Effectiveness of the Internet-based Versus Face-to-Face Interaction on Reduction of Tobacco use among Adults: A Systematic Review and Meta-Analysis

Literature reported the effectiveness of internet-based interventions over face-to-face interaction on tobacco quitting; however, limited sample size reinforces to integrate and analyze these studies' ndings to reach a single conclusion. Therefore, we evaluated the effectiveness of the internet-based versus face-to-face interventions on reducing tobacco use with a systematic search through various electronic databases such as Medline, PsychInfo, PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), ResearchGate, Google Scholar, and Academia. Reference lists of the eligible articles were also screened. Full-text articles were included as per eligibility criteria (PICO framework). A total of 13 studies were selected for meta-analysis, with 3852 and 3908 participants in intervention and control groups. Forest plot favours the intervention group at one month follow up for tobacco quitting (OR: 2.37, CI: 1.86–3.02, P-0.00001, I 2 = 0%), at three months (OR: 1.88, CI: 1.48–2.40, P-0.00001, I 2 = 42%) at six months (OR: 2.02, CI: 1.64–2.50, P-0.00001, I 2 = 38%) and at 1 year of follow-up (OR: 1.43, CI: 1.18–1.74, P-0.00001, I 2 = 36%). Internet and web-based interventions are highly useful in tobacco quitting at one month, three months, six months, and one year of follow-up compared to face-to-face interaction or no intervention, although the evidence level was moderate.


Introduction
Tobacco consumption is the leading cause of avertible and premature deaths worldwide. [1,2] Burden of tobacco-related disease is increasing in developed and developing countries as well. [3] Interestingly, the deaths are declining in developed countries, and the burden is shifting to developing countries. [4] However, tobacco consumption pattern varies across gender; male vs. female, domicile; rural vs. urban, regions, cultural practices, and family income. [5] Men are more frequently (23%) indulging in tobacco use than their counterparts (3%). [6] Quitting any form of smoking is challenging and involves physiological, psychological, and many other factors, including social and environmental milieu. [7,8] to become successful. In the case of smoking cessation, the best use of positive and negative reinforcements helps alleviate the withdrawal symptoms, also the role of behavioral approaches in tobacco cessation cannot be denied. [9,10] Over the years, many innovative forms of internet-based approaches have been tried to quit tobacco use globally. The use of health communication and internet-based interventions, [11,12] tailored computerized programs, [13] text messages, [14] mobile or telephone, and WhatsApp for reminder or call, [15][16][17][18] app-based intervention, [19] chat-based instant messaging, [20] video assistance using the website and mobile [8] and use of social media, [21] has been vividly used in recent decades to quit smoking among different age groups. Although there is ample research and data regarding the potential in uence of the media, [22] face to face health education, [23] cognitive behavior therapy, [5,23] motivational in uences, [6] nurses assisted counseling, [24,25] on behavioral changes among smokers, there are scanty reports on the internet use or behavioral interventions. They are neither planned nor conducted rigorously to indicate rm evidence of any encouraging effects on health outcomes. [26] Interestingly, internet use and other electronic platforms are abundantly present in this era and have almost become part and parcel of the health care system. [26] A medical expert with just a computer device and internet access, and some necessary handling skills can reach many people and communicate inexpensively. Though the effectiveness of internet-based and face-to-face interventions on quitting smoking are very well reported in the literature, every study carries one or another limitation in methodology and limited sample size. Therefore, it is required to integrate and analyze these studies' ndings to reach a single conclusion. This study was planned to assess the effectiveness of the internet versus face-to-face interactions on reducing tobacco use among adults.
Aim-To study the effectiveness of the internet versus face-to-face interventions on reducing tobacco use among adults.

Results
A systematic search was done by two reviewers independently. PRISMA ow chart displays all the steps followed in inclusion and exclusion of studies. [27] (Figure 1) Characteristics of included studies, including the author's name with publication year, country, sample size, the mean age of participants, male to female ratio, baseline tobacco consumption, and follow-up period after the intervention, have been mentioned.

Risk of bias assessment
Risk of bias has been assessed and created a risk of bias graph, and summary of included studies under the heads of selection bias, performance bias, detection bias, attrition bias, reporting bias, and any other bias observed across the studies. [28] It depicts that there was no serious risk of bias across the studies (Figure 2). RevMan les were exported to the GRADE Pro ler to assess the quality of studies and create a "Summary of Findings" table. [29] (Table 2) Data analysis Tobacco quitting among participants has been analyzed at one, three, six, and twelve months of follow-up and presented in a forest plot (Figure 3). The Funnel plots have also been created to assess the publication bias across studies. It measures an effect estimate against its standard error for an outcome (Figure 4).  Tobacco quitting outcome A total of 13 articles were found suitable for meta-analysis, with 3852 and 3908 participants in intervention and control groups. [30][31][32][33][34][35][36][37][38][39][40][41][42] All studies revealed data with a sample size ranging from 160 [32] to 2159 [39] . Baseline characteristics of included studies have respectively. [37,40] Calhoun et al. had the majority of male participants in the intervention (85%) and control group (84 %). [34] Two studies measured the outcome at four steps: one, three, six months, and one year. [30,312] Two studies followed up the participants only for one month. [39,42] Two studies measured the outcome at six months only. [32,36] Calhoun et al. measured the outcome at three months and twelve months of internet intervention and telehealth medication clinic unite with a telehealth medication clinic for nicotine replacement therapy. [34] Even four studies assessed the outcome of tobacco use at one year of different web or internet-based intervention. [33,35,38. 40] Subgroup analysis with tobacco quitting outcomes at one, three, six months, and one-year follow-up further lowers the heterogeneity across studies. Sensitivity analysis was done to nd the better result with a random effect model. We observed similar results with the random effect model also. Pike KJ et al. have been removed from the analysis due to the vast difference in the number of participants in both groups, creating heterogeneity. [43] The Forest plot favors the intervention group (OR: 2.37, CI: 1.86-3.02, P-0.00001, I 2 =0%) in comparison to the control group for quitting tobacco at a one-month follow-up ( Figure 3).
The Forest plot also favors the intervention group compared to the control group (OR: 1.88, CI: 1.48-2.40, P-0.00001, I 2 =42%) for quitting tobacco at three months follow up (Figure 3).
The Forest plot also favors the intervention group compared to the control group (OR: 2.02, CI: 1.64-2.50, P-0.00001, I 2 =38%) for quitting tobacco at six months follow up (Figure 3).
The Forest plot also favors the intervention group compared to the control group (OR: 1.43, CI: 1.18-1.74, P-0.00001, I 2 = 36%) for quitting tobacco at one year follow up ( Figure 3).
The forest plot suggests signi cantly higher tobacco quitting events in the internet intervention group at one, three, six, and twelve months of participants' follow-up with moderate heterogeneity across the studies.

Discussion
Over the year, many innovative forms of internet-based approaches [11] tailored computerized programs, [13] text messages, [14] mobile or telephone, and WhatsApp for reminder or call, [15][16][17][18] app-based intervention, [19] chat-based instant messaging, [20] video assistance using the website and mobile [8] and use of social media, [21] have been practiced commonly to quit tobacco in different age group population. Although, various methodological issues reduce the ability to esti mate the effects of internet-based approaches.
This study evaluated the impact of the internet approaches versus face-to-face interaction on reducing tobacco use in the adult population. Results suggestive of signi cantly higher tobacco quitting events in the internet intervention group than the control group at one month, three months, six months, and one year of follow-up of participants with moderate heterogeneity across the studies. Happy ending, a digital multi-media smoking cessation intervention consists of more than 400 contacts through emails, interactive voice responses, Web pages, and short message service compared with self-help booklet, reported higher point abstinence rates in the treatment group in the long-term effect of the intervention. [30,31] A written list of internet resources for smoking cessation was found more helpful than written self-help material to quit smoking for a long-term period of one year. [33] Internet-based self-help smoking cessation program, interactive, individual advice, multiple computer-tailored smoking cessation internet interventions, and a video-based internet site presented strategies for motivational materials and smoking cessation found no effect at six months of intervention but the signi cant effect at 12 months of follow up. [38,40,41,42] Personalized smoking cessation through an online life magazine in the young population enhanced smoking cessation at the end of 12 months. [37] Internet use and telehealth medication clinic combined with a telehealth medication clinic for nicotine replacement therapy reported no signi cant difference (17% vs. 12%) in comparison to clinical-based smoking cessation after three months of intervention. [34] However, Burford O et al. compared a computer-generated photoaging intervention with no treatment group and reported a higher (27.5%) incidence of smoking quit than the control group (6.3%) at six months follow up. [32] Rabius V et al. reported the follow-up response rate as 38%, and Feil EG et al. achieved 50% responses from participants with monetary incentives. [44,45] Findings were also reinforced by the researchers that the participants' loss inevitably in uences research on the internet for health purposes. [46,47] After the quit attempts, web-based interventions could be more effective in preventing relapse in the long term, which requires adherence to the intervention for its effectiveness. [35] Additionally, an approach to a website supporting smoking abstinence is not related to smoking cessation. [36] Civljak M et al.
reported the strong effect of uniting tailored materials with nicotine replacement therapy on tobacco cessation and a signi cant positive impact of tailored materials among pre-contemplators. [48] Previous studies compared the tailored to untailored web-based intervention; few studies have observed that tailored web-based intervention is equally e cacious to untailored intervention [49][50][51][52][53][54], while others have found the tailored intervention to be better in comparison to untailored one. [39,55,56] The internet services should be based on their preference and easily accessible to those who want to quit smoking and seek related information through the internet and utilize the internet services. [57] Presently, internet interventions' incremental cost is less than other modalities, facilitating and evaluating online programs for effectiveness. [58] Online interventions also can access smokers and support them in quitting tobacco, which is also rmly associated with the total and physical quality of life among adults. [58,59] Despite variations in trials, this meta-analysis adds to the evidence for the promising approach of the internet-based intervention in modifying behavior, reducing tobacco use, and enhancing positive health practices among adults.

Strength and limitations
Subgroup analysis explored and discussed the possibility of tobacco quitting in the adult population at different time points. Sensitivity analysis strengthened the evidence by exploring possible alternate ndings.
Although there was lack of uniformity of internet-based approaches in included trials, they had different internet approaches, which have also been discussed. (Table 1) The risk of included bias in the individual trial also contributed towards the limitation of metaanalysis. (Figure 2)

Conclusion
This meta-analysis pooled the data of randomized controlled trials with limited sample size. It winded up that internet use is highly effective in tobacco quitting at one, three, six, and twelve months of follow-up of participants compared to face-to-face intervention or no intervention with moderate heterogeneity across the studies a moderate level of evidence to support the ndings. Further studies are required to explore internet interventions' durable adherence among the adult population who their spared maximum time with the internet in any form. Additionally, limited availability of trials in developing countries, arising need for research of internet use in developing countries to quit tobacco. Findings provide evidence to policymakers to utilize the internet as an effective instrument for tobacco control in their countries.

Data sources and search strategy
The electronic databases, such as Medline, Embase, PsychInfo, Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Google Scholar, Academia, and ResearchGate, were explored. Reference lists of the selected articles were also screened. All relevant studies available on the topic were included irrespective of time duration. The systematic search was restricted to studies published in the English language. The keywords were "smoker or smokers OR smoking," "tobacco" OR cigarette OR nicotine OR smoking cessation OR "tobacco consumption OR cessation, OR abstain* OR quit* OR stop* OR computer OR computer-aided design, OR internet, OR computer, OR networks, OR media, OR cellular phone OR mobile, OR text OR message* OR SMS, OR web, OR electronic mail OR Chat, OR video recording.

Eligibility criteria PICO Framework
Participants Inclusion: Adults aged more than 18 years use the internet or face-to-face interventions to reduce or quit tobacco use. No ethnicity restrictions were applied.
Exclusion: Cochrane studies that compare the internet to face-to-face interventions with other interventions.

Intervention
Internet interventions (Phone, mobile, WhatsApp, Facebook, Online network group, Online Support group, text messaging, other internet media) Comparator Face-to-face interventions or no intervention in the comparator group.
Face-to-face interventions include counseling, cognitive behavior therapy, or health education forms with control or routine care.

Outcome
Post-intervention tobacco quitting -number of participants quitting tobacco after the intervention (internet use)

Study design
Randomized controlled trials

Time frame
No restriction to the time frame was applied.

Screening of eligible studies
A systematic search was done by two reviewers independently. After searching, studies were screened with titles and abstracts of respective studies. All selected studies were imported to Rayyan (https://rayyan.qcri.org), a free web-based software. [60] Two reviewers screened the full text of articles based on eligibility criteria determined as per review protocol. Any relevant discrepancy has been resolved by consensus with the help of a third reviewer. We adhered to the guidelines of Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2009. [28] (Additional File. 1)

Data extraction
Two reviewers extracted the data from the full text of eligible studies. Corresponding authors of included studies were contacted for the relevant data. Data excel sheet was prepared to note the characteristics of selected studies. It includes the author's name with publication year, country, sample size, participants' mean age, male to female ratio, baseline tobacco consumption, and follow-up period after the intervention (Table 1).
Eligible studies were exported to RevMan software for data analysis. Forest plots have been created to present the results with Odds ratio (OR), con dence interval (CI), and effect size.

Risk of bias assessment
Two reviewers independently assessed the quality of included studies. Risk of bias graph and summary has been created in Review Manager software 5.4 version under the heads of Random sequence generation (selection bias), Blinding of participants and personnel (performance bias), Allocation concealment (selection bias), Blinding of outcome assessment, (detection bias), Incomplete outcome data (attrition bias), Selective reporting (reporting bias), and Other bias (Figure 2). [29] The GRADE approach was also followed to explore the quality of evidence on high, moderate, and low levels. [30] RevMan les were exported to the GRADE Pro ler to assess the quality of studies and create a "Summary of Findings" table (Table 2).

Data analysis
Review Manager software 5.4 version was used for meta-analysis. [29] The xed-effects model and effect measures were calculated as the OR with p-value < 0.05 considered statistically signi cant. I 2 statistics with 25, 50, and 75 % were measured to compute statistical heterogeneity in low, moderate, and high grades. The tabulated data presented in a forest plot (Figure 3). Funnel plots have also been created to assess the publication bias across studies. It measures an effect estimate against its standard error for an outcome (Figure 4).
Tobacco quitting among participants has been analyzed at one, three, six, and twelve months of follow-up and presented in a forest plot (Figure 3).