Spirometers are important devices for following up patients with respiratory diseases. These are mainly located only at hospitals, with all the disadvantages that this can entail. This limits their use and consequently, the supervision of patients. Research efforts focus on providing digital alternatives to spirometers. Although less accurate, the authors claim they are cheaper and usable by many more people worldwide at any given time and place. In order to further popularize the use of spirometers even more, we are interested in also providing user-friendly lung-capacity metrics instead of the traditional-spirometry ones. The main objective, which is also the main contribution of this research, is to obtain a person’s lung age by analyzing the properties of their exhalation by means of a machine-learning method. To perform this study, 188 samples of blowing sounds were used. These were taken from 91 males (48.4%) and 97 females (51.6%) aged between 17 and 67. A total of 42 spirometer and frequency-like features, including gender, were used. Traditional machine-learning algorithms used in voice recognition applied to the most significant features were used. We found that the best classification algorithm was the Quadratic Linear Discriminant algorithm when no distinction was made between gender. By splitting the corpus into age groups of 5 consecutive years, accuracy, sensitivity and specificity of, respectively, 94.69%, 94.45% and 99.45% were found. Features in the audio of users’ expiration that allowed them to be classified by their corresponding lung age group of 5 years were successfully detected. Our methodology can become a reliable tool for use with mobile devices to detect lung abnormalities or diseases.

Background: Health specialists take care of us, but who takes care of them? These professionals are the most vulnerable to the increasingly common syndrome known as burnout. Burnout is a syndrome conceptualized as a result of chronic workplace stress that has not been successfully managed. Objective: This study aims to develop a useful app providing burnout self-diagnosis and tracking of burnout through a simple, intuitive, and user-friendly interface. Methods: We present the BurnOut app, an Android app developed using the Xamarin and MVVMCross platforms, which allows users to detect critical cases of psychological discomfort by implementing the Goldberg and Copenhagen Burnout Inventory tests. Results: The BurnOut app is robust, user-friendly, and efficient. The good performance of the app was demonstrated by comparing its features with those of similar apps in the literature. Conclusions: The BurnOut app is very useful for health specialists or users, in general, to detect burnout early and track its evolution.

Currently, a multitude of smoking cessation applications exists, a significant portion of which functions as self-help tools without specialized assistance. This study presents a thorough five-year examination, delving into the impact of the TControl application on augmenting patients' success rates in smoking cessation.TControl, a monitoring and support application for non-smoking patients, has undergone iterative enhancements from its inception, concentrating on refining its user interface, support messages, and integration with social networks. Subsequently, a comprehensive 5-year cohort study was conducted to assess the impact of the application to the smoking cessation program. Patients underwent regular weekly monitoring through spirometry tests to detect potential relapses.Throughout these five years, the cohort experienced consistent fluctuations, leading to variable patient numbers annually. Within these cohorts, distinct program states were identified as Treating, Relapse/Abandon, and Success. Analysis of the five-year observation period unveiled that TControl program participation, initially lower than the traditional program in 2018 (TControl: 13.2% - Traditional: 86.8%), steadily rose to 50.5% in TControl and 49.5% in the Traditional group by 2022. Crucially, these trends resulted in favorable outcomes, as demonstrated by sustained patient adherence, contributing to an average 7.48% reduction in relapses compared to the traditional method.In conclusion, a marked enhancement in the performance of the TControl group compared to the traditional group is evident. The TControl group displayed a higher success rate, indicative of a positive trajectory in smoking cessation outcomes.