Effectiveness of a smoking cessation decision-making electronic tool ( Pare de Fumar Conosco ): A randomized clinical trial

Abstract

There is an underutilization of smoking cessation treatment among patients with chronic conditions, which indicates a need for new strategies to engage them. Web-based smoking cessation decision-making tools can be beneficial. This study assessed the effectiveness of the Pare de Fumar Conosco software at increasing engagement to smoking cessation counseling groups among Brazilian smokers with chronic conditions. Clinical trial participants were randomized to Pare de Fumar Conosco or standard of care. Engagement in the smoking cessation counseling group was the primary outcome. Secondary outcomes included motivation level, cessation rate, completion, and treatment adherence. The t-tests and Chi-square analyzed baseline differences between groups and the Poisson models, the effects of the intervention. Engagement rates were higher in the Pare de Fumar Conosco (IR=2.22; 95% CI: 1.06 - 4.63) concerning the standard of care. Pare de Fumar Conosco group was more adherent to treatment over time (6.4% versus 4.2% in the final week). Cessation rate was 26.3% in standard care and 23.1% in Pare de Fumar Conosco. Both interventions maintained significant motivation levels to quit smoking. The Pare de Fumar Conosco intervention effectively increases the engagement in the smoking cessation counseling groups. There is a need to improve the smoking cessation rate.

Keywords

decision making educational technology tobacco use cessation tobacco use disorder software

Introduction

Tobacco use remains the leading cause of reversible morbimortality worldwide.¹ By 2030, tobacco will be responsible for approximately 8000 deaths per year. Of these, more than 80% will be in low- and middle-income countries.² Offering smoking cessation treatment in various modalities is one-actions indicated to minimize this projected impact.³

In Brazil, population-based surveys show a decrease in the prevalence of tobacco use in the past 30 years.⁴ However, Brazil still has more than 20 million tobacco users and is listed among the ten countries with the highest number of smokers.^5,6 Brazilian clinical practice guidelines recommend the combination of counseling and pharmacotherapy for smoking cessation.⁷ As part of Brazil’s universal healthcare system, group counseling is offered to all smokers at no cost. Group counseling covers coping methods, social skills training, contingency management, self-control, and cognitive-behavioral strategies.^8,9 The Brazilian universal health care system also covers pharmacotherapy (nicotine replacement therapy, bupropion, and varenicline). However, the uptake of these resources remains low.⁷ This underutilization of smoking cessation treatment is even more evident among patients with chronic conditions (e.g. hypertension, diabetes, kidney disease, and obesity). Studies demonstrate the limited engagement and low cessation rate in smokers with multiple conditions.^10,11

There is an urgent need to develop new strategies to engage smokers with chronic conditions in smoking cessation treatment.¹² Web-based interventions are a promising strategy for smoking cessation,¹³ particularly in developing countries.¹⁴ There are several types of web-based smoking cessation interventions available (e.g. standardized or tailored information, interactive features, multimedia resources, etc.).^15–20 Web-based interventions stimulate the desire to quit smoking and guide smokers to achieve this goal.²¹ Moreover, these interventions are a credible source of information and mitigate barriers to smoking cessation.²² Previous studies showed that web-based smoking cessation interventions increase motivation to quit and pharmacotherapy use, which caused remarkable abstinence rates.^21,22,23,24

Pare de Fumar Conosco, a web-based decision-making tool for smoking cessation, has significantly increased interest in pharmacotherapy and counseling among Brazilian smokers with chronic conditions.²⁵ However, additional testing as a formal randomized controlled trial is needed to test its effectiveness. This study aimed to assess the effectiveness of Pare de Fumar Conosco at increasing engagement to smoking cessation counseling groups among Brazilian smokers with chronic conditions.

Methods

Trial design

The study hypothesis was: Among Brazilian smokers with chronic conditions, the Pare de Fumar Conosco software increases engagement in the smoking cessation counseling groups. This engagement in cessation demonstrated by attendance to the first in-person smoking cessation counseling group was the primary outcome. Motivation level, cessation rate, completion, and treatment adherence were secondary outcomes. Study procedures were approved and monitored by the Research Ethics Committee of the Hospital University of UFJF (24883413.5.0000.5133). The study' report follows the recommendation of the CONSORT Declaration 26.

Participants

Brazilian smokers with chronic conditions were recruited in the waiting rooms of two secondary care clinics by a research assistant. Eligible participants were ≥18 years of age, self-identified as current smokers, were treated at healthcare centers for chronic conditions, and were willing to sign an informed consent form. We obtained written informed consent was from each participant and did not reimburse them for their participation in the study.

Interventions

We submitted participants in the intervention group to the Pare de Fumar Conosco software and the control group to the standard of care. Both interventions and group counseling for smoking cessation are specified below.

Pare de Fumar Conosco

The one-arm feasibility study of the Pare de Fumar Conosco software describes in detail this intervention.²⁵ In summary, a panel of Brazilian healthcare professionals adapted Pare de Fumar Conosco from an existing web-based decision-making tool for smoking cessation developed in English and Spanish for use among Latinos in the United States²¹ and Mexico.^27–29 They adapted the content to reflect the resources, perceptions, and sociocultural characteristics of Brazilian smokers. Pare de Fumar Conosco follows the Social Cognitive Theory, which comprises motivational messages, behavioral change support, and smoking cessation pharmacotherapy use. The decision-making tool included videos featuring a professional actor guiding smokers through the program, as well as a testimonial from an ex-smoker. The software also collected sociodemographic information and smoking behaviors. It used this information to develop a personalized quit plan: pharmacotherapy selection and an invitation to a counseling group. Finally, the software asked participants to set up a smoking quit date. The engagement in cessation demonstrated by the attendance to the first in-person smoking cessation counseling group is the main functionality of the software. The use of Pare de Fumar Conosco took approximately 15–25 minutes per participant to complete. Figure 1 shows some aspects of Pare de Fumar Conosco.

Figure 1.

Pare de Fumar Conosco software in its original language (Portuguese).

Standard of care

Following Brazil’s National Cancer Institute (INCA, based on its acronym in Portuguese), standard of care, basic approach for smoking cessation treatment, was substantiated by the 5 A’s approach: Ask, Advise, Assess, Assist, and Arrange.³⁰ The procedures consisted of asking participants about their smoking behaviors, advising participants to quit smoking, assessing their willingness to make a quit attempt, assisting participants with a quit date selection, and inviting them to participate in the smoking cessation counseling groups. These procedures took approximately 15-25 minutes per participant to complete. Lastly, we gave participants printed educational material with motivational content and the contact information for the counseling groups.

Smoking cessation counseling group

We invited all participants to the in-person group-based counseling program. This program consisted of intense cognitive-behavioral therapy, which included behavioral skills training and pharmacological intervention if indicated. The program was structured in four 90-minute weekly sessions. Subsequently, these meetings were held every fifteen days until the conclusion of the twelve-week follow-up. The smoking cessation counselor conducting the sessions was not aware of the participants’ allocation group.

Outcomes

Research staff conducted an in-person assessment at baseline and follow-up (Week 12). The baseline survey collected sociodemographic information (e.g. age, gender, and educational level). Smoking-related variables included two questions from the Fagerström Test for Nicotine Dependence³¹ (number of cigarettes smoked per day and time of smoking the first cigarette). Moreover, we assessed if participants made a previous quit attempt. The Patient Health Questionnaire (PHQ-2)³² screened depression.

The engagement rate in cessation (percentage of participants who attended the first in-person smoking cessation counseling group) was the primary outcome. As part of the counseling program, we assessed: motivation level, evaluated by an analog scale of 0 to 10 (10 being the maximum motivation level), self-reported smoking abstinence (7-day point prevalence abstinence), completion rate (percentage of participants who attended all the sessions in the counseling program), and treatment adherence (percentage of participants who attended in each session in the counseling program). We analyzed smoking abstinence, completion rate, and treatment adherence using intention to treat analysis.

Participants in the intervention group were contacted eight weeks after utilization of the software to assess satisfaction. This over-the-phone survey consisted of questions about the degree of satisfaction with the software, its most valuable aspects, and suggestions for improvement.

Sample size

Data from a study conducted in one of the secondary healthcare centers that reported 11.9% of the patients as current smokers³³ supported the sample size calculation performed in the OpenEpi software. The primary outcome was the engagement in the smoking cessation counseling group. We expected a 25%³⁴ and 10% (assumed effect size) engagement rate in the Pare de Fumar Conosco group and standard of care group, respectively. The effect size in terms of relative risk was 2.5 in favor of the intervention group. We set the significance level and statistical power at 5% and 80%, respectively. As a result, the estimated sample size was 100 subjects per group. With an assumed loss of 10%, the adequate size of eligible participants should total at least 110 subjects per group, or 220 in total.

Randomization

After the baseline assessment completion, participants were randomized to one of two conditions: (1) Pare de Fumar Conosco or (2) standard of care. For allocation of the participants, the statistical program R created a computer-generated list of random numbers. This allocation was open and strictly followed the random number list to avoid cross-contamination between groups. Researchers and participants were not blinded. Figure 2 shows the summary of the entire clinical trial design.

Figure 2.

Clinical trial design.

We continuously trained the research team to standardize both interventions – standard of care and Pare de Fumar Conosco. Standard of care is part of the regular training of health professionals, with at least two training per year. Training on the implementation of Pare de Fumar Conosco followed the same periodicity during the study time.

Statistical methods

Based upon the data stored in REDCap³⁵ (Research Electronic Data Capture), we performed descriptive and inferential statistical analyses with the statistical package R version 3.4.4 and STATA 13.0. The confidence level adopted was 95%, and we interpreted the p-values and confidence intervals following the literature recommendation.^36,37

We reported the categorical variables in absolute (n) and relative (%) frequencies and continuous variables in mean, median, and standard deviation. Differences in baseline between groups were analyzed using Chi-square and t-tests.

To analyze the effects of the intervention, we used regression models appropriate to the outcome type and always adjusted or stratified for potentially confounding variables. In estimating treatment effects, we used Generalized Linear Models,³⁸ a class of statistical models that allows for highly flexible probability distributions, according to the type of response variable. In the case of dichotomous variables (e.g. engagement in the smoking cessation counseling group), we used Poisson models with a log-link function to provide the incidence ratio as a measure of association.

Results

During the recruitment process, 281 participants were eligible for the study. However, it was not possible to contact 38 individuals. Hence, the final sample comprised was 243 participants, with 124 (51%) in the Pare de Fumar Conosco group (intervention group) and 119 (49%) in the standard of care (control group) (Figure 3).

Figure 3.

Participant recruitment process and follow-up.

Participants were primarily middle-aged women. Most of them were daily smokers, had screened negative for depression, and had incomplete or no elementary education. Almost half of the participants (44%) consumed a maximum of ten cigarettes a day, and 41% smoked their first cigarette five minutes after waking up.

Randomization resulted in some similar baseline characteristics between the intervention group (IG) and the control group (CG) (e.g. gender, age, and daily smoking). However, compared to the IG, more participants in the CG were light smokers, reported having made a previous quit attempt, and screened positive for depression. Moreover, more participants in the IG were intermediate smokers (11-20 CPD) and smoked over 60 minutes after waking up, compared to participants in the CG (Table 1).

Table 1.

Baseline characteristics of study participants.

Variable	Total, n = 243	IG, n = 124	CG, n = 119
General data
Female, n (%)	145 (59.3)	72 (58.1)	72 (60.5)
Median age, years (mean ± SD)	56 (54.8±11.9)	55 (53.9±11.5)	57 (55.9±12.3)
Educational level*
Incomplete or no elementary school	151 (63.7)	76 (61.3)	75 (66.4)
Elementary school, n (%)	37 (15.6)	15(12.1)	22 (19.5)
High school graduate or college graduate, n (%)	49 (20.7)	33(26.6)	16 (14.1)
Depression screening (PHQ-2)*, n (%)	91 (38.6)	32 (25.8)	59 (52.7)
Smoking history
Daily smoker, n (%)	233 (95.9)	117 (94.4)	116 (97.5)
Time of smoking the 1st cigarette*
Within 5 min, n (%)	97 (41.1)	45 (36.3)	52 (46.4)
6–30 min, n (%)	43 (18.2)	8 (6.4)	35 (31.2)
31–60 min, n (%)	23 (9.8)	14 (11.3)	9 (8.1)
>60 min, n (%)	73 (30.9)	57 (46)	16 (14.3)
N° of cigarettes daily*
Light smokers (≤10 CPD), n (%)	106 (44)	45 (36.3)	61 (52.1)
Intermediate smokers (11-20 CPD), n (%)	97 (40.2)	55 (44.3)	42 (35.9)
Heavy smokers (>20 CPD), n (%)	38 (15.8)	24 (19.4)	14 (12)
Previous attempt to quit smoking*, n (%)	168 (69.7)	71 (57.3)	97 (82.9)

CG: control group; CPD: cigarettes a day; IG: intervention group; n: total; N°: number; PHQ-2: Patient Health Questionnaire - 2 items; SD: standard deviation; * - p-value < 0,05.

The engagement rate in the smoking cessation counseling group, the focus of this study, was 18.5% (45/243). The median time between randomization and attending the first in-person smoking cessation counseling group was 45 days (42 in the intervention group and 48 in the control). At Week 4, the median motivation level was 10, and seven of the 24 participants who completed the fourth week of treatment quit smoking. At Week 12, the completion rate was 28.9% (13/45), with only two smokers finishing treatment without successfully quitting smoking (Table 2).

Table 2.

Comparison of outcomes between study groups.

Outcomes	Total	IG	CG	p-value
Engagement rate, n (%)	45 (18.5)	26 (21)	19 (16)	0.32^a
Median motivation level (Week 4) (mean ± SD)	10 (9.6 ± 0.8)	10 (9.6 ± 0.7)	10 (9.5 ± 1.0)	0.75*
Cessation rate at week 4, n (%)	7 (15.6)	4 (15.4)	3 (15.8)	0.97^a
Completion rate at week 4, n (%)	24 (53.3)	14 (53.8)	10 (52.6)	0.93^a
Cessation rate at week 12, n (%)	11 (24.4)	6 (23.1)	5 (26.3)	0.80^a
Completion rate at week 12, n (%)	13 (28.9)	8 (30.8)	5 (26.3)	0.74^a

CG: control group; IG: intervention group; N: total; SD: standard deviation. *: t-test.

^achi-square test.

At Week 4, eleven participants used smoking cessation medications (43% in the intervention group and 50% in the control group). Sessions adherence of the in-person smoking cessation counseling group decreased over time (Figure 4). At week 4, adherence treatment was 53% (24/45). At Week 12, 6% of participants in the intervention group attended the counseling group session, compared to 4% in the control group.

Figure 4.

Treatment adherence (intention to treat).

For Poisson model estimates, the engagement in the cessation demonstrated by attendance at the first in-person smoking cessation counseling group and treatment completion (intention to treat) were considered response variables. According to the Chi-Square test for differences between groups, the study participants' characteristics with p <0.05 and the intervention group were the explanatory variables included. After adjusting for these confounding factors, the intervention group and screening for depression influenced the engagement in the smoking cessation counseling group (Table 3).

Table 3.

Multivariate Poisson regression models for primary and secondary outcomes.

Adjusted variables	Engagement in the smoking cessation counseling group		Treatment completion (ITT)
Adjusted variables	IR	95% CI	IR	95% CI
Group [intervention]	2.22	1.06 - 4.63	3.39	0.88 – 12.97
Incomplete or no elementary school	0.73	0.47 - 1.12	0.79	0.36 – 1.72
Depression screening	2.00	1.05 - 3.83	3.25	0.96 – 10.98
Previous attempt to stop	1.54	0.74 - 3.17	1.03	0.28 – 3.72
Time to first cigarette [within 5 minutes]	0.89	0.71 - 1.14	0.63	0.38 – 1.04
Number of cigarettes daily [light smokers]	1.08	0.69 – 1.66	0.52	0.19 – 1.36

CI: confidence interval; ITT: intention to treat; IR: incidence ratio.

Eighteen participants in the intervention group completed the over-the-phone survey at Week 8. Almost all participants (94.4%) reported high satisfaction with the technology and the setting of utilization (e.g. waiting areas). The most valuable aspects of the interventions were information about smoking cessation medications (27%), testimonials (20%), interest in smoking cessation (20%), the importance of quitting smoking (13.3%), and technology (20%). Two participants suggested enhancing the psychological aspects of smoking cessation and developing more innovative technological features. A total of 83% of participants reported that they cut down the number of cigarettes in the past seven days, with a median (mean ± standard deviation) of consuming six (9.2 ± 6.3) cigarettes per day.

Discussion

As focus of this study, the clinical trial showed higher engagement in the counseling group session among participants using Pare de Fumar Conosco concerning those who received standard of care. Moreover, participants in the intervention group were more adherent to the treatment. However, smoking cessation was higher among participants receiving standard of care than participants in the intervention group.

Participants using Pare de Fumar Conosco reported a similar engagement rate (21%) to smokers in Mexico using a similar web-based smoking cessation decision-making tool (19.7%).²⁷ The cessation rate described in this study (4.5%, 11/243) is consistent with the rate found in population-based surveys.^39-41 However, this result was lower than that obtained by the web-based smoking cessation decision-making tools in the United States²¹ and Mexico²⁷ (11.8% in the United States and 19.1% in Mexico).

A systematic review and network meta-analysis showed that cessation, overall, is higher among participants who use smoking cessation electronic tools compared to participants who do not receive an intervention or use generic self-help resources (RR=1.14, 95% CI 1.07 to 1.22).⁴² The same effect occurs when comparing electronic tools with computer-generated and tailored printed materials concerning no intervention, standard of care, or untailored printed self-help materials, with or without concurrent co-interventions (RR=1.51. 95% CI 1.15 to 1.97).⁴² In the Social-Local-Mobile study, the combination of psychopharmacological therapy with a digital mobile health solution favored smoking cessation by per-protocol analysis (adjusted OR = 3.45, p = 0.01) and by intention-to-treat analysis (adjusted OR = 3.13, p = 0.002).⁴³

In our study, cessation rates in the control group were higher than in the Pare de Fumar Conosco group, especially in Week 12. One aspect that could explain this difference is that of the total number of individuals in the intervention group who had quit smoking in Week 4, 50% abandoned the treatment before the end of the 12 weeks. Cessation rates may differ depending on the length of treatment.⁴⁴ Studies reported higher success rates in early periods than in later treatment periods.^45–47 In pooled analyzes of the effects of the web- and computer-based smoking cessation programs, short-term (3 months), midterm (6–10 months), and long-term (12 months) follow-up resulted in a decline in smoking cessation rates over time of treatment.⁴⁸ It is worth mentioning the need to improve the effectiveness of the cessation of a decision-support tool. Web-based interventions have room for improvement.^49,50 Its combination with different types of interventions can enhance smoking cessation.^42,51-53

Even with the tobacco cessation treatment (combination of counseling and pharmacotherapy)^54–59 available at no cost, treatment adherence decreased over time, especially in the control group. This finding corroborates with those described in the literature. Similar to our study, in Brazilian patients with chronic conditions, the adherence rate to the 4-week treatment of the smoking cessation counseling group was 57%.⁶⁰ Despite lack of standardized research in adherence to smoking treatment, the counseling sessions adherence is positively associated with cessation.^55,61-63

Audio-visual communication can eliminate the barrier of low literacy.^64,65 Accessibility via the Internet or internal networks permits a single production to be used several times with a minimal recurring cost.⁶⁶ Unlike standardized health, electronic tools have a personalized focus on the choices and results of users, guiding them to decision-making according to their preferences, values, and beliefs.⁶⁷ Multimedia interventions can standardize counseling for decision-making, saving the time healthcare providers spending to educating their patients.⁶⁸ The reduction of health system costs and the potential informed decision support has on patient health are policymakers' objectives for improving health, equity, and patient satisfaction.⁶⁹ The SmokeFreeBrain project, funded by the European Commission H2020 program,⁷⁰ analyzed the success rate of various smoking cessation interventions in terms of health economics.⁷¹

The opportunity to help smokers, who mostly live in low- and middle-income countries, may lie within their public health systems.⁷² Incorporating smoking cessation into the assistance programs of health centers facilitates the successful integration of approaches.⁷² Undoubtedly, technology-enabled healthcare has a high potential to help improve outcomes in key-risk patients.⁷³ Computer-tailored interventions have proven to be effective in smoking cessation.^74–76 By achieving an increase in cessation rates, these approaches can contribute to reducing the incidence of diseases directly associated with smoking and those resulting from exposure to secondhand smoke.⁷⁷

Pare de Fumar Conosco should be linked, in the future, with counseling resources such as telephone calls and or text messages. Text messaging is a powerful resource in health education.⁷⁸ There is evidence that the potential for text messages to deliver smoking cessation treatment may be even more among hard-to-reach and socioeconomically disadvantaged populations,⁷⁹ like the one assessed in this study. Cruvinel et al. showed the feasibility of a hybrid telephone and text messaging counseling intervention among post-discharge hospitalized smokers in Brazil.⁸⁰ This hybrid intervention was well accepted by participants and resulted in noteworthy retention and cessation rates at 12 weeks.

The results can be generalized with caveats. We cannot eliminate differences in the implementation of the intervention. Standard of care and smoking cessation counseling groups follow Brazilian guidelines. Differences between what guidelines recommend and what healthcare centers implement can occur. Moreover, groups were not well balanced at baseline. Important variables such as depression screening, educational level, time to first cigarette, previous attempts to quit smoking, and the number of cigarettes daily were significantly different between groups. The sample size and type of block randomization generated by a statistical program can explain these differences. There is the possibility of favorable or unfavorable results because of the unbalanced allocation.⁸¹ Randomization did not eliminate but reduced biases in potential. By being performed by a list generated on the computer, the investigator, even unconsciously, would not influence the participants’ allocation in the treatment groups. Randomization enabled a valid static inference.⁸¹

Some factors may still have biased the study’s outcomes after multivariate adjustments. The lower education level (incomplete or no elementary school) may have caused difficulty understanding the information provided related to smoking cessation.^82,83 Smoking within the first five minutes after waking up, an indicator of high nicotine dependence, may interfere with the decision-making process and treatment uptake.^84,85

Depression could negatively affect the study results. This condition is related to heavier smoking, relapses, and difficulties for smoking cessation.⁸⁶ Pare de Fumar Conosco has obtained a level of user motivation, which we reiterated due to the engagement in the smoking cessation counseling group being two times higher for the group intervention.

This study had several limitations. First, we were not able to contact 13.5% (38/281) of eligible subjects. There is a need to further evaluate the barriers and facilitators in clinic settings to reach and recruit Brazilian smokers into cessation studies. Second, we did not assess nicotine dependence. So it remains unknown whether these results vary by nicotine dependence level. Third, follow-up was limited to 12 Weeks. Fourth, we did not use biomarkers to verify smoking status. Fifth, at the end of the treatment, we did not measure the motivation level. Sixth, we did not present the results of the Poisson model for smoking cessation in the 12th week. The number of participants who reached this outcome was small, making meta-regression difficult. Seventh, this study was conducted at two clinics with wireless internet capacity; many healthcare clinics in Brazil still lack high-speed internet connections, especially in rural areas.

Conclusions

The Pare de Fumar Conosco intervention effectively increases the engagement in the smoking cessation counseling groups among Brazilian smokers with chronic conditions. However, more research is needed to find innovative ways to enhance adherence to smoking cessation treatment and improve the cessation rate. Future studies should evaluate the integration of Pare de Fumar Conosco as part of routine care and its cost-effectiveness.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Foundation of the State of Minas Gerais [grant number FAPEMIG - CBB APQ 03603-13]; the Fogarty International Center at the National Institutes of Health [grant number R03TW008723]; and the Mineiro Researcher Program [grant number PPM-00129-18].

Trial registry

Brazil’s Registry of Clinical Trials; number RBR-6tjmw3 (https://ensaiosclinicos.gov.br/rg/RBR-6tjmw3).

Data accessibility

The clinical trial participants’ data used to support this study’s findings are available from the corresponding author upon request.

ORCID iD

Roberta da Silva Teixeira

References

Chandler

Rennard

. Smoking cessation. Chest 2010; 137(2): 431–435.

Instituto Nacional do Câncer. Agência de Notícias . Organização Mundial de Saúde: mais de 94% da população mundial está exposta ao fumo passivo, 2009. http://www2.inca.gov.br/wps/wcm/connect/agencianoticias/site/home/noticias/2009/oms_populacao_fufu_passivo (accessed 22 August 2018).

World Health Organization . Report on the global tobacco epidemic: the MPOWER package. Geneva: World Health Organization, 2008.

Wünsch Filho

Mirra

López

RVM

, et al. Tabagismo e câncer no Brasil: evidências e perspectivas. Rev Bras Epidemiol 2010; 13(2): 175–187.

Cavalcante

. Tobacco control in Brazil: advances and challenges. Rev Psiquiatr Clin 2005; 32: 283–300.

Instituto Brasileiro de Geografia e Estatística - IBGE. Pesquisa Nacional de Saúde . Percepção do estado de saúde, estilo de vida E doenças crônicas, 2013. Rio de Janeiro: IBGE, 2015.

Ministério da Saúde. Instituto Nacional de Câncer (INCA) - Coordenação de Prevenção e Vigilância (CONPREV). Prevenção e Vigilância (CONPREV) . Abordagem e Tratamento do Fumante - Consenso 2001. Rio de Janeiro: INCA, 2001.

Antal

Ramseier

Barabás

, et al. Current concepts in tobacco cessation and prevention. Fogorv Sz 2012; 105: 99–103.

Tsiapa

Gkiozos

Souliotis

, et al. Smoking cessation strategies in patients with lung disease. Vivo 2013; 27: 171–176.

10.

Manso

MEG

Silveira

RSM

Godoy

, et al. Programa para cessação de tabagismo em uma operadora de planos de saúde - relato de experiência. Rev Bras Med 2014; 71(5): 142–147.

11.

Ferreira

Cruz

Castro

LFR

, et al. Como aumentar a aderência à cessação do tabagismo em usuários com múltiplas condições crônicas. J Bras Pneumol 2015; 41(Suppl 2R): R69–R69.

12.

Hartmann-Boyce

Stead

Cahill

, et al. Efficacy of interventions to combat tobacco addiction: cochrane update of 2012 reviews. Addiction 2013; 108: 1711–1721.

13.

Barak

Hen

Boniel-Nissim

, et al. A comprehensive review and a meta-analysis of the effectiveness of Internet-based psychotherapeutic interventions. J Technol Hum Serv 2008; 26: 109–160.

14.

Muñoz

. Using evidence-based internet interventions to reduce health disparities worldwide. J Med Internet Res 2010; 12: e60.

15.

Shahab

McEwen

. Online support for smoking cessation: a systematic review of the literature. Addiction 2009; 104(11): 1792–1804.

16.

Munoz

Lenert

Delucchi

, et al. Toward evidence-based internet interventions: a Spanish/English web site for international smoking cessation trials. Nicotine Tob Res 2006; 8: 77–87.

17.

Patten

Croghan

Meis

, et al. Randomized clinical trial of an internet-based versus brief office intervention for adolescent smoking cessation. Patient Educ Couns 2006; 64: 249–258.

18.

McKay

Danaher

Seeley

, et al. Two web-based smoking cessation programs: randomized controlled trial. J Med Internet Res 2008; 10: e40.

19.

Pike

Rabius

McAlister

, et al. American cancer society’s quitlink: randomized trial of internet assistance. Nicotine Tob Res 2007; 9: 415–420.

20.

Swartz

Noell

Schroeder

, et al. A randomised control study of a fully automated internet based smoking cessation programme. Tob Control 2006; 15: 7–12.

21.

Cupertino

Richter

Sanderson

, et al. Feasibility of a Spanish/english computerized decision aid to facilitate smoking cessation efforts in underserved community. J Health Care Poor Underserved 2010; 21(2): 504–517.

22.

Willemsen

Wiebing

van Emst

, et al. Helping smokers to decide on the use of efficacious smoking cessation methods: a randomized controlled trial of a decision aid. Addiction 2006; 101: 441–449.

23.

Brunette

Ferron

McHugo

, et al. An electronic decision support system to Motivate people with severe mental illnesses to quit smoking. Psychiatr Serv 2011; 62: 360–366.

24.

Brunette

Gunn

Alvarez

, et al. A pre-post pilot study of a brief, web-based intervention to engage disadvantaged smokers into cessation treatment. Addict Sci Clin Pract 2015; 10: 3.

25.

Cupertino

Cartujano-Barrera Colugnati

FAB

, et al. Web-based decision-making tool for smoking cessation (Pare De Fumar Conosco) among patients with chronic conditions in Brazil: one-arm feasibility study. BMJ Health Care Inform 2020; 27(1): e100063.

26.

Schulz

Altman

Moher

, et al. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ 2010; 340: c332.

27.

Cupertino

Cartujano-Barrera

Perales

, et al. Vive Sin Tabaco ¡Decídete!” feasibility and acceptability of an e-health smoking cessation informed decision-making tool integrated in primary healthcare in Mexico. Telemed J E Health 2019; 25: 425–431.

28.

Ponciano-Rodríguez

Reynales-Shigematsu

Rodríguez-Bolaños

, et al. Enhancing smoking cessation in Mexico using an e-health tool in primary healthcare. Salud Publica Mex 2018; 60: 549–558.

29.

Cupertino

Cartujano-Barrera

Ramírez

, et al. A mobile smoking cessation intervention for Mexico (Vive sin Tabaco. ¡Decídete!): single-arm pilot study. JMIR Mhealth Uhealth 2019; 7: e12482.

30.

Rosemberg

. Nicotina droga universal. Rio de Janeiro: Instituto Nacional de Câncer, 2004.

31.

JTd

Pueyo

. A Adaptaçäo Ao Português Do Fagerström Test For Nicotine Dependence (FTND) Para Avaliar A Dependência E Tolerância À Nicotina Em Fumantes Brasileiros. Rev Bras Med 2002; 59: 73–80.

32.

Kroenke

Spitzer

Williams

. The patient health questionnaire-2: validity of a two item depression screener. Med Care 2003; 41(11): 1284–1292.

33.

Campos

Richter

Cupertino

, et al. Cigarette smoking among patients with chronic diseases. Int J Cardiol 2014; 174(3): 808–810.

34.

Galil

AGS

Ferreira

Campos

, et al. Cessação do tabagismo e adesão ao tratamento entre pacientes acompanhados em ambulatório de condições crônicas do SUS. Arq Bras Cardiol 2013; 101: 75.

35.

Harris

Taylor

Thielke

, et al. Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42(2): 377–381.

36.

Altman

Bland

. Statistics notes: absence of evidence is not evidence of absence. BMJ 1995; 311: 485.

37.

Gardner

Altman

. Confidence intervals rather than P values: estimation rather than hypothesis testing. Br Med J (Clin Res Ed) 1986; 292(6522): 746–750.

38.

Nelder

Wedderburn

RWM

. Generalized linear models. J R Stat Soc Ser A Stat Soc 1972; 135: 370–384.

39.

Agaku

King

Dube

, et al. Current cigarette smoking among adults - United States, 2005-2012. Morb Mortal Wkly Rep 2014; 63(2): 29–34.

40.

Bala

Strzeszynski

Topor-Madry

, et al. Mass media interventions for smoking cessation in adults. Cochrane Database Syst Rev 2013; 6(6): CD004704.

41.

Centers for Disease Control and Prevention (CDC) . Cigarette smoking among adults–United States, 2007. MMWR Morb Mortal Wkly Rep 2008; 57(45): 1221–1226.

42.

Chen

Madan

Welton

, et al. Effectiveness and cost-effectiveness of computer and other electronic aids for smoking cessation: a systematic review and network meta-analysis. Health Technol Assess 2012; 16(38): 1–205.

43.

Carrasco-Hernandez

Jódar-Sánchez

Núñez-Benjumea

, et al. A mobile health solution complementing psychopharmacology-supported smoking cessation: randomized controlled trial. JMIR Mhealth Uhealth 2020; 8(4): e17530.

44.

Esen

Soylem

Arica

, et al. Factors affecting success and abstinence within a smoking cessation clinic: a one-year follow-up study in Turkey. Tob Prev Cessat 2020; 6: 71–78.

45.

Sağlam

. Investigation of the results of a smoking cessation clinic and the factors associated with success. Turk J Med Sci 2012; 42(3): 515–522.

46.

Peña

Zagolin

Acuña

, et al. Factores asociados con el éxito de la terapia antitabaco en pacientes tratados con vareniclina: 10 años de experiencia en un programa multidisciplinario [Factors associated with success of a smoking cessation program]. Rev Med Chil 2016; 144(8): 965–971.

47.

Kanatsız

Başlılar

Şaylan

, et al. Evaluation of the factors affecting success rate and medical treatments in smoking cessation. Euras J Fam Med 2017; 6(2): 65–71.

48.

Myung

McDonnell

Kazinets

, et al. Effects of Web- and computer-based smoking cessation programs: meta-analysis of randomized controlled trials. Arch Intern Med 2009; 169(10): 929–937.

49.

Civljak

Sheikh

Stead

, et al. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev 2010; 9: CD007078.

50.

Taylor

GMJ

Dalili

Semwal

, et al. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev 2017; 9: Cd007078.

51.

Trujillo-Gómez

Díaz-Gete

Martín-Cantera

, et al. Intervention for smokers through new communication technologies: what perceptions do patients and healthcare professionals have? A qualitative study. PLoS One 2015; 10(9): e0137415.

52.

Graham

Carpenter

Cha

, et al. Systematic review and meta-analysis of Internet interventions for smoking cessation among adults. Subst Abuse Rehabil 2016; 7: 55–69.

53.

Puigdomènech

Trujillo-Gómez

Martín-Cantera

, et al Information and communications technologies for approaching smokers: a descriptive study in primary healthcare. BMC Public Health 2015; 15: 2.

54.

Japuntich

Piper

Leventhal

, et al. The effect of five smoking cessation pharmacotherapies on smoking cessation milestones. J Consult Clin Psychol 2011; 79(1): 34–42.

55.

Secades-Villa

Alonso-Perez

Garcia-Rodriguez

, et al. Effectiveness of three intensities of smoking cessation treatment in primary care. Percept Mot Skills 2009; 105: 747–758.

56.

Shiffman

Brockwell

Pillitteri

, et al. Individual differences in adoption of treatment for smoking cessation: demographic and smoking history characteristics. Drug Alcohol Depend 2008; 93: 121–131.

57.

Cahill

Stevens

Perera

, et al. Pharmacological interventions for smoking cessation: an overview and network meta-analysis. Cochrane Database Syst Rev 2013; 31(5): CD009329.

58.

Ortega

Vellisco

Márquez

, et al. Effectiveness of a cognitive orientation program with and without nicotine replacement therapy in stopping smoking in hospitalized patients. Arch Bronconeumol 2011; 47(1): 3–9.

59.

Rigotti

Clair

Munafò

, et al. Interventions for smoking cessation in hospitalised patients. Cochrane Database Syst Rev 2012; 16(5): CD001837.

60.

Galil

AGS

Ferreira

Silva

, et al. Evaluating smoking cessation among chronic disease patients within the public health system in Brazil. In: SRNT ANNUAL MEETING, Boston, United States, 13-16 March 2013, paper no. POS4-134, pp. 261. Massachusetts: SRNT.

61.

Hays

Leischow

Lawrence

, et al. Adherence to treatment for tobacco dependence: association with smoking abstinence and predictors of adherence. Nicotine Tob Res 2010; 12(6): 574–581.

62.

Ramos

Ripoll

Estrades

, et al. Effectiveness of intensive group and individual interventions for smoking cessation in primary health care settings: a randomized trial. BMC Public Health 2010; 23(10): 89–99.

63.

Shiffman

. Reflections on smoking relapse research. Drug Alcohol Rev 2006; 25(1): 15–20.

64.

Borrayo

. Where’s Maria? A video to increase awareness about breast cancer and mammography screening among low-literacy Latinas. Prev Med 2004; 39(1): 99–110.

65.

Cassey

. Building a case for using technology: health literacy and patient education. Nurs Econ 2007; 25(3): 186–188.

66.

Tate

Finkelstein

Khavjou

, et al. Cost effectiveness of internet interventions: review and recommendations. Ann Behav Med 2009; 38(1): 40–45.

67.

Mistler

Brunette

Ferron

, et al. Shared decision making and behavioral support interventions for people with severe mental illness and tobacco dependence. J Dual Diagn 2012; 8(2): 99–103.

68.

Winston

Grendarova

Rabi

. Video-based patient decision aids: a scoping review. Patient Educ Couns 2018; 101(4): 558–578.

69.

Butt

. Measuring the benefits of decision aids for economic evaluation. Pharmacoecon Open 2019; 3(2): 143–150.

70.

Bamidis

Paraskevopoulos

Konstantinidis

, et al. Multimodal e-health services for smoking cessation and public health: the smokefreebrain project approach. Stud Health Technol Inform 2017; 245: 5–9.

71.

Smokefreebrain.eu . SmokeFreeBrain, 2017, http://smokefreebrain.eu/ (accessed 06 January 2022).

72.

Vardavas

Kyriakos

Fernández

, et al. H2020 funding for respiratory research: scaling up for the prevention and treatment of lung diseases. Eur Respir J 2019; 54(3): 1901417.

73.

Jacobs

Lou

Ownby

, et al. A systematic review of eHealth interventions to improve health literacy. Health Inform J 2016; 22(2): 81–98.

74.

Gilbert

Sutton

Morris

, et al. Effectiveness of personalized risk information and taster sessions to increase the uptake of smoking cessation services (Start2quit): a randomized controlled trial. Lancet 2017; 389(10071): 823–833.

75.

Abroms

Ahuja

Kodl

, et al. Text2Quit: results from a pilot test of a personalized, interactive mobile health smoking cessation program. J Health Comm 2012; 17(Suppl 1): 44–53.

76.

Te Poel

Bolman

Reubsaet

, et al. Efficacy of a single computer-tailored e-mail for smoking cessation: results after 6 months. Health Educ Res 2009; 24(6): 930–940.

77.

Hors-Fraile

Malwade

Spachos

, et al. A recommender system to quit smoking with mobile motivational messages: study protocol for a randomized controlled trial. Trials 2018; 19(1): 618.

78.

Speirs

Grutzmacher

Munger

, et al. Recruitment and retention in an SMS-based health education program: lessons learned from Text2BHealthy. Health Inform J 2016; 22(3): 651–658.

79.

Kong

Ells

Camenga

, et al. Text messaging-based smoking cessation intervention: a narrative review. Addict Behav 2014; 39: 907–917.

80.

Cruvinel

Richter

Colugnati

, et al. An experimental feasibility study of a hybrid telephone Counseling/Text messaging intervention for post-discharge cessation support among hospitalized smokers in Brazil. Nicotine Tob Res 2019; 21: 1700–1705.

81.

Aickin

. Randomization, balance, and the validity and e ciency of design-adaptive allocation methods. J Stat Plan Inference 2001; 94(1): 97–119.

82.

Nerín

Crucelaegui

Mas

, et al. Profile of smokers who seek treatment at a smoking cessation clinic. Arch Bronconeumol 2003; 39(7): 298–302.

83.

Siddiqui

Ogbeide

. Profile of smoking amongst health staff in a primary care unit at a general hospital in Riyadh, Saudi Arabia. Saudi Med J 2001; 22(12): 1101–1104.

84.

US Department of Health Services . Those who continue to smoke. Smoking and Tobacco Control Monograph. Bethesda: U.S. Department of Health Services, 2003.

85.

Fagerström

Furberg

. A comparison of the Fagerström Test for Nicotine Dependence and smoking prevalence across countries. Addiction 2008; 103: 841–845.

86.

Pisinger

Aadahl

Toft

, et al. Motives to quit smoking and reasons to relapse differ by socioeconomic status. Prev Med 2011; 52(1): 48–52.