See Me Smoke-Free: Protocol for a Research Study to Develop and Test the Feasibility of an mHealth App for Women to Address Smoking, Diet, and Physical Activity

Background: This paper presents the protocol for an ongoing research study to develop and test the feasibility of a multi-behavioral mHealth app. Approximately 27 million women smoke in the US, and more than 180,000 women die of illnesses linked to smoking annually. Women report greater difficulties quitting smoking. Concerns about weight gain, negative body image, and low self-efficacy may be key factors affecting smoking cessation among women. Recent studies suggest that a multi-behavioral approach, including diet and physical activity, may be more effective at helping women quit. Guided imagery has been successfully used to address body image concerns and self-efficacy in our 3 target behaviors—exercise, diet and smoking cessation. However, it has not been used simultaneously for smoking, diet, and exercise behavior in a single intervention. While imagery is an effective therapeutic tool for behavior change, the mode of delivery has generally been in person, which limits reach. mHealth apps delivered via smart phones offer a unique channel through which to distribute imagery-based interventions. Objective: The objective of our study is to evaluate the feasibility of an mHealth app for women designed to simultaneously address smoking, diet, and physical activity behaviors. The objectives are supported by three specific aims: (1) develop guided imagery content, user interface, and resources to reduce weight concern, and increase body image and self-efficacy for behavior change among women smokers, (2) program a prototype of the app that contains all the necessary elements of text, graphics, multimedia and interactive features, and (3) evaluate the feasibility, acceptability, and preliminary efficacy of the app with women smokers. Methods: We created the program content and designed the prototype application for use on the Android platform in collaboration with 9 participants in multiple focus groups and in-depth interviews. We programmed and tested the application’s usability with 6 participants in preparation for an open, pre- and posttest trial. Currently, we are testing the feasibility and acceptability of the application, evaluating the relationship of program use to tobacco cessation, dietary behaviors, and physical activity, and assessing consumer satisfaction with approximately 70 women smokers with Android-based smart phones. Results: The study was started January 1, 2014. The app was launched and feasibility testing began in April 1, 2015. Participants were enrolled from April 1-June 30, 2015. During that time, the app was downloaded over 350 times using no paid advertising. Participants were required to use the app “most days” for 30 days or they would be dropped from the study. We enrolled 151 participants. Of those, 78 were dropped or withdrew from the study, leaving 73 participants. We have completed the 30-day assessment, with a 92% response rate. The 90-day assessment is ongoing. During the final phase of the study, we will be conducting data analyses and disseminating study findings via presentations and publications. Feasibility will be demonstrated by successful participant retention and a high level of app use. We will examine individual metrics (eg, duration of use, number of screens viewed, change in usage patterns over time) and engagement with interactive activities (eg, activity tracking). Conclusions: We will aggregate these data into composite exposure scores that combine number of visits and overall duration to calculate correlations between outcome and measures of program exposure and engagement. Finally, we will compare app use between participants and non-participants using Google Analytics

Network anomaly detection using autonomous system flow aggregates

Abstract—Detecting malicious traffic streams in modern com-puter networks is a challenging task due to the growing traffic volume that must be analyzed. Traditional anomaly detection systems based on packet inspection face a scalability problem in terms of computational and storage capacity. One solution to this scalability problem is to analyze traffic based on IP flow aggregates. However, IP aggregates can still result in prohibitively large datasets for networks with heavy traffic loads. In this paper, we investigate whether anomaly detection is still possible when traffic is aggregated at a coarser scale. We propose a volumetric analysis methodology that aggregates traffic at the Autonomous System (AS) level. We show that our methodology reduces the number of flows to be analyzed by several orders of magnitude compared with IP flow level analysis, while still detecting traffic anomalies. I

Uma arquitetura de computação pervasiva para trabalho de campo

Os ambientes de trabalho de campo possuem diversas restrições, como falta de infraestrutura e dispositivos de baixa capacidade. Trabalhos atuais sobre redes ad hoc e computação pervasiva deixam de considerar diversos aspectos que poderiam melhorar os ambientes de campo. Este trabalho de tese descreve uma arquitetura composta de serviços e um protocolo projetados para dar suporte aos requisitos do trabalho de campo. Os serviços foram projetados para dar suporte aos diversos tipos de dispositivos, com diversos padrões de mobilidade. Os dados de roteamento dentro da área de trabalho consideram aspectos dos tipos dos dispositivos, indicando ao serviço de adaptação de conteúdo os tipos de nós do ambiente. O uso de uma estratégia de atualização de informações de localização física também reduz a carga de dados que é transmitida no ambiente. Também é apresentado um protocolo de roteamento que usa informações de localização física de diversas formas. Essas informações podem gerar sub-áreas de trabalho, limitando o escopo das mensagens que trafegam na rede, sendo utilizadas apenas pelos trabalhadores de uma área. Também permitem a troca de dados entre diferentes locais, através do uso de nós que controlam a borda das áreas de trabalho. O conhecimento dos nós vizinhos permite o roteamento por localização e a troca de informações somente entre os nós próximos. Estas contribuições podem também ser utilizadas por outros sistemas de computação pervasiva que tenham interesse em melhorar seu desempenh

Uma arquitetura de computação pervasiva para trabalho de campo

Os ambientes de trabalho de campo possuem diversas restrições, como falta de infraestrutura e dispositivos de baixa capacidade. Trabalhos atuais sobre redes ad hoc e computação pervasiva deixam de considerar diversos aspectos que poderiam melhorar os ambientes de campo. Este trabalho de tese descreve uma arquitetura composta de serviços e um protocolo projetados para dar suporte aos requisitos do trabalho de campo. Os serviços foram projetados para dar suporte aos diversos tipos de dispositivos, com diversos padrões de mobilidade. Os dados de roteamento dentro da área de trabalho consideram aspectos dos tipos dos dispositivos, indicando ao serviço de adaptação de conteúdo os tipos de nós do ambiente. O uso de uma estratégia de atualização de informações de localização física também reduz a carga de dados que é transmitida no ambiente. Também é apresentado um protocolo de roteamento que usa informações de localização física de diversas formas. Essas informações podem gerar sub-áreas de trabalho, limitando o escopo das mensagens que trafegam na rede, sendo utilizadas apenas pelos trabalhadores de uma área. Também permitem a troca de dados entre diferentes locais, através do uso de nós que controlam a borda das áreas de trabalho. O conhecimento dos nós vizinhos permite o roteamento por localização e a troca de informações somente entre os nós próximos. Estas contribuições podem também ser utilizadas por outros sistemas de computação pervasiva que tenham interesse em melhorar seu desempenh

Exploiting a Generic Approach for Constructing Mobile Device Applications

We are witnessing increasing demand for applications that are runnable on a wide range of mobile devices (e.g. wireless laptops, mobile phones, sensors). In addition, the emergence of new software technologies (e.g. component approaches, publish subscribe bindings, web services, service discovery protocols) has demanded that such applications face heterogeneous software platforms. However, existing approaches for building mobile device applications are often targeted to a particular platform (e.g. mobile phones, PDAs, sensors) and software technology (Web Services, Microsoft COM, Java components). This paper discusses the use of a generic component approach for the construction of adaptive applications that can integrate and re-use technologies (e.g. middleware and legacy components) and deploy them across heterogeneous devices. We have implemented a Java prototype for J2ME virtual machines and evaluated the potential benefits using development case-studies and performance measures. We show that we can address a wide range of heterogeneity with minimal resource overheads

Development and evaluation of the See Me Smoke-Free multi-behavioral mHealth app for women smokers

Background: Women face particular challenges when quitting smoking, especially those with weight concerns. A multi-behavioral smoking cessation intervention addressing these concerns and incorporating guided imagery may assist women to engage in healthy lifestyle behaviors. An mHealth app can easily disseminate such an intervention. Purpose: The goals of this pilot study were to develop and test the feasibility and potential of the See Me Smoke-Free™ mHealth app to address smoking, diet and physical activity among women smokers. Methods: We used pragmatic, direct-to-consumer methods to develop and test program content, functionality, and the user interface, and conduct a pre-/post-test, 90-day pilot study. Results: We enrolled 151 participants. Attrition was 52%, leaving 73 participants. At 90 days, 47% of participants reported 7-day abstinence, and significant increases in physical activity and fruit consumption. Conclusions: Recruitment methods worked well, but similar to other mHealth studies, we experienced high attrition. This study suggests that a guided imagery mHealth app has the potential to address multiple behaviors. Future research should consider different methods to improve retention and assess efficacy.National Cancer Institute [R21-CA174639]12 month embargo; first published: 02 February 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]