# Comparative Analysis of MoCA and DigiMoCA Test Results: A Pilot Study
## 2 Materials and Methods
### Statistical Analysis
This approach enabled the identification of patterns and relationships among variables, thereby facilitating correlation analysis. Initially, the Kaiser-Meyer-Olkin (KMO) and Bartlett tests yielded a KMO mean of 0.604 (cf. Table A1, Appendix A). Upon the examination of the table of communalities (cf. Table 1, Appendix A), it was noted that the extraction of variables transport, day, and organization increased the KMO mean to 0.668, resulting in communalities exceeding 0.4 for the remaining variables. Table 2 in Appendix A displays the distribution of the DigiMoCa variables into seven factors, which explain 73.7% of the total variance. In Figure 2, a Scree plot depicts the variables that determine the number of factors to retain in an exploratory factor analysis. Finally, Varimax was used in the present study as the most common rotation technique in statistical analysis. Table 3 in Appendix A presents Varimax-rotated components exhibiting the relationship between the original variables and the components derived from the principal component analysis. Each number in the table reflects the loading of individual variables onto each component, resulting in seven distinct factors, namely, factor 1 (calculations, subtraction, words, and F words), factor 2 (letter mistakes, letters, second sentence, and week), factor 3 (mean response time, uncued recalls, first sentence), factor 4 (backward numbers, forward numbers, and month), factor 5 (choice recalls and measure), factor 6 (year), and factor 7 (city and cued recalls). The Spearman correlation analysis was employed to assess the relationships among sociodemographic variables (i.e., age, gender, educational level, residency, marital status). Furthermore, associations between MoCA test scores and dimensions, as well as DigiMoCA factors with the GDS-15 scale, are highlighted, considering only values close to or exceeding 0.3 (cf. Table 5). No correlations greater than 0.3 were found for the sex variable. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **GDS-15** & **RT Mean** & **Age** & **Ed. Level** & **Household** \\ \hline CDT & Total score & \(-0.469\), \(p<0.001\) & \(-0.355\), \(p=0.007\) & \(-0.660\), \(p<0.001\) & 0.513, \(p<\)0.001 & – \\ \hline \multirow{8}{*}{MoCA} & Visuospatial/FE & \(-0.429\), \(p\ll 0.001\) & \(-0.437\), \(p=0.001\) & \(-0.709\), \(p<0.001\) & 0.510, \(p<0.001\) & – \\  & Identification & – & – & \(-0.375\), \(p=0.001\) & 0.426, \(p<0.001\) & – \\  & Attention & \(-0.447\), \(p<0.001\) & \(-0.324\), \(p=0.014\) & \(-0.466\), \(p<0.001\) & 0.525, \(p<0.001\) & – \\  & Abstraction & – & \(-0.341\), \(p=0.009\) & \(-0.494\), \(p<0.001\) & 0.466, \(p<0.001\) & – \\  & Language & \(-0.391\), \(p=0.001\) & & \(-0.403\), \(p<0.001\) & 0.453, \(p<0.001\) & – \\  & Memory & \(-0.301\), \(p=0.010\) & 0.478, \(p<0.001\) & \(-0.463\), \(p<0.001\) & 0.463, \(p<0.001\) & – \\  & Orientation & – & – & \(-0.428\), \(p<0.001\) & 0.428, \(p<0.001\) & – \\  & Total score & \(-0.422\), \(p<0.001\) & \(-0.482\), \(p<0.001\) & \(-0.684\), \(p<0.001\) & 0.524, \(p<0.001\) & – \\ \hline \multirow{8}{*}{DigiMoCA} & Factor 1 & – & – & \(-0.308\), \(p=0.020\) & \(-0.296\), \(p=0.025\) \\  & Factor 2 & \(-0.321\), \(p=0.015\) & & \(-0.508\), \(p<0.001\) & 0.371, \(p=0.005\) & – \\  & Factor 3 & – & \(-0.861\), \(p<0.001\) & \(-0.426\), \(p=0.001\) & – & – \\  & Factor 6 & – & – & \(-0.614\), \(p<0.001\) & \(-0.665\), \(p<0.001\) & 0.382, \(p=0.003\) & – \\ \hline Lawton’s & Total score & \(-0.367\), \(p=0.001\) & \(-0.324\), \(p=0.014\) & \(-0.582\), \(p<0.001\) & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Spearman correlations. Figure 2: Scree plot depicted to identify the variables that determine the number of factors to retain in an exploratory factor analysis. **GDS-15**: correlates negatively with the CDT score, visuospatial, attention, language, overall MoCA score, memory, and factor 2.