Janet Brigham

Reliability of Adult Retrospective Recall of Lifetime Tobacco Use

Retrospective assessment of tobacco use underlies much of the data collected in epidemiological and genetic epidemiological research. Although individuals are asked to report lifetime tobacco use for periods spanning months to decades, the test-retest reliability intervals of the instruments often span only a few weeks to several months. The present analyses examined the test-retest reliability of retrospective tobacco use measures, including details of first use, circumstances of first use, and initial subjective reactions. The questions were part of the Lifetime Tobacco Use Questionnaire (LTUQ), a Web-based questionnaire designed to assess use of most forms of tobacco or nicotine retrospectively across the lifespan. A convenience sample of 236 men and women with history of tobacco use (Time 1 mean age, 44.9 years; 74.2% females; 75.1% regular monthly tobacco use) responded verifiably to invitations to self-administer the LTUQ two times, 2 years apart. Test-retest reliability analyses reflected high reliability for salient tobacco-use questions. Acceptable levels of reliability were observed for initial subjective reactions to smoking, if the scaled response options were dichotomized. Few differences in the reliability of recall were apparent between sexes and between age groups. These results indicate that recall of important tobacco use information can form a reliable basis for research.

A multidimensional model for characterizing tobacco dependence.

The standard tool for assessing tobacco dependence is the Fagerström Tolerance Questionnaire (FTQ) or its more recent variant, the Fagerström Test for Nicotine Dependence (FTND). Although both of these scales reportedly assess physiological dependence on nicotine, they might not tap some facets of dependence, particularly psychosocial factors. To determine whether tobacco dependence exhibits multidimensional properties, we examined two existing, independent data sets, one from SRI International (n=443) and another from the University of Michigan (n=445). Based on our knowledge from existing literature, standard psychometric statistical analyses, and results from exploratory factor analysis using SRIs data set, we identified two competing models for dependence representing a hybrid of the FTQ/FTND and the Smoking Motives Questionnaire. We then examined these models using confirmatory factor analysis with data from the University of Michigan. We characterized the final model by five first-order factors, each consisting of two to four items, and one higher-order factor. The first-order factors were termed stimulation, automaticity, sedation, psychosocial motives, and morning smoking; the higher-order factor, tobacco dependence, underlay each of the first-order factors. The ranges of interitem correlations and Cronbachs alpha estimates of internal consistency for the first-order factors were .34 - .68 and .64 - .81, respectively. Results of these analyses support the hypothesis that tobacco dependence is multidimensional.

Test-Retest Reliability of Web-Based Retrospective Self-Report of Tobacco Exposure and Risk

Background Retrospectively collected data about the development and maintenance of behaviors that impact health are a valuable source of information. Establishing the reliability of retrospective measures is a necessary step in determining the utility of that methodology and in studying behaviors in the context of risk and protective factors. Objective The goal of this study was to examine the reliability of self-report of a specific health-affecting behavior, tobacco use, and its associated risk and protective factors as examined with a Web-based questionnaire. Methods Core tobacco use and risk behavior questions in the Lifetime Tobacco Use Questionnaire—a closed, invitation-only, password-controlled, Web-based instrument—were administered at a 2-month test-retest interval to a convenience sample of 1229 respondents aged 18 to 78 years. Tobacco use items, which covered cigarettes, cigars, smokeless tobacco, and pipe tobacco, included frequency of use, amount used, first use, and a pack-years calculation. Risk-related questions included family history of tobacco use, secondhand smoke exposure, alcohol use, and religiosity. Results Analyses of test-retest reliability indicated modest (.30 to .49), moderate (.50 to .69), or high (.70 to 1.00) reliability across nearly all questions, with minimal reliability differences in analyses by sex, age, and income grouping. Most measures of tobacco use history showed moderate to high reliability, particularly for age of first use, age of first weekly and first daily smoking, and age at first or only quit attempt. Some measures of family tobacco use history, secondhand smoke exposure, alcohol use, and religiosity also had high test-retest reliability. Reliability was modest for subjective response to first use. Conclusions The findings reflect the stability of retrospective recall of tobacco use and risk factor self-report responses in a Web-questionnaire context. Questions that are designed and tested with psychometric scrutiny can yield reliable results in a Web setting.

Adolescent smoking trajectories and nicotine dependence

The present study correlates empirically constructed prospective adolescent smoking trajectories with indicators of nicotine dependence assessed in adolescence and in adulthood. Excluding individuals who reported no smoking during repeat assessment (nonadopters), we identified five smoking trajectory groups: experimenters (n=116, 48.5%), late increasers (n=39, 16.3%), early increasers (n=37, 15.5%), quitters (n=22, 9.2%), and persistent smokers (n=25, 10.5%). Higher frequency of nicotine dependence symptoms in adolescence occurred in the quitters and persistent smokers groups, who smoked at higher levels relative to the experimenters, late increasers, and early increasers groups, who reported a similar frequency of nicotine dependence symptoms and smoked at low levels. Lifetime nicotine dependence was assessed in adulthood in lifetime daily smokers using the Fagerström Test for Nicotine Dependence (FTND) and the Nicotine Dependence Scale (NDS). Lifetime FTND levels were similar across trajectory groups. Relative to experimenters, all remaining smoking trajectory groups had higher NDS levels that were similar to one another. These results suggest that higher levels of adolescent nicotine dependence were associated with heavier smoking trajectory groups, and that regardless of trajectory group membership, smoking more than a few cigarettes per week throughout adolescence resulted in similar levels of lifetime nicotine dependence as measured by the FTND and NDS.

Validity of Recall of Tobacco Use in Two Prospective Cohorts

This project studied the convergent validity of current recall of tobacco-related health behaviors, compared with prospective self-report collected earlier at two sites. Cohorts were from the Oregon Research Institute at Eugene (N = 346, collected 19.5 years earlier) and the University of Pittsburgh, Pennsylvania (N = 294, collected 3.9 years earlier). Current recall was examined through computer-assisted interviews with the Lifetime Tobacco Use Questionnaire from 2005 through 2008. Convergent validity estimates demonstrated variability. Validity estimates of some tobacco use measures were significant for Oregon subjects (age at first cigarette, number of cigarettes/day, quit attempts yes/no and number of attempts, and abstinence symptoms at quitting; all P < 0.03). Validity estimates of Pittsburgh subjects’ self-reports of tobacco use and abstinence symptoms were significant (P < 0.001) for all tobacco use and abstinence symptoms and for responses to initial use of tobacco. These findings support the utility of collecting recalled self-report information for reconstructing salient lifetime health behaviors and underscore the need for careful interpretation.

Introduction to Technologies for Clinical Decision-Making, Interventions, and Wellness Minitrack

The objective of this minitrack is to address the challenges facing mobile and Internet-based health-related applications and devices that are geared toward facilitating and improving clinical decision-making and enhancing wellness. These goals address the promise of mobile health to direct healthcare professionals and consumers toward proven methods that will increase the likelihood of positive outcomes. The papers in this minitrack explore innovative approaches that include analytic strategies to improve care efficiency, an ingestible device to monitor treatment adherence, a scale for assessing attachment to mobile devices, and vital-sign monitoring.

Introduction to Evidence-Based Mobile and Web Health Design and Analysis Minitrack

The objective of this minitrack is to address the challenges facing mobile and Internet-based health-related applications and devices. Healthcare professionals and consumers alike are vulnerable to unproven methods that have not been subjected to testing for usability, efficacy, effectiveness, or positive outcomes. The papers in this minitrack explore ways to encourage adherence to treatment protocols, employment and engagement of persons with disabilities, improve monitoring of communicable diseases, and design interactions that draw on patients as stakeholders.

Introduction to Evidence-Based Health Design and Analysis Minitrack

The focus of this mini-track is to address the challenges of the rapidly evolving mobile health (mHealth) field. As the mHealth landscape evolves quickly, consumers and practitioners alike are incorporating mobile devices and sensors into their daily routines in ways that were not possible just a couple of years ago. Although thousands of mobile apps present a path to significant health improvements, often in areas of critical health concern, a far smaller number are built on an evidence base of scientific data and established care guidelines, or are verified before being widely implemented. Fewer still are tested for efficacy, effectiveness, or other measures. This not only leaves healthcare professionals and consumers vulnerable to unprooven methods, but it crowds out potentially useful novel approaches. The need is pressing for mHealth applications that are evaluated with rigor in order to bring about meaningful change to the way health care is delivered. Yet, there is a parallel need for methods of evaluation across the design, development, and implementation continuum that do not hamper innovation and take into account the fast pace of technological change. The objective of this mini-track is to address these challenges by showcasing novel methodological, conceptual, and design research studies on mHealth that:(1) evaluate the design, development, and implementation of mHealth, (2) assess the impact of mHealth applications, (3) create an evidence base, taking into account the pace of change in mHealth, and (4) develop models to better understand the evidence base for mHealth systems. The following presentations comprise the minitrack: • “Enabling Patient Information Handoff from Pre-hospital Transport Providers to Hospital Emergency Departments: Design-Science Approach to Field Testing”; this presentation addresses challenges in transferring complete patient information between emergency medical personnel and hospitals emergency department staff. • “Mobile Medical Applications for Melanoma Risk Assessment: False Assurance or Valuable Tool?” This presentation examines commercially available apps for identifying melanoma risk and explores the current validity of autonomous risk assessment of melanoma. • “Building an Evidence Base Using Qualitative Data for mHealth Development” models using qualitative methods and an iterative approach to blend consumer-driven and investigator-driven aims to produce paradigm-shifting, novel intervention applications to impact health behavior. • “A Tailoring Algorithm to Optimize Behavior Change” details the development and implementation of an algorithm for tailoring a behavioral intervention based on published treatment guidelines and on outcome data from two large clinical trials. The algorithm pinpoints behavioral, cognitive, and emotional issues that can optimize specific behavior change. 2014 47th Hawaii International Conference on System Science

A Tailoring Algorithm to Optimize Behavior Change

Effective computerized tailoring can enhance the impact of health interventions. Long-term success rates can be improved with prospective tailoring that builds on evidence-based research. A new algorithm, developed with data from smoking cessation clinical trials and published practice guidelines, is designed to predict the likelihood of abstinence. The algorithm prioritizes the content of a stop-smoking intervention individually for each user and indicates the potential effect of utilizing various stop-smoking medications and stop-smoking approaches. Thus, it has the potential to guide a smoker through the cessation process by dynamically optimizing the likelihood of success. Importantly, the algorithm predicts that even a daily smoker may be able to substantially improve the likelihood of quitting and staying quit both by using stop-smoking techniques and medications and by addressing emotional and cognitive issues that sustain smoking.

Lessons from an Online Stop-Smoking Intervention: Adaptations for Mobile Implementation

A web-based tailored intervention yields valuable lessons for transitioning content from personal computers to the smaller form factor of a mobile device. Lessons learned from a stop-smoking intervention designed for the National Cancer Institute site www.smokefree.gov can provide a useful framework for developing tailored mHealth apps. Issues of instrument deployment, data quality, item design, user characteristics, motivation to improve health, barriers to improvement, and health behavior history and current status may be essential considerations in creating an engaging, effective user experience. Appropriate, relevant inclusion of alternative approaches could help users set and maintain health behaviors such as tobacco abstinence.