Individual and Conjoint Factors Associated With Beliefs About the Harmfulness of Nicotine Replacement Therapies Relative to Combustible Cigarettes Among People Who Smoke: Findings From the 2020 ITC Four Country Smoking and Vaping Survey.

INTRODUCTION: This study examined individual and conjoint factors associated with beliefs about the harmfulness of nicotine replacement therapies (NRTs) relative to combustible cigarettes (CCs). AIMS AND METHODS: Data analyzed came from 8642 adults (≥18 years) who smoked daily/weekly and participated in the 2020 ITC Four Country Smoking and Vaping Survey in Australia (n = 1213), Canada (n = 2633), England (n = 3057), and United States (n = 1739). Respondents were asked: "Compared to smoking cigarettes, how harmful do you think nicotine replacement products are?" Responses were dichotomized into "much less" versus otherwise for analysis using multivariable logistic regression models, complemented by decision-tree analysis to identify conjoint factors. RESULTS: Percentages believing that NRTs are much less harmful than CCs were 29.7% (95% CI = 26.2% to 33.5%) in Australia, 27.4% (95% CI = 25.1% to 29.8%) in England, 26.4% (95% CI = 24.4% to 28.4%) in Canada, and 21.7% (95% CI = 19.2% to 24.3%) in the United States. Across all countries, believing nicotine is not at all/slightly harmful to health (aOR = 1.53-2.27), endorsing nicotine vaping products (NVPs) as less harmful than CCs (much less harmful: aOR = 7.24-14.27; somewhat less harmful: aOR = 1.97-3.23), and possessing higher knowledge of smoking harms (aOR = 1.23-1.88) were individual factors associated with increased odds of believing NRTs are much less harmful than CCs. With some country variations, these nicotine-related measures also interacted with each other and sociodemographic variables to serve as conjoint factors associated with the likelihood of accurate NRT relative harm belief. CONCLUSIONS: Many people who regularly smoke cigarettes are unaware that NRTs are much less harmful than cigarettes. Additionally, beliefs about NRTs relative harmfulness appear to be influenced by both individual and conjoint factors. IMPLICATIONS: This study demonstrates that despite past efforts to educate people who smoke about the harms of NRTs relative to CCs, misperceptions around the relative harmfulness of NRTs remain substantial. In all four studied countries, subgroups of people who smoke regularly who are misinformed about the relative harmfulness of NRTs, and who may be reluctant to use NRTs for smoking cessation can be reliably identified for corrective interventions based on their understanding of the harms related to nicotine, NVPs and smoking along with sociodemographic markers. The identified subgroup information can be used to prioritize and inform the development of effective interventions to specifically address the gaps in knowledge and understanding of the various subgroups identified. Our results suggest these may need to be tailored for each country.

Design and development of a smartphone app for hypertension management: An intervention mapping approach.

Background: Several research studies have demonstrated the potential of mobile health apps in supporting health management. However, the design and development process of these apps are rarely presented. Objective: We present the design and development of a smartphone-based lifestyle app integrating a wearable device for hypertension management. Methods: We used an intervention mapping approach for the development of theory- and evidence-based intervention in hypertension management. This consisted of six fundamental steps: needs assessment, matrices, theoretical methods and practical strategies, program design, adoption and implementation plan, and evaluation plan. To design the contents of the intervention, we performed a literature review to determine the preferences of people with hypertension (Step 1) and necessary objectives toward the promotion of self-management behaviors (Step 2). Based on these findings, we implemented theoretical and practical strategies in consultation with stakeholders and researchers (Steps 3), which was used to identify the functionality and develop an mHealth app (Step 4). The adoption (Step 5) and evaluation (Step 6) of the mHealth app will be conducted in a future study. Results: Through the needs analysis, we identified that people with hypertension preferred having education, medication or treatment adherence, lifestyle modification, alcohol and smoking cessation and blood pressure monitoring support. We utilized MoSCoW analysis to consider four key elements, i.e., education, medication or treatment adherence, lifestyle modification and blood pressure support based on past experiences, and its potential benefits in hypertension management. Theoretical models such as (i) the information, motivation, and behavior skills model, and (ii) the patient health engagement model was implemented in the intervention development to ensure positive engagement and health behavior. Our app provides health education to people with hypertension related to their condition, while utilizing wearable devices to promote lifestyle modification and blood pressure management. The app also contains a clinician portal with rules and medication lists titrated by the clinician to ensure treatment adherence, with regular push notifications to prompt behavioral change. In addition, the app data can be reviewed by patients and clinicians as needed. Conclusions: This is the first study describing the design and development of an app that integrates a wearable blood pressure device and provides lifestyle support and hypertension management. Our theory-driven intervention for hypertension management is founded on the critical needs of people with hypertension to ensure treatment adherence and supports medication review and titration by clinicians. The intervention will be clinically evaluated in future studies to determine its effectiveness and usability.

Identifying factors that conjointly influence nicotine vaping product relative harm perception among smokers and recent ex-smokers: Findings from the 2016 ITC Four Country Smoking and Vaping Survey.

BACKGROUND: Use of nicotine vaping products (NVPs) to replace smoking is often influenced by perceived harmfulness of these products relative to smoking. This study aimed to identify factors that conjointly influenced NVP relative harm perception among smokers and ex-smokers. METHODS: Data (n = 11,838) from adult smokers and ex-smokers (quit < 2 years) who participated in the 2016 ITC 4 Country Smoking and Vaping Surveys in Australia, Canada, England and the US were analyzed. Decision tree models were used to classify respondents into those who perceived vaping as less harmful than smoking ("correct" perception) versus otherwise ("incorrect" perception) based on their socio-demographic, smoking and vaping related variables. RESULTS: Decision tree analysis identified nicotine replacement therapy (NRT) harmfulness perceptions relative to smoking, perceived vaping portrayal in the media and other sources as positive, negative or balanced, recency of seeking online vaping information, and age as the key variables that interacted conjointly to classify respondents into those with "correct" versus "incorrect" harm perceptions of vaping relative to smoking (model performance accuracy = 0.70-0.74). In all countries, NRT relative harmfulness perception and vaping portrayal perception were consistently the two most important classifying variables, with other variables showing some country differences. CONCLUSIONS: In all four countries, perception of NVP relative harmfulness among smokers and recent ex-smokers is strongly influenced by a combination of NRT relative harmfulness perception and vaping portrayal in the media and other sources. These conjoint factors can serve as useful markers for identifying subgroups more vulnerable to misperception about NVP relative harmfulness to benefit from corrective intervention.

Identifying smoker subgroups with high versus low smoking cessation attempt probability: A decision tree analysis approach.

BACKGROUND: Regression-based research has successfully identified independent predictors of smoking cessation, both its initiation and maintenance. However, it is unclear how these various independent predictors interact with each other and conjointly influence smoking behaviour. As a proof-of-concept, this study used decision tree analysis (DTA) to identify the characteristics of smoker subgroups with high versus low smoking cessation initiation probability based on the conjoint effects of four predictor variables, and determine any variations by socio-economic status (SES). METHODS: Data come from the Australian arm of the ITC project, a longitudinal cohort study of adult smokers followed up approximately annually. Reported wanting to quit smoking, worries about smoking negative health impact, quitting self-efficacy and quit intentions assessed in 2005 were used as predictors and reported quit attempts at the 2006 follow-up survey were used as the outcome for the initial model calibration and validation analyses (n = 1475), and further cross-validated using the 2012-2013 data (n = 787). RESULTS: DTA revealed that while all four predictor variables conjointly contributed to the identification of subgroups with high versus low smoking cessation initiation probability, quit intention was the most important predictor common across all SES strata. The relative importance of the other predictors showed differences by SES. CONCLUSIONS: Modifiable characteristics of smoker subgroups associated with making a quit attempt and any variations by SES can be successfully identified using a decision tree analysis approach, to provide insights as to who might benefit from targeted intervention, thus, underscoring the value of this approach to complement the conventional regression-based approach.

Non-invasive sensor based automated smoking activity detection.

Although smoking prevalence is declining in many countries, smoking related health problems still leads the preventable causes of death in the world. Several smoking intervention mechanisms have been introduced to help smoking cessation. However, these methods are inefficient since they lack in providing real time personalized intervention messages to the smoking addicted users. To address this challenge, the first step is to build an automated smoking behavior detection system. In this study, we propose an accelerometer sensor based non-invasive and automated framework for smoking behavior detection. We built a prototype device to collect data from several participants performing smoking and other five confounding activities. We used three different classifiers to compare activity detection performance using the extracted features from accelerometer data. Our evaluation demonstrates that the proposed approach is able to classify smoking activity among the confounding activities with high accuracy. The proposed system shows the potential for developing a real time automated smoking activity detection and intervention framework.

Multi-task transfer learning for in-hospital-death prediction of ICU patients.

Multi-Task Transfer Learning (MTTL) is an efficient approach for learning from inter-related tasks with small sample size and imbalanced class distribution. Since the intensive care unit (ICU) data set (publicly available in Physionet) has subjects from four different ICU types, we hypothesize that there is an underlying relatedness amongst various ICU types. Therefore, this study aims to explore MTTL model for in-hospital mortality prediction of ICU patients. We used single-task learning (STL) approach on the augmented data as well as individual ICU data and compared the performance with the proposed MTTL model. As a performance measurement metrics, we used sensitivity (Sens), positive predictivity (+Pred), and Score. MTTL with class balancing showed the best performance with score of 0.78, 0.73, o.52 and 0.63 for ICU type 1 (Coronary care unit), 2 (Cardiac surgery unit), 3 (Medical ICU) and 4 (Surgical ICU) respectively. In contrast the maximum score obtained using STL approach was 0.40 for ICU type 1 & 2. These results indicates that the performance of in-hospital mortality can be improved using ICU type information and by balancing the `non-survivor' class. The findings of the study may be useful for quantifying the quality of ICU care, managing ICU resources and selecting appropriate interventions.