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

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

mHealth Support System for Researchers and Participants

With the proliferation of mobile technologies, there is a significant increase of research using mobile devices in the medical and public health area. Mobile technology has improved the efficiency of healthcare delivery effectively. Mobile Health or mHealth is an interdisciplinary research area which has been active for more than a decade. Much research has been conducted and many software research tools (mHealth Support System) have been developed. Despite the time length, there is a significant gap in the mHealth research area regarding software research tools. Individual research groups are developing their own software research tool though there is a significant similarity among them. Most of the research tools are study or disease specific. Some of the tools are device specific (desktop/laptop, mobile phone, and tablet) and some are platform specific (web, android, iOS, and windows). This costs each research study their precious time, money, and workforce to develop similar service or software research tools. Based on the mHealth research characteristics, it is possible to design and implement a customizable generic software research tool. In this thesis, we have proposed, designed, and implemented a customizable generic mHealth software research tool. It has most of the common software research modules that are needed for an mHealth research study. These include real-time data collection, research participant management, research staff management, role based access control, research data anonymization, customizable surveys, report generation, study forum, and activity tracking. This software research tool is responsive and HIPAA compliant which makes it device independent, privacy-aware, and security-aware

Realising the technological promise of smartphones in addiction research and treatment: An ethical review

Background Smartphone technologies and mHealth applications (or apps) promise unprecedented scope for data collection, treatment intervention, and relapse prevention when used in the field of substance abuse and addiction. This potential also raises new ethical challenges that researchers, clinicians, and software developers must address. Aims This paper aims to identify ethical issues in the current uses of smartphones in addiction research and treatment. Methods A search of three databases (PubMed, Web of Science and PsycInfo) identified 33 studies involving smartphones or mHealth applications for use in the research and treatment of substance abuse and addiction. A content analysis was conducted to identify how smartphones are being used in these fields and to highlight the ethical issues raised by these studies. Results Smartphones are being used to collect large amounts of sensitive information, including personal information, geo-location, physiological activity, self-reports of mood and cravings, and the consumption of illicit drugs, alcohol and nicotine. Given that detailed information is being collected about potentially illegal behaviour, we identified the following ethical considerations: protecting user privacy, maximising equity in access, ensuring informed consent, providing participants with adequate clinical resources, communicating clinically relevant results to individuals, and the urgent need to demonstrate evidence of safety and efficacy of the technologies. Conclusions mHealth technology offers the possibility to collect large amounts of valuable personal information that may enhance research and treatment of substance abuse and addiction. To realise this potential researchers, clinicians and app-developers must address these ethical concerns to maximise the benefits and minimise risks of harm to users

Motivational and Intervention Systems and Monitoring with mHealth Tools

Use of mobile and telecommunication technologies has become widespread in the last decade. With this development, use of mobile devices in healthcare (mHealth) is also increasing. Mobile phones, smartphones, and other mobile devices are affordable tools for different health-related services. In my research, with my research team, I have helped to develop several mHealth tools to address the quality of life of cancer survivors, cancer patients, and individuals at increased risk for cancer. Tobacco smoking is the major cause of several types of often-fatal cancers and cardio-respiratory diseases. Optimally, we hypothesize that the most effective mHealth tools should be customized and personalized. For smokers, the goal is to encourage cessation. For cancer survivors, one goal is to increase physical activity, which is associated with decreased rates of recurrent disease. In patients with incurable cancers, efficient and current monitoring of symptoms should contribute to better palliation. This dissertation explores multiple issues in use of mHealth tools with these medical populations. We discuss a general framework for collecting and managing healthcare data and mathematical models for data analysis. The specific contributions of this dissertation are: 1.) The design and development of a culturally tailored customized text messaging system for motivation and intervention; 2.) The design and development of a data collection system for an mHealth intervention, and; 3.) A model for monitoring pain levels using mobile devices

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

A Namibian digital health innovation ecosystem framework

Digital Health relates to “health information systems which enable the merging of social-care and healthcare systems. This would impact on the organisation, service delivery as well as the technological infrastructure” (Herselman & Botha, 2016, p.10). However, with relatively sparse research publications emanating from within the Namibian Health domain, and the concept of Namibian Digital Health as an emergent phenomenon, a Namibian Digital Health Innovation Ecosystem Framework would provide a start to conceptualising, developing and implementing such an ecosystem for Namibia and thus unlocking the potential of Digital Health in this country. The purpose of this study is to develop a Namibian Digital Health Innovation Ecosystem Framework based on literature reviews and the feedback from knowledgeable professionals (KPs) in Namibia, as well as global experts. The methodology which was applied in this study to address the purpose, and to answer the research questions, was Design Science Research Methodology and the Design Science Research Methodology (DSRM) process of Peffers, Tuunanen, Rothenberger and Chatterjee (2008), was adopted. Pragmatism is the overall philosophy guiding the study, as proposed by Ackoff’s theory regarding the hierarchy of human understanding (1989) and Shneiderman’s visual information seeking mantra (1996). During Phases 2 and 3 of the study interpretivism and positivism were applied as philosophies, guided by hermeneutics and triangulation, towards understanding the feedback of Knowledgeable Professionals (KPs) in Namibia, as well as the global experts. The study was divided into three phases. The first phase entailed a literature study which identified the components of Digital Health, Innovation and Digital Ecosystems as well as related research of Digital health, Innovation and Digital Ecosystems in developed and developing countries. This process led to the compilation of the initial Namibian Digital Health Innovation Ecosystem Framework using a conceptual approach. In the second phase of the study, the initial Namibian Digital Health Innovation Ecosystem was evaluated by KPs in Namibia using the Delphi method and interviews. Phase 2 adopted both quantitative and qualitative approaches. The findings from Phase 2 resulted in the development of the intermediate Namibian Digital Health Innovation Ecosystem Framework. In Phase 3 of the study, the intermediate framework was validated by global experts. Feedback was collected from global experts through questionnaires which were analysed through qualitative content analysis. The findings, from Phase 3 led to the development of the final Namibian Digital Health Innovation Ecosystems Framework. The guidelines, which can be used by the Namibian government to implement the suggested digital health innovation ecosystem framework, were also provided.Information ScienceD. Litt. et Phil. (Information Systems

An exploration of the strengths and weaknesses of using text messaging as a tool for self-report data collection in psychological research

Short Message Service (SMS) has immense potential for self-report data collection because it makes use of mobile phones that people already own, and allows researchers to communicate with participants regardless of physical location. Though interest in the possibilities of SMS as a tool for psychological research is slowly growing, to date, there has been no structured investigation of how this potential may be applied in psychological research. The research within this thesis examined the feasibility of using SMS as a tool for self-report psychological research, focussing on its strengths and weaknesses as a research mode. Across fifteen studies, this was investigated using a mixture of literature review, meta-analysis, surveys, and interviews. Participant samples varied from the broad (general population, university students) to specific (the elderly, the deaf). Strengths of SMS as a tool for self-report psychological research included growing interest in research community; positive perceptions of SMS as a research tool amongst potential sample; prompt responses and high response rate; suitability for frequent repeated sampling; and usefulness as a reminder prompt to support other modes of data collection. Weaknesses included a disconnect between stated willingness to participate and actual participation; response incompleteness; unsuitability for infrequent sampling; and some problems with psychometric equivalence in relation to other research modes like online or paper surveys. This was the first structured evaluation of SMS as a tool for self-report data collection in psychological research. Conclusions are limited by somewhat arbitrary design choices (such as the psychological topic within surveys) made in the absence of guiding background literature. Future research can refine these choices and use the logic presented here to guide further investigation into how SMS performs with more varied samples, different psychological topics, and as part of different research designs. This research has shown that while SMS has great potential as a tool for psychological self-report research, it has a number of weaknesses. Identifying these strengths and weaknesses, and some design choices which may mitigate the weaknesses, will open up possibilities for a wide range of future psychological research