A Research on the Classification and Applicability of the Mobile Health Applications

Mobile health applications are applied for different purposes. Healthcare professionals and other users can use this type of mobile applications for specific tasks, such as diagnosis, information, prevention, treatment, and communication. This paper presents an analysis of mobile health applications used by healthcare professionals and their patients. A secondary objective of this article is to evaluate the scientific validation of these mobile health applications and to verify if the results provided by these applications have an underlying sound scientific foundation. This study also analyzed literature references and the use of mobile health applications available in online application stores. In general, a large part of these mobile health applications provides information about scientific validation. However, some mobile health applications are not validated. Therefore, the main contribution of this paper is to provide a comprehensive analysis of the usability and user-perceived quality of mobile health applications and the challenges related to scientific validation of these mobile applications.This work was funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

Mobile Applications for the Promotion and Support of Healthy Nutrition and Physical Activity Habits: A Systematic Review, Extraction of Features and Taxonomy Proposal

Background: Mobile applications can be used for the monitoring of lifestyles and physical activity. It can be installed in commodity mobile devices, which are currently used by different types of people in their daily activities worlwide. Objective: This paper reviews and categorizes the mobile applications related to diet, nutrition, health, physical activity and education, showing the analysis of 73 mobile applications available on Google Play Store with the extraction of the different features. Methods: The mobile applications were analyzed in relation to each proposed category and their features, starting with the definition of the search keywords used in the Google Play Store. Each mobile application was installed on a smartphone, and validated whether it was researched in scientific studies. Finally, all mobile applications and features were categorized. Results: These mobile applications were clustered into four groups, including diet and nutrition, health, physical activity and education. The features of mobile applications were also categorized into six groups, including diet, anthropometric parameters, social, physical activity, medical parameters and vital parameters. The most available features of the mobile applications are weight, height, age, gender, goals, calories needed calculation, diet diary, food database with calories, calories burned and calorie intake. Conclusion: With this review, it was concluded that most mobile applications available in the market are related to diet, and they are important for different types of people. A promising idea for future work is to evaluate the acceptance by young people of such mobile applications.This work is funded by FCT/MEC through national funds and when applicable co-funded by FEDER – PT2020 partnership agreement under the project UID/EEA/50008/2019

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).This work is funded by FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.This work was supported by FCT project UID/EEA/50008/2013. The authors would also like to acknowledge the contribution of the COST Action IC1303–AAPELE–Architectures, Algorithms and Protocols for Enhanced Living Environments

Chaotic Genetic Patchiness in the Highly Valued Atlantic Stalked Barnacle Pollicipes pollicipes from the Iberian Peninsula: Implications for Fisheries Management. Frontiers in Marine Science

The stalked barnacle Pollicipes pollicipes inhabits rocky shores from the Atlantic coasts of Brittany (France) to Senegal. Because of the culinary traditions of southern Europe, stalked barnacles represent an important target species for local fisheries on the Iberian Peninsula. To manage this fishery sustainably, it is therefore important to assess the dynamics of local populations over the Iberian coast, and how they are interconnected at a wider scale using finely tuned genetic markers. In this work, a new enriched library of GT microsatellites for P. pollicipes was prepared and sequenced using Ion TorrentTM Next Gen-Sequencing Technology. 1,423 adults and juveniles were sampled in 15 localities of three geographic regions: southern Portugal, Galicia and Asturias (both in northern Spain). Twenty polymorphic loci arranged in five multiplex PCRs were then tested and validated as new molecular tools to address the spatial and temporal genetic patterns of P. pollicipes. Our results revealed high genetic diversity among adults. However, juveniles were genetically more structured than their adult counterparts, which alternatively displayed much more connectivity among the three studied regions. The lack of spatial genetic heterogeneity in adults may be due to the overlapping of several generations of settlers coming from different geographic origins, which mainly depends on the orientation of residual currents along the coast during reproduction. The genetic differentiation of juveniles may indeed be congruent with Iberian Peninsula hydrodynamics, which can produce chaotic genetic patchiness (CGP) at small temporal scales due to sweepstake reproductive success, collective dispersal and/or self-recruitment. Remarkably, most of the genetic heterogeneity of juveniles found in this work was located in Galicia, which could represent an admixture between distinct metapopulations or an old refuge for the most northern populations. To conclude, high genetic variation in P. pollicipes can lead to the false impression of population panmixia at the Iberian scale by masking more restricted and current-driven larval exchanges between regions. This possibility should be taken into consideration for further specific management and conservation plans for the species over the Iberian Peninsula

Validation Techniques for Sensor Data in Mobile Health Applications

Mobile applications have become a must in every user's smart device, and many of these applications make use of the device sensors' to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also discusses the process of achieving statistical significance and trust for the desired output