mHealth and global mental health: still waiting for the mH2 wedding?

BACKGROUND: Two phenomena have become increasingly visible over the past decade: the significant global burden of disease arising from mental illness and the rapid acceleration of mobile phone usage in poorer countries. Mental ill-health accounts for a significant proportion of global disability-adjusted life years (DALYs) and years lived with disability (YLDs), especially in poorer countries where a number of factors combine to exacerbate issues of undertreatment. Yet poorer countries have also witnessed significant investments in, and dramatic expansions of, mobile coverage and usage over the past decade. DEBATE: The conjunction of high levels of mental illness and high levels of mobile phone usage in poorer countries highlights the potential for "mH(2)" interventions--i.e. mHealth (mobile technology-based) mental health interventions--to tackle global mental health challenges. However, global mental health movements and initiatives have yet to engage fully with this potential, partly because of scepticism towards technological solutions in general and partly because existing mH(2) projects in mental health have often taken place in a fragmented, narrowly-focused, and small-scale manner. We argue for a deeper and more sustained engagement with mobile phone technology in the global mental health context, and outline the possible shape of an integrated mH(2) platform for the diagnosis, treatment, and monitoring of mental health. SUMMARY: Existing and developing mH(2) technologies represent an underutilised resource in global mental health. If development, evaluation, and implementation challenges are overcome, an integrated mH2 platform would make significant contributions to mental healthcare in multiple settings and contexts.

A global model for effective use and evaluation of e-learning in health.

Healthcare systems worldwide face a wide range of challenges, including demographic change, rising drug and medical technology costs, and persistent and widening health inequalities both within and between countries. Simultaneously, issues such as professional silos, static medical curricula, and perceptions of "information overload" have made it difficult for medical training and continued professional development (CPD) to adapt to the changing needs of healthcare professionals in increasingly patient-centered, collaborative, and/or remote delivery contexts. In response to these challenges, increasing numbers of medical education and CPD programs have adopted e-learning approaches, which have been shown to provide flexible, low-cost, user-centered, and easily updated learning. The effectiveness of e-learning varies from context to context, however, and has also been shown to make considerable demands on users' motivation and "digital literacy" and on providing institutions. Consequently, there is a need to evaluate the effectiveness of e-learning in healthcare as part of ongoing quality improvement efforts. This article outlines the key issues for developing successful models for analyzing e-health learning.

A global model for effective use and evaluation of e-learning in health.

Healthcare systems worldwide face a wide range of challenges, including demographic change, rising drug and medical technology costs, and persistent and widening health inequalities both within and between countries. Simultaneously, issues such as professional silos, static medical curricula, and perceptions of "information overload" have made it difficult for medical training and continued professional development (CPD) to adapt to the changing needs of healthcare professionals in increasingly patient-centered, collaborative, and/or remote delivery contexts. In response to these challenges, increasing numbers of medical education and CPD programs have adopted e-learning approaches, which have been shown to provide flexible, low-cost, user-centered, and easily updated learning. The effectiveness of e-learning varies from context to context, however, and has also been shown to make considerable demands on users' motivation and "digital literacy" and on providing institutions. Consequently, there is a need to evaluate the effectiveness of e-learning in healthcare as part of ongoing quality improvement efforts. This article outlines the key issues for developing successful models for analyzing e-health learning.

Air quality and public health impacts of UK airports. Part I: Emissions

The potential adverse human health and climate impacts of emissions from UK airports have become a significant political issue, yet the emissions, air quality impacts and health impacts attributable to UK airports remain largely unstudied. We produce an inventory of UK airport emissions - including aircraft landing and takeoff (LTO) operations and airside support equipment - with uncertainties quantified. The airports studied account for more than 95% of UK air passengers in 2005. We estimate that in 2005, UK airports emitted 10.2 Gg [-23 to +29%] of NOx, 0.73 Gg [-29 to +32%] of SO2, 11.7 Gg [-42 to +77%] of CO, 1.8 Gg [-59 to +155%] of HC, 2.4 Tg [-13 to +12%] of CO2, and 0.31 Gg [-36 to +45%] of PM2.5. This translates to 2.5 Tg [-12 to +12%] CO2-eq using Global Warming Potentials for a 100-year time horizon. Uncertainty estimates were based on analysis of data from aircraft emissions measurement campaigns and analyses of aircraft operations.The First-Order Approximation (FOA3) - currently the standard approach used to estimate particulate matter emissions from aircraft - is compared to measurements and it is shown that there are discrepancies greater than an order of magnitude for 40% of cases for both organic carbon and black carbon emissions indices. Modified methods to approximate organic carbon emissions, arising from incomplete combustion and lubrication oil, and black carbon are proposed. These alterations lead to factor 8 and a 44% increase in the annual emissions estimates of black and organic carbon particulate matter, respectively, leading to a factor 3.4 increase in total PM2.5 emissions compared to the current FOA3 methodology. Our estimates of emissions are used in Part II to quantify the air quality and health impacts of UK airports, to assess mitigation options, and to estimate the impacts of a potential London airport expansion. © 2011 Elsevier Ltd.