Can mHealth Improve Risk Assessment in Underserved Populations? Acceptability of a Breast Health Questionnaire App in Ethnically Diverse, Older, Low-Income Women.

Background: Use of mobile health (mHealth) tools has expanded rapidly but little research has been done on its acceptability by low-income, diverse, older patient populations. Objective: To assess the attitudes of a diverse group of underserved women on the acceptability and usability of mHealth tools in a clinical setting using a breast health questionnaire application (app) at a public hospital mammography clinic. Methods: Semi-structured interviews were conducted in a breast-imaging center of an urban safety net institution from July-August 2012. Interviews included pre- and post-questions. Women completed the Athena breast health questionnaire app on an iPad and were asked about their experience and ways to improve the tool. Results: Fifteen women age 45-75 years from diverse ethnic and educational backgrounds were interviewed. The majority of women, 11 of 15, preferred the Athena app over a paper version and all the women thought the app was easy to use. Two Spanish-speaking Latinas preferred paper; and two women, with limited mobile phone use, did not have a preference. Many women indicated that it would be necessary to have staff available for instruction and assistance if the app were to be implemented. Conclusions: mHealth tools are an acceptable, if not preferred, method of collecting health information for diverse, older, low-income women. Further studies are required to evaluate the reliability and accuracy of data collection using mHealth tools in underserved populations. mHealth tools should be explored as a novel way to engage diverse populations to improve clinical care and bridge gaps in health disparities

Taking Afrobarometer Data Everywhere

According to statistics gathered by research group Afrobarometer, many countries in Africa lack infrastructure and basic necessities. In fact, Afrobarometer knows the specific rates of need and availability sampled across thirty-six countries but more prosperous African countries do not know these numbers. These more developed countries are in a position to help their less fortunate neighbors if only made aware of the social and economic climate in the respective areas. Our partnership with Afrobarometer will allow us to advertise these statistics through the use of a mobile application. The data will be displayed in a way that is easy for the average reader to digest and understand. By exposing a larger African audience to the results from these public opinion surveys, Afrobarometer hopes to inspire these people to take action and make donations to the appropriate social benefit groups. The countries represented by the surveys can then receive help in the areas expressing need

An fMRI Compatible Touchscreen to Measure Hand Kinematics During a Complex Drawing Task

ACKNOWLEDGMENTS This study was funded by the Northwood Trust and the Aberdeen Biomedical Imaging Centre, University of Aberdeen. GDW is part of the SINASPE collaboration (Scottish Imaging Network - A Platform for Scientific Excellence www.SINAPSE.ac.uk). The authors thank Baljit Jagpal, Nichola Crouch, Beverly Maclennan and Katrina Klaasen for their help with running the experiment and Dawn Younie and Teresa Morris for their help with recruitment and scheduling. We also thank the participants for their generous participation.Peer reviewedPublisher PD

Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap with arXiv:1703.0352