Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (Ra), and insulin sensitivity (S I ). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in dailylife conditions, using minimally-invasive (MI) technologies, and validated it against the R-OMM. Methods: Forty-seven individuals with T1D (weight=78±13kg, age=42±10yr) underwent three 23hour visits, during which G and I were frequently sampled, while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. Results: The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and Ra model parameters were not significantly different using the MI-OMM and R-OMM (p>0.05) and the correlation between the two S I was satisfactory (ρ=0.77). Conclusion: The MI-OMM is usable to estimate GR, Ra, and S I from data collected in real-life conditions with minimally-invasive technologies. Significance: Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, Ra, and S I . DSS could finally exploit this information to improve diabetes management.