Jiang Bian

Low Quality of Free Coaching Apps With Respect to the American College of Sports Medicine Guidelines: A Review of Current Mobile Apps

Background Low physical activity level is a significant contributor to chronic disease, weight dysregulation, and mortality. Nearly 70% of the American population is overweight, and 35% is obese. Obesity costs an estimated US

Off-the-Record Instant Messaging for Group Conversation

147 billion annually in health care, and as many as 95 million years of life. Although poor nutritional habits remain the major culprit, lack of physical activity significantly contributes to the obesity epidemic and related lifestyle diseases. Objective Over the past 10 years, mobile devices have become ubiquitous, and there is an ever-increasing number of mobile apps that are being developed to facilitate physical activity, particularly for active people. However, no systematic assessment has been performed about their quality with respect to following the parameters of sound fitness principles and scientific evidence, or suitability for a variety of fitness levels. The aim of this paper is to fill this gap and assess the quality of mobile coaching apps on iOS mobile devices. Methods A set of 30 popular mobile apps pertaining to physical activity programming was identified and reviewed on an iPhone device. These apps met the inclusion criteria and provided specific prescriptive fitness and exercise programming content. The content of these apps was compared against the current guidelines and fitness principles established by the American College of Sports Medicine (ACSM). A weighted scoring method based on the recommendations of the ACSM was developed to generate subscores for quality of programming content for aerobic (0-6 scale), resistance (0-6 scale), and flexibility (0-2 scale) components using the frequency, intensity, time, and type (FITT) principle. An overall score (0-14 scale) was generated from the subscores to represent the overall quality of a fitness coaching app. Results Only 3 apps scored above 50% on the aerobic component (mean 0.7514, SD 1.2150, maximum 4.1636), 4 scored above 50% on the resistance/strength component (mean 1.4525, SD 1.2101, maximum 4.1094), and no app scored above 50% on the flexibility component (mean 0.1118, SD 0.2679, maximum 0.9816). Finally, only 1 app had an overall score (64.3%) above 50% (mean 2.3158, SD 1.911, maximum 9.0072). Conclusions There are over 100,000 health-related apps. When looking at popular free apps related to physical activity, we observe that very few of them are evidence based, and respect the guidelines for aerobic activity, strength/resistance training, and flexibility, set forth by the ACSM. Users should exercise caution when adopting a new app for physical activity purposes. This study also clearly identifies a gap in evidence-based apps that can be used safely and effectively to start a physical routine program, develop fitness, and lose weight. App developers have an exciting opportunity to improve mobile coaching app quality by addressing these gaps.

Mining Twitter to Assess the Public Perception of the "Internet of Things".

Instant messaging (IM) is becoming an integral part of social as well as business life. The main concern with IM systems is that the information being transmitted is easily accessible. Although some protection could be achieved with the use of a secure tunneling (i.e. VPN etc.), they do not provide end-to-end secrecy. Off-the-record (OTR) is a protocol which enables IM users to have private conversations over the open and insecure public Internet. However, the OTR protocol currently does not support multi-user chat rooms. There is a need for such a product that provides users an opportunity to meet in an IM-based, virtual, and encrypted chat room. This project implements an extension of the two-party OTR protocol, named Group OTR-GOTR. GOTR enables users to have a free and secure multi-user communication environment with no proprietary software requirement. The case study describes a proof of concept plug-in of GOTR developed for the GAIM, as well as the plug-in implementation details. Such a product is believed to be beneficial to small businesses to keep their privacy and their competitiveness.

CollaborationViz: interactive visual exploration of biomedical research collaboration networks.

Social media analysis has shown tremendous potential to understand publics opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the publics perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public’s attitude towards the IoT. As anticipated, our analysis indicates that the publics perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public.

A Secure Distributed File System for Medical Image Archiving

Social network analysis (SNA) helps us understand patterns of interaction between social entities. A number of SNA studies have shed light on the characteristics of research collaboration networks (RCNs). Especially, in the Clinical Translational Science Award (CTSA) community, SNA provides us a set of effective tools to quantitatively assess research collaborations and the impact of CTSA. However, descriptive network statistics are difficult for non-experts to understand. In this article, we present our experiences of building meaningful network visualizations to facilitate a series of visual analysis tasks. The basis of our design is multidimensional, visual aggregation of network dynamics. The resulting visualizations can help uncover hidden structures in the networks, elicit new observations of the network dynamics, compare different investigators and investigator groups, determine critical factors to the network evolution, and help direct further analyses. We applied our visualization techniques to explore the biomedical RCNs at the University of Arkansas for Medical Sciences – a CTSA institution. And, we created CollaborationViz, an open-source visual analytical tool to help network researchers and administration apprehend the network dynamics of research collaborations through interactive visualization.

A role-based secure group communication framework

The explosion of medical image usage in clinical and research domains brings us a great challenge of securely handling, storing, retrieving and transmitting biomedical images. Medical images are often large files and they have to be stored for a long time if they are part of a patient’s medical record. As medical images usually contain Protected Health Information (PHI), such data is also subjected to various regulations such as HIPAA. Cost effective measures which provide strong security for such data are essential. Therefore, we present a secure and cost effective distributed file system, JigDFS, for archiving medical images/data.

CLARA: an integrated clinical research administration system

Building a secure group communication system is an active research topic. Several studies have focused on achieving a good level of privacy among a group of people via agreement on a shared encryption key. However, there is not much work published on easily manageable, simple, and effective systems that can provide secure communication in a role-based environment. In this paper, we propose a comprehensive solution to the key exchange problem for group communication. A centralized key server is used to produce a key chain, based on recursive hashing, and securely distributing the keys to the recipients according to their roles. The proposed work makes it possible that a user with a higher clearance can audit the communications among the users that are hierarchically below him/her. Moreover, the system has the ability to isolate communications among different groups, which means the compartmentation is reserved.

Comparison of PIN- and pattern-based behavioral biometric authentication on mobile devices

Administration of human subject research is complex, involving not only the institutional review board but also many other regulatory and compliance entities within a research enterprise. Its efficiency has a direct and substantial impact on the conduct and management of clinical research. In this paper, we report on the Clinical Research Administration (CLARA) platform developed at the University of Arkansas for Medical Sciences. CLARA is a comprehensive web-based system that can streamline research administrative tasks such as submissions, reviews, and approval processes for both investigators and different review committees on a single integrated platform. CLARA not only helps investigators to meet regulatory requirements but also provides tools for managing other clinical research activities including budgeting, contracting, and participant schedule planning.

Improving the precision of fMRI BOLD signal deconvolution with implications for connectivity analysis.

Personal identification numbers (PIN) and unlock patterns are highly popular authentication mechanisms on smart mobile devices but they are not sufficiently secure. PIN or pattern mechanisms enhanced by additional, implicit behavioral biometric authentication can offer stronger authentication assurance while preserving usability, therefore becoming very attractive. Individual studies on PIN- and pattern-based behavioral biometric authentication on smartphones were conducted but their results cannot be directly compared. In this work, we present a comparison study on the authentication accuracy between PIN-based and pattern-based behavioral biometric authentication using both smartphone and tablet. We developed a uniform framework for both PIN-based and pattern-based schemes and used two representative methods-Histogram and DTW-for user verification. We recruited 15 users and collected behavioral biometric data for both simple and complex PINs and patterns. Our experimental results show that PIN-based and pattern-based behavioral biometric authentication schemes can achieve about the same level of accuracy but not all verification methods are equal. The Histogram method can achieve more consistent results and handle template aging better than the DTW method based on our results. Our findings are expected to shed light on the exploration and analysis of effective behavioral biometric verification methods and facilitate more comprehensive investigation on behavioral biometric authentication for mobile devices.

LightGraph: Lighten Communication in Distributed Graph-Parallel Processing

An important, open problem in neuroimaging analyses is developing analytical methods that ensure precise inferences about neural activity underlying fMRI BOLD signal despite the known presence of confounds. Here, we develop and test a new meta-algorithm for conducting semi-blind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that estimates, via bootstrapping, both the underlying neural events driving BOLD as well as the confidence of these estimates. Our approach includes two improvements over the current best performing deconvolution approach; 1) we optimize the parametric form of the deconvolution feature space; and, 2) we pre-classify neural event estimates into two subgroups, either known or unknown, based on the confidence of the estimates prior to conducting neural event classification. This knows-what-it-knows approach significantly improves neural event classification over the current best performing algorithm, as tested in a detailed computer simulation of highly-confounded fMRI BOLD signal. We then implemented a massively parallelized version of the bootstrapping-based deconvolution algorithm and executed it on a high-performance computer to conduct large scale (i.e., voxelwise) estimation of the neural events for a group of 17 human subjects. We show that by restricting the computation of inter-regional correlation to include only those neural events estimated with high-confidence the method appeared to have higher sensitivity for identifying the default mode network compared to a standard BOLD signal correlation analysis when compared across subjects.