According to the ideomotor theory, action selection is done by the mental anticipation of its perceptual consequences. If the distal information processed mainly by vision and hearing are considered essential for the representation of the action, the proximal information processed by the sense of touch and proprioception is of less importance. Recent works seem to show the opposite. Nevertheless, it is necessary to complete these results by offering a situation, more ecological, where response and effect can occur on the same effector. So, the goal of our work was to implement a more relevant spatial correspondence because to touch is not the same action that to hear or to see. To do so, participants pressed a specific key after the presentation of a stimulus. The key vibrated depending on the pressure exerted on it. In a compatible condition, high pressure on a key triggered a high vibration, while in an incompatible condition high pressure triggered a low vibration on the same effectors. As expected, the response times were faster in the compatible condition than the incompatible condition. This means that proximal information participates actively in the selection of action.

Background: Self-monitoring blood glucose (SMBG) is facilitated by application available to analyze these data. They are mainly based on descriptive statistical analyses. In this study, we are proposing a method inspired by artificial intelligence algorithm for displaying glycemic data in an intelligible way with high-level information that is compatible with the short duration allocated to medical visits. Method: We propose a display method based on a numerical glycemic data conversion using a qualitative color scale that exhibits the patient’s overall glycemic state. Moreover, a machine learning algorithm inputs these displays to exhibit recurrent glycemic pattern over configurable extended time period. Results: A demonstrator of our method, output as a glycemic map, could be used by the physician during quarterly patient consultations. We have tested this methodology retrospectively on a database in order to observe the behavior of our algorithm. In some data files we were able to highlight some of the glycemic patterns characteristics that remain invisible on the tabular representations or through the use of descriptive statistic. In a next step the interpretation will have to be done by physicians to confirm they underlie knowledge. Conclusions: Our approach with artificial intelligence algorithm paired up with graphical color display allow a large database fast analysis to provide insights on diabetes knowledge. The next steps are first to set up a clinical trial to validate this methodology with dedicated patients and physicians then we will adapt our methodology for the huge data sets generated by continuous glycemic measurement (CGM) devices.