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The POSTCOVID-AI project continues to make progress in achieving its objectives. Following the first informative group session, held to introduce the project to social agents, representatives from the Social Services of the Honorable City Council of Montilla showed interest in analyzing the feasibility of a possible collaboration. For this reason, on Tuesday, March 7th, a personalized meeting was held with the councilwoman, the delegate, and the psychologist of the Social Services of the Montilla city council.

During this online meeting, we had the opportunity to get to know the interesting and necessary work they carry out through programs that ensure the well-being of the residents of this Cordovan town. The meeting focused on the possibility of using the project's tool in one of the programs carried out by the council, which aims to promote physical activity among retired people and which is expected to start in the coming months. Within this context, the tool would allow for the monitoring of different indicators related to well-being over time, such as the level of physical activity, emotional state, or social interaction, obtaining data that validates the advantages of participating in such a program, complementing and reinforcing the beneficial results already observed. The meeting was very productive, and the exchange of ideas around the tool's design was enriching and fluid.

This meeting is the first of the action plan aimed at establishing this collaboration, which will mean achieving one of the fundamental goals of this project, the co-design of the tool hand in hand with professionals with a broad trajectory in social contexts, whose work has an impact on the well-being of society. We greatly appreciate the interest and contribution to this project from the Honorable City Council of Montilla, and especially Carmen, Marta, and Alicia for their dedication.

On February 8, a briefing was held online for representatives of third sector entities, public institutions and associations. In this meeting (video available at https://projects.ugr.es/postcovid-ai/en/results/)), we had the opportunity to present the project and the POSTCOVID-AI tools, as well as establish a communication channel with these outstanding professionals who work on the front line with both the general population and vulnerable groups and people at risk of social exclusion.

For this occasion, we were fortunate to have the attendance and participation of representatives from two Spanish municipalities, who work in the social and health areas, as well as professionals from various parts of Spain who belong to organizations such as Red Cross, the Spanish Association Against Cancer, the Anti-AIDS Citizen Association or the Mental Health Federation. All participants were very cooperative and interested in the project. As concluded during the session, the main objective is to create synergies between the project team and stakeholders, trying to better understand the difficulties and needs of the work they do daily, the population they work with, and how POSTCOVID-AI can help them in this task. To this end, we propose a series of group and individual meetings that will take place in the coming weeks where the characteristics and peculiarities of each use case will be analysed.

We take this opportunity to note that any person or organization interested in the project can contact the POSTCOVID-AI team via our email: postcovid.ai@gmail.com. We will be happy to hear about your needs and interests to determine how the project can be useful to you.

Do you work in a body that represents part of the population? Does your work consist of solving problems for the population, and ensuring their physical, mental or social wellbeing? Would you like to have access to a digital tool to better understand the situation of society and its evolution over time? This is of interest to you.

The POSTCOVID-AI team, a project of the Social Research Call of the "la Caixa" Foundation 2020, invites you to attend an informative online meeting where we will present our R&D project (https://projects.ugr.es/postcovid-ai/). The project, developed by researchers from the University of Granada and the Complutense University of Madrid, consists of building a novel tool based on artificial intelligence which is designed to answer the question: how does the crisis in the post-pandemic era affect the wellbeing of the population?

This meeting aims to establish a communication channel with the administrations, care services and social organisations working to understand and respond to this question. In doing so, we would like to learn first-hand about the challenges and difficulties you encounter in your daily work and identify how our tool can help you make more informed decisions.

The meeting will take place on 7-9 February 2023 and is scheduled to last one hour. If you are interested in attending, please fill in the following form (sl.ugr.es/form_postcovidai).

If you think that this information may be of interest to others, please do not hesitate to share it.

Thank you very much for your collaboration.

We are waiting for you!

POSTCOVID-AI Team

The dataset collected in the first POSTCOVID-AI study contains a wide range of variables. One of the most important data is the mood of the participants. Thus, after preprocessing the data from the daily surveys where participants reported their mood, we identified 57 participants with sufficient responses, at least 80% of the total number of the 180 responses that could be recorded over the month-long study, to extract indices of psychological well-being. Of these 57 participants, 30 were male (53%) and 27 were female (47%). The average age of the participants was 44 years with a standard deviation (s.d.) of approximately 17 years. The youngest participant was 18 years old and the oldest was 70 years old.

With regard to the psychological characteristics of the participants, based on the questionnaires they filled out at the beginning of the study, we can compare the results obtained with the mean scores of the general pre-pandemic population reported in other studies, in terms of symptoms of anxiety and depression. Specifically, the mean score obtained by our participants in the anxiety questionnaire used (Generalized Anxiety Disorder Scale - 7) was 5.95 (s.d. = 4.85), which corresponds to a mild level of anxiety, while the mean of the general population in Spain before the pandemic was 3.54 (s.d. = 3.32). In the depression questionnaire (Patient Health Questionnaire - 9), our participants scored a mean of 6.86 (s.d. = 4.72), which corresponds to a mild level, in this case of depression. The mean of the general population before the pandemic corresponded to a value of 2.91 (s.d. = 3.52).

These data indicate that our participants show slightly higher levels of both anxiety and depression compared to those obtained in the general population in other studies before the pandemic. Moreover, these results align with those obtained in another study with almost 2000 participants conducted during the pandemic in Spain, where a mean GAD-7 score of 5.86 (s.d. = 5.24) and a PHQ-9 score of 6.50 (s.d. = 5.65) were obtained. While this gives us confidence in the validity of the data and highlights once again the mental health effects of the pandemic that have been reported in numerous studies, one of our group's goals is to conduct further studies to validate these findings.

While we continue working on the analysis of all the information we talked about in previous posts, we believe it is appropriate to highlight the difficulty of collecting some of this data. Specifically, we are referring to the data coming from the smartphone's sensors and that we must collect continuously: physical activity, network, light, noise, screen status, etc.

It is common knowledge that there is a wide variety of companies that manufacture smartphones (Samsung, Huawei, Xiaomi, etc.) and that each of them offers the user different models. Samsung, for example, manufactures dozens of different models. Many will also know that there are several operating systems that manage the resources of these smartphones, the most widely used at present being Android (with approximately 71% of the market share) and iOS (28%).

If this diversity seems little to anyone, perhaps they will change their mind if they consider that for each particular operating system there are different versions that co-exist in the same period of time, each with its own functionalities (Android version 13 has been released just some days ago). Finally, to put the icing on this "cake of different smartphones", each manufacturer usually adds an additional layer of its own to the operating system used, so that even if two cell phones use, for example, the same version of Android, both may have a different user interface and resource management (especially those related to energy saving).

In this context, the development of the POSTCOVID-AI mobile app is proposed, an application that must be able to measure the sensor data of all these smartphones in a continuous way. Some of the most important decisions we have taken for its development are:

- To use Flutter as the programming language (developed by Google). Programs written in Flutter have the advantage of being able to be used on multiple platforms using different operating systems, including Android and iOS.

- To use Flutter libraries that are compatible with the largest possible number of operating systems currently used by cell phones.

Although these decisions seem to solve all our problems, experience has shown us that all that glitters is not gold. Let's remember that we need an application that is continuously collecting information from a large number of sensors on the cell phone. Obviously, these measurements must be performed transparently to the user, so this part of the application must run silently in the background, without disturbing the user. And this is a problem for several reasons:

- These processes run without a "graphical interface", i.e., they do not have an easy way to communicate with the user. In our case, the developed application has a part with a graphical interface that only appears at the beginning of the study. It is only at this precise moment that we must take the opportunity to request the user's permission to access the information from the smartphone's sensors.

- Operating systems do not like processes of this type because they could be malicious processes that are spying on the user and, in addition, they can involve high energy consumption. iOS, in fact, does not like it at all, so it does not allow unknown applications (such as ours) to use this type of processes. The only alternative in this case would be to have a timer that starts the silent app every so often but, again, the power optimizations added by iOS make the frequency with which this can be done too low and, on top of that, very unreliable. This has forced us to discard, for the time being, iPhones from our study.

In the case of Android, it has been possible to adopt this solution, although it has not been easy either: the application cannot be downloaded from the Google Play Store due to its "complex" features and the permissions to allow the silent application to run without restrictions and to run every time the cell phone starts, in many cases, must be activated manually by the user. To compensate for this circumstance, we have written a simple instruction manual for the installation of the application. And considering the enormous diversity of manufacturers, models and versions of Android that we mentioned at the beginning... we will only say in this regard that, as Michael Ende said in "The Neverending Story": that is another story and shall be told another time.