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Social-Local-Mobile intervention for supporting smoking cessation in the SmokeFreeBrain project: a study protocol of a 12-month randomized open-label parallel-group trial (Preprint)

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BACKGROUND Smoking is considered the main cause of preventable illness and premature deaths worldwide. The treatment usually prescribed to subjects who wish to quit smoking is a multidisciplinary intervention, combining both psychological advice and pharmacological therapy, since the application of both strategies significantly increases the chance of success in a quit attempt. OBJECTIVE This paper presents a study protocol of a 12-month randomized open-label parallel-group trial which primary objective is to analyze the efficacy and efficiency of the usual psycho-pharmacological therapy plus Social-Local-Mobile app (intervention group) applied to the smoking cessation process compared to usual psycho-pharmacological therapy (control group). METHODS The target population consist of smokers attending the Smoking Cessation Unit at Virgen del Rocío University Hospital. Social-Local-Mobile is an innovative intervention based on mobile technologies and its capacity to trigger behavioral changes. The App is a complement to pharmacological therapies to quit smoking providing personalised motivational messages, physical activity monitoring, lifestyle advices and distractions (mini-games) to help pass the cravings. Usual pharmacological therapy consists of bupropion (Zyntabac® 150 mg) or varenicline (Champix® 0.5 mg or 1 mg). The main clinical outcome will be the smoking abstinence rate at 1 year measured by means of exhaled carbon monoxide and urinary cotinine tests. The result of cost-effectiveness analysis will be expressed in terms of incremental cost-effectiveness ratio. Secondary objectives are to analyze safety of pharmacological therapy; to analyze health-related quality of life of patients; and to monitor healthy lifestyle and physical exercise habits. RESULTS We identified 548 patients using the hospital’s electronic records system. From this initial selection, 308 patients were excluded: 188 declined to participate and 120 not meeting the inclusion criteria. A total of 240 patients were enrolled: the control group (n=120) will receive usual psycho-pharmacological therapy, while the intervention group (n=120) will receive usual psycho-pharmacological therapy plus the So-Lo-Mo app. CONCLUSIONS Nowadays, social networks and mobile technologies influence our daily lives and, therefore, may influence our smoking habits as well. As part of the SmokeFreeBrain H2020 European Commission project (GA 681120), this study aims at elucidating the potential role of these technologies when used as an extra aid to quit smoking. CLINICALTRIAL ClinicalTrials.gov identifier: NCT03553173. Retrospectively registered, 12 June 2018.
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Protocol
Using the Social-Local-Mobile App for Smoking Cessation in the
SmokeFreeBrain Project: Protocol for a Randomized Controlled
Trial
Francisco Jódar-Sánchez1, PhD; Laura Carrasco Hernández2,3, MD, PhD; Francisco J Núñez-Benjumea1, MSc; Marco
Antonio Mesa González2, MSc; Jesús Moreno Conde1, MSc; Carlos Luis Parra Calderón1, MSc; Luis Fernandez-Luque4,
PhD; Santiago Hors-Fraile5,6, MSc; Anton Civit5, PhD; Panagiotis Bamidis7, PhD; Francisco Ortega-Ruiz2, MD, PhD
1Research and Innovation Group in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, Virgen
del Rocío University Hospital / Spanish National Research Council / University of Seville, Seville, Spain
2Smoking Cessation Unit, Medical-Surgical Unit of Respiratory Diseases, Virgen del Rocío University Hospital, Sevilla, Spain
3Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Carlos III Institute of Health, Madrid, Spain
4Salumedia Tecnologías, Sevilla, Spain
5Department of Architecture and Computer Technology, School of Computer Engineering, University of Seville, Sevilla, Spain
6Department of Health Promotion, School for Public Health and Primary Care, Maastricht University, Maastricht, Netherlands
7Medical Physics Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
Corresponding Author:
Francisco J Núñez-Benjumea, MSc
Research and Innovation Group in Biomedical Informatics, Biomedical Engineering and Health Economy
Institute of Biomedicine of Seville
Virgen del Rocío University Hospital / Spanish National Research Council / University of Seville
Avenida Manuel Siurot, s/n
Centro de Documentación Clínica Avanzada, Virgen del Rocío University Hospital
Seville, 41013
Spain
Phone: 34 955013616
Email: francisco.nunez.exts@juntadeandalucia.es
Abstract
Background: Smoking is considered the main cause of preventable illness and early deaths worldwide. The treatment usually
prescribed to people who wish to quit smoking is a multidisciplinary intervention, combining both psychological advice and
pharmacological therapy, since the application of both strategies significantly increases the chance of success in a quit attempt.
Objective: We present a study protocol of a 12-month randomized open-label parallel-group trial whose primary objective is
to analyze the efficacy and efficiency of usual psychopharmacological therapy plus the Social-Local-Mobile app (intervention
group) applied to the smoking cessation process compared with usual psychopharmacological therapy alone (control group).
Methods: The target population consists of adult smokers (both male and female) attending the Smoking Cessation Unit at
Virgen del Rocío University Hospital, Seville, Spain. Social-Local-Mobile is an innovative intervention based on mobile
technologies and their capacity to trigger behavioral changes. The app is a complement to pharmacological therapies to quit
smoking by providing personalized motivational messages, physical activity monitoring, lifestyle advice, and distractions
(minigames) to help overcome cravings. Usual pharmacological therapy consists of bupropion (Zyntabac 150 mg) or varenicline
(Champix 0.5 mg or 1 mg). The main outcomes will be (1) the smoking abstinence rate at 1 year measured by means of exhaled
carbon monoxide and urinary cotinine tests, and (2) the result of the cost-effectiveness analysis, which will be expressed in terms
of an incremental cost-effectiveness ratio. Secondary outcome measures will be (1) analysis of the safety of pharmacological
therapy, (2) analysis of the health-related quality of life of patients, and (3) monitoring of healthy lifestyle and physical exercise
habits.
Results: Of 548 patients identified using the hospital’s electronic records system, we excluded 308 patients: 188 declined to
participate and 120 did not meet the inclusion criteria. A total of 240 patients were enrolled: the control group (n=120) will receive
usual psychopharmacological therapy, while the intervention group (n=120) will receive usual psychopharmacological therapy
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plus the So-Lo-Mo app. The project was approved for funding in June 2015. Enrollment started in October 2016 and was completed
in October 2017. Data gathering was completed in November 2018, and data analysis is under way. The first results are expected
to be submitted for publication in early 2019.
Conclusions: Social networks and mobile technologies influence our daily lives and, therefore, may influence our smoking
habits as well. As part of the SmokeFreeBrain H2020 European Commission project, this study aims at elucidating the potential
role of these technologies when used as an extra aid to quit smoking.
Trial Registration: ClinicalTrials.gov NCT03553173; https://clinicaltrials.gov/ct2/show/record/NCT03553173 (Archived by
WebCite at http://www.webcitation.org/74DuHypOW).
International Registered Report Identifier (IRRID): PRR1-10.2196/12464
(JMIR Res Protoc 2018;7(12):e12464) doi: 10.2196/12464
KEYWORDS
smoking cessation; mobile applications; randomized controlled trial; economic evaluation
Introduction
Background
Smoking is considered the main cause of preventable illness
and early deaths worldwide [1]. Mathers and Loncar [2]
estimated that about 100 million deaths were caused by tobacco
addiction in the 20th century. Also, 5.4 million people
worldwide die each year of tobacco-related diseases, and it is
estimated that by 2030 smoking will cause 8 to 10 million deaths
a year, over 80% of them in low- and middle-income countries
[3]. According to the World Health Organization, in 2025, about
22% of the adult populations of Europe will be regular smokers
[4]. Together with the Americas, Europe has the highest
proportion of all deaths attributable to tobacco, estimated at
16% [1].
In Spain, in 2017, 22.08% of the population aged over 15 years
smoked daily and 2.34% were occasional smokers. In Spain
25.58% of males and 18.76% of females are smokers; and
17.56% of young persons aged 15 to 24 years old have a tobacco
habit, showing a relevant difference by sex (19.96% of males
compared with 15.05% of females) [5].
In the Andalusian Health Service region, the largest public
health problem is the smoking epidemic. To address this
problem, a plan called “Comprehensive Tobacco Action Plan
for Andalusia” has been defined and promoted [6] by the
Andalusian Ministry of Health. This plan expects to reduce the
incidence and prevalence of smoking in Andalusia, reducing
complications and morbidity and mortality related to tobacco
among the Andalusian population, and improving the quality
of life of both smokers and nonsmokers. The plan hopes to
create a smokefree future in a climate of social well-being and
mutual respect, promoting healthy lifestyles, ensuring the right
to health for all, and promoting public participation, with the
final aim to guarantee smokers the best health care, based on
scientific evidence, and ensuring continuity of care as an element
of integral quality.
Smoking is usually considered to be exclusively a personal
decision. This statement seems not to be true, since the clear
majority of smokers claim that they wish to stop consuming
tobacco when they are deeply aware of all the negative side
effects to their health, yet they find it difficult to stop smoking
due to the great addictiveness of nicotine. Fortunately, there are
a variety of useful pharmaceutical products to help them quit.
Among them, bupropion and varenicline are 2 drugs usually
prescribed. At the Smoking Cessation Unit of the Virgen del
Rocío University Hospital (VRUH) in Seville, Spain, patients
willing to quit smoking are provided with a combination of both
psychological advice and pharmacological treatment using any
of the 2 previously mentioned drugs. This multidisciplinary
strategy to quit has significantly improved the success rate [7].
Some research has been performed regarding the use of tailored
mobile- and Web technology–based interventions and their
impact on the smoking cessation process. Hébert et al [8]
showed the utility of mobile phones for assessing the risk for
smoking lapse in real time, and their findings endorsed the
statement that tailored content may affect users’ urge to smoke,
stress, and cigarette availability. Chakraborty et al [9]
investigated the correlation between the personalization level
of Web-based interventions and participants’ educational level
when dealing with their smoking behavior, finding that highly
individually tailored interventions were more effective for
smokers with a low level of education. However, it is not clear
what impact such interventions may have on smoking cessation
efficacy in the long term. A recent observational study
highlighted key insights related to participant engagement and
cessation among adults who voluntarily subscribed to a 42-day
mobile phone text message smoking cessation program [10],
uncovering the need to improve program engagement. On the
other hand, evidence showed a beneficial impact of mobile
phone–based smoking cessation interventions on 6-month
cessation outcomes, although caution should be taken in
generalizing these results outside this type of intervention and
context [11]. Nonetheless, there is still a need to demonstrate
the added value that a tailored smoking cessation intervention
based on mobile technologies has for the efficacy of long-term
abstinence when added to psychopharmacological therapies.
Craving is a key component that has been shown to vary over
time during a smoking cessation attempt and is highly related
to treatment efficacy and cessation success [12]. In addition, it
is documented that craving fades away during the first 2 weeks
of abstinence [13]. However, cravings may return if former
smokers’ coping strategies lose effectiveness over time, leaving
smokers with less and less ability to resist the urge to smoke.
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There is scarce evidence about the valuable support that physical
activity and information and communication technologies (app
gamification, short text sent as push notifications, and short
message service [SMS] text messaging and Facebook) may
provide to the smoking cessation process [14,15].
Objectives
As part of the SmokeFreeBrain H2020 European Commission
project (Grant Agreement 681120) [16,17], this study aims at
elucidating the potential role of the aforementioned technologies
when used as an extra aid to quit. The Social-Local-Mobile
(So-Lo-Mo) intervention focuses on providing health goals,
including physical activity, with immediate feedback through
the mobile app, reinforcing patients’ ability to stay abstinent
with motivational messages and offering patients tools to
overcome cravings, such as playing specifically designed
minigames.
The main objectives of this study are to analyze the efficacy
and cost effectiveness of usual psychopharmacological therapy
plus the So-Lo-Mo app (intervention group) compared with
usual psychopharmacological therapy alone (control group)
applied to the smoking cessation process.
Secondary objectives are the following: (1) to analyze the safety
of pharmacological therapy, (2) to analyze patients’
health-related quality of life (HRQoL), and (3) to monitor
patients’ healthy lifestyle and physical exercise habits.
Methods
Design and Setting
This is a 12-month randomized open-label parallel-group trial
performed at the VRUH. It was retrospectively registered on
June 12, 2018 (NCT03553173).
Participants and Recruitment Strategy
We calculated sample size during the study design phase
according to the following parameters: (1) confidence level:
95%, (2) statistical power: 80%, (3) success rate in the control
group: 35%, (4) success rate in the intervention group: 55%,
and (5) expected dropout rate: 20%. This calculation yielded a
sample size of N=236 participants. However, because this study
was framed within a research project with tight deadlines for
recruitment, we had only 12 months available for recruiting
participants. Therefore, we calculated the sample size according
to the average consultations performed by the Smoking
Cessation Unit of VRUH during the last 5 years, which resulted
in a slightly higher N=240.
Inclusion criteria are as follows: (1) the smoking population
attending the Smoking Cessation Unit of VRUH, (2) age 18
years or older and the desire to give up smoking, (3) availability
of an Android-based mobile phone, (4) ability to interact with
the mobile phone (we will assess mobile phone literacy by
asking patients if they commonly use other text exchange mobile
phone apps such as Mail, SMS, or WhatsApp), and (5)
willingness to sign an informed consent form.
We excluded patients who had some previous adverse effects
related to the pharmacological treatment used in the study.
It should be noted that we will use the allocation to different
pharmacological therapies as a reflection of usual care rather
than a confounding factor for the analysis. We generated a list
of 240 consecutive items by randomly assigning 1 of the study
groups to each item. Participants were assigned to each of the
study groups according to this list and following their order of
enrollment.
We obtained written informed consent from all study
participants.
Usual Psychopharmacological Therapy
Usual care consists of pharmacological therapy with bupropion
(Zyntabac 150 mg; Glaxosmithkline SA, Tres Cantos, Spain)
or varenicline (Champix 0.5 mg or 1 mg; Pfizer SL, San
Sebastián de los Reyes, Spain) and behavioral therapy. In routine
care, patients must pay for these treatments in the pharmacy.
To facilitate patient recruitment and to avoid bias due to the
treatment cost, the SmokeFreeBrain project is funding these
costs, so participants receive the assigned treatment free of
charge.
The psychological intervention process, which is performed
individually, starts with an assessment of the smoker to
complement his or her medical record. The first step is to assess
the patient’s smoking record (number of daily cigarettes, age
at the start of regular smoking, previous quit attempts, and
methods followed for quitting). The next step is to assess the
patient’s nicotine dependence level using the Fagerström Test
for Nicotine Dependence [18] and motivation to quit smoking
according to the Richmond test [19].
The psychological intervention starts once these assessments
have been performed. This intervention includes providing
information about smoking and the smoking cessation process,
as well as supporting behavioral changes by providing new
skills and strategies. Although there is a wide variety of
psychological interventions to support the smoking cessation
process, such as group behavior therapy programs and self-help
materials [20], the most used methods delivered by the Smoking
Cessation Unit of the VRUH are the motivational interview and
cognitive behavioral therapy.
The motivational interview is performed in 2
stages—exploratory and decisive—and includes the following
basic principles: (1) to express empathy, (2) to create
discrepancy, (3) to overcome resistance, (4) to avoid arguing,
and (5) to encourage self-efficacy.
The objective of cognitive behavioral therapy is to reconvert
the smoker’s thoughts, knowledge, and behavior related to
smoking. To achieve this, the following are applied: (1) problem
solving skills training, (2) social support (by phone and email),
(3) self-analysis of reasons to quit, (4) cigarette consumption
registry, (5) progressive reduction of cigarette consumption,
and (6) relapse prevention strategies.
The Social-Local-Mobile Intervention
So-Lo-Mo is an innovative intervention based on mobile
technologies and their capacity to trigger behavioral changes.
In this sense, the app is a complement to pharmacological
therapies to quit smoking providing, among other features,
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personalized motivational messages sent by the system,
peer-to-peer messaging, physical activity monitoring, lifestyle
advice, and distractions (minigames) to help overcome cravings.
The main objective of this app is to improve patients’ adherence
to the smoking cessation process by use of behavioral techniques
in the form of motivational messages and SMS text messages.
To this aim, the underlying algorithm is able to do the following:
Send motivational messages as SMS text messages or in a
notification-based format. High-priority messages—those
related to specific dates such as New Year’s Eve or holidays
when people may relapse—are sent by SMS so that their
delivery is guaranteed even when users do not have an
internet connection on their mobile, while the rest of the
messages are delivered via app push notification
mechanisms, which uses an internet connection.
Dynamically schedule message frequency according to the
phase of the transtheoretical model of behavior change [21]
that the individual is undergoing (preparation, action, or
maintenance) and when the notifications should be delivered
according to the individual’s preferences.
Dynamically determine the category (ie, content and type)
of the message that is delivered to the individual according
to the phase of the transtheoretical model of behavior
change that the individual is undergoing, user health
conditions, user feedback, and user filtering strategies. For
this, we have defined the following motivational message
categories: reduce tobacco consumption, increase risk
perception, and increase benefit perception during the
preparation phase; and general motivation, diet tips, exercise
and active life recommendations, personal physical activity
level, and positive facts of being a former smoker during
the action and maintenance phases.
Figure 1 shows the 3 specific sections of the app that address
how to resist cravings: (1) motivational messages that the user
can request immediately from the system, (2) relaxation tools
such as breathing exercises, and (3) minigames specifically
designed to help the user overcome cravings. Hors-Fraile et al
[22] provide further information on the methods and the
underlying algorithm for delivering these text messages.
Study Outcomes
The main outcomes will be the efficacy and efficiency of the
So-Lo-Mo intervention.
To assess efficacy, the main clinical outcome will be the
smoking abstinence rate at 1 year measured by means of exhaled
carbon monoxide and urine cotinine tests.
Figure 1. Sections of the app that address how to resist cravings.
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Exhaled carbon monoxide is part of the smoke and can be
measured by a carbon monoxide tester. The patient must perform
a deep inspiration and hold the air for 15 seconds. Then, the
patient must exhale the air inside the carbon monoxide tester
(Micro+ Smokerlyzer; Bedfont Scientific Ltd, Maidstone, UK)
in a slow, sustained, and complete fashion. The carbon
monoxide tester then yields the exact amount of exhaled carbon
monoxide in parts per million. Exhaled carbon monoxide levels
are highest between 3 and 6 hours after smoking a cigarette. A
person is considered to be smoker when his or her exhaled
carbon monoxide is higher than 6 ppm [23].
The urine cotinine (SmokeScreen test; GFC Diagnostics Ltd,
Chipping Warden, UK) is a colorimetric test that measures the
main metabolites of nicotine, including cotinine. In Spain,
patients with cotinine concentrations over 200 ng/mL are
considered to be smokers [24].
To consider a participant to be a smoker, only 1 of the
aforementioned conditions needs to be met.
To assess efficiency, we will carry out a cost-effectiveness
analysis considering the recommendations of the proposed
guidelines for economic evaluation of health technologies [25].
The analysis will adopt the perspective of the Spanish National
Health System. We will express results of the cost-effectiveness
analysis in terms of the incremental cost-effectiveness ratio,
calculated by dividing the difference in total costs between the
intervention group and the control group by the difference in
quality-adjusted life-years (QALYs) between the 2 groups [26].
Cost analysis will include costs of the prescribed medication,
time spent by the health professionals on the So-Lo-Mo
intervention, health care resources utilization, and equipment
and software costs related to the So-Lo-Mo intervention. We
will calculate QALYs in order to assess the health benefit of
the intervention regarding the cost-effectiveness analysis. QALY
is a health outcome that includes both the quality and the
quantity of life, and we will calculate it through 5-level EuroQol
5 dimensions questionnaire (EQ-5D-5L) scores [27], according
to the Spanish validation [28].
The secondary outcomes will be safety, patients’ HRQoL, and
patients’ healthy lifestyle and exercise habits.
We will measure safety as the number of adverse events related
to pharmacological therapies. The following adverse events
have been identified related to each pharmacological therapy:
(1) for varenicline (Champix 0.5 mg or 1 mg): nausea (feeling
sick), insomnia (difficulty sleeping), abnormal dreams,
headache, and nasopharyngitis (inflammation of the nose and
throat) [29]; and (2) for bupropion (Zyntabac 150 mg): insomnia,
headache, dryness in the mouth (alteration of taste), skin
reactions, convulsions, cardiovascular side effects, and severe
skin reactions [30].
We will measure HRQoL through the EQ-5D-5L questionnaire
[27] and the 36-item Short Form Health Survey [31].
We will monitor physical activity through the International
Physical Activity Questionnaire [32] and healthy lifestyle in
terms of the variation of body mass index during the follow-up
consultations.
Moreover, information from all patients undergoing this study
will include demographic data (age and sex) and socioeconomic
data (profession and employment status); consumption history
(eg, daily cigarettes, living with smokers, partner smokers, quit
attempts); clinical information (eg, weight, size, blood pressure,
comorbidities); and nicotine dependence measured through the
Fagerström Test for Nicotine Dependence [18]. Multimedia
Appendix 1 shows every variable of the evaluation framework
and its use in each follow-up session.
Data Collection
We will record information subsets in the So-Lo-Mo clinical
database according to the following schedule (Figure 2):
In session 1 (baseline), patients are assessed for the first time
in the Smoking Cessation Unit as they are referred from either
the Pneumology Unit or another clinical department. We will
collect information regarding the following sections: general
information, consumption history, smoking-related symptoms,
clinical information, dependency, motivation, treatment
assigned, HRQoL, physical exercise monitoring, and
observations.
In session 2, 15 (±5) days after the baseline consultation, we
will collect information regarding the following sections:
consumption history, clinical information, motivation, symptoms
related to abstinence, and observations. Relaxation techniques
and risk-avoidance techniques will also be explained to the
patient.
In session 3, 30 (±5) days after the baseline consultation, we
will collect information regarding the following sections:
consumption history, clinical information, motivation, symptoms
related to abstinence, and observations. New relaxation
techniques will be explained in case those previously explained
proved to be useless. Patients will be coached for relapse
prevention.
In session 4, 60 (±5) days after the baseline consultation, we
will collect information regarding the following sections:
motivation, symptoms related to abstinence, and observations.
Risk-avoidance techniques will be reinforced.
In session 5, 90 (±5) days after the baseline consultation, we
will collect information regarding the following sections:
consumption history, clinical information, motivation, symptoms
related to abstinence, and observations. Patients will be coached
for relapse prevention and confrontational techniques will be
explained.
In session 6, 120 (±5) days after the baseline consultation,
patients can be assessed by phone if they have completed the
pharmacological treatment. In this session we will collect
information regarding motivation, symptoms related to
abstinence, and observations. Patients will be coached for
relapse prevention and confrontational techniques will be
explained.
In session 7, 180 (±5) days after the baseline consultation, we
will collect information regarding the following sections:
consumption history, clinical information, motivation, HRQoL,
physical exercise monitoring, and observations (including
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information on health care resources). Relaxation and confrontational techniques will be reinforced when needed.
Figure 2. Information subsets and follow-up schedule to record in the study clinical database. HRQoL: health-related quality of life; S1-S8: sessions
1-8.
In session 8, 365 (±5) days after the baseline consultation, we
will collect information regarding the following sections:
consumption history, clinical information, motivation, HRQoL,
physical exercise monitoring, and observations (including
information on health care resources).
We will develop a case report form built on the OpenClinica
[33] tool to facilitate information management in the study. It
is worth noting that OpenClinica is compliant with the Guideline
for Good Clinical Practice, US Code of Federal Regulations,
Title 21, Part 11, the US Health Insurance Portability and
Accountability Act of 1996, and other regulations. This case
report form is integrated with the VRUH’s electronic health
record system, so some data elements are automatically loaded
into the case report form, thus avoiding double recording for
clinicians.
Statistical Analysis
At the end of the study, we will carry out a descriptive analysis
of patients’ characteristics by absolute and relative frequencies
for qualitative variables and mean (SD) for quantitative
variables. We will perform a bivariate analysis of study groups
for qualitative variables by chi-square test or Fisher exact test.
Comparison of quantitative variables will differ depending on
technical assumptions, such as parametric (Student t test or
analysis of variance) or nonparametric (Mann-Whitney U or
Kruskal-Wallis) tests.
To analyze the uncertainty of the incremental cost-effectiveness
ratio results, we will provide a tornado diagram of the univariate
sensitivity analysis, incorporating variations in the components
of cost and QALY. The data analysis team will not be blinded
to the allocation of participants.
Results
We identified 548 patients using the hospital’s electronic records
system. From this initial selection, we excluded 308 patients:
188 declined to participate (149 did not show up for the baseline
consultation, 27 refused the medication, 7 did not want to enroll
in the study, and 5 did not want to quit smoking), while 120 did
not meet the inclusion criteria (98 reported previous adverse
effects related to the medication assigned, 15 were not smokers
at baseline, and 7 did not have an Android-based smartphone
available). A total of 240 patients were enrolled: the control
group (n=120) will receive usual psychopharmacological
therapy, while the intervention group (n=120) will receive usual
psychopharmacological therapy plus the So-Lo-Mo app. Figure
3 shows the enrollment and allocation phases of the study.
The project was approved for funding in June 2015. Enrollment
started in October 2016 and was completed in October 2017.
Data gathering was completed in November 2018 and data
analysis is under way. We expect to submit the first results for
publication in early 2019.
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Virgen Macarena–Virgen del Rocío University Hospitals Ethics Committee approved the study protocol in July 2016.
Figure 3. Flow diagram of the randomized open-label parallel-group trial of the Social-Local-Mobile (So-Lo-Mo) app.
Discussion
Nowadays, the treatment usually prescribed to patients who
wish to give up smoking is a multidisciplinary intervention,
combining both psychological advice and pharmacological
therapy, since the application of both strategies significantly
increases the chance of success in a quit attempt [34].
Psychological interventions can be performed at different levels
depending on resource availability and the level of care, without
a difference in efficacy between individual and group therapies.
In this scenario, psychological treatments are based on
confrontational techniques, including behavioral and cognitive
behavioral therapies [35]. Behavioral therapies are designed to
help smokers to recognize and avoid external stimuli temporarily
associated with the consumption of tobacco, while cognitive
behavioral therapies provide the tools to confront both
physiological and cognitive stimuli with the urge to smoke.
During the smoking cessation process, 1 in 3 smokers rely on
the pharmacological treatment too [36]. There is a wide range
of drugs that could support this process, and the physicians in
the Smoking Cessation Unit at VRUH usually prescribe either
varenicline or bupropion. Varenicline, bupropion, and nicotine
replacement therapy reduce-to-quit interventions have all been
found to be effective cessation interventions in smokers who
would like to quit [37].
A recent systematic review of smartphone apps for smoking
cessation highlighted that future studies should aim to develop
and standardize an innovative and timely approach to evaluate
apps for commitment to evidenced-based practice; to explore
strategies to make scientifically supported apps easily searchable
and more accessible to consumers, including indexing with
plain language terminology; and to explore ways to inform the
development of health apps to better align app content with
evidence-based medicine [38].
In this sense, we will compare the efficacy of usual
pharmacological treatment plus behavioral and cognitive
behavioral therapies routinely delivered at VRUH when added
to the So-Lo-Mo intervention. If adding the So-Lo-Mo
intervention provides any meaningful increase in the smoking
cessation treatment effectiveness rate, we can infer that this
increment will also take place when added to nicotine
replacement therapy, given that it is not as effective as
varenicline or bupropion. Furthermore, we are conducting a
parallel study to assess the perceived quality of the health
recommender system and the level of engagement with the
motivational messages. The analysis of these technical aspects
will provide a more comprehensive understanding of the
So-Lo-Mo intervention [39].
Cultural and material factors are also key elements that need to
be further investigated to uncover their correlation with smoking
habit in different geographical regions [40,41]. Recently, the
Taipei Medical University joined the SmokeFreeBrain project
and they are currently developing an intervention similar to
So-Lo-Mo [42]. Their involvement reflects not only the capacity
of this intervention to cross borders and go beyond the frame
of a European project, but will also shed some light on the
cultural grounding of the efficacy and effectiveness of the use
of mobile technologies applied to the smoking cessation process.
Understanding the way social networks and mobile technologies
can influence individual behavior and the decision of whether
to smoke is essential to optimize their use as a means of
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prevention and treatment in the future. A recent scoping review
[43] showed how health recommender systems, like the one
used in So-Lo-Mo, are being used in health care with many
potential benefits for their users. However, most of the systems
lacked any description of their design, follow-up of
implementation, and behavioral change models they were based
on. So-Lo-Mo is, thus, an innovative intervention based on
mobile technologies and its capacity to trigger behavioral
changes following the transtheoretical behavioral change model
[21].
Although a large number of studies have assessed the effects
of interventions intended to reduce the harm to health of
continued tobacco use, it is important that more high-quality
randomized controlled trials be conducted, and that these also
measure the long-term health effects of treatments [44]. Another
innovative contribution of our study is the focus on the
cost-effectiveness analysis that will consider the economic
impact on the Spanish National Health System and on patients’
HRQoL.
Some limitations of our study should be acknowledged. First,
the So-Lo-Mo app is developed for Android-based users only.
However, in Spain the market share for Android-based devices
in 2018 is 78.09% [45]. This means that only a small percentage
of patients will be excluded from the study due to this criterion.
Second, we did not calculate the sample size based on power
calculations, as explained above, which will limit our ability to
extract strong evidence about the efficacy and efficiency of the
intervention applied to the smoking cessation process after
completion of the study.
Acknowledgments
The So-Lo-Mo intervention is a joint effort among Salumedia, Aristotle University of Thessaloniki, Servicio Andaluz de Salud,
and Universidad de Sevilla in the frame of the SmokeFreeBrain project.
The research study is funded by H2020 European Commission project (Grant Agreement 681120), as part of the SmokeFreeBrain
project. Further information about the project can be found on the SmokeFreeBrain project website (www.smokefreebrain.eu).
Authors' Contributions
FJS, CLPC, and FOR designed the study protocol of the So-Lo-Mo intervention in the frame of the SmokeFreeBrain project.
FJS, LCH, FJNB, CLPC, MAM, and FOR contributed to the study protocol refinement. FJS and FJNB drafted the manuscript.
SHF, LFL, AC, and PB took part in the design of the So-Lo-Mo intervention. All authors edited and revised the manuscript draft.
All authors reviewed and approved the final paper.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Variables of the evaluation framework.
[XLSX File (Microsoft Excel File), 22KB-Multimedia Appendix 1]
Multimedia Appendix 2
Peer-reviewer report from the European Commission.
[PDF File (Adobe PDF File), 94KB-Multimedia Appendix 2]
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Abbreviations
EQ-5D-5L: 5-level EuroQol 5 dimensions questionnaire
HRQoL: health-related quality of life
QALY: quality-adjusted life-year
SMS: short message service
So-Lo-Mo: Social-Local-Mobile
VRUH: Virgen del Rocío University Hospital
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Edited by G Eysenbach; submitted 17.10.18; peer-reviewed by A Martinez-Millana, L Wesselman; comments to author 06.11.18;
revised version received 19.11.18; accepted 20.11.18; published 06.12.18
Please cite as:
Jódar-Sánchez F, Carrasco Hernández L, Núñez-Benjumea FJ, Mesa González MA, Moreno Conde J, Parra Calderón CL,
Fernandez-Luque L, Hors-Fraile S, Civit A, Bamidis P, Ortega-Ruiz F
Using the Social-Local-Mobile App for Smoking Cessation in the SmokeFreeBrain Project: Protocol for a Randomized Controlled
Trial
JMIR Res Protoc 2018;7(12):e12464
URL: http://www.researchprotocols.org/2018/12/e12464/
doi: 10.2196/12464
PMID: 30522992
©Francisco Jódar-Sánchez, Laura Carrasco Hernández, Francisco J Núñez-Benjumea, Marco Antonio Mesa González, Jesús
Moreno Conde, Carlos Luis Parra Calderón, Luis Fernandez-Luque, Santiago Hors-Fraile, Anton Civit, Panagiotis Bamidis,
Francisco Ortega-Ruiz. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 06.12.2018. This
is an open-access article distributed under the terms of the Creative Commons Attribution License
(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information,
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Quitting cigarette smoking benefits smokers at any age (1). Individual, group, and telephone counseling and seven Food and Drug Administration-approved medications increase quit rates (1-3). To assess progress toward the Healthy People 2020 objectives of increasing the proportion of U.S. adults who attempt to quit smoking cigarettes to ≥80.0% (TU-4.1), and increasing recent smoking cessation success to ≥8.0% (TU-5.1),* CDC assessed national estimates of cessation behaviors among adults aged ≥18 years using data from the 2000, 2005, 2010, and 2015 National Health Interview Surveys (NHIS). During 2015, 68.0% of adult smokers wanted to stop smoking, 55.4% made a past-year quit attempt, 7.4% recently quit smoking, 57.2% had been advised by a health professional to quit, and 31.2% used cessation counseling and/or medication when trying to quit. During 2000-2015, increases occurred in the proportion of smokers who reported a past-year quit attempt, recently quit smoking, were advised to quit by a health professional, and used cessation counseling and/or medication (p<0.05). Throughout this period, fewer than one third of persons used evidence-based cessation methods when trying to quit smoking. As of 2015, 59.1% of adults who had ever smoked had quit. To further increase cessation, health care providers can consistently identify smokers, advise them to quit, and offer them cessation treatments (2-4). In addition, health insurers can increase cessation by covering and promoting evidence-based cessation treatments and removing barriers to treatment access (2,4-6).
Smoking is the largest avoidable cause of preventable morbidity worldwide. It causes most of the cases of lung cancer and chronic obstructive pulmonary disease (COPD) and contributes to the development of other lung diseases. SmokeFreeBrain aims to address the effectiveness of a multi-level variety of interventions aiming at smoking cessation in high risk target groups within High Middle Income Countries (HMIC) such as unemployed young adults, COPD and asthma patients, and within the general population in Low-Middle Income Countries (LMIC). The project addresses existing approaches aimed to prevent lung diseases caused by tobacco while developing new treatments and evaluating: (i) Public Service Announcement (PSA) against smoking, (ii) the use of electronic cigarettes, (iii) neurofeedback protocols against smoking addiction, (iv) a specifically developed intervention protocol based on behavioral therapy, social media/mobile apps and short text messages (sms) and (v) pharmacologic interventions. Emphasis in this paper, however, is placed on the e-heath, m-health, open (big) data, mobile game and neuroscientific challenges and developments upon facilitating the aforementioned interventions.
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Background: Smartphone apps can provide real-time, tailored interventions for smoking cessation. The current study examines the effectiveness of a smartphone-based smoking cessation application that assessed risk for imminent smoking lapse multiple times per day and provided messages tailored to current smoking lapse risk and specific lapse triggers. Methods: Participants (N=59) recruited from a safety-net hospital smoking cessation clinic completed phone-based ecological momentary assessments (EMAs) 5 times/day for 3 consecutive weeks (1week pre-quit, 2weeks post-quit). Risk for smoking lapse was estimated in real-time using a novel weighted lapse risk estimator. With each EMA, participants received messages tailored to current level of risk for imminent smoking lapse and self-reported presence of smoking urge, stress, cigarette availability, and motivation to quit. Generalized linear mixed model analyses determined whether messages tailored to specific lapse risk factors were associated with greater reductions in these triggers than messages not tailored to specific triggers. Results: Overall, messages tailored to smoking urge, cigarette availability, or stress corresponded with greater reductions in those triggers than messages that were not tailored to specific triggers (p's=0.02 to <0.001). Although messages tailored to stress were associated with greater reductions in stress than messages not tailored to stress, the association was non-significant (p=0.892) when only moments of high stress were included in the analysis. Conclusions: Mobile technology can be used to conduct real-time smoking lapse risk assessment and provide tailored treatment content. Findings provide initial evidence that tailored content may impact users' urge to smoke, stress, and cigarette availability.
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Background: Group therapy offers individuals the opportunity to learn behavioural techniques for smoking cessation, and to provide each other with mutual support. Objectives: To determine the effect of group-delivered behavioural interventions in achieving long-term smoking cessation. Search methods: We searched the Cochrane Tobacco Addiction Group Specialized Register, using the terms 'behavior therapy', 'cognitive therapy', 'psychotherapy' or 'group therapy', in May 2016. Selection criteria: Randomized trials that compared group therapy with self-help, individual counselling, another intervention or no intervention (including usual care or a waiting-list control). We also considered trials that compared more than one group programme. We included those trials with a minimum of two group meetings, and follow-up of smoking status at least six months after the start of the programme. We excluded trials in which group therapy was provided to both active therapy and placebo arms of trials of pharmacotherapies, unless they had a factorial design. Data collection and analysis: Two review authors extracted data in duplicate on the participants, the interventions provided to the groups and the controls, including programme length, intensity and main components, the outcome measures, method of randomization, and completeness of follow-up. The main outcome measure was abstinence from smoking after at least six months follow-up in participants smoking at baseline. We used the most rigorous definition of abstinence in each trial, and biochemically-validated rates where available. We analysed participants lost to follow-up as continuing smokers. We expressed effects as a risk ratio for cessation. Where possible, we performed meta-analysis using a fixed-effect (Mantel-Haenszel) model. We assessed the quality of evidence within each study and comparison, using the Cochrane 'Risk of bias' tool and GRADE criteria. Main results: Sixty-six trials met our inclusion criteria for one or more of the comparisons in the review. Thirteen trials compared a group programme with a self-help programme; there was an increase in cessation with the use of a group programme (N = 4395, risk ratio (RR) 1.88, 95% confidence interval (CI) 1.52 to 2.33, I(2) = 0%). We judged the GRADE quality of evidence to be moderate, downgraded due to there being few studies at low risk of bias. Fourteen trials compared a group programme with brief support from a health care provider. There was a small increase in cessation (N = 7286, RR 1.22, 95% CI 1.03 to 1.43, I(2) = 59%). We judged the GRADE quality of evidence to be low, downgraded due to inconsistency in addition to risk of bias. There was also low quality evidence of benefit of a group programme compared to no-intervention controls, (9 trials, N = 1098, RR 2.60, 95% CI 1.80 to 3.76 I(2) = 55%). We did not detect evidence that group therapy was more effective than a similar intensity of individual counselling (6 trials, N = 980, RR 0.99, 95% CI 0.76 to 1.28, I(2) = 9%). Programmes which included components for increasing cognitive and behavioural skills were not shown to be more effective than same-length or shorter programmes without these components. Authors' conclusions: Group therapy is better for helping people stop smoking than self-help, and other less intensive interventions. There is not enough evidence to evaluate whether groups are more effective, or cost-effective, than intensive individual counselling. There is not enough evidence to support the use of particular psychological components in a programme beyond the support and skills training normally included.