Are you smoking? Automatic alert system helping people keep away from cigarettes

Theoretically Guided Iterative Design of the Sense2Quit App for Tobacco Cessation in Persons Living with HIV

The use of mobile health (mHealth technology) can be an effective intervention when considering chronic illnesses. Qualitative research methods were used to identify specific content and features for a mobile app for smoking cessation amongst people living with HIV (PWH). We conducted five focus group sessions followed by two Design Sessions with PWH who were or are currently chronic cigarette smokers. The first five groups focused on the perceived barriers and facilitators to smoking cessation amongst PWH. The two Design Sessions leveraged the findings from the focus group sessions and were used to determine the optimal features and user interface of a mobile app to support smoking cessation amongst PWH. Thematic analysis was conducted using the Health Belief Model and Fogg's Functional Triad. Seven themes emerged from our focus group sessions: history of smoking, triggers, consequences of quitting smoking, motivation to quit, messages to help quit, quitting strategies, and mental health-related challenges. Functional details of the app were identified during the Design Sessions and used to build a functional prototype.

Machine learning applications in tobacco research: a scoping review

OBJECTIVE

Identify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis.

DATA SOURCES

MEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded.

STUDY SELECTION

Two reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis.

DATA EXTRACTION

Using data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted.

DATA SYNTHESIS

74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed.

CONCLUSIONS

ML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.

The mutagenic effect of tobacco smoke on male fertility

Despite the association between tobacco use and the harmful effects on general health as well as male fertility parameters, smoking remains globally prevalent. The main content of tobacco smoke is nicotine and its metabolite cotinine. These compounds can pass the blood-testis barrier, which subsequently causes harm of diverse degree to the germ cells. Although controversial, smoking has been shown to cause not only a decrease in sperm motility, sperm concentration, and an increase in abnormal sperm morphology, but also genetic and epigenetic aberrations in spermatozoa. Both animal and human studies have highlighted the occurrence of sperm DNA-strand breaks (fragmentation), genome instability, genetic mutations, and the presence of aneuploids in the germline of animals and men exposed to tobacco smoke. The question to be asked at this point is, if smoking has the potential to cause all these genetic aberrations, what is the extent of damage? Hence, this review aimed to provide evidence that smoking has a mutagenic effect on sperm and how this subsequently affects male fertility. Additionally, the role of tobacco smoke as an aneugen will be explored. We furthermore aim to incorporate the epidemiological aspects of the aforementioned and provide a holistic approach to the topic.

A Smoking Cessation Mobile App for Persons Living With HIV: Preliminary Efficacy and Feasibility Study

BACKGROUND

The prevalence of smoking in the United States general population has gradually declined to the lowest rate ever recorded; however, this has not been true for persons with HIV.

OBJECTIVE

We conducted a pilot test to assess the feasibility and efficacy of the Lumme Quit Smoking mobile app and smartwatch combination with sensing capabilities to improve smoking cessation in persons with HIV.

METHODS

A total of 40 participants were enrolled in the study and randomly assigned 1:1 to the control arm, which received an 8-week supply of nicotine replacement therapy, a 30-minute smoking cessation counseling session, and weekly check-in calls with study staff, or to the intervention arm, which additionally received the Lumme Quit Smoking app and smartwatch.

RESULTS

Of the 40 participants enrolled, 37 completed the follow-up study assessments and 16 used the app every day during the 56-day period. During the 6-month recruitment and enrollment period, 122 people were screened for eligibility, with 67.2% (82/122) deemed ineligible. Smoking criteria and incompatible tech were the major reasons for ineligibility. There was no difference in the proportion of 7-day point prevalence abstinence by study arm and no significant decrease in exhaled carbon monoxide for the intervention and control arms separately. However, the average exhaled carbon monoxide decreased over time when analyzing both arms together (P=.02).

CONCLUSIONS

Results suggest excellent feasibility and acceptability of using a smoking sensor app among this smoking population. The knowledge gained from this research will enable the scientific community, clinicians, and community stakeholders to improve tobacco cessation outcomes for persons with HIV.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04808609; https://clinicaltrials.gov/ct2/show/NCT04808609.

Smoking Cessation System for Preemptive Smoking Detection

Smoking cessation is a significant challenge for many people addicted to cigarettes and tobacco. Mobile health-related research into smoking cessation is primarily focused on mobile phone data collection either using self-reporting or sensor monitoring techniques. In the past 5 years with the increased popularity of smartwatch devices, research has been conducted to predict smoking movements associated with smoking behaviors based on accelerometer data analyzed from the internal sensors in a user's smartwatch. Previous smoking detection methods focused on classifying current user smoking behavior. For many users who are trying to quit smoking, this form of detection may be insufficient as the user has already relapsed. In this paper, we present a smoking cessation system utilizing a smartwatch and finger sensor that is capable of detecting pre-smoking activities to discourage users from future smoking behavior. Pre-smoking activities include grabbing a pack of cigarettes or lighting a cigarette and these activities are often immediately succeeded by smoking. Therefore, through accurate detection of pre-smoking activities, we can alert the user before they have relapsed. Our smoking cessation system combines data from a smartwatch for gross accelerometer and gyroscope information and a wearable finger sensor for detailed finger bend-angle information. We compare the results of a smartwatch-only system with a combined smartwatch and finger sensor system to illustrate the accuracy of each system. The combined smartwatch and finger sensor system performed at an 80.6% accuracy for the classification of pre-smoking activities compared to 47.0% accuracy of the smartwatch-only system.

A context-adaptive smoking cessation system using videos

Cigarette smoking is the primary preventable cause of death and disease worldwide. Studies reveal that smoking is associated with psychiatric symptoms, sociodemographic characteristics, social stressors, and lack of social support. In general, smokers report poorer mental health and benefit from support to be able to quit smoking (Jorm et al., 1999). In this paper, a tailored smoking cessation system has been developed in which the counseling and support is delivered via video-messaging. The system engages users in adaptive motivating video access. Users can interact with the system and the system selects the best matching video for them by processing their messages using Natural Language Processing (NLP). We have tailored 77 videos for interactive contents that encompass important issues users might face during the process of smoking cessation. A novel application-based data driven approach has been taken for categorizing videos to push to participants. The approach is based on analyzing 750 messages of people in the cessation process. We observed that most of the messages' contents were about smoking health effects, cravings, triggers, relapse, positive mood, low cessation self efficacy, medications, and culturally specific targeting inquiries. Considering these categories, videos are categorized to the corresponding groups by an intelligent approach. The information underlying the data driven categories allows for improving and facilitating smoking status assessment. The system has the potential for improving future smoking cessation decision-making adaptive interventions and health monitoring systems. The goal is to tailor the system to meet the needs of the users in real-time and maximize the potential impact.

A Report on Smoking Detection and Quitting Technologies

Mobile health technologies are being developed for personal lifestyle and medical healthcare support, of which a growing number are designed to assist smokers to quit. The potential impact of these technologies in the fight against smoking addiction and on improving quitting rates must be systematically evaluated. The aim of this report is to identify and appraise the most promising smoking detection and quitting technologies (e.g., smartphone apps, wearable devices) supporting smoking reduction or quitting programs. We searched PubMed and Scopus databases (2008-2019) for studies on mobile health technologies developed to assist smokers to quit using a combination of Medical Subject Headings topics and free text terms. A Google search was also performed to retrieve the most relevant smartphone apps for quitting smoking, considering the average user's rating and the ranking computed by the search engine algorithms. All included studies were evaluated using consolidated criteria for reporting qualitative research, such as applied methodologies and the performed evaluation protocol. Main outcome measures were usability and effectiveness of smoking detection and quitting technologies supporting smoking reduction or quitting programs. Our search identified 32 smoking detection and quitting technologies (12 smoking detection systems and 20 smoking quitting smartphone apps). Most of the existing apps for quitting smoking require the users to register every smoking event. Moreover, only a restricted group of them have been scientifically evaluated. The works supported by documented experimental evaluation show very high detection scores, however the experimental protocols usually lack in variability (e.g., only right-hand patients, not natural sequence of gestures) and have been conducted with limited numbers of patients as well as under constrained settings quite far from real-life use scenarios. Several recent scientific works show very promising results but, at the same time, present obstacles for the application on real-life daily scenarios.

Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review

Globally, cigarette smoking is widespread among all ages, and smokers struggle to quit. The design of effective cessation interventions requires an accurate and objective assessment of smoking frequency and smoke exposure metrics. Recently, wearable devices have emerged as a means of assessing cigarette use. However, wearable technologies have inherent limitations, and their sensor responses are often influenced by wearers' behavior, motion and environmental factors. This paper presents a systematic review of current and forthcoming wearable technologies, with a focus on sensing elements, body placement, detection accuracy, underlying algorithms and applications. Full-texts of 86 scientific articles were reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines to address three research questions oriented to cigarette smoking, in order to: (1) Investigate the behavioral and physiological manifestations of cigarette smoking targeted by wearable sensors for smoking detection; (2) explore sensor modalities employed for detecting these manifestations; (3) evaluate underlying signal processing and pattern recognition methodologies and key performance metrics. The review identified five specific smoking manifestations targeted by sensors. The results suggested that no system reached 100% accuracy in the detection or evaluation of smoking-related features. Also, the testing of these sensors was mostly limited to laboratory settings. For a realistic evaluation of accuracy metrics, wearable devices require thorough testing under free-living conditions.