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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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358 Nicotine & Tobacco Research, 2016, Vol. 18, No. 3
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.
1. Statistics Canada. Canadian Community Health Survey, 2010. Ottawa,
ON: Statistics Canada; 2011.
2. Minian N, Schwartz R, Di Sante E, Philipneri A. Impact of the Smoking
Cessation System on Young Male Smokers. Special Report. Toronto, ON:
Ontario Tobacco Research Unit; 2010.
3. Bader P, Travis HE, Skinner HA. Knowledge synthesis of smoking cessa-
tion among employed and unemployed young adults. Am J Public Health.
2007;97(8):1434–1443. doi:10.2105/AJPH.2006.100909.
4. Curry SJ, Sporer AK, Pugach O, Campbell RT, Emery S. Use of tobacco
cessation treatments among young adult smokers: 2005 National
Health Interview Survey. Am J Public Health. 2007;97(8):1464–1469.
5. Fagan P, Augustson E, Backinger CL, et al. Quit attempts and intention
to quit cigarette smoking among young adults in the United States. Am J
Public Health. 2007;97(8):1412–1420. doi:10.2105/AJPH.2006.103697.
Table3. Multiple Logistic Regression for the Associations Between the Social Media Campaigns and 3-Month Smoking Cessation
Outcomes Favorable outcomesa, n (%) OR crude (95% CI) OR adjustedb (95% CI)
Primary outcomes
7-day PPA
Smokers’ helplinec21 (15.4) 1.00 1.00
Break-it-off 48 (47.1) 4.87 (2.66 to 8.93) 3.89 (1.98 to 7.67)
30-day PPA
Smokers’ helplinec19 (14.0) 1.00 1.00
Break-it-off 33 (32.4) 2.95 (1.56 to 5.57) 2.14 (1.05 to 4.38)
Secondary outcomes
Made a quit attempts (stopped for ≥24 hours)
Smokers’ helplinec87 (79.1) 1.00 1.00
Break-it-off 61 (91.0) 2.69 (1.03 to 6.99) 2.05 (0.77 to 5.49)
Cut-down amount smoked
Smokers’ helplinec85 (79.4) 1.00 1.00
Break-it-off 59 (89.4) 2.18 (0.88 to 5.44) 2.18 (0.76 to 6.24)
Set a quite date
Smokers’ helplinec69 (63.3) 1.00 1.00
Break-it-off 42 (65.6) 1.11 (0.58 to 2.11) 1.30 (0.61 to 2.73)
CI=condence interval; OR=odds ratio; PPA=point prevalence abstinence.
aNumber and percent of participants who obtained favorable outcomes at 3months follow-up in each group.
bConfounders included in all nal models: education, ethnicity, and cigarette consumption at baseline.
cReferent group.
at University of Waterloo on June 28, 2016 from
359Nicotine & Tobacco Research, 2016, Vol. 18, No. 3
6. Jha P, Ramasundarahettige C, Landsman V, et al. 21st-century hazards
of smoking and benets of cessation in the United States. N Engl J Med.
2013;368(4):341–350. doi:10.1056/NEJMsa1211128.
7. Villanti AC, McKay HS, Abrams DB, Holtgrave DR, Bowie JV. Smoking-
cessation interventions for U.S.young adults: a systematic review. Am J
Prev Med. 2010;39(6):564–574. doi:10.1016/j.amepre.2010.08.009.
8. Norman CD. Social Media for Health Promotion With Youth and Young
Adult Substance Use: A Resource and Evidence Review. Ottawa, ON:
Health Canada, Tobacco and Drugs Initiative; 2012.
9. Statistics Canada. Canadian Internet Use Survey, Internet Use, by Age
Group and Internet Activity. Table 358-0153. Ottawa, ON: Statistics
Canada; 2011.
10. Bennett GG, Glasgow RE. The delivery of public health interventions
via the Internet: actualizing their potential. Annu Rev Public Health.
2009;30:273–292. doi:10.1146/annurev.publhealth.031308.100235.
11. Civljak M, Sheikh A, Stead LF, Car J. Internet-based interven-
tions for smoking cessation. Cochrane Database Syst Rev. 2010;9.
12. Crutzen R, de Nooijer J, Brouwer W, Oenema A, Brug J, de Vries NK.
Strategies to facilitate exposure to internet-delivered health behavior change
interventions aimed at adolescents or young adults: a systematic review.
Health Educ Behav. 2011;38(3):49–62. doi:10.1177/1090198110372878.
13. Shahab L, McEwen A. Online support for smoking cessation: a sys-
tematic review of the literature. Addiction. 2009;104(11):1792–1804.
14. Brouwer W, Kroeze W, Crutzen R, etal. Which intervention characteristics
are related to more exposure to internet-delivered healthy lifestyle promo-
tion interventions? Asystematic review. J Med Internet Res. 2011;13(1):e2.
15. Graham AL, Cha S, Papandonatos GD, etal. Improving adherence to web-
based cessation programs: a randomized controlled trial study protocol.
Trials. 2013;14:48. doi:10.1186/1745-6215-14-48.
16. Free C, Phillips G, Watson L, etal. The effectiveness of mobile-health tech-
nologies to improve health care service delivery processes: a systematic
review and meta-analysis. PLoS Med. 2013;10(1):e1001363. doi:10.1371/
17. Cobb NK, Graham AL, Byron MJ, Niaura RS, Abrams DB; Workshop
Participants. Online social networks and smoking cessation: a scientic
research agenda. J Med Internet Res. 2011;13(4):e119. doi:10.2196/
18. Gold J, Pedrana AE, Sacks-Davis R, etal. A systematic examination of the
use of online social networking sites for sexual health promotion. BMC
Public Health. 2011;11:583. doi:10.1186/1471-2458-11-583.
19. Struik LL, Baskerville NB. The role of Facebook in Crush the Crave,
a mobile- and social media-based smoking cessation interven-
tion: qualitative framework analysis of posts. J Med Internet Res.
20. Chou WY, Prestin A, Lyons C, Wen KY. Web 2.0 for health promotion:
reviewing the current evidence. Am J Public Health. 2013;103(1):e9–18.
doi:10.2105/AJPH.2012.301071; 10.2105/AJPH.2012.301071.
21. Andreasen AR. Marketing social marketing in the social change
marketplace. J Public Policy Mark. 2002;21(1):3–13. doi:10.1509/
22. Fiore M, Jaen C, Baker TB, etal. Treating Tobacco Use and Dependence:
2008 Update. Clinical Practice Guidline. Rockville, MD: U.S. Department
of Health and Human Services, Public Health Service; 2008.
23. Stead LF, Perera R, Lancaster T. A systematic review of interventions for
smokers who contact quitlines. Tob Control. 2007;16:i3–8. doi:10.1136/
24. Stead LF, Hartmann-Boyce J, Perera R, Lancaster T. Telephone
counselling for smoking cessation. Cochrane Database Syst Rev.
2013;8:CD002850. doi:10.1002/14651858.CD002850.pub3
25. World Health Organization. Developing and Improving Toll-Free Tobacco
Quitlines: AWorld Health Organization Manual. Geneva, Switzerland:
World Health Organization; 2011.
26. Campbell DT, Stanley JC. Experimental and Quasi-Experimental Designs
for Research. Boston, MA: Houghton Mifin Company; 1963.
27. Shadish WR, Cook TD, Campbell DT. Experimental and Quasi-
Experimental Designs for Generalized Causal Inference. New York, NY:
Houghton Mifin Company; 2002.
28. Des Jarlais DC, Lyles C, Crepaz N; TREND Group. Improving the
reporting quality of nonrandomized evaluations of behavioral and pub-
lic health interventions: the TREND statement. Am J Public Health.
29. Campbell HS, Ossip-Klein D, Bailey L, Saul J; North American Quitline
Consortium. Minimal dataset for quitlines: a best practice. Tob Control.
2007;16:i16–i20. doi:10.1136/tc.2007.019976.
30. Dillman D, Smyth J, Christian L. Internet, Mail, and Mixed-Mode Surveys:
The Tailored Design Method. New York, NY: Wiley; 2009.
31. Gupta SK. Intention-to-treat concept: a review. Perspect Clin Res.
2011;2(3):109–112. doi:10.4103/2229–3485.83221.
32. Heatherton TF, Kozlowski LT, Frecker RC, Rickert W, Robinson J.
Measuring the heaviness of smoking: using self-reported time to the rst
cigarette of the day and number of cigarettes smoked per day. Br J Addict.
33. John U, Meyer C, Schumann A, etal. A short form of the Fagerström
Test for Nicotine Dependence and the Heaviness of Smoking Index in
two adult population samples. Addict Behav. 2004;29(6):1207–1212.
34. Pérez-Ríos M, Santiago-Pérez MI, Alonso B, Malvar A, Hervada X,
de Leon J. Fagerstrom test for nicotine dependence vs heavy smoking
index in a general population survey. BMC Public Health. 2009;9:493.
35. Larsen DL, Attkisson CC, Hargreaves WA, Nguyen TD. Assessment of
client/patient satisfaction: development of a general scale. Eval Program
Plann. 1979;2(3):197–207. doi:10.1016/0149-7189(79)90094-6.
36. Civljak M, Stead LF, Hartmann-Boyce J, Sheikh A, Car J. Internet-based
interventions for smoking cessation. Cochrane Database Syst Rev.
2013;7:CD007078. doi:10.1002/14651858.CD007078.pub4.
37. Brendryen H, Kraft P. Happy ending: a randomized controlled trial
of a digital multi-media smoking cessation intervention. Addiction.
2008;103(3):478–84. doi:10.1111/j.1360-0443.2007.02119.x.
38. Smith PM, Cameron R, McDonald PW, Kawash B, Madill C, Brown KS.
Telephone counseling for population-based smoking cessation. Am J
Health Behav. 2004;28(3):231–241.
39. Zhu SH, Lee M, Zhuang YL, Gamst A, Wolfson T. Interventions to
increase smoking cessation at the population level: how much progress
has been made in the last two decades? Tob Control. 2012;21(2):110–118.
40. Bull SS, Levine D, Schmiege S, Santelli J. Recruitment and retention of
youth for research using social media: experiences from the just/us study.
Vulnerable Child Youth Stud. 2013;8(2):171–181.
41. Maher CA, Lewis LK, Ferrar K, Marshall S, De Bourdeaudhuij I, Vandelanotte
C. Are health behavior change interventions that use online social networks
effective? Asystematic review. J Med Internet Res. 2014;16(2):e40.
42. Benowitz NL, Jacob P, Ahijevych K, et al. Biochemical verication
of tobacco use and cessation. Nicotine Tob Res. 2002;4(2):149–159.
43. Wong SL, Shields M, Leatherdale S, Malaison E, Hammond D.
Assessment of validity of self-reported smoking status. Health Rep.
44. Canadian Radio-Television and Telecommunications Commission.
Navigating Convergence: Charting Canadian Communications Change
and Regulatory Implications. (No. Convergence Consulting Group’s fore-
cast). Ottawa, ON: Canadian Radio-Television and Telecommunications
Commission; 2010.
45. Miller G. The smartphone psychology manifesto. Perspect Psychol Sci.
46. Abrams D. Comprehensive smoking cessation policy for all smokers:
systems integration to save lives and money. In Bonnie RJ, Stratton K,
Wallace RB eds. Ending the Tobacco Problem: Blueprint for the Nation.
Washington, DC: National Academies Press; 2010:A-1–A-50.
47. Health Canada. Break it off smoking cessation campaign. http://news. September 23, 2014 Accessed
October 30, 2014.
at University of Waterloo on June 28, 2016 from
360 Nicotine & Tobacco Research, 2016, Vol. 18, No. 3
48. World Health Organization. World Health Organization. mhealth: New
Horizons for Health Through Mobile Technologies. Global Observatory
for eHealth Series (vol. 3). Geneva, Switzerland: World Health
Organization; 2011.
49. Abroms LC, Padmanabhan N, Thaweethai L, Phillips T. iPhone apps for
smoking cessation: a content analysis. Am J Prev Med. 2011;40(3):279–
285. doi:10.1016/j.amepre.2010.10.032; 10.1016/j.amepre.2010.10.032.
50. Richardson CG, Vettese L, Sussman S, Small SP, Selby P. An investiga-
tion of smoking cessation video content on YouTube. Subst Use Misuse.
2011;46(7):893–897. doi:10.3109/10826084.2011.570628.
51. Abroms LC, Lee Westmaas J, Bontemps-Jones J, Ramani R, Mellerson J. A
content analysis of popular smartphone apps for smoking cessation. Am J
Prev Med. 2013;45(6):732–736.
52. Backinger CL, Pilsner AM, Augustson EM, Frydl A, Phillips T, Rowden
J. YouTube as a source of quitting smoking information. Tob Control.
2011;20(2):119–122. doi:10.1136/tc.2009.035550.
53. Prochaska JJ, Pechmann C, Kim R, Leonhardt JM. Twitter=quitter?
An analysis of Twitter quit smoking social networks. Tob Control.
at University of Waterloo on June 28, 2016 from
... Previous research has suggested that social features complement the apps by providing an engaging platform [38]. Social media can also provide social support for quitting [39], which was found as a positive development of smoking cessation mobile apps (e.g., [27,40]. ...
... Previous research has suggested that social features complement the apps by providing an engaging platform [38], and provide crucial components (e.g., social support) of successful smoking cessation interventions [40]. Despite the evidence of using social features for physical activity [41], mental health [42], and sexual health [43], the effectiveness of social features has not been thoroughly tested for smoking cessation, which leaves unclear which specific forms of social engagement features (i.e., social support, social announcement, and social referencing) lead to higher engagement and effectiveness. ...
Full-text available
Despite the ubiquity of smartphone ownership and the increasing integration of social engagement features in smoking cessation apps to engage users, the social and non-social engagement features that are present in current smoking cessation apps and the effectiveness of these features in engaging users remain understudied. To fill the gap in the literature, a content analysis of free and paid smoking cessation mobile apps was conducted to examine (a) the presence of social features (i.e., social support, social announcement, and social referencing) and non-social engagement features (e.g., personal environmental changes, goal setting, progress tracking, reinforcement tracking, self-monitoring, and personalized recommendations) and (b) their relationships with user engagement scores measured by the Mobile App Rating Scale. In this study, 28.2% of the smoking cessation apps enable social announcement and 8.1% offered the social support feature. Only two apps provided a social referencing feature (1.3%). No app included reinforcement tracking, with the percentage of other non-social engagement features ranging from 9.4% to 49.0%. Social support (β = 0.30, p < 0.001), social announcement (β = 0.21, p < 0.05), and social referencing (β = 0.18, p < 0.05) were significant predictors of user engagement. Regarding the non-social engagement features, personal environment changes (β = 0.38, p < 0.001), progress tracking (β = 0.18, p < 0.05), and personalized recommendations (β = 0.37, p < 0.001) significantly predicted user engagement. The findings not only contribute to the mobile communication literature by applying and extending the theory-based mobile health apps engagement typology, but also inform the future architecture design of smoking cessation mobile apps.
... Past studies have examined whether this health information seeking activity on the Internet and SM can lead to increased knowledge (Gough et al., 2017;Lemire et al., 2008) and behavior change (Elaheebocus et al., 2018;Frost & Massagli, 2008;Webb et al., 2010). Research has demonstrated that Internet and SM campaigns (i.e., a strategically coordinated online marketing effort designed to reinforce information or raise awareness on a specific topic) (Baskerville et al., 2016) have positively impacted various health behaviors such as smoking cessation (Baskerville et al., 2016), alcohol consumption (Lehto & Oinas-Kukkonen, 2011), skin cancer prevention (Gough et al., 2017), weight loss (Merchant et al., 2014;Merchant et al., 2017;Patrick et al., 2014), and physical activity (Maher et al., 2015). However, the effects of Internet and SM posts on pediatric injury prevention behaviors is not well understood (Drake et al., 2017). ...
... Past studies have examined whether this health information seeking activity on the Internet and SM can lead to increased knowledge (Gough et al., 2017;Lemire et al., 2008) and behavior change (Elaheebocus et al., 2018;Frost & Massagli, 2008;Webb et al., 2010). Research has demonstrated that Internet and SM campaigns (i.e., a strategically coordinated online marketing effort designed to reinforce information or raise awareness on a specific topic) (Baskerville et al., 2016) have positively impacted various health behaviors such as smoking cessation (Baskerville et al., 2016), alcohol consumption (Lehto & Oinas-Kukkonen, 2011), skin cancer prevention (Gough et al., 2017), weight loss (Merchant et al., 2014;Merchant et al., 2017;Patrick et al., 2014), and physical activity (Maher et al., 2015). However, the effects of Internet and SM posts on pediatric injury prevention behaviors is not well understood (Drake et al., 2017). ...
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Social media (SM) offers an opportunity for injury professionals to disseminate reliable safety recommendations to parents, yet little is known about the reach and impact of SM messages on parental safety knowledge and safety behavior adoption. It is also unclear whether electronic health (eHealth) literacy level is associated with understanding of messages. Parents of children (< 7 years) were recruited from a nationally representative consumer panel to complete an online survey assessing their Internet and SM usage and eHealth literacy level using the eHealth Literacy Scale (eHEALS). Participants were shown three safety SM posts where images and text matched or did not match. A post-exposure survey captured participant understanding of SM post message. Five-hundred eighty parents completed the survey. A majority of participants were female (58.6%) with high eHealth literacy (84.5%). Compared to low eHealth literate parents, a larger proportion of high eHealth literate parents correctly identified the message in mismatched posts (safe sleep: p = .0081; poison prevention: p = .0052), while similar proportions of parents with high and low eHealth literacy correctly identified a matched post for bike safety (p = .7022). Within each eHealth literacy level, high eHealth literate parents were more often able to correctly identify SM post messaging when the photo and text matched. Parents are using SM to acquire safety, health, and parenting information; therefore, it is incumbent upon disseminators to create content with clear messages. SM posts should utilize matching text with imagery that illustrates the recommended safety behavior to facilitate parental understanding of safety recommendations, regardless of audience eHealth literacy level.
... According to the global survey on eHealth by the World Health Organization (WHO) in 2016, citizens from nearly 80% (range: 70-90%) of the participating member countries across all income groups used social media as a source of information for learning about health issues [1]. Social media-based information can influence health perceptions and attitudes and foster changes in health-related behavior [2][3][4][5]. Therefore, social K. Bora et al. media is increasingly considered as a powerful tool for improving health. ...
... Social media or internet websites offer a promising opportunity in this context since these platforms are widely sought by the public for COVID-19-related information [21,22,24]. In addition to acting as tools for public health education and awareness, social mediabased content can catalyze and effectuate behavioral changes for promoting health and controlling diseases [2,4]. Multimedia materials such as videos are particularly appealing and were previously proven to be very effective for health education and behavioral modification [5,44]. ...
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Social media offers an opportune platform for educating the public about the recommended interventions during global health emergencies. This case study evaluated information in the popular social media platform YouTube about two key interventions (namely, 'social distancing' and 'hand washing') recommended during coronavirus disease-2019. Using the keywords 'social distancing' and 'hand washing', 77 and 78 videos, respectively, were selected from YouTube through pre-defined criteria. The understandability, actionability and quality of information in these videos were assessed. Cumulatively, the social distancing videos received >9 million views and the hand-washing videos received >37 million views. Thirteen social distancing videos (16.9%) and 46 hand-washing videos (58.9%) provided understandable, actionable and good-quality information. The non-understandable, non-actionable or poor-quality videos had paradoxically more viewer engagements than the understandable, actionable or good-quality videos, respectively. Most social distancing videos came from news agencies (68.8%). Hand-washing videos were mostly uploaded by health agencies or academic institutes (52.6%). The videos were less likely to be understandable and actionable and to be of good quality when uploaded by sources other than health agencies or academic institutes. The paucity of adequate information and the limited representation of 'authoritative' sources were concerning. Strategies for harnessing social media as an effective medium for public health education are necessary during pandemics.
... Online social networks are an emerging trend that provide opportunities to encourage peer support in various settings. Online peer support has been oriented toward patients with chronic conditions (e.g., cancer, diabetes) [31,32] and others intending to change a health-risk behavior (e.g., smoking cessation) [33]. These online communities offer experiencedbased information and reciprocal emotional support. ...
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Mothers have traditionally sought child feeding information from social connections. While mothers are heavily engaged on social media and value peer support in online communities, very little is known about how they use online communities for information about child feeding practices after exclusive breastfeeding cessation. This study explores mothers’ perceptions of joining Facebook child feeding support groups. Individual semi-structured interviews with ten Thai mothers were conducted. The transcribed interviews were analyzed using a phenomenological hermeneutical approach. Our findings highlighted that Thai mothers participated in Facebook child feeding support groups in a deliberate effort to reduce their uncertainty by normalizing the process through accessing the shared experiences of others. One of their intentions was to seek menu recipes based on favorable psychosocial and environmental factors. Implications for using social media in health promotion and communication include the importance of building appropriate common practices through social collaboration and interactivity to supplement traditional knowledge and attitudes.
... To achieve a minimal power level of 0.80 with a type I error probability (significance level) of 0.05, a sample size of n = 232 was considered necessary. Based on previous social media-based smoking cessation intervention studies, we expected an attrition rate between 20% and 50% (Danaher et al., Sep 2013;Naughton et al., 2014;YTD et al., 2015;Pechmann et al., 2015b;Ramo et al., 2015c;Baskerville et al., Mar 2016;Pechmann et al., Mar 2016;Kim et al., 2017), and allowed for a 30% attrition rate in our sample size calculations. Overall, we projected a minimal required sample size of n = 330. ...
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Background China is the largest tobacco producer and has the highest number of tobacco consumers in the world. Extensive research has demonstrated the utility of social media for smoking cessation. WeChat is the most commonly used social media platform in China, but has not yet been utilized for smoking cessation interventions. The objectives of this study are (1) to evaluate the efficacy of a WeChat-based smoking cessation intervention; and (2) to examine a possible additive effect of integrating oral health and smoking-related information into a tailored, Transtheoretical Model (TTM) guided smoking cessation intervention. Methods Eligible adults were recruited through WeChat from July 1 to August 6, 2019, to participate in a 3-arm, single-blinded, randomized controlled trial. We enrolled and randomized 403 participants into three groups: the Standard Group, Enhanced Group, or a Waitlist-Control Group. Participants in the Standard Group received 20 smoking cessation-related messages for 2 weeks; participants in the Enhanced Group received this same protocol plus 6 oral health-related messages over an additional week. Participants in the Control Group received smoking cessation-related messages, after the post-intervention assessment. The primary outcome was TTM Stage of Change, and the secondary outcomes were 7-day Point Prevalence Abstinence (PPA), 24-h PPA, daily cigarette use, and nicotine dependence at 4 weeks follow-up post intervention, comparing intervention groups with the control group. The overall program attrition rate was 46%. Paired t-tests, McNemar tests, and linear and logistic regression were used to examine differences in smoking cessation outcomes within and between groups. Results Participants in the Enhanced Group (β = −1.28, 95%CI: −2.13, −0.44) and the Standard Group (β = −1.13, 95%CI: −1.95, −0.30) reported larger changes in nicotine dependence scores, compared to participants in the Waitlist Group. No statistically significant differences were found between the Enhanced Group and the Standard Group. Discussion This WeChat-based intervention was effective for smoking cessation overall. The addition of oral health information did not significantly improve the intervention.
... Higher problematic social media use was not significantly correlated with dependence to smoking cigarettes, according to our results. A possible explanation for our results was conveyed in efforts towards smoking cessation through social media platforms that have significantly increased during the last few years, mainly targeting cigarette and e-cigarette smoking (Baskerville et al., 2016). Antismoking campaigns on Facebook and Twitter proved to be beneficial in promoting cessation and were able to reach a large number of individuals (Watkins et al., 2019). ...
Purpose: to evaluate the correlation between problematic social media use, alcohol use disorder and smoking addiction through both cigarettes and waterpipe, among a sample of Lebanese adults. Methods: This cross-sectional study was carried out between January and April 2019. It enrolled 461 residents of the community randomly selected from Lebanon's districts in a proportionate rate. Results: Higher problematic social media use was significantly associated with more waterpipe addiction but not cigarette dependence and alcohol use disorder. Practice implications: This study’s findings might serve as a starting point towards the development and implementation of anti-smoking waterpipe awareness campaigns, similar to those negatively representing cigarette smoking.
... In addition to mobile apps, patient digitization should also include health games (with a focus on health education and/or behaviour change), portable digital devices (sensors, trackers, etc.), and social media (getting general health information). The relevant studies show that middle-aged and specifically young people demonstrate a high level of active participation and satisfaction with the use of various clinical preventive care services, in particular those relating to physical activity, weight loss, addiction recovery (sobriety & addiction apps), mental and sexual health [37][38][39][40]. Such features as exchanging messages aiming to get support from friends and peers, achieving goals set in apps, updating information about the users' own progress and failures, uploading photos and videos not only add motivation, but also become trendy among more and more people. ...
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A business model is a key tool for companies and their employees to achieve market success. It can be used by healthcare providers, though this is not a common practice. While the number of publications about business models is growing, there is no universal description of a model which can be used by hospitals. The purpose of this article is to present a new business model on the healthcare market, launching of which will be accompanied by an adjustment of the value linkage and aims to generate a sustainable competitive advantage. This proposal for a business model is based on the analysis of business models available in the literature for healthcare providers and of business models for service companies. The business model proposed serves as a main key to achieving market success by entities and their employees. The healthcare providers delivering medical services through digital technologies improve communication between doctors and patients, employees of the healthcare services and stakeholders. These technologies increase patients’ quality of life and have a special meaning to increase their overall health. The digital business model provides increased values to the patients which manifests itself in service reliability information and customer focus.
... Besides the regulation of tobacco and nicotine marketing, our findings also suggested social media as a promising platform that health communication scholars and practitioners could take advantage of to disseminate anti-tobacco/ENDS messages, and ultimately to prevent young adults from initiating ENDS product use. Despite the paucity of ENDS interventions, previous social-media-based anti-tobacco interventions and campaigns were effective in promoting quitting attempts and abstinence among young adult smokers (Baskerville et al., 2015;Ramo et al., 2015). Furthermore, the recent "Real Cost" campaign addressing the rising epidemic of ENDS use among young adults conducted by the FDA was delivered on a variety of social media channels, such as YouTube, Facebook and Instagram (Zeller, 2019). ...
Electronic nicotine delivery systems (ENDS) products have been marketed heavily on social media throughout the past years, which exerts great influence on young adults’ ENDS use. Despite scholars’ pioneering efforts in investigating the influence of tobacco and nicotine products marketing on young adults’ vaping behavior, scholarly attention has been paid primarily to passive exposure to rather than active engagement with the information on social media. In addition, the majority of existing research has been cross-sectional or focused on the unidirectional path from marketing information to behavior. To extend previous research in tobacco regulatory science on new media, we examined the bidirectional associations between self-reported exposure to and engagement with tobacco and nicotine products messaging on social media, and subsequent use of ENDS products one year later among a large, diverse sample of young adults. Results from cross-lagged panel analyses indicated that pro-tobacco/ENDS engagement and advertising exposure elevated risk whereas anti-tobacco/ENDS engagement decreased risk for the subsequent use of ENDS products one year later. On the other hand, the use of ENDS products positively predicted both pro- and anti-tobacco/ENDS engagement one year later. Findings provide empirical support for the reasoned action approach and the confirmation bias rooted in cognitive dissonance theory through rigorous longitudinal examination. Our findings not only point to the imperativeness of and offer guidance for regulating marketing information on social media, but also suggest social media as a promising platform to prevent young adults from initiating ENDS product use.
Reviews published over the past decade confirm tobacco control campaigns can be effective for influencing adult and youth tobacco use behaviours, with strengthening evidence for high cost-effectiveness. Evidence is also accumulating for positive campaign effects on interpersonal discussions, social norms and policy support that can help motivate and sustain quitting and reduce uptake. Research needs over the next decade centre on the rapidly changing media environment and the equity of campaign effects among high smoking prevalence communities. The field needs specific evidence on: how to measure total campaign reach and frequency across the diverse range of media platforms and channels; the optimum mix of traditional, digital and social media to achieve behaviour change, especially among high smoking prevalence communities; the relative reach and impact of the wide variety of integrated, digital and social media message delivery methods; the relative effectiveness of messages that aim to build capacity to quit and optimum methods for combining motivational and capacity-building messages, especially for high prevalence groups who face additional barriers to staying quit; the ongoing effectiveness of traditional versus new versions of messages highlighting tobacco industry practices; the influence of e-cigarette use on tobacco control campaign effects; and the effectiveness of different types of campaigns aiming to prevent e-cigarette uptake and motivate e-cigarette cessation. Research is also needed to investigate the potential for campaigns to influence the public’s understanding and support for endgame tobacco control policies and for campaign elements that may influence the social and environmental contexts surrounding smokers that support and maintain behaviour change.
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Introduction Physical distancing (PD) is an important public health strategy to reduce the transmission of COVID-19 and has been promoted by public health authorities through social media. Although youth have a tendency to engage in high-risk behaviors that could facilitate COVID-19 transmission, there is limited research on the characteristics of PD messaging targeting this population on social media platforms with which youth frequently engage. This study examined social media posts created by Canadian public health entities (PHEs) with PD messaging aimed at youth and young adults aged 16–29 years and reported behavioral change techniques (BCTs) used in these posts. Methods A content analysis of all social media posts of Canadian PHEs from Facebook, Twitter, Instagram and YouTube were conducted from April 1st to May 31st, 2020. Posts were classified as either implicitly or explicitly targeting youth and young adults. BCTs in social media posts were identified and classified based on Behavior Change Technique Taxonomy version 1 (BCTTv1). Frequency counts and proportions were used to describe the data. Results In total, 319 youth-targeted PD posts were identified. Over 43% of the posts originated from Ontario Regional public health units, and 36.4 and 32.6% of them were extracted from Twitter and Facebook, respectively. Only 5.3% of the total posts explicitly targeted youth. Explicit posts were most frequent from federal PHEs and posted on YouTube. Implicit posts elicited more interactions than explicit posts regardless of jurisdiction level or social media format. Three-quarters of the posts contained at least one BCT, with a greater portion of BCTs found within implicit posts (75%) than explicit posts (52.9%). The most common BCTs from explicit posts were i nstructions on how to perform a behavior (25.0%) and restructuring the social environment (18.8%). Conclusions There is a need for more PD messaging that explicitly targets youth. BCTs should be used when designing posts to deliver public health messages and social media platforms should be selected depending on the target population.
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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.