Lessons learned from unsuccessful use of personal carbon monoxide monitors to remotely assess abstinence in a pragmatic trial of a smartphone stop smoking app - A secondary analysis

Reintroducing face-to-face support alongside remote support to form a hybrid stop smoking service in England: a formative mixed methods evaluation

BACKGROUND

During the COVID-19 pandemic, United Kingdom (UK) stop smoking services had to shift to remote delivery models due to social distancing regulations, later reintroducing face-to-face provision. The "Living Well Smokefree" service in North Yorkshire County Council adopted a hybrid model offering face-to-face, remote, or a mix of both. This evaluation aimed to assess the hybrid approach's strengths and weaknesses and explore potential improvements.

METHODS

Conducted from September 2022 to February 2023, the evaluation consisted of three components. First, qualitative interviews involved 11 staff and 16 service users, analysed thematically. Second, quantitative data from the QuitManager system that monitored the numbers and proportions of individuals selecting and successfully completing a 4-week quit via each service option. Third, face-to-face service expenses data was used to estimate the value for money of additional face-to-face provision. The qualitative findings were used to give context to the quantitative data via an "expansion" approach and complementary analysis.

RESULTS

Overall, a hybrid model was seen to provide convenience and flexible options for support. In the evaluation, 733 individuals accessed the service, with 91.3% selecting remote support, 6.1% face-to-face, and 2.6% mixed provision. Remote support was valued by service users and staff for promoting openness, privacy, and reducing stigma, and was noted as removing access barriers and improving service availability. However, the absence of carbon monoxide monitoring in remote support raised accountability concerns. The trade-off in "quantity vs. quality" of quits was debated, as remote support reached more users but produced fewer carbon monoxide-validated quits. Primarily offering remote support could lead to substantial workloads, as staff often extend their roles to include social/mental health support, which was sometimes emotionally challenging. Offering service users a choice of support options was considered more important than the "cost-per-quit". Improved dissemination of information to support service users in understanding their options for support was suggested.

CONCLUSIONS

The hybrid approach allows smoking cessation services to evaluate which groups benefit from remote, face-to-face, or mixed options and allocate resources accordingly. Providing choice, flexible provision, non-judgmental support, and clear information about available options could improve engagement and match support to individual needs, enhancing outcomes.

Relative Efficacy of an Acceptance and Commitment Therapy-Based Smartphone App with a Standard US Clinical Practice Guidelines-Based App for Smoking Cessation in Dual Users of Combustible and Electronic Cigarettes: Secondary Findings from a Randomized Trial

BACKGROUND

While smartphone apps for smoking cessation have shown promise for combustible cigarette smoking cessation, their efficacy in helping dual users of combustible and electronic cigarettes (e-cigarettes) to quit cigarettes remains unknown. This study utilized data from a randomized trial to determine if an Acceptance and Commitment Therapy (ACT)-based app (iCanQuit) was more efficacious than a US Clinical Practice Guidelines-based app (QuitGuide) for combustible cigarette smoking cessation among 575 dual users.

METHODS

The primary cessation outcome was self-reported, complete-case 30-day abstinence from combustible cigarettes at 12 months. Logistic regression assessed the interaction between dual use and treatment arm on the primary outcome in the full trial sample (N = 2,415). We then compared the primary outcome between arms among dual users (iCanQuit: n = 297; QuitGuide: n = 178). Mediation analyses were conducted to explore mechanisms of action of the intervention: acceptance of cues to smoke and app engagement. Results: There was an interaction between dual use of combustible and e-cigarettes and treatment arm on the primary outcome (p = 0.001). Among dual users, 12-month abstinence from cigarettes did not differ between arms (23% for iCanQuit vs. 27% for QuitGuide, p = 0.40). Mediation analysis revealed a significant positive indirect effect of the iCanQuit app on 12-month abstinence from cigarettes through acceptance of emotions that cue smoking (p = 0.004).

CONCLUSIONS

Findings from this study of dual users of combustible and e-cigarettes showed no evidence of a difference in quit rates between arms. Acceptance of emotions that cue smoking is a potential mechanism contributing to cigarette smoking abstinence among dual users.

Do Smartphone Apps Impact Long-Term Smoking Cessation for Sexual and Gender Minority Adults? Exploratory Results from a 2-Arm Randomized Trial Comparing Acceptance and Commitment Therapy with Standard US Clinical Practice Guidelines

Sexual and gender minority (SGM) adults face unique challenges in accessing smoking cessation care due to stigma tied to their identities and smoking. While cessation apps show promise in the general population, their efficacy for SGM adults is unclear. This study utilized data from a randomized trial to compare two cessation apps, iCanQuit (Acceptance and Commitment Therapy-based) and QuitGuide (US Clinical Practice Guidelines-based) among 403 SGM adults. The primary outcome was self-reported complete-case 30-day abstinence from cigarette smoking at 12 months. Mediation analyses explored whether interventions operated through acceptance of cues to smoke and app engagement. At 12 months, quit rates did not differ between arms (26% iCanQuit vs. 22% QuitGuide, OR = 1.22; 95% CI: 0.74 to 2.00, p = .43). iCanQuit positively impacted cessation via acceptance of cues to smoke (indirect effect = 0.23; 95% CI: 0.06 to 0.50, p < .001) and demonstrated higher engagement (no. logins, 28.4 vs. 12.1; p < .001) and satisfaction (91% vs. 75%, OR = 4.18; 95% CI: 2.12 to 8.25, p < .001) than QuitGuide. Although quit rates did not differ between arms, acceptance of cues to smoke seemed to play a crucial role in helping SGM adults quit smoking. Future interventions should consider promoting acceptance of cues to smoke in this population.

Outback Quit Pack: Feasibility trial of outreach smoking cessation for people in rural, regional, and remote Australia

BACKGROUND

Tobacco smoking rates are higher in rural, regional, and remote (RRR) areas in Australia, and strategies to improve access to quit supports are required. This pilot study examined the feasibility of a smoking cessation intervention for people in RRR areas who smoke with the intention of using this data to design a powered effectiveness trial.

METHODS

A randomised controlled trial (RCT) of the feasibility of a 12-week 'Outback Quit Pack' intervention consisting of mailout combination nicotine replacement therapy (NRT) and a proactive referral to Quitline, compared with a minimal support control (1-page smoking cessation support information mailout) was conducted between January and October 2021. Participants recruited via mailed invitation or Facebook advertising, were adults who smoked tobacco (≥10 cigarettes/day) and resided in RRR areas of New South Wales, Australia. Participants completed baseline and 12-week follow-up telephone surveys. Outcomes were feasibility of trial procedures (recruitment method; retention; biochemical verification) and acceptability of intervention (engagement with Quitline; uptake and use of NRT).

RESULTS

Facebook advertising accounted for 97% of participant expressions of interest in the study (N = 100). Retention was similarly high among intervention (39/51) and control (36/49) participants. The intervention was highly acceptable: 80% of the intervention group had ≥1 completed call with Quitline, whilst Quitline made 3.7 outbound calls/participant (mean 14:05 mins duration). Most of the intervention group requested NRT refills (78%). No differences between groups in self-reported cessation outcomes. Biochemical verification using expired air breath testing was not feasible in this study.

CONCLUSION

The Outback Quit Pack intervention was feasible and acceptable. Alternative methods for remote biochemical verification need further study. SO WHAT?: A powered RCT to test the effectiveness of the intervention to improve access to evidence-based smoking cessation support to people residing in RRR areas is warranted.

Use of e-Cigarettes in Cigarette Smoking Cessation: Secondary Analysis of a Randomized Controlled Trial

BACKGROUND

Many adults use e-cigarettes to help them quit cigarette smoking. However, the impact of self-selected use of e-cigarettes on cigarette smoking cessation, particularly when concurrently receiving app-based behavioral interventions, remains unexplored.

OBJECTIVE

This study used data from a randomized trial of 2 smartphone apps to compare 12-month cigarette smoking cessation rates between participants who used e-cigarettes on their own (ie, adopters: n=465) versus those who did not (ie, nonadopters: n=1097).

METHODS

The study population included all participants who did not use e-cigarettes at baseline. "Adopters" were those who self-reported the use of e-cigarettes at either 3- or 6-month follow-ups. "Nonadopters" were those who self-reported no use of e-cigarettes at either follow-up time point. The primary cessation outcome was self-reported, complete-case, 30-day point prevalence abstinence from cigarette smoking at 12 months. Secondary outcomes were missing-as-smoking and multiple imputation analyses of the primary outcome, prolonged abstinence, and cessation of all nicotine and tobacco products at 12 months. In logistic regression models, we first examined the potential interaction between e-cigarette use and treatment arm (iCanQuit vs QuitGuide) on the primary cessation outcome. Subsequently, we compared 12-month cigarette smoking cessation rates between adopters and nonadopters separately for each app.

RESULTS

There was suggestive evidence for an interaction between e-cigarette use and treatment arm on cessation (P=.05). In the iCanQuit arm, 12-month cigarette smoking cessation rates were significantly lower among e-cigarette adopters compared with nonadopters (41/193, 21.2% vs 184/527, 34.9%; P=.003; odds ratio 0.55, 95% CI 0.37-0.81). In contrast, in the QuitGuide arm, 12-month cigarette smoking cessation rates did not differ between adopters and nonadopters (46/246, 18.7% vs 104/522, 19.9%; P=.64; odds ratio 0.91, 95% CI 0.62-1.35).

CONCLUSIONS

The use of e-cigarettes while concurrently receiving an app-based smoking cessation intervention was associated with either a lower or an unimproved likelihood of quitting cigarette smoking compared to no use. Future behavioral treatments for cigarette smoking cessation should consider including information on the potential consequences of e-cigarette use.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02724462; https://clinicaltrials.gov/study/NCT02724462.

A Scoping Review and Meta-analysis of the Use of Remote Biochemical Verification Methods of Smoking Status in Tobacco Research

INTRODUCTION

Increasing digital delivery of smoking cessation interventions has resulted in the need to employ novel strategies for remote biochemical verification.

AIMS AND METHODS

This scoping review and meta-analysis aimed to investigate best practices for remote biochemical verification of smoking status. The scientific literature was searched for studies that reported remotely obtained (not in-person) biochemical confirmation of smoking status (ie, combustible tobacco). A meta-analysis of proportions was conducted to investigate key outcomes, which included rates of returned biological samples and the ratio of biochemically verified to self-reported abstinence rates.

RESULTS

A total of 82 studies were included. The most common samples were expired air (46%) and saliva (40% of studies), the most common biomarkers were carbon monoxide (48%) and cotinine (44%), and the most common verification methods were video confirmation (37%) and mail-in samples for lab analysis (26%). Mean sample return rates determined by random-effects meta-analysis were 70% for smoking cessation intervention studies without contingency management (CM), 77% for CM studies, and 65% for other studies (eg, feasibility and secondary analyses). Among smoking cessation intervention studies without CM, self-reported abstinence rates were 21%, biochemically verified abstinence rates were 10%, and 47% of individuals who self-reported abstinence were also biochemically confirmed as abstinent.

CONCLUSIONS

This scoping review suggests that improvements in sample return rates in remote biochemical verification studies of smoking status are needed. Recommendations for reporting standards are provided that may enhance confidence in the validity of reported abstinence rates in remote studies.

IMPLICATIONS

This scoping review and meta-analysis included studies using remote biochemical verification to determine smoking status. Challenges exist regarding implementation and ensuring high sample return rates. Higher self-reported compared to biochemically verified abstinence rates suggest the possibility that participants in remote studies may be misreporting abstinence or not returning samples for other reasons (eg, participant burden, inconvenience). Remote biochemical confirmation of self-reported smoking abstinence should be included in smoking cessation studies whenever feasible. However, findings should be considered in the context of challenges to sample return rates. Better reporting guidelines for future studies in this area are needed.

Efficacy of smartphone applications for smoking cessation in heavy-drinking adults: Secondary analysis of the iCanQuit randomized trial

INTRODUCTION

Efficacious smoking cessation treatments are needed for heavy-drinking adults who often have difficulty quitting smoking. In a secondary analysis of a parent randomized controlled trial, we explored the efficacy of an Acceptance and Commitment Therapy (ACT)-based smartphone application (iCanQuit) versus a US Clinical Practice Guidelines (USCPG)-based smartphone application (QuitGuide) for smoking cessation among heavy-drinking participants (4 + drinks/day for women; 5 + drinks/day for men).

METHODS

Participants were randomized to receive iCanQuit (n = 188) or QuitGuide (n = 160) for 12-months. Smoking cessation outcomes were measured at 3, 6 and 12-months. The primary outcome was self-reported complete-case 30-day point prevalence abstinence (PPA) at 12-months. Secondary outcomes were 7-day PPA at all timepoints; prolonged abstinence; and cessation of all nicotine-containing products at 12-months. Multiple imputation and missing-as-smoking analyses were also conducted. Exploratory outcomes were cessation of both smoking and heavy drinking and change in alcohol use (drinks/day) at 12-months. Treatment engagement and satisfaction and change in ACT-based processes were compared between arms.

RESULTS

Retention rate was 85% at 12-months and did not differ by arm. At 12-months, iCanQuit participants had nearly double the odds of smoking cessation compared to QuitGuide (complete-case 30-day PPA = 24% vs. 15%; OR = 1.87 95% CI: 1.03, 3.42). Findings were similar for the multiple imputation and missing-as-smoking outcomes at 12-months. Combined cessation of smoking and heavy drinking, and alcohol use at 12-months did not differ by arm. iCanQuit was significantly more engaging and satisfying than QuitGuide. Increased acceptance of thoughts about smoking mediated the effect of treatment on cessation of heavy drinking at 12-months.

CONCLUSIONS

The iCanQuit smartphone application was more efficacious and engaging for smoking cessation among heavy-drinking adults than a USCPG-based smartphone application.

Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial

BACKGROUND

Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes.

OBJECTIVE

In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership.

METHODS

Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app.

RESULTS

For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app.

CONCLUSIONS

Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.

Efficacy and utilization of an acceptance and commitment therapy-based smartphone application for smoking cessation among Black adults: secondary analysis of the iCanQuit randomized trial

BACKGROUND AND AIMS

Black adults who smoke are less likely to seek treatment and to succeed in quitting compared with other racial groups. The lack of efficacious and engaging trials for smoking cessation further contributes to this disparity. This study explored whether an acceptance and commitment therapy (ACT)-based smartphone application (iCanQuit) was more efficacious for smoking cessation than a United States Clinical Practice Guidelines (USCPG)-based smartphone application (QuitGuide) among Black adults.

DESIGN

Secondary analysis of a two-arm randomized trial with 12-month follow-up.

SETTING

United States (US).

PARTICIPANTS

A total of 554 Black adults who smoke daily were recruited from 34 US states and enrolled between May 2017 and September 2018.

INTERVENTIONS

Participants were randomized to receive iCanQuit (n = 274) or QuitGuide (n = 280) for 12 months.

MEASUREMENTS

Smoking cessation outcomes were measured at 3, 6, and 12 months. The primary outcome was self-reported complete-case 30-day point prevalence abstinence (PPA) at 12 months. Secondary outcomes were 7-day PPA, missing-as-smoking imputation, multiple imputation, prolonged abstinence, and cessation of all tobacco products at 12 months. Study retention, treatment engagement, and change in ACT-based processes were also compared between arms.

FINDINGS

Study retention was 89% at 12 months and did not differ by arm (P > 0.05). The complete-case 30-day PPA was 28% for iCanQuit versus 20% for QuitGuide at 12 months (odds ratio [OR] = 1.60; 95% confidence interval [CI] = 1.03, 2.46). Similar associations were observed for the missing-as-smoking imputation, although non-significant (25% iCanQuit vs 18% QuitGuide; OR = 1.50; 95% CI = 0.98, 2.30). iCanQuit vs QuitGuide participants were significantly more engaged with iCanQuit application as measured by the number of logins from baseline to 6 months (incidence rate ratio = 3.26; 95% CI = 2.58, 4.13). Increased acceptance of cues to smoke mediated the effect of treatment on cessation (indirect effect: OR = 0.20; 95% CI = 0.05, 0.29).

CONCLUSIONS

Among Black adults, an acceptance and commitment therapy-based smartphone application appeared to be more efficacious and engaging for smoking cessation than the United States Clinical Practice Guidelines-based QuitGuide application.

Supporting smoking cessation and preventing relapse following a stay in a smoke-free setting: a meta-analysis and investigation of effective behaviour change techniques

BACKGROUND AND AIMS

Admission to a smoke-free setting presents a unique opportunity to encourage smokers to quit. However, risk of relapse post-discharge is high, and little is known about effective strategies to support smoking cessation following discharge. We aimed to identify interventions that maintain abstinence following a smoke-free stay and determine their effectiveness, as well as the probable effectiveness of behaviour change techniques (BCTs) used in these interventions.

METHODS

Systematic review and meta-analyses of studies of adult smokers aged ≥ 18 years who were temporarily or fully abstinent from smoking to comply with institutional smoke-free policies. Institutions included prison, inpatient mental health, substance misuse or acute hospital settings. A Mantel-Haenszel random-effects meta-analysis of randomized controlled trials (RCTs) was conducted using biochemically verified abstinence (7-day point prevalence or continuous abstinence). BCTs were defined as 'promising' in terms of probable effectiveness (if BCT was present in two or more long-term effective interventions) and feasibility (if BCT was also delivered in ≥ 25% of all interventions).

RESULTS

Thirty-seven studies (intervention n = 9041, control n = 6195) were included: 23 RCTs (intervention n = 6593, control n = 5801); three non-randomized trials (intervention n = 845, control n = 394) and 11 cohort studies (n = 1603). Meta-analysis of biochemically verified abstinence at longest follow-up (4 weeks-18 months) found an overall effect in favour of intervention [risk ratio (RR) = 1.27, 95% confidence interval (CI) = 1.08-1.49, I²  = 42%]. Nine BCTs (including 'pharmacological support', 'goal-setting (behaviour)' and 'social support') were characterized as 'promising' in terms of probable effectiveness and feasibility.

CONCLUSIONS

A systematic review and meta-analyses indicate that behavioural and pharmacological support is effective in maintaining smoking abstinence following a stay in a smoke-free institution. Several behaviour change techniques may help to maintain smoking abstinence up to 18 months post-discharge.

Feasibility and Acceptability of a Smoking Cessation Smartphone App (My QuitBuddy) in Older Persons: Pilot Randomized Controlled Trial

Background

Although many smoking cessation smartphone apps exist, few have been independently evaluated, particularly in older populations. In 2017, of the 112 commercially available smoking cessation apps in Australia, only 6 were deemed to be of high quality, in that they partially adhered to Australian guidelines. Mobile health (mHealth) apps have the potential to modify smoking behavior at a relatively low cost; however, their acceptability in older smokers remains unknown. Rigorous scientific evaluation of apps is thus urgently needed to assist smokers and clinicians alike.

Objective

We conducted a pilot randomized controlled trial to evaluate the feasibility of a large-scale trial to assess the use and acceptability of a high-quality smoking cessation app in older smokers.

Methods

Adult inpatient and outpatient smokers with computer and smartphone access were recruited face to face and via telephone interviews from Metropolitan Hospitals in Brisbane, Australia. Participants were randomized 1:1 to the intervention (requested to download the “My QuitBuddy” smoking cessation app on their smartphone) or the control group (provided access to a tailored smoking cessation support webpage [Quit HQ]). The My QuitBuddy app is freely available from app stores and provides personalized evidenced-based smoking cessation support. Quit HQ offers regular email support over 12 weeks. No training or instructions on the use of these e-resources were given to participants. Outcomes at 3 months included recruitment and retention rates, use and acceptability of e-resource (User Version of the Mobile App Rating Scale [uMARS]), changes in quitting motivation (10-point scale), and self-reported smoking abstinence.

Results

We randomized 64 of 231 potentially eligible individuals (27.7%). The mean age of participants was 62 (SD 8). Nicotine dependence was moderate (mean Heaviness of Smoking Index [HSI] 2.8 [SD 1.2]). At 3 months the retention rate was (58/64, 91%). A total of 15 of 31 participants in the intervention arm (48%) used the app at least once, compared with 10 of 33 (30%) in the control arm. uMARS scores for e-resource use and acceptability were statistically similar (P=.29). Motivation to quit was significantly higher in the intervention arm compared with the control arm (median 6 [IQR 4-8] versus 4 [IQR 4-5], respectively, P=.02). According to the intention-to-treat analysis, smoking abstinence was nonsignificantly higher in the intervention group (4/31 [13%], 95% CI 4%-30%, versus 2/33 [6%], 95% CI 1%-20%; P=.42). The estimated number needed to treat was 14.

Conclusions

Internet and mHealth smoking cessation resources appear acceptable to a minority of older smokers. Smokers who engaged with the allocated e-resources rated them equally, and there were trends toward greater uptake, increased motivation, and higher abstinence rates in the app group; however, only the change in motivation reached statistical significance (median score 6 versus 4, respectively, P=.02). This results of this pilot study suggest that apps may improve quit outcomes in older adults who are willing to use them. Further research into user–app interactions should be undertaken to facilitate improvements in app design and consumer engagement. These favorable trends should be explored in larger trials with sufficient statistical power.

Trial Registration

Australian New Zealand Clinical Trials Registry ACTRN12619000159156; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=376849&isReview=true

Mobile, Remote, and Individual Focused: Comparing Breath Carbon Monoxide Readings and Abstinence Between Smartphone-Enabled and Stand-Alone Monitors

INTRODUCTION

Newly available, smartphone-enabled carbon monoxide (CO) monitors are lower in cost than traditional stand-alone monitors and represent a marked advancement for smoking research. New products are promising, but data are needed to compare breath CO readings between smartphone-enabled and stand-alone monitors. The purpose of this study was to (1) determine the agreement between the mobile iCO (Bedfont Scientific Ltd) with two other monitors from the same manufacturer (Micro+ pro and Micro+ basic) and (2) determine optimal, monitor-specific, cotinine-confirmed abstinence cutoff values.

METHODS

Adult (≥18) smokers (n = 26) and nonsmokers (n = 21) provided three breath CO samples (using three different monitors) in each of 10 sessions, and urine cotinine was measured for gold standard determination of abstinence. CO comparisons (N = 437) were analyzed using regression-based Bland-Altman Analysis of Agreement; receiver operating characteristics curves were used to determine optimal abstinence cutoffs.

RESULTS

Bland-Altman analyses indicated that the iCO monitor provided higher CO results than both Micro+ monitors. Sensitivity and specificity analyses showed that the optimal CO cutoff for determining abstinence was <3 ppm for the Micro+ pro (88% sensitivity, 93% specificity) and Micro+ basic (83% sensitivity, 98% specificity), but was higher for the iCO (<6 ppm; 73% sensitivity, 100% specificity).

CONCLUSIONS

Relative to both Micro+ monitors, the smartphone-enabled iCO provided systematically higher CO values and required a higher cutoff to reliably determine smoking abstinence. This does not indicate that CO values obtained using the iCO are not valid; instead, these results suggest that monitor-specific abstinence cutoffs are needed to ensure accurate bioverification of smoking status.

IMPLICATIONS

Results from this study indicate that CO values from the smartphone-enabled iCO should not be used interchangeably with the stand-alone Micro+ pro and Micro+ basic, particularly when lower CO values (<10 ppm) are critical (ie, determination of abstinence vs confirming smoking status for study inclusion). Optimal CO cutoffs recommended for determining abstinence on Micro+ and iCO monitors are at <3 and <6 ppm, respectively.

A Facebook intervention to address cigarette smoking and heavy episodic drinking: A pilot randomized controlled trial

BACKGROUND

Co-occurrence of tobacco use and heavy episodic drinking (HED; 5+ drinks for men and 4+ drinks for women per occasion) is common among young adults; both warrant attention and intervention. In a two-group randomized pilot trial, we investigated whether a Facebook-based smoking cessation intervention addressing both alcohol and tobacco use would increase smoking abstinence and reduce HED compared to a similar intervention addressing only tobacco.

METHODS

Participants were 179 young adults (age 18-25; 49.7% male; 80.4% non-Hispanic white) recruited from Facebook and Instagram who reported smoking 4+ days/week and past-month HED. The Smoking Tobacco and Drinking (STAND) intervention (N = 84) and the Tobacco Status Project (TSP), a tobacco-only intervention (N = 95), both included daily Facebook posts for 90 days and weekly live counseling sessions in private "secret" groups. We verified self-reported 7-day smoking abstinence via remote salivary cotinine tests at 3, 6, and 12 months (with retention at 83%, 66%, and 84%, respectively). Participants self-reported alcohol use.

RESULTS

At baseline, the participants averaged 10.4 cigarettes per day (SD = 6.9) and 8.9 HED occasions in the past month (SD = 8.1), with 27.4% in a preparation stage of change for quitting smoking cigarettes. Participants reported significant improvements in cigarette smoking and alcohol use outcomes over time, with no significant differences by condition. At 12 months, intent-to-treat smoking abstinence rates were 3.5% in STAND vs. 0% in TSP (biochemically verified) and 29.4% in STAND vs. 25.5% in TSP (self-reported). Compared to TSP, participants rated the STAND intervention more favorably for supporting health and providing useful information.

CONCLUSIONS

Adding an alcohol treatment component to a tobacco cessation social media intervention was acceptable and engaging but did not result in significant differences by treatment condition in smoking or alcohol use outcomes. Participants in both conditions reported smoking and drinking less over time, suggesting covariation in behavioral changes.

Does addition of craving management tools in a stop smoking app improve quit rates among adult smokers? Results from BupaQuit pragmatic pilot randomised controlled trial

OBJECTIVES

Delivery of craving management tools via smartphone applications (apps) may improve smoking cessation rates, but research on such programmes remains limited, especially in real-world settings. This study evaluated the effectiveness of adding craving management tools in a cessation app (BupaQuit).

METHODS

The study was a two-arm pragmatic pilot parallel randomised controlled trial, comparing a fully-automated BupaQuit app with craving management tool with a control app version without craving management tool. A total of 425 adult UK-based daily smokers were enrolled through open online recruitment (February 2015-March 2016), with no researcher involvement, and individually randomised within the app to the intervention (n = 208) or control (n = 217). The primary outcome was self-reported 14-day continuous abstinence assessed at 4-week follow-up. Secondary outcomes included 6-month point-prevalence and sustained abstinence, and app usage. The primary outcome was assessed with Fisher's exact test using intent to treat with those lost to follow-up counted as smoking. Participants were not reimbursed.

RESULTS

Re-contact rates were 50.4% at 4 weeks and 40.2% at 6 months. There was no significant difference between intervention and control arms on the primary outcome (13.5% vs 15.7%; p = 0.58; relative risk = 0.86, 95% confidence interval = 0.54-1.36) or secondary cessation outcomes (6-month point prevalence: 14.4% vs 17.1%, p = 0.51; relative risk = 0.85, 95% confidence interval = 0.54-1.32; 6-month sustained: 11.1% vs 13.4%, p = 0.55; relative risk = 0.83, 95% confidence interval = 0.50-1.38). Bayes factors supported the null hypothesis (B[0, 0, 1.0986] = 0.20). Usage was similar across the conditions (mean/median logins: 9.6/4 vs 10.5/5; time spent: 401.8/202 s vs 325.8/209 s).

CONCLUSIONS

The addition of craving management tools did not affect cessation, and the limited engagement with the app may have contributed to this.

Remote Methods for Conducting Tobacco-Focused Clinical Trials

Most tobacco-focused clinical trials are based on locally conducted studies that face significant challenges to implementation and successful execution. These challenges include the need for large, diverse, yet still representative study samples. This often means a protracted, costly, and inefficient recruitment process. Multisite clinical trials can overcome some of these hurdles but incur their own unique challenges. With recent advances in mobile health and digital technologies, there is now a promising alternative: Remote Trials. These trials are led and coordinated by a local investigative team, but are based remotely, within a given community, state, or even nation. The remote approach affords many of the benefits of multisite trials (more efficient recruitment of larger study samples) without the same barriers (cost, multisite management, and regulatory hurdles). The Coronavirus Disease 2019 (COVID-19) global health pandemic has resulted in rapid requirements to shift ongoing clinical trials to remote delivery and assessment platforms, making methods for the conduct of remote trials even more timely. The purpose of the present review is to provide an overview of available methods for the conduct of remote tobacco-focused clinical trials as well as illustrative examples of how these methods have been implemented across recently completed and ongoing tobacco studies. We focus on key aspects of the clinical trial pipeline including remote: (1) study recruitment and screening, (2) informed consent, (3) assessment, (4) biomarker collection, and (5) medication adherence monitoring. Implications With recent advances in mobile health and digital technologies, remote trials now offer a promising alternative to traditional in-person clinical trials. Remote trials afford expedient recruitment of large, demographically representative study samples, without undo burden to a research team. The present review provides an overview of available methods for the conduct of remote tobacco-focused clinical trials across key aspects of the clinical trial pipeline.

Decrease in Resting Heart Rate Measured Using Smartphone Apps to Verify Abstinence From Smoking: An Exploratory Study

INTRODUCTION

Verifying self-reports of smoking abstinence is challenging in studies that involve remote data collection. Resting heart rate (HR) decreases during smoking abstinence. This study assessed whether a decrease in resting HR measured using freely available smartphone apps could potentially be used to verify smoking abstinence.

METHODS

This study involved a repeated measures experimental design, with data collection in natural setting. Participants were 18 adult, daily smokers. They recorded resting HR in beats per minute (bpm) using freely available smartphone apps during five timepoints (two in the morning and three postnoon) on each of 3 days. The outcome measure was the mean of the postnoon HR recordings. The experimental condition for each of the 3 days (counterbalanced order) was as follows: (1) smoking as usual, (2) not smoking without nicotine replacement therapy (NRT), or (3) not smoking but using NRT. Abstinence was verified using expired-air carbon monoxide (CO) concentration.

RESULTS

Compared with the smoking as usual condition, mean HR was 13.4 bpm lower (95% confidence interval [CI] = 5.4 to 21.4, p = .001) in the not smoking without NRT condition and 10.4 bpm lower (95% CI = 3.1 to 17.8, p = 0.004) in the not smoking with NRT condition. There was no statistically significant difference in HR between the two not smoking conditions (p = .39). Abstinence during not smoking days without and with NRT was CO-verified in 18/18 and in 16/18 cases, respectively.

CONCLUSIONS

Self-recording of resting HR in natural setting using smartphone apps shows a reliable decrease in response to smoking abstinence and may provide a basis for remote verification in smoking cessation studies.

IMPLICATIONS

Remote verification of self-reported abstinence in smoking cessation studies remains challenging. Smoking abstinence has been shown to decrease resting HR under laboratory conditions. This study demonstrated that self-recording using freely available smartphone apps shows reliable decreases in resting HR during smoking abstinence and may provide a basis for inexpensive remote verification of smoking abstinence.

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

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.