Alcuni abstract di articoli che trattano argomenti relativi all'eHealth

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A sustainable village phone model to serve the rural developing world

Wireless technologies have created an unprecedented opportunity for rural customers in the developing world to solve their communication and information problems in an instantaneous, interactive and customized way. The framework of the study focuses on existing mobile village phone model in Bangladesh and suggests ways to make it sustainable through mobile information services marketing. The study has treated ‘village phone’ as a cost effective and interactive channel through which various time befitting information can be marketed to serve customers in the rural settings

Digital communities: context for leading learning into the future?

In 2011, a robust, on-campus, three-element Community of Practice model consisting of growing community, sharing of practice and building domain knowledge was piloted in a digital learning environment. An interim evaluation of the pilot study revealed that the three-element framework, when used in a digital environment, required a fourth element. This element, which appears to happen incidentally in the face-to-face context, is that of reflecting, reporting and revising. This paper outlines the extension of the pilot study to the national tertiary education context in order to explore the implications for the design, leadership roles, and selection of appropriate technologies to support and sustain digital communities using the four-element model

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Regulating Mobile Mental Health Apps

Mobile medical apps (MMAs) are a fast‐growing category of software typically installed on personal smartphones and wearable devices. A subset of MMAs are aimed at helping consumers identify mental states and/or mental illnesses. Although this is a fledgling domain, there are already enough extant mental health MMAs both to suggest a typology and to detail some of the regulatory issues they pose. As to the former, the current generation of apps includes those that facilitate self‐assessment or self‐help, connect patients with online support groups, connect patients with therapists, or predict mental health issues. Regulatory concerns with these apps include their quality, safety, and data protection. Unfortunately, the regulatory frameworks that apply have failed to provide coherent risk‐assessment models. As a result, prudent providers will need to progress with caution when it comes to recommending apps to patients or relying on app‐generated data to guide treatment