## 3 Materials and Methods
### Web Ecosystem Design and Proof of Concept Implementation
#### 3.2.3 Web Platform
Thus, the web platform sends to these external services the coordinates of each user, without any further identification data from the user. All data requested from these external services are presented in Figure 8.  
Management of the Database System  
Once a new user registers on the platform, the web platform creates an individual database for the user, aiming to preserve the confidentiality of the information stored by not mixing data from multiple uses. Within each of these databases, multiple collections are created, defining different data categories. Figure 9 presents this structure to easily understand its modeling, where we can observe particular collections for physical measurements, users' profile data, and data concerning the results of the completed surveys. Additionally, we can identify a general database containing weather and pollution data, shared by all users' databases. This approach was motivated by multiple users using the application in the same city, generating duplicate environmental data that could be unified to improve the efficiency of the platform. In terms of its implementation, we followed a nonrelational approach for our storage, using MongoDB [23] and implementing the communication with the web platform using  
Figure 8: Data obtained from each external service. PyMongo [24]. We made this decision considering that the volume of the stored data would considerably increase when using the web ecosystem in comprehensive clinical trials with thousands of patients and larger acquisition temporal windows. Additionally, the acquired data coded in JSON was compatible with nonrelational data structures, easing the implementation. Prediction of Blood Pressure Values  
This last component focuses on the application of machine learning techniques to predict real-time and future blood pressure values. For that, the first step in this process is to extract datasets from the database. These datasets are essential since they are structured following a format compatible with ML algorithms. In particular, each tuple of the dataset contains all the relevant data associated with a blood pressure measurement. Additionally, it ensures a plain representation of the data, removing the complexity and particularities of the database design and implementation. In this sense, we consider essential to indicate how the datasets have been created. First, we have defined a dataset per user, extracting the data stored in each individual database. Additionally, we were interested in having a global dataset including the acquired data from all users studied to identify differences in subsequent results. This general dataset contains an additional field indicating the identifier of the user. It is essential to highlight that each tuple of any dataset corresponds to a blood pressure measurement, completing the rest of the data based on the following criteria:  
* Measurements: additionally to the blood pressure value included in the tuple, we also store the heart rate and the number of steps registered at the moment of the blood pressure verification. * Periodic surveys: for each survey of this category, we select the closer data after each given blood pressure measurement, storing them in the tuple. We select future data because these surveys contain questions regarding the previous week. * Alcohol and smoking habits: we obtain those entries from the database whose registration is within the 24 h previous to the pressure measurement. We consider this temporal window to contemplate the elimination of alcohol in the blood. * Medicine surveys: for occasional medicines, we include in the tuple the name of those medicines consumed in the 24 h previous to the measurement. For chronic treatments, we always consider the closest past survey since the doctor can change chronic medication. Figure 9: Structure of the database system implemented in the proposed web ecosystem. * Height and weight survey: as before, we acquire the closest data to the pressure measurement.