The Use of Web-Based Support Groups Versus Usual Quit-Smoking Care for Men and Women Aged 21-59 Years: Protocol for a Randomized Controlled Trial

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.

Understanding Hesitation to Use Nicotine Replacement Therapy: A Content Analysis of Posts in Online Tobacco-Cessation Support Groups

PURPOSE

We aimed to better understand hesitancy to use nicotine replacement therapy (NRT) to quit smoking.

DESIGN

We content coded and analyzed NRT-related posts in online quit smoking support groups to understand NRT-use hesitancy and to examine associations with health outcomes.

SETTING

NRT posts were analyzed in unmoderated social-media support groups with free NRT.

SAMPLE

Adults who smoked daily (n = 438) and posted about NRT were studied, 339 of whom reported on NRT usage and 403 reported on smoking abstinence.

MEASURES

Surveys at 1-month post-quit date assessed NRT usage and smoking abstinence.

ANALYSIS

Relationships among NRT posts, NRT usage and smoking abstinence were analyzed using GEE models accounting for support group and covariates.

RESULTS

Nearly all (96.17%) participants reported using the study-provided NRT once, most (70.21%) used NRT during the past week, but less than half (45.72%) used NRT daily for the full month as recommended. Nearly two-thirds (65.34%) of NRT posts were negative. Posts reflecting dislike or no longer needing NRT were associated with a lower likelihood of using NRT in the past week at least once (B = -.66, P = .005 and B = -.37, P = .045), use occasions (B = -1.86, P = .018 and B = -1.10, P = .016) and used daily for full month (B = -.56, P = .044 and B = -.53, P = .009). Posts related to the effectiveness of NRT related to past-week NRT used at least once (B = .15, P = .023), used daily for full month (B = .25, P = .001), and smoking abstinence (B = .27, P = .002).

CONCLUSION

Strategies are needed to address dislike of NRT and strengthen perceptions of NRT efficacy, especially on social media where posts may be amplified.

Buddies as In-Group Influencers in Online Support Groups: A Social Network Analysis of Processes and Outcomes

Buddies, serving as in-group influencers to aid demographically similar cobuddies, are extensively used in face-to-face support groups to enhance positive social influence. The authors examine the efficacy of buddies in online support groups and investigate underlying mediating processes using social network analysis. They observe what happens when members of support groups for quitting smoking, including members who are relatively active and less active in the group, after a few days are called on to be buddies and assigned to specific cobuddies. The findings indicate that, consistent with normative expectations for buddies, members form especially strong ties with their designated cobuddies. The more active buddies are in the group, the stronger the ties they form with their cobuddies and, in turn, their cobuddies form stronger ties with group members overall, which then relates to cobuddy goal attainment. The findings suggest that interactive marketers should consider using buddies in online support groups but observe activity levels before making buddy assignments, because positive outcomes are contingent on buddies being active in the group. Marketers should also ensure that online support group members post to everyone, not just their buddies, because ties formed among group members as a whole are crucial for goal attainment.

Application of Automated Text Analysis to Examine Emotions Expressed in Online Support Groups for Quitting Smoking

Online support groups offer social support and an outlet for expressing emotions when dealing with health-related challenges. This study examines whether automated text analysis of emotional expressions using Linguistic Inquiry and Word Count (LIWC) can identify emotions related to abstinence expressed in online support groups for quitting smoking, suggesting promise for offering targeted mood management to members. The emotional expressions in 1 month of posts by members of 36 online support groups were related to abstinence at month end. Using the available LIWC dictionary, posts were scored for overall positive emotions, overall negative emotions, anxiety, anger, sadness, and an upbeat emotional tone. Greater expressions of negative emotions, and specifically anxiety, related to nonabstinence, while a more upbeat emotional tone related to abstinence. The results indicate that automated text analysis can identify emotions expressed in online support groups for quitting smoking and enable targeted delivery of mood management to group members.

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.