Social Media and mHealth Technology for Cancer Screening: Systematic Review and Meta-analysis
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JOURNAL OF MEDICAL INTERNET RESEARCH Ruco et al
Review
Social Media and mHealth Technology for Cancer Screening:
Systematic Review and Meta-analysis
Arlinda Ruco1,2, MPH; Fahima Dossa3, MD; Jill Tinmouth1,4,5, MD, PhD, FRCPC; Diego Llovet1,4, PhD; Jenna
Jacobson1,6, PhD; Teruko Kishibe7, MISt; Nancy Baxter1,2,8, MD, PhD, FRCSC
1
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
2
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
3
Division of General Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
4
Prevention & Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, ON, Canada
5
Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
6
Ted Rogers School of Management, Ryerson University, Toronto, ON, Canada
7
Library Services, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
8
Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
Corresponding Author:
Nancy Baxter, MD, PhD, FRCSC
Melbourne School of Population and Global Health
University of Melbourne
207 Bouverie Street
Melbourne, 3053
Australia
Phone: 61 39035551
Email: Nancy.baxter@unimelb.edu.au
Abstract
Background: Cancer is a leading cause of death, and although screening can reduce cancer morbidity and mortality, participation
in screening remains suboptimal.
Objective: This systematic review and meta-analysis aims to evaluate the effectiveness of social media and mobile health
(mHealth) interventions for cancer screening.
Methods: We searched for randomized controlled trials and quasi-experimental studies of social media and mHealth interventions
promoting cancer screening (breast, cervical, colorectal, lung, and prostate cancers) in adults in MEDLINE, Embase, PsycINFO,
Scopus, CINAHL, Cochrane Central Register of Controlled Trials, and Communication & Mass Media Complete from January
1, 2000, to July 17, 2020. Two independent reviewers screened the titles, abstracts, and full-text articles and completed the risk
of bias assessments. We pooled odds ratios for screening participation using the Mantel-Haenszel method in a random-effects
model.
Results: We screened 18,008 records identifying 39 studies (35 mHealth and 4 social media). The types of interventions included
peer support (n=1), education or awareness (n=6), reminders (n=13), or mixed (n=19). The overall pooled odds ratio was 1.49
(95% CI 1.31-1.70), with similar effect sizes across cancer types.
Conclusions: Screening programs should consider mHealth interventions because of their promising role in promoting cancer
screening participation. Given the limited number of studies identified, further research is needed for social media interventions.
Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019139615;
https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=139615
International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-035411
(J Med Internet Res 2021;23(7):e26759) doi: 10.2196/26759
KEYWORDS
social media; mHealth; cancer screening; digital health; mass screening; mobile phone
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JOURNAL OF MEDICAL INTERNET RESEARCH Ruco et al
participation or intention. We included studies pertaining to
Introduction breast, cervical, colorectal, prostate, or lung cancer, as guidelines
Background for screening exist for these cancers. We defined mHealth
interventions as those that delivered health-related information
The use of mobile health (mHealth) technologies and social via telecommunication or other wireless technologies (eg,
media in the health care sphere has now become widespread smartphones and tablets) [4]. Social media interventions
[1-6] and has enabled the rapid sharing of health information, included those delivered on an already established or new
the launching of health promotional campaigns, access to peer purpose-built social media platform where users could create a
support groups, and facilitation of appointment reminders profile and share content with other users (virtual communities)
[1,2,4,6]. The World Health Organization has defined mHealth [1]. Any comparator was acceptable, including a nonintervention
as the use of mobile wireless devices for medical and public group; an alternate, nonsocial media, non-mHealth intervention;
health practice [1]. Social media allows those with access to or studies with a pre- and postintervention design. We included
information and communication technology to become content studies with multifaceted interventions if at least one component
creators and share content with others in virtual communities involved a social media– or mHealth-based strategy. Studies
or networks in addition to accessing information and connecting were restricted to those conducted in adults aged 18 years or
communities [1,6]. The use of mHealth and social media for older and articles published in English. In case we were unable
health presents an important opportunity to reach health to access full-text articles for relevant abstracts, we contacted
consumers, as these technologies and platforms can provide study authors to obtain the articles. If the authors did not
more frequent interactions, deliver tailored material, and increase respond, we included the abstract if we could ascertain the
accessibility to health information [1], and they now constitute eligibility criteria and if the data on the primary or secondary
a major way of communicating and advertising. In addition, as outcome were available. Commentaries, editorials, letters, and
access to mobile devices and the internet in low- and reviews were excluded. We also excluded articles published
middle-resource nations is reported to be comparable with those before 2000 because the use of social media was not widespread
in developed countries, mHealth and social media may play a before this time [4].
role in closing the gap in health disparities between high- and
low-resource nations [1,7]. Search Strategy
With almost 19 million people expected to be diagnosed with The search strategy was developed by a senior information
cancer in 2020, cancer is one of the leading causes of death specialist (TK) and used a combination of text words and MeSH
globally [8]. Cancer screening has been shown to reduce (Medical Subject Headings) terms depending on the database
disease-specific mortality for a number of cancers [9-12], and to capture the following concepts: cancer, screening, and social
as a result, many jurisdictions have implemented media or mHealth interventions. The search strategy was peer
population-based screening programs [13,14]. However, reviewed by a second information specialist in accordance with
screening participation remains suboptimal across jurisdictions the Peer Review of Electronic Search Strategies checklist [23]
and cancer types [13-16]. Emerging research has explored the and has been previously published [24].
use of social media and mHealth for cancer screening [17-21]. Information Sources
However, we currently lack an understanding of how effective
The search was conducted using the following databases:
mHealth and social media can be for cancer screening
MEDLINE, Embase, PsycINFO, Scopus, CINAHL, the
participation.
Cochrane Central Register of Controlled Trials, and
Objectives Communication & Mass Media Complete from inception to
This systematic review and meta-analysis aims to explore the May 31, 2019. The search was updated on July 17, 2020.
effectiveness of social media and mHealth interventions to Data Management
increase cancer screening participation and intention for screen
We used systematic review software (DistillerSR, Evidence
detectable cancers.
Partners Incorporated) to manage records during the screening
and study selection phases.
Methods
Study Selection
Study Design and Registration
Two independent reviewers (AR and FD) used a piloted data
This systematic review was registered with the International collection form and screened the studies in three stages: title,
PROSPERO (Prospective Register of Systematic Reviews; abstract, and full text. Citations that either reviewer considered
registration #CRD42019139615) and was written and reported potentially eligible at the title stage were included to maximize
according to the PRISMA (Preferred Reporting Items for sensitivity in the early stages of screening. Inclusion in the
Systematic Reviews and Meta-Analyses) checklist [22]. abstract and full-text screening stages required consensus
Inclusion and Exclusion Criteria between the reviewers. Discrepancies between the reviewers at
the abstract or full-text stages were resolved by discussion.
Studies included in this systematic review were randomized
controlled trials (RCTs) or quasi-experimental studies with a Data Extraction
pre- and postintervention design reporting on the effectiveness Two reviewers independently extracted data from the included
of an mHealth or social media intervention on cancer screening studies using a piloted data collection form in Excel (Version
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15.0; Microsoft). Any discrepancies were resolved by group equally by the number of intervention arms of interest to
discussion. Information extracted from each study included maintain the same proportion of those screened while not
study characteristics (authors, date of publication, location or counting the sample size of the control group more than once,
country, funding, and study design), participant characteristics as recommended by Cochrane [28]. Forest plots were created
(sample size, age, sex, ethnicity, and eligibility), intervention to graphically display results stratified by cancer type and the
details (type of intervention, components, comparator or control nature of the intervention. Statistical heterogeneity was
group interventions, follow-up or duration, technology platform, calculated using the I2 statistic, where a cutoff of ≥75% was
and delivery of intervention by whom), and outcomes of interest defined as considerable heterogeneity [28]. We conducted a
(screening participation or intention including timeframe). sensitivity analysis in which we excluded articles that were
Outcomes assessed to have a high risk of bias. In addition, we conducted
sensitivity analyses to explore whether the overall pooled effect
Screening participation (primary outcome) was defined as the estimate would differ for studies measuring the outcome of
proportion of adults who participated in the screening. This cancer screening participation through self-reporting compared
included self-reported outcomes as well as those confirmed with objective or administrative records and for studies
through administrative records. Screening intention (secondary conducted in low- and middle-income countries (LMICs). We
outcome) was defined as per the primary study authors. checked for publication bias for the primary outcome among
Typically, this is measured as the written intention to undergo the RCTs using a funnel plot. Statistical significance was set at
screening within a specified timeframe (eg, within the next 3 a two-tailed P
JOURNAL OF MEDICAL INTERNET RESEARCH Ruco et al
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram outlining the steps involved in identifying screened
and included studies in the meta-analysis.
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Table 1. Summary of included randomized controlled trials (n=30).
Study Location Type of Intervention Nature of in- Total Population Summary of inter- Outcomes
cancer type tervention sample vention
size
Arcas et al Spain Breast mHealtha Reminder 703 Women (aged 50- Invitation letter • Proportion that
[29] 69 years) with a and text message screened for breast
registered mobile reminder 2 days cancer during the
phone number before the mam- 2-month rescreen-
mography appoint- ing period
ment
Vidal et al Barcelona, Breast mHealth Reminder 12,786 Breast cancer Text message re- • Proportion attend-
[51] Spain screening target minder 3 days be- ing an appoint-
population of the fore a scheduled ment before Octo-
southern Barcelona appointment with ber 31, 2011 (3-5
metropolitan area or without a mes- months after the
sage, with a new intervention)
appointment date
if requested
Kerrison et al United King- Breast mHealth Reminder 2240 Women (aged 47- Text message re- • Proportion attend-
[41] dom 53 years) who minder 48 hours ing the appoint-
were due to be in- before the appoint- ment within 60
vited for their first ment and an addi- days of the initial
routine breast tional text message appointment
screen if they did not at-
tend the initial ap-
pointment
Rashid et al Klang, Cervical mHealth Reminder 1000 Women (aged 20- Text message re- • Proportion com-
[47] Malaysia 65 years) residing minder for a repeat pleting the Papani-
in Klang who had Papanicolaou test colaou test within
a nonpositive Pa- within a month 8 weeks
panicolaou test in from the date of re-
the previous year call
and were due for
repeat screening
Wanyoro and Thika, Cervical mHealth Reminder 286 Women (aged 25- 4 text message re- • Proportion
Kabiru [52] Kenya 70 years) attending minders in a period screened for cervi-
the general outpa- of 2 weeks cal cancer at the
tient clinic who same site within 2
had never had cer- weeks
vical cancer
screening, who
owned a mobile
phone, and who
had normal cervi-
cal Papanicolaou
test after the initial
baseline screening
Huf et al [39] United King- Cervical mHealth Reminder 14,587 Women (aged 24- 1 of 6 text message • Proportion who
dom 64 years) reminders: a sim- screened within 18
ple reminder, gener- weeks after the re-
al practice endorse- minder
ment, total and
proportional social
norms messages,
and gain- and loss-
framed messages
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Study Location Type of Intervention Nature of in- Total Population Summary of inter- Outcomes
cancer type tervention sample vention
size
Sly et al [50] New York, CRCb mHealth Reminder 24 Adults (aged >50 Standard naviga- • Colonoscopy com-
United years) with referral tion, a scheduling pletion within 3
States for screening telephone call and months
colonoscopy with 2 text message ap-
no personal or pointment re-
family history of minders
CRC or any chron-
ic gastrointestinal
disorder, with tele-
phone service, and
who spoke English
Hagoel et al Israel CRC mHealth Reminder 48,091 Adults (aged 50-74 Text message re- • Proportion com-
[36] years) with no diag- minders including pleting FOBT at 6
nosis of an inflam- interrogative or months
matory bowel dis- noninterrogative
ease or a bowel messages
malignancy, who
had not undergone
colonoscopy with-
in the previous 3
years, and who had
not performed
FOBTc in the previ-
ous year
Coronado et al United CRC mHealth Reminder 2010 Adults (aged 50-75 2 text message re- • FITd kit return rate
[32] States years) not up to minders with or
date with CRC without a live
screening and with phone call
a clinic visit in the
previous year
Hirst et al [38] United King- CRC mHealth Reminder 8269 Adults (aged 60-74 Usual care and a • Proportion return-
dom years) text message re- ing test kit at the
minder if they had end of an 18-week
not returned their screening episode
test kit within 8
weeks
Lam et al [61] Hong Kong CRC mHealth Reminder 500 Adults (aged 40-70 A WhatsApp mes- • Proportion success-
years) who were sage reminder sent fully returning the
asymptomatic and 1 month before the FIT kit
had a previous due date for subse-
negative FIT test quent FIT
and who were ex-
pected for an annu-
al FIT screening in
the subsequent
year
Coronado et al Los Angeles, CRC mHealth Reminder 1767 Adults (aged 50-75 Text message • Proportion com-
[33] United years) who were prompt before re- pleting the FIT kit
States overdue for CRC ceipt of the FIT kit within 6 months
screening and had with 2 automated
attended at least phone call re-
two clinic visits minders or with 2
within the past 24 automated phone
months calls and up to 3
live phone call re-
minders
Hwang et al United CRC Social media Peer support 306
[40] States
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Study Location Type of Intervention Nature of in- Total Population Summary of inter- Outcomes
cancer type tervention sample vention
size
Adults (aged 50-75 Study-specific • Proportion
years) who had no web-based Spark- screened for CRC
previous diagnosis Team to access the at 6 months
of CRC, had no narratives and inter- (FOBT, sigmoi-
history of inflam- act with the narra- doscopy, or
matory bowel dis- tors (positive role colonoscopy)
ease, and were not models) and other
up to date with participants
CRC screening
Lakkis et al Beirut, Breast mHealth Mixed (edu- 385 Women (aged 40- Educational and • Completion of a
[43] Lebanon cation and 75 years) who had general invitation mammography
reminder) not undergone a text message for
mammogram in the mammography and
past 2 years 3 additional text
reminders
Chung et al Republic of Breast mHealth Mixed (edu- 202 Women (aged 20- Usual care and 1 • Adherent to
[31] Korea cation and 65 years) who un- text message re- monthly BSEe for
reminder) derwent surgery minder and 1 edu- 5 out of 6 months
for breast cancer, cational text mes-
excluding those sage
with distant metas-
tasis or recurrent
breast cancer
Heydari and Bushehr, Breast mHealth Mixed (edu- 120 Women (aged ≥40 Multimedia educa- • Proportion com-
Noroozi [37] Iran cation and years) who were tion session pleting mammogra-
reminder) elementary school through a CD and phy
teachers, were not text messages; 1-2 • Intention to get a
pregnant or breast- educational text mammography
feeding, had no messages sent on a
history of cancer, weekly basis for 1
had no family histo- month and a re-
ry of breast cancer, minder about
had not had breast mammography
biopsy experience
and mammography
in the past 3 years
Lee et al [44] Minnesota, Breast mHealth Mixed (edu- 131 Korean American mMammogram • Proportion receiv-
United cation and immigrant women mobile app deliver- ing mammography
States navigation) (aged 40-79 years) ing 8-21 messages or with a sched-
who had not re- over a 7-day period uled appointment
ceived a mammo- within 6 months
gram in the past 2 • Intention to re-
years ceive a mammogra-
phy in the future
on a 4-point scale
(1=not within a
year, 2=within a
year, 3=within 3
months, and
4=within 1 month)
Khademolhos- Bushehr, Cervical mHealth Mixed (edu- 95 Educational train- • Completion of the
seini et al [42] Iran cation and ing through text Papanicolaou test
reminder) messaging, elec- within 3 months
tronic posters, info-
graphics, podcasts,
and video tutorial
and a reminder to
perform a Papanico-
laou smear test
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Study Location Type of Intervention Nature of in- Total Population Summary of inter- Outcomes
cancer type tervention sample vention
size
Women who were
able to read and
write, were mar-
ried for at least 6
months, had a
smartphone, had
no history of geni-
tal tract cancer in
their family, and
had no experience
of doing a Papani-
colaou smear test
in the past 3 years
Richman et al North Caroli- Cervical mHealth Mixed (edu- 264 Adults (aged 18-26 7 electronic email • Proportion com-
[49] na, United or rectal cation and years) who attend- or text messages pleting HPV dose
States reminder) ed the university once per month for 3 vaccine
and who were vol- 7 months
untarily initiating
the first HPVf vac-
cine dose from the
campus student
health center
Adler et al United Cervical mHealth Mixed (edu- 95 Women (aged 21- Referral and 3 text • Proportion who
[62] States cation and 65 years) with no messages delivered underwent cervi-
reminder) past hysterectomy at 30-day intervals cal cancer screen-
with cervical re- over a period of 90 ing 150 days after
moval or known days after enroll- enrollment
HIV infection ment
Erwin et al Kilimanjaro Cervical mHealth Mixed (edu- 851 Women (aged 25- 15 unique text • Proportion attend-
[34] and Arusha cation and 49 years) with ac- messages delivered ing cervical cancer
regions, Tan- reminder) cess to a mobile over 21 days with screening within
zania phone living in the or without a trans- 60 days
catchment areas of portation e -vouch-
Mawenzi Regional er covering return
Referral Hospital transportation to
and Meru District the nearest screen-
Hospital ing clinic
Firmino- Portugal Cervical mHealth Mixed (edu- 1220 Women (aged 25- Automated or cus- • Proportion adher-
Machado et al cation and 49 years) eligible tomized text mes- ent to cervical
[35] reminder) for screening and sages and phone cancer screening
registered at prima- calls, followed by at 45 (step 1), 90
ry health care units text message re- (step 1+2), and
that perform sys- minders of the ap- 150 days after the
tematic written let- pointment (step 1), initial invitation
ter invitations for phone calls by (step 1+2+3)
screening clinical secretaries
(step 2), and phone
calls or face-to-
face interviews by
doctors (step 3)
Linde et al Tanzania Cervical mHealth Mixed (edu- 689 Women (aged 25- 10 educative text • Proportion attend-
[65] cation and 60 years) who had messages (1 per ing the scheduled
reminder) tested positive for month) and 5 re- screening appoint-
HPV during a pa- minders (14, 7, and ment within 30
tient-initiated op- 1 day before the days
portunistic screen- scheduled screen-
ing 14 months ear- ing appointment)
lier over a 10-month
period
Romli et al Kedah, Cervical mHealth 210
[63] Malaysia
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Study Location Type of Intervention Nature of in- Total Population Summary of inter- Outcomes
cancer type tervention sample vention
size
Mixed (edu- Women en- A 30-minute educa- • Proportion having
cation and trepreneurs (aged tional talk, a 5- a Papanicolaou
reminder) 20-65 years) who minute video on smear test
received financial Papanicolaou
help from Amanah smear test proce-
Ikhtiar Malaysia dures, experience
and who were or sharing from a cer-
had been previous- vical cancer sur-
ly married vivor, distribution
of pamphlet on
cervical cancer and
Papanicolaou
smear testing, and
2 text message re-
minders sent over
a 3-month period
Baker et al Chicago, CRC mHealth Mixed (edu- 450 Adults (aged 51-75 A mailed reminder • Proportion com-
[30] United cation, re- years) with pre- letter and FIT kit pleting either
States minder, and ferred language with postage-paid FOBT or
navigation) listed as English or envelope, automat- colonoscopy with-
Spanish and with a ed telephone and in 6 months of the
negative FOBT text message re- date the patient
minders, and per- was due for annual
sonal telephone screening
outreach by a
screening naviga-
tor after 3 months
Muller et al Anchorage, CRC mHealth Mixed (edu- 2386 Alaska Native or A maximum of 3 • Proportion
[46] Alaska cation and American Indian text messages over screened (FIT,
reminder) adults (aged 40-75 2 months FOBT, flexible
years) with no his- sigmoidoscopy, or
tory of CRC or colonoscopy)
colectomy enrolled
with the Southcen-
tral Foundation
health care system
and eligible for
screening
Miller et al North Caroli- CRC mHealth Mixed (edu- 450 English-speaking mPATH-CRC, an • CRC screening
[45] na, United cation and adults (aged 50-74 iPad app providing completed within
States decision aid) years) who were screening informa- 24 weeks
scheduled to see a tion, help with
primary care screening decision, • Intention to re-
provider and were self-ordering a ceive screening
due for CRC screening test, and within the next 6
screening automated electron- months
ic messages to
complete the cho-
sen test
Reiter et al United Rectal mHealth Mixed (edu- 150 Gay or bisexual Population-target- • Proportion com-
[48] States cation and men (aged 18-25 ed, individually pleting all 3 doses
reminder) years) residing in tailored content of the HPV vac-
the United States about HPV and cine
who had not re- monthly HPV vac-
ceived any HPV cination reminders
vaccine doses sent via email
and/or text mes-
sage
Wong et al Hong Kong CRC mHealth Mixed (edu- 629
[53] cation and
reminder)
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Study Location Type of Intervention Nature of in- Total Population Summary of inter- Outcomes
cancer type tervention sample vention
size
Adults (aged 40-70 Generic text mes- • Proportion success-
years) at average sage about the im- fully returning
risk of CRC who portance of regular completed FIT
had a negative FIT CRC screening and specimen within 6
result in their first the time and venue months
screening round for of FIT tube re-
the study trieval
Mahmud et al United CRC mHealth Mixed (edu- 71 Adults (aged 18-75 9 text messages • Proportion who at-
[64] States cation and years) scheduled sent in the week tended their
reminder) for outpatient before the sched- scheduled appoint-
colonoscopy with- uled procedure ment
in 2 months of ini-
tial contact
a
mHealth: mobile health.
b
CRC: colorectal cancer.
c
FOBT: fecal occult blood test.
d
FIT: fecal immunochemical test.
e
BSE. breast self-exam.
f
HPV: human papilloma virus.
The most common reminder strategies used were text message scheduled appointment or those participating in screening within
reminders [29-39,41-43,46-55,57-65]. Educational strategies 2 weeks [52], a month [65], 45 days [35], 60 days [29,34,41,47],
most commonly included general health information about the 3 months [35,42,50], 3-5 months [38,39,51], or 6 months
specific cancer and information about cancer screening, [30,31,33,36,40,45,53].
including the importance of screening. Although text messages
There was wide variability in the study participants. For
were commonly used to deliver educational information
example, the included participants were targeted based on
[34,35,37,42-44,46,48,49,53-55,59,62,64,65], some studies also
geographical region in some studies [34,51,56] or by their
used electronic posters or infographics, CDs, videos, mobile
profession as elementary school teachers [37], entrepreneurs
apps, and podcasts [37,42,44,45,55,59,63]. Education was also
[63], or university students [49,59]. Some studies were targeted
provided through in-person educational or training sessions in
to specific racial and cultural groups [44,46,54,58,67], whereas
some cases in addition to a social media or mHealth strategy or
others included gay and bisexual men only [48] or women who
in the comparison groups [55,63]. Educational interventions
were HIV positive [60]. The intervention intensity also differed
using social media included social media campaigns [56] or
between the studies. For example, some interventions included
sharing information or daily posts about screening or cancer
sending only a single text message reminder
with participants who were members of a group (virtual
[29,31,33,38,39,41,51], whereas others included sending 22
community) on a social media platform [66,67]. Peer support
text messages over 16 days [54] or 21 messages over a 7-day
interventions on social media also leveraged groups to support
period [44]. For social media interventions, participants in one
participants of that virtual community through the sharing of
study received three daily posts over a 12-week period [67] or
personal stories and narratives [40]. Outcomes were measured
as many as 20 posts per day over 5 days [66].
at several time points, including the proportion attending a
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Table 2. Summary of included pre- and postintervention studies (n=9).
Study Location Type of Intervention Nature of in- Total sam- Population Summary of inter- Outcomes
cancer type tervention ple size vention
Ganta et Nevada, United Cervical mHealtha Reminder 473 HIV-infected Reminders to • Proportion complet-
al [60] States women (aged schedule a Papan- ing the Papanico-
≥18 years) at icolaou test via 3 laou test
the HIV Well- sequential text
ness Center messages and
subsequently by
3 phone call at-
tempts
Lee et al Minnesota, Cervical mHealth Education or 30 Korean Ameri- 7-day text mes- • Proportion receiving
[58] United States awareness can women sage–based inter- a Papanicolaou test
(aged 21-29 vention including within 3 months
years) with no quizzes and ques- • Intent to receive a
previous receipt tions and engage- Papanicolaou test
of a Papanico- ment in conversa- within a year
laou test with tion
up-to-date
health insurance
Lemos et Madeira, Portu- Cervical mHealth Education or 144 Female college 5 structured text • Intention to get a
al [59] gal awareness students recruit- messages deliv- Papanicolaou test
ed from various ered over 5 measured on a 5-
undergraduate weeks and an edu- point Likert scale
courses of cational video in- from 1 (definitely
Madeira Univer- tervention lasting will not do) to 5
sity 12 minutes (definitely will do)
Le and United States Cervical mHealth Education or 52 Church-attend- 22 text messages • Intent to get a Papan-
Holt [54] awareness ing African- delivered over 16 icolaou smear test in
American wom- days, containing the next 6 months
en (aged 21-65 health-specific
years) with no and spiritually
previous medi- based content
cal history of
cervical cancer
or hysterectomy
Lyson et United States Cervical Social media Education or 782 Women (aged Health Connect • Proportion ever had
al [66] awareness ≥18 years) who web-based plat- a Papanicolaou test
lived in the form where partic- • Proportion ever re-
United States, ipants were as- ceived the HPVb
spoke English signed to groups vaccine
as their primary of 9 and where
language, and each participant
did not have was randomly
cervical cancer distributed a set
of 20 tweets or
messages per day
over 5 days in a
personalized
message feed
Key et al Kentucky, Unit- CRCc Social media Education or 60 Appalachian Participants • Proportion ever re-
[67] ed States awareness Kentuckians joined a closed ceived a
(aged ≥50 Facebook group colonoscopy or
years) noncom- and were present- FOBTd
pliant with cur- ed with 3 daily
rent screening Facebook posts
guidelines during the 12-
week intervention
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Study Location Type of Intervention Nature of in- Total sam- Population Summary of inter- Outcomes
cancer type tervention ple size vention
Jessup et Massachusetts, Lung Social media Education or Variable Patients, care- Patient awareness • Number of LDCTe
al [56] United States awareness depending givers, and campaign on examinations per
on plat- health care Facebook and week before and af-
form providers with- Google and ter the campaign
in a 60-mile ra- provider cam-
dius of a large paign on
quaternary med- LinkedIn and
ical center and Twitter
2 affiliated off-
campus imag-
ing sites. Pa-
tient campaign
targeted current
and former
smokers (aged
≥55 years), fe-
males (aged
≥55 years), pa-
tients and em-
ployees of the
academic medi-
cal center (aged
≥18 years), and
caregivers
(aged ≥18
years)
Fornos et Texas, United Cervical mHealth Mixed (edu- 32,807 Women (aged Newsletters, pub- • 3-year cervical can-
al [57] States cation, re- ≥18 years) en- lic service an- cer screening rate
minders, and rolled in Care- nouncements, au-
navigation) Link who were tomated client re-
not up to date minders includ-
with Papanico- ing text mes-
laou screening sages, and com-
or actively ob- munity outreach
taining Papani-
colaou test ap-
pointments
Capik Erzurum, Prostate mHealth Mixed (edu- 75 Men (aged 41- Poster announce- • Proportion having
and Turkey cation and 65 years) work- ments, interactive had a PSAf test in
Gozum reminders) ing in 2 public educational ses- the last 3 months
[55] institutions who sion, access to • Proportion having
had not re- website, desk cal- had a prostate exam-
ceived a endar information ination in the last 3
prostate cancer and reminders, months
diagnosis monthly email re-
minders, flyers,
and 1 text mes-
sage
a
mHealth: mobile health.
b
HPV: human papilloma virus.
c
CRC: colorectal cancer.
d
FOBT: fecal occult blood test.
e
LDCT: low-dose computed tomography.
f
PSA: prostate-specific antigen.
having some concerns in several domains, including bias arising
Quality Assessment from the randomization process, effect of assignment to
Risk of bias assessments for the included studies are shown in intervention, and measurement of the outcome. All pre- and
Figures 2 and 3. Briefly, 27% (8/30) of the included RCTs were postintervention studies were classified as high risk. Figure 4
classified as high risk, 23% (7/30) as having some concerns, displays the funnel plot used to check for publication bias. The
and the remainder (15/30, 50%) were classified as low risk. x-axis represents the effect estimates, whereas the y-axis
Common reasons for being classified as high risk included represents the study size or precision. The funnel plot generated
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may suggest some publication bias because of the lack of studies small effect sizes and variances.
in the bottom left corner of the plot representing studies with
Figure 2. Risk of bias assessment for the included randomized controlled trials (n=30) created using the Robvis tool.
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Figure 3. Risk of bias assessment for the included pre- and postintervention studies (n=9).
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Figure 4. Funnel plot of publication bias for the randomized controlled trials reporting on the primary outcome. OR: odds ratio.
type, with the largest effect observed for cervical cancer
Primary and Secondary Outcomes screening studies (OR 1.71, 95% CI 1.34-2.19; Figure 5).
The absolute effect of being screened in the intervention arms Stratification by cancer type did not reduce the heterogeneity.
was 22.22% (13,115/59,017). There was an absolute risk When we conducted a sensitivity analysis excluding trials
difference of 14% (95% CI 13.12-14.33) between the assessed to have a high risk of bias, the overall pooled OR and
intervention and comparison arms, with the proportion screened I2 remained stable (OR 1.54, 95% CI 1.33-1.78; Figure 6). The
in the comparison arms being 35.94% (12,524/34,872). When overall pooled OR was not significant when including only
stratified by cancer type, the absolute proportion screened in studies measuring screening participation through self-reporting
the intervention arms was 71.68% (3935/5489) for breast cancer (OR 2.09, 95% CI 0.96-4.53). The overall pooled effect estimate
compared with 64.11% (7096/11,067) in the comparison arms remained stable when including only studies that captured the
(risk difference 8%; 95% CI 6.08-9.06). For cervical cancer, outcome through administrative records (OR 1.46, 95% CI
there were 35.23% (2382/6760) screened in the intervention 1.28-1.66). When we included only studies conducted in LMIC
arms compared with 28.26% (1548/5478) in the comparison settings (n=3), the overall pooled OR was 3.29 (95% CI
arms. For CRC, the proportion screened in the intervention arms
1.02-10.60) with considerable heterogeneity (I2=93%). However,
was 14.53% (6798/46,768) and 21.17% (3880/18,327) in the
the pooled OR increased to 5.50 (95% CI 3.19-9.51) with only
comparison arms, with a risk difference of 6% (95% CI
5.96-7.31). moderate heterogeneity (I2=38%) when only studies with a low
risk of bias were included (n=2). We also conducted subgroup
The overall pooled OR for cancer screening participation among analyses by meta-analyzing studies based on the nature of the
the included RCTs was 1.49 (95% CI 1.31-1.70; Figure 5), intervention. The results showed an overall pooled effect
indicating that the odds of getting screened increased by 49% estimate of 1.23 (95% CI 1.08-1.41) for reminder interventions
for those who received a social media or mHealth intervention. (Figure 7) and 2.07 (95% CI 1.49-5.83) for mixed interventions
However, considerable heterogeneity was observed (I2=88%). (Figure 8). Heterogeneity did not change when subgroup
Similar effect estimates were observed when stratified by cancer analyses were conducted.
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Figure 5. Forest plot for the randomized controlled trials reporting on the primary outcome of cancer screening participation categorized by type of
cancer (n=30).
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Figure 6. Sensitivity analysis for the primary outcome of interest of cancer screening participation without inclusion of randomized controlled trials
with a high risk of bias (n=22).
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Figure 7. Forest plot for the reminder interventions reporting on the primary outcome of cancer screening participation (n=12).
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Figure 8. Forest plot for the mixed interventions reporting on the primary outcome of cancer screening participation (n=17).
Table 3 presents the results of the secondary outcomes of outcome, except for one in which it decreased. The highest
screening intention. Six studies (3 RCTs and 3 pre- and increase in screening intention was observed in the study by
postintervention studies) reported on screening intention, with Lee et al [58], where there was a 24% absolute increase in the
two studies reporting on screening intention only. There was intent to receive a Papanicolaou test postintervention (19/30,
minor variability in the measurement of screening intention 63% preintervention and 26/30, 87% postintervention). The
among the studies. For example, screening intention was treated study included a 7-day text message–based intervention that
as a dichotomous variable in some studies [37,45,54,58] or included a high level of engagement with participants through
scored using a four-point [44] or five-point [59] Likert scale in quizzes, questions, and engagement in conversation [58]. Owing
others. Half of the studies (3/6, 50%) focused on cervical cancer, to the variability in how screening intention was measured or
followed by breast cancer (2/6, 33%) and CRC (1/6, 17%). The captured, we did not perform a meta-analysis on these data.
intention to screen increased in all studies reporting on this
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Table 3. Cancer screening intention outcome among included studies (n=6).
Study Study design Outcome definition Timeframe for as- Outcome in compari- Outcome in interven-
sessing outcome son group (if RCTa) tion group (if RCT) or
or preintervention postintervention
Heydari and RCT Intention to get a mammogram 3 months 93% (56/60) 83% (50/60)
Noroozi [37] (yes or no)
Lee et al [44] RCT Intention to receive a mammo- 1-week postinterven- Group differences Group differences
gram in the future on a 4-point tion preintervention −0.64 postintervention 3.48
scale (1=not within a year,
2=within a year, 3=within 3
months, and 4=within 1 month)
among intervention and control
groups
Miller et al RCT Intention to receive screening 6 months 49% (112/227) 62% (138/223)
[45] measured through the postpro-
gram iPad survey
Le and Holt Pre- and postintervention Intent to get a Papanicolaou 6 months 48% (22/46) 52% (24/46)
[54] smear test (yes or no)
Lee et al [58] Pre- and postintervention Intent to receive a Papanicolaou Within 1 year 63% (19/30) 87% (26/30)
test (yes or no)
Lemos et al Pre- and postintervention Intention to get a Papanicolaou 6 weeks 4.50 (SD 0.64) 4.82 (SD 0.48)
[59] test measured on a 5-point Lik-
ert scale from 1 (definitely will
not do) to 5 (definitely will do)
a
RCT: randomized controlled trial.
message reminders are different from these other approaches
Discussion because they are sent only to mobile devices compared with
Principal Findings telephone calls, which may be made to landlines, for which
coverage has been decreasing. In addition, text messages can
Our systematic review identified 39 studies describing the be sent instantly, whereas letter or postcard reminders need to
effectiveness of social media and mHealth interventions on be delivered by the post. Moreover, text messages have the
cancer screening participation and/or intention. The overall opportunity to reach those with no fixed addresses. For example,
pooled OR for cancer screening participation was significant, a recent systematic review on technology use among homeless
favoring the intervention arm (OR 1.49, 95% CI 1.31-1.70). adults showed that a majority (94%) owned a cell phone [68].
Effect sizes were similar across all cancer types, and estimates Overall, Tamuzi et al [18] found that call reminders were the
remained stable when trials deemed to be at high risk of bias only intervention to show a statistically significant pooled effect
were excluded, indicating that social media, and particularly estimate. Only one study included in their review reported on
mHealth interventions, can be effective for increasing cancer the effect of text message reminders, and a meta-analysis of this
screening participation. type of intervention was, therefore, not possible [18].
Two systematic reviews on this topic were published in 2017 The results of this study enhance our understanding of the
[17,18]. Uy et al [17] evaluated the effectiveness of text effectiveness of social media and mHealth interventions for
messaging interventions on cancer screening and identified nine cancer screening. Although both previous reviews were
studies that met the inclusion criteria. Absolute screening rates published in 2017, nearly 44% (17/39) of the studies in this area
for text messaging interventions were 1%-15% higher and have been published since that time. Our review provides a
relative screening rates were 4%-63% higher for intervention comprehensive and more contemporary understanding of this
recipients in their study [17]. The authors concluded that text topic. In addition, although previous reviews focused primarily
messaging interventions moderately increased screening rates on breast and cervical cancer, our study provides valuable
for breast and cervical cancer; however, additional research is insights into the effectiveness of these interventions in CRC
needed to better quantify this relationship [17]. Tamuzi et al screening as well. We included 13 studies focused on CRC in
[18] explored mHealth interventions for cervical cancer our meta-analysis and found a significant pooled effect estimate,
screening only. Their review identified 17 studies, and the suggesting that the use of these types of interventions can be
authors were able to perform a meta-analysis on the results by extended to CRC as well. In comparison with the study by Uy
type of intervention [18]. However, their definition of mHealth et al [17], we found that absolute screening rates between the
was different from ours. In their study, Tamuzi et al [18] intervention and comparison groups were higher in our study.
included telephone, letter, and text message reminders, whereas This may suggest that multicomponent interventions that couple
only text message reminders were included in our study based social media or mHealth with additional strategies may be more
on our adopted definition of mHealth interventions. Text
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effective at increasing screening rates compared with mHealth may be because there may be a limited number of other
or social media strategies alone. campaigns in these resource-low settings, whereas access to
mobile phones and the internet has been reported to be
The results of our study must also be understood within the
comparable with that of developed nations [1].
larger context of interventions for cancer screening. Brouwers
et al [69] conducted a systematic review of interventions for Only a limited number of studies (n=4) tested social media
increasing cancer screening rates and looked at client reminders, interventions. As such, our results are more indicative of the
client incentives, mass media, small media, group education, effectiveness of mHealth interventions. A narrative systematic
one-on-one education, reducing structural barriers, reducing review focusing on describing the characteristics of social media
out-of-pocket costs to clients, provider assessment and feedback, interventions used for cancer prevention and management found
and provider incentives. Similarly, the authors found wide that cancer screening participation or intention was not measured
heterogeneity across studies and interventions and chose not to in any of the 18 studies included in the review [70]. The most
meta-analyze their data. For example, their results showed that common outcome measured in these studies was knowledge
small media interventions, including videos or printed materials [70]. Although research related to social media and cancer
such as letters, brochures, newspapers, magazines, and screening participation has started to emerge [71], the inclusion
billboards, resulted in a point percentage increase for cancer of this work was limited in our review, as there are few RCTs
screening participation ranging from −32.8% to 26% among and before and after comparisons also capturing the outcome
studies on breast cancer, cervical cancer, and CRC [69]. Our of screening participation or intention. This suggests areas for
review showed that the absolute difference between the future research to generate more evidence on the use of social
intervention and comparison arms was 14%. The magnitude of media interventions for cancer screening participation. In
effect varied considerably among and between intervention addition, very few studies have been conducted on prostate and
categories in the review by Brouwers et al [69], suggesting that lung cancer screening, which is similar to what was observed
additional evidence is needed for interventions related to client in a previous study [17].
reminders, mass media, group education, one-on-one education,
Our review and meta-analysis included a variety of mHealth
reduction of structural barriers and out-of-pocket costs, and
and social media interventions and multicomponent
provider incentive interventions. Given the need for additional,
interventions. Our review is comprehensive and contemporary
high-quality evidence, it is difficult to ascertain whether social
and uses a rigorous systematic approach to screen and review
media and mHealth interventions fare similar, better, or worse
the literature. As such, it includes a large number of studies for
than non-mHealth or non–social media interventions. In
the most established screening programs for breast cancer,
addition, costs should also be considered when making any
cervical cancer, and CRC. Owing to the large number of studies
comparisons between the effectiveness of these interventions
included in our review, we were able to calculate pooled effect
to inform the translation of these findings into practice.
estimates by cancer type to inform practice and future research.
Although the pooled effect estimate in our meta-analysis was However, this study has limitations. Although we made every
consistent in the subgroup and sensitivity analyses, significant effort to obtain full-text articles, there were some records
heterogeneity remained. This may be because of the variability identified from our search that we could not locate. We also did
in populations, interventions, or outcome measurement across not calculate a Cohen κ coefficient to report the interrater
studies. For example, the populations randomized in the studies reliability between the 2 reviewers. Our review is also limited
in our review included all adults up to 79 years [44], or highly in regard to social media interventions, as only four studies were
specialized populations such as emergency department patients identified, with only one RCT included in the meta-analysis.
[62] or HIV-positive individuals [60]. Moreover, many of the This may be a reflection of current practice or due to the fact
studies included insured samples, which may not be reflective that it may be more difficult to link direct patient outcomes with
of population-level interventions, and therefore, must be the use of social media.
considered in the generalizability of these results. In addition,
the follow-up and the intensity of each intervention varied across
Conclusions
studies. For example, some studies may have sent a single text In conclusion, our results suggest that mHealth interventions
message reminder [37], whereas other interventions included may have a significant effect on cancer screening participation,
sending multiple text messages in combination with telephone particularly for breast cancer, cervical cancer, and CRC
reminders [33]. Interestingly, when we looked at studies screening. Screening programs should consider the use of
conducted in LMIC settings and excluded those with a high risk mHealth interventions to increase screening participation.
of bias, the overall pooled OR was even larger with only Further research focusing on social media interventions for
moderate heterogeneity. These results suggest that the cancer screening participation is needed, as there was
effectiveness of these interventions for cancer screening insufficient evidence available at the time of this review.
participation may be more pronounced in these settings. This
Acknowledgments
This study was supported by the Canadian Institutes of Health Research (grants FDN-148470 and GSO-157853). The funding
agency had no role in the design or conduct of the study. The authors would like to acknowledge the contributions of Amina
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Benmessaoud and Alawia Sherif for their help with data management and Dr Rahim Moineddin, a thesis committee member for
AR’s doctoral dissertation.
Conflicts of Interest
None declared.
References
1. World Health Organization. Global Difusion of eHealth: Making Universal Health Coverage Achievable. Report of the
Third Global Survey on eHealth. Geneva: World Health Organization; 2016:1-160.
2. Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health care: systematic review
of the uses, benefits, and limitations of social media for health communication. J Med Internet Res 2013;15(4):e85 [FREE
Full text] [doi: 10.2196/jmir.1933] [Medline: 23615206]
3. 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 [FREE Full text] [doi:
10.2196/jmir.2952] [Medline: 24550083]
4. Korda H, Itani Z. Harnessing social media for health promotion and behavior change. Health Promot Pract 2013
Jan;14(1):15-23. [doi: 10.1177/1524839911405850] [Medline: 21558472]
5. Bull SS, Levine DK, Black SR, Schmiege SJ, Santelli J. Social media-delivered sexual health intervention: a cluster
randomized controlled trial. Am J Prev Med 2012 Nov;43(5):467-474 [FREE Full text] [doi: 10.1016/j.amepre.2012.07.022]
[Medline: 23079168]
6. Prochaska JJ, Coughlin SS, Lyons EJ. Social media and mobile technology for cancer prevention and treatment. Am Soc
Clin Oncol Educ Book 2017;37:128-137 [FREE Full text] [doi: 10.1200/EDBK_173841] [Medline: 28561647]
7. Hagg E, Dahinten VS, Currie LM. The emerging use of social media for health-related purposes in low and middle-income
countries: a scoping review. Int J Med Inform 2018 Jul;115:92-105. [doi: 10.1016/j.ijmedinf.2018.04.010] [Medline:
29779724]
8. Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Pineros M. Global cancer observatory: cancer tomorrow. International
Agency for Research on Cancer. 2018. URL: https://gco.iarc.fr/tomorrow [accessed 2020-09-15]
9. Hewitson P, Glasziou P, Watson E, Towler B, Irwig L. Cochrane systematic review of colorectal cancer screening using
the fecal occult blood test (hemoccult): an update. Am J Gastroenterol 2008 Jun;103(6):1541-1549. [doi:
10.1111/j.1572-0241.2008.01875.x] [Medline: 18479499]
10. Nelson HD, Tyne K, Naik A, Bougatsos C, Chan BK, Humphrey L, U.S. Preventive Services Task Force. Screening for
breast cancer: an update for the U.S. Preventive Services Task Force. Ann Intern Med 2009 Nov 17;151(10):727-737 [FREE
Full text] [doi: 10.7326/0003-4819-151-10-200911170-00009] [Medline: 19920273]
11. Peirson L, Fitzpatrick-Lewis D, Ciliska D, Warren R. Screening for cervical cancer: a systematic review and meta-analysis.
Syst Rev 2013 May 24;2:35 [FREE Full text] [doi: 10.1186/2046-4053-2-35] [Medline: 23706117]
12. Sadate A, Occean BV, Beregi J, Hamard A, Addala T, de Forges H, et al. Systematic review and meta-analysis on the
impact of lung cancer screening by low-dose computed tomography. Eur J Cancer 2020 Jul;134:107-114. [doi:
10.1016/j.ejca.2020.04.035] [Medline: 32502939]
13. Schreuders EH, Ruco A, Rabeneck L, Schoen RE, Sung JJ, Young GP, et al. Colorectal cancer screening: a global overview
of existing programmes. Gut 2015 Oct;64(10):1637-1649. [doi: 10.1136/gutjnl-2014-309086] [Medline: 26041752]
14. Cancer Screening in Canada: An overview of screening participation for breast, cervical, and colorectal cancer. Toronto:
Canadian Partnership Against Cancer. 2015. URL: https://s22457.pcdn.co/wp-content/uploads/2019/01/
Breast-Cervical-Colorectal-Screening-Participate-2015-EN.pdf [accessed 2020-09-15]
15. Youlden DR, Cramb SM, Dunn NA, Muller JM, Pyke CM, Baade PD. The descriptive epidemiology of female breast
cancer: an international comparison of screening, incidence, survival and mortality. Cancer Epidemiol 2012
Jun;36(3):237-248. [doi: 10.1016/j.canep.2012.02.007] [Medline: 22459198]
16. Singh H, Bernstein CN, Samadder JN, Ahmed R. Screening rates for colorectal cancer in Canada: a cross-sectional study.
CMAJ Open 2015;3(2):149-157 [FREE Full text] [doi: 10.9778/cmajo.20140073] [Medline: 26389092]
17. Uy C, Lopez J, Trinh-Shevrin C, Kwon SC, Sherman SE, Liang PS. Text messaging interventions on cancer screening
rates: a systematic review. J Med Internet Res 2017 Aug 24;19(8):e296 [FREE Full text] [doi: 10.2196/jmir.7893] [Medline:
28838885]
18. Tamuzi JL. Effectiveness of mHealth to increase cervical cancer screening: systematic review of interventions. Int J Pul
Res Sci 2017 Oct 27;2(3):555586. [doi: 10.19080/ijoprs.2017.02.555586]
19. Parackal M, Parackal S, Eusebius S, Mather D. The use of Facebook advertising for communicating public health messages:
a campaign against drinking during pregnancy in New Zealand. JMIR Public Health Surveill 2017 Aug 10;3(3):e49 [FREE
Full text] [doi: 10.2196/publichealth.7032] [Medline: 28798011]
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