OBJECTIVE: After comparing the top 10 menstrual tracking applications, there are no applications that address symptoms specific to Polycystic Ovary Syndrome (PCOS). This thesis demonstrates the need for a comprehensive menstrual tracking application that caters toward all populations, including patients with PCOS. METHODS: Mobile application usage was evaluated through data from the Ovulation and Menstruation (OM) Health Study to view relationships between PCOS/demographic groups and preference for tracking methods. The top 10 most popular mobile menstrual tracking applications were compiled through a search on the iOS operating system. Mobile menstrual tracking applications were then evaluated through an adapted APPLICATIONS system, which includes categories to score for PCOS-specific symptoms. RESULTS: PCOS groups showed clear preference for tracking methods overall, and are more likely to view their own health as fair/poor. P values for tests between other demographic variables in the OM Health study were insignificant, but there are observable trends in education, income, and age and usage of tracking methods. Evaluation of the top 10 mobile menstrual applications resulted in Clue scoring the highest, but no applications scored a complete PCOS-specific score. CONCLUSIONS: With continual variance in menstrual cycles, there needs to be development of a mobile menstrual application that is effective for all populations. Mobile menstrual applications have proven their popularity through PCOS groups and the rising usage within younger age groups. However, through the adapted APPLICATIONS system, major features are still missing, and are necessary to cater towards unique groups such as people with menstrual irregularities and PCOS.

Artificial intelligence (AI) in mobile applications has grown in popularity, changing the way people engage with technology. AI improves functionality, personalization, and efficiency across a wide range of mobile applications. One specific group of apps powered by AI is period-tracking applications, which is the focus of this master thesis. These apps utilize AI for features such as cycle predictions, fertility windows, symptom predictions, health insights, and chatbots. The aim of this project was to evaluate the performance of these advanced features, leading to the research question: “How do users perceive and experience the AI features in period-tracking applications?” To address this question, an observational mixed-method approach was employed. The research began with a survey, shared via social media platforms from February 19th, 2024, to April 2nd, 2024. The questionnaire included 20 open-ended and multiple-choice questions centered on AI features. By the end of the survey period, 61 responses were collected and analyzed. During the survey period, four participants were chosen for follow-up interviews, but three users’ responses were used in the analysis. The survey results underwent statistical tests to explore the relationship between monthly app usage, duration of app use, and satisfaction with cycle predictions. Later, interview results analyzed by using thematic analysis were integrated to the walkthrough method while assessing the functionality, interface, and user experience of AI features. The mixed-method study offered comprehensive insights into users' practices and experiences with AI features in period-tracking apps, revealing the need for future work to enhance the performance of these AI features for better user satisfaction.

This applied research Project presents the methodology of development understood in the achievement of a tracking solution for vehicles with GPS devices, the proposed software is part of a service of vehicle tracking offered by a telecommunications company. The system’s architecture is described and specified around the use cases in the multiples disciplines of the software process development. Although, there are multiples vehicle tracking solutions which are used in differents control centers, the offered solution gather the most representative functionalities from the best tracking solutions in the market and present it on objective way through in a complete and punctual proposal where the final user can use the solution using a web browser, likewise is shown the design of the solution using the disciplines and patterns requested by the software engineering. / Este proyecto de investigación aplicada presenta la metodología de desarrollo comprendida en la realización de una solución de monitoreo para vehículos con dispositivos GPS, el software propuesto forma parte del servicio de monitoreo vehicular que ofrece una empresa de telecomunicaciones. Se describe y especifica la arquitectura del sistema a través de casos de uso en las diversas disciplinas del proceso de desarrollo de software. Si bien existen diversas soluciones de monitoreo vehicular que son usadas en distintos centros de control, la solución propuesta agrupa las funcionalidades representativas de las mejores soluciones de monitoreo del mercado y las presenta de manera objetiva a través de una propuesta completa y puntual donde el usuario final pueda dar uso de la solución a través de un navegador web, así mismo se muestra el diseño de la solución utilizando las disciplinas y patrones que exige la ingeniería de software.