ABSTRACT
AIM: To summarise the research literature on the impacts or perceptions of policies to end tobacco use at a population level (ie, tobacco endgame policies) among people from eight priority population groups (experiencing mental illness, substance use disorders, HIV, homelessness, unemployment or low incomes, who identify as lesbian, gay, bisexual, transgender, queer or intersex (LGBTQI+) or who have experienced incarceration). METHODS: Guided by JBI Scoping Review Methodology, we searched six databases for original research examining the impacts or perceptions of 12 tobacco endgame policies among eight priority populations published since 2000. We report the results according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. RESULTS: Of the 18 included studies, one described perceptions of five endgame policies among people on low incomes in Aotearoa (New Zealand), and 17 focused on the effectiveness or impacts of a very low nicotine content (VLNC) cigarette standard among people experiencing mental illness (n=14), substance use disorders (n=8), low incomes (n=6), unemployment (n=1) or who identify as LGBTQI+ (n=1) in the USA. These studies provide evidence that VLNC cigarettes can reduce tobacco smoking, cigarette cravings, nicotine withdrawal and nicotine dependence among these populations. CONCLUSIONS: Most of the tobacco endgame literature related to these priority populations focuses on VLNC cigarettes. Identified research gaps include the effectiveness of endgame policies for reducing smoking, impacts (both expected and unexpected) and policy perceptions among these priority populations.
ABSTRACT
Personal Health Intervention Toolkit (PHIT) is an advanced cross-platform software framework targeted at personal self-help research on mobile devices. Following the subjective and objective measurement, assessment, and plan methodology for health assessment and intervention recommendations, the PHIT platform lets researchers quickly build mobile health research Android and iOS apps. They can (1) create complex data-collection instruments using a simple extensible markup language (XML) schema; (2) use Bluetooth wireless sensors; (3) create targeted self-help interventions based on collected data via XML-coded logic; (4) facilitate cross-study reuse from the library of existing instruments and interventions such as stress, anxiety, sleep quality, and substance abuse; and (5) monitor longitudinal intervention studies via daily upload to a Web-based dashboard portal. For physiological data, Bluetooth sensors collect real-time data with on-device processing. For example, using the BinarHeartSensor, the PHIT platform processes the heart rate data into heart rate variability measures, and plots these data as time-series waveforms. Subjective data instruments are user data-entry screens, comprising a series of forms with validation and processing logic. The PHIT instrument library consists of over 70 reusable instruments for various domains including cognitive, environmental, psychiatric, psychosocial, and substance abuse. Many are standardized instruments, such as the Alcohol Use Disorder Identification Test, Patient Health Questionnaire-8, and Post-Traumatic Stress Disorder Checklist. Autonomous instruments such as battery and global positioning system location support continuous background data collection. All data are acquired using a schedule appropriate to the app's deployment. The PHIT intelligent virtual advisor (iVA) is an expert system logic layer, which analyzes the data in real time on the device. This data analysis results in a tailored app of interventions and other data-collection instruments. For example, if a user anxiety score exceeds a threshold, the iVA might add a meditation intervention to the task list in order to teach the user how to relax, and schedule a reassessment using the anxiety instrument 2 weeks later to re-evaluate. If the anxiety score exceeds a higher threshold, then an advisory to seek professional help would be displayed. Using the easy-to-use PHIT scripting language, the researcher can program new instruments, the iVA, and interventions to their domain-specific needs. The iVA, instruments, and interventions are defined via XML files, which facilities rapid app development and deployment. The PHIT Web-based dashboard portal provides the researcher access to all the uploaded data. After a secure login, the data can be filtered by criteria such as study, protocol, domain, and user. Data can also be exported into a comma-delimited file for further processing. The PHIT framework has proven to be an extensible, reconfigurable technology that facilitates mobile data collection and health intervention research. Additional plans include instrument development in other domains, additional health sensors, and a text messaging notification system.
ABSTRACT
BACKGROUND AND OBJECTIVE: We evaluated patient acceptance of an electronic questionnaire to collect breast cancer risk-factor data in a mammography setting. METHODS: We developed an electronic questionnaire on a tablet computer incorporating prefilled answers and skip patterns. Using a randomized controlled study design, we tested the survey in a mammography clinic that administers a paper risk-factor questionnaire to every woman at her screening mammogram. We randomized 160 women to use the electronic survey (experimental group, n = 86) or paper survey (control group, n = 74). We evaluated patient acceptance and data completeness. RESULTS: Overall, 70.4% of the experimental group women thought the survey was very easy to use, compared to 55.6% of women in the control group. Ninety percent of experimental group women preferred using the tablet, compared to the paper questionnaire. Preference for the tablet did not differ by age; however, women > or = 60 years did not find the tablet as easy to use as did women < 60 years. The proportion of missing data was significantly lower on the tablet compared to the paper questionnaire (4.6% vs. 6.2%, P = .04). CONCLUSION: Electronic questionnaires are feasible to use in a mammography setting, can improve data quality, and are preferred by women regardless of age.
