We realize the potential of reaching the remotest of people via mHealth Technology and together with our partners aim to improve the health of girls and young women through USSD technology - Unstructured Supplementary Service Data is low cost, interactive, real time, interactive, does not require data or an internet connection and works on any kind of phone - to name a few benefits of USSD.
mHealth technology is increasingly becoming an important tool in global health programs. In South Africa 92% of the population have access to a mobile phone and close to 100% of our country's population enjoys mobile network coverage. Gateway realizes the potential of reaching the remotest of people via mHealth Technology and together with our partners aim to improve the health of the population by focusing on the youth aged 10 to 24. This age group is disproportionately affected by HIV and urgent interventions are needed to educate these girls and young women and to advocate for better services, information and knowledge management.
1. Using USSD technology to reach and engage rural youth
Rural youth are hard to reach - using USSD technology developed by partner Digitata, GHI is able to engage the youth on issues such as Sexual and Reproductive Health and Rights.
mD-SAM (mobile Diagnosis of Severe Acute Malnutrition) is an innovative mHealth tool, using basic mobile phones to assist in the rapid and accurate diagnosis of severe acute malnutrition by calculating a malnutrition score based on anthropometric, clinical and independent risk factors.
MUAC (Mid Upper Arm Circumference) and Weight for Height z-scores arecurrently the *imperfect* gold standards for diagnosis of SAM. In most developing countries scales are often unavailable or not calibrated, and MUAC alone is subject to observer error. Our aim is to develop an innovative mHealth tool that will enable fieldworkers to rapidly diagnose severe acute malnutrition in sick children using basic mobile phones. MUAC for Height (*Z. Mei et al*, 1997) is a reliable indicator for SAM and we propose using MAUC for Height in combination with MUAC and pre-selected
clinical and independent risk factors to calculate a ‘malnutrition score which will enable accurate, reliable diagnosis of SAM in sick children.
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