Comparing the Efficacy of an Identical, Tailored Smoking Cessation Intervention Delivered by Mobile Text Messaging Versus Email: Randomized Controlled Trial

A systematic review on eHealth technology personalization approaches

Despite the widespread use of personalization of eHealth technologies, there is a lack of comprehensive understanding regarding its application. This systematic review aims to bridge this gap by identifying and clustering different personalization approaches based on the type of variables used for user segmentation and the adaptations to the eHealth technology and examining the role of computational methods in the literature. From the 412 included reports, we identified 13 clusters of personalization approaches, such as behavior + channeling and environment + recommendations. Within these clusters, 10 computational methods were utilized to match segments with technology adaptations, such as classification-based methods and reinforcement learning. Several gaps were identified in the literature, such as the limited exploration of technology-related variables, the limited focus on user interaction reminders, and a frequent reliance on a single type of variable for personalization. Future research should explore leveraging technology-specific features to attain individualistic segmentation approaches.

The Framing Effect of Digital Textual Messages on Uptake Rates of Medical Checkups: Field Study

BACKGROUND

Health care authorities often use text messages to enhance compliance with medical recommendations. The effectiveness of different message framings has been studied extensively over the past 3 decades. Recently, health care providers have begun using digital media platforms to disseminate health-related messages.

OBJECTIVE

This study aimed to examine the effectiveness of some of the most widely used message framings on the uptake rates of medical checkups.

METHODS

This study used a large-scale digital outreach campaign conducted by Maccabi Healthcare Services (MHS) during 2020-2021, involving a total of 113,048 participants. MHS members aged 50-74 years were invited to take their recommended medical actions from the following list: human papillomavirus (HPV), mammography, abdominal aortic aneurysm, fecal occult blood test (FOBT), and pneumococcal vaccination. Each member was randomly assigned to receive 1 of 6 message framings: control (neutrally framed; n=20,959, 18.5%), gains (benefits of compliance; n=20,393, 18%), losses (negative consequences of noncompliance; n=15,165, 13.4%), recommendation (a recommendation by an authoritative figure, in this context by a physician; n=20,584, 18.2%), implementation intentions (linking potential outcomes to future reactions; n=20,701, 18.3%), and empowerment (emphasizing personal responsibility for maintaining good health; n=15,246, 13.5%). The time frames for measuring a successful intervention were 14 days for scheduling screenings (ie, HPV, mammography, or abdominal aortic aneurysm), 30 days for performing the FOBT, and 60 days for receiving pneumococcal vaccination. We also examined the effectiveness of media channels (text message or email) on uptake rates and whether the subject-line length is correlated with message-opening rates.

RESULTS

No significant effect of message framing on uptake rates of medical checkups was observed. The rates of appointments for screening ranged from 12.9% to 14.1% across treatments. Based on a chi-square test, there was no evidence to reject the null hypothesis that these compliance rates are independent of the treatments (P=.35). The uptake rates for the FOBT and pneumococcal vaccination ranged from 23.3% to 23.8% across treatments, and we could not reject the hypothesis that they are independent of the treatments (P=.88). We also found that emails are more effective than text messages (P<.001) and that the subject-line length is negatively correlated with message-opening rates.

CONCLUSIONS

No evidence was found for an effect of the 5 message framings on uptake rates of medical checkups. To enhance compliance rates, public health officials may consider alternative framings. Furthermore, media channels and the subject-line length should be given careful consideration in the planning stages of health care campaigns.

TRIAL REGISTRATION

AEA RCT Registry AEARCTR-0006317; https://www.socialscienceregistry.org/trials/6317/history/201365.

Systematic review and meta-analysis of text messaging interventions to support tobacco cessation

OBJECTIVE

To review randomised controlled trials (RCTs) investigating the effectiveness of text message-based interventions for smoking cessation, including the effects of dose (number of text messages) and concomitant use of behavioural or pharmacological interventions.

DATA SOURCES

We searched seven databases (PubMed, CINAHL, PsycINFO, Scopus, EMBASE, Cochrane Library and Web of Science), Google Scholar and the reference lists of relevant publications for RCTs. Eligible studies included participants aged ≥15 years who smoked tobacco at enrolment.

STUDY SELECTION

One reviewer screened titles and abstracts and two reviewers independently screened full texts of articles.

DATA EXTRACTION

One of three reviewers independently extracted data on study and intervention characteristics and smoking abstinence rates using Qualtrics software.

DATA SYNTHESIS

30 of the 40 included studies reported higher rates of smoking cessation among those receiving text messaging interventions compared with comparators, but only 10 were statistically significant. A meta-analysis of seven RCTs found that participants receiving text messages were significantly more likely to quit smoking compared with participants in no/minimal intervention or 'usual care' conditions (risk ratio 1.87, 95% CI 1.52 to 2.29, p <0.001). Three trials found no benefit from a higher dose of text messages on smoking cessation. Two trials that tested the added benefit of text messaging to pharmacotherapy reported outcomes in favour of adding text messaging.

CONCLUSIONS

Findings suggest that text messaging-based interventions are effective at promoting smoking cessation. Further research is required to establish if any additional benefit is gained from an increased number of text messages or concurrent pharmacotherapy or behavioural counselling.

Effects of interventions on smoking cessation: A systematic review and network meta-analysis

A network meta-analysis (NMA) including randomized controlled trials (RCTs) was conducted to evaluate the effects of different interventions on smoking cessation. Studies were collected from online databases including PubMed, EMBASE, Cochrane Library, and Web of Science based on inclusion and exclusion criteria. Eligible studies were further examined in the NMA to compare the effect of 14 interventions on smoking cessation. Thirty-four studies were examined in the NMA, including a total of 14 interventions and 28 733 participants. The results showed that health education (HE; odds ratio ([OR] = 200.29, 95% CI [1.62, 24 794.61])), other interventions (OI; OR = 29.79, 95% CI [1.07, 882.17]) and multimodal interventions (MUIs; OR = 100.16, 95% CI [2.06, 4867.24]) were better than self-help material (SHM). HE (OR = 243.31, 95% CI [1.39, 42531.33]), MUI (OR = 121.67, 95% CI [1.64, 9004.86]) and financial incentive (FI; OR = 14.09, 95% CI [1.21, 164.31]) had positive effects on smoking cessation rate than smoking cessation or quitting APP (QA). Ranking results showed that HE (83.6%) and motivation interviewing (MI; 69.6%) had better short-term effects on smoking cessation. HE and MUI provided more smoking cessation benefits than SHM and QA. FI was more effective at quitting smoking than QA. Also, HE and MI were more likely to be optimal smoking cessation interventions.

Effectiveness of eHealth Smoking Cessation Interventions: Systematic Review and Meta-Analysis

BACKGROUND

Rapid advancements in eHealth and mobile health (mHealth) technologies have driven researchers to design and evaluate numerous technology-based interventions to promote smoking cessation. The evolving nature of cessation interventions emphasizes a strong need for knowledge synthesis.

OBJECTIVE

This systematic review and meta-analysis aimed to summarize recent evidence from randomized controlled trials regarding the effectiveness of eHealth-based smoking cessation interventions in promoting abstinence and assess nonabstinence outcome indicators, such as cigarette consumption and user satisfaction, via narrative synthesis.

METHODS

We searched for studies published in English between 2017 and June 30, 2022, in 4 databases: PubMed (including MEDLINE), PsycINFO, Embase, and Cochrane Library. Two independent reviewers performed study screening, data extraction, and quality assessment based on the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework. We pooled comparable studies based on the population, follow-up time, intervention, and control characteristics. Two researchers performed an independent meta-analysis on smoking abstinence using the Sidik-Jonkman random-effects model and log risk ratio (RR) as the effect measurement. For studies not included in the meta-analysis, the outcomes were narratively synthesized.

RESULTS

A total of 464 studies were identified through an initial database search after removing duplicates. Following screening and full-text assessments, we deemed 39 studies (n=37,341 participants) eligible for this review. Of these, 28 studies were shortlisted for meta-analysis. According to the meta-analysis, SMS or app text messaging can significantly increase both short-term (3 months) abstinence (log RR=0.50, 95% CI 0.25-0.75; I2=0.72%) and long-term (6 months) abstinence (log RR=0.77, 95% CI 0.49-1.04; I2=8.65%), relative to minimal cessation support. The frequency of texting did not significantly influence treatment outcomes. mHealth apps may significantly increase abstinence in the short term (log RR=0.76, 95% CI 0.09-1.42; I2=88.02%) but not in the long term (log RR=0.15, 95% CI -0.18 to 0.48; I2=80.06%), in contrast to less intensive cessation support. In addition, personalized or interactive interventions showed a moderate increase in cessation for both the short term (log RR=0.62, 95% CI 0.30-0.94; I2=66.50%) and long term (log RR=0.28, 95% CI 0.04-0.53; I2=73.42%). In contrast, studies without any personalized or interactive features had no significant impact. Finally, the treatment effect was similar between trials that used biochemically verified or self-reported abstinence. Among studies reporting outcomes besides abstinence (n=20), a total of 11 studies reported significantly improved nonabstinence outcomes in cigarette consumption (3/14, 21%) or user satisfaction (8/19, 42%).

CONCLUSIONS

Our review of 39 randomized controlled trials found that recent eHealth interventions might promote smoking cessation, with mHealth being the dominant approach. Despite their success, the effectiveness of such interventions may diminish with time. The design of more personalized interventions could potentially benefit future studies.

TRIAL REGISTRATION

PROSPERO CRD42022347104; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347104.

Assessment of a Text Message-Based Smoking Cessation Intervention for Adult Smokers in China: A Randomized Clinical Trial

IMPORTANCE

Successful smoking cessation strategies are an important part of reducing tobacco use. However, providing universal smoking cessation support can be a challenge for most countries because it requires sufficient resources. One way to expand access is to use mobile technologies to provide cessation support.

OBJECTIVE

To assess the efficacy of a behavior change theory-based smoking cessation intervention using personalized text messages.

DESIGN, SETTING, AND PARTICIPANTS

This study was a 2-arm double-blind randomized clinical trial conducted in 5 cities in China. Daily or weekly smokers 18 years or older were eligible for inclusion if they owned a mobile phone and used the WeChat social media app. A total of 722 participants were randomized to the intervention or control group between April 1 and July 27, 2021.

INTERVENTIONS

Intervention group participants received a personalized text message-based smoking cessation intervention that was based on the transtheoretical model and protection motivation theory and developed by this study's investigators. Control group participants received a nonpersonalized text message-based smoking cessation intervention developed by the US National Cancer Institute. Both groups received 1 to 2 text messages per day for 3 months through the app.

MAIN OUTCOMES AND MEASURES

The primary outcome was the biochemically verified 6-month sustained abstinence rate, defined as the self-report of no smoking of any cigarettes after the designated quit date, which was validated biochemically by an expired air carbon monoxide level of less than 6 ppm at each follow-up point.

RESULTS

A total of 722 participants (mean [SD] age, 41.5 [12.7] years; 716 men [99.2%]; all of Chinese ethnicity) were randomly assigned to the intervention group (360 participants) or the control group (362 participants). Biochemically verified continuous abstinence at 6 months was 6.9% in the intervention group and 3.0% in the control group (odds ratio [OR], 2.66; 95% CI, 1.21-5.83). Among smokers with low nicotine dependence, the intervention group had significantly better abstinence rates for most of the indicators after adjusting for covariates (eg, biochemically verified 24-hour point prevalence of abstinence at 1 month: adjusted OR, 2.15; 95% CI, 1.05-4.38). Among smokers with moderate and high nicotine dependence, only the biochemically verified 24-hour point prevalence of abstinence at 6 months was statistically significant (adjusted OR, 4.17; 95% CI, 1.34-3.00). The pattern was similar for quitting intention, and the personalized text message-based intervention was more effective for smokers who had strong quitting intention than for those who had weak quitting intention.

CONCLUSIONS AND RELEVANCE

In this study, the behavior change theory-based smoking cessation intervention using personalized text messages was more effective than an intervention using nonpersonalized text messages. The intervention was most effective among smokers with low nicotine dependence and strong quitting intention. This study's findings also provide further evidence regarding the potential benefits of mobile health interventions for other behaviors.

TRIAL REGISTRATION

Chinese Clinical Trial Registry Identifier: ChiCTR2100041942.

Real-world utilization and effectiveness of Message-Based Tobacco Cessation Program (mCessation) in Chinese general population (Preprint)

BACKGROUND

Randomized controlled trials on text message interventions for smoking cessation have shown they are effective and recommended for tobacco control. However, the effectiveness in real-world settings is largely unknown, especially in low- and middle-income countries.

OBJECTIVE

This study aimed to provide real-world evidence about the utilization and effectiveness of a message-based tobacco cessation program (mCessation) in China.

METHODS

From May 2021 to September 2022, 16,746 people from the general population participated in the mCessation program provided by the World Health Organization. All participants received text messages on smoking cessation via instant messaging for 6 months, and they were also required to report smoking status. We randomly selected 2500 participants and interviewed them by telephone to determine the 7-day point prevalence abstinence rate at 6 months. Descriptive statistics were used to analyze population characteristics and abstinence rate. Logistic regression analysis was performed to explore risk factors for the abstinence rate.

RESULTS

Among the 2500 participants, the mean age was 35 years, and most (2407/2500, 96.20%) were male. The prevalence of tobacco dependence and light degree of tobacco dependence were 85.70% (2142/2500) and 89.10% (2228/2500), respectively. For respondents (953/2500, 38.10%), the 7-day point prevalence abstinence rate at 6 months was 21.90% (209/953). Participants older than 40 years or with tobacco dependence had significantly higher abstinence rates than those who were younger than 30 years old (odds ratio [OR] 1.77, 95% CI 1.06-3.29) or without dependence (OR 1.64, 95% CI 1.08-2.51), respectively. However, married people or heavily dependent smokers tended to find it more difficult to successfully quit smoking compared with unmarried people (OR 0.57, 95% CI 0.34-0.93) or lightly dependent smokers (OR 0.16, 95% CI 0.02-0.98), respectively.

CONCLUSIONS

In a real-world setting, mCessation China was generally acceptable to men and lightly dependent smokers, and it could help 1 in 5 smokers aged 18 years to 67 years quit smoking. However, strategies to increase awareness of young and married adults may improve implementation and abstinence rates.

Tobacco quitlines: Opportunities for innovation to increase reach and effectiveness

The largest tobacco treatment network in North America, Tobacco Quitlines are an effective population-based approach to increase tobacco cessation; however, overall reach has decreased significantly in the past decade. A new generation of innovations responsive to evolving shifts in communication preferences, supported by research, and focused on increasing the impact of services have the potential to reinvigorate this network. The goal of this narrative review was to identify opportunities for innovation in Quitline service delivery, synthesize evidence for these opportunities, and identify gaps in the research. Innovation was defined as significant shift in current practice by utilizing novel theoretical concepts, approaches, methodologies, or interventions. The Experimental Medicine Approach informed the identification of gaps in the research. The specific domains were selected by reviewing previous reviews, commentaries, calls for action, and a recent report on promising practices. Evidence was garnered primarily from systematic reviews. Opportunities included automated and interactive digital therapeutics, novel health communications for stigma-free media campaigns, methods to increase access to nicotine replacement therapies, novel treatment options and combinations, and methods to promote engagement with digital therapeutics. Research topics that cross multiple domains include the consideration of theoretical frameworks, the identification of therapeutic targets and mechanisms of action, and the development of adapted approaches to address specific challenges and cultural responsivity. Finally, an examination is needed to understand how to improve the speed with which innovations are developed and implemented in this network.

Factors associated with predictors of smoking cessation from a Norwegian internet-based smoking cessation intervention study

INTRODUCTION

We examined if we could identify predictors for smoking cessation at six months post cessation, among smokers enrolled in a large Norwegian population-based intervention study.

METHODS

We followed 4333 (72.1% women) smokers who enrolled in an internet-based smoking cessation intervention during 2010–2012. The baseline questionnaire collected information on sociodemographic and lifestyle factors, including current snus use. The cessation outcome was self-reported no smoking past seven days, at six months. We used logistic regression to estimate odds ratios (ORs) with 95% confidence intervals, to identify predictors of smoking cessation, adjusting for potential confounders.

RESULTS

Women (OR=1.30; 95% CI: 1.01–1.69) compared with men, and those with medium (OR=1.31; 95% CI: 1.02–1.68) and longer (OR=1.42; 95% CI: 1.06–1.90) education compared with those with shorter education, were more likely to be successful quitters.

Overall, being a student (OR=0.56; 95% CI: 0.37–0.85) compared with having full-time work, and a moderate to high Fagerström test for nicotine dependence (FTND) score (OR=0.69; 95% CI: 0.55–0.87) compared with a low score, were predictors for unsuccessful cessation. Current snus use was a predictor for unsuccessful cessation compared to no snus use for both men (OR=0.49; 95% CI: 0.28–0.88) and women (OR=0.49; 95% CI: 0.32–0.75).

CONCLUSIONS

Our study identifies female sex and longer education as predictors for successful smoking cessation, while a medium or high FTND score, being a student, and current snus use, were predictors for unsuccessful smoking cessation. Only current snus use was a predictor for unsuccessful cessation for both sexes. Our results indicate that smokers should be warned that snus use may prevent successful smoking cessation.

A Randomized Controlled Trial to Address Consumer Food Waste with a Technology-aided Tailored Sustainability Intervention

There is growing concern whether pro-environmental behavioral interventions can generate sufficient reductions in carbon emissions to address climate change. While many have suggested enhanced tailoring of interventions to increase effect sizes, and while individual tailoring is common among health interventions, little is known about how individual tailoring may impact effect sizes for pro-environmental behavioral interventions. Using a novel technology-aided delivery and measurement approach, we conduct a randomized controlled trial featuring an individually tailored intervention focused on reducing the amount of food wasted by participants over approximately one week in their normal living conditions. We find large significant effects for the focal area of food wasted during dining (a 79% reduction), a null effect on food wasted over all household stages (preparation, dining and clean outs), and desirable or null effects for critical antecedent (e.g., waste during preparation, continued purchases of fresh produce), concurrent (e.g., food selection and consumption), and attendant behaviors (e.g., waste from storage clean outs, avoiding waste deposits in landfills).

Non-pharmacological interventions for tobacco cessation: A systematic review of existing practices and their effectiveness

Smoking tobacco is associated with lung cancer and other life-threatening diseases which requires serious action to curb it. Tobacco cessation interventions are available as pharmacological and non-pharmacological methods or a combination of both. The present review examines the effectiveness of the existing non-pharmacological tobacco cessation interventions and synthesizes the result for the future development of drug-free treatment in the community for tobacco cessation. The literature search was conducted in August 2020, using two electronic databases (PubMed and JSTOR), with search terms: ['tobacco cessation' OR 'smoking cessation'] AND ['intervention'] which included studies published during 2010 and 2020 (till 31st July 2020). All studies were limited to English language, human participants and excluded patients with comorbidities. A total of 2,114 publications were retrieved out of which 11 articles were reviewed. On the basis of intervention used in reviewed studies, we categorized them into seven categories: i. incentive-based intervention, ii. exercise based, iii. telephone-based proactive counselling, iv. mobile phone SMS (Short Message Service) based, v. smartphone app (application) based, vi. web-based intervention, vii. self-help material. Incentives were provided in most of the studies to maintain the retention rate and motivate the participants for completing follow-up. Non-pharmacological interventions for tobacco cessation include a combination of various elements. Our findings suggest that behavioural counselling is one of the most important elements of any non-pharmacological intervention. In addition to behaviour counselling, yoga and exercises along with self-help material, video and phone counselling may have higher efficacy. Thus, practicing non-pharmacological interventions may also increase the cessation rate and reduce the tobacco use burden.

A Smartphone-Based Information Communication Technology Solution for Primary Modifiable Risk Factors for Noncommunicable Diseases: Pilot and Feasibility Study in Norway

Background

Cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes are the 4 main noncommunicable diseases. These noncommunicable diseases share 4 modifiable risk factors (tobacco use, harmful use of alcohol, physical inactivity, and unhealthy diet). Short smartphone surveys have the potential to identify modifiable risk factors for individuals to monitor trends.

Objective

We aimed to pilot a smartphone-based information communication technology solution to collect nationally representative data, annually, on 4 modifiable risk factors.

Methods

We developed an information communication technology solution with functionalities for capturing sensitive data from smartphones, receiving, and handling data in accordance with general data protection regulations. The main survey comprised 26 questions: 8 on socioeconomic factors, 17 on the 4 risk factors, and 1 about current or previous noncommunicable diseases. For answers to the continuous questions, a keyboard was displayed for entering numbers; there were preset upper and lower limits for acceptable response values. For categorical questions, pull-down menus with response options were displayed. The second survey comprised 9 yes-or-no questions. For both surveys, we used SMS text messaging. For the main survey, we invited 11,000 individuals, aged 16 to 69 years, selected randomly from the Norwegian National Population Registry (1000 from each of the 11 counties). For the second survey, we invited a random sample of 100 individuals from each county who had not responded to the main survey. All data, except county of residence, were self-reported. We calculated the distribution for socioeconomic background, tobacco use, diet, physical activity, and health condition factors overall and by sex.

Results

The response rate was 21.9% (2303/11,000; women: 1397/2263; 61.7%, men: 866/2263, 38.3%; missing: 40/2303, 1.7%). The median age for men was 52 years (IQR 40-61); the median age for women was 48 years (IQR 35-58). The main reported reason for nonparticipation in the main survey was that the sender of the initial SMS was unknown.

Conclusions

We successfully developed and piloted a smartphone-based information communication technology solution for collecting data on the 4 modifiable risk factors for the 4 main noncommunicable diseases. Approximately 1 in 5 invitees responded; thus, these data may not be nationally representative. The smartphone-based information communication technology solution should be further developed with the long-term goal to reduce premature mortality from the 4 main noncommunicable diseases.

Smoking Prevalence among Physicians: A Systematic Review and Meta-Analysis

BACKGROUND

Smoking is a major public health problem. Although physicians have a key role in the fight against smoking, some of them are still smoking. Thus, we aimed to conduct a systematic review and meta-analysis on the prevalence of smoking among physicians.

METHODS

PubMed, Cochrane, and Embase databases were searched. The prevalence of smoking among physicians was estimated and stratified, where possible, by specialties, continents, and periods of time. Then, meta-regressions were performed regarding putative influencing factors such as age and sex.

RESULTS

Among 246 studies and 497,081 physicians, the smoking prevalence among physicians was 21% (95CI 20 to 23%). Prevalence of smoking was 25% in medical students, 24% in family practitioners, 18% in surgical specialties, 17% in psychiatrists, 16% in medical specialties, 11% in anesthesiologists, 9% in radiologists, and 8% in pediatricians. Physicians in Europe and Asia had a higher smoking prevalence than in Oceania. The smoking prevalence among physicians has decreased over time. Male physicians had a higher smoking prevalence. Age did not influence smoking prevalence.

CONCLUSION

Prevalence of smoking among physicians is high, around 21%. Family practitioners and medical students have the highest percentage of smokers. All physicians should benefit from targeted preventive strategies.

Evaluating the use of a recommender system for selecting optimal messages for smoking cessation: patterns and effects of user-system engagement

Background

Motivational messaging is a frequently used digital intervention to promote positive health behavior changes, including smoking cessation. Typically, motivational messaging systems have not actively sought feedback on each message, preventing a closer examination of the user-system engagement. This study assessed the granular user-system engagement around a recommender system (a new system that actively sought user feedback on each message to improve message selection) for promoting smoking cessation and the impact of engagement on cessation outcome.

Methods

We prospectively followed a cohort of current smokers enrolled to use the recommender system for 6 months. The system sent participants motivational messages to support smoking cessation every 3 days and used machine learning to incorporate user feedback (i.e., user’s rating on the perceived influence of each message, collected on a 5-point Likert scale with 1 indicating strong disagreement and 5 indicating strong agreement on perceiving the influence on quitting smoking) to improve the selection of the following message. We assessed user-system engagement by various metrics, including user response rate (i.e., the percent of times a user rated the messages) and the perceived influence of messages. We compared retention rates across different levels of user-system engagement and assessed the association between engagement and the 7-day point prevalence abstinence (missing outcome = smoking) by using multiple logistic regression.

Results

We analyzed data from 731 participants (13% Black; 73% women). The user response rate was 0.24 (SD = 0.34) and user-perceived influence was 3.76 (SD = 0.84). The retention rate positively increased with the user response rate (trend test P < 0.001). Compared with non-response, six-month cessation increased with the levels of response rates: low response rate (odds ratio [OR] = 1.86, 95% confidence interval [CI]: 1.07–3.23), moderate response rate (OR = 2.30, 95% CI: 1.36–3.88), high response rate (OR = 2.69, 95% CI: 1.58–4.58). The association between perceived message influence and the outcome showed a similar pattern.

Conclusions

High user-system engagement was positively associated with both high retention rate and smoking cessation, suggesting that investigation of methods to increase engagement may be crucial to increase the impact of the recommender system for smoking cessation.

Trial registration

Registration Identifier: NCT03224520. Registration date: July 21, 2017.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11803-8.

mHealth Technology and CVD Risk Reduction

PURPOSE OF REVIEW

To review existing mHealth-based interventions and examine their efficacy in reducing cardiovascular disease (CVD) risk factors.

RECENT FINDINGS

A total of 50 articles are included in this review. The majority of the mHealth interventions targeted a specific CVD risk factor, while 4 addressed 2 or more CVD risk factors. Of the 9 mHealth-supported weight loss intervention trials, 4 resulted in significant weight loss. Four out of 7 RCTs targeting improvement in physical activity reported significant improvement, while 4 of the 8 mHealth-supported smoking cessation intervention trials resulted in smoking abstinence. Of the 10 mHealth-based diabetes intervention trials, 5 reported significant reductions in HbA1c; however, only 3 out of the 9 antihypertension interventions resulted in significant reductions in blood pressure. There is a growing body of literature focused on mHealth interventions that address CVD risk factors. Despite the immense potential of mHealth interventions, evidence of their efficacy in mitigating cardiovascular risk is heterogeneous.

A digital behaviour change intervention to increase booking and attendance at Stop Smoking Services: the MyWay feasibility RCT

Background

Smoking remains a leading cause of illness and preventable death. NHS Stop Smoking Services increase quitting, but, as access is in decline, cost-effective interventions are needed that promote these services. StopApp™ (Coventry University, Coventry, UK) is designed to increase booking and attendance at Stop Smoking Services.

Design

A two-arm feasibility randomised controlled trial of StopApp (intervention) compared with standard promotion and referral to Stop Smoking Services (control) was conducted to assess recruitment, attrition and health equity of the design, alongside health economic and qualitative process evaluations.

Setting

Smokers recruited via general practitioners, community settings and social media.

Participants

Smokers aged ≥ 16 years were recruited in one local authority. Participants had to live or work within the local authority area, and there was a recruitment target of 120 participants.

Interventions

StopApp to increase booking and attendance at Stop Smoking Services.

Main outcome measures

Participants completed baseline measures and follow-up at 2 months post randomisation entirely online. Objective data on the use of Stop Smoking Services were collected from participating Stop Smoking Services, and age groups, sex, ethnicity and socioeconomic status in baseline recruits and follow-up completers/non-completers were assessed for equity.

Results

Eligible participants (n = 123) were recruited over 116 days, with good representation of lower socioeconomic status groups; black, Asian and minority ethnic groups; and all age groups. Demographic profiles of follow-up completers and non-completers were broadly similar. The attrition rate was 51.2%, with loss to follow-up lowest in the social media setting (n = 24/61; 39.3%) and highest in the general practitioner setting (n = 21/26; 80.8%). Most measures had < 5% missing data. Social media represented the most effective and cost-efficient recruitment method. In a future, definitive, multisite trial with recruitment driven by social media, our data suggest that recruiting ≥ 1500 smokers over 12 months is feasible. Service data showed that five bookings for the Stop Smoking Services were scheduled using StopApp, of which two did not attend. Challenges with data access were identified. A further five participants in the intervention arm self-reported booking and accessing Stop Smoking Services outside StopApp compared with two control arm participants. Event rate calculations for the intervention were 8% (Stop Smoking Services data), 17% (including self-reports) and 3.5% from control arm self-reports. A conservative effect size of 6% is estimated for a definitive full trial. A sample size of 840 participants would be required to detect an effect for the primary outcome measure of booking a Stop Smoking Services appointment in a full randomised controlled trial. The process evaluation found that participants were satisfied with the research team contact, study methods and provision of e-vouchers. Staff interviews revealed positive and negative experiences of the trial and suggestions for improvements, including encouraging smokers to take part.

Conclusion

This feasibility randomised controlled trial found that, with recruitment driven wholly or mainly by social media, it is possible to recruit and retain sufficient smokers to assess the effectiveness and cost-effectiveness of StopApp. The study methods and measures were found to be acceptable and equitable, but accessing Stop Smoking Services data about booking, attendance and quit dates was a challenge. A full trial may be feasible if service data are accessible. This will require careful planning with data controllers and a targeted social media campaign for recruitment. Changes to some study measures are needed to avoid missing data, including implementation of a more intensive follow-up data collection process.

Future work

We plan a full, definitive randomised controlled trial if the concerns around data access can be resolved, with adaptations to the recruitment and retention strategy.

Limitations

Our trial had high attrition and problems with collecting Stop Smoking Services data, which resulted in a reliance on self-reporting.

Trial registration

Research Registry: 3995. The trial was registered on 18 April 2018.

Funding

This project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 9, No. 5. See the NIHR Journals Library website for further project information.

Preferences for Tobacco Cessation Information and Support During Covid-19

OBJECTIVE

To identify how best to provide smoking cessation advice and support during the Covid-19 pandemic. Preferences were assessed in relation to: (i) specific forms of tobacco cessation support (eg, nicotine replacement therapy (NRT) and various counseling formats); (ii) information sources (eg, government officials, doctors); and (iii) channels via which to receive relevant information (eg, television, social media).

METHODS

An online survey was administered to adults who smoke tobacco in Australia (n = 604) and the UK (n = 600). Descriptive analyses were conducted to identify levels of interest in cessation support and information provision. Differences in responses according to demographic characteristics and smoking history were assessed.

RESULTS

Around half of the respondents were interested in receiving personal counseling and/or participating in a text support program over the next month. By far the most popular delivery mechanism for personal counseling was email. Three-quarters of the sample expressed an interest in receiving free, home-delivered NRT. The most popular information sources nominated by respondents seeking more information about smokers' Covid-related risks were government departments and their doctor/general practitioner. Television and online news sources were the most preferred information dissemination channels.

CONCLUSIONS

The substantial levels of interest expressed in accessing various forms of cessation assistance within the next month suggest that Covid-19 may be increasing receptiveness to quitting. The strong interest in free, home-delivered NRT indicates that this may be a useful mechanism for facilitating quit attempts during the pandemic.

The Effects of Text Message and Infographic on Reducing the Number Cigarettes Consumption: A Randomized Controlled Trial

OBJECTIVE

To test the effect of a text-message and infographic to promote smokers quit smoking.

Methods: A randomized control trial was conducted in two provinces of northeast Thailand. Three hundred and ninety-six participants were allocated to either a text-message and infographic group or a control group. We assessed the primary outcome by self-reported 7-day point prevalence smoking abstinence. Multiple logistic regression was used to test the effect of quitting smoking.

Results: At 3-month follow-up, lost to follow-up 16 participants, 380 participants were included for analysis. The difference in the rate of quitting smoking between the intervention and control groups was not found a statistical significance (17.8% versus 11.6%). However, we found a statistically significant difference in the number of cigarettes smokes (the difference: -1.74; 95%CI: -2.63, -0.84).

Conclusion: No effect of text message and infographic for help smokers to quit smoking. However, the intervention showed a decrease in the number of cigarettes smoked.

Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text-Based Secondary Prevention Program: Feasibility Study

BACKGROUND

SMS text messaging programs are increasingly being used for secondary prevention, and have been shown to be effective in a number of health conditions including cardiovascular disease. SMS text messaging programs have the potential to increase the reach of an intervention, at a reduced cost, to larger numbers of people who may not access traditional programs. However, patients regularly reply to the SMS text messages, leading to additional staffing requirements to monitor and moderate the patients' SMS text messaging replies. This additional staff requirement directly impacts the cost-effectiveness and scalability of SMS text messaging interventions.

OBJECTIVE

This study aimed to test the feasibility and accuracy of developing a machine learning (ML) program to triage SMS text messaging replies (ie, identify which SMS text messaging replies require a health professional review).

METHODS

SMS text messaging replies received from 2 clinical trials were manually coded (1) into "Is staff review required?" (binary response of yes/no); and then (2) into 12 general categories. Five ML models (Naïve Bayes, OneVsRest, Random Forest Decision Trees, Gradient Boosted Trees, and Multilayer Perceptron) and an ensemble model were tested. For each model run, data were randomly allocated into training set (2183/3118, 70.01%) and test set (935/3118, 29.98%). Accuracy for the yes/no classification was calculated using area under the receiver operating characteristics curve (AUC), false positives, and false negatives. Accuracy for classification into 12 categories was compared using multiclass classification evaluators.

RESULTS

A manual review of 3118 SMS text messaging replies showed that 22.00% (686/3118) required staff review. For determining need for staff review, the Multilayer Perceptron model had highest accuracy (AUC 0.86; 4.85% false negatives; and 4.63% false positives); with addition of heuristics (specified keywords) fewer false negatives were identified (3.19%), with small increase in false positives (7.66%) and AUC 0.79. Application of this model would result in 26.7% of SMS text messaging replies requiring review (true + false positives). The ensemble model produced the lowest false negatives (1.43%) at the expense of higher false positives (16.19%). OneVsRest was the most accurate (72.3%) for the 12-category classification.

CONCLUSIONS

The ML program has high sensitivity for identifying the SMS text messaging replies requiring staff input; however, future research is required to validate the models against larger data sets. Incorporation of an ML program to review SMS text messaging replies could significantly reduce staff workload, as staff would not have to review all incoming SMS text messages. This could lead to substantial improvements in cost-effectiveness, scalability, and capacity of SMS text messaging-based interventions.

Enhancing Patient Activation and Self-Management Activities in Patients With Type 2 Diabetes Using the US Department of Defense Mobile Health Care Environment: Feasibility Study

Background

Past mobile health (mHealth) efforts to empower type 2 diabetes (T2D) self-management include portals, text messaging, collection of biometric data, electronic coaching, email, and collection of lifestyle information.

Objective

The primary objective was to enhance patient activation and self-management of T2D using the US Department of Defense’s Mobile Health Care Environment (MHCE) in a patient-centered medical home setting.

Methods

A multisite study, including a user-centered design and a controlled trial, was conducted within the US Military Health System. Phase I assessed preferences regarding the enhancement of the enabling technology. Phase II was a single-blinded 12-month feasibility study that randomly assigned 240 patients to either the intervention (n=123, received mHealth technology and behavioral messages tailored to Patient Activation Measure [PAM] level at baseline) or the control group (n=117, received equipment but not messaging. The primary outcome measure was PAM scores. Secondary outcome measures included Summary of Diabetes Self-Care Activities (SDSCA) scores and cardiometabolic outcomes. We used generalized estimating equations to estimate changes in outcomes.

Results

The final sample consisted of 229 patients. Participants were 61.6% (141/229) male, had a mean age of 62.9 years, mean glycated hemoglobin (HbA_1c) of 7.5%, mean BMI of 32.7, and a mean duration of T2D diagnosis of 9.8 years. At month 12, the control group showed significantly greater improvements compared with the intervention group in PAM scores (control mean 7.49, intervention mean 1.77; P=.007), HbA_1c (control mean −0.53, intervention mean −0.11; P=.006), and low-density lipoprotein cholesterol (control mean −7.14, intervention mean 4.38; P=.01). Both groups showed significant improvement in SDSCA, BMI, waist size, and diastolic blood pressure; between-group differences were not statistically significant. Except for patients with the highest level of activation (PAM level 4), intervention group patients exhibited significant improvements in PAM scores. For patients with the lowest level of activation (PAM level 1), the intervention group showed significantly greater improvement compared with the control group in HbA_1c (control mean −0.09, intervention mean −0.52; P=.04), BMI (control mean 0.58, intervention mean −1.22; P=.01), and high-density lipoprotein cholesterol levels (control mean −4.86, intervention mean 3.56; P<.001). Significant improvements were seen in AM scores, SDSCA, and waist size for both groups and in diastolic and systolic blood pressure for the control group; the between-group differences were not statistically significant. The percentage of participants who were engaged with MHCE for ≥50% of days period was 60.7% (68/112; months 0-3), 57.4% (62/108; months 3-6), 49.5% (51/103; months 6-9), and 43% (42/98; months 9-12).

Conclusions

Our study produced mixed results with improvement in PAM scores and outcomes in both the intervention and control groups. Structural design issues may have hampered the influence of tailored behavioral messaging within the intervention group.

Trial Registration

ClinicalTrials.gov NCT02949037; https://clinicaltrials.gov/ct2/show/NCT02949037

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.6993

They Came, But Will They Come Back? An Observational Study of Re-Enrollment Predictors for the Oklahoma Tobacco Helpline

PURPOSE

Although quitlines reach 1% to 2% of tobacco users annually, additional efforts are needed to increase their impact. We hypothesized that offering less intensive services would increase the rate of re-enrollment in any service, as well as re-enrollment in more intensive services. This study describes the enrollment patterns and identifies re-enrollment predictors for Oklahoma Tobacco Helpline (OTH) participants.

DESIGN

This study used a comparative observational design.

SETTING

The setting for this study was the OTH, a telephone-based cessation program funded by the Oklahoma Tobacco Settlement Endowment Trust. The OTH participants could select either a multicall telephone-based cessation program (MC) or one or more individual services (IS), including a 2-week nicotine replacement therapy (NRT) starter kit, e-mail or text-based support, and a printed quit guide.

PARTICIPANTS

A total of 35 648 first-time adult OTH participants eligible for the multicall program from October 2015 through September 2018 were included.

MEASURES

Demographic and tobacco use variables and initial quitline service selection were collected at intake. Additional service utilization was tracked for 6 months following initial registration.

ANALYSIS

Pearson chi-square and t tests were used to test for significant differences between groups. Multinomial logistic regression was used to examine predictors of re-enrollment.

RESULTS

Individual services were more frequently selected (n = 17 266) than MC (n = 14 326), despite all users being eligible for MC. A much higher proportion of IS registrants re-enrolled than MC registrants (16% vs 3%, P < .0001) Among the IS cohort, those who received an NRT follow-up call were 14.7 times more likely to re-enroll in IS, and 7.8 times more likely to re-enroll in MC, than those who were not reached by phone.

CONCLUSIONS

Access to free NRT without a telephone-coaching requirement is a draw for tobacco users, especially those with lower income and the uninsured. The results suggest the value of increasing use of nonphone services in an effort to increase interest in quitting and reach.