FAMAP: A Framework for Developing m-Health Apps

The edge-cutting mobile technologies have allowed the expansion of m-health applications for both patients and doctors. However, the variety of technologies, platforms and general-purpose development frameworks make developers and researchers to spend a considerable amount of time in developing m-health apps from scratch. This papers presents an ongoing research project about the creation of a framework for assisting developers and researchers in creating m-health apps called FAMAP. This framework is presented for the first time in the current article. Among others, this framework contains components for respectively (1) collecting data, (2) visualizing data analytics, (3) automating the definition and management of questionnaires, (4) implementing agent-based decision support systems and (5) supporting multi-modal communication. To show the utility of the proposed framework, this article presents some well-known and in-progress m-health apps developed with this framework. This work is assessed by considering (a) the usage data to show the commitment of users in one of the apps, and (b) the downloads and ranking in stores of another of the apps.

[1]  Bran Selic,et al.  The Pragmatics of Model-Driven Development , 2003, IEEE Softw..

[2]  Iván García-Magariño,et al.  ABSEM: An agent-based simulator of emotions in mindfulness programs , 2017, Expert Syst. Appl..

[3]  Shah Jahan Miah,et al.  Extending the framework for mobile health information systems Research: A content analysis , 2017, Inf. Syst..

[4]  P. Ekman.  An argument for basic emotions , 1992 .

[5]  Iván García-Magariño,et al.  ABS-MindHeart: An agent based simulator of the influence of mindfulness programs on heart rate variability , 2017, J. Comput. Sci..

[6]  Stoyan R. Stoyanov,et al.  Review and Evaluation of Mindfulness-Based iPhone Apps , 2015, JMIR mHealth and uHealth.

[7]  Iván García-Magariño,et al.  A metrics suite for evaluating agent-oriented architectures , 2010, SAC '10.

[8]  Iván García-Magariño,et al.  TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions , 2017, Simul. Model. Pract. Theory.

[9]  Mark Begale,et al.  Uptake and usage of IntelliCare: A publicly available suite of mental health and well-being apps , 2016, Internet interventions.

[10]  Manasi Sheth-Chandra,et al.  Big Data Knowledge in Global Health Education. , 2017, Annals of global health.

[11]  Harsha N. Halahalli,et al.  Resting heart rate variability and cardiorespiratory fitness in healthy young adults , 2017 .

[12]  Giuseppe Piro,et al.  A policy enforcement framework for Internet of Things applications in the smart health , 2017 .

[13]  Mirta Galesic,et al.  Dropouts on the Web: Influence of changes in respondents' interest and perceived burden during the Web survey , 2004 .

[14]  Zhicheng Wang,et al.  Challenges in Assessing Mobile Health App Quality: A Systematic Review of Prevalent and Innovative Methods. , 2016, American journal of preventive medicine.

[15]  Mark Sagar,et al.  Upright posture improves affect and fatigue in people with depressive symptoms. , 2017, Journal of behavior therapy and experimental psychiatry.

[16]  Iván García-Magariño,et al.  Development and initial evaluation of a mobile application to help with mindfulness training and practice , 2017, Int. J. Medical Informatics.

[17]  Porfirio Tramontana,et al.  A general framework for comparing automatic testing techniques of Android mobile apps , 2017, J. Syst. Softw..

[18]  Jasmine Travers,et al.  A user-centered model for designing consumer mobile health (mHealth) applications (apps) , 2016, J. Biomed. Informatics.

[19]  Iván García-Magariño,et al.  A model-driven approach for constructing ambient assisted-living multi-agent systems customized for Parkinson patients , 2016, J. Syst. Softw..

[20]  Iván García Magariño,et al.  A metrics suite for the communication of multi-agent systems , 2009 .

[21]  Iván García-Magariño,et al.  Agent-Based Simulation of Smart Beds With Internet-of-Things for Exploring Big Data Analytics , 2018, IEEE Access.

[22]  Jorge J. Gómez-Sanz,et al.  A multi-agent based implementation of a Delphi process , 2008, AAMAS.

[23]  L. Cadmus-Bertram,et al.  Use of the Fitbit to Measure Adherence to a Physical Activity Intervention Among Overweight or Obese, Postmenopausal Women: Self-Monitoring Trajectory During 16 Weeks , 2015, JMIR mHealth and uHealth.

[24]  Iván García-Magariño,et al.  PEABS: A Process for developing Efficient Agent-Based Simulators , 2015, Eng. Appl. Artif. Intell..

[25]  Jorge J. Gómez-Sanz,et al.  A Tool for Generating Model Transformations By-Example in Multi-Agent Systems , 2009, PAAMS.