Effectiveness of mobile applications to quit smoking: Systematic review and meta-analysis

Varenicline Combined With Oral Nicotine Replacement Therapy and Smartphone-Based Medication Reminders for Smoking Cessation: Feasibility Randomized Controlled Trial

BACKGROUND

Varenicline and oral nicotine replacement therapy (NRT) have each been shown to increase the likelihood of smoking cessation, but their combination has not been studied. In addition, smoking cessation medication adherence is often poor, thus, challenging the ability to evaluate medication efficacy.

OBJECTIVE

This study examined the effects of combined varenicline and oral NRT and smartphone medication reminders on pharmacotherapy adherence and smoking abstinence among adults enrolled in smoking cessation treatment.

METHODS

A 2×2 factorial design was used. Participants (N=34) were randomized to (1) varenicline + oral NRT (VAR+NRT) or varenicline alone (VAR) and (2) smartphone medication reminder messages (REM) or no reminder messages (NREM) over 13 weeks. Participants assigned to VAR+REM received varenicline reminder prompts, and those assigned to VAR+NRT+REM also received reminders to use oral NRT. The other 2 groups (VAR+NREM and VAR+NRT+NREM) did not receive medication reminders. Participants were not blinded to intervention groups. All participants received tobacco cessation counseling. Smartphone assessments of smoking as well as varenicline and NRT use (if applicable) were prompted daily through the first 12 weeks after a scheduled quit date. Descriptive statistics were generated to characterize the relations between medication and reminder group assignments with daily smoking, daily varenicline adherence, and daily quantity of oral NRT used. Participants completed follow-up assessments for 26 weeks after the quit date.

RESULTS

Participants were predominantly White (71%), and half were female (50%). On average, participants were 54.2 (SD 9.4) years of age, they smoked an average of 19.0 (SD 9.0) cigarettes per day and had smoked for 34.6 (SD 12.7) years. Descriptively, participants assigned to VAR+NRT reported more days of smoking abstinence compared to VAR (29.3 vs 26.3 days). Participants assigned to REM reported more days of smoking abstinence than those assigned to NREM (40.5 vs 21.8 days). Participants assigned to REM were adherent to varenicline on more days compared to those assigned to NREM (58.6 vs 40.5 days), and participants assigned to VAR were adherent to varenicline on more days than those assigned to VAR + NRT (50.7 vs 43.3 days). In the subsample of participants assigned to VAR+NRT, participants assigned to REM reported more days where ≥5 pieces of NRT were used than NREM (14.0 vs 7.4 days). Average overall medication adherence (assessed via the Medication Adherence Questionnaire) showed the same pattern as the daily smartphone-based adherence assessments.

CONCLUSIONS

Preliminary findings indicated that smoking cessation interventions may benefit from incorporating medication reminders and combining varenicline with oral NRT, though combining medications may be associated with poorer adherence. Further study is warranted.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03722966; https://classic.clinicaltrials.gov/ct2/show/NCT03722966.

Non-pharmacological interventions for smoking cessation: analysis of systematic reviews and meta-analyses

BACKGROUND

Although non-pharmacological smoking cessation measures have been widely used among smokers, current research evidence on the effects of smoking cessation is inconsistent and of mixed quality. Moreover, there is a lack of comprehensive evidence synthesis. This study seeks to systematically identify, describe, and evaluate the available evidence for non-pharmacological interventions in smoking populations through evidence mapping (EM), and to search for best-practice smoking cessation programs.

METHODS

A comprehensive search for relevant studies published from the establishment of the library to January 8, 2023, was conducted in PubMed, Web of Science, Embase, the Cochrane Library, CNKI, CBM, Wan Fang, and VIP. Two authors independently assessed eligibility and extracted data. The PRISMA statement and AMSTAR 2 tool were used to evaluate the report quality and methodology quality of systematic reviews/meta-analyses (SRs/MAs), respectively. Bubble plots were utilized to display information, such as the study population, intervention type, evidence quality, and original study sample size.

RESULTS

A total of 145 SRs/MAs regarding non-pharmacological interventions for smoking cessation were investigated, with 20 types of interventions identified. The most commonly used interventions were cognitive behaviour education (n = 32, 22.07%), professional counselling (n = 20, 13.79%), and non-nicotine electronic cigarettes (e-cigarettes) (n = 13, 8.97%). Among them, counselling and behavioural support can improve smoking cessation rates, but the effect varies depending on the characteristics of the support provided. These findings are consistent with previous SRs/MAs. The general population (n = 108, 74.48%) was the main cohort included in the SRs/MAs. The total score of PRISMA for the quality of the reports ranged from 8 to 27, and 13 studies (8.97%) were rated as high confidence, and nine studies (6.21%) as moderate confidence, in the AMSTAR 2 confidence rating.

CONCLUSIONS

The abstinence effect of cognitive behaviour education and money incentive intervention has advantages, and non-nicotine e-cigarettes appear to help some smokers transition to less harmful replacement tools. However, the methodological shortcomings of SRs/MAs should be considered. Therefore, to better guide future practice in the field of non-pharmacological smoking cessation, it is essential to improve the methodological quality of SRs and carry out high-quality randomized controlled trials (RCTs).

Use of the Smoking Cessation App Ex-Smokers iCoach and Associations With Smoking-Related Outcomes Over Time in a Large Sample of European Smokers: Retrospective Observational Study

BACKGROUND

Digital interventions are increasingly used to support smoking cessation. Ex-smokers iCoach was a widely available app for smoking cessation used by 404,551 European smokers between June 15, 2011, and June 21, 2013. This provides a unique opportunity to investigate the uptake of a freely available digital smoking cessation intervention and its effects on smoking-related outcomes.

OBJECTIVE

We aimed to investigate whether there were distinct trajectories of iCoach use, examine which baseline characteristics were associated with user groups (based on the intensity of use), and assess if and how these groups were associated with smoking-related outcomes.

METHODS

Analyses were performed using data from iCoach users registered between June 15, 2011, and June 21, 2013. Smoking-related data were collected at baseline and every 3 months thereafter, with a maximum of 8 follow-ups. First, group-based modeling was applied to detect distinct trajectories of app use. This was performed in a subset of steady users who had completed at least 1 follow-up measurement. Second, ordinal logistic regression was used to assess the baseline characteristics that were associated with user group membership. Finally, generalized estimating equations were used to examine the association between the user groups and smoking status, quitting stage, and self-efficacy over time.

RESULTS

Of the 311,567 iCoach users, a subset of 26,785 (8.6%) steady iCoach users were identified and categorized into 4 distinct user groups: low (n=17,422, 65.04%), mild (n=4088, 15.26%), moderate (n=4415, 16.48%), and intensive (n=860, 3.21%) users. Older users and users who found it important to quit smoking had higher odds of more intensive app use, whereas men, employed users, heavy smokers, and users with higher self-efficacy scores had lower odds of more intensive app use. User groups were significantly associated with subsequent smoking status, quitting stage, and self-efficacy over time. For all groups, over time, the probability of being a smoker decreased, whereas the probability of being in an improved quitting stage increased, as did the self-efficacy to quit smoking. For all outcomes, the greatest change was observed between baseline and the first follow-up at 3 months. In the intensive user group, the greatest change was seen between baseline and the 9-month follow-up, with the observed change declining gradually in moderate, mild, and low users.

CONCLUSIONS

In the subset of steady iCoach users, more intensive app use was associated with higher smoking cessation rates, increased quitting stage, and higher self-efficacy to quit smoking over time. These users seemed to benefit most from the app in the first 3 months of use. Women and older users were more likely to use the app more intensively. Additionally, users who found quitting difficult used the iCoach app more intensively and grew more confident in their ability to quit over time.

Smokers' user experience of smoking cessation apps: A systematic review

OBJECTIVES

To explore how smokers view common functions and characteristics of smoking cessation apps.

DESIGN

Systematic review.

SEARCH SOURCES

CINAHL PLUS, MEDLINE, PsycINFO, EMBASE, IEEE Xplore, ACM Digital Library, and Google Scholar.

REVIEW METHODS

Seven digital databases were searched separately using relevant search terms. Search results were uploaded to Covidence. Inclusion and exclusion criteria were identified with the expert team in advance. Titles, abstracts, and full texts were screened by two reviewers independently. Any disagreements were discussed in research meetings. Pertinent data were extracted and analysed using qualitative content analysis. Findings were presented in a narrative approach.

RESULTS

28 studies were included in this review. The overarching themes were app functionality and app characteristics. Under app "functionality", six subthemes emerged: 1) education; 2) tracking; 3) social support; 4) compensation; 5) distraction, and 6) reminding. Under "app characteristics", five subthemes emerged: 1) simplification, 2) personalisation, 3) diverse content forms, 4) interactivity, and 5) privacy and security.

CONCLUSION

Understanding user needs and expectations is crucial for developing a programme theory for smoking cessation app interventions. Relevant needs identified in this review should be linked to broader theories of smoking cessation and app-based intervention.

Effectiveness of a Conversational Chatbot (Dejal@bot) for the Adult Population to Quit Smoking: Pragmatic, Multicenter, Controlled, Randomized Clinical Trial in Primary Care

Background

Tobacco addiction is the leading cause of preventable morbidity and mortality worldwide, but only 1 in 20 cessation attempts is supervised by a health professional. The potential advantages of mobile health (mHealth) can circumvent this problem and facilitate tobacco cessation interventions for public health systems. Given its easy scalability to large populations and great potential, chatbots are a potentially useful complement to usual treatment.

Objective

This study aims to assess the effectiveness of an evidence-based intervention to quit smoking via a chatbot in smartphones compared with usual clinical practice in primary care.

Methods

This is a pragmatic, multicenter, controlled, and randomized clinical trial involving 34 primary health care centers within the Madrid Health Service (Spain). Smokers over the age of 18 years who attended on-site consultation and accepted help to quit tobacco were recruited by their doctor or nurse and randomly allocated to receive usual care (control group [CG]) or an evidence-based chatbot intervention (intervention group [IG]). The interventions in both arms were based on the 5A’s (ie, Ask, Advise, Assess, Assist, and Arrange) in the US Clinical Practice Guideline, which combines behavioral and pharmacological treatments and is structured in several follow-up appointments. The primary outcome was continuous abstinence from smoking that was biochemically validated after 6 months by the collaborators. The outcome analysis was blinded to allocation of patients, although participants were unblinded to group assignment. An intention-to-treat analysis, using the baseline-observation-carried-forward approach for missing data, and logistic regression models with robust estimators were employed for assessing the primary outcomes.

Results

The trial was conducted between October 1, 2018, and March 31, 2019. The sample included 513 patients (242 in the IG and 271 in the CG), with an average age of 49.8 (SD 10.82) years and gender ratio of 59.3% (304/513) women and 40.7% (209/513) men. Of them, 232 patients (45.2%) completed the follow-up, 104/242 (42.9%) in the IG and 128/271 (47.2%) in the CG. In the intention-to-treat analysis, the biochemically validated abstinence rate at 6 months was higher in the IG (63/242, 26%) compared with that in the CG (51/271, 18.8%; odds ratio 1.52, 95% CI 1.00-2.31; P=.05). After adjusting for basal CO-oximetry and bupropion intake, no substantial changes were observed (odds ratio 1.52, 95% CI 0.99-2.33; P=.05; pseudo-R²=0.045). In the IG, 61.2% (148/242) of users accessed the chatbot, average chatbot-patient interaction time was 121 (95% CI 121.1-140.0) minutes, and average number of contacts was 45.56 (SD 36.32).

Conclusions

A treatment including a chatbot for helping with tobacco cessation was more effective than usual clinical practice in primary care. However, this outcome was at the limit of statistical significance, and therefore these promising results must be interpreted with caution.

Trial Registration

Clinicaltrials.gov NCT 03445507; https://tinyurl.com/mrnfcmtd

International Registered Report Identifier (IRRID)

RR2-10.1186/s12911-019-0972-z