Thienne Johnson

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.

A new proposal of data mule network focused on Amazon riverine population

This paper presents a new proposal to provide electronic health care for riverine communities through Vehicular Ad Hoc Networks (VANETs), using boats as data mules. The focus of this project is based in specific scenarios without telecommunications infrastructure, like Marajó Archipelago, located in north of Brazil, at Amazon Forest.

Providing adaptive smartphone interfaces targeted at elderly people: an approach that takes into account diversity among the elderly

Abstract The growth of the elderly population in many countries makes it necessary to develop appropriate technologies for them. These include mobile phones, as they are increasingly becoming cheaper and more widespread. Furthermore, many families would like their elderly relatives to use this technology as a means of fostering digital inclusion. However, the current designs for mobile devices software are aimed at a young audience, rather than taking account of the different needs of the elderly. The elderly population can be characterized by their wide diversity which can be attributed to decades of varied experiences. Moreover, this is heightened by sharp differences in acquired education levels, use of technology at work, cognitive skills and physical dexterity. The authors believe that this diversity is even more striking in developing countries, such as Brazil, since in these countries a huge economic gap still exists between different elderly people. This paper seeks to investigate: (1) What can be done to solve the problem of enabling the elderly to use smartphone interfaces and (2) how one should develop adaptive smartphone interfaces that can be targeted to the elderly. With this in mind, this paper shows how a prototyped platform was implemented and evaluated. It is worth stressing that real-life experiments with Brazilian elderly people were carried out. The results suggested that there had been a reduction in the interaction time as well as a significant increase in user satisfaction.

Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors

Users’ emotional states influence decision making and are essential for the knowledge and explanation of users’ behavior with computer applications. However, collecting emotional states during the interaction time with users is a onerous task because it requires very careful handling of the empirical observation, leading researchers to carry out assessments of emotional responses only at the end of the interaction. This paper reports our research in assessing users’ behavior at interaction time and also describes the results of a case study which analyzed users’ emotional responses while interacting with a game. We argue that capturing emotions during interaction time can help us in making changes on users’ behavior (e.g., changing from stressed to a less stressed state) or even suggesting an user to have a break. This can be all possible if both (1) emotions are captured during interaction and (2) changes are suggested at runtime (e.g., through persuasion). The results of this study suggest that there are significant differences between emotional responses captured during the interaction and those declared at the end.

IMap: visualizing network activity over internet maps

We propose a novel visualization, IMap, which enables the detection of security threats by visualizing a large volume of dynamic network data. In IMap, the Internet topology at the Autonomous System (AS) level is represented by a canonical map (which resembles a geographic map of the world), and aggregated IP traffic activity is superimposed in the form of heat maps (intensity overlays). Specifically, IMap groups ASes as contiguous regions based on AS attributes (geo-location, type, rank, IP prefix space) and AS relationships. The area, boundary, and relative positions of these regions in the map do not reflect actual world geography, but are determined by the characteristics of the Internets AS topology. To demonstrate the effectiveness of IMap, we showcase two case studies, a simulated DDoS attack and a real-world worm propagation attack.

Development of a Multi-Behavioral mHealth App for Women Smokers

This article describes the development of the See Me Smoke-Free™ (SMSF) mobile health application, which uses guided imagery to support women in smoking cessation, eating a healthy diet, and increasing physical activity. Focus group discussions, with member checks, were conducted to refine the intervention content and app user interface. Data related to the context of app deployment were collected via user testing sessions and internal quality control testing, which identified and addressed functionality issues, content problems, and bugs. Interactive app features include playback of guided imagery audio files, notification pop-ups, award-sharing on social media, a tracking calendar, content resources, and direct call to the local tobacco quitline. Focus groups helped design the user interface and identified several themes for incorporation into app content, including positivity, the rewards of smoking cessation, and the integrated benefits of maintaining a healthy lifestyle. User testing improved app functionality and usability on many Android phone models. Changes to the app content and function were made iteratively by the development team as a result of focus group and user testing. Despite extensive internal and user testing, unanticipated data collection and reporting issues emerged during deployment due not only to the variety of Android software and hardware but also to individual phone settings and use.

Strong authentication countermeasures using dynamic keying for sinkhole and distance spoofing attacks in smart grid networks

This paper presents a proposal to provide secure communications in smart grid / utility metering networks using RPL (Routing Protocol for Low Power and Lossy Networks). The RPL protocol provides optimal routing performance in some wireless sensor networks and may become a strong standard in utility metering networks. However, the protocol does have numerous security flaws which should be addressed prior to its use in critical infrastructure such as automated metering infrastructure (AMI). Among those flaws, this paper examines the sinkhole and distance spoofing attacks; in addition, it proposes modifications to RPL addressing routing vulnerabilities, including using node-to-node encrypted authentication with dynamically served encryption keys and key-compromising detection using data mining. Simulation results show that the proposed resolution provides good performance characteristics for use in utility grid networks.

Chains of trust for on-demand requests of electronic health records

As mobile devices become more prevalent in everyones lives, they are being used as a interaction point for health applications. And because of the inherited mobility of such devices, healthcare applications can benefit and create new ways to provide services, such as on-demand exchange of medical records or emergency response scenarios. Thus it is necessary to develop infrastructure to allow these devices to securely participate in such applications. In this work, on-demand access to Electronic Health Records of a patient is analyzed and a protocol based on chains of trust for such a scenario is presented. As these records may not be immediately available to the requesting party, several parties may need to be contacted to ensure to validate the identity of the requester and deliver the records. The construction of a prototype is discussed, focusing on the implementation decisions as well as its evaluation and the difficulties encountered during development.

Network anomaly detection using autonomous system flow aggregates

Detecting malicious traffic streams in modern computer 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.

Context routing in heterogeneous pervasive computing fieldwork environments

This article presents a routing protocol that uses physical location information in diverse ways. This information can generate local working sub-areas, limiting the target of messages that pass through the network. Also it allows the exchange of data between different sub-areas, through the use of nodes that control the borders of the work areas. The knowledge of neighboring areas allows routing based on physical location and the exchange of information only to nodes that are physically adjacent