):1 (predictor variables) was maintained for all of the multivariable models. Emphasis was placed on the odds ratio (OR) and confidence intervals as measures of effect and precision.
Sample size justification for Aim 2:
Aim 2 power was determined based upon the sample size needed to complete Aim 1 (n = 56). Assuming the addition of the primary exposure increased the explained variance (R2) by 20% and additional covariates in the model (confounders) at an alpha of 0.05 there was 92% power to detect an overall model effect size of 0.20. Therefore, we expected to have adequate power to conduct Aim 2 analyses with the sample size calculated to complete the primary aim of the study. While the a priori power analysis was based on a continuous outcome, during data processing and analysis, we found that the outcome variable, SPD, was not normally distributed. No post hoc power analysis was performed.
Table 3.7. Summary of previous reported exposure and outcome odds ratios and confidence intervals
Author, Year Participants Time post stroke
Exposure Outcome OR CI
Olsson et al, 2017 117 1 year Pre-stroke
activity level Activity Level 7.46 1.51 – 36.82
Olsson et al, 2017 117 1 year Age Activity
Level
1.13 1.06 – 1.21
Exploratory Aim 3:
To determine if the observed estimates of association between attention demanding tasks at hospital discharge and fall or physical activity at 3-months post hospital discharge are different based on whether or not therapy was received post hospital discharge or the location of discharge (acute care or acute inpatient rehabilitation).
We explored stratified estimates based on the discharging facility (acute care versus acute inpatient rehabilitation) and whether or not therapy was received in the first 3 months post hospital discharge.
Management of missing data
Overall, there were minimal missing data for our primary outcome measures. For Aim 1, obstacle-crossing gait speed data were missing for two participants due to inability to cross the obstacle. Visual tracking parameters were missing for five participants due to technical difficulties with eye-tracking data acquisition or data processing within the software. For Aim 2, walking activity measures were missing for two participants, one due to death and the other due to refusal to wear the activity monitor as instructed.
Descriptive data extracted from the medical record were missing for NIHSS (n = 13) and AM-PAC (n = 14). Further, one participant did not complete the IPAQ and two participants did not complete the 2-minute walk test due to time constraints at hospital discharge in the acute care setting and fatigue during the baseline assessments in the acute inpatient rehabilitation setting respectively.
We used multiple imputation for two physical performance measures including one missing data point for the variable 5-meter walk test self-selected walking speed (m/s) and two missing data points for the variable 2-minute walk test distance (m). We imputed data for these specific variables because they were important potential predictors for our models based on previous published findings. The imputed variables did not change the outcome of our analyses due to the small number of missing data points. We verified this by running the models with and without imputed data. Due to the percentage of missing data for other variables (e.g.,13 data points [28%] for NIHSS ), we did not impute missing data for all variables.
CHAPTER 4: RESULTS