## 6 Discussion  
We implemented an adaptive user authentication model for IoMT users with a particular focus on improving usable security. The model, which was implemented on Android smartphones, demonstrated promising results in terms of accuracy, precision, recall, and overall performance. The model calculates the initial risk score by utilizing various features like user ID, device ID, network, location, and habits and performed stepwise authentication guided by the hardware of the device. The model demonstrated high accuracy in identifying authorized and unauthorized access attempts during cross-validation, indicating effective risk calculation. These ideal outcomes, however, could not always be practical and might point to possible problems like overfitting, particularly given that the evaluation was mostly focused on training data rather than a distinct test set. To ensure the model maintained its excellent performance in real-world scenarios, it was crucial to determine consistency in its performance on unobserved test data. The Kappa value of one indicated a perfect agreement between the model's predictions and the actual values after adjusting for chance. The risk calculation mechanism accurately detected anomalies between legitimate and fraudulent access attempts, with a 1.0 sensitivity and specificity, ensuring no false positives or negatives. Combining the AUC with additional performance indicators showed that our model accurately recognized all positive and negative classifications, predicting data distributions. The results suggested that there may have been overfitting, which may necessitate cross-validation. Nevertheless, our accuracy of 98.5% after applying Lasso and Elastic Net normalization provided us with confidence that our model was resistant to overfitting. The authentication paradigm, which had zero false acceptance and rejection rates, exhibited high security and usability. Although these results are ideal, the model's performance in real-world scenarios and against different user types is crucial for ensuring its robustness and generalizability. The health impact accuracy rate was 80%, indicating accurate detection of positive situations with high recall and precision. We can infer that physical health conditions have an impact on the success of authenticators like a fingerprint or gait, while mental health conditions affect the success of knowledge-based authenticators that are recall-based, based on our analysis of authenticators and their suitability for elderly users. As a result, we used rule-based selection to allocate authenticators related to health conditions. Nevertheless, the impact of each health condition on the outcome of authentication was not examined in this experiment. Therefore, to strengthen our user authentication model, it is crucial to discover any particular risk factors associated with health issues that are correlated with lower trust ratings or greater failure rates. To ensure equitable treatment for older users with specific medical conditions and appropriate authentication mechanisms, the model was further tested for usability taking health conditions and distance from known locations into consideration. However, this is only applicable to specific smartphone's hardware. A further analysis and investigation may be necessary to identify the most significant predictor features and their impact on model performance. When evaluating using the train-test split, the model's Kappa, precision, and NPV showed accurate forecasts for each class, indicating good generalization to test data. The study found a mix of high- and low-trust users, with a median trust value of 0.5, influenced by contextual factors. Health conditions, age, and location data in that case were significant predictors of trust score. In line with the logic of the model, which holds that a greater distance diminishes confidence, authorization was significantly negatively impacted by distance from the known location.