Data Analysis

Descriptive and regression analyses were stratified by dementia care and general care units, as it was hypothesized that physical features of these environments or the extent of environmental press might be different. To determine statistical significance by unit type of key descriptives, student t-tests were computed using a p-value of 0.01. There were a total of 184 residents in dementia care units; those with less than six meals of food intake data and those with missing CPS were excluded, leaving a total of 180 for this analysis. In the dementia care units, none of the dining rooms had an unsecured stove, thus analyses could not be performed on this DEAP variable. There were a total of 455 residents in general care units, four were removed for having less than six days of food intake data, five were missing body weight data and three were missing CPS, thus were not included, leaving a total of 443 for this analysis.

Each individual feature and subscale of the DEAP was summarized descriptively as frequency or mean and analyzed, using hierarchical regression analysis to determine its association with energy and protein intake (kilocalorie per kilogram body weight (kcal/kg); grams of protein per kilogram of body weight (g/kg)). These outcome variables were created by taking the average energy and protein intake variables and dividing by the resident’s body weight. Gender, age and CPS were used as covariates as these variables were anticipated to strongly predict energy and protein intake. Bivariate analyses using cluster regression, adjusting for age, gender and CPS score, were performed for each DEAP variable stratified by dementia and general care units; a p-value<0.25 was used as an indicator for inclusion of the variable in the multivariate model used to predict energy and protein intake in each of the unit types. The multivariate model was built using backwards elimination and a final p-value of 0.05 was used to determine the variables to be retained. Multicollinearity was assessed for

each final model; however none of the correlations were greater than 0.5 indicating that multicollinearity was not present.

The regression procedure was also used to determine those DEAP variables which predicted the homelikeness and functionality summative scales when adjusted for other variables in this tool. First, a bivariate analysis was performed between homelikeness and each variable from the DEAP; those that had a p-value<0.25 were included in the

multivariate model. Next a multivariate model of all variables found to have potential association was built and backwards elimination was performed using a p-value of 0.05 to determine order for removal and retention of variables. The final model was achieved when all variables had a p-value of 0.05 or lower. The same method was used for the functionality scale. When the multivariate model for functional ability was conducted, the variable that represented “respecting and responding to resident’s opinions” was found to interact with the variable “residents are able to see the dining area from their bedroom”. Both of these

variables were eliminated from the multivariate model as the first could not be reliably assessed in an empty dining room while the second had only three dining rooms with this feature. The adjusted R-squared was noted at each step of the multivariate model to

understand how each variable was affecting the overall model. Further, collinearity tests were performed using the tolerance values and cooks d to gather information on the existing relationships between each of the variables that remained in the final model. Due to tolerance values being >0.2 in all models, it was determined that multicollinearity was not present. Upon conducting cooks d, outliers were detected and removed; however, this did not alter the interpretation of the model, therefore supporting their inclusion.

The construct validity of the summary scales of homelikeness and functionality from the DEAP were assessed by contrasting with resident energy and protein intake, nutritional risk as measured with MNA-SF as well as several other scales in the data set. Descriptive statistics were computed for each of the instruments that the scales were compared to, dining room level M-RCC, MTS summary scales, staff PDC, resident Food and Food Service Satisfaction survey, resident DRS, nutritional risk, CPS score and resident level M-RCC. Since the DEAP was collected at the unit level, when performing correlations to resident

level instruments the median was used for instruments that had a high level of variance (e.g. energy intake and resident level M-RCC ratio) and the mean was used for those that had little variability. A Spearman rho correlation was computed for each instrument with the

homelikeness and functionality scales, and a p<0.05 was used to indicate statistical significance. To determine the association between CPS and the homelikeness and

functionality scale, the CPS score was dichotomized into none to mild cognitive impairment (scores 0-2) and moderate to severe cognitive impairment (3-6). Using a Student t-test, it was determined if homelikeness and functionality varied by cognitive status.

Chi squared tests were computed to determine if the provinces were statistically different from one another with respect to individual characteristics captured with DEAP. If the Chi squared test was significant (p<0.01), a Fisher’s test was performed to address potential non-independence between variables. For continuous variables, such as the

homelikeness and functionality scales, analysis of variance was computed and Tukey’s tests were conducted to determine significant differences among the four provinces. All analyses were performed using SAS University (version 9.4).