AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

被引:2
|
作者
Gonzalez-Perez, Alberto [1 ]
Matey-Sanz, Miguel [1 ]
Granell, Carlos [1 ]
Diaz-Sanahuja, Laura [2 ]
Breton-Lopez, Juana [2 ,3 ]
Casteleyn, Sven [1 ]
机构
[1] Univ Jaume 1, Inst New Imaging Technol, GEOTEC Res Grp, Castellon de La Plana 12071, Spain
[2] Univ Jaume 1, Dept Basic Psychol Clin & Psychobiol, Castellon de La Plana 12071, Spain
[3] Inst Salud Carlos III, CIBER Fisiopatol Obesidad & Nutr CIBEROBN, Madrid 28029, Spain
关键词
mHealth; Smartphone; Data collection; Intervention; Context; -awareness; Digital phenotyping; right [12; 13; Technical complexity; development costs; regulatory; SMARTPHONE APPS; MHEALTH; TECHNOLOGY; FUTURE;
D O I
10.1016/j.jbi.2023.104359
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, welldesigned software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.
引用
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页数:20
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