Live counselor contact in a Facebook intervention predicts smoking cessation outcomes

Smoking cessation message testing to inform app-based interventions for young adults - an online experiment

BACKGROUND

To improve the efficacy of digital smoking cessation interventions for young adults, intervention messages need to be acceptable and appropriate for this population. The current study compared ratings of smoking cessation and urge reduction messages based on Cognitive Behavioral Therapy (distraction themed) and Acceptance and Commitment Therapy (acceptance themed) in young adults who smoke.

METHODS

A total of 124 intervention messages were rated by an online Qualtrics panel of N = 301 diverse young adults who currently smoked tobacco cigarettes (Age M = 26.6 years; 54.8% male; 51.5% racial/ethnic minority; 16.9% sexual or gender minority (SGM); 62.5% daily smoking). Each participant rated 10 randomly selected messages (3,010 total message ratings; 24.3 ratings per message) on 5-point scales (higher scores representing more favorable ratings) evaluating quality of content, quality of design, perceived support for coping with smoking urges, and perceived support for quitting smoking. Mixed models examined associations between message category (distraction vs. acceptance), participant level predictors (sociodemographic variables, readiness and motivation to quit, daily smoking, psychological flexibility), and message ratings.

RESULTS

Overall ratings ranged from M = 3.61 (SD = 1.25) on support for coping with urges to M = 3.90 (SD = 1.03) on content, with no differences between distraction and acceptance messages. Male participants gave more favorable ratings on the dimensions of support for coping (p < 0.01) and support for quitting (p < 0.01). Participants identifying as SGM gave lower ratings for message design (p < 0.05). Participants with a graduate degree gave higher ratings on support for coping with urges and support for quitting (both p < 0.05). Higher motivation to quit was associated with more favorable scores across all dimensions (all p < 0.01). Those smoking daily rated messages as less helpful for coping with urges (p < 0.01) and quitting smoking (p < 0.05) compared to those smoking non-daily. Few interactions were found between message category distraction vs. acceptance and participant characteristics.

CONCLUSIONS

Distraction and acceptance messages received similar ratings among young adults who smoke cigarettes. Message revisions may be needed to increase appeal to women, SGM, those with lower education, and those less motivated to quit. Messages will be refined and used in an ongoing micro-randomized trial to investigate their real-time impact on smoking urges and behaviors.

Smoking cessation message testing to inform app-based interventions - an online experiment

Background: To improve the efficacy of digital smoking cessation interventions for young adults, intervention messages need to be acceptable and appropriate for this population. The current study compared ratings of smoking cessation and urge reduction messages based on Cognitive Behavioral Therapy (distraction themed) and Acceptance and Commitment Therapy (acceptance themed) in young adults who smoke. Methods: A total of 124 intervention messages were rated by an online Qualtrics panel of N=301 diverse young adults who currently smoked tobacco cigarettes (Age M=26.6 years; 54.8% male; 51.5% racial/ethnic minority; 16.9% sexual or gender minority (SGM); 62.5% daily smoking). Each participant rated 10 randomly selected messages (3,010 total message ratings; 24.3 ratings per message) on 5-point scales (higher scores representing more favorable ratings) evaluating quality of content, quality of design, perceived support for coping with smoking urges, and perceived support for quitting smoking. Mixed models examined associations between message category (distraction vs. acceptance), participant level predictors (sociodemographic variables, readiness and motivation to quit, daily smoking, psychological flexibility), and message ratings. Results: Overall ratings ranged from M=3.61 (SD=1.25) on support for coping with urges to M=3.90 (SD=1.03) on content, with no differences between distraction and acceptance messages. Male participants gave more favorable ratings on the dimensions of support for coping (p<0.01) and support for quitting (p<0.01). Participants identifying as SGM gave lower ratings for message design (p<0.05). Participants with a graduate degree gave higher ratings on support for coping with urges and support for quitting (both p<0.05). Higher motivation to quit was associated with more favorable scores across all dimensions (all p<0.01). Those smoking daily rated messages as less helpful for coping with urges (p<0.01) and quitting smoking (p<0.05) compared to those smoking non-daily. Few interactions were found between message category distraction vs. acceptance and participant characteristics. Conclusions: Distraction and acceptance messages received similar ratings among young adults who smoke cigarettes. Message revisions may be needed to increase appeal to women, SGM, those with lower education, and those less motivated to quit. Messages will be refined and used in an ongoing micro-randomized trial to investigate their real-time impact on smoking urges and behaviors.

Elements Influencing User Engagement in Social Media Posts on Lifestyle Risk Factors: Systematic Review

BACKGROUND

The high prevalence of noncommunicable diseases and the growing importance of social media have prompted health care professionals (HCPs) to use social media to deliver health information aimed at reducing lifestyle risk factors. Previous studies have acknowledged that the identification of elements that influence user engagement metrics could help HCPs in creating engaging posts toward effective health promotion on social media. Nevertheless, few studies have attempted to comprehensively identify a list of elements in social media posts that could influence user engagement metrics.

OBJECTIVE

This systematic review aimed to identify elements influencing user engagement metrics in social media posts by HCPs aimed to reduce lifestyle risk factors.

METHODS

Relevant studies in English, published between January 2006 and June 2023 were identified from MEDLINE or OVID, Scopus, Web of Science, and CINAHL databases. Included studies were those that examined social media posts by HCPs aimed at reducing the 4 key lifestyle risk factors. Additionally, the studies also outlined elements in social media posts that influenced user engagement metrics. The titles, abstracts, and full papers were screened and reviewed for eligibility. Following data extraction, narrative synthesis was performed. All investigated elements in the included studies were categorized. The elements in social media posts that influenced user engagement metrics were identified.

RESULTS

A total of 19 studies were included in this review. Investigated elements were grouped into 9 categories, with 35 elements found to influence user engagement. The 3 predominant categories of elements influencing user engagement were communication using supportive or emotive elements, communication aimed toward behavioral changes, and the appearance of posts. In contrast, the source of post content, social media platform, and timing of post had less than 3 studies with elements influencing user engagement.

CONCLUSIONS

Findings demonstrated that supportive or emotive communication toward behavioral changes and post appearance could increase postlevel interactions, indicating a favorable response from the users toward posts made by HCPs. As social media continues to evolve, these elements should be constantly evaluated through further research.

Factors Associated With Abstinence Among Young Adult Smokers Enrolled in a Real-world Social Media Smoking Cessation Program

OBJECTIVE

Social media platforms are promising to provide smoking cessation support. This study aimed to identify baseline factors associated with cigarette smoking abstinence among young adult smokers enrolled in a real-world social media-based smoking cessation program.

AIMS AND METHODS

We analyzed data from young adult smokers (aged 18-30 years) participating in a publicly available Facebook-based smoking cessation program serving the San Francisco Bay Area. The analytic sample consisted of 248 participants who completed both the baseline and follow-up surveys at 3 months. Multivariable logistic regression analysis determined baseline factors significantly associated with self-reported 7-day cigarette smoking abstinence at 3 months.

RESULTS

Participants were race/ethnically diverse, well-educated, and 47.6% reported LGB + sexual identity. Those who reported dual use of cigarettes and e-cigarettes in the past 30 days (vs. cigarette use only), current alcohol users (vs. non-users), and those aged 25-30 years (vs. 18-24 years) were significantly less likely to report 7-day abstinence at 3 months. Non-daily smokers (vs. daily smokers) and those with high desire to quit smoking (vs. low to moderate desire) were more likely to report abstinence. Results also showed reduction in the percentage of e-cigarette and other tobacco product use among participants.

CONCLUSIONS

Social media interventions may be more effective for young adult non-daily smokers and those with high desire to quit smoking. Smoking cessation programs may help reduce use of other tobacco products among treatment-seeking smokers. Smoking cessation interventions for young adults need to explicitly address dual use of cigarettes and e-cigarettes and use of alcohol.

IMPLICATIONS

Findings of this study highlight the need for future interventions to address dual use of cigarettes and e-cigarettes and use of alcohol to improve cigarette smoking abstinence outcomes. The reduction in the use of other tobacco products among program participants indicates that social media smoking cessation programs may exert a broader positive influence on overall tobacco consumption. The large number of LGB+ smokers participating in the program suggests social media is a promising cessation channel for this hard-to-reach group, warranting further study.

Acceptability and Utility of a Smartphone App to Support Adolescent Mental Health (BeMe): Program Evaluation Study

BACKGROUND

Adolescents face unprecedented mental health challenges, and technology has the opportunity to facilitate access and support digitally connected generations. The combination of digital tools and live human connection may hold particular promise for resonating with and flexibly supporting young people's mental health.

OBJECTIVE

This study aimed to describe the BeMe app-based platform to support adolescents' mental health and well-being and to examine app engagement, usability, and satisfaction.

METHODS

Adolescents in the United States, aged 13 to 20 years, were recruited via the web and enrolled between September 1 and October 31, 2022. App engagement, feature use, clinical functioning, and satisfaction with BeMe were examined for 30 days. BeMe provides content based on cognitive behavioral therapy, dialectical behavior therapy, motivational interviewing, and positive psychology; interactive activities; live text-based coaching; links to clinical services; and crisis support tools (digital and live).

RESULTS

The average age of the sample (N=13,421) was 15.04 (SD 1.7) years, and 56.72% (7612/13,421) identified with she/her pronouns. For the subsample that completed the in-app assessments, the mean scores indicated concern for depression (8-item Patient Health Questionnaire mean 15.68/20, SD 5.9; n=239), anxiety (7-item Generalized Anxiety Disorder Questionnaire mean 13.37/17, SD 5.0; n=791), and poor well-being (World Health Organization-Five Well-being Index mean 30.15/100, SD 16.1; n=1923). Overall, the adolescents engaged with BeMe for an average of 2.38 (SD 2.7) days in 7.94 (SD 24.1) sessions and completed 11.26 (SD 19.8) activities. Most adolescents engaged with BeMe's content (12,270/13,421, 91.42%), mood ratings (13,094/13,421, 97.56%), and interactive skills (10,098/13,421, 75.24%), and almost one-fifth of the adolescents engaged with coaching (2539/13,421, 18.92%), clinical resources (2411/13,421, 17.96%), and crisis support resources (2499/13,421, 18.62%). Overall app engagement (total activities) was highest among female and gender-neutral adolescents compared with male adolescents (all P<.001) and was highest among younger adolescents (aged 13-14 years) compared with all other ages (all P<.001). Satisfaction ratings were generally high for content (eg, 158/176, 89.8% rated as helpful and 1044/1139, 91.66% improved coping self-efficacy), activities (5362/8468, 63.32% helpful and 4408/6072, 72.6% useful in coping with big feelings), and coaching (747/894, 83.6% helpful and 747/894, 83.6% improved coping self-efficacy). Engagement (total activities completed) predicted the likelihood of app satisfaction (P<.001).

CONCLUSIONS

Many adolescents downloaded the BeMe app and completed multiple sessions and activities. Engagement with BeMe was higher among female and younger adolescents. Ratings of BeMe's content, activities, and coaching were very positive for cognitive precursors aimed at reducing depression and anxiety and improving well-being. The findings will inform future app development to promote more sustained engagement, and future evaluations will assess the effects of BeMe on changes in mental health outcomes.

Facebook Intervention to Connect Alaska Native People With Resources and Support to Quit Smoking: CAN Quit Pilot Randomized Controlled Trial

INTRODUCTION

There is some evidence that social media interventions can promote smoking cessation. This randomized controlled pilot study is the first to evaluate the feasibility and potential efficacy of a Facebook smoking cessation intervention among Alaska Native (AN) adults.

AIMS AND METHODS

Recruitment and data collection occurred from December 2019 to March 2021. Participants were recruited statewide in Alaska using Facebook advertisements with a targeted sample of 60 enrolled. Participants were stratified by gender, age, and rural or urban residence and randomly assigned to receive referral resources on evidence-based cessation treatments (EBCTs) (control, n = 30) or these resources plus a 3-month, closed (private), culturally tailored, Facebook group (intervention, n = 31) that connected participants to EBCT resources and was moderated by two Alaska Native Trained Tobacco Specialists. Assessments were conducted online post-randomization at 1, 3, and 6 months. Outcomes were feasibility (recruitment, retention, and intervention engagement), self-reported use of EBCTs, and biochemically confirmed seven-day point-prevalence smoking abstinence.

RESULTS

Of intervention participants, 90% engaged (eg posted, commented) more than once. Study retention was 57% at 6 months (no group differences). The proportion utilizing EBCTs was about double for intervention compared with the control group participants at 3 and 6 months. Smoking abstinence was higher for intervention than control participants at 3 months (6.5% vs. 0%, p = .16) but comparable at 6 months (6.4% vs. 6.7%, p = .97).

CONCLUSIONS

While additional research is needed to promote long-term cessation, this pilot trial supports recruitment feasibility during the Coronavirus Disease 2019 (COVID-19) pandemic, consumer uptake, and a signal for intervention efficacy on the uptake of cessation treatment and short-term smoking abstinence.

IMPLICATIONS

This study is the first evaluation of a social media intervention for smoking cessation among Indigenous people. We learned that statewide Facebook recruitment of AN adults who smoke was feasible and there was a signal for the efficacy of a Facebook intervention on the uptake of EBCT and short-term (3 months) biochemically verified smoking abstinence. Clinically, social media platforms may complement current care models by connecting AN individuals and others living in hard-to-reach communities to cessation treatment resources.

A pilot randomized controlled trial of automated and counselor-delivered text messages for e-cigarette cessation

INTRODUCTION

Automated text messaging programs show promise for e-cigarette cessation. Adding live text counseling could make them more engaging. We developed Quit the Vape (QTV), an automated e-cigarette cessation text messaging program, designed to be delivered as stand-alone or with counselor-delivered messages (QTV-C), and evaluated the acceptability and preliminary efficacy of QTV and QTV-C.

METHODS

Between May and August 2021, we recruited 58 e-cigarette users, aged 20-43 years, 53.5% male, 63.8% non-Hispanic White, from an ongoing cohort study in the United States. Inclusion criteria were: using nicotine-containing e-cigarettes on ≥4 days per month; smartphone ownership; and not receiving tobacco cessation treatment. Motivation to quit did not impact eligibility. Participants were randomized to QTV (n=20), QTV-C (n=19), or control (link to e-cigarette cessation website, n=19). At end-of-treatment, we assessed program engagement and satisfaction, and self-reported quitting behaviors (e.g. point prevalence abstinence, PPA).

RESULTS

At baseline, average past-month e-cigarette use was 26.8 days (SD=6.2). At follow-up at 4 weeks, among QTV and QTV-C participants, ≥85% replied to ≥1 message, ≥35% set a quit date, and ≤15% opted out. More QTV and QTV-C participants (55.6%) versus control (17.7%) reported program satisfaction (p=0.034). QTV-C participants (vs QTV and control) trended more favorably on 7-day e-cigarette PPA [27.8% (95% CI: 11.5-53.3) vs 11.1% (95% CI: 2.6-37.0) and 5.9% (95% CI: 0.7-34.5)] and quit attempts [66.7% (95% CI: 41.6-84.9) vs 50.0% (95% CI: 27.4-72.6) and 52.9% (95% CI: 29.2-75.5)].

CONCLUSIONS

Adding live text counseling to an automated text messaging program is acceptable and shows promise for e-cigarette cessation. A larger trial is warranted to assess its efficacy.

Behavioural interventions delivered through interactive social media for health behaviour change, health outcomes, and health equity in the adult population

BACKGROUND

Social networking platforms offer a wide reach for public health interventions allowing communication with broad audiences using tools that are generally free and straightforward to use and may be combined with other components, such as public health policies. We define interactive social media as activities, practices, or behaviours among communities of people who have gathered online to interactively share information, knowledge, and opinions.

OBJECTIVES

We aimed to assess the effectiveness of interactive social media interventions, in which adults are able to communicate directly with each other, on changing health behaviours, body functions, psychological health, well-being, and adverse effects. Our secondary objective was to assess the effects of these interventions on the health of populations who experience health inequity as defined by PROGRESS-Plus. We assessed whether there is evidence about PROGRESS-Plus populations being included in studies and whether results are analysed across any of these characteristics.

SEARCH METHODS

We searched CENTRAL, CINAHL, Embase, MEDLINE (including trial registries) and PsycINFO. We used Google, Web of Science, and relevant web sites to identify additional studies and searched reference lists of included studies. We searched for published and unpublished studies from 2001 until June 1, 2020. We did not limit results by language.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), controlled before-and-after (CBAs) and interrupted time series studies (ITSs). We included studies in which the intervention website, app, or social media platform described a goal of changing a health behaviour, or included a behaviour change technique. The social media intervention had to be delivered to adults via a commonly-used social media platform or one that mimicked a commonly-used platform. We included studies comparing an interactive social media intervention alone or as a component of a multi-component intervention with either a non-interactive social media control or an active but less-interactive social media comparator (e.g. a moderated versus an unmoderated discussion group). Our main outcomes were health behaviours (e.g. physical activity), body function outcomes (e.g. blood glucose), psychological health outcomes (e.g. depression), well-being, and adverse events. Our secondary outcomes were process outcomes important for behaviour change and included knowledge, attitudes, intention and motivation, perceived susceptibility, self-efficacy, and social support.

DATA COLLECTION AND ANALYSIS

We used a pre-tested data extraction form and collected data independently, in duplicate. Because we aimed to assess broad outcomes, we extracted only one outcome per main and secondary outcome categories prioritised by those that were the primary outcome as reported by the study authors, used in a sample size calculation, and patient-important.

MAIN RESULTS

We included 88 studies (871,378 participants), of which 84 were RCTs, three were CBAs and one was an ITS. The majority of the studies were conducted in the USA (54%). In total, 86% were conducted in high-income countries and the remaining 14% in upper middle-income countries. The most commonly used social media platform was Facebook (39%) with few studies utilising other platforms such as WeChat, Twitter, WhatsApp, and Google Hangouts. Many studies (48%) used web-based communities or apps that mimic functions of these well-known social media platforms. We compared studies assessing interactive social media interventions with non-interactive social media interventions, which included paper-based or in-person interventions or no intervention. We only reported the RCT results in our 'Summary of findings' table. We found a range of effects on health behaviours, such as breastfeeding, condom use, diet quality, medication adherence, medical screening and testing, physical activity, tobacco use, and vaccination. For example, these interventions may increase physical activity and medical screening tests but there was little to no effect for other health behaviours, such as improved diet or reduced tobacco use (20,139 participants in 54 RCTs). For body function outcomes, interactive social media interventions may result in small but important positive effects, such as a small but important positive effect on weight loss and a small but important reduction in resting heart rate (4521 participants in 30 RCTs). Interactive social media may improve overall well-being (standardised mean difference (SMD) 0.46, 95% confidence interval (CI) 0.14 to 0.79, moderate effect, low-certainty evidence) demonstrated by an increase of 3.77 points on a general well-being scale (from 1.15 to 6.48 points higher) where scores range from 14 to 70 (3792 participants in 16 studies). We found no difference in effect on psychological outcomes (depression and distress) representing a difference of 0.1 points on a standard scale in which scores range from 0 to 63 points (SMD -0.01, 95% CI -0.14 to 0.12, low-certainty evidence, 2070 participants in 12 RCTs). We also compared studies assessing interactive social media interventions with those with an active but less interactive social media control (11 studies). Four RCTs (1523 participants) that reported on physical activity found an improvement demonstrated by an increase of 28 minutes of moderate-to-vigorous physical activity per week (from 10 to 47 minutes more, SMD 0.35, 95% CI 0.12 to 0.59, small effect, very low-certainty evidence). Two studies found little to no difference in well-being for those in the intervention and control groups (SMD 0.02, 95% CI -0.08 to 0.13, small effect, low-certainty evidence), demonstrated by a mean change of 0.4 points on a scale with a range of 0 to 100. Adverse events related to the social media component of the interventions, such as privacy issues, were not reported in any of our included studies. We were unable to conduct planned subgroup analyses related to health equity as only four studies reported relevant data.

AUTHORS' CONCLUSIONS

This review combined data for a variety of outcomes and found that social media interventions that aim to increase physical activity may be effective and social media interventions may improve well-being. While we assessed many other outcomes, there were too few studies to compare or, where there were studies, the evidence was uncertain. None of our included studies reported adverse effects related to the social media component of the intervention. Future studies should assess adverse events related to the interactive social media component and should report on population characteristics to increase our understanding of the potential effect of these interventions on reducing health inequities.