## 4 Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Total (n = 39)**} & \multicolumn{3}{c}{**Women (n = 11)**} & \multicolumn{3}{c}{**Men (n = 28)**} \\ \hline  & **B** & **SE** & \(p\) & **B** & **SE** & \(p\) & **B** & **SE** & \(p\) \\ \hline Basic Model (R\({}^{2}\) = 0.2\%, W: 0\%, M: 0.1\%) & & & & & & & & & \\ Age (y) & 0.005 & 0.041 & 0.903 & 0.004 & 0.086 & 0.967 & 0.006 & 0.047 & 0.908 \\ Gender (men vs. women *) & 0.320 & 1.208 & 0.792 & NA & NA & NA & NA & NA & NA \\ Model 1 (R\({}^{2}\) = 1.1\%, W: 18.6\%, M: 2\%) & & & & & & & & \\ Age (y) & 0.002 & 0.042 & 0.967 & \(-\)0.009 & 0.083 & 0.913 & 0.007 & 0.049 & 0.888 \\ Gender (men vs. women *) & 0.228 & 10.232 & 0.854 & NA & NA & NA & NA & NA & NA \\ Physical activity at baseline \$ & \(-\)0.393 & 0.724 & 0.591 & \(-\)1.915 & 10.419 & 0.214 & 0.160 & 0.849 & 0.852 \\ \hline Model 2 (R\({}^{2}\) T: 13.5\%, W: 83.6\%, M: 4.6\%) & & & & & & & & \\ Age (y) & 0.004 & 0.041 & 0.928 & \(-\)0.069 & 0.049 & 0.207 & 0.023 & 0.053 & 0.665 \\ Gender (men vs. women *) & 2.757 & 1.804 & 0.136 & NA & NA & NA & NA & NA \\ Physical activity at baseline \$ & \(-\)0.017 & 0.785 & 0.983 & \(-\)2.568 & 1.093 & 0.057 & 0.524 & 0.941 & 0.583 \\ Pattern 1: High BMI-high jaw fit & 0.282 & 0.628 & 0.657 & \(-\)0.469 & 0.738 & 0.549 & 0.522 & 0.869 & 0.554 \\ Pattern 2: High BMI-high lean tissue & 1.611 & 0.803 & 0.050 & 3.353 & 0.692 & 0.003 & 0.769 & 1.167 & 0.517 \\ \hline Model 3 (R\({}^{2}\) T: 20.6\%, W: 88.5\%, M: 18.6\%) & & & & & & & & \\ Age (y) & \(-\)0.006 & 0.040 & 0.887 & \(-\)0.040 & 0.044 & 0.424 & 0.003 & 0.054 & 0.949 \\ Gender (men vs. women *) & 2.579 & 1.734 & 0.147 & NA & NA & NA & NA & NA \\ Physical activity at baseline \$ & \(-\)0.290 & 0.762 & 0.706 & \(-\)30.416 & 0.979 & 0.025 & 0.248 & 0.915 & 0.789 \\ Pattern 1: High BMI-high jaw fit & 0.128 & 0.610 & 0.835 & \(-\)10.245 & 0.703 & 0.151 & 0.411 & 0.840 & 0.630 \\ Pattern 2: High BMI-high lean tissue & 10.307 & 0.818 & 0.121 & 20.415 & 0.717 & 0.028 & 0.727 & 10.123 & 0.524 \\ Triglycerides (mg/dL) & \(-\)0.018 & 0.009 & 0.050 & \(-\)0.005 & 0.009 & 0.618 & \(-\)0.022 & 0.011 & 0.069 \\ \hline \hline \end{tabular}  
* T: Total sample, M: Men, W: Women, NA: Not applicable; y = years,
* Men = 1, Women = 0, $ 1 = mildly active, 2 = moderately active, 3 = active, 4 = intensely active or athlete.  
\end{table} 
Table 5: Linear regression analysis with % weight loss after a CDSS intervention as dependent variable in the total sample, men and women. (after appropriate training), since it \"allows\" patients to visit their personal profile and record/track their personal progress, e.g., weight goals, physical activity goals, etc. [4, 12]. The tracking feature of the present CDSS also enables a \"continuous treatment model\" approach, which is crucial to combat a chronic condition, such as obesity [34]. In parallel, the Nutrient(r) CDSS endorses patient stimulation, a powerful mechanism for addressing health inequalities. The use of CDSS tools by patients themselves has shown promising results in a hospital-based intervention [35]. Similarly, an electronic nutritional assessment tool improved dietary habits, as well as motivation and self-efficiency, when used by patients [36]. A bi-directional communication of the patient and health provider can be achieved through the Nutrinet(r) CDSS, which is particularly important in the COVID-19 era [4]. In a wider perspective, new technology-assisted dietary assessment has been connected to higher acceptability, self-adherence and self-monitoring compared to paper records in persons with obesity [16, 37, 38, 39] or type 2 diabetes [40, 41]. In line with our results suggesting a decrease in body weight and body fat, website-assisted interventions combining nutrition intervention and dietary self-monitoring have shown higher rates of weight loss and body fat loss compared to standard treatment [37, 38], although not all studies agree [42]. A borderline reduction of lean mass was observed in the present study.