Table 4.

Comparison of the OR and corresponding 95% C.I. of bad performances, total mistakes, and mistakes in good performances for the prediction of MMSE decline in the binary logistic regression models.

MMSE Decline
	Bad Performances			Total Mistakes			Mistakes in Good Performances
	OR	95% C.I.	p	OR	95% C.I.	p	OR	95% C.I.	p
Model 1	1.120	1.037–1.208	0.004	1.019	1.012–1.027	<0.001	1.026	1.016–1.036	<0.001
Model 2	1.067	0.981–1.159	0.129	1.014	1.004–1.024	0.004	1.019	1.005–1.033	0.006
Model 3	1.030	0.944–1.124	0.503	1.007	0.997–1.017	0.186	1.009	0.995–1.023	0.219
Model 4	1.067	0.969–1.174	0.187	1.010	0.999–1.021	0.082	1.011	0.995–1.026	0.178
Model 4a	1.063	0.965–1.170	0.216	1.010	0.998–1.021	0.090	1.011	0.995–1.027	0.180

Models for each main predictor, i.e., bad performances, total mistakes, or mistakes in good performances: model 1, with just the main predictor; model 2, adjusted with mean RT and SD RT; model 3, which was model 2 with the addition of age, sex, and education level; model 4, the fully adjusted regression model, considering also all the other covariates mentioned in Section 2.1.5 (anxiety, depression, hypertensives, diabetes, smoking, alcohol, and IPAQ); and model 4a, which was model 4 adjusted by UGS at baseline (wave 1). The odds ratio (OR) and corresponding 95% confidence interval (C.I.) give a measure of the influence of the predictor on the outcome; the p-value expresses the statistical significance of the predictor in the model.