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Social media technologies offer a novel opportunity for scalable health interventions that can facilitate user engagement and social support, which in turn may reinforce positive processes for behavior change.
By using principles from health communication and social support literature, we implemented a Facebook group–based intervention that targeted smoking reduction and cessation. This study hypothesized that participants’ engagement with and perceived social support from our Facebook group intervention would predict smoking reduction.
We recruited 16 regular smokers who live in the United States and who were motivated in quitting smoking at screening. We promoted message exposure as well as engagement and social support systems throughout the intervention. For message exposure, we posted prevalidated, antismoking messages (such as national antismoking campaigns) on our smoking reduction and cessation Facebook group. For engagement and social support systems, we delivered a high degree of engagement and social support systems during the second and third week of the intervention and a low degree of engagement and social support systems during the first and fourth week. A total of six surveys were conducted via Amazon Mechanical Turk (MTurk) at baseline on a weekly basis and at a 2-week follow-up.
Of the total 16 participants, most were female (n=13, 81%), white (n=15, 94%), and between 25 and 50 years of age (mean 34.75, SD 8.15). There was no study attrition throughout the 6-time-point baseline, weekly, and follow-up surveys. We generated Facebook engagement and social support composite scores (mean 19.19, SD 24.35) by combining the number of likes each participant received and the number of comments or wall posts each participant posted on our smoking reduction and cessation Facebook group during the intervention period. The primary outcome was smoking reduction in the past 7 days measured at baseline and at the two-week follow-up. Compared with the baseline, participants reported smoking an average of 60.56 fewer cigarettes per week (SD 38.83) at the follow-up, and 4 participants out of 16 (25%) reported 7-day point prevalence smoking abstinence at the follow-up. Adjusted linear regression models revealed that a one-unit increase in the Facebook engagement and social support composite scores predicted a 0.56-unit decrease in cigarettes smoked per week (standard error
This study is the first Facebook group–based intervention that systemically implemented health communication strategies and engagement and social support systems to promote smoking reduction and cessation. Our findings imply that receiving one like or posting on the Facebook-based intervention platform predicted smoking approximately one less cigarette in the past 7 days, and that interventions should facilitate user interactions to foster user engagement and social support.
Tobacco use is the primary cause of premature mortality and is responsible for almost half a million deaths every year in the United States and nearly 5 million deaths globally [
Significant advances in social media technologies and their ubiquity offer novel opportunities to provide geographically distant users with easily accessible, cost-effective, personalized health content, and social network-based support. For example, Facebook, one of the most widely adopted social media platforms, hosts approximately 1.22 billion daily active users [
In this regard, social media such as Facebook provide a range of communication features for putative processes of behavior change that are important to individuals with health problems. Those social media features and related processes include “posting” features for self-disclosure [
Facebook groups, in particular, can be used as a designated online social support community for members with similar health concerns [
In this study, we utilized Facebook group features to effectively disseminate prevalidated antismoking messages with high frequency and longitudinal exposure. Message exposure frequency and exposure duration are pivotal factors for successful health campaigns [
Despite the potential benefits of harnessing social media for health interventions, a critical gap in knowledge persists in terms of how to best utilize social media features to achieve positive health intervention outcomes. The intent of this study is to strategically leverage communication features that are available on Facebook groups to implement a smoking reduction and cessation intervention among regular smokers who are interested in quitting. For our intervention, we promoted smoking cessation as the optimal outcome to achieve, but we also accepted smoking reduction as a positive change for those who could not immediately quit smoking, as smoking reduction is a common step toward eventual cessation [
Prior studies on health promotions and health behavior models have demonstrated that exposure to health communications can enhance one’s health behavior by changing core beliefs and attitudes about expected health outcomes [
Social support and engagement systems were additional theoretical components that were applied in our Facebook group–based smoking reduction and cessation intervention. Social support is defined as informational, emotional, reassuring, or tangible resources [
Based on the four dimensions of the social support conceptual framework [
One’s perceived social support can help them enhance their self-efficacy beliefs in order to overcome barriers to adopting the health behavior being promoted. To deliver social support and engagement in relation to promoting smoking reduction and cessation during the 4-week intervention period, we manipulated the level of engagement and social support systems (high vs low) and juxtaposed it with high versus low message exposure.
We examined the feasibility of a Facebook group–based smoking reduction and cessation intervention. Additionally, the preliminary efficacy on smoking reduction (the reduced number of cigarettes consumed per week) and on 7-day point smoking abstinence at the follow-up was tested. We also tested whether the intervention components (social support and engagement systems) predict smoking reduction.
Recruitment messages and preliminary screening questions were disseminated through Amazon Mechanical Turk (MTurk) and social media platforms. MTurk is an anonymous Web-based labor market with over 500,000 registered workers worldwide. MTurk workers complete tasks distributed by requesters for small financial rewards. MTurk has been used as a recruitment pool in various fields of research for an array of tasks, including decision-making [
Over 200 applicants who were interested in our four-week smoking reduction and cessation interventions were screened based on their self-reported characteristics. The inclusion criteria were regular smokers (smoking 5 days per week) who were between the ages of 18 and 65 years and living in the United States. To be eligible, participants had to have no chronic disease interfering with their daily lives, no use of illicit drugs, and be motivated to quit smoking (> 80, on a 100-point motivation to quit smoking scale [
Qualified applicants (N=132) were invited to participate in our study. The eligible participants who responded to our invitation (N=46) were randomly assigned to one of the following conditions: email condition, MTurk-only condition, or the Facebook condition. Participants were introduced to their intervention and coached on how to participate in it. This report focuses on the subjects who were randomized to the Facebook condition (n=16) in order to give special attention to the findings that are unique due to the social media features exclusively available on Facebook (eg, comments, share, likes, and wall postings). Primary outcomes from all three conditions will be published in a separate report.
Participants were first provided with an electronic informed consent form. Before the start date of the intervention, researchers contacted consenting individuals through an individual phone call meeting and provided guidelines in greater detail on how to participate in the Facebook intervention. For example, participants were encouraged to share their thoughts, progress, and peer support. We also provided practical methods on engaging with the intervention materials on a daily basis by leaving comments, liking posts, and interacting with other peers in the group throughout the four-week intervention period. The participants were informed that there is no incentive for intervention engagement in our smoking reduction and cessation Facebook group. We informed participants that our research members would post different antismoking messages throughout the intervention period and provide social support to keep participants motivated to engage in action for smoking reduction and cessation. In addition, on the start date, the research team greeted all participants on the Facebook group wall by posting encouraging statements such as “…If you are having a hard time quitting, let us hear. We are here to support you and encourage you to achieve your goal.” This greeting statement was used to set a positive tone and an atmosphere inclusive of all participants. All procedures, materials, and study protocols were reviewed and approved by the university’s Institutional Review Board.
We implemented and targeted different levels of message exposure and engagement and social support systems over four weeks. During week 1 (high message exposure combined with low engagement and social support), we posted antismoking messages three times per day without directly encouraging people to respond to the materials or share their thoughts. During week 2 (low message exposure combined with high engagement and social support), we posted antismoking materials once per day and delivered supportive comments and fostered user engagement by directly asking participants to share their motivating factors, their thoughts on posted antismoking messages, and their progress on quitting smoking with the group. During week 3 (high message exposure combined with high engagement and social support), we posted antismoking messages three times per day. In addition, a professional clinical expert joined the Facebook group and provided guidance on smoking reduction and cessation as well as methods to cope with nicotine withdrawal. We also continued our targeted engagement and social support communications by asking people to share their thoughts toward the guidance. During the last week (low message exposure and low engagement and social support), we posted antismoking materials once per day that focused on mindfulness, self-regulatory tips, and resources for smoking reduction and cessation. Participants were blinded from the intention of the intervention designs regarding message exposure frequency and levels of engagement and social support systems.
In order to prepare intervention materials to be disseminated on our smoking reduction and cessation Facebook group for four weeks, a total of 80 different antismoking advertisements, campaign messages, and news articles were collected from publicly available online sources, such as smokefree.gov, cancer.gov, and the CDC’s Media Campaign Resource Center (MCRC), a rich database with more than 10,000 antismoking ads produced by different states and federal agencies. The collected antismoking materials were either video-based or text-and-image-based materials that have shown population-level success or promising evidence on promoting tobacco control and prevention (eg, the “Tips From Former Smokers” campaign). To select the final set of intervention materials, in a separate MTurk-based randomized experiment, we evaluated the relative effectiveness of 80 antismoking materials among 1288 smokers prior to the interventions. Based on composite scores of message effectiveness and post-antismoking attitudes toward randomly assigned antismoking material, a total of 56 antismoking messages out of the 80 units were selected as intervention materials (3 messages × 7 days for the first week, 1 message × 7 days for the second week, 3 messages × 7 days for the third week, and 1 message × 7 days for the last week).
The 56 units of antismoking messages were posted in a random order on our smoking reduction and cessation Facebook group. Based on the ongoing feedback from our participants and weekly surveys, 5 message units of these 56 (approximately 9%) were replaced with other antismoking materials to correspond to the needs of participants (eg, asking for more information on smoking cessation tips).
Our smoking reduction and cessation Facebook group intervention started in late November 2015 and ended in early January 2016. We delivered antismoking materials with different frequencies across four intervention weeks (as described above) but with fixed time schedules: 8:00 AM, 12:00 PM, and 5:00 PM (Pacific Time) for the first and third week; and 11:00 AM (Pacific Time) for the second and fourth week.
In addition to using social media as an intervention modality where participants were exposed to antismoking messages frequently, the research team utilized communication features on the Facebook group, such as pressing the “like” button to express support and affective responses toward users’ wall postings and comments and leaving “comments” to provide constructive feedback. These activities were implemented to synchronously reciprocate them with information and foster social support and user engagement.
A baseline survey, all weekly surveys administered during the four-week intervention period, and a two-week follow-up survey were conducted to participants via MTurk by using the “qualification type” function on MTurk. This function made the survey available only to our intervention participants. Participants were compensated with US $8 for each baseline and weekly survey and US $15 for the two-week follow-up survey for a total of US $55 over the study period. The median values of the time spent by participants on survey assessments throughout the entire intervention period ranged between 4.21 minutes and 13.96 minutes.
Demographic information such as age, gender, marital status, ethnicity, and race, self-reported smoking status (the average number of cigarettes participants smoked in the past 7 days), motivation to quit [
The primary outcome was self-reported smoking reduction reported at baseline and the last follow-up (adopted from [
For the key independent variable, we constructed individual-level Facebook engagement and social support composite scores (referred to as “Facebook ESSC Scores” hereafter) to capture user engagement and the social support received from our Facebook group. The Facebook ESSC score was aggregated for each participant by combining the number of postings each participant generated (both wall postings and comments) and the number of “likes” each participant received during the intervention period. Two trained coders verified the number of likes each participant received and the number of comments or wall posts each participant made. The two coders reached a consensus on these results.
Secondary constructs measured at baseline, weekly, and follow-up surveys include the antismoking attitudes scale on a 7-point semantic differential scale [
We generated the Facebook Intervention Feasibility Inventory by adopting and modifying questionnaires from usability and acceptability scales that were validated in the mHealth intervention context [
We monitored participants’ engagement with the intervention content and other members by looking at the frequency of postings and “likes” that participants generated on the Facebook group. Due to limited access to extract user data specific to “seen by” activities, our trained research members counted the number of “seen by” activities per post. We also unobtrusively observed participants’ exposure to intervention messages by checking the “seen by” feature on a daily basis, which enabled the research team to track whether each user had seen the materials posted on the wall of our Facebook group.
R package version 3.2.5 [
The majority of participants were white (n=15, 94%), female (n=13, 81%), and between 25 and 50 years old (mean 34.75, SD 8.15). On average, participants smoked 11.31 cigarettes per day (SD 6.81) and 6.93 days per week (SD 0.25) at baseline. The degree of readiness scores for smoking cessation was 7.50 (SD 1.59), which indicates high readiness to quit smoking. During the past 12 months prior to baseline, participants stopped smoking 1.81 times (SD 1.47 times) for at least one day or longer. The 7-item antismoking attitude scale at baseline had a high inter-reliability (Cronbach alpha=.91) and loaded on a single confirmatory factor (with an eigenvalue=4.965 with 70% variances being explained by this one factor). Thus, we created a composite score (mean 2.37, SD 1.22): lower values indicate strong antismoking attitudes, such as smoking cigarettes is “bad, useless, and harmful for my health”; and higher values indicate positive attitudes toward smoking, such as smoking cigarettes is “good, beneficial, and useful.”
All participants (N=16) completed all 6-time-point surveys. Descriptive statistics of predictors (Facebook ESSC scores), primary and secondary outcomes, and Facebook intervention feasibility questionnaires are presented in
Seven-item antismoking attitudes showed a high reliability for each time-point survey, ranging from Cronbach alpha=.91 to .99. Thus, we created a composite score for each time-point. Similarly, 5-item self-efficacy questionnaires had a high reliability for each time-point survey, ranging from Cronbach alpha=.88 to .98. We generated a composite score for self-efficacy beliefs measured at each time-point of the surveys. For perceived social support questionnaires, five items were averaged to a single factor for each time-point survey after verifying a good reliability score for each survey (Cronbach alpha scores ranged from .84 to .92). The descriptive statistics of secondary outcomes are reported in
Predictors, primary and secondary outcomes, and Facebook feasibility.
Variables | Statistics | ||||||
Mean (SD) | |||||||
Number of Facebook “likes” received | 13.25 (17.67) | ||||||
Number of Facebook “comments/wall posts” generated | 5.94 (6.96) | ||||||
Number of Facebook engagement scores | 19.19 (24.35) | ||||||
Baseline | Week 1 | Week 2 | Week 3 | Week 4 | Follow-up | ||
Mean number of cigarettes smoked per week among smokers mean (SD) | 79.19 (47.66) | 44.38 (60.09) | 32.44 (44.42) | 29.81 (41.84) | 20.44 (36.51) | 18.63 (35.33) | |
Number of people who quit smoking in the past 7 days, n (%) | 0 (0) | 0 (0) | 2 (13) | 3 (19) | 3 (19) | 4 (25) | |
Mean Antismoking attitude scale score | 2.37 (1.22) | 1.76 (1.33) | 1.73 (1.34) | 1.66 (1.32) | 1.60 (1.51) | 1.60 (1.53) | |
Mean self-efficacy for smoking cessation scale score | – | 6.06 (0.70) | 5.83 (0.83) | 6.18 (0.75) | 6.40 (0.67) | 6.05 (1.50) | |
Mean score on readiness to quit item | 7.50 (1.59) | 8.19 (1.22) | 7.88 (1.59) | 7.94 (1.53) | 8.63 (1.20) | 8.56 (1.41) | |
Mean perceived social support scale score | – | 3.95 (0.87) | 4.10 (0.64) | 4.03 (0.79) | 4.18 (0.84) | 3.89 (0.88) | |
Response efficacy (alpha =.96) | – | – | – | – | – | 5.47 (1.20) | |
Perceived technology barriers (alpha=.97) | – | – | – | – | – | 2.23 (1.45) | |
Easiness to use (alpha=.96) | – | – | – | – | – | 6.02 (0.98) |
Correlations across predictors, smoking reduction, and Facebook feasibility subfactors. Facebook engagement and social support composite scores (Facebook ESSC scores) are combined values of the number of Facebook “likes” one received (1 in the table) and the number of Facebook “comments” and “wall postings” each person generated (2 in the table).
Variables | 1 | 2 | 3 | 4 | 5 | 6 | |||||||
1 | Number of Facebook “likes” received | – | |||||||||||
2 | Number of Facebook “comments/wall posts” generated | .95a | – | ||||||||||
3 | Facebook ESSC scores | .996a | .97a | – | |||||||||
4 | Reduced number of cigarettes smoked | .49c | .48 | .49c | – | ||||||||
5 | Facebook response efficacy | −.19 | .01 | −.01 | .14 | – | |||||||
6 | Perceived technology barriers | −.34 | −.35 | −.34 | −.56b | .12 | – | ||||||
7 | Easiness to use | .24 | .20 | .23 | .35 | .62b | −.15 |
a
b
c
A confirmatory factor analysis with a direct oblimin rotation was performed on the 24-item Facebook intervention feasibility questionnaires. Items within each subfactor with loading scores greater than 0.6 were averaged to compose three subconcepts under the umbrella concept of perceived Facebook feasibility for health interventions. Those subfactors represent Facebook response efficacy (alpha=.96), perceived barriers of using Facebook (alpha=.97), and easiness of using Facebook for smoking reduction and cessation interventions (alpha=.96), respectively (
User engagement during the four-week intervention period. The y-axis indicates the number of wall posts and comments participants generated within the smoking reduction and cessation Facebook group. The values are indicative of user engagement.
Using a closed Facebook group, we developed and delivered a smoking reduction and cessation intervention in a cost-effective manner while overcoming geolocational barriers and time constraints. We found that Facebook was highly feasible and we demonstrated 100% study retention and survey completion rates. In this study, 25% of participants reported 7-day smoking abstinence at the follow-up, and those who continued had dramatically reduced the number of cigarettes they smoked weekly. These outcomes are consistent with and comparable with previous studies of Facebook-delivered interventions for smoking [
Although we promoted smoking cessation as an optimal outcome for our intervention, we also accepted smoking reduction as a positive form of behavior change. Helping smokers to cut down on cigarette use has been attempted in many controlled trials [
Results also suggest that engagement and receipt of social support within this Facebook health communications intervention predicted smoking reduction among motivated smokers. Specifically, a one-unit increase in Facebook ESSC scores predicted a 0.56 unit decrease in cigarettes consumed in the past 7 days. That is, participants who received more “likes” and those who posted more content on our Facebook group, indicative of social support and user engagement, were more likely to reduce their weekly smoking (
We generated the term “Facebook engagement and social support composite scores” (ESSC [/ˈesit/] scores) in this study and tested the predictive validity of the ESSC scores on smoking reduction. The composite score was based on our conceptualization that “writing comments and wall posts” is an indicator of user engagement, as also defined by Facebook [
Given this conceptualization, we were interested in how the user engagement and social support systems worked synergistically to enhance intervention outcomes. Determining whether user engagement is exclusively more important than perceived social support or vice versa for predicting smoking reduction was beyond the scope of our research. Thus, we proposed a composite score by combining them to serve our conceptual approach. In addition, our approach was aligned with the principle from test theory that composite scores are more reliable than individual items [
Throughout the intervention period, on average, participants generated six comments during the four-week intervention period (SD 6.96, median 3.50). Thrul and colleague [
To understand how and for whom social media–based interventions work, future work may examine potential moderating factors that impact the relationship between user engagement and intervention outcomes. An array of baseline characteristics have predicted technology-based intervention outcomes [
We operationalized two key intervention components to maximize the persuasive effects of social media platforms in promoting smoking reduction and cessation: (1) exposure to antismoking messages and (2) participant engagement and social support systems. Prior studies have demonstrated that exposure to health campaign messages can enhance health behavior by changing one’s beliefs about expected health outcomes [
CDC’s Best Practices Report, released in 2014, recommends the reuse of existing advertisements and campaign messages rather than producing new content in order to reduce the cost, time, and untested risks associated with developing new ones [
At the time we developed this study, there was no standardized, evidence-based model or framework applicable to designing Facebook group–based interventions for smoking reduction and cessation. Thus, based on prior health communication and technology literature, we developed an intervention model using two main components: “persuasive message exposure” and “supportive engagement systems.” We used a varied frequency of message exposure (three times per day or one time per day), as there was no empirical evidence on the optimal dose of message exposure for a social media–based intervention. We randomly juxtaposed these two components (high vs low message exposure frequency × high vs low engagement and social support) to develop our intervention model. This randomly juxtaposed combination led to four weekly designs, including high message exposure and low engagement and social support systems for week 1; low message exposure and high engagement and social support systems for week 2; high message exposure and high engagement and social support systems for week 3; and low message exposure and low engagement and social support systems for week 4. Our findings should be understood with caution. We did not examine which weeks resulted in the most successful intervention outcome (smoking reduction), but we tested the overall impact of the intervention as a whole (before and after the intervention) on smoking reduction. Thus, the risk of any possible confounding effect due to the varied frequency of message exposure is minimized because we did not test smoking reduction by individual week.
An interesting finding about user engagement is that although we manipulated the frequency of message exposure, there was no resulting effect on increasing user engagement, as shown in
Another strength of the study was 100% study retention throughout the MTurk-linked surveys at six different time points. Various technology features on MTurk, such as qualification assignment and the online payment system allowed us to conduct longitudinal surveys. We demonstrated that MTurk can be a platform for a wide range of research activities, ranging from recruitment of smokers living in the United States to multiple times of follow-up assessments with participants.
Our findings successfully demonstrated the feasibility of social media technologies to offer smoking reduction and cessation interventions with the strategic delivery of engagement and social support systems. Our findings, however, should be understood within the limitations imposed by research budgets and the scope of the study. We did not objectively verify self-reported abstinence; thus, it is possible that the impact of the intervention may be inflated. The reported outcome on the reduced number of cigarettes per week does not correspond to the same level of reduction in toxicant exposure. In future technology-based interventions for smoking reduction and cessation, researchers should embrace practically feasible methods for measuring objective markers of nicotine toxicology [
Our sample size in this study was relatively small. Thus, rather than using a complex modeling approach such as latent growth curve modeling, we simplified our statistical model and directly examined the predictive value of Facebook-mediated engagement and social support in explaining smoking reduction outcomes. The dataset of 16 participants with no missing data still provided enough statistical power to detect the effect of the primary regression model outcomes.
Another limitation is that the gender and race of our sample were relatively homogeneous, mostly white women. In future research, we hope to replicate the interventions with bigger sample sizes and involve participants with characteristics that are more heterogeneous than the current sample to establish the generalizability and reproducibility of the findings. With an increased sample size, future studies should examine pathways of intervention processes with intermediate factors, such as enhanced self-efficacy and perceived social support, to reflect the dynamics of behavior change [
We examined the feasibility of the communication features in our Facebook group that were utilized to deliver theory-guided intervention components such as message exposure and engagement and social support systems among the optimal set of participants (self-motivated participants who wanted to quit smoking at baseline). In future work, another important question might be whether social media–based interventions can have a significant impact on enhancing these intermediate factors (eg, enhancing motivation to quit, and pro-quitting attitudes), even among those with low motivation to quit smoking at baseline. If social media–based interventions can successfully enhance those factors and smoking reduction and cessation among participants with low motivation, the expected significance of the interventions can be much greater than this study.
After our four-week intervention, followed by a two-week follow-up survey, we learned that participants continued to use our Facebook group and some participants expressed that they wanted the interventions for a longer period of time. Social media platforms provide novel opportunities to operationalize persuasive technologies for scalable interventions and to maintain active engagement and long-lasting intervention outcomes [
As reviewed, theory-driven and evidence-based interventions using Facebook for health promotions are promising. A growing line of research has shown positive effects of Facebook use on various health outcomes, from smoking cessation [
This study is the first Facebook-mediated intervention research that systemically promoted antismoking communication strategies and social support and engagement systems as mechanisms of behavior change within a Facebook group. We conceptualized Facebook “likes” and “wall postings and comments” as the manifestation of social support and user engagement. Our findings imply that receiving one Facebook “like” or posting on the Facebook group at least once predicts almost one less cigarette in the past 7 days. The study supports positive effects of Facebook-mediated communication, engagement and social support systems for smoking reduction and cessation, and highlights the public health potential of social media interventions for scaling-up tobacco control and prevention efforts. It also provides practical guidelines for designing communication strategies and persuasive, social media–based smoking reduction and cessation interventions that might be useful for future research.
Facebook engagement and social support composite score
This study was funded by the National Institutes of Health (NIH) NIDA Grant No. P30DA029926 Pilot Core. NIDA had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. The authors would like to thank Gene Geter for his assistance in data collection, as well as Lamar D Moss for his sharp proofreading and valuable feedback.
None declared.