Mobile phone text messaging and app‐based interventions for smoking cessation

Robyn Whittaker; Hayden McRobbie; Chris Bullen; Anthony Rodgers; Yulong Gu; Rosie Dobson

doi:10.1002/14651858.CD006611.pub5

. 2019 Oct 22;2019(10):CD006611. doi: 10.1002/14651858.CD006611.pub5

Mobile phone text messaging and app‐based interventions for smoking cessation

Robyn Whittaker ^1,^✉, Hayden McRobbie ², Chris Bullen ¹, Anthony Rodgers ³, Yulong Gu ⁴, Rosie Dobson ¹

Editor: Cochrane Tobacco Addiction Group

PMCID: PMC6804292 PMID: 31638271

Abstract

Background

Mobile phone‐based smoking cessation support (mCessation) offers the opportunity to provide behavioural support to those who cannot or do not want face‐to‐face support. In addition, mCessation can be automated and therefore provided affordably even in resource‐poor settings. This is an update of a Cochrane Review first published in 2006, and previously updated in 2009 and 2012.

Objectives

To determine whether mobile phone‐based smoking cessation interventions increase smoking cessation rates in people who smoke.

Search methods

For this update, we searched the Cochrane Tobacco Addiction Group's Specialised Register, along with clinicaltrials.gov and the ICTRP. The date of the most recent searches was 29 October 2018.

Selection criteria

Participants were smokers of any age. Eligible interventions were those testing any type of predominantly mobile phone‐based programme (such as text messages (or smartphone app) for smoking cessation. We included randomised controlled trials with smoking cessation outcomes reported at at least six‐month follow‐up.

Data collection and analysis

We used standard methodological procedures described in the Cochrane Handbook for Systematic Reviews of Interventions. We performed both study eligibility checks and data extraction in duplicate. We performed meta‐analyses of the most stringent measures of abstinence at six months' follow‐up or longer, using a Mantel‐Haenszel random‐effects method, pooling studies with similar interventions and similar comparators to calculate risk ratios (RR) and their corresponding 95% confidence intervals (CI). We conducted analyses including all randomised (with dropouts counted as still smoking) and complete cases only.

Main results

This review includes 26 studies (33,849 participants). Overall, we judged 13 studies to be at low risk of bias, three at high risk, and the remainder at unclear risk. Settings and recruitment procedures varied across studies, but most studies were conducted in high‐income countries. There was moderate‐certainty evidence, limited by inconsistency, that automated text messaging interventions were more effective than minimal smoking cessation support (RR 1.54, 95% CI 1.19 to 2.00; I² = 71%; 13 studies, 14,133 participants). There was also moderate‐certainty evidence, limited by imprecision, that text messaging added to other smoking cessation interventions was more effective than the other smoking cessation interventions alone (RR 1.59, 95% CI 1.09 to 2.33; I² = 0%, 4 studies, 997 participants). Two studies comparing text messaging with other smoking cessation interventions, and three studies comparing high‐ and low‐intensity messaging, did not show significant differences between groups (RR 0.92 95% CI 0.61 to 1.40; I² = 27%; 2 studies, 2238 participants; and RR 1.00, 95% CI 0.95 to 1.06; I² = 0%, 3 studies, 12,985 participants, respectively) but confidence intervals were wide in the former comparison. Five studies compared a smoking cessation smartphone app with lower‐intensity smoking cessation support (either a lower‐intensity app or non‐app minimal support). We pooled the evidence and deemed it to be of very low certainty due to inconsistency and serious imprecision. It provided no evidence that smartphone apps improved the likelihood of smoking cessation (RR 1.00, 95% CI 0.66 to 1.52; I² = 59%; 5 studies, 3079 participants). Other smartphone apps tested differed from the apps included in the analysis, as two used contingency management and one combined text messaging with an app, and so we did not pool them. Using complete case data as opposed to using data from all participants randomised did not substantially alter the findings.

Authors' conclusions

There is moderate‐certainty evidence that automated text message‐based smoking cessation interventions result in greater quit rates than minimal smoking cessation support. There is moderate‐certainty evidence of the benefit of text messaging interventions in addition to other smoking cessation support in comparison with that smoking cessation support alone. The evidence comparing smartphone apps with less intensive support was of very low certainty, and more randomised controlled trials are needed to test these interventions.

Plain language summary

Can programmes delivered by mobile phones help people to stop smoking?

Background

Tobacco smoking is a leading cause of preventable death. Mobile phones can be used to support people who want to quit smoking. In this review, we have focused on programmes that use text messages or smartphone apps to do so.

Search date

We searched for published and unpublished studies in October 2018.

Study characteristics

We included 26 randomised controlled studies (involving over 33,000 people) that compared smoking quit rates in people who received text messages or smartphone apps to help them quit, with people who did not receive these programmes. We were interested in studies that measured smoking for six months or longer.

Key results

We found that text messaging programmes may be effective in supporting people to quit, increasing quit rates by 50% to 60%. This was the case when they were compared to minimal support or were tested as an addition to other forms of stop‐smoking support. There was not enough evidence to determine the effect of smartphone apps.

Quality and completeness of the evidence

Most of the studies were of high quality, although three studies had high drop out rates. We are moderately confident in the results of the text messaging interventions, but there were some issues with unexplained differences between study findings and for some comparisons there was not much data. We have low confidence in the results concerning smartphone apps, and more studies are needed in this field.

Summary of findings

Background

Description of the condition

Tobacco remains one of the most important risk factors for poor health across the globe (IHME 2018). Many countries are looking for sustainable options for the provision of smoking cessation support on a large scale.

Description of the intervention

'mHealth' describes the use of mobile communications technologies and mobile phones to support health care. In this review, we are specifically interested in the use of text messaging and smartphone applications (apps) to support smoking cessation.

How the intervention might work

The benefits of mobile phone‐based smoking cessation support (mCessation) interventions are: the ease of use anywhere at any time; cost‐effective delivery and scalability to large populations, regardless of location; the ability to tailor messages to key user characteristics (such as age, sex, ethnicity); the ability to send time‐sensitive messages with an 'always on' device; the provision of content that can distract the user from cravings; and the ability to link the user with others for social support.

A key benefit of the use of mobile phones for health programmes is their widespread uptake in those areas where health services are not easily accessible or used. In 2018, the number of mobile phone subscriptions globally topped 8 billion, with the developing world now having more mobile phone subscriptions than population (population penetration of 102%; ITU 2018). There is evidence to suggest that people from lower socioeconomic groups may prefer mCessation interventions due to the greater feeling of control associated with the ability to decide when and where they engage with messages, and the perception of around‐the‐clock support (Boland 2017). Focusing mCessation efforts on the populations in greatest need, could help to address the health inequalities that come about from high use of tobacco and lack of accessible health promotion and prevention services in low‐resource settings globally.

Furthermore, initial research suggests that the use of text messaging for smoking cessation is cost effective. Guerriero 2013 found that the cost of text message‐based support was GBP 278 per quitter. When the future health service costs saved (as a result of smoking cessation) were included, with 0.5 quality‐adjusted life years (QALYs) gained per quitter, text‐based support was considered to be cost saving.

Why it is important to do this review

Smartphones (mobile phones with a computer operating system) are fast becoming the computer of choice, or at least the most accessible computer, in many countries. According to the International Telecommunications Union only 36.3% of low‐and middle‐income countries has a computer in the household, but 61% have mobile broadband subscriptions (allowing mobile phones to access to the Internet; ITU 2018). Therefore, it was important to update this review to include studies on the effectiveness of smartphone apps, as well as text messaging interventions, for smoking cessation.

Objectives

To determine whether mobile phone‐based smoking cessation interventions increase smoking cessation rates in people who smoke.

Methods

Criteria for considering studies for this review

Types of studies

Randomised or quasi‐randomised trials. Cluster‐randomised trials were eligible for inclusion.

Types of participants

People who smoked at study enrolment.

Types of interventions

We included studies that examined any intervention that could be considered predominantly a mobile phone‐based programme (such as text messaging or smartphone apps) for smoking cessation. We excluded interventions where mobile phones were seen as an adjunct to a predominantly face‐to‐face or Internet programme, such as to remind participants of appointments, or where the effects of the various components of a multi‐faceted programme could not be separated. We also excluded interventions that could be performed via any type of telephone such as telephone counselling. We did not exclude any studies based on comparator, but instead grouped studies by comparators in the analyses.

Types of outcome measures

The primary outcome was smoking abstinence at longest follow‐up, and at least six months from baseline. Where multiple measures were available, we preferred sustained abstinence to point prevalence abstinence, and biochemically validated results to self‐report.

There is no obvious risk of adverse events for text messaging or smartphone app interventions, and so we have not included this as an outcome in this review.

Search methods for identification of studies

For the present update of the review, we searched the Cochrane Tobacco Addiction Group's Specialised Register on 29 October 2018 using the terms 'mobile phone', 'cell phone', 'txt', 'pxt', 'sms', or 'mms' in the title, abstract or keyword fields. The Specialised Register includes reports of possible controlled trials of smoking cessation interventions identified from sensitive searches of databases. At the time of the search, the Register included the following results of searches

Cochrane Central Register of Controlled trials (CENTRAL; 2018, Issue 1)
MEDLINE (via Ovid, to 26 October 2018)
Embase (via Ovid, to 28 October 2018)
PsycINFO (via Ovid; to 22 October 2018)

See the Cochrane Tobacco Addiction Group website for full search strategies and a list of other resources searched. We also searched the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP; apps.who.int/trialsearch/) and ClinicalTrials.gov trials registers for ongoing or recently completed studies. We searched through the reference lists of identified studies for any additional eligible studies and attempted to contact the authors of ongoing studies.

We placed no restrictions on publication language or date.

Data collection and analysis

Selection of studies

The Cochrane Tobacco Addiction Group's Information Specialist ran the searches and provided the results. Two review authors (YG, HM) independently pre‐screened the titles and abstracts of records identified in duplicate to exclude reports that had no relevance to the topic and to provide a list of potentially relevant citations. A third reviewer (CB) resolved any differences in initial screening. Two review authors (from RW, YG, CB, RD) independently reviewed full‐text manuscripts in duplicate for the final eligibility screen.

We resolved any disagreements by discussion or by obtaining further information through contacting study authors. We recorded reasons for exclusion of studies in the Characteristics of excluded studies table. We contacted authors of unpublished, registered studies, which could potentially have been completed, to determine ongoing status or to request unpublished data.

Data extraction and management

We extracted the following methodological details from the included study reports and presented them in the Characteristics of included studies table. Two review authors (from RW, YG, RD, CB, HM) independently extracted data using the standardised Covidence data extraction form. A third review author provided a review of the quality assessment and a consensus check.

Funding source
Authors' declarations of interest
Country and context of the study
Study design
Number of participants
Age and other relevant recorded characteristics of study participants
Inclusion criteria
Exclusion criteria
Intervention details
Control details
Definition of abstinence outcome
Smoking cessation rates at six months (self‐reported abstinence or biochemically verified abstinence, or both)
Smoking cessation rates at final follow‐up (if follow‐up greater than six months and where these data were available)

Assessment of risk of bias in included studies

Two review authors (from RW, YG, RD, CB, HM) independently assessed the risk of bias for included studies, based on the guidance of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2017), and the Cochrane Tobacco Addiction Group. For each study, we assessed the following domains.

Random sequence generation
Allocation concealment
Blinding of outcome assessment
Incomplete outcome data
Other sources of bias

Specific 'Risk of bias' guidance developed by the Cochrane Tobacco Addiction Group to assess smoking cessation studies states that performance bias (relating to the blinding of participants and providers) should not be assessed for behavioural interventions, as it is impossible to blind people to these types of interventions. We graded detection bias as low where there was biochemical verification of abstinence, or where abstinence was self‐reported with no difference in face‐to‐face contact between control and intervention arms. We considered bias due to incomplete outcome as low risk where numbers lost to follow‐up were clearly reported for each group, the overall loss was not greater than 50%, and the difference between groups was not greater than 20%, or sensitivity analysis showed that the direction of effect was not sensitive to different imputation methods for loss to follow‐up.

Each review author recorded information in study reports relevant to each relevant domain and then judged each domain as either at low, high, or unclear risk of bias. We resolved disagreements through discussion with a third review author.

Measures of treatment effect

We recorded the information below.

Smoking cessation rates at six months or longer using the most stringent measure available
Biochemically verified abstinence, where available

We calculated risk ratios (RR) and 95% confidence intervals (CI) for the smoking cessation outcome for each included study. We calculated outcomes on an intention‐to‐treat basis, including all participants randomised to a trial arm and assuming that participants lost to follow‐up had continued to smoke or relapsed.

Dealing with missing data

If we found any important study characteristics or outcome data to be missing, we followed up with study authors where possible.

Assessment of heterogeneity

In order to assess whether it was appropriate to pool studies and conduct meta‐analyses we assessed the characteristics of included studies to identify any clinical or methodological heterogeneity. Where we deemed studies homogeneous enough to be combined meaningfully, we conducted a meta‐analysis, and we assessed statistical heterogeneity using the I² statistic; we deemed an I² value greater than 50% to indicate substantial heterogeneity (Higgins 2003).

Assessment of reporting biases

We planned to use funnel plots to assess reporting bias for any comparisons where we identified and analysed abstinence rates from at least 10 studies. Only the 'text messaging versus minimal smoking cessation support' comparison met this criteria in this review; therefore a funnel plot was generated for this comparison only. Funnel plots illustrate the relationship between the effect estimates from individual studies against their size or precision. The greater the degree of asymmetry, the greater the risk of reporting bias.

Data synthesis

We conducted meta‐analyses of the included studies, using the Mantel‐Haenszel random‐effects method to pool RRs and 95% CIs calculated for the smoking abstinence outcome, across the following comparisons.

Text messaging versus minimal smoking cessation support (including standard self‐help materials, as is standard practice in the Cochrane Tobacco Addiction Group)
Text messaging in addition to another form of smoking cessation support
Text messaging versus other smoking cessation support
Higher‐ versus lower‐frequency text messaging
Smartphone app versus less intensive smoking cessation support

Where studies had multiple intervention arms relevant to a single meta‐analysis, we split control arm data to avoid double‐counting.

Subgroup analysis and investigation of heterogeneity

We carried out the following subgroup analyses.

We split the 'smartphone app versus less intensive smoking cessation support' comparison into two subgroups to reflect the different comparators used across studies; either minimal non‐app smoking cessation support (e.g. self‐help materials, information on existing stop‐smoking services) or a less intensive smartphone app.

Sensitivity analysis

We conducted the following sensitivity analyses.

We calculated pooled RRs and 95% CIs for all analyses using complete case data to calculate quit rates. People may drop out of studies for reasons other than still smoking, and these reasons may differ between groups. For example, people who successfully stop smoking may withdraw from receiving an intervention if the text messages remind them of smoking. Therefore, this analysis tests whether assuming that all people lost to follow‐up are smoking (as in our primary analyses of all participants randomised) is potentially biasing our results.
Removing any studies judged to be at high risk of bias from all comparisons
Removing the only cluster‐RCT (Haug 2013), as information was not available to adjust for any potential clustering effect
Removing the two studies carried out in a pregnant (Abroms 2017), or postnatal population (Yu 2017), as these populations differ substantially from those recruited in the other studies.

'Summary of findings' tables

Following standard Cochrane methods (Schünemann 2017), we created a 'Summary of findings' table for the primary outcome (smoking abstinence), for the following comparisons.

Text messaging versus minimal smoking cessation support
Text messaging in addition to other smoking cessation support
Smartphone app versus less intensive smoking cessation support

Also following standard Cochrane methodology (Schünemann 2017), we used the five GRADE considerations (risk of bias, inconsistency, imprecision, indirectness and publication bias) to assess the certainty of the body of evidence for the abstinence outcome for each comparison, and to draw conclusions about the certainty of evidence within the text of the review.

Results

Description of studies

See Characteristics of included studies; Characteristics of excluded studies; and Characteristics of ongoing studies for further details.

Results of the search

The previous version of this review (Whittaker 2016), included 12 studies (Abroms 2014; Bock 2013; Borland 2013; Ferguson 2015; Free 2009; Free 2011; Gritz 2013; Haug 2013; Naughton 2014; Rodgers 2005; Tseng 2017; Whittaker 2011). Gritz 2013 was excluded at this update, as their intervention (telephone counselling and help line) was significantly different from the other interventions included in this review. A telephone help line intervention does not need to be carried out using a mobile phone specifically. Therefore, 11 of the previously included studies were included at this update, as well as one previously 'ongoing' study that was changed to 'included' as the study is now complete and data was available (Danaher 2019).

For this update of our review, the new literature search identified 370 studies (Figure 1). Many were duplicates, or unrelated and were immediately excluded at the title and abstract screening phase. We screened the full‐text of 71 reports of 62 studies, excluding 16 studies, and leaving 14 new studies eligible for inclusion at this update (Abroms 2017; Alessi 2017; Augustson 2017; Baskerville 2018; BinDhim 2018; Chan 2015; Cobos‐Campos 2017; Garrison 2018; Herbec 2019; Liao 2018; Peiris 2019; Squiers 2017; Wilson 2016; Yu 2017). Data were supplied by the authors for two studies (Danaher 2019; Herbec 2019). Reasons for excluding studies included: intervention that was not predominantly a mobile phone programme; not a randomised controlled trial; relapse prevention only; or no abstinence outcome measured at ≥ 6 months follow‐up (see Characteristics of excluded studies table for further details).

We also identified 32 ongoing studies at this update. When added to the previously identified ongoing studies there was a total of 34 ongoing studies (for further details see the Characteristics of ongoing studies table).

Included studies

Context and participants

The settings and recruitment methods, and therefore the participants, varied considerably across studies. Where previously this review had included only studies from a small range of high‐income countries, the new studies included in this update provided greater variation in settings, including China (Augustson 2017; Chan 2015; Liao 2018).

Bock 2013 (USA) found usual in‐person recruitment methods slow and shifted to online recruitment methods during the study. Baskerville 2018 (Canada), Borland 2013 (Australia), Danaher 2019 (USA), Garrison 2018 (USA), Squiers 2017 (USA), Herbec 2019 (UK), and Abroms 2014 (USA) also used online recruitment via Internet advertisements. In Abroms 2014 this initially led to some fraudulent participants who were discovered and disqualified, and extra procedures were put in place to prevent this from happening again. Free 2009 and Free 2011 recruited via advertisements at UK primary care centres, smoking cessation clinics, pharmacies, newspapers, websites, bus billboards and on the radio in the UK, and Liao 2018 used similar advertising methods in China. Rodgers 2005 also used direct advertising via websites, email, and posters at tertiary institutions across New Zealand. Similarly Whittaker 2011 (New Zealand) used a wide range of advertising media, including Māori‐specific media, and targeted young people. Alessi 2017 recruited through email, flyers, and print advertisements and Ferguson 2015 (Australia) used advertisements in papers, radio, and Facebook. Abroms 2017 was embedded in the Text4Baby text message (three messages a week) health information programme for pregnant women in the USA. Women who had smoked at least one puff in the past two weeks were eligible to also receive Quit4Baby text messages (between one to eight messages a day) to support smoking cessation. Augustson 2017 recruited through Nokia Life Tools, a service providing more than 100 million users with tools pre‐installed on their Nokia mobile phones, in urban and rural areas of China’s Zhejiang, Heilongjiang and Shaanxi provinces. BinDhim 2018 recruited through the Apple App Store in several countries (Australia, Singapore, UK, USA). Participants were advised that by downloading the app they would be participating in a study. Chan 2015 recruited through a Quit and Win competition in Hong Kong that was promoted in shopping malls and other public areas. Wilson 2016 mailed letters to potential participants in the US Veterans Administration health system. Naughton 2014 was set in primary care practices in the UK with trained smoking cessation advisors providing smoking cessation advice; Cobos‐Campos 2017 in two health clinics in Spain with health advice provided by a doctor or nurse; and Tseng 2017 in large urban HIV clinics. Haug 2013 recruited in vocational schools and differed from the other studies by allowing the inclusion of occasional smokers (at least four cigarettes in the past month or at least one in the preceding week). Peiris 2019 (Australia) recruited via an Aboriginal Community Controlled Health Service, a regional community event, and the New South Wales Government telephone coaching service. Yu 2017 recruited in maternal‐child health centres in China after asking mothers about household second‐hand smoke exposure. The intervention included messages on both the harms of second‐hand smoke (to the mother and her husband) and additional messages to the husband to encourage quitting.

Four studies deliberately targeted young adults (Baskerville 2018 in Canada, Haug 2013 in Switzerland; Squiers 2017 in USA; Whittaker 2011 in New Zealand). Most studies had similar proportions of men and women or slightly more women than men. The exceptions were Abroms 2017, as the intervention was targeted at pregnant women (100% women), Wilson 2016, which recruited 89% male veterans, and the studies in China, where the rates of smoking in women are low (Chan 2015 > 80% men, Liao 2018 94.6% men, Yu 2017 100% men).

Intervention programmes

Text messaging

All studies tested automated text messaging interventions. Eighteen of the included studies used text messaging (SMS) as a central component of the intervention (Abroms 2014; Abroms 2017; Augustson 2017; Bock 2013; Borland 2013; Chan 2015; Cobos‐Campos 2017; Ferguson 2015; Free 2009; Free 2011; Haug 2013; Liao 2018; Naughton 2014; Rodgers 2005; Tseng 2017; Squiers 2017; Whittaker 2011; Yu 2017). Whittaker 2011 sent text messages containing links to theoretically driven video messages from 'ordinary' role models coping with quitting. Several studies paired text messages with in‐person visits or assessments (Bock 2013; Cobos‐Campos 2017; Haug 2013; Naughton 2014).

The text message interventions varied in length from one week (Chan 2015), to five weeks (Ferguson 2015), six weeks (Augustson 2017; Yu 2017), eight weeks (Bock 2013;Squiers 2017), three months (Abroms 2017; Haug 2013; Naughton 2014; Tseng 2017), and six months (Abroms 2014; Cobos‐Campos 2017; Free 2009; Free 2011; Liao 2018; Rodgers 2005; Whittaker 2011), or were variable (Borland 2013).

Eight studies did not state that text messages were tailored to the individual (Abroms 2017; Augustson 2017; Chan 2015; Cobos‐Campos 2017; Liao 2018; Tseng 2017; Squiers 2017; Yu 2017). In other studies using text messages, the degree of individual tailoring varied:

Abroms 2014 tailored messages to include first name, quit date, top three reasons for quitting, money saved by quitting, and use of quit‐smoking medications;
Bock 2013 and Haug 2013 tailored messages to the stage of readiness to quit;
Borland 2013's programme could be interacted with by reporting changes in smoking behavior (e.g. a quit attempt, relapse), so that appropriate stage‐specific messages could be sent;
Ferguson 2015 tailored their intervention text messages to contain advice and encouragement tailored to participants' current quit status (preparing to quit, first week of the quit attempt, second week of attempt etc.)
Free 2009 and Free 2011 tailored the messages to information collected at baseline about the individual;
Naughton 2014 individually tailored messages using 24 items from the iQuit questionnaire and information on smoking status at three and seven weeks;
Rodgers 2005 matched participant characteristics to messages by keyword to create an individualised programme;
Whittaker 2011's participants selected the role model from whom they wished to receive messages.

A number of text messaging interventions included interactive components such as:

the ability to text for more support in the instance of cravings or lapses (Abroms 2014; Bock 2013; Free 2011; Liao 2018; Naughton 2014; Rodgers 2005);
an optional Quit Buddy in Rodgers 2005 and Free 2011;
a Quit support network in Bock 2013;
polls and quizzes (Rodgers 2005);
regular checking in on smoking status (Haug 2013).

Borland 2013 was the only study to include some degree of choice. Participants received offers of support via a personalised tailored Internet programme, a text message programme, both programmes, a choice of all three, or a minimal control. For the purposes of meta‐analyses, we compared the text message group with the control group.

Some of the included interventions were somewhat related to each other. The text messaging intervention in Rodgers 2005 was developed in New Zealand, and later adapted and tested in a UK pilot study (Free 2009), and then a large randomised controlled study (Free 2011). The intervention in Abroms 2017 was developed for pregnant women from the same group’s previous intervention for adult smokers in Abroms 2014. The Augustson 2017 intervention in China was adapted from the smoke‐free text programme that was evaluated in Squiers 2017. For further details of the messaging interventions across individual studies see the Characteristics of included studies table.

The control conditions used in the text message studies could be categorised into four groups.

Minimal smoking cessation support (13 studies): the control programmes across the studies in this category varied from no smoking cessation support (Haug 2013; Yu 2017), to non‐smoking‐related text messages sent two‐weekly (Free 2009; Free 2011; Rodgers 2005; Whittaker 2011), or weekly (Liao 2018), to written or Internet untailored materials (Abroms 2014; Chan 2015; Ferguson 2015), to links to smoking cessation support (Borland 2013; Rodgers 2005), or regular general health advice provided by a clinician (Cobos‐Campos 2017). Abroms 2017's control group participants received standard non‐smoking‐related Text4Baby text messages (three a week) without the extra smoking cessation‐related Quit4Baby text messages.
Another form of smoking cessation support (matched to support received by the intervention group, but without the text messaging intervention; four studies): support varied across studies and included a single session of smoking cessation counselling plus non‐smoking‐related text messages (Bock 2013); smoking cessation behavioural support and pharmacotherapy (Naughton 2014), and behavioural support and pharmacotherapy (Tseng 2017). Participants in the comparison arm of Yu 2017 received in‐person counselling and materials on establishing a smoke‐free home.
Another form of smoking cessation support (not matched in the intervention arm; two studies): an Internet‐based interactive smoking cessation programme (Borland 2013), and a five‐minute smoking cessation counselling session (Chan 2015).
Higher‐ versus lower‐frequency text messaging. Three studies examined the effect of higher‐ versus lower‐frequency text messages (Augustson 2017; Liao 2018; Squiers 2017). In Augustson 2017 this was comparing 91 messages over six weeks (three a day initially, followed by two a day, then one a day), with one text message a week for six weeks. In Liao 2018 this was three to five messages per day compared with three to five messages per week. Squiers 2017 compared smoking assessment and quit date messages only, with those messages plus motivational preparatory messages for two weeks prior to quitting, and with all of those messages plus six weeks of follow‐up post‐quit messages.

Smartphone apps

Five studies tested the effectiveness of smoking cessation smartphone apps alone (Baskerville 2018; BinDhim 2018; Garrison 2018; Herbec 2019; Peiris 2019). These apps varied considerably in intervention content and components. The app in Baskerville 2018 was described as comprehensive and evidence‐informed, including components such as a quit plan, contingency reinforcement, a link to an online Facebook community, supportive messages through the app, web‐based distraction, information and performance feedback, access to evidence‐based cessation services. BinDhim 2018 described their app as a decision aid (based on the Ottawa Decision Support Framework drawing from a number of psychological and behavioural theories) with additional support with push notifications, messages, diary and benefits tracker. The Garrison 2018 app training modules taught mindfulness for smoking cessation and how to work through cravings. Herbec 2019 included craving management tools within an app that supported smokers to be smoke free for 28 days.

The control conditions used in these smoking cessation app studies could be categorised into two groups:

minimal non‐app smoking cessation support that included: a printed self‐help guide (Baskerville 2018), and encouragement to access available smoking cessation services (Peiris 2019);
less intensive app support that included an app that provided only basic information. In BinDhim 2018 this included information only on quitting (no structured process or support). Garrison 2018 delivered experience sampling to query smoking, craving, and mindfulness in real time, and the control app in Herbec 2019 was designed to be a minimally credible intervention that resembled the intervention but without key intervention components.

Carbon monoxide (CO) monitoring and contingency management

Alessi 2017 and Wilson 2016 used mobile phone technology slightly differently to the above studies, by specifically using mobile phones to monitor the concentration of carbon monoxide in end‐expiratory air (CO levels). In Alessi 2017 interactive voice response calls would prompt the participant to conduct a CO test using a CO monitor. This was video recorded on the mobile phone and submitted using multimedia messaging. The CO result was provided via interactive voice response call. In the reinforcement arm of the trial, this was supplemented by negative CO test results (not smoking), which were rewarded with chances to win prizes. Therefore, the study had two arms that received mHealth CO monitoring as well as counselling and nicotine replacement therapy (NRT), with one of the arms also receiving rewards for smoking abstinence. Wilson 2016 combined cognitive behavioural telephone counselling and access to NRT with a mobile app for CO monitoring and contingency management in one study arm, and compared this to the same intervention without the CO monitoring and contingency management app. Participants provided CO readings twice a day by video through the app and received payment for abstinence in the intervention arm.

Smartphone app plus text messaging

Danaher 2019 tested an intervention that used both an integrated mobile web app and text messaging. Text messages were prompts and motivations to visit parts of the web programme as well as information, motivation and smoking questions (290 messages over six months). The control group received a PC‐based web intervention with interactive and multimedia features based on phases of quitting, the main difference to the intervention app being that it was not adapted for the small screen and did not include text messaging. Emails were sent as prompts if there were periods of inactivity.

Outcome

The included studies provided a range of abstinence outcome measures. Five studies (Cobos‐Campos 2017; Free 2009; Free 2011; Liao 2018; Peiris 2019), reported the strictest outcome as biochemically verified sustained/continuous abstinence, and Abroms 2014 and Alessi 2017 defined abstinence as biochemically confirmed repeat point prevalence at six months.

Seven additional studies reported self‐reported continuous abstinence at six months, without biochemical verification Baskerville 2018; BinDhim 2018; Borland 2013; Herbec 2019; Naughton 2014; Rodgers 2005; Whittaker 2011).

Two studies used self‐reported four‐week or 30‐day point prevalence abstinence at six‐month follow‐up (Abroms 2017; Haug 2013), three studies used self‐reported seven‐day point prevalence at six months (Augustson 2017; Bock 2013; Danaher 2019), one used self‐reported point prevalence abstinence at 32 weeks (Squiers 2017), one at 12 months (Yu 2017), and an additional four studies used six‐month biochemically verified measures of seven‐day point prevalence (Chan 2015; Ferguson 2015; Garrison 2018; Tseng 2017). Chan 2015 also provided biochemically verified seven‐day point prevalence abstinence rates at 12‐month follow‐up. Wilson 2016 reported six month follow‐up data in their trial registry entry; however they do not specify whether these rates were validated or not.

Risk of bias in included studies

The Characteristics of included studies table provides details of 'Risk of bias' judgements for each domain of each included study. Figure 2 illustrates judgements for each included study. Overall, we judged 13 studies to be at low risk of bias (judged at low risk for all domains), and three to be at high risk (judged to be at high risk in at least one domain). We judged the remaining studies to be at unclear risk (judged to be at unclear risk of bias for at least one domain, but with no judgements of high risk).

'Risk of bias' summary: review authors' judgements about each 'Risk of bias' item for each included study

Selection bias

The majority of studies (17 of 26) appeared to have adequate procedures for random sequence generation and allocation concealment, so we judged them to be at low risk of bias for these domians; however Alessi 2017; Augustson 2017; Chan 2015; Ferguson 2015; Garrison 2018; Haug 2013; Squiers 2017; Wilson 2016 and Yu 2017 did not provide sufficient description of either randomisation, concealment procedures, or both. Therefore, it is impossible to know whether the lack of information is due to actual bias or simply because it has not been reported, and we judged them to be at unclear risk of bias for at least random sequence generation or allocation concealment.

Detection bias

Blinding of participants is not possible in studies of behavioural interventions. In this case participants knew if they were receiving text messages or using an app. Therefore, we did not assess performance bias, and instead judged the likelihood of detection bias. We did not deem a study to be high risk for this domain where there was biochemical verification of abstinence, or where both arms received the same amount of face‐to‐face contact (or none).

In most cases, studies collected outcomes electronically and remotely (Abroms 2014; Augustson 2017; Baskerville 2018; BinDhim 2018; Bock 2013; Danaher 2019; Free 2009; Free 2011; Garrison 2018; Squiers 2017). Chan 2015; Herbec 2019; Liao 2018; Haug 2013 and Wilson 2016 all collected outcomes by phone, and Naughton 2014 by mailed questionnaire or in person. Cobos‐Campos 2017 collected outcomes in person in the clinic and this was not blinded, however this was mitigated by biochemical verification of quitting.

A number of the trials sought biochemical verification of long‐term abstinence with salivary cotinine (Abroms 2014; Free 2009; Free 2011), urinary cotinine (Liao 2018), or expired CO (Cobos‐Campos 2017; Garrison 2018). Chan 2015 assessed both CO and cotinine concentrations. Abroms 2017 biochemically validated their primary outcome at three months, but not at six months, and Rodgers 2005 validated abstinence at six weeks but not long‐term follow‐up. Similarly, Naughton 2014 used verification at four weeks only. Wilson 2016 stated that they planned to verify abstinence at all follow‐up points using salivary cotinine; however it is not stated whether the abstinence rates reported in their trial registry entry were the validated rates or not. However, as data was collected remotely this study was still deemed to be at low risk of bias for this domain. In fact, we deemed all studies to be at low risk of detection bias.

Attrition bias

We judged three studies to be at high risk of bias due to greater than 50% of participants lost to follow‐up at six months (Augustson 2017; Cobos‐Campos 2017; Herbec 2019). Several other studies had moderately high loss to follow‐up but the numbers were clearly reported. The difference between groups was not greater than 20%, and overall loss was not greater than 50%. Ferguson 2015 did not report loss to follow‐up and so we judged it to be at unclear risk of attrition bias.

Other

In Abroms 2014 there were some issues with fraudulent enrolment at the outset of the study, although this was corrected once detected. In Haug 2013, although clustering is adjusted for in this study's analysis the authors do not report the clustering effect, making it impossible to adjust for this in our analysis. Therefore, it is not clear how much the clustering adjustment influences the result from this study and our meta‐analyses. Rodgers 2005 suggested that some participants in their control group may have thought their incentive at follow‐up (a month of free text messaging) depended on reporting quitting. This could account for an unexpected increase in control group participants reporting quitting from six weeks (109 participants) to six months (202 participants reporting no smoking in the past seven days), which could have led to an underestimation of the effect of the intervention.

Effects of interventions

See: Table 1; Table 2; Table 3

Summary of findings for the main comparison. Text messaging compared to minimal support for smoking cessation.

Text messaging compared to minimal support for smoking cessation
Patient or population: people who smoke  Setting: community  Intervention: text messaging  Comparison: minimal smoking cessation support
Outcomes	*Anticipated absolute effects^ (95% CI)**		Relative effect  (95% CI)	№ of participants  (studies)	Certainty of the evidence  (GRADE)	Comments
	Risk with minimal SC support	Risk with text messaging
Long‐term abstinence (all randomised) Measured with self‐report and biochemical validation at 6 to 12 months	Study population		RR 1.54  (1.19 to 2.00)	14,133  (13 RCTs)	⊕⊕⊕⊝  Moderate^a
	6 per 100	9 per 100  (7 to 11)
*The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).    CI: confidence interval; RCT: randomised controlled trial; RR: risk ratio; SC: smoking cessation
GRADE Working Group grades of evidence  High certainty: we are very confident that the true effect lies close to that of the estimate of the effect.  Moderate certainty: we are moderately confident in the effect estimate: the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.  Low certainty: our confidence in the effect estimate is limited: the true effect may be substantially different from the estimate of the effect.  Very low certainty: we have very little confidence in the effect estimate: the true effect is likely to be substantially different from the estimate of effect.

Text messaging in addition to other smoking cessation support compared to other smoking cessation support alone for smoking cessation
Patient or population: people who smoke  Setting: community  Intervention: text messaging + other smoking cessation support  Comparison: other smoking cessation support alone
Outcomes	*Anticipated absolute effects^ (95% CI)**		Relative effect  (95% CI)	№ of participants  (studies)	Certainty of the evidence  (GRADE)	Comments
	Risk with other SC support alone	Risk with text messaging + other SC support
Long‐term abstinence (all randomised) Measured as self‐reported and biochemical validation at 6 to 12 months	Study population		RR 1.59  (1.09 to 2.33)	997  (4 RCTs)	⊕⊕⊕⊝  Moderate^a
	8 per 100	12 per 100  (9 to 18)
*The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).    CI: confidence interval; RCT: randomised controlled trial; RR: risk ratio; SC: smoking cessation
GRADE Working Group grades of evidence  High certainty: we are very confident that the true effect lies close to that of the estimate of the effect.  Moderate certainty: we are moderately confident in the effect estimate: the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.  Low certainty: our confidence in the effect estimate is limited: the true effect may be substantially different from the estimate of the effect.  Very low certainty: we have very little confidence in the effect estimate: the true effect is likely to be substantially different from the estimate of effect.

Smartphone app compared to lower‐intensity support for smoking cessation
Patient or population: people who smoke  Setting: community Intervention: smartphone app  Comparison: lower‐intensity smoking cessation support
Outcomes	*Anticipated absolute effects^ (95% CI)**		Relative effect  (95% CI)	№ of participants  (studies)	Certainty of the evidence  (GRADE)	Comments
	Risk with lower intensity SC support	Risk with Smartphone app
Long‐term abstinence (all randomised) Measured with self‐report and biochemical validation at 6 months	Study population		RR 1.00  (0.66 to 1.52)	3079  (5 RCTs)	⊕⊝⊝⊝  Very low^a,b
	8 per 100	8 per 100  (5 to 12)
*The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).    CI: confidence interval; RCT: randomised controlled trial; RR: risk ratio; SC: smoking cessation
GRADE Working Group grades of evidence  High certainty: we are very confident that the true effect lies close to that of the estimate of the effect.  Moderate certainty: we are moderately confident in the effect estimate: the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.  Low certainty: our confidence in the effect estimate is limited: the true effect may be substantially different from the estimate of the effect.  Very low certainty: we have very little confidence in the effect estimate: the true effect is likely to be substantially different from the estimate of effect.

Date	Event	Description
23 September 2019	New citation required but conclusions have not changed	Search updated and 13 new studies added
19 June 2019	New search has been performed	Search updated 2018, new studies added and text updated

Date	Event	Description
1 October 2015	New search has been performed	Updated 2015, seven new studies added and text updated
1 October 2012	New citation required and conclusions have changed	Three new included studies added, meta‐analysis conducted, conclusions changed (pooled effect statistically significant)
1 October 2012	New search has been performed	Updated 2012, three new studies added and text updated
15 July 2008	Amended	Converted to new review format.
5 September 2006	New citation required and conclusions have changed	Substantive amendment

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Long‐term abstinence (all randomised))	13	14133	Risk Ratio (M‐H, Random, 95% CI)	1.54 [1.19, 2.00]
2 Long‐term abstinence (complete case)	13	11969	Risk Ratio (M‐H, Random, 95% CI)	1.56 [1.21, 2.02]

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Long‐term abstinence (all randomised)	2	2238	Risk Ratio (M‐H, Random, 95% CI)	0.92 [0.61, 1.40]
2 Long‐term abstinence (complete case)	2	1813	Risk Ratio (M‐H, Random, 95% CI)	0.93 [0.63, 1.36]

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Long‐term abstinence (all randomised)	4	997	Risk Ratio (M‐H, Random, 95% CI)	1.59 [1.09, 2.33]
2 Long‐term abstinence (complete case)	4	796	Risk Ratio (M‐H, Random, 95% CI)	1.63 [1.12, 2.37]

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Long‐term abstinence (all randomised)	3	12985	Risk Ratio (M‐H, Random, 95% CI)	1.00 [0.95, 1.06]
2 Long‐term abstinence (complete case)	3	6798	Risk Ratio (M‐H, Random, 95% CI)	1.04 [1.00, 1.09]

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Long‐term abstinence (all randomised)	5	3079	Risk Ratio (M‐H, Random, 95% CI)	1.00 [0.66, 1.52]
1.1 Comparison: minimal non‐app SC support	2	1645	Risk Ratio (M‐H, Random, 95% CI)	0.82 [0.56, 1.18]
1.2 Comparator: less intensive smartphone app	3	1434	Risk Ratio (M‐H, Random, 95% CI)	1.12 [0.60, 2.09]
2 Long‐term abstinence (complete case)	5	1774	Risk Ratio (M‐H, Random, 95% CI)	1.06 [0.72, 1.54]
2.1 Comparison: minimal non‐app SC support	2	771	Risk Ratio (M‐H, Random, 95% CI)	0.87 [0.62, 1.23]
2.2 Comparator: less intensive smartphone app	3	1003	Risk Ratio (M‐H, Random, 95% CI)	1.18 [0.67, 2.09]

Outcome or subgroup title	No. of studies	Statistical method	Effect size
1 Long‐term abstinence (all randomised)	2	Risk Ratio (M‐H, Random, 95% CI)	Totals not selected
1.1 Comparison: counselling, nicotine replacement therapy and monitoring only app	1	Risk Ratio (M‐H, Random, 95% CI)	0.0 [0.0, 0.0]
1.2 Comparison: counselling and nicotine replacement therapy	1	Risk Ratio (M‐H, Random, 95% CI)	0.0 [0.0, 0.0]
2 Long‐term abstinence (complete case)	2	Risk Ratio (M‐H, Random, 95% CI)	Totals not selected
2.1 Comparison: counselling, nicotine replacement therapy and monitoring only app	1	Risk Ratio (M‐H, Random, 95% CI)	0.0 [0.0, 0.0]
2.2 Comparison: counselling and nicotine replacement therapy	1	Risk Ratio (M‐H, Random, 95% CI)	0.0 [0.0, 0.0]

Methods	Study design: RCT Country: USA Recruitment: Internet. Individuals who were searching on Google with keywords related to quitting smoking saw study ads in conjunction with their search results. Dates of study: 2011‐13
Participants	Baseline characteristics (n = 503) Mean age: 35.7 years Female: 65.6% High school or lower education: 21.9% FTND: 5.33 White: 78.5% Inclusion criteria: to be eligible for the study, participants were required to (1) be ≥18 years of age; (2) smoke ≥ 5 cigarettes/day; (3) have a US mailing address; (4) have a working e‐mail address; (5) have a cell phone number with an unlimited SMS (i.e. text messaging) plan; (6) express an interest in quitting smoking within the next month; and (7) not be pregnant. Exclusion criteria: pregnant
Interventions	Text2Quit: automated, tailored, interactive and bidirectional text messaging programme that was supported by email and web portal. Based on social cognitive theory and practice guidelines. Duration 6 months with decreasing frequency of messages. Control: weblink to smokefree.gov website, weblink to a guidebook, and study related reminder text messages
Outcomes	Definition of abstinence: 6‐month biochemically confirmed repeat point prevalence
Funding source	This research was supported by grant no. 5K07 CA124579‐02 and the American Recovery and Reinvestment Act supplement to Dr. Lorien Abroms, from the National Cancer Institute of the NIH. Support also came from an award from the Department of Prevention and Community Health at the George Washington University School of Public Health and Health Services to Dr. Lorien Abrom
Conflicts of interest	The George Washington University/Lorien Abroms has licensed the Text@Quit program to Voxiva Inc; Dr Abroms has stock options in Voxiva Inc
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Computer‐generated
Allocation concealment (selection bias)	Low risk	Central randomisation online
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Abstinence was biochemically verified
Incomplete outcome data (attrition bias)   All outcomes	Low risk	At 6 months, 52 lost to follow‐up in control group (21.6%) and 70 lost in intervention group (26.7%)
Other bias	Unclear risk	Some issues with fraudulent enrolment at outset of study, corrected process once detected

Methods	Study design: RCT Study grouping: parallel group Country: USA Recruitment: through E‐mail, flyers, and print advertisements Dates of study: 2012‐2014
Participants	Baseline characteristics (n = 90) Mean age: 45 Female: 59% (N = 53) High school or lower education: not stated FTND: approx 3 White: 74% (N = 67) Inclusion criteria: inclusion criteria were (1) ≥ 10 cigarettes daily verified by CO ≥ 8 ppm, (2) no past‐year abstinence > 3 months, (3) intent to quit within 3 weeks (score ≥ 7 out of 10, “How much do you want to quit smoking within the next 3 weeks?”15, (4) aged ≥ 18 years, and (5) mailing address and valid photo ID Exclusion criteria: Exclusion criteria were (1) past month behavioral or pharmacotherapy for smoking, (2) serious and unstable psychiatric illness (e.g. schizophrenia, non‐nicotine substance use disorder) or medical disease, or contraindication for transdermal nicotine, (3) pregnant, nursing a child, or not using effective contraceptive if female, (4) ongoing use of monoamine oxidase inhibitors, antipsychotics, mood stabilisers, bupropion, or naltrexone, and (5) not English‐speaking
Interventions	mHealth monitoring: for all participants, brief counselling (˜10 min) was scheduled to occur twice weekly for 4 weeks by phone. Discussion included personal reasons for quitting, skills‐based items, and craving control strategies. Self‐reported smoking status was documented. The study also provided 8 weeks of transdermal nicotine (typically 21 mg patches for 4 weeks, 14 mg for 2 weeks, and 7 mg for 2 weeks). All participants were instructed that an IVR system would send prompts to conduct CO self‐tests up to 3 times daily between 7 a.m. and 10 p.m. for the next 4 weeks, with the exact number and timing not disclosed. When prompted, participants used the video‐record function on their study cell phone (with a front‐facing lens) to record the CO self‐test process, and sent the date and time‐stamped video to research staff using multimedia messaging. Participants also reported the CO results and number of cigarettes smoked using the IVR. Video test results were compared against IVR reports to confirm accuracy (confirmed in all but 2 instances). mHealth reinforcement: as above, plus mHealth reinforcement participants earned chances for prizes contingent on on‐time and smoking‐negative breath tests (CO ≤ 6 ppm). Earnings were determined immediately via computer algorithm during IVR calls, and were available for redemption after IVR reports were confirmed against video clips.
Outcomes	Definition of abstinence: 6‐month biochemically confirmed repeat point prevalence
Funding source	National Institutes of Health grants R21‐DA029215 and R01‐DA01344
Conflicts of interest	None declared
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Quote: "On the target quit date, participants (N = 90) were randomly assigned (allocated 1:1) to one of two treatment conditions using an urn procedure 34 and stratified on at least one smoking‐negative CO during baseline"
Allocation concealment (selection bias)	Unclear risk	Not stated
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Abstinence was biochemically verified.
Incomplete outcome data (attrition bias)   All outcomes	Low risk	Follow‐up rates did not differ between conditions (P > 0.05). Reinforcement group (n = 45) follow‐up questionnaires: 38, samples: 33; usual care group (n = 45) questionnaires 43, samples 39. Used ITT analysis

Methods	Study design: RCT Country: Canada Setting: recruited through web‐based media including Facebook, Google, and other sources Study dates: 2014‐15
Participants	Baseline characteristics (n = 1599) Age: 49.1% aged 19‐23 years Female: 45.6% High school or lower education: 44.5% Moderate to high nicotine dependence: 26.5% White: 73% Smokes at least a packet a day: 25.6% Inclusion criteria: aged 19‐29 years, smoked cigarettes daily, resided in Canada, were considering quitting smoking in the next 30 days, had an Android (version 2.0‐5.0) or iPhone (version 4.0‐7.0) smartphone, were able to provide informed consent, were able to comprehend English, and were not referred to the study by an existing study participant. Exclusion criteria: not explicitly stated
Interventions	Crush the Crave: a comprehensive and evidence‐informed SC smartphone app; enabled users to customise a quit plan by choosing a QD and whether to quit or reduce every week; reminders of money saved and health improvements; contingency reinforcement with milestones tracked as rewards, choose to share to Facebook/Twitter; Facebook community for additional support; supportive messages and inspirational photos; recording smoking; feedback; web‐based distractions; evidence‐informed information for relapses and cravings; access to cessation services Control: standard print‐based self‐help guide 'On the Road to Quitting' for young adult smokers
Outcomes	Definition of abstinence: self‐reported continuous 6‐month abstinence
Funding source	Health Canada, Federal Tobacco Control Strategy and a grant from the Canadian Institutes of Health Research
Conflicts of interest	NBB received salary support from the Canadian Cancer Society Research Institute
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Computer‐generated randomisation procedure
Allocation concealment (selection bias)	Low risk	Participants were blinded to group allocation and were not aware of which was the control and intervention condition. Investigators were blinded to group allocation until completion of the trial after initial analysis of the primary and secondary outcomes.
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Contact with investigators was minimal
Incomplete outcome data (attrition bias)   All outcomes	Low risk	ITT analysis was conducted. High rates of attrition, but no significant difference between groups. Follow‐up at 6 months was 60.48%, however complete case follow‐up was considered at both 3 and 6 months for primary outcomes data; 43.2% intervention and 47.6% control groups (no significant difference)

Methods	Study design: RCT Country: USA Recruitment: advertisements in local media outlets, Internet sites, radio programmes, asking interested individuals to call or text Study date: 2011
Participants	Baseline characteristics(n = 60) Mean age: 30.7 (9.0) Female: 57% (N = 34) High school or less: 30% (N = 18) FTND: not stated White: 66% (N = 40) Inclusion criteria: current daily smoker, interested in quitting smoking in the next 30 days, have a mobile phone with SMS text messaging capability, use SMS messaging at least once monthly Exclusion criteria: not explicitly stated
Interventions	All participants received a single, individual 30‐min SC counselling session. TXT‐2‐Quit: an 8‐week programme with 1‐4 text messages/day (depending on quit stage). SC messages were tailored to the participant's stage of SC, with specialised messages provided on demand, based on user requests for additional support, and an optional peer‐to‐peer social support network Control: an 8‐week programme of daily non‐smoking‐related text messages
Outcomes	Primary outcome: self‐reported 7‐day point‐prevalence abstinence at 6 months
Funding source	National Institute on Drug Abuse
Conflicts of interest	None declared
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Simple randomisation via computerised random number generator
Allocation concealment (selection bias)	Low risk	Assignments in a sealed envelope delivered after completion of the baseline data collection
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Questionnaires were filled in online with minimal investigator contact
Incomplete outcome data (attrition bias)   All outcomes	Low risk	Only 2 participants in control group appeared to be missing at 6 months; ITT analysis presented

Methods	Study design: RCT Country: China Recruitment: recruited from subscribers to Nokia Life Tools on Nokia phones in both urban and rural areas of China’s Zhejiang, Heilongjiang, and Shaanxi provinces Date of study: 2013
Participants	Baseline characteristics (n = 8000) Female %: not stated Mean age: not stated High school or lower education: not stated FTND: not stated White: not stated Inclusion criteria: Nokia Life Tools users; adult smokers Exclusion criteria: none specifically stated
Interventions	High‐frequency text contact (HFTC): 91 messages during the 6 weeks; 3 messages/day for weeks 1 and 2, 2/day for weeks 3‐5, and 1/day for week 6. At the end of each text message, participants in both groups were offered the opportunity to cancel the service via text. The text messages provided encouragement, practical advice to help maintain cessation, and information on the health effects of smoking. Low‐frequency text contact (LFTC): 1 text message/week, for a total of 6 text messages during the 6‐week intervention period; a subset of text messages on smoking’s health effects
Outcomes	Definition of abstinence: 7‐day point prevalence abstinence self‐reported via text message at 6‐month follow‐up
Funding source	National Cancer Institute, National Institutes of Health, U.S. Dept of Health Human Services
Conflicts of interest	None declared
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Unclear risk	Quote: "Intervention participants who opted into the phase 3 SC trial were randomly assigned to the intervention or comparison group (n ¼ 4000 in each group)" Therefore, participants were randomly assigned but it was not stated how
Allocation concealment (selection bias)	Unclear risk	Not stated
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Outcome assessed by text message with no face‐to‐face contact Quote: "participants were not aware of the separate intervention arms and therefore did not know what group they were assigned to"
Incomplete outcome data (attrition bias)   All outcomes	High risk	Loss to follow‐up clearly stated but > 50%; 58.4% of intervention group and 56.1% of control group lost to follow‐up at 6‐month assessment. ITT analysis

Methods	Study design: RCT Countries: USA, Australia, UK and Singapore Recruitment: users of the Apple App Store in the 4 countries were recruited passively via the app’s download page in the Apple App Store Study date: 2014
Participants	Baseline characteristics (n = 684) Mean age: 28.3 (SD 10.0) Female: 55% (N = 376) < graduate level education: 46.3% (N = 317) FTND: proportion 6‐10 (high‐very high): 36.7% (N = 251) White: not stated Inclusion criteria: the eligibility criteria were daily smokers of cigarettes, ≥ 18 years and from USA, UK, Singapore, Australia Exclusion criteria: occasional smokers and users of other tobacco products
Interventions	SSC app: decision aid app that included 4 main components that made optimal use of smartphone features: (1) mandatory information about quitting options, with their benefits and harms; (2) daily motivational messages using push notifications sent from the study server, (3) a quitting diary and (4) a quitting benefits tracker. The decision‐aid app allowed smokers to freely choose a quit method through a structured process of weighing up the available options and their benefits and harms. Control App: both groups encouraged to set QD. App with information about quitting only
Outcomes	Definition of abstinence: self‐reported continuous abstinence at 6 months
Funding source	The app was developed by NFB as part of a PhD degree, advertisement was covered by a small fund from the PhD sponsor (Ministry of Education, Saudi Arabia)
Conflicts of interest	None declared
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Quote: "The study app automatically randomised eligible participants (daily cigarette smokers, aged 18 years and above and from the four countries) to either the intervention or the control sub‐app using stratified block (age, gender, country) randomisation. The strata were defined by age, country and gender."
Allocation concealment (selection bias)	Low risk	Quote: "The study app automatically randomised eligible participants (daily cigarette smokers, aged 18 years and above and from the four countries) to either the intervention or the control sub‐app using stratified block (age, gender, country) randomisation. The strata were defined by age, country and gender."
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	All participant involvement was remote, through the apps, in both study arms
Incomplete outcome data (attrition bias)   All outcomes	Low risk	Numbers reported, similar in both groups (289/342 completed follow‐up in control group, 294/342 in intervention group). ITT analysis, imputation of missing data including sensitivity analysis and all missing as smoking

Methods	Study design: RCT. Participants were not pre‐committed to consider using the interventions they were offered Country: Australia Recruitment: from those having recently sought cessation assistance (mainly Quitline callers who were not seeking help from a counsellor) via the study website, and from a cold‐contacted sample taken from two Internet survey panels Study dates: 2008‐09
Participants	Baseline characteristics (n = 3530) Mean age: 42.1 Female: 60% (N = 2118) High school or less: not stated Smoking: 16.9 cigarettes/day White: not stated Inclusion criteria: none stated Exclusion criteria: none stated
Interventions	onQ programme: provides a stream of SMS messages that mix snippets of advice on strategy and motivational messages. The user can interact with it by indicating their stage of quitting so that appropriate stage‐specific messages are sent, and once quit can also call up messages in crisis situations. QuitCoach: a personalised, automated tailored cessation programme delivered via the Internet. It generates letters of advice based on answers to an assessment questionnaire, including suggestions about strategy and motivational messages. It also provides further untailored supplementary resources. Control: brief information on Internet‐ and phone‐based assistance available in Australia
Outcomes	Definition of abstinence: self‐reported 6‐months, sustained abstinence at 7‐month follow‐up Intention‐to‐quit analysis and sensitivity analysis around treatment of missing data
Funding source	National Health and Medical Research Council (Australia)
Conflicts of interest	JB is currently employed part‐time through the University of Freiburg, Germany, on a project funded by Pfizer Global Health Partnership
Notes	OnQ and control arms used in comparison of text messaging with minimal SC. OnQ and QuitCoach arms used in comparison of text messaging with other SC support
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Computerised random number generator embedded within the baseline survey
Allocation concealment (selection bias)	Low risk	Participant allocation was embedded into the baseline survey, which appeared to be carried out online
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Outcome data assessed through online surveys with no difference in contact between study arms
Incomplete outcome data (attrition bias)   All outcomes	Low risk	Loss to follow‐up 475 (13% total) with similar numbers across groups (control = 66, onQ = 89, QuitCoach = 104, both = 121, participant choice = 95); 2 excluded as reported to have died at 7‐month follow‐up

Methods	Study design: RCT Country: Spain Recruitment: participants recruited from 2 health centres, identified through their electronic health record and sent a letter of invitation Study dates: 2013‐2014
Participants	Baseline characteristics (n = 320) Mean age: 45.0 Female: 44.0% High school or lower education: not stated FTND: 17.5% high dependence White: not stated Inclusion criteria: smokers aged ≥ 18 years, had a mobile phone, were able to receive and send text messages, and were motivated to start a SC programme (based on a score of ≥ 5 on the Richmond test) Exclusion criteria: people who were on drug treatment for SC or had a history of mental or behavioral disorders or a diagnosis of depression (using the Goldberg scale; 23), as well as women who were pregnant
Interventions	Health advice: usual clinical practice (health advice provided by a doctor or nurse). Both groups followed the usual protocol (health advice) with its 4 visits (protocol according to recommendations of Spanish Society of Family and Community Medicine) Text messaging + health advice: as above, plus reinforcement text messages to their mobile phones. 2 automatically generated text messages/day (1 in the morning and 1 in the evening) for the first 5 weeks and 3 messages/week from weeks 6 to 26. Messages were motivational in intent, to encourage participants in their efforts to stop smoking, and also provided information about the health‐related risks of smoking. (SMSalud®)
Outcomes	Definition of abstinence: biochemically confirmed prolonged abstinence (participant reporting not having smoked > 5 cigarettes since the start of the follow‐up period) at 6 months
Funding source	Departamento de Industria del Gobierno Vasco of the Basque Country under the 2012 Saiotek funding round (reference number SAIO12‐OA12BF001) Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco (IT620‐13)
Conflicts of interest	None declared
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Randomly assigned to groups using computer‐generated sequence
Allocation concealment (selection bias)	Low risk	Researchers involved were blind to the computer‐generated sequence used for randomisation until the moment of group allocation
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Biochemical confirmation of abstinence
Incomplete outcome data (attrition bias)   All outcomes	High risk	Dropout rate was > 50%: 48.75% intervention and 43.75% provided 6‐month follow‐up data, similar in both groups, all numbers reported, ITT analysis

Methods	Study design: RCT Country: Australia Recruitment: advertisements in papers, radio, social media, Facebook, in Tasmania Study dates: not stated
Participants	Baseline characteristics (n = 284) Mean age: 42.1 (SD 13.2) Female: 51.1% (N = 145) Household income < AUD 45,000: 66.7% (N = 189) FTND: mean 4.8 (SD 2.0) White: 93.7% (N = 266) Inclusion criteria: daily smokers of > 10 cigarettes/day for past 3 years Exclusion criteria: not explicitly stated
Interventions	Control: self‐help quit booklet containing tips for quitting and cognitive and behavioural coping mechanisms Intervention: as above, plus 4 or 5 randomly timed text messages/day containing quit smoking advice and encouragement tailored to participants' current quit status (preparing to quit, first week of the quit attempt, second week of attempt etc.). Participants could request additional text messages
Outcomes	Definition of abstinence: 7‐day point prevalence abstinence verified by expired CO at 6 months
Funding source	National Health and Medical Research Council
Conflicts of interest	SF has consulted for GlaxoSmithKline Consumer Healthcare on matters relating to SC and has received researcher‐initiated project grant funding from Pfizer (through the GRAND initiative)
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Unclear risk	Randomisation schedules for sequential allocation. No further information provided
Allocation concealment (selection bias)	Unclear risk	Quote: "The group allocation procedure was designed to blind study staff with direct participant contact from knowing group assignment"
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Abstinence was biochemically verified
Incomplete outcome data (attrition bias)   All outcomes	Unclear risk	Loss to follow‐up not stated

Methods	Study design: RCT Country: UK Setting: advertisements on radio, bus billboards, websites, newspapers, primary care centres, pharmacies, SC services. Participants registered their interest by text message or online. Study dates: 2007
Participants	Baseline characteristics (n = 200) Mean age: 36 years (SD 9) Female: 47% (N = 94) Education: not stated Cigarettes per day: mean 20 White: not stated Inclusion criteria: aged ≥ 16 years; smoking daily and interested in quitting; current owner of mobile phone Exclusion criteria: not explicitly stated
Interventions	Intervention: 6‐month text messaging programme delivered solely over mobile phone based on programme in Rodgers 2005 but messages adapted for UK population. Participant nominated QD and received regular personalised text messages with advice, support and distraction, with a countdown to QD, intensive 4 weeks of 5 or 6 messages/day then maintenance phase of 1 message/2 weeks. Messages selected from database matched to participant characteristics. Free month of text messaging from QD. Optional Quit Buddy, and Text Crave (messages on demand). Interactive polls and quizzes  Control: 1 generic text message every 2 weeks about study participation, with no SC content
Outcomes	Definition of abstinence: salivary cotinine verified continuous abstinence (< 5 cigarettes) at 6 months
Funding source	UK Medical Research Council, Primary Care Research Networks
Conflicts of interest	None declared
Notes	Pilot study carried out as a precursor to Free 2011
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Central computerised randomisation
Allocation concealment (selection bias)	Low risk	Participants sent consent via text message, RA entered data into web‐based form, system automatically generated intervention or control group texts according to the computer‐generated allocation
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Verified with salivary cotinine
Incomplete outcome data (attrition bias)   All outcomes	Low risk	Lost to follow‐up: 8/98 (control) and 8/102 (intervention) at 6 months (92% follow‐up)

Methods	Study design: cluster‐RCT Country: Switzerland Recruitment: from students in vocational schools Study dates: 2011‐12
Participants	Baseline characteristics (n = 755) Mean age: 18.2 years (SD 2.3) Female: 51.9% (N = 392) Secondary school or less: 81.6% (N = 616) Cigarettes per day mean: 10.6 (SD 7.6) No immigration background: 53.2% (N = 399) Inclusion criteria: daily or occasional cigarette smoking (at least 4 cigarettes in the preceding month and at least 1 cigarette during the preceding week), ownership of a mobile phone Exclusion criteria: not explicitly stated
Interventions	SMS‐COACH: a 3‐month programme including a weekly SMS text message assessment of smoking‐related target behaviour, 2 weekly text messages tailored to baseline data and responses to the SMS text message assessments, and an optional further integrated QD preparation and relapse prevention SMS programme. Participants who did not use the integrated programme for QD preparation and relapse prevention received a total of 37 text messages (1 welcome message, 11 assessment messages, 24 tailored feedback messages, 1 goodbye message). Participants, who used the QD preparation and relapse‐prevention programme for the whole period from 1 week before the scheduled QD until 3 weeks afterwards, received an additional 42 text messages Control: all students in participating classes were invited to participate in an online health screening survey during a regular school lesson reserved for health education. The control group did not receive anything else
Outcomes	Definition of abstinence: self‐reported 4‐week point prevalence abstinence at 6‐month follow‐up
Funding source	Swiss Tobacco Prevention Fund
Conflicts of interest	SH and CM were involved in the development of the intervention
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Block randomisation with computer‐generated randomly permuted blocks of 4 cases
Allocation concealment (selection bias)	Unclear risk	No information provided
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Minimal contact with study investigators in both trial arms
Incomplete outcome data (attrition bias)   All outcomes	Low risk	111/383 in control and 85/372 in intervention were lost to follow‐up at 6 months. ITT analysis conducted
Other bias	Unclear risk	Although clustering is adjusted for in this study's analysis the authors do not report the clustering effect, making it impossible to adjust for this in our analysis. Therefore, it is not clear how much the clustering adjustment influences the result from this study.

Methods	Study design: RCT Country: New Zealand Recruitment: advertisements on websites, media articles, email and text message mailing lists, and posters at tertiary institutions Study date: 2001
Participants	Baseline characteristics (n = 1705) Mean age: 22 years Female: 58.5% (N = 997) High school or lower education: not stated FTND: 5 Maori (indigenous population): 20.8% (N = 355) Inclusion criteria: aged ≥ 16 years, smoking daily, wanting to quit within the next month, and were able to send and receive text messages on their own mobile phone Exclusion criteria: not explicitly stated
Interventions	All participants were informed at their baseline interview of the smoking Quitline and the government subsidy for nicotine replacement therapy that was available. Intervention: 6‐month programme delivered solely over mobile phone. Participant nominated QD and received regular personalised text messages with advice, support and distraction, with a countdown to QD, intensive 4 weeks of 5 or 6 messages/day then maintenance phase of 1 message/2 weeks. Messages selected from database matched to participant characteristics. Free month of text messaging from QD. Optional Quit Buddy and Text Crave (messages on demand). Interactive polls and quizzes  Control: 1 text message/2 weeks thanking participants for taking part (text messages had no SC content)
Outcomes	Definition of abstinence: self‐reported continuous abstinence at 26 weeks
Funding source	National Heart Foundation of New Zealand, the Cancer Society of New Zealand, Vodafone NZ, Alcatel and Auckland UniServices. AR held a Senior Fellowship from the National Heart Foundation.
Conflicts of interest	None declared
Notes
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Low risk	Central computerised randomisation
Allocation concealment (selection bias)	Low risk	Central computerised randomisation, concealed until after assignment
Blinding of outcome assessment (detection bias)   All outcomes	Low risk	Minimal contact with investigators across groups
Incomplete outcome data (attrition bias)   All outcomes	Low risk	Loss to follow‐up at 6 months was 179/853 in the control arm versus 261/852 in the intervention arm
Other bias	Unclear risk	The authors suggested that some participants in the control group may have thought their incentive at follow‐up (month of free text messaging) depended on reporting quitting. This could account for an unexpected increase in control group participants reporting quitting from 6 weeks (109 participants) to 6 months (202 participants reporting no smoking in the past 7 days), which could have led to an underestimation of the effect of the intervention.

Study	Reason for exclusion
ACTRN12617000491369	Follow‐up < 6 months
Aigner 2017	Wrong intervention
Applegate 2007	Follow‐up < 6 months
Bamidis 2017	Wrong study design
Bernstein 2018	Follow‐up < 6 months
Blasco 2012	Wrong intervention
Brendryen 2008	Wrong intervention
Bricker 2014	Follow‐up < 6 months
Brinker 2016	Wrong intervention
Bronshtein 2016	Wrong intervention
Buller 2014	Follow‐up < 6 months
Chow 2012	Wrong intervention
Dale 2014	Wrong intervention
Fingrut 2014	Wrong intervention
Fraser 2014	Wrong intervention
Gritz 2013	Wrong intervention
Halpern 2018	Wrong intervention
Hammett 2018	Follow‐up < 6 months
Hassandra 2017	Wrong patient population
Haug 2008	Follow‐up < 6 months
Haug 2009	Follow‐up < 6 months
Haug 2014	Wrong intervention
Kiselev 2011	Wrong intervention
Lazev 2004	Wrong study design
Mason 2016	Wrong outcomes
Mehring 2014	Wrong intervention
Naughton 2012	Follow‐up < 6 months
Naughton 2017	Follow‐up < 6 months
NCT01454999	Follow‐up < 6 months
NCT02245308	Wrong intervention
NCT02844296	Wrong intervention
NCT03177265	Wrong intervention
Obermayer 2004	Wrong study design
Pechmann 2015	Wrong study design
Peng 2013	Wrong intervention
Pollak 2013	Wrong intervention
Riley 2008	Wrong study design
Shi 2013	Follow‐up < 6 months
Skov‐Ettrup 2013	Wrong intervention
Skov‐Ettrup 2014	Wrong study design
Skov‐Ettrup 2016	Wrong intervention
Snider 2011	Wrong study design
Stanczyk 2014	Wrong intervention
Vidrine 2006	Follow‐up < 6 months
Vilaplana 2014	Follow‐up < 6 months
Wizner 2009	Wrong intervention
Ybarra 2012	Follow‐up < 6 months
Ybarra 2013	Follow‐up < 6 months
Yuhongxia 2011	Wrong study design

Trial name or title	Pragmatic randomised controlled trial evaluating effectiveness of a smoking cessation e‐ intervention "Tabac Info Service"
Methods	RCT
Participants	Smokers aged 18+ years in France
Interventions	E‐intervention, Tabac Info Service (TIS), by website and mobile application Control: current practices of smoking cessation in France
Outcomes	Point prevalence abstinence at 6 months
Starting date	January 2017
Contact information	Dr Linda Cambon; Linda.cambon@ehesp.fr
Notes	Funding: this study is funded by the CNAMTS for the period 2016–2018

PERMALINK

Mobile phone text messaging and app‐based interventions for smoking cessation

Robyn Whittaker

Hayden McRobbie

Chris Bullen

Anthony Rodgers

Yulong Gu

Rosie Dobson

Abstract

Background

Objectives

Search methods

Selection criteria

Data collection and analysis

Main results

Authors' conclusions

Plain language summary

Summary of findings

Background

Description of the condition

Description of the intervention

How the intervention might work

Why it is important to do this review

Objectives

Methods

Criteria for considering studies for this review

Types of studies

Types of participants

Types of interventions

Types of outcome measures

Search methods for identification of studies

Data collection and analysis

Selection of studies

Data extraction and management

Assessment of risk of bias in included studies

Measures of treatment effect

Dealing with missing data

Assessment of heterogeneity

Assessment of reporting biases

Data synthesis

Subgroup analysis and investigation of heterogeneity

Sensitivity analysis

'Summary of findings' tables

Results

Description of studies

Results of the search

1.

Included studies

Context and participants

Intervention programmes

Text messaging

Smartphone apps

Carbon monoxide (CO) monitoring and contingency management

Smartphone app plus text messaging

Outcome

Risk of bias in included studies

2.

Selection bias

Detection bias

Attrition bias

Other

Effects of interventions

Summary of findings for the main comparison. Text messaging compared to minimal support for smoking cessation.

Summary of findings 2. Text messaging in addition to other smoking cessation support.

Summary of findings 3. Smartphone app compared to lower‐intensity support for smoking cessation.

Text messaging versus minimal smoking cessation support

1.1. Analysis.

1.2. Analysis.

Text messaging versus other smoking cessation intervention

2.1. Analysis.

2.2. Analysis.

Text messaging plus other smoking cessation support versus other smoking cessation support alone

3.1. Analysis.

3.2. Analysis.

High‐frequency versus low‐frequency text messaging

4.1. Analysis.

4.2. Analysis.

Smartphone app versus lower‐intensity smoking cessation support

5.1. Analysis.

5.2. Analysis.

Trial name or title	Babies living safe and smokefree (BLiSS)
Methods	RCT
Participants	Female smokers aged 18+, living with a child < 6 years old, in USA
Interventions	Multimodal behavioural intervention (MBI) treatment: mobile app on cessation + Ask advise refer (AAR) + telephone cessation counselling + NRT gum/lozenge Control: AAR + telephone nutrition counselling + mobile phone nutrition app
Outcomes	Point prevalence abstinence at 12 months
Starting date	February 2016
Contact information	Bradley N. Collins, collinsb@temple.edu
Notes	Funding: this project was supported by a grant from the National Institutes of Health (CA188813) to Lepore and Collins (multi‐PIs).

Trial name or title	Tobacco cessation at non communicable disease clinics
Methods	RCT
Participants	Smokers aged 30‐80, with 1+ non communicable disease (diabetes, hypertension, CVD, stroke, cancer), in India
Interventions	Mobile messages and calls on tobacco cessation + counselling Control: counselling
Outcomes	Point prevalence abstinence at 6 months
Starting date	May 2018
Contact information	Garima Bhatt, garimabhatt.90@gmail.com; Dr Sonu Goel, sonugoel007@yahoo.co.in
Notes	Source of monetary or material support: Post Graduate Institute of Medical Education and Research Sector 12 Chandigarh

Trial name or title	A clinical trial to study the effect of WhatsApp and pamphlet based quit smoking interference among software professionals in Bengaluru City
Methods	RCT
Participants	Smokers aged 20‐40, software professionals, in India
Interventions	WhatsApp text on tobacco cessation counselling Control: self‐help pamphlet
Outcomes	Smoking abstinence at 6 months
Starting date	November 2017
Contact information	Silpi Chatterjee, dr.silpi510@gmail.com; Archana Krishna Murty, archanakm20@gmail.com
Notes

Trial name or title	Optimizing text messaging to improve adherence to web‐based cessation treatment
Methods	RCT
Participants	Smokers aged 18+ in USA
Interventions	Phase 1: mobile text messaging (personalisation, integration with web‐based programme, tailoring, varying levels of message intensity). Phase 2: BecomeAnEX.org web‐based smoking cessation programme + optimal‐adherence text message programme from Phase 1. Phase 1: full factorial design of 4 factors each with 2 levels (16 arms); Phase 2: 2‐arm randomised trial. Phase 1 control: standard text message programme. Phase 2 control: BecomeAnEX.org web‐based smoking cessation program. Funding: National Institute on Drug Abuse of the National Institutes of Health (#1 R01 DA 038139‐01A1; Graham, PI)
Outcomes	Point prevalence abstinence at 9 months
Starting date	March 2018
Contact information	Amanda L Graham, agraham@truthinitiative.org
Notes

Trial name or title	An integrated digital/clinical approach to smoking cessation in lung cancer screening
Methods	RCT
Participants	Smokers aged 18+ in USA
Interventions	Mobile text messaging + access to BecomeAnEx website + consult with a trained tobacco treatment specialist Control: brief cessation counselling
Outcomes	Smoking abstinence at 6 months
Starting date	August 2017
Contact information	Amanda L Graham, agraham@truthinitiative.org
Notes	Funding: National Cancer Institute of the National Institutes of Health under Award Number R01CA207048

Trial name or title	Efficacy of a smoking cessation intervention using smartphones
Methods	RCT
Participants	Smokers aged 18+ and buddies non‐smoker aged 18+ in Switzerland
Interventions	Smartphone app (SmokeFree Buddy) to support cessation. The participants choose a buddy (self‐chosen from the personal social network) Control: announces a self‐set QD, and try to stop smoking on their own
Outcomes	Smoking abstinence at 6 months
Starting date	June 2017
Contact information	Philipp Schwaninger, philipp.schwaninger@uzh.ch
Notes	Funding: University of Zurich (Switzerland)

Trial name or title	Impact of a smartphone application on smoking cessation: a RCT
Methods	RCT
Participants	Smokers aged 18+ in Switzerland or in France
Interventions	Smartphone app (Stop‐tabac) with (1) immediate feedback during episodes of craving and tobacco withdrawal symptoms; (2) an interactive 'coach' who provides individually‐tailored counselling messages; (3) a discussion forum (The Tribe) where participants receive support from other users; (4) fact sheets; a calculator of cigarettes not smoked, money saved, and years of life gained; (5) a module on NRT Control: a placebo smartphone app
Outcomes	Smoking abstinence at 6 months
Starting date	September 2018
Contact information	Jean‐François Etter. ORCID ID: orcid.org/0000‐0002‐1426‐3157. ISG‐Campus Biotech, 9 ch. des Mines, Geneva 1202 Switzerland
Notes	Funder: Swiss National Science Foundation

Trial name or title	Evaluation of the effectiveness of a text‐based mHealth smoking cessation intervention among high school students in Sweden
Methods	RCT
Participants	Smokers, as high school students, in Sweden
Interventions	Mobile phone text messages of 13 weeks Control: treatment as usual
Outcomes	Point prevalence abstinence at 6 months
Starting date	November 2017
Contact information	Ulrika Müssener. ORCID ID: orcid.org/0000‐0001‐5173‐5419. Department of Medical and Health Sciences, Faculty of Medicine and Health, Linköping university, Linköping 58183 Sweden
Notes	Funder: Linköping University (Sweden)

Trial name or title	Mobile health interventions for smoking cessation services uptake and smoking cessation: a factorial randomised trial in Thailand
Methods	RCT
Participants	Smokers aged 18+ in Thailand
Interventions	Mobile phone text messages for 30 days
Outcomes	Smoking abstinence at 6 months Control: mobile messages thanking participants for being part of project (contain no behaviour change)
Starting date	December 2015
Contact information	Pritaporn Kingkaew, umpk@leeds.ac.uk
Notes	Funder: Health Promotion Economic Evaluation Collaborative Center (Thailand)

Trial name or title	Evaluation of the "Stop Tabac" Android phone application
Methods	RCT
Participants	Smokers aged 18+ in Switzerland
Interventions	Smartphone app (Stop Tabac) to support cessation Control: placebo (smartphone application with minimal content on stopping smoking
Outcomes	Smoking abstinence at 6 months
Starting date	June 2015
Contact information	Céline Mavrot, celine.mavrot@kpm.unibe.ch
Notes	Funder: Tobacco Control Fund of the Swiss Federal Office of Public Health