Toward an mHealth Intervention for Smoking Cessation

The prevalence of tobacco dependence in the United States (US) remains alarming. Invariably, smoke-related health problems are the leading preventable causes of death in the US. Research has shown that a culturally tailored cessation counseling program can help reduce smoking and other tobacco usage. In this paper, we present a mobile health (mHealth) solution that leverages the Short Message Service (SMS) or text messaging feature of mobile devices to motivate behavior change among tobacco users. Our approach implements the Theory of Planned Behavior (TPB) and a phase-based framework. We make contributions to improving previous mHealth intervention approaches by delivering personalized and evidence-based motivational SMS messages to participants. Our proposed solution implements machine learning algorithms that take the participant\u27s demographic profile and previous smoking behavior into account. We discuss our preliminary evaluation of the system against a couple of pseudo-scenarios and our observation of the system\u27s performance

Building a Tailored Text Messaging System for Smoking Cessation in Native American Populations

When starting new and healthy habits or encouraging vigilance against returning to poor habits, a simple text message can be beneficial. Text messages also have the advantage of being easily accessible for lower-income populations spread over a rural area, who may not be able to afford smartphones with apps or data plans. Users benefit the most from text messages that are customized for them, but personalization requires time and effort on part of the user and the counselor. However, personalization that focuses on the cultural background of a pool of recipients, in addition to general personal preferences, can be a low-cost method of ensuring the best experience for patients interested in taking up new habits. In this paper, we discuss the development of a system for motivating users to quit smoking designed for Native American users in South Dakota, using text messaging as a daily intervention method for patients. Our results show that focusing on modular message customization options and messages with a conversational tone best helps our goal of providing users with customization options that help motivate them to live happy and healthy lifestyles

Challenges in Developing Applications for Aging Populations

Elderly individuals can greatly benefit from the use of computer applications, which can assist in monitoring health conditions, staying in contact with friends and family, and even learning new things. However, developing accessible applications for an elderly user can be a daunting task for developers. Since the advent of the personal computer, the benefits and challenges of developing applications for older adults have been a hot topic of discussion. In this chapter, the authors discuss the various challenges developers who wish to create applications for the elderly computer user face, including age-related impairments, generational differences in computer use, and the hardware constraints mobile devices pose for application developers. Although these challenges are concerning, each can be overcome after being properly identified

Reality Versus Grant Application Research “Plans”

This article describes the implementation of the American Indian mHealth Smoking Dependence Study focusing on the differences between what was written in the grant application compared to what happened in reality. The study was designed to evaluate a multicomponent intervention involving 256 participants randomly assigned to one of 15 groups. Participants received either a minimal or an intense level of four intervention components: (1) nicotine replacement therapy, (2) precessation counseling, (3) cessation counseling, and (4) mHealth text messaging. The project team met via biweekly webinars as well as one to two in-person meetings per year throughout the study. The project team openly shared progress and challenges and collaborated to find proactive solutions to address challenges as compared to what was planned in the original grant application. The project team used multiple strategies to overcome unanticipated intervention issues: (1) cell phone challenges, (2) making difficult staffing decisions, (3) survey lessons, (4) nicotine replacement therapy, (5) mHealth text messages, (6) motivational interviewing counseling sessions, and (7) use of e-cigarettes. Smoking cessation studies should be designed based on the grant plans. However, on the ground reality issues needed to be addressed to assure the scientific rigor and innovativeness of this study

Applying Affective Feedback to Reinforcement Learning in ZOEI, a Comic Humanoid Robot

As robotic technologies of varying shapes and forms continue to make their way into our everyday lives, the significance of a humanoid robot\u27s ability to make a human interaction feel natural, engaging and entertaining becomes an area of keen interest in sociable robotics. In this paper, we present our findings on how affective feedback can be used to drive reinforcement learning in human-robot interactions (HRI) and other dialogue systems. We implemented a system where a humanoid robot, named ZOEI, acts as a standup comedian by entertaining a human audience in a bid to generate humor and positively influence the emotional state of the humans. The mood rating of the audience is recorded prior to the interaction session. Using a survey, the eventual emotional state of the human participant is captured after the HRI session. For each audience member, we capture feedback regarding how funny each joke was. We present the implementation of the content selection framework. We share our findings to substantiate the idea that by using expressive behaviors of the humanoid to influence the delivery of content (in this case, jokes) as well as employing reinforcement learning techniques for driving targeted content selection, the robot was able to improve the human mood score progressively across the 16 people who engaged in the study

A Reference Architecture for High-Availability Automatic Failover between PaaS Cloud Providers

As the adoption rate of Cloud Computing continues to clamber on among various application archetypes, there is a growing concern for identifying reliable automatic failover solutions between various cloud providers in an attempt to minimize the effect of recent cloud provider outages among diverse always-on and mission-critical applications in healthcare, e-Commerce and ancillary settings. Automatic failover between cloud providers stands out as a solution for course-plotting application reliability requirements in support of high-availability, disaster recovery and high-performance scenarios. Using a case study involving Microsoft\u27s Windows Azure cloud and the Google App Engine cloud solution, we investigate some of the key characteristics in this area of concern and present a reference architecture for automatic failover between multiple Platform-as-a-Service (PaaS) cloud delivery providers in a bid to maximize the delivery of architecturally significant quality attributes pertaining to High-Availability, Performance and Disaster Recovery in a mission-critical application prototype

Toward Collective Intelligence for Fighting Obesity

The emergent prevalence of childhood and adolescent obesity remains one of the most significant health care challenges facing the United States today. On the other hand, breakthroughs in Human-Robot Interaction (HRI) research and the diminishing cost of personal robots and virtual agents along with the ever-increasing use of smart personal devices, suggests that there is room for harnessing the power of ubiquitous intelligent systems that can work in partnership to solve some of our most difficult challenges in the very near future. In this paper, we present the design and prototype implementation of a collective intelligence approach aimed at employing machine learning algorithms that work in concert to facilitate the personalization of a humanoid robot Health Coach with a focus on childhood obesity intervention through Child-Robot Interactions and other adaptive Ubiquitous Computing (UbiComp) solutions

Towards Modeling Confidentiality in Persuasive Robot Dialogue

In many persuasive health interventions, humanoid robots and other intelligent systems are capable of carrying out meaningful conversations with human subjects in an effort to influence humans towards behavior or attitudinal change. In human-to-human conversations, the listening party often has the ability to discern whether or not certain aspects of the conversation should be kept confidential. Consequently, in conversational service robot scenarios (including elderly care use cases), humans often have the expectation that humanoid robots are capable of preserving the privacy and confidentiality of a given human-robot dialogue. In this literature, we explore the inherent challenges and approaches to modeling confidentiality in human-robot interaction (HRI) dialogue scenarios involving a cloud-enabled networked robot. As a result, we share a novel reference model for designing persuasive dialogue systems

SPTP: A Trust Management Protocol for Online and Ubiquitous Systems

With the recent proliferation of ubiquitous, mobile and cloud-based systems, security, privacy and trust concerns surrounding the use of emerging technologies in the ensuing wake of the Internet of Things (IoT) continues to mount. In most instances, trust and privacy concerns continuously surface as a key deterrent to the adoption of these emergent technologies. The ensuing literature presents a Secure, Private and Trustworthy protocol (named SPTP) that was prototyped for addressing critical security, privacy and trust concerns surrounding mobile, pervasive and cloud services in Collective Intelligence (CI) scenarios. The efficacy of the protocol and its associated characteristics are evaluated in CI-related scenarios including multimodal monitoring of Elderly people in smart home environments, Online Advertisement targeting in Computational Advertising settings, and affective state monitoring through game play as an intervention for Autism among Children. We present our evaluation criteria for the proposed protocol, our initial results and future work

A Reference Architecture for Social Media Intelligence Applications in the Cloud

As the social media upsurge of today continues to mount, opportunities to derive collective intelligence from online social networking (OSN) content sources are inevitably expected to grow. While enterprise organizations and research institutions make a dash for identifying rich insights and opportunities to tap into the millions of conversations and user profile relationships exposed by this new social-influenced big data phenomenon, architectural concerns regarding the storage and processing of large datasets unearthed by OSNs, along with performance, scalability, fault-tolerance, security, privacy, and high-availability solutions have become an area of concern for social media intelligence (SMI) solutions. In this literature, we present a reference architecture, for designing SMI solutions. In addition, we showcase two key case studies for SMI applications built on this architecture. Our selected case studies are focused on the analysis of User-Generated Content (i.e. With Sentiment Analysis in Twitter data) and Social Graph Influence (i.e. In a Facebook-influenced Movie Recommendations solution). We evaluate the \u27goodness-of-fit\u27 in applying our model to these case study solutions and present results from our performance evaluation of these cloud-hosted solutions across multiple cloud providers like Amazon AWS, Microsoft Azure and Google Cloud