m-Health: Lessons Learned by m-Experiences

Abstract

m-Health is an emerging area that is transforming how people take part in the control of their wellness condition. This vision is changing traditional health processes by discharging hospitals from the care of people. Important advantages of continuous monitoring can be reached but, in order to transform this vision into a reality, some factors need to be addressed. m-Health applications should be shared by patients and hospital staff to perform proper supervised health monitoring. Furthermore, the uses of smartphones for health purposes should be transformed to achieve the objectives of this vision. In this work, we analyze the m-Health features and lessons learned by the experiences of systems developed by MAmI Research Lab. We have focused on three main aspects: m-interaction, use of frameworks, and physical activity recognition. For the analysis of the previous aspects, we have developed some approaches to: (1) efficiently manage patient medical records for nursing and healthcare environments by introducing the NFC technology; (2) a framework to monitor vital signs, obesity and overweight levels, rehabilitation and frailty aspects by means of accelerometer-enabled smartphones and, finally; (3) a solution to analyze daily gait activity in the elderly, carrying a single inertial wearable close to the first thoracic vertebra.

A Randomized Controlled Study of Cognitive–Behavioral Therapy for Posttraumatic Stress in Street Children in Mexico City

“…Interrater reliability of the PTSD module of the DISC was evaluated in 13 children in the sample by two raters (κ = .81). In the present study, a Spanishlanguage version of the DISC-2.3 (Bravo, Woodbury-Farina, Canino, & Rubio-Stipec, 1993) was used.…”

Big data analytics in healthcare: a systematic literature review

“…This theme captures the application of technology to meet the responsibilities of healthcare through e-Health (Shao et al 2016) and m-Health (Bravo et al 2018) services. This theme also encompasses related issues, such as disease (Amirian et al 2017); e-Health networks (Shao et al 2016); monitoring and alerting system (Manogaran et al 2018); m-Health services (Bravo et al 2018;Navaz et al 2018;Li et al 2015;Wu et al 2016, Wu et al 2017; privacy and security issues (Patil and Seshadri 2014) surveillance (Amirian et al 2017) and alert systems (Manogaran et al 2018). Shao et al (2016) developed an algorithm based on a reduced variable neighbourhood search to improve the functioning of e-Health networks.…”

Thermodynamics and Cardy-like formula for nonminimally dressed, charged Lifshitz black holes in new massive gravity

“…Some natural extensions of this work would be to extend this approach to higher dimensions [42] or, complementarily, to consider a more general asymptotic metric in which the scaling transformation does not act as an isometry but rather like a conformal transformation. This type of metrics are commonly referred to as hyperscaling violation metrics and are, in general, described by [43]…”