UthealthHouston researchers fight smoking with mHealth interventions

Smoking rates among adults in the United States have fallen precipitously since the first Great American Smokeout was organized in San Francisco in 1977, when over a third of Americans smoked cigarettes. Today, that number is less than 12.5%. While those changes are dramatic, and worthy of celebration - called “one of the most significant public health successes in modern U.S. history” by Surgeon General Jerome M. Adams in his 2020 report on smoking cessation - it is important to acknowledge that cigarette smoking remains the leading cause of preventable disease, disability, and death in the United States, accounting for 1,300 deaths every day. And while cigarette smoking has decreased broadly across the country, tobacco use among some minors has actually increased over the last two decades. Clearly, there is still work to be done, and researchers at the UTHealth Houston School of Public Health are developing new and intriguing techniques, utilizing mobile health technology, to help smokers quit.

移动医疗、移动健康是常用的一个术语to reference the use of mobile communication devices - mobile phones, tablets, and wearable smart-devices, for health services. With the ubiquity of the technology and devices across demographics, mHealth has emerged as an important tool for reaching hard-to-reach and underserved populations, and improving access to public health information and tools.

Irene Tami-Maury, DMD, DrPH, MSc, assistant professor of Epidemiology, Human Genetics & Environmental Sciences at the School of Public Health, leads the Health Equity Research Group (HERG) at the school, who have adapted and pilot tested a text-message-based smoking cessation intervention for sexual and gender minority (SGM) individuals – lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people.Smokefreesgm根据LGBTQ+吸烟者的需求和经验量身定制。SmokeFreegSM基于国家癌症研究所（NCI）开发的SmokeFreetxt干预措施，但专门针对LGBTQ+社区量身定制，使用烟草专家，当前和前LGBTQ+吸烟者以及行为健康，流行病学和生物统计学的领先专家。NCI资助计划的目标是使用随机控制试验来确定基于文本的干预措施（例如SmokeFreetxt和SmokeFreesgm）在多大程度上可以帮助想要戒烟的LGBTQ+人。

参与者进入项目后，对参与者进行筛选和评估，然后随机分配给SmokeFreetxt（对照组）或SmokeFreesGM。参与者和研究人员均未被告知分配了哪个组。beplay苹果手机能用吗总体而言，参与者花了八个月的时间参加该计划。通过吸烟状况自我报告和尼古丁唾液测试以一个，三个月和六个月的间隔进行后续评估。

Smokefreesgmparticipants receive complimentary nicotine patches to help wean them off of cigarettes, and they receive daily text messages two weeks before, and up to four weeks after they quit smoking – motivational messaging encouraging them to stay smoke-free, informational messages reminding them of the important reasons why they quit, strategies for dealing with cravings, triggers, and stressful situations, among others. They also receive prompts and queries designed to track their mental and emotional well-being. Importantly, on-demand help is available to participants who need help staying smoke-free.

At this time, SmokefreeSGM is in its final phase (randomized control trial) recruiting potential study participants who self-identify as LGBTQ+ adult individuals and are currently smoking cigarettes. Interested individuals will participate in a two-part virtual screening process, and if enrolled, are given access to the text-messaging program, receive a supply of nicotine patches, and participate in the four virtual assessments and a final virtual interview. Each of these assessments (including the individual interview) last less than six minutes, for which study participants are compensated.

A separate study, funded by SWOG/The Hope Foundation and conducted by Tami-Maury and HERG, uses a mobile phone application to enhance the knowledge and skills of healthcare professionals providing smoking cessation services to their patients. The Decision-T app aims to help providers make quicker and better decisions about cessation treatment approaches for their patients, and is based on the 5A’s framework: Ask, Advise, Assess, Assist, and Arrange. Providers问patients about their smoking behavior,advisethem to quit smoking,assess他们愿意辞职，协助them with finding available resources, andarrangefollow-up meetings to evaluate patient progress. Decision-T guides healthcare professionals in estimating their patients’ smoking behavior and provides a personalized quitting plan for the patient to use. The research team has completed the beta testing phase of the app. The feedback from the small sample of healthcare providers testing Decision-T has been outstanding in terms of its usability, functionality, and acceptability. These preliminary findings are encouraging for wider implementation of the Decision-T app, which planned by HERG.

Emily T. Hébert, DrPH,协助ant professor in the Department of Health Promotion and Behavioral Sciences is also developing interventions that utilize mobile technology to aid smoking cessation. Hébert was awarded an NIH Career Development Award in 2019 fora study using machinelearning to develop just-in-time adaptive interventions (JITAI) for smoking cessation. Machine learning is a robust data analytic strategy that can produce highly accurate predictive models from large and dynamic datasets in real-time. The objective is to use supervised machine learning to develop an automated algorithm to quantify smoking lapse risk at the individual level, and deliver a personalized intervention in real-time.

电话传感器，可穿戴技术和实时数据收集方法使得可以收集大量个性化的环境和生理数据，例如位置，心率和情绪。使用该技术提供的环境和情境提示，例如渴望或与其他吸烟者接近，该技术可以在最需要的情况下通过移动设备提供及时且量身定制的支持来预测失误，并通过及时且量身定制的支持打断它们。如果成功的话，该项目及其实施方法可能是机器学习力量的深刻证明，以及如何适应其他健康行为和关注点，包括饮食，体育锻炼或其他物质使用障碍。该研究将于2023年开始入学。

“Most smokers want to quit,” says Hébert, “but many don’t use evidence-based treatments like behavioral counseling or nicotine replacement therapy. Since 85% of adults in the U.S. own a smartphone, our hope is to use mHealth interventions to help increase access to effective treatments like these, and help more people quit.”

While this project will be among the first to use machine learning methods to predict risk of individual smoking lapse in real time, Hébert has participated in other related research using mobile technology and machine learning. In astudy published in 2020赫伯特和她的同事发现,动态smartphone apps that tailor intervention content in real-time may increase user engagement and exposure to treatment-related materials, and may be capable of providing similar outcomes to traditional, in-person counseling.第二项研究published that year used an algorithm to develop a just-in-time adaptive intervention for individuals in a clinic-based smoking cessation program. The study demonstrated the utility of data-driven approaches in estimating smoking lapses and developing JITA.