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A Report on Smoking Detection and Quitting Technologies.

ABSTRACT: 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.

SUBMITTER: Ortis A

PROVIDER: S-EPMC7177980 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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A Report on Smoking Detection and Quitting Technologies.

Ortis Alessandro A Caponnetto Pasquale P Polosa Riccardo R Urso Salvatore S Battiato Sebastiano S

International journal of environmental research and public health 20200410 7

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 ...[more]

PMID: 32290288

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Project description:INTRODUCTION The number of cessation clinics in China have been increasing ever since the Chinese government supported the establishment of smoking cessation clinics (SCCs) in each province in 2014. Many studies have examined smoking cessation behaviors among male smokers, but few of female smokers. This study aimed to understand female smokers’ quitting behaviors in SCCs and identify predictors of successful cessation. METHODS This study used data of the SCCs Platform in China from 2018 to 2020. The self-reported 7-day point prevalence of abstinence rate (PPAR) at 1 month and at 3 months follow-up and the continuous abstinence rate (CAR) at 3 months follow-up are reported based on smokers’ characteristics and intention to treat analysis. A multiple logistic regression model was used to identify predictors of continuous abstinence at 3 months follow-up. RESULTS The 7-day PPAR of female outpatients in SCCs was 29.20% at 1 month follow-up and 28.36% at 3 months follow-up. The CAR at 3 months follow-up was 19.88%. Female smokers who were prepared to quit within 7 days (AOR=2.86; 95% CI: 1.53–5.32), today (AOR=4.01; 95% CI: 2.35–6.85), had started to quit (AOR=7.11; 95% CI: 4.12–12.27), and used a combination of counseling and drugs (AOR=2.41; 95% CI: 1.73–3.35) were more likely to quit smoking. Associated with lower quitting rates were: living in the central region of China (AOR=0.47; 95% CI: 0.31–0.73) and the west region (AOR=0.48; 95% CI: 0.31–0.73); being aged 30–39 years (AOR=0.39; 95% CI: 0.23–0.64), and 40–49 years (AOR=0.41; 95% CI:0.24–0.69); being unemployed (AOR=0.64; 95% CI: 0.45–0.91); having a fair perceived health status at the first visit (AOR=0.65; 95% CI: 0.47–0.91) and a poor one (AOR=0.37; 95% CI: 0.21–0.64); having a moderate nicotine dependence (AOR=0.64; 95% CI: 0.44–0.92) and a severe one (AOR=0.50; 95% CI: 0.34–0.72). CONCLUSIONS In our study, the region of residence, age, employment, perceived health status, Fagerström test for nicotine dependence (FTND), readiness to quit, and intervention model were independent predictors of quitting for female smokers. Improving the motivation to quit, providing intensive psychological interventions and equipping SCCs with cessation medication would assist female smokers to quit.

Project description:BackgroundCigarette smoking is widespread among HIV-infected patients, who confront increased risk of smoking-related co-morbidities. The effects of HIV infection and HIV-related variables on smoking and smoking cessation are incompletely understood. We investigated the correlates of smoking and quitting in an HIV-infected cohort using a validated natural language processor to determine smoking status.MethodWe developed and validated an algorithm using natural language processing (NLP) to ascertain smoking status from electronic health record data. The algorithm was applied to records for a cohort of 3487 HIV-infected from a large health care system in Boston, USA, and 9446 uninfected control patients matched 3:1 on age, gender, race and clinical encounters. NLP was used to identify and classify smoking-related portions of free-text notes. These classifications were combined into patient-year smoking status and used to classify patients as ever versus never smokers and current smokers versus non-smokers. Generalized linear models were used to assess associations of HIV with 3 outcomes, ever smoking, current smoking, and current smoking in analyses limited to ever smokers (persistent smoking), while adjusting for demographics, cardiovascular risk factors, and psychiatric illness. Analyses were repeated within the HIV cohort, with the addition of CD4 cell count and HIV viral load to assess associations of these HIV-related factors with the smoking outcomes.ResultsUsing the natural language processing algorithm to assign annual smoking status yielded sensitivity of 92.4, specificity of 86.2, and AUC of 0.89 (95% confidence interval [CI] 0.88-0.91). Ever and current smoking were more common in HIV-infected patients than controls (54% vs. 44% and 42% vs. 30%, respectively, both P<0.001). In multivariate models HIV was independently associated with ever smoking (adjusted rate ratio [ARR] 1.18, 95% CI 1.13-1.24, P <0.001), current smoking (ARR 1.33, 95% CI 1.25-1.40, P<0.001), and persistent smoking (ARR 1.11, 95% CI 1.07-1.15, P<0.001). Within the HIV cohort, having a detectable HIV RNA was significantly associated with all three smoking outcomes.ConclusionsHIV was independently associated with both smoking and not quitting smoking, using a novel algorithm to ascertain smoking status from electronic health record data and accounting for multiple confounding clinical factors. Further research is needed to identify HIV-related barriers to smoking cessation and develop aggressive interventions specific to HIV-infected patients.

Project description:AimsTo estimate (1) associations between self-reported COVID-19, hand-washing, smoking status, e-cigarette use and nicotine replacement therapy (NRT) use and (2) the extent to which COVID-19 has prompted smoking and vaping quit attempts and more smoking inside the home.DesignCross-sectional household surveys.Setting and participantsA representative sample of the population in England from April to May 2020. The sample included 3179 adults aged ≥ 18 years.MeasurementsParticipants who reported that they definitely or thought they had coronavirus were classified as having self-reported COVID-19. Participants were asked how often they wash their hands after returning home, before preparing foods, before eating or before touching their face. They were also asked whether, due to COVID-19, they had (i) attempted to quit smoking, (ii) attempted to quit vaping and (iii) changed the amount they smoke inside the home.FindingsOdds of self-reported COVID-19 were significantly greater among current smokers [20.9%, adjusted odds ratio (aOR) = 1.34, 95% confidence interval (CI) = 1.04-1.73] and long-term (> 1-year) ex-smokers (16.1%, aOR = 1.33, 95% CI = 1.05-1.68) compared with never smokers (14.5%). Recent (< 1-year) ex-smokers had non-significantly greater odds of self-reported COVID-19 (22.2%, aOR = 1.50, 95% CI = 0.85-2.53). Bayes factors indicated there was sufficient evidence to rule out large differences in self-reported COVID-19 by NRT use and medium differences by e-cigarette use. With the exception of hand-washing before face-touching, engagement in hand-washing behaviours was high (> 85%), regardless of nicotine use. A minority (12.2%) of quit attempts in the past 3 months were reportedly triggered by COVID-19, and approximately one in 10 current e-cigarette users reported attempting to quit vaping because of COVID-19.ConclusionsIn England, current smokers and long-term ex-smokers appear to have higher odds of self-reported COVID-19 compared with never smokers in adjusted analyses, but there were no large differences between people who used nicotine replacement therapy or e-cigarettes. Engagement in hand-washing appears to be high, regardless of nicotine or tobacco use. A minority of past-year smokers and current e-cigarette users, respectively, report attempting to quit smoking/vaping due to COVID-19.

Dataset Information

A Report on Smoking Detection and Quitting Technologies.

Publications

A Report on Smoking Detection and Quitting Technologies.

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