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Effect of a Digital Social Media Campaign on Young Adult Smoking Cessation

  • Canadian Forces Morale and Welfare Services

Abstract and Figures

Social Media (SM) may extend the reach and impact for smoking cessation among young adult smokers. To-date little research targeting young adults has been done on the use of social media to promote quitting smoking. We assessed the effect of an innovative multi-component web-based and SM approach known as Break-it-Off (BIO) on young adult smoking cessation. The study employed a quasi-experimental design with baseline and 3-month follow-up data from 19 to 29 year old smokers exposed to BIO (n=102 at follow-up) and a comparison group of Smokers' Helpline (SHL) users (n=136 at follow-up). Logistic regression analysis assessed differences between groups on self-reported 7-day and 30-day point prevalence cessation rates, adjusting for ethnicity, education level, and cigarette use (daily or occasional) at baseline. The campaign reached 37,325 unique visitors with a total of 44,172 visits. BIO users had significantly higher 7-day and 30-day quit rates compared with users of SHL. At three month follow-up, BIO participants (32.4%) were more likely than SHL participants (14%) to have quit smoking for 30 days (odds ratio = 2.95, 95% CI = 1.56- 5.57, p < .001)and BIO participants (91%) were more likely than SHL participants (79%) to have made a quit attempt (odds ratio = 2.69, 95% CI = 1.03-6.99, p = .04). The reach of the campaign and findings on quitting success indicate that a digital/social media platform can complement the traditional SHL cessation service for young adult smokers seeking help to quit. © The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail:
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© The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved.
For permissions, please e-mail:
Nicotine & Tobacco Research, 2016, 351–360
Original investigation
Advance Access publication June 4, 2015
In Canada, young adults (19–29) have the highest rate of smoking
at 24.4%,1 but their use of cessation services and products is low.2
This may reect the common belief among young adults that the
best way to quit is on their own.3 More than half of young adult
smokers want to quit, but few are successful,4,5 suggesting that there
may be a misalignment between the needs of young people and the
cessation methods available to them. However, those who are able to
quit before the age of 30 will have a life expectancy nearly as long as
those who never smoked,6 which holds hope for achieving popula-
tion health impact if effective, accessible and attractive interventions
are developed for this population. Innovative intervention solutions
for reaching young adult smokers are needed.
Web and mobile phone-based interventions have shown prom-
ise for encouraging smoking cessation in young adults7 and social
media (SM) tools and technologies have become a key resource for
young adults. SM is dened as “any electronic, networked informa-
tion resource that derives its principal value from user contributions,
Original investigation
Effect of a Digital Social Media Campaign on
Young Adult Smoking Cessation
Neill BruceBaskerville PhD1,2, SundayAzagba PhD1,2,
CameronNorman PhD3,4, KyleMcKeown BA5, K. StephenBrown PhD2,6
1Applied Health Sciences, University of Waterloo, Waterloo, ON, Canada; 2Propel Centre for Population Health
Impact, University of Waterloo, Waterloo, ON, Canada; 3Dalla Lana School of Public Health, University of Toronto,
Toronto, ON, Canada; 4CENSE Research + Design, Toronto, ON, Canada; 5Smokers’ Helpline, Canadian Cancer
Society, Toronto, ON, Canada; 6Statistics and Actuarial Sciences, University of Waterloo, Waterloo, ON, Canada
Corresponding Author: Neill Bruce Baskerville, PhD, Propel Centre for Population Health Impact, 200 University Ave.
West, University of Waterloo, ON N2L 3G1, Canada. Telephone: 519-888-4567 ext. 35236; Fax: 519-886-6424;
Introduction: Social media (SM) may extend the reach and impact for smoking cessation among
young adult smokers. To-date, little research targeting young adults has been done on the use of
SM to promote quitting smoking. We assessed the effect of an innovative multicomponent web-
based and SM approach known as Break-it-Off (BIO) on young adult smoking cessation.
Methods: The study employed a quasi-experimental design with baseline and 3-month follow-up
data from 19 to 29-year old smokers exposed to BIO (n=102 at follow-up) and a comparison group
of Smokers’ Helpline (SHL) users (n = 136 at follow-up). Logistic regression analysis assessed
differences between groups on self-reported 7-day and 30-day point prevalence cessation rates,
adjusting for ethnicity, education level, and cigarette use (daily or occasional) at baseline.
Results: The campaign reached 37 325 unique visitors with a total of 44 172 visits. BIO users had
significantly higher 7-day and 30-day quit rates compared with users of SHL. At 3-month follow-
up, BIO participants (32.4%) were more likely than SHL participants (14%) to have quit smoking for
30days (odds ratio=2.95, 95% CI=1.56 to 5.57, P < .001) and BIO participants (91%) were more
likely than SHL participants (79%) to have made a quit attempt (odds ratio=2.69, 95% CI=1.03 to
6.99, P=.04).
Conclusion: The reach of the campaign and findings on quitting success indicate that a digital/SM
platform can complement the traditional SHL cessation service for young adult smokers seeking
help to quit.
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352 Nicotine & Tobacco Research, 2016, Vol. 18, No. 3
engagement, and interaction”.8 Among Canadian Internet users aged
16 to 24, 91% use the SM platform Facebook, 73% use instant mes-
saging, and 33% participate in web based discussions on microblog-
ging sites such as Twitter or post images on sites such as YouTube9
suggesting that these tools may be a promising means to engage
young smokers.
Several reviews of web-based research have noted the importance
of tailoring content to individuals and providing interactive compo-
nents to promote greater engagement with the site and greater success
in quitting.10–13 Other research suggests the importance of support
from peers, and the constant updating of website content.10,14,15
Arecent systematic review of mobile interventions found that text
messaging was the most commonly identied smoking cessation
intervention and pooled analysis showed that it doubled cessation
rates.16 However, there is little evidence concerning the effectiveness
of SM interventions for changing health behaviors using platforms
such as Facebook, Twitter, or YouTube.8,10,17–19 Furthermore, a recent
systematic review of SM-based interventions for health promotion
noted the scarcity of empirical evidence and the need for more inter-
ventions with participatory and user-generated features.20
Social marketing provides a means of raising awareness of an
issue or intervention and drawing attention to a specic interven-
tion aimed at large-scale social change issues.21 Implementing a
social marketing approach through SM to drive interest and engage-
ment for a web-based program is both practical and consistent with
the way young people use the media to share ideas and promote
themselves, thus the intervention was considered a central focus of a
larger campaign. The aim of this study was to examine the effect of
the Break-it-Off (BIO) campaign, a digital SM campaign on young
adult smoking cessation.
The BIO campaign was developed and implemented by the
Canadian Cancer Society and funded by Health Canada. It started
January 2012 and was promoted through to the end of March 2012
and continues to be available— The January to
March 2012 period represents the rst time the campaign ran. The
campaign was aimed at young adult smokers aged 19–29. The prin-
cipal aim of the campaign was to engage young adults in smoking
cessation through use of a web-site and SM. The campaign targeted
young adults in six provinces across Canada.
BIO used a “break-up” metaphor, comparing quitting smoking
with ending a romantic relationship, to provide support and encour-
age young adults to “break up” with their smoking addiction. The
campaign’s website guided users through the challenging stages of
ending an unhealthy relationship with smoking: getting it over with,
staying split up, and moving on with life (Figure1). Through the site,
users could learn about established quit methods, such as telephone
counseling, nicotine patches, and nicotine gum. Visitors to the site
could upload a video of their “break-up with smoking” experience
as well as announce their break-up status to friends via Facebook.
One of the central features of the campaign was a multi-platform
smartphone app that provided instant support during specic trigger
points (eg, when stressed, angry, tipsy, or bored). Afree BIO smart-
phone app was available for download (Figure2). The app provided
information that was designed to be time-sensitive in its response,
allowing the information to be accessed at the moment a smoker felt
an urge to smoke. BIO was promoted primarily through paid (eg,
Online Banner Ads through Facebook, Google, Yahoo, Microsoft,
etc.), and earned (eg, television, radio and print)media.
Smokers’ Helpline (SHL), the comparison intervention, is a quit-
line that provides a telephone-based smoking cessation service to
support people who would like to quit smoking, maintain their quit,
or help someone else to quit. Quitlines are an evidence-based smok-
ing cessation intervention22–24 offered in at least 53 countries25 and
usually at no cost to the user. SHL is an established intervention and
provides smokers who want to quit information, self-help materials,
referrals to other resources, tailored motivational counseling, as well
as proactive follow-up counseling to adult smokers, including young
adults 19–29, in six Canadian provinces. During the intervention
period, SHL was promoted through both paid and earned media as
well as referrals from health organizations and professionals.
Figure1. Break-it-off homepage.
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353Nicotine & Tobacco Research, 2016, Vol. 18, No. 3
Design and Participants
This study employed a quasi-experimental, pretest, and posttest
design with one experimental and one unmatched comparison
group26,27 to test the effect of the BIO intervention. The Transparent
Reporting of Evaluations with Non-randomized Designs guideline
statement was used to assist in the reporting of this study.28
Participants were English speaking smoking young adults
19–29 years of age in the Canadian provinces of Saskatchewan,
Manitoba, Ontario, New Brunswick, Nova Scotia, and Prince
Edward Island. BIO participants were recruited through a link to
the study registration form on from February to
September 2012 and through ads placed in the general labor section
of (an online classied service) in various Canadian
cities from June 29 until September 20, 2012. Three months after
registration and completing an online baseline survey, participants
were emailed a link to an online follow-up survey. BIO Participants
received a $10 iTunes redemption code as incentive for register-
ing and another $15 iTunes redemption code upon completion of
the follow-up survey. Young adult SHL participants were recruited
through the SHL’s usual administrative and evaluation procedures
where participants complete baseline demographic and smoking sta-
tus questions over the telephone and a follow-up telephone survey
interview between October 1, 2010 and September 30, 2011. All
study participants provided informed consent at registration and
the study was approved by the University of Waterloo, Ofce of
Research Ethics.
Data Collection
The baseline and follow-up questionnaires were administered to
both the intervention and comparison groups. Questionnaires were
based on the questions contained within the minimal data set for
tobacco cessation29 with new questions added for the current study.
In addition to nancial incentives for completing the study, a modi-
ed Dillman method30 was employed by contacting participants that
registered at baseline up to ve times either by email or telephone
to increase response rates. The baseline questionnaire consisted of
demographic and baseline smoking behavior questions. The follow-
up questionnaire included questions on use of cessation services, sat-
isfaction with services, cessation support relationships, and smoking
Seven-day and 30-day point prevalence abstinence (PPA) rates were
measured at 3-month follow-up for both groups. SHL participants
provided the date of last cigarette smoked to determine abstinence
at 3months based on a 7-month follow-up and according to SHL’s
administrative procedures. Quit rates were based on those partici-
pants who completed the follow-up surveys. For both treatment
groups, respondents who completed the follow-up survey, but did
not provide answers to the point prevalence questions were consid-
ered to be smokers. We followed the intention-to-treat principle in
that participants were analyzed in the groups to which they were
allocated, regardless of whether they received or adhered to the allo-
cated intervention.31
The self-reported 7-day point prevalence cessation rate was deter-
mined by asking, “Have you smoked any cigarettes, even a single
puff, in the last 7days?” The self-reported 30-day point prevalence
cessation rate was determined by asking, “Have you smoked any
cigarettes, even a single puff, in the last 30- days?”
Cigarette Consumption
At registration, participants were asked whether their current ciga-
rette use was daily, occasional, or had quit and were trying to stay
Heaviness of SmokingIndex
Level of addiction was measured using the heaviness of smoking
index (HSI) that combines the number of cigarettes smoked per day
and the time to rst cigarette in the morning.32–34 High scores on the
Figure2. Break-it-off smartphone app page.
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354 Nicotine & Tobacco Research, 2016, Vol. 18, No. 3
HSI indicate higher levels of addiction and greater difculty in quit-
ting. HSI was categorized as low (scores of 0 to 2), medium (scores
of 3 and 4), and high (scores of 5 and 6).
Intention toQuit
Intention to quit smoking was measured by asking current smokers
if they intended to quit in the next 30days or not. Valid responses
were “yes” or “no.
Used any CessationAid
At follow-up, participants were asked if they had used any of the fol-
lowing cessation aids other than SHL or BIO to help them quit and
included the options: Champix; Zyban; nicotine replacement therapy
gum, lozenges, inhaler, or nasal spray; advice from a physician, phar-
macist, nurse or other health professional; group cessation program;
self-help materials; quit contests; websites; and, smart phone apps.
At least One Support Person was Available
At follow-up, participants were asked if they had at least one person
they could count on for support in quitting smoking. Valid responses
were “yes” or “no.
At least One Action Taken Toward Quitting
At follow-up, participants were asked if they had taken any of the
following actions toward quitting: cutting down the number of ciga-
rettes smoked; stopping smoking for 24 hours in an attempt to quit;
and set a quit date.
Larsen SatisfactionScore
User satisfaction was measured using the Larsen Satisfaction Score,35
which was computed by summing the responses for three questions
about satisfaction, each coded on a four-point scale with 1 being low.
The satisfaction questions were: To what extent has the program met
your needs? Overall, how satised were you with the service you
received from the program? If you were to seek help again, would
you use the program? The resulting Larsen Score ranges from 3 to
12, with 12 indicating “very high” satisfaction and 3“very low”.
Statistical Analyses
We compared baseline characteristics, use of cessation aids/supports,
service satisfaction, and smoking abstinence outcomes between BIO
and SHL young adult participants. Characteristics, smoking absti-
nence rates at 3 months after registration, use of cessation aids/
supports, satisfaction, and other cessation related outcomes were
summarized using the mean ± SD for continuous variables and fre-
quency percentages for categorical variables, and they were com-
pared between programs using the t test for independent groups or
the chi-square test, respectively. Further, to test for attrition bias,
baseline demographic characteristics and baseline smoking behav-
iors of participants that completed the follow-up survey and those
that did not were compared using the chi-square test. Finally, a sepa-
rate analysis of smoking abstinence rates was conducted using the
chi-square test that assumed participants lost to 3-month follow-up
were smokers.
First, logistic regression models were tted to examine the asso-
ciations of variables to the odds of having two primary outcomes—
7-day PPA and 30-day PPA. Variables signicantly associated with
quitting were included as covariates in the subsequent analysis to
examine the effect of BIO on smoking cessation. Second, regression
models were tted to examine the associations between the exposure
to the programs and the primary outcomes adjusting for possible
confounders identied at rst step. Avariable was considered to
enter the nal model if it produced a signicant association with the
outcome with a P value less than .05 for any level of the variable.
Assumptions such as sufcient sample size for single cell counts and
Goodness-of-t tests were used. Abackward elimination strategy
was employed in order to evaluate each covariate in the presence
of others. Potential confounders were removed one at a time start-
ing with the least signicant predictor and continuing until the P
value was less than 10% for all variables in the model. Findings
were summarized using unadjusted and adjusted odds ratios (ORs)
and corresponding 95% condence intervals (CIs). We used PROC
LOGISTIC in SAS 9.3.2 for analyses.
Participant Characteristics
The ow of participants through the study is depicted in Figure3.
One-hundred twenty-two participants were excluded after baseline
given that they had already quit smoking for more than 30 days
prior to the commencement of the intervention. Atotal of 322 par-
ticipants were lost to follow-up (66% for BIO vs. 48% for SHL).
Atotal of 238 participants completed follow-up and contributed to
the analysis with overall follow-up rates for BIO and SHL of 34%
and 52%, respectively.
Important differences were found between BIO and SHL par-
ticipants at baseline (Table1). Users of SHL were more likely to be
female [63.2% vs. 49.0%, χ2 (1, N =238) = 4.81, P = .03], white
[89.1% vs. 76.2%, χ2 (1, N = 229)= 6.71, P= .01], have high
school education or less [50.7% vs. 36.3%, χ2 (1, N =238)=4.93,
P=.03], intended to quit in the next 30days [80.9% vs. 69.6%,
χ2 (1, N= 238) = 4.07, P = .04], and were much more likely to
be daily smokers [81.6% vs. 59.1%, χ2 (1, N = 224)=13.68, P <
.001]. Level of addiction in terms of cigarettes smoked per day and
time to rst cigarette in the morning was not signicantly different
between groups with 59.6% of SHL participants and 63.5% of BIO
participants reporting a low HSI. Signicant demographic, baseline
cigarette consumption and HSI differences were not found between
those that completed follow-up and those that did not for both BIO
and SHL participants (data not shown).
Program Reach and Participation
From January 2012 to March 2012, the total visits to http:// were 44 172 with 37 325 unique visitors. The aver-
age user viewed 2.47 pages during their visit. Ontario was the most
prevalent audience with an average of 59% of visits, followed by
Saskatchewan with 22%. There were also 3937 installations of the
mobile app, 339 visitors interacted by posting content with the SM
components (Facebook and YouTube). Only 21 visitors connected
to SHL via the BIO campaign website. Larson satisfaction was
9.7 ± 1.97 and 78% of study participants visited the BIO website
two or more times with 18% visiting six or moretimes.
SHL reached 488 young adult smokers (19 to 29years of age)
from six provinces during the January 2012 to March 2012 inter-
vention period. Ontario represented 75% of users. Similar to BIO,
Larson satisfaction was 9.5 ± 2.19 and 68% of SHL participants 19
to 29years of age received two or more telephone calls for coun-
seling with 10% receiving six or more calls.
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355Nicotine & Tobacco Research, 2016, Vol. 18, No. 3
BIO—Regression Analysis
Table2 provides the results of association between participant char-
acteristics and the primary study outcomes. As expected, having
post-secondary education or higher is signicantly associated with
increased odds of quitting smoking for 7-day PPA, adjusted odds
ratio (AOR) = 2.16, 95% CI= 1.06 to 4.41, P= .03. In addition,
occasional cigarette use is signicantly associated with increased
odds of quitting for both 7-day PPA and 30-day PPA (AO R=6.26,
95% CI=3.10 to 12.67, P < .001 and AOR=5.93, 95% CI=2.82
to 12.46, P < .001, respectively). Other characteristics that were
positively associated but not signicant were being 25 to 30years
of age, non-white, intending to quit in the next 30days and having
social support (Table2).
BIO users had signicantly higher 7-day and 30-day quit rates
compared with users of SHL (Table3). The 7-day quit rate for BIO
(47.1%) was more than double that of SHL (15.4%), OR= 4.87,
95% CI=2.66 to 8.93, P < .001 and AOR=3.89, 95% CI=1.98 to
7.67, P < .001, controlling for education, ethnicity and daily or occa-
sional cigarette use. Although it was less prominent, the same pattern
was seen with 30-day quit rates, BIO (32.4%) and SHL (14.0%),
OR=2.95, 95% CI=1.56 to 5.57, P < .001 and AOR=2.14, 95%
CI=1.05 to 4.38, P=.04, controlling for covariates. For second-
ary outcomes, 91% of BIO participants made a quit attempt dur-
ing the 3-month intervention period compared to 79.1% of SHL
participants, OR=2.69, 95% CI=1.03 to 6.99, P=.04. Although
not statistically signicant, 89.4% of BIO participants versus 79.4%
of SHL participants cut-down amount smoked, OR= 2.18, 95%
CI = 0.88 to 5.44, P= .09. Both BIO and SHL participants were
equally likely to have set a quit date (Table3).
Treating all those who did not complete follow-up as smokers,
there was a signicant difference [χ2 (1, N=560)=7.55, P=.004]
for 7-day PPA between users of BIO (16.1%) and SHL (8.0%) and
the 30-day point prevalence was also higher for BIO participants
(11.0%) compared to SHL (7.3%); however, the difference was not
statistically signicant [χ2 (1, N=560)=1.91, P=.15].
The current study found that the BIO campaign had a signicant
effect on young adult cessation rates. The results compare favorably
to other digital SM interventions36 such as “Happy Ending” in the
Netherlands which reported a 44.7% 7-day PPA rate at 3months.37
Further, a randomized trial of SHL had similar results to those
reported here. Participants in the SHL arm of the trial had a 7-day
quit rate at 3-month follow-up of 19.8%38 as compared to 15.4% in
this study. The doubling of quit rates for the digital SM intervention
group (AOR=2.14, 95% CI=1.05 to 4.38) was similar to the nd-
ings of a recent systematic review of internet-based and text messag-
ing interventions that also reported a doubling of quit rates.16,36 In
addition, BIO participants were signicantly more likely to make a
quit attempt as compared to SHL participants (91% vs. 79%). BIO
is helping smokers to try to quit and it is argued that increasing quit
attempts is key to improving the overall population cessation rate.39
Study Limitations
There are important limitations of the current study that are worth
noting. First, some of the limitations of this study are inherent in
the nature of the quasi-experimental design27 and internet-based
research in general such as the low study recruitment and high attri-
tion rate.40,41 However, attrition bias was not a limitation as the
baseline characteristics between those who completed follow-up
and those that did not were not signicantly different for both com-
parison groups. Second, threats to internal validity, such as selection
effects, were mediated by adjusting for observed group differences.
Break It Off
Smokers’ Helpline
BIO registrants
consented and
completed baseline
SHL registrants
consented and
completed baseline
Already quit
Lost to
Completed 3
month follow-up
Assessed for smoking
cessation outcomes
(n = 136)
Assessed for smoking
cessation outcomes
(n = 102)
Completed 3
month follow-up
Already quit
(n = 43)
Lost to
(n= 197)
3 month intervention: n = 299
Smokers’ Helpline
3 month intervention: n = 261
Final N = 238
Figure3. Participant flow-diagram of recruitment and data collection.
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356 Nicotine & Tobacco Research, 2016, Vol. 18, No. 3
However, there is possibility of bias due to unobserved characteris-
tics. In addition, BIO participants may have been more motivated to
participate due to receiving an incentive of up to CaD $25. Third,
in terms of generalizability, the BIO campaign results may only be
applicable to those young adults who are motivated to use SM.
Given the novelty of the BIO concept, the campaign may not work
as effectively at another time and setting. Fourth, we were not able
to obtain corresponding SHL data for the same time periods as BIO;
however, using the older data may have prevented contamination of
our ndings given that a SHL specic promotion campaign happened
during the fall of 2012. Finally, while biochemical validation was
not conducted, the recommendation of the Society for Research on
Nicotine and Tobacco is that biochemical validation is not required
for large population health trials.42 In fact, a Cochrane Review of
Internet-based interventions for smoking cessation found that very
few studies used this method11 and accurate estimates of the preva-
lence of cigarette smoking among Canadians can be derived from
self-reported smoking status data.43
Implications for Practice
There are important policy and practice considerations given the
reach of a campaign such as BIO compared to traditional quitline
services using telephone-based counseling. Most young adults who
live on their own do not have landlines, prefer other modes of
communication such as texting, and the percentages are growing
for other population groups.44 Older technology is quickly being
replaced by mobile smartphone technology which act as computers,
are projected to be used by 5 billion people world-wide by 2025, and
have almost limitless functionality for accessing information and
facilitating interaction.45 Our ndings support the need for deter-
mining the role of SM and mobile technology interventions within
a tobacco cessation system, given the effectiveness and potential for
greater reach into population groups not served by or motivated to
use traditional cessation services.46 For example, building on the
results of the initial BIO campaign ndings, Health Canada has com-
mitted to extending the BIO campaign for 5years Canada-wide,47
expanding the reach and the potential depth of information tools
and resources available through BIO.
Implications for Research
Given that research on the effectiveness of SM for health pro-
motion and behavior change is still new, there are a number of
areas for further research. More formative evaluation is needed to
answer such questions as how SM are used by different audiences.
Table1. Sociodemographic Characteristics, Smoking Behaviors at Baseline, and Use of Cessation Aids and Supports by Young Adult
Smokers in Break-It-Off (BIO) and Smokers’ Helpline (SHL) Programs
BIO user (N=102), (n, %) SHL user (N=136), (n, %) Pa
Male 52 (51.0) 50 (36.8) .028
Female 50 (49.0) 86 (63.2)
19 to 24 54 (54.5) 64 (47.1) .257
25 to 29 45 (45.5) 72 (52.9)
Highest level of education
High school or less 37 (36.3) 69 (50.7) .026
Post-secondary or higher 65 (63.7) 67 (49.3)
White 77 (76.2) 114 (89.1) .010
Other ethnicity 24 (23.8) 14 (10.9)
Working statusb
Not working 49 (49.5) 62 (45.6) .554
Working (full or part-time) 50 (50.5) 74 (54.4)
Smoking behavior at baseline
Cigarette consumptionb
Smoking daily 52 (59.1) 111 (81.6) <.001
Not smoking daily 36 (40.9) 25 (18.4)
Heaviness of smoking indexb
Medium or high 31 (36.5) 55 (40.4)
Low 54 (63.5) 81 (59.6) .556
Intent to quit in next 30days
No 31 (30.4) 26 (19.1) .044
Yes 71 (69.6) 110 (80.9)
Use of cessation aids and supports
Have used cessation aids
No 40 (39.2) 48 (35.3) .535
Yes 62 (60.8) 88 (64.7)
Social support: at least one support person was available
No 11 (12.0) 22 (16.2) .374
Yes 81 (88.0) 114 (83.8)
aP value for chi-square test of association.
bSome missing values for this variable in the study population.
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Table2. Multiple Logistic Regression for the Association Between Participant Characteristics With 7-day and 30-day Smoking Abstinence at 3 Months
7-day point prevalence at 3months 30-day point prevalence at 3months
Abstinenta n (%) OR crude (95% CI) OR adjustedb (95% CI) Abstinent, n (%) OR crude (95% CI) OR adjustedc (95% CI)a
Maled31 (30.4) 1.00 1.00 23 (22.6) 1.00 1.00
Female 38 (27.9) 0.89 (0.51 to 1.56) 0.90 (0.45 to 1.82) 29 (21.3) 0.93 (0.50 to 1.73) 1.12 (0.53 to 2.39)
19 to 24d31 (26.3) 1.00 1.00 21 (17.8) 1.00 1.00
25 to 29 31 (31.6) 1.30 (0.74 to 2.29) 1.09 (0.54 to 2.21) 31 (26.5) 1.67 (0.89 to 3.11) 1.62 (0.76 to 3.45)
Highest level of education
High school or lessd21 (19.8) 1.00 1.00 18 (17.0) 1.00 1.00
Post-secondary or higher 48 (36.4) 2.31 (1.28 to 4.19) 2.16 (1.06 to 4.41) 34 (25.6) 1.70 (0.89 to 3.22) 1.38 (0.64 to 2.97)
Whited53 (27.8) 1.00 1.00 40 (20.9) 1.00 1.00
Other ethnicity 15 (39.5) 1.70 (0.82 to 3.50) 1.86 (0.74 to 4.69) 11 (29.0) 1.54 (0.70 to 3.37) 2.06 (0.77 to 5.52)
Working status
Not workingd29 (26.1) 1.00 1.00 20 (18.0) 1.00 1.00
Working (full or part time) 39 (31.4) 1.30 (0.74 to 2.29) 1.15 (0.57 to 2.29) 31 (25.0) 1.52 (0.81 to 2.85) 0.94 (0.44 to 2.00)
Smoking behavior at baseline
Cigarette consumption
Smoking dailyd29 (17.8) 1.00 1.00 21 (12.9) 1.00 1.00
Not smoking daily 36 (59.0) 6.65 (3.48 to 12.74) 6.26 (3.10 to 12.67) 28 (45.9) 5.74 (2.90 to 11.34) 5.93 (2.82 to 12.46)
Heaviness of smoking index
Medium or high 16 (18.6) 1.00 1.00 13 (15.1) 1.00 1.00
Low 49 (36.3) 2.49 (1.31 to 4.76) 1.00 (0.42 to 2.34) 36 (26.7) 2.04 (1.01 to 4.12) 0.90 (0.35 to 2.32)
Intent to quit in next 30days
Nod10 (17.5) 1.00 1.00 8 (14.0) 1.00 1.00
Yes 59 (32.6) 2.27 (1.07 to 4.81) 2.19 (0.79 to 6.12) 44 (24.3) 1.97 (0.87 to 4.47) 2.56 (0.79 to 8.28)
Use of cessation aids and supports
Have used cessation aids
Nod27 (30.7) 1.00 1.00 20 (22.7) 1.00 1.00
Yes 42 (28.0) 0.88 (0.49 to 1.56) 0.70 (0.34 to 1.45) 32 (21.3) 0.92 (0.48 to 1.74) 0.72 (0.33 to 1.56)
Social support: at least one support person was available
Nod5 (15.2) 1.00 1.00 4 (12.1) 1.00 1.00
Yes 59 (30.3) 2.43 (0.89 to 6.60) 2.45 (0.79 to 7.62) 45 (23.1) 2.18 (0.73 to 6.52) 2.22 (0.64 to 7.70)
CI=condence interval; OR=odds ratio.
aNumber and percent of participants who were abstinent at 7days or 30days at 3months follow-up in each group.
bConfounders included in all nal models: education, ethnicity, cigarette consumption, intent to quit in next 30days, and social support.
cConfounders included in all nal models: age, education, ethnicity, cigarette consumption, intent to quit in next 30days, and social support.
dReferent group.
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Other questions relate to implementation science; for example,
what are the design features that encourage deeper engagement
with SM to support behavior change and the dose needed to
produce a behavior change response.17 BIO is a multicompo-
nent intervention; thus, it was not possible to determine to what
extent each component of BIO contributed to the overall cam-
paign’s impact. For example, it is not known if the smartphone
app is more effective than the website itself. However, the BIO
website “Get it over with” section was the most popular aspect
of the campaign among BIO users and the “Break-up methods”
section of the website received the most page views. Research to
disentangle which elements of a multicomponent intervention are
accounting for change is needed as is further research to explore
the cumulative effect of the intervention and how it can connect
with other SM or technological resources.
Overall, there is very little evidence on the effectiveness and cost-
effectiveness of SM interventions available to decision makers. The
World Health Organization recently made a call to action for more
research on the effectiveness of SM and mHealth interventions for
behavior change.48 In addition, further research is needed to ensure
that smartphone and SM interventions incorporate evidence-based
practices22 rather than nonevidence-based practices such as hypno-
sis49,50 and that this knowledge is translated for policy-makers.50–53
A large number of young adults prefer a forum such as BIO for
help to quit smoking in comparison to traditional quitline services.
The reach of the campaign and ndings on quitting success indicate
that a multicomponent digital and SM campaign offers a promis-
ing opportunity to promote smoking cessation. While there is no
one-size-ts-all policy for smoking cessation, an integrated approach
that combines traditional quitline cessation services and a BIO type
SM campaign may be more effective, particularly in reaching young
adult smokers.
This work was supported by research grants from the Canadian Cancer
Society Research Institute (grant numbers 2011-70099 and 2011-701019).
Declaration of Interests
None declared.
NBB led the conceptualization and design of the study and CN, KM and KSB
contributed to the design of the study. NBB and SA drafted the manuscript.
NBB, SA, CN, KM, and KSB critically revised the manuscript for important
intellectual content. NBB is principal investigator and CN and KSB are co-
investigators on the research funding application. KM provided administra-
tive, technical, and material support. NBB is the guarantor. We thank Barbara
Zupko, Nghia C.Nguyen, Larry Squire, Lynda Hayward, and Matt Grey of
the University of Waterloo Propel Centre for Population Health Impact for
assistance in conducting the research, data analysis and helpful comments.
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... A loss to follow up was seen in most studies. We took a good response rate as 60%, which four RCTs (Bewick et al., 2010;Deady et al., 2016;Ekman et al., 2011;Skov-Ettrup et al., 2014) and one non-RCT (Baskerville et al., 2016) failed to achieve, suggesting high attrition rates. Their statistical analyses sought to minimise attrition bias in analyses, using methods such as multiple imputation, last observation carried forward, and sensitivity analysis. ...
... A total of twelve studies could not be included in the smoking meta-analysis Baskerville et al., 2016;Kong et al., 2017;Mason et al., 2015;Mays et al., 2020;Obermayer et al., 2004;Ramo et al., 2018;Riley et al., 2008;Shrier et al., 2014;Skov-Ettrup et al., 2014;Woodruff et al., 2001;Ybarra et al., 2013). Seven were excluded because they were non-RCTs (Baskerville et al., 2016;Kong et al., 2017;Mays et al., 2020;Obermayer et al., 2004;Riley et al., 2008;Shrier et al., 2014;Woodruff et al., 2001) and three studies contained elements or variations of the digital intervention Skov-Ettrup et al., 2014;Ybarra et al., 2013). ...
... A total of twelve studies could not be included in the smoking meta-analysis Baskerville et al., 2016;Kong et al., 2017;Mason et al., 2015;Mays et al., 2020;Obermayer et al., 2004;Ramo et al., 2018;Riley et al., 2008;Shrier et al., 2014;Skov-Ettrup et al., 2014;Woodruff et al., 2001;Ybarra et al., 2013). Seven were excluded because they were non-RCTs (Baskerville et al., 2016;Kong et al., 2017;Mays et al., 2020;Obermayer et al., 2004;Riley et al., 2008;Shrier et al., 2014;Woodruff et al., 2001) and three studies contained elements or variations of the digital intervention Skov-Ettrup et al., 2014;Ybarra et al., 2013). There were no comparable outcome measures in the other two studies despite authors being contacted (Mason et al., 2015;Ramo et al., 2018). ...
Full-text available
Background: Substance use amongst young people poses developmental and clinical challenges, necessitating early detection and treatment. Considering the widespread use of technology in young people, delivering interventions digitally may help to reduce and monitor their substance use. Aims: We conducted a systematic review and two meta-analyses to assess the effectiveness of digital interventions for reducing substance use (alcohol, smoking, and other substances) among young people aged 10 to 24 years old. Method: Embase, Global Health, Medline, PsychINFO, Web of Science and reference lists of relevant papers were searched in November 2020. Studies were included if they quantitatively evaluated the effectiveness of digital health technologies for treating substance use. A narrative synthesis and meta-analysis were conducted. Results: Forty-two studies were included in the systematic review and 18 in the meta-analyses. Digital interventions showed small, but statistically significant reductions in weekly alcohol consumption compared to controls (SMD= -0.12, 95% CI= -0.17 to -0.06, I2=0%), but no overall effect was seen on 30-day smoking abstinence (OR = 1.12, 95% CI = 0.70 to 1.80, I2=81%). The effectiveness of digital interventions for reducing substance use is generally weak, however, promising results such as reducing alcohol use were seen. Large-scale studies should investigate the viability of digital interventions, collect user feedback, and determine cost-effectiveness. Prisma/prospero: This systematic review was conducted following Cochrane methodology PRISMA guidelines. The review was registered with PROSPERO in November 2020 (CRD42020218442).
... The QUIT WG is an online group chat function embedded in the WeChat app (up to 500 persons), which could offer real-time online cessation counseling, group interventions, and facilitate the formation of mutually reciprocated, strong, and long-lasting social bounds that support smoking cessation in a similar manner to that of Twitter and Facebook [30][31][32]. Three counselors are arranged to provide online counseling, and a group administrator is arranged to manage the group and encourage participant to send message. Stage-matched online group interventions will be provided via two manners, including video courses on smoking cessation by clinical expert, and Q&A session after the courses and at 12:00 a.m.-13:00 p.m. from Monday to Friday. ...
... Moreover, their effectiveness was limited in real-world settings due to the high attrition and low utilization rates [52]. Social media could provide online guidance and increase the interactivity between smokers, but its effectiveness is still uncertain due to the low intensity of intervention [30][31][32]. In light of these above mentioned, the Surgeon General's 2020 report suggested integrating multiple treatment resources, as a potential strategy for increasing the reach and engagement of mHealth-based interventions for smoking cessation [10], while at the same time maintaining or improving their effectiveness. ...
Full-text available
Background and aims: Developing accessible, affordable, and effective approaches to smoking cessation is crucial for tobacco control. Mobile health (mHealth) based interventions have the potential to aid smokers in quitting, and integrating treatments from multiple sources may further enhance their accessibility and effectiveness. As part of our efforts in smoking cessation, we developed a novel behavioral intervention delivery modality for smoking cessation that integrated three interventions using the WeChat app, called the "Way to Quit" modality (WQ modality). It is presented here the protocol for a randomized controlled trial evaluating the effectiveness, feasibility, and cost-effectiveness of the WQ modality in Chinese smokers. Methods: Eligible participants (n = 460) will be recruited via online advertisement in Beijing, China. They will be randomly assigned to receive either quitline-based treatment (QT, n = 230) or WQ modality-based treatment (WQ, n = 230) using a block randomization method. Participants in the QT group will receive telephone-assisted treatment over a four-week period (multi-call quitline protocol), while those in the WQ group will receive integrated interventions based on the WQ modality for four weeks. A four-week supply of nicotine replacement therapy (gums) will be provided to all participants. Participants will be asked to complete phone or online follow-up at 1, 3, 6, and 12-months. At 1-month follow-up, individuals with self-reported smoking abstinence for more than 7 days will be invited to receive an exhaled carbon monoxide (CO) test for biochemical validation. The primary aim is to determine whether the WQ modality is effective in assisting smokers in quitting smoking. The secondary aims are to evaluate the acceptability, satisfaction, and cost-effectiveness of the WQ modality. Discussion: If the WQ modality is determined to be effective, acceptable, and affordable, it will be relatively easy to reach and provide professional cessation treatments to the communities, thus helping to reduce the disparities in smoking cessation services between different regions and socioeconomic groups. Trial registration: Chinese Clinical Trial Registry: ChiCTR2200066427, Registered December 5, 2022.
... Visitors to the Break It Off (BIO) campaign website ( could upload a video of their "break-up with smoking" experience as well as announce their break-up status to friends via Facebook (Baskerville, Azagba, Norman, McKeown, & Brown, 2015). In the first four months of the campaign, total visits to the website were 44,172, there were 3,937 installations of the app, and 339 interactions via social media components (Facebook and YouTube). ...
... In the first four months of the campaign, total visits to the website were 44,172, there were 3,937 installations of the app, and 339 interactions via social media components (Facebook and YouTube). The evaluation of BIO compared quit rates among users of the campaign materials and users of the telephone quitline of the same age (Baskerville et al., 2015); BIO users had significantly higher 7-day and 30-day quit rates compared with users of the quitline. The Crush the Crave campaign was promoted through Google and Facebook ads from April 2012 to April 2013 (Struik & Baskerville, 2014). ...
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p>Misinformation studies have focused on traditional news formats, overlooking prevalent visual forms such as political memes. However, if citizens systematically respond differently to claims conveyed by memes, their effects on the broader information ecosystem may be underestimated. This study (N = 598) uses a 2 (partisan news/meme) X 2 (oppositional/congenial) design to examine perceptions of political memes’ influence on self and others, and the format’s effect on willingness to engage in corrective discussion. Results indicate that meme format enhances individuals’ tendency to see messages as less influential on oneself than on others, and individuals are less likely to correct claims presented in meme format. This decrease in corrective intent is mediated by the decrease in perceived influence over self. These findings have practical implications for those combating inaccurate claims in the public sphere, and call attention to the role format differences may play in the psychological processes underlying political discussion as it becomes increasingly mediated and visual.</p
... Promising results in terms of high impressions on social media were observed in many public health studies [35,36], however, stroke awareness campaigns have barely used social media so far. While the WSD Campaign provides comprehensive material for social media use (https://, a recent systematic literature review [37] revealed that none of the thirteen identified stroke education studies has evaluated a social media-based approach. ...
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Background: Stroke is a major global health problem and was the second leading cause of death worldwide in 2020. However, the lack of public stroke awareness especially in low- and middle-income countries (LMICs) such as Nepal severely hinders the effective provision of stroke care. Efficient and cost-effective strategies to raise stroke awareness in LMICs are still lacking. This study aims to (a) explore the feasibility of a social media-based stroke awareness campaign in Nepal using a cost-benefit analysis and (b) identify best practices for social media health education campaigns. Methods: We performed a stroke awareness campaign over a period of 6 months as part of a Stroke Project in Nepal on four social media platforms (Facebook, Instagram, Twitter, TikTok) with organic traffic and paid advertisements. Adapted material based on the World Stroke Day Campaign and specifically created videos for TikTok were used. Performance of the campaign was analyzed with established quantitative social media metrics (impressions, reach, engagement, costs). Results: Campaign posts were displayed 7.5 million times to users in Nepal. 2.5 million individual social media users in Nepal were exposed to the campaign on average three times, which equals 8.6% of Nepal's total population. Of those, 250,000 users actively engaged with the posts. Paid advertisement on Facebook and Instagram proved to be more effective in terms of reach and cost than organic traffic. The total campaign cost was low with a "Cost to reach 1,000 users" of 0.24 EUR and a "Cost Per Click" of 0.01 EUR. Discussion: Social media-based campaigns using paid advertisement provide a feasible and, compared to classical mass medias, a very cost-effective approach to inform large parts of the population about stroke awareness in LMICs. Future research needs to further analyze the impact of social media campaigns on stroke knowledge.
... Social media have been crucial in promoting health and educating about various diseases 4 . Social media can provide services for young adult smokers seeking smoking cessation help 5 . Research shows that interactive web-based smoking cessation interventions contribute to quitting smoking 6 . ...
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Introduction: Smoking cessation is a significant public health issue for young people. Social media provide the public with health knowledge through various types of videos. Bilibili is a trendy social video platform among the young population in China, and the number of smoking cessation videos on this platform is continuously increasing. Different content creators advocating smoking cessation through videos may influence young people's attitudes and behaviors towards tobacco and smoking. This study aims to measure the message sensation value (MSV) and the information appeals in smoking cessation videos on Bilibili, examining their impact on communication effectiveness. Methods: This study collected 337 videos from Bilibili and conducted a content analysis regarding the content creator's information, video presentation, MSV, and message appeals. The communication effectiveness of the videos was defined as a dependent variable and was divided into three dimensions: communication breadth, recognition, and participation. Results: The average MSV (rated on a scale of 0 to 11) for smoking cessation-themed videos was 4.49 (SD=2.23). Chi-squared analysis revealed significant differences among different types of videos in the use of threat appeal (p<0.001), humor appeal (p<0.001), and psychological benefit (p<0.05). Additionally, different types of creators showed differences in the use of threat appeal (p<0.05), humor appeal (p<0.001), and psychological benefit (p<0.05). ANOVA results indicated significant differences in the level of MSV among different smoking cessation videos (F=39.775, p<0.001). Linear regression analysis showed that MSV, threat appeal, humor appeal, and economic benefit positively impacted dissemination effects (p<0.001). Conclusions: The results indicate that young people are likelier to watch smoking cessation videos with higher MSV and information appeal. These elements can enhance persuasion and the effectiveness of communication. Therefore, when video creators aim to promote smoking cessation among young people, they can consider factors such as MSV, threat appeal, humor appeal, and economic benefit to enhance communication effects.
... 47 Existing evidence supports the use of social media for tobacco control. [48][49][50][51][52] For example, a recent social media campaign sought to educate high school students on e-cigarettes led to greater knowledge and beliefs about the harmful effects of e-cigarette use, suggesting social media as a promising tool for tobacco education, among young people. 53 Another study also demonstrated the use of digital media to empower adolescents in smoking prevention. ...
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Objective This study explored multistakeholder perspectives on existing adolescent-specific tobacco control policies and programmes, to advance India’s transition towards a tobacco-free generation. Design Qualitative semi-structured interviews. Setting Interviews were conducted with officials involved in tobacco control at the national (India), state (Karnataka), district (Udupi) and village level. Interviews were audio recorded, transcribed verbatim and analysed thematically. Participants Thirty-eight individuals representing national (n=9), state (n=9), district (n=14) and village (n=6) levels, participated. Results The study findings highlighted the need to strengthen and amend the existing Tobacco Control Law (2003) provisions, particularly in the vicinity of schools (Sections 6a and 6b). Increasing the minimum legal age to buy tobacco from 18 to 21 years, developing an ‘application’ for ‘compliance and monitoring indicators’ in Tobacco-Free Educational Institution guidelines were proposed. Policies to address smokeless tobacco use, stricter enforcement including regular monitoring of existing programmes, and robust evaluation of policies was underscored. Engaging adolescents to co-create interventions was advocated, along with integrating national tobacco control programmes into existing school and adolescent health programmes, using both an intersectoral and whole-societal approach to prevent tobacco use, were recommended. Finally, stakeholders mentioned that when drafting and implementing a comprehensive national tobacco control policy, there is a need to adopt a vision striving toward a tobacco-free generation. Conclusion Strengthening and developing tobacco control programmes and policies are warranted which are monitored and evaluated rigorously, and where adolescents should be involved, accordingly.
... The QUIT WG could remove the barrier of participating in face-to-face group interventions. More importantly, it facilitates the formation of mutually reciprocated, strong, and long-lasting social bonds that support smoking cessation in a similar manner to that of Twitter and Facebook [34][35][36] . The QUIT WOA provides non-tailored electronic smoking cessation self-help material, updated regularly and providing a lot of smoking cessation information. ...
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Introduction: Many smokers have not accessed professional smoking cessation assistance due to limited smoking cessation services. We developed a novel mHealth-based integrated modality for smoking cessation (WeChat + Quitline modality, WQ modality) and applied it to a large public welfare project (China Western-QUIT Program) in western China. This study evaluated the usage, acceptability, and preliminary effectiveness of the WQ modality in the population of western China. Methods: A prospective cohort study was conducted between April and August 2021. Smokers or their relatives were recruited through online advertisements and medical staff referrals. After using the services of the WQ modality for one month, the self-reported awareness, use, and satisfaction with each service among the participants were collected by a telephone interview. We also evaluated the self-reported 7-day point prevalence of abstinence (PPA) and quit attempt rate among baseline current smokers. The usage data of each service were downloaded from quitline and WeChat platforms. Results: Of the 17326 people from western China using the WQ modality, the largest number of users was WeChat official account (11173), followed by WeChat mini program (3734), WeChat group (669), and quitline (541 inbound calls, 605 outbound calls). At one month follow-up, over 70% of participants who completed the baseline survey (n=2221) were aware of WeChat-based services, and over 50% used them. However, the awareness rate (11.1%) and utilization rate (0.5%) of quitline were relatively low. The median satisfaction scores across all services were 9 out of 10 points (IQR: 8-9). Among the baseline current smokers (n=1257), self-reported 7-day PPA was 41.8% (526/1257), and another 225 smokers (17.9%) reported making a quit attempt. Conclusions: The WQ modality could be well used and accepted, and it has great potential to motivate and aid short-term smoking cessation in smokers from western China.
... Furthermore, owing to the widespread penetration of the internet and mobile devices, numerous health care professionals increasingly use web-based or online materials to provide information to patients [4]. Previous studies have demonstrated that digital information can be implemented and used positively for public health projects, such as smoking cessation, weight control, and alcohol addiction management [5][6][7]. Although access to a wide range of information has improved with the internet, information on the internet comes from a variety of providers and sources that are difficult to control, which can lead to problems with quality and the risk of circulating biased content according to the interests and purposes involved [8]. ...
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Background With widespread use of the internet and mobile devices, many people have gained improved access to health-related information online for health promotion and disease management. As the health information acquired online can affect health-related behaviors, health care providers need to take into account how each individual’s online health literacy (eHealth literacy) can affect health-related behaviors. Objective To determine whether an individual’s level of eHealth literacy affects actual health-related behaviors, the correlation between eHealth literacy and health-related behaviors was identified in an integrated manner through a systematic literature review and meta-analysis. Methods The MEDLINE, Embase, Cochrane, KoreaMed, and Research Information Sharing Service databases were systematically searched for studies published up to March 19, 2021, which suggested the relationship between eHealth literacy and health-related behaviors. Studies were eligible if they were conducted with the general population, presented eHealth literacy according to validated tools, used no specific control condition, and measured health-related behaviors as the outcomes. A meta-analysis was performed on the studies that could be quantitatively synthesized using a random effect model. A pooled correlation coefficient was generated by integrating the correlation coefficients, and the risk of bias was assessed using the modified Newcastle-Ottawa Scale. Results Among 1922 eHealth literacy–related papers, 29 studies suggesting an association between eHealth literacy and health-related behaviors were included. All retrieved studies were cross-sectional studies, and most of them used the eHealth Literacy Scale (eHEALS) as a measurement tool for eHealth literacy. Of the 29 studies, 22 presented positive associations between eHealth literacy and health-related behaviors. The meta-analysis was performed on 14 studies that presented the correlation coefficient for the relationship between eHealth literacy and health-related behaviors. When the meta-analysis was conducted by age, morbidity status, and type of health-related behavior, the pooled correlation coefficients were 0.37 (95% CI 0.29-0.44) for older adults (aged ≥65 years), 0.28 (95% CI 0.17-0.39) for individuals with diseases, and 0.36 (95% CI 0.27-0.41) for health-promoting behavior. The overall estimate of the correlation between eHealth literacy and health-related behaviors was 0.31 (95% CI 0.25-0.34), which indicated a moderate correlation between eHealth literacy and health-related behaviors. Conclusions Our results of a positive correlation between eHealth literacy and health-related behaviors indicate that eHealth literacy can be a mediator in the process by which health-related information leads to changes in health-related behaviors. Larger-scale studies with stronger validity are needed to evaluate the detailed relationship between the proficiency level of eHealth literacy and health-related behaviors for health promotion in the future.
The prevalence of cigarette smoking in young adults is higher among those with socioeconomic disadvantage than those without. Low treatment-seeking among young adult smokers is compounded by few efficacious smoking cessation interventions for this group, particularly socioeconomically-disadvantaged young adults (SDYA) who smoke cigarettes. The goal of this study was to test a tailored smoking-cessation intervention for SDYA. 343 SDYA aged 18–30 living in the U.S. (85% female) who smoke cigarettes with access to a smartphone and interest in quitting smoking in the next six months were recruited online in Spring 2020 and randomized to referral to online quit resources (usual care control; n = 171) or a 12-week tailored text message smoking-cessation program with a companion web-based intervention (n = 172). Intent to treat analyses examined associations between study condition, self-reported 30-day point prevalence abstinence (PPA), and confidence to quit smoking at 12 weeks, controlling for potential confounders. Intervention group participants had greater self-reported 30-day PPA at 12-weeks than controls (adjusted relative risk 3.93, 95% CI 2.14–7.24). Among those who continued smoking, the intervention increased confidence to quit (0.81 points, 95% confidence interval 0.08–1.53). Weekly engagement in the intervention predicted greater cessation. A tailored text message intervention for SDYA increased smoking abstinence and confidence to quit at the end-of-treatment. Findings may have been influenced by recruitment at the start of the COVID pandemic but suggest that text messaging is an acceptable and efficacious cessation strategy for SDYA smokers. Future studies should examine the impact on longer-term smoking-cessation and importance of intervention tailoring for SDYA.
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Background: There is little consensus regarding effective digital health interventions for diverse populations, which is due in part to the difficulty of quantifying the impact of various media and content and the lack of consensus on evaluating dosage and outcomes. In particular, digital smoking behavior change intervention is an area where consistency of measurement has been a challenge because of emerging products and rapid policy changes. This study reviewed the contents and outcomes of digital smoking interventions and the consistency of reporting to inform future research. Objective: This study aims to systematically review digital smoking behavior change interventions and evaluate the consistency in measuring and reporting intervention contents, channels, and dose and response outcomes. Methods: PubMed, Embase, Scopus, PsycINFO, and PAIS databases were used to search the literature between January and May 2021. General and journal-based searches were combined. All records were imported into Covidence systematic review software (Veritas Health Innovation) and duplicates were removed. Titles and abstracts were screened by 4 trained reviewers to identify eligible full-text literature. The data synthesis scheme was designed based on the concept that exposure to digital interventions can be divided into intended doses that were planned by the intervention and enacted doses that were completed by participants. The intended dose comprised the frequency and length of the interventions, and the enacted dose was assessed as the engagement. Response measures were assessed for behaviors, intentions, and psychosocial outcomes. Measurements of the dose-response relationship were reviewed for all studies. Results: A total of 2916 articles were identified through a database search. Of these 2916 articles, the title and abstract review yielded 324 (11.11%) articles for possible eligibility, and 19 (0.65%) articles on digital smoking behavior change interventions were ultimately included for data extraction and synthesis. The analysis revealed a lack of prevention studies (0/19, 0%) and dose-response studies (3/19, 16%). Of the 19 studies, 6 (32%) reported multiple behavioral measures, and 5 (23%) reported multiple psychosocial measures as outcomes. For dosage measures, 37% (7/19) of studies used frequency of exposure, and 21% (4/19) of studies mentioned the length of exposure. The assessment of clarity of reporting revealed that the duration of intervention and data collection tended to be reported vaguely in the literature. Conclusions: This review revealed a lack of studies assessing the effects of digital media interventions on smoking outcomes. Data synthesis showed that measurement and reporting were inconsistent across studies, illustrating current challenges in this field. Although most studies focused on reporting outcomes, the measurement of exposure, including intended and enacted doses, was unclear in a large proportion of studies. Clear and consistent reporting of both outcomes and exposures is needed to develop further evidence in intervention research on digital smoking behavior change.
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Background: Social networking sites, particularly Facebook, are increasingly included in contemporary smoking cessation interventions directed toward young adults. Little is known about the role of Facebook in smoking cessation interventions directed toward this age demographic. Objective: The aim of this study was to characterize the content of posts on the Facebook page of Crush the Crave, an evidence-informed smoking cessation intervention directed toward young adults aged 19 to 29 years. Methods: Crush the Crave Facebook posts between October 10, 2012 and June 12, 2013 were collected for analysis, representing page activity during the pilot phase of Crush the Crave. Of the 399 posts included for analysis, 121 were original posts, whereas the remaining 278 were reply posts. Posts were coded according to themes using framework analysis. Results: We found that the original Crush the Crave Facebook posts served two main purposes: to support smoking cessation and to market Crush the Crave. Most of the original posts (86/121, 71.1%) conveyed support of smoking cessation through the following 7 subthemes: encouraging cessation, group stimulation, management of cravings, promoting social support, denormalizing smoking, providing health information, and exposing tobacco industry tactics. The remaining original posts (35/121, 28.9%) aimed to market Crush the Crave through 2 subthemes: Crush the Crave promotion and iPhone 5 contest promotion. Most of the reply posts (214/278, 77.0%) were in response to the supporting smoking cessation posts and the remaining 64 (23.0%) were in response to the marketing Crush the Crave posts. The most common response to both the supporting smoking cessation and marketing Crush the Crave posts was user engagement with the images associated with each post at 40.2% (86/214) and 45% (29/64), respectively. The second most common response consisted of users sharing their smoking-related experiences. More users shared their smoking-related experiences in response to the supporting smoking cessation posts (81/214, 37.9%) compared to the marketing Crush the Crave posts (11/64, 17%). With the exception of 4 posts, a moderator posted all the original posts. In addition, although 56.00% (18,937/33,815) of Crush the Crave Facebook page users were men, only 19.8% (55/278) of the reply posts were made by men. Finally, men were found to be more likely to express sarcasm or make strong assertions about quitting smoking and Crush the Crave than women. Conclusions: The CTC Facebook page presents as a unique platform for supporting young adult smoking cessation at all stages of the cessation process. The findings of this study indicate that social networking sites, especially Facebook, warrant inclusion in tobacco control efforts directed towards young adults. Research on effectiveness of the Facebook page for quitting smoking is needed.
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The dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change. The intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions. Eight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where "population" included child or adult populations, including healthy and disease populations; "intervention" involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; "comparator" was either a control group or within subject in the case of pre-post study designs; "outcomes" included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg, self-efficacy); and "study design" included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen's d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized. A total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online health social network websites (n=2), research health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine of the 10 included studies reported significant improvements in some aspect of health behavior change or outcomes related to behavior change. Effect sizes for behavior change ranged widely from -0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement and fidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity). To date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether behavior change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided.
Background: Cigarette smoking is associated with adverse health effects, including cancer, respiratory illness, heart disease and stroke. National data on smoking prevalence often rely on self-reports. This study assesses the validity of self-reported cigarette smoking status among Canadians. Data and methods: Data are from the 2007 to 2009 Canadian Health Measures Survey, a nationally representative cross-sectional survey of 4,530 Canadians aged 12 to 79. The survey included self-reported smoking status and a measure of urinary cotinine, a biomarker of exposure to tobacco smoke. The prevalence of cigarette smoking was calculated based on self-reports and also on urinary cotinine concentrations. Results: Compared with estimates based on urinary cotinine concentration, smoking prevalence based on self-report was 0.3 percentage points lower. Sensitivity estimates (the percentage of respondents who reported being smokers among those classifi ed as smokers based on cotinine concentrations) were similar for males and females (more than 90%). Although sensitivity tended to be lower for respondents aged 12 to 19 than for those aged 20 to 79, the difference did not attain statistical signifi cance. Interpretation: Accurate estimates of the prevalence of cigarette smoking among Canadians can be derived from self-reported smoking status data.
Youth, particularly youth of color, continue to face disparities with regard to sexually transmitted infections (STIs), including HIV. Yet, comprehensive education about sex and sexual risk for STIs is not universal for youth in the United States. While Internet research on HIV prevention has demonstrated that the medium can be as effective as face-to-face education and prevention approaches, Internet-based research has faced challenges in recruitment of diverse samples, has not consistently been able to retain adequate samples, and do so for long-term follow-up. Additionally, we have few examples of research on social networking sites, which are particularly popular with youth and represent locations where they spend the majority of their time online. In this work, we describe efforts to recruit and retain youth on My Space and Facebook for a randomized controlled trial testing the efficacy of using social media for HIV prevention. Findings demonstrate no success in using My Space to recruit for our STI prevention intervention, but success in recruitment of a diverse sample and short-term retention of this sample using Facebook. We recruited 1578 diverse youth aged 16–24 years for the study and retained 69% of them for a two-month follow-up; follow-up dropped to 50% at six months, demonstrating challenges with longer-term retention. This work represents innovation in recruitment and retention of individuals and networks using social media.
Abstract BACKGROUND: The Internet is now an indispensable part of daily life for the majority of people in many parts of the world. It offers an additional means of effecting changes to behaviour such as smoking. OBJECTIVES: To determine the effectiveness of Internet-based interventions for smoking cessation. SEARCH METHODS: We searched the Cochrane Tobacco Addiction Group Specialized Register. There were no restrictions placed on language of publication or publication date. The most recent search was conducted in April 2013. SELECTION CRITERIA: We included randomized and quasi-randomized trials. Participants were people who smoked, with no exclusions based on age, gender, ethnicity, language or health status. Any type of Internet intervention was eligible. The comparison condition could be a no-intervention control, a different Internet intervention, or a non-Internet intervention. DATA COLLECTION AND ANALYSIS: Two authors independently assessed and extracted data. Methodological and study quality details were extracted using a standardized form. We extracted smoking cessation outcomes of six months follow-up or more, reporting short-term outcomes where longer-term outcomes were not available. We reported study effects as a risk ratio (RR) with a 95% confidence interval (CI). Clinical and statistical heterogeneity limited our ability to pool studies. MAIN RESULTS: This updated review includes a total of 28 studies with over 45,000 participants. Some Internet programmes were intensive and included multiple outreach contacts with participants, whilst others relied on participants to initiate and maintain use.Fifteen trials compared an Internet intervention to a non-Internet-based smoking cessation intervention or to a no-intervention control. Ten of these recruited adults, one recruited young adult university students and two recruited adolescents. Seven of the trials in adults had follow-up at six months or longer and compared an Internet intervention to usual care or printed self help. In a post hoc subgroup analysis, pooled results from three trials that compared interactive and individually tailored interventions to usual care or written self help detected a statistically significant effect in favour of the intervention (RR 1.48, 95% CI 1.11 to 2.78). However all three trials were judged to be at high risk of bias in one domain and high statistical heterogeneity was detected (I² = 53%), with no obvious clinical explanation. Pooled results from two studies of an interactive, tailored intervention involving the Internet and automated phone contacts also detected a significant effect (RR 2.05, 95% CI 1.42 to 2.97, I² = 42%). Results from a sixth study comparing an interactive but non-tailored intervention to control did not detect a significant effect, nor did the seventh study, which compared a non-interactive, non-tailored intervention to control. Three trials comparing Internet interventions to face-to-face or phone counselling also did not detect evidence of an effect, nor did two trials evaluating Internet interventions as adjuncts to other behavioural interventions. A trial in college students increased point prevalence abstinence after 30 weeks but had no effect on sustained abstinence. Two small trials in adolescents did not detect an effect on cessation compared to control.Fourteen trials, all in adult populations, compared different Internet sites or programmes. Pooled estimates from three trials that compared tailored and/or interactive Internet programmes with non-tailored, non-interactive Internet programmes did not detect evidence of an effect (RR 1.12, 95% CI 0.95 to 1.32, I² = 0%). One trial detected evidence of a benefit from a tailored email compared to a non-tailored one, whereas a second trial comparing tailored messages to a non-tailored message did not detect evidence of an effect. Trials failed to detect a benefit of including a mood management component (three trials), or an asynchronous bulletin board. AUTHORS' CONCLUSIONS: Results suggest that some Internet-based interventions can assist smoking cessation at six months or longer, particularly those which are interactive and tailored to individuals. However, the trials that compared Internet interventions with usual care or self help did not show consistent effects and were at risk of bias. Further research is needed despite 28 studies on the subject. Future studies should carefully consider optimising the interventions which promise most effect such as tailoring and interactivity. Update of: Cochrane Database Syst Rev. 2010;(9):CD007078.