Identifying effective components for mobile health behaviour change interventions for smoking cessation and service uptake: protocol of a systematic review and planned meta-analysis
Kingkaew et al. Systematic Reviews
Identifying effective components for mobile health behaviour change interventions for smoking cessation and service uptake: protocol of a systematic review and planned meta-analysis
Pritaporn Kingkaew 0 1
Liz Glidewell 0
Rebecca Walwyn 3
Hamish Fraser 0
Jeremy C. Wyatt 2
0 Leeds Institute of Health Sciences, University of Leeds , Worsley Building, Clarendon Way, Leeds , UK
1 Health Intervention and Technology Assessment Program, Ministry of Public Health , Nonthaburi , Thailand
2 Wessex Institute of Health and Research, Faculty of Medicine, University of Southampton , Southampton , UK
3 Leeds Institute of Clinical Trials Research, University of Leeds , Worsley Building, Clarendon Way, Leeds , UK
Background: Mobile health (mHealth) interventions for smoking cessation have been shown to be associated with an increase in effectiveness. However, interventions using mobile phones to change people's behaviour are often perceived as complex interventions, and the interactions between several components within them may affect the outcome. Therefore, it is important to understand how we can improve the design of mHealth interventions using mobile phones as a medium to deliver services. Methods: Randomised controlled trials (RCTs) of mHealth interventions to support smoking cessation or uptake of smoking cessation services for smokers will be included in this systematic review. A search will be performed by searching MEDLINE, MEDLINE(R) In-Process & Other Non-Indexed Citations, EMBASE, PsycINFO, Web of Science, and CINAHL. A search for new publications will be conducted 3 months prior to submission for publication as mHealth is an emerging area of research. A random-effects meta-analysis model will be used to summarise the effectiveness of mHealth interventions. The risk ratio will be used for the primary outcome, self-reported or verified smoking abstinence, and any binary outcomes for uptake of smoking cessation services. The standardised mean difference using Hedges' g will be reported for continuous data. Heterogeneity will be assessed using I2 statistics. Where feasible, meta-regression analysis using random-effects multilevel modelling will be conducted to examine the association of pre-specified characteristics (covariates) at the study level with the effectiveness of interventions. Publication bias will be explored using Egger's test for continuous outcomes and Harbord and Peters tests for dichotomous outcomes. The funnel plot will be used to evaluate the presence of publication bias. The Cochrane Risk of Bias Tool will be used to assess differences in risks of bias. Discussion: The results of this systematic review will provide future research with a foundation for designing and evaluating complex interventions that use mobile phones as a platform to deliver behaviour change techniques. Systematic review registration: PROSPERO CRD42016026918.
Complex interventions; Tobacco cessation; Mobile phones; Mobile health; Behaviour change techniques; Theory-based interventions; Systematic review; Meta-analysis; Protocol
Tobacco smoking is widely recognised as one of the
leading threats to population health. Tobacco use is an
avoidable behavioural risk factor that causes many
diseases such as cardiovascular disease, respiratory diseases,
and several cancers and neoplasms. It is estimated that
there are one billion adult smokers in the world [
around 10% of deaths globally per year will be
attributable to tobacco use [
Mobile health (mHealth) is an emerging area of
research with various fields for application, one of which is
for behaviour change. mHealth interventions have been
shown to be associated with better behaviour, especially
in the smoking cessation field [
]. Results from
published meta-analyses suggest that mobile phone-based
interventions for smoking cessation are beneficial
compared to controls without mobile phone interventions
]. mHealth can also be used as an intervention to
add onto existing smoking cessation services by
identifying more people to engage in smoking cessation services.
However, these existing systematic reviews have not
examined whether mobile phone interventions can
increase the proportion of participants deciding to engage
with smoking cessation services, and only consider
smoking cessation outcomes.
mHealth for behaviour change interventions are often
perceived as complex interventions because they contain
several interacting components and have several
dimensions of complexity. However, defining intervention
components can be challenging due to the nature of complex
]. The typical question of whether it works
may no longer be sufficient but understanding how it
works is also crucial [
]. Several components within
these complex interventions may affect the outcome such
as mode of delivery [
], duration and intensity of the
], and tailored functionality [
The use of theory to design and develop complex
interventions is recommended by the UK Medical
Research Council (MRC) [
]. Theory-based behaviour
change interventions have been used in many mHealth
interventions. Theory can be applied in various
approaches such as recruiting participants, designing an
intervention, and planning for evaluation . Though
the use of theory—classified very simply as yes or
no—was not found to be associated with the effect size
of text messaging-based health promotion interventions
], it was found to differ where extensive use of theory
was associated with intervention effect size of
internetbased behaviour change interventions [
Behaviour change techniques (BCTs) are identifiable
‘active ingredients’ of behaviour change interventions derived
from behaviour change theory [
]. A taxonomy of BCTs
specific to smoking cessation has been developed [
has been used to identify behaviour change techniques
that are associated with higher effect size such as action
planning, self-monitoring, social support, and advice on
weight control [
]. The ability to define and recognise
such BCTs could help researchers improve knowledge
regarding effective behaviour change interventions. The
latest hierarchically structured taxonomy of 93
behaviour change techniques was developed to serve as a
more reliable and systematic specification of BCTs for
generic use [
Defining intervention components can be challenging.
A Template for Intervention Description and Replication
(TIDieR) checklist can be used to assess the quality of
reporting of complex interventions [
]. It can also help
future research identify the important items to be
reported for complex interventions. Therefore, the use of
the TIDieR checklist would allow readers to
systematically assess the quality of the report but would also allow
them to systematically synthesise the components of
A lack of evidence on effective components of mHealth
interventions for smoking cessation and a lack of
outcomes reported in terms of smoking cessation service
uptake limit the future design of these interventions.
Therefore, to understand how we can improve the design
of mHealth interventions as a medium to deliver smoking
cessation services, the review questions for this study
include: (1) what is the effectiveness of mHealth behaviour
change interventions for smoking cessation?; (2) what is
the effectiveness of mHealth behaviour change
interventions for uptake of smoking cessation services?; (3) which
components of mHealth behaviour change interventions
are associated with improvements in smoking cessation
rates?; and (4) which components of mHealth behaviour
change interventions are associated with improvements in
uptake of smoking cessation services? The results from
this review should be beneficial to researchers planning
for and developing complex mHealth interventions.
Protocol and study registration
The Preferred Reporting Items for Systematic Review
and Meta-Analysis Protocols (PRISMA-P) 2015 [
its supporting paper [
] were used as a guide to
develop this protocol. See Additional file 1 for the
PRISMA-P checklist. The protocol of this systematic
review was registered on the PROSPERO International
prospective register of systematic reviews (PROSPERO
There will be no restrictions for settings and countries
of origin. Studies will be selected for analysis according
to the following criteria.
Participants are all smokers, including those who intend
to quit smoking and those who do not intend to quit
smoking, from any sources or settings.
Interventions aimed at smoking cessation that are
delivered through or in combination with mobile phones via
short messaging services (SMS), multimedia messaging
services (MMS), phone calls, interactive voice responses
(IVR), email, web browsers, social media, and apps will
be included. The use of mobile phones only for research
design facilitation or for data collection purposes will be
excluded. Interventions aimed at preventing new
smokers will also be excluded from this study.
Comparators include no interventions or usual care or
alternative mHealth interventions.
Primary outcome includes biochemically verified or
selfreported smoking abstinence at any follow-up period
(e.g. 1, 3, 6 months or longer). Secondary outcomes
include only biochemically verified smoking abstinence
at any follow-up period and reported uptake of smoking
cessation services at any follow-up period. Reported
uptake of smoking cessation services includes
behaviourrelated outcomes such as the number of smoking
cessation services attendance.
Randomised controlled trials (RCTs) will be included in
the review. Controlled clinical trials (CCTs), controlled
before-after (CBA) studies, and studies that do not have a
control group such as cross-sectional studies, case series,
and case reports will be excluded from the review.
A search will be performed by searching Ovid
MEDLINE(R), Ovid MEDLINE(R) In-Process & Other
NonIndexed Citations, EMBASE, PsycINFO, Web of Science,
CINAHL (EbscoHOST), and LILACS. Text words and, if
available, subject heading terms for mobile phone, text
messaging, mobile application, interactive voice response,
email, internet, web browser, social media, smoking
cessation, tobacco use, smoking, tobacco-use disorder,
smoking behaviour, and randomised control trial will be used.
To ensure the quality of the search strategy, it was peer
reviewed by an information specialist using the Peer
Review for Electronic Search Strategies (PRESS) template
]. Additional file 2 shows the search strategy from
Ovid MEDLINE(R). A review of grey literature will be
conducted through a search from Open Grey and
WorldCat. Reference lists of included studies will be
screened for relevant studies. An update search will
commence in August 2017 to check for new publications.
All studies retrieved using the search strategy will be
managed using EndNote X7 reference (Thomson Reuters
(Scientific) LLC, New York). All duplicates will be
screened within EndNote X7 reference. Titles and
abstracts of studies will be screened by one reviewer (PK)
using the eligibility form (see Additional file 3). A random
sample of 20% of all abstracts will be independently
checked by two reviewers (PK and LG). Reliability and
agreement rate will be tested prior to full review. The full
text of included studies will then be retrieved and assessed
for eligibility by one reviewer (PK). A random sample of
20% of the full texts will be independently checked by two
reviewers (PK and LG). Disagreements between the review
authors over the inclusion of the sample of 20% of the full
texts will be resolved by discussion. Cohen’s kappa (Cohen
1960) will be used to report the degree of agreement.
Data collection and data extraction
Full text of eligible studies will be explored for
information related to the development of the interventions. If it
states elsewhere, such as the study protocol, descriptive
and qualitative study, additional references will be
included for data extraction purposes only. Separate
notation of sources of information will be identified.
Information to be extracted are presented in Table 1.
Coding will be conducted for both intervention groups
and comparator groups (control, usual care, and
Coding procedures for behaviour change techniques
Generic behaviour change technique taxonomy will be
used in this study [
]. Standardising the coding of the
behaviour change techniques requires experience [
Therefore, prior to coding, training will be carried out
using the BCTTv1 training online. Subsequently, the
trained coders (LG and PK) will independently identify
the BCTs from 20% of all included studies. Discrepancies
regarding the BCT coding will be resolved through
discussion. BCTs will then be mapped to the COM-B
system where the interaction of capability, opportunity, and
motivation leads to behaviour change [
Risk of bias and quality assessment
Studies that are included for data synthesis will be
assessed for risks of bias using the Cochrane Risk of Bias
]. A random sample of 20% of the full texts will
be independently checked by two reviewers (PK and
RW). Disagreements between the review authors over
the risk of bias will be resolved through discussion. A
sensitivity analysis to exclude studies that are shown
to have high risk of bias will be conducted. In
addition, all included studies will be assessed using
the Template for Intervention Description and
Replication (TIDieR) checklist—used to assess the quality
of reporting of complex interventions—to provide
more information about complex interventions [
We will report this as a percentage of studies that are
reported in each criterion.
A descriptive summary table of the included studies will
be summarised (see Table 1). When there is a sufficient
number of studies (k > 10) reporting for similar outcomes
], meta-analysis will be used to estimate the pooled
treatment effect. To determine the effect size, the risk
ratio (RR) will be used for the primary outcome,
selfreporting, or verified smoking abstinence (e.g., smoking
status: yes/no). The standardised mean difference, using
Hedges’ g [
], will be used for continuous outcome
measurements comparing between the treatment and control
groups (e.g., increase number of smoking cessation service
Information to be extracted
First author name, year of publication
Country and source of participants, e.g. primary/secondary care, v
Reported information on the duration of study and follow up for
Year of study
Participant mean age, percentage of current smokers, average
number of cigarettes per day
Sample sizes included in the analysis
Identified barriers of behaviour change prior to intervention design
No theory used/the use of theory to inform interventions/the use of
theory to classify participants/the use of theory to tailor interventions
according to participants
19 Theory Coding Scheme (when > 50 studies are included)
Coding scheme for behaviour change technique using BCTTv1 [
SMS, MMS, email, phone call, internet, apps, IVR
No tailoring function (fixed intervention)/personalisation based on
participant characteristics or personal preferences/tailored to
Detailed description of any form of measurement for engagement in
technology, e.g. automated monitoring of the users’ interactions with
the system, etc.
Detailed description of control and intervention groups
Self-reported smoking abstinence
Verified smoking abstinence
Uptake of smoking cessation services, e.g. the number of service attendance
Additional information elsewhere that is related to the intervention design
attendance). All statistical analyses will be undertaken
using Stata 14 software [
A meta-analysis (the metan command) will be conducted
using random-effects model (inverse-variance methods and
DerSimonian and Laird methods of moment estimator),
with 95% confidence intervals and significance level at 5%
]. Random-effects model recognises within-study
variance and between-study variance. The nature of the studies
included in this systematic review is likely to be different
(intervention and patient population), and the
heterogeneity between studies is expected. Consistent with this
assumption, random-effects model will be used to estimate a
pooled treatment effect. Heterogeneity between studies will
be assessed using I2 statistics. When I2 is over 50%
(moderate heterogeneity), heterogeneity will be addressed through
meta-regression and sensitivity analyses [
In order to address any publication bias, a funnel plot of
log odds ratio against standard error of log odds will be
conducted for each outcome. Egger’s test will be used to
test for asymmetry for continuous outcomes [
] and Harbord [
] tests will be used for binary
data. Peters and Harbord tests were proposed to be used
to avoid the mathematical association between the log
odds ratio and its standard error (false-positive test
results) that occurs from Egger’s test [
]. The fixed- and
random-effects estimates of the intervention effect will be
compared if there are any small-study effects. When there
is an evidence of small-study effects on the pooled effect,
sensitivity analysis based on selection models proposed by
Terrin et al. [
] will be used to estimate a pooled effect
adjusted for selection bias [
]. This method is
recommended over the trim-and-fill method as it provided
better performance in simulation study.
A meta-regression analysis will be conducted in order to
define which components of mHealth behaviour change
interventions are associated with improvements in
smoking cessation rates and uptake of smoking cessation
services when there is at least 10 studies [
Prespecified covariates are recommended in order to avoid
false-positive conclusions. Covariates that will be fitted
in univariate and multivariate analyses include the
duration of intervention, the use of theory to design the
intervention, behaviour change techniques, mobile
functionality, tailored design, and communication pathway.
Two approaches for the meta-regression analysis will be
used. The first approach assumes all control interventions
across studies are the same (the metareg command). This
first approach will regress only covariates (characteristics)
from intervention arms using random-effects
metaregression. The second approach will consider the
characteristics of controls in addition. A multilevel logistic
regression for repeated measures, allowing for different
follow-up times, will be used to estimate the effect of
interventions towards binary outcomes, including the
primary outcome—self-reporting smoking abstinence (the
meqrlogit command). If available, a multilevel regression
analysis will be used to estimate the effects of
interventions for other continuous outcomes.
Sub-group analyses will explore when studies with high
risk of bias are excluded. Sub-group analysis will also be
conducted to consider characteristics of intervention
and control groups specified in Table 1, including the
use of theory to design intervention, behaviour change
techniques, mobile functionality, tailored design, and
This protocol states the plan for a systematic review and
meta-analysis of mobile health behaviour change
interventions for smoking cessation and service uptakes. While
there are a number of meta-analysis and meta-regression
studies for smoking cessation, the majority of mHealth
systematic reviews for smoking cessation focuses more on
the technology components rather than behaviour change
components except for a systematic review conducted by
Free et al. [
]. However, this review only showed the
descriptive results of the number of BCTs used in each study
]. To our knowledge, there is a similar planned
systematic review and meta-analysis of behaviour change
interventions to support smoking cessation [
]. However, our
study will be focusing on the technology aspects as well as
behaviour change techniques and will include studies with
all follow-up periods. While de Bruin [
] plans to extract
information regarding BCTs from primary research
groups, the BCTs identified from research groups can be
subjected to bias due to retrospective data collection.
A potential limitation of this study is that the
outcomes for smoking cessation service uptakes are still
unknown. This may include a wide range of indicators, and
therefore a meta-regression may not be able to be
conducted where the number of papers is a significant
factor for analysis. As such, descriptive data synthesis is
expected in this case. We hope that this study will help
provide a platform for future designs of smoking
cessation using mobile phones as a medium.
Additional file 1: PRISMA-P checklist. (DOCX 15 kb)
Additional file 2: Search strategy. (DOCX 15 kb)
Additional file 3: Study eligibility form. (DOCX 29 kb)
BCTs: Behaviour change techniques; BCTTv1: Behaviour change technique
taxonomy version 1; CBA: Controlled before-after study; CCTs: Controlled
clinical trials; IVR: Interactive voice responses; mHealth: Mobile health;
MMS: Multimedia messaging services; MRC: Medical Research Council;
PRESS: Peer Review for Electronic Search Strategies; PRISMA-P: Preferred
Reporting Items for Systematic Review and Meta-Analysis Protocols;
RCTs: Randomised controlled trials; SMS: Short messaging services;
TIDieR: Template for Intervention Description and Replication
The authors would like to thank Judy Wright, Senior Information Specialist at
the University of Leeds, for information specialist support on the development
of search strategy used in this study. We would also like to extend our thanks
to Rocio Rodriguez Lopez, an Information Specialist at the University of Leeds,
for the peer review of electronic search strategies.
There is no funding to support the work of this study. This work is carried out
as part of a PhD study at the University of Leeds.
Availability of data and materials
All authors contributed to the development of the selection criteria, data
extraction criteria, and analyses plan. PK developed the search strategy. PK
and LG screened eligible studies and abstracted data. PK and RW conducted
the risk assessment. All authors contributed to the drafting of the manuscript.
All authors read and provided comments and feedback to the draft manuscript.
All authors read and approved the final manuscript.
Pritaporn Kingkaew PhD is funded by the HPSR fellowship programme from
the International Health Policy Programme Foundation. Dr. Fraser was a
Marie Skłodowska-Curie Fellow, funded from the European Union’s Horizon
2020 research and innovation programme under grant agreement No.
661289: ‘Global eHealth’.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
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