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Original Paper
A Mobile Phone-Based Life Skills Training Program for Substance
Use Prevention Among Adolescents: Pre-Post Study on the
Acceptance and Potential Effectiveness of the Program, Ready4life
Severin Haug1, PhD; Raquel Paz Castro1, MSc; Christian Meyer2, PhD; Andreas Filler3,4, MSc; Tobias Kowatsch3,
PhD; Michael P Schaub1, PhD
1Swiss Research Institute for Public Health and Addiction at the University of Zurich, Zurich University, Zurich, Switzerland
2Institute of Social Medicine and Prevention, University of Greifswald, Greifswald, Germany
3Centre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
4Energy Efficient Systems Group, University of Bamberg, Bamberg, Germany
Corresponding Author:
Severin Haug, PhD
Swiss Research Institute for Public Health and Addiction
University of Zurich
Konradstrasse 32
Zurich, 8031
Switzerland
Phone: 41 444481174
Email: severin.haug@isgf.uzh.ch
Abstract
Background: Substance use and misuse often first emerge during adolescence. Generic life skills training that is typically
conducted within the school curriculum is effective at preventing the onset and escalation of substance use among adolescents.
However, the dissemination of such programs is impeded by their large resource requirements in terms of personnel, money, and
time. Life skills training provided via mobile phones might be a more economic and scalable approach, which additionally matches
the lifestyle and communication habits of adolescents.
Objective: The aim of this study was to test the acceptance and initial effectiveness of an individually tailored mobile phone–based
life skills training program in vocational school students.
Methods: The fully automated program, named ready4life, is based on social cognitive theory and addresses self-management
skills, social skills, and substance use resistance skills. Program participants received up to 3 weekly text messages (short message
service, SMS) over 6 months. Active program engagement was stimulated by interactive features such as quiz questions, message-
and picture-contests, and integration of a friendly competition with prizes in which program users collected credits with each
interaction. Generalized estimating equation (GEE) analyses were used to investigate for changes between baseline and 6-month
follow-up in the following outcomes: perceived stress, self-management skills, social skills, at-risk alcohol use, tobacco smoking,
and cannabis use.
Results: The program was tested in 118 school classes at 13 vocational schools in Switzerland. A total of 1067 students who
owned a mobile phone and were not regular cigarette smokers were invited to participate in the life skills program. Of these, 877
(82.19%, 877/1067; mean age=17.4 years, standard deviation [SD]=2.7; 58.3% females) participated in the program and the
associated study. A total of 43 students (4.9%, 43/877) withdrew their program participation during the intervention period. The
mean number of interactive program activities that participants engaged in was 15.5 (SD 13.3) out of a total of 39 possible
activities. Follow-up assessments were completed by 436 of the 877 (49.7%) participants. GEE analyses revealed decreased
perceived stress (odds ratio, OR=0.93; 95% CI 0.87-0.99; P=.03) and increases in several life skills addressed between baseline
and the follow-up assessment. The proportion of adolescents with at-risk alcohol use declined from 20.2% at baseline to 15.5%
at follow-up (OR 0.70, 95% CI 0.53-0.93; P=.01), whereas no significant changes were obtained for tobacco (OR 0.94, 95% CI
0.65-1.36; P=.76) or cannabis use (OR 0.91, 95% CI 0.67-1.24; P=.54).
Conclusions: These results reveal high-level acceptance and promising effectiveness of this interventional approach, which
could be easily and economically implemented. A reasonable next step would be to test the efficacy of this program within a
controlled trial.
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(JMIR Mhealth Uhealth 2017;5(10):e143) doi:10.2196/mhealth.8474
KEYWORDS
coping skills; social skills; substance use disorder; adolescents; students; mobile phone
Introduction
Several biological, psychological, and social transitions that
occur during adolescence are essential for a young person’s
later-life trajectory [1,2]. These transitions offer opportunities
for them to gain skills to achieve greater autonomy from adults,
build social connections with peers, develop a positive body
image, and form a sense of identity. However, these transitions
also facilitate exploration and risk taking at a stage when
cognitive functions of the brain are not yet fully developed [3].
Shifts of emotional regulations and increased risky behaviors
result in vulnerabilities for mental and substance use disorders,
which constitute the biggest contributors to the health burden
of 10- to 24-year-old individuals [4]. Substance use and the
development of substance use disorders often first emerge during
adolescence and co-occur with mental disorders [1].
The age of onset of substance use is similar across high-income
countries, with increasing levels and frequency of use beginning
in mid-adolescence, peaking during early adulthood [5].
According to the World Health Organization (WHO) World
Mental Health Surveys [6], the interquartile range of the
age-of-onset distributions is typically 14 to 21 years for alcohol,
15 to 21 years for tobacco, 16 to 22 years for cannabis, and 19
to 28 years for cocaine.
As most young people do not fulfill criteria for problematic or
disordered use, prevention programs and early intervention
should be the focus, rather than substance-related treatment
measures. A recent systematic review of studies assessing the
effectiveness of prevention, early intervention, and harm
reduction in young people for tobacco, alcohol, and illicit drugs
demonstrated the effectiveness of taxation, public consumption
bans, advertising restrictions, and minimum legal age, as well
as the potential effectiveness of preventative interventions that
deliver life skills training in educational settings [7]. Schools
are particularly suitable settings to reach adolescents with
preventative interventions because of the ease of delivery and
access to young people within compulsory secondary education
[7].
A Cochrane review on school-based programs for the prevention
of tobacco smoking [8] concluded that combined social
competence and social influence interventions had a significant
effect at 1 year and at longest follow-up, whereas a social
influences program on its own, multimodal community-wide
initiatives, and information-only interventions were found to
be ineffective. Another Cochrane review on school-based
prevention programs for alcohol misuse in young people [9]
concluded that generic psychosocial and developmental
prevention programs can be effective. However, the
methodological quality of the trials included in the analysis was
poor, and this did not allow for any quantitative pooling of data.
A Cochrane review on school-based prevention of illicit drug
use [10] concluded that programs based on a combination of
social competence and social influence approaches were most
promising, on average exhibiting small but consistent protective
effects to prevent drug use.
According to the WHO, life skills are “abilities for adaptive and
positive behavior that enable individuals to deal effectively with
the demands and challenges of everyday life” [11]. The majority
of the generic programs addressing social competences and
social influences that were included in the aforementioned
reviews were based on Bandura’s social learning theory [12],
which hypothesizes that children and adolescents learn substance
use by modeling, imitation, and reinforcement, influenced by
individual cognitions, attitudes, and skills. Moreover, substance
use susceptibility is increased by poor personal and social skills.
Generic life skills programs to prevent substance use, such as
the IPSY (Information + Psychosocial Competence = Protection)
program developed in Germany [13] or the ALERT [14] or
LifeSkills Training [15] programs developed in the United
States, typically combine training in self-management skills,
social skills (eg, self-awareness, coping strategies, assertiveness,
or communication skills), and substance use resistance skills
(eg, resisting peer pressure to drink alcohol and recognizing
and resisting media influences promoting cigarette smoking).
Although these life skills training programs were effective at
preventing the onset of specific substances [8,13] or at
decreasing problematic substance use [9], their implementation
and dissemination in schools present serious challenges [16].
First, teachers and other professionals need the time, motivation,
knowledge, and skills to deliver the program. Second, extensive
resources—in terms of personnel, money, and time allocated
to deliver substance use prevention—are required to prepare
and administer such programs.
Electronically delivered interventions (eg, via computer, Internet,
or mobile phone) have the potential to overcome the
aforementioned obstacles that hinder successful program
implementation and dissemination of life skills training in
schools at a larger scale. Electronically delivered interventions
have a wide reach at a low cost and offer the opportunity to
automatically deliver individually tailored contents that can be
accessed at any time and in any place [17]. Furthermore,
electronically delivered interventions might be more appealing
for adolescents because they can better ensure privacy and tailor
contents to their needs.
Beyond traditional personal computers, a promising means of
delivering prevention programs is to do so remotely through
the use of mobile technologies. In Switzerland, as in most other
developed countries, almost all (98%) adolescents between the
ages of 12 and 19 years own a mobile phone, and 97% of these
phones are smartphones [18]. Most adolescents are familiar
with how to use mobile phones and typically use them on a
daily basis for texting, taking pictures, playing games, and so
on. Mobile phone–based interventions can provide almost
constant support to users, relative to interventions that can only
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be accessed at specific times or locations; and they provide a
discrete and confidential means of intervention delivery [19].
Several studies have underlined the potential and effectiveness
of substance-specific mobile phone–based programs for early
interventions in adolescents already consuming specific
substances such as tobacco or alcohol [20-22]. However, the
feasibility and effectiveness of more generic, life skills
interventions to prevent substance use via mobile phones have
not been addressed to date.
Consequently, the objectives of this study were (1) to test the
acceptance, use, and evaluation of a mobile phone–based life
skills program among vocational school students and (2) to
explore its potential effectiveness.
Methods
Setting
In most European countries, vocational schools are
postsecondary public schools that are analogous to American
community colleges. They are a part of the dual educational
system that combines apprenticeships in a business context and
vocational training in a school context. Vocational schools
provide general education and specific skills for each particular
profession.
On the basis of data from the Swiss Federal Statistical Office,
approximately half of all Swiss adolescents aged 16 to 19 years
currently attend vocational schools [23], with the highest
proportions among adolescents aged 17 years (males: 60%,
females: 48%) and 18 years (males: 58%, females: 47%).
Design and Procedures
A longitudinal pre-post study design with assessments at
baseline and after program completion (month 6) was used to
test the initial effectiveness of the program. Prevention
specialists from branches of the Swiss Lung Association
(Aargau, Basel-Land, Basel-Stadt, Berne, Vaud, and St Gallen),
with particular training in the study and program to be delivered,
arranged sessions lasting 30 min in participating vocational
school classes during regular school lessons reserved for health
education. Within this session, the students were informed about
and invited to participate in a study testing innovative channels
for the provision of health-related information and life skills.
The students were informed by the prevention specialists about
the study’s aims and assessments, reimbursement, and data
protection. Students were also informed that they could
withdraw from program participation at any time, simply by
sending a short service message (SMS) expressing their request
to stop the program.
The mobile phone–based program and its association with a
friendly competition with prizes were described in detail by the
prevention specialists. To ensure sufficient participation and,
thus, representativeness of the sample [24], students were
informed that they would also receive a small reward for
participating in the study. Each student was provided with a
tablet computer or used his or her mobile phone for the screening
process to assess for study eligibility and for study registration
and the baseline assessment. Inclusion criteria for this study
were (1) a minimum age of 16 years and (2) possession of a
mobile phone. After being screened for the inclusion criteria
and giving informed consent, study participants were invited to
choose a username and provide their mobile phone number.
On the basis of previous results on the efficacy of texting-based
programs for smoking cessation [25,26], vocational school
students who smoked cigarettes regularly (at least four cigarettes
over the preceding month and at least one cigarette within the
preceding week) received a program version combining smoking
cessation support based on the MobileCoach Tobacco program
[27] and strategies for stress management. As this program
primarily focused on smoking cessation and did not include
comprehensive life skills training, we excluded regular smokers
from this study.
After they had given their informed consent, study participants
completed a baseline assessment directly on their mobile phone
or on the tablet computer that they had been provided with.
They received additional questions that were necessary to tailor
their intervention’s content. Subsequently, participants received
individually tailored Web-based feedback directly on their
mobile phone. Over the subsequent 6 months, they received
individually tailored life skills training provided via mobile
phone texting. All subjects were invited to complete a
Web-based follow-up assessment after program completion 6
months after their enrollment in the study. For this, they received
a text message (SMS) with a Web link to the Web-based
assessment. Up to two text message–based reminders were sent
to those who failed to complete the follow-up assessment upon
initial request.
The study protocol was approved by the Ethics Committee in
the Faculty of Philosophy at the University of Zurich,
Switzerland (date of approval: June 24, 2016) and the trial
conducted in compliance with the Declaration of Helsinki.
The Intervention Program ready4life
Theoretical Background and Intervention Contents
The intervention elements of the program called ready4life are
based on social cognitive theory [28,29]. This theory relies on
social learning theory, as it was founded on principles of
learning within the human social context [12], though it has
also integrated several concepts from cognitive psychology.
Key concepts of this theory that are incorporated within the
Web-based and text messaging–based life skills program are
(1) outcome expectations (ie, beliefs about the likelihood and
impact of the consequences of behavioral choices), (2)
self-efficacy (ie, beliefs about one’s personal ability to perform
a desired behavior that could be stimulated; eg, by mastery,
experience, or persuasion), (3) observational learning (ie,
learning new behaviors via exposure to them through
interpersonal or media displays; eg, through peer modeling),
(4) facilitation (ie, providing strategies, tools, and resources that
make new behaviors easier to perform), and (5) self-regulation
(ie, controlling oneself via monitoring, goal setting, feedback,
and self-instruction).
The contents of ready4life rely on proven and widely
disseminated life skills programs such as IPSY [13], ALERT
[14], and LifeSkills Training [15]. The program addresses (1)
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self-management skills, (2) social skills, and (3) substance use
resistance skills. As recruitment for this study was conducted
within the German- and French-speaking part of Switzerland,
all intervention contents were available in both German and
French.
Technological Background
The intervention program was developed using the MobileCoach
system. Technical details of the system are described elsewhere
[30,31]. The MobileCoach system is available as an open-source
project. Password protection and Secure Sockets Layer encoding
are used to ensure the privacy and safety of data transfer.
Individually Tailored Feedback
Individually tailored Web-based feedback was given
immediately after subjects completed the Web-based baseline
assessment during school classes using tablet computers or
mobile phones. This feedback comprised five screens, which
included textual and graphical feedback on the following: (1)
stress in general; (2) the individual level of stress in various
domains; (3) the individual level of stress compared with an
age- and gender-specific reference group, based on the Perceived
Stress Scale (PSS) [32] and on data derived from a survey
among Swiss vocational school students [33]; and (4)
individually applied and suggested strategies to cope with stress.
Text Messages
For a period of 6 months, program participants received between
two and four individualized text messages per week on their
mobile phone. These messages were generated and sent by the
fully automated MobileCoach system. Within the first 9 weeks,
the messages focused on self-management skills; for example,
coping with stress, emotional self-regulation, or management
of feelings of anger and frustration. In the weeks 10 to 15, the
messages focused on social skills, for example, making requests,
refusing unreasonable requests, and meeting new people. In
weeks 16 to 20, the text messages focused on substance use
resistance skills, for example, recognizing and resisting media
influences, social norms of substance use, or the associations
of self-management skills and interpersonal competences with
substance use. Boosters for each of the components were
provided in weeks 22 to 24. The program concluded with
information about a prize draw and an invitation to participate
in the follow-up assessment. The messages were tailored
according to the individual data from the baseline assessment
and on-text messaging assessments during program runtime,
for example, on substance use or the individual’s emotional
state. Sample messages from different intervention components
are displayed in Figure 1.
To exploit the full potential of current mobile phones, several
interactive features—such as quiz questions, tasks to create
individually tailored if-then behavior plans based on
implementation intentions, and message contests—were
implemented within the program. Within picture and message
contests, participants were invited to create and upload text
messages or photos on specific topics, for example, on
individually preferred strategies to cope with stress or on
relaxation possibilities. The messages or pictures provided by
other participants could be rated anonymously on a separate
responsive website by all participants, and the top three postings
were presented anonymously to all other participants after 48
hours on a website, which was only accessible for program
participants. All messages and photos created by the participants
were checked by a junior scientist with respect to the
appropriateness of their content. Inappropriate content was
excluded and not presented to the other participants.
Due to the wide dissemination of mobile phones in adolescents
[18], several messages also included hyperlinks to audio files
(eg, audio testimonials and motivational podcasts), as well as
thematically appropriate video clips, pictures, and related
websites.
Figure 1. Sample messages (translated from the German program version).
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Figure 2. Individual profile page (translated from the German program version).
Prize Draw
To stimulate active program engagement, program use was
associated with a friendly competition that allowed program
users to collect credits for each interaction (eg, answering and
monitoring text messages, participating in quizzes, creating
messages or pictures within contests, and accessing video links
integrated in text messages). The more credits participants
collected, the higher were their chances of winning one of
several attractive prizes that were part of a prize draw after
program completion. Participants could retrieve their number
of credits compared with the number of credits of other program
participants of their group (similar starting date and Canton) at
any time from an individual profile page (Figure 2).
Focus Groups for Pilot Testing
Before the study, a prototype of this program was tested and
evaluated in five focus groups. Within these focus groups,
individuals in the age range of 16 to 20 years, drawn from local
vocational schools were asked to evaluate program flow, the
layout and content of the Web-based assessment and feedback,
and the content of text messages. Optimizations that resulted
from these focus groups were integrated into the final version
of the program.
Measures and Outcome Criteria
Demographics
The baseline survey included questions about the following
demographic variables: gender, age, and migrant background.
For the last of these, we asked about the country of birth of both
parents of each vocational school student to identify potential
migrant backgrounds. On the basis of this information, persons
were assigned to one of the following categories: (1) persons
with neither parent born outside Switzerland—no migrant
background and (2) persons with one or both parents born
outside Switzerland—migrant background.
Life Skills
Life skills were assessed at baseline and 6-month follow-up.
The life skills assessed were related to the contents of the
program and focused on (1) stress, (2) self-management skills,
and (3) social skills.
A four-item version [32] of the PSS [34] was used to measure
the degree to which students appraised situations as stressful
over the preceding month. Responses were scored on a 1- to
5-point scale from (1) “never” to (5) “very often.” These four
items were “In the last month...,” (1) “how often have you felt
that you were unable to control the important things in your
life?” (2) “how often have you felt confident about your ability
to handle your personal problems?” (3) “how often have you
felt that things were going your way?” and (4) “how often have
you felt difficulties were piling up so high that you could not
overcome them?” This scale showed comparable acceptable
psychometric properties when administered online and in
paper-pencil format, with a Cronbach alpha of .72 for the
Web-based version [32].
Self-management and coping behavior within vocational training
were assessed using one item derived from each of the five
subscales of the Questionnaire for the Measurement of Stress
and Coping in Children and Adolescents (SSKJ 3-8) [35]. The
items were selected based upon their item-subscale correlation
and the relevance of their content for vocational school students.
Students indicated, on a 5-point rating scale ranging from never
(1) to always (5), how often they use each of the presented
coping strategies in response to stressful situations during
vocational training. These five items were “If I am stressed
during vocational training...,” (1) “I tell others, how I feel”
(seeking social support); (2) “I change something so that things
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are getting better” (problem solving); (3) “I tell myself that
things will resolve themselves” (avoidant coping); (4) “I try to
relax” (palliative emotion regulation); and (5) “I get totally
upset” (anger-related emotion regulation). According to
Eschenbeck et al [35], constructive coping behavior is
particularly indicated by higher values on subscales (1), (2),
and (4); conversely, higher values on subscales (3) and (5) are
less desirable.
Social skills were assessed using a scale with seven items
derived from the Assertion Inventory [36]. These items
addressed (1) expressing an opinion that differs from that of
other persons, (2) resisting social pressure to drink or smoke
cigarettes, (3) telling a work colleague when he or she says or
does something that bothers you, (4) asking questions to find
out more about something, (5) apologizing when you are wrong,
(6) accepting yourself even while being criticized, and (7) telling
someone good news about yourself. Students indicated, on a
5-point rating scale ranging from never (1) to always (5), the
frequency that they display each of the indicated behaviors.
These seven items exhibited acceptable internal consistency,
with a Cronbach alpha of .65.
Substance Use
Indicators of substance use were assessed at baseline and
follow-up and included (1) at-risk alcohol use, (2) tobacco
smoking, and (3) cannabis use.
At-risk alcohol use was assessed through the consumption items
of the Alcohol Use Disorder Identification Test (AUDIT), the
AUDIT-C [37]. The AUDIT-C assesses drinking quantity,
drinking frequency, and binge drinking. On the basis of
recommendations for adolescents [38], we used a cut-off value
of ≥5 to determine whether risky drinking was present.
Tobacco smoking was assessed by the yes or no question: “have
you taken at least one puff of a cigarette within the past 30
days?”
Cannabis use was assessed with the item “Within the last six
months, how often did you use cannabis or marijuana?” with
the response options (1) “never,” (2) “1-5 times,” (3) “6-20
times,” and (4) “more often than 20 times.” To estimate the
prevalence of cannabis use within the last 6 months, we
collapsed response options 2, 3, and 4 into a single category:
cannabis use.
Program Use and Evaluation
To obtain the number of program participants who unsubscribed
from the program within the program runtime of 6 months, we
analyzed the log files of the MobileCoach system in which all
incoming and outgoing text messages were recorded. Using
these log files, we also assessed the mean number of replies to
the 12 text message assessments during the program. At
follow-up, we assessed another aspect of SMS usage by asking
the participants whether they usually (1) read through the text
messages thoroughly, (2) took only a short look at them, or (3)
did not read the text messages.
Using a yes or no question, we evaluated whether the times
when participants received the text messages were deemed to
be appropriate. Furthermore, we assessed whether the number
of received text messages was felt to be appropriate, or whether
the participants would have preferred fewer or more messages.
Finally, program participants were asked to rate the program
and different program elements using the response categories
“very good,” “good,” “less than good,” “bad,” and “don’t know.”
Outcome Criteria
To explore the intervention’s effectiveness, the pre-post changes
between baseline and 6-months follow-up of the following
variables were investigated: (1) perceived stress [32], (2)
self-management and coping behaviors [35], (3) interpersonal
skills [36], (4) at-risk alcohol use [37], (5) tobacco smoking,
and (6) cannabis use.
Data Analysis
To test for baseline differences between study participants and
nonparticipants, Pearson χ2analysis for categorical variables
and nonpaired student’s ttests for continuous variables were
applied. For the attrition analysis (program participants lost to
follow-up), we used χ2analysis for categorical variables and t
tests for continuous variables. Baseline equivalence and lack of
attrition bias were assumed for tests with P>.10.
We used generalized estimating equation (GEE) analyses to
investigate the longitudinal course of the outcome criteria over
the study period of 6 months. GEE is a repeated-measures
regression model that takes into account the correlation of
repeated measures within each subject [32]. It is a powerful and
versatile procedure for analyzing longitudinal data, with minimal
assumptions about time dependence, and it allowed us to use
all available longitudinal data, irrespective of single missing
values at follow-up.
We used logistic GEE models for binary outcomes and linear
GEE models for continuous variable outcomes. To control for
attrition bias, we additionally added the respective baseline
variables and variables on program use as covariates to the GEE
models. Each GEE model included (1) the examined time
variable (baseline vs follow-up assessment) as a predictor, (2)
covariates to account for selective attrition, and (3) some
outcome variable as the dependent variable.
Given the clustered nature of the data (students within school
classes and intraclass correlation for the considered outcomes
ranged from .02-.06), we computed robust variance estimators
for all GEE analyses. An alpha level of .05 (two-tailed) was
chosen for all statistical tests conducted in the study. All
analyses were performed using the Stata software package,
version 12 (StataCorp).
Results
Study Participants
Participants’ progression through the study is depicted in Figure
3. At the time of the Web-based assessment in 118 vocational
school classes at 13 Swiss vocational schools, a total of 2032
students were present. Among them, 1889 (92.96%, 1889/2032)
had a minimum age of 16 years and owned a mobile phone and,
as such, fulfilled the inclusion criteria for study participation.
A total of 822 (43.51%, 822/1889) regular tobacco smokers
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were not considered within this study because they received a
program focusing on smoking cessation. Among the 1067
(56.48%, 1067/1889) students who were invited to participate
in ready4life, 877 (82.19%, 877/1067) agreed to participate.
Nonparticipants were subjects who met enrollment criteria but
did not agree to participate in the study.
Table 1 summarizes characteristics of study participants and
nonparticipants. Study participants had a mean age of 17.4 years
(standard deviation, SD 2.7) and consisted of 58.3% females.
Study participants differed from nonparticipants with respect
to the baseline variables gender and migrant background. A
greater percentage of study participants than nonparticipants
were female (χ2
1=5.5, P=.02) and had no migrant background
(χ2
1=4.8, P=.03).
Table 1. Baseline characteristics of study participants and nonparticipants.
Pvalue
Nonparticipants
(N=190)
Study participants
(N=877)
Variable
.0293 (48.9)511 (58.3)Female gender, n (%)
.1017.9 (4.5)17.4 (2.7)
Age in years, mean (SDa)
.03Migrant background, n (%)
83 (43.7)460 (52.5)No migrant background
107 (56.3)417 (47.5)Migrant background
.652.53 (0.64)2.51 (0.66)
Perceived stress (PSSb, range 1-5), mean (SD)
Self-management skills (range 1-5), mean (SD)
--2.9 (1.3)Seeking social support
--3.3 (1.0)Problem solving
--2.5 (1.1)Avoidant coping
--3.3 (1.2)Palliative emotion regulation
--2.6 (1.2)Anger-related emotion regulation
--3.7 (0.6)Social skills (scale, range 1-5), mean (SD)
.43
Alcohol use (AUDIT-Cc), n (%)
128 (83.7)d
710 (81.0)Not at risk (<5)
25 (16.3)167 (19.0)At risk (≥5)
Tobacco smoking in the previous 30 days, n (%)
--796 (90.8)No
--81 (9.2)Yes
.25Cannabis use in the previous 6 months, n (%)
129 (84.3)d
769 (87.7)No
24 (15.7)108 (12.3)Yes
aSD: standard deviation.
bPSS: Perceived Stress Scale.
cAUDIT-C: Alcohol Use Disorders Identification Test-C.
dn=37 missing values in nonparticipants.
Follow-up assessments were completed by 436 of the 877
(49.7%) study participants. Concerning attrition bias, the
analysis revealed that follow-up assessments were completed
more likely by female than male participants (χ2
1=6.7, P<.01),
by participants without a migrant background (χ2
1=9.9, P<.01),
by participants with higher values on the item on social support
seeking (t875=−2.47, P=.01), and by participants with a higher
number of program activities during the program period
(t875=37.3, P<.001). To account for this attrition bias, these
variables were entered as covariates within the GEE models
that compared changes in outcomes between baseline and
6-month follow-up.
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Figure 3. Participants’ progress through the study.
Acceptability of the Intervention
During the program, which lasted for 6 months, 43 (4.9%) of
the initial 877 program participants unsubscribed from the
program, resulting in a total of 834 (95.1%, 834/877) participants
who completed the entire program.
A total of 39 activities (eg, replies to texting prompts, accessing
Web links within text messages, and participating in contests)
were prompted over the 6-month program. The mean number
of activities carried out by participants was 15.5 (SD 13.3). Of
the 877 participants, 134 (15.3%) participated in no activities,
223 (25.4%) carried out 1 to 7 activities, 102 (11.6%) engaged
in 8 to 14 activities, 82 (9.4%) in 15 to 21 activities, 123 (14.0%)
in 22 to 28 activities, 156 (17.8%) in 29 to 35 activities, and 57
(6.5%) in 36 to 39 activities.
Of the 387 subjects with valid follow-up data, 323 (83.4%)
indicated that they “read the SMS messages thoroughly,” 61
persons (15.8%) reported that they “took a short look at the
feedback messages,” and only 3 persons (0.8%) chose the
predefined response category “I did not read the feedback
messages.”
The duration of the program was rated as appropriate by 334
(86.7%, 334/385) program participants with valid follow-up
data. The number of received SMS messages was rated as
appropriate by 84.9% (328/386); 6.7% (26/386) would have
preferred more, and 8.3% (32/386) would have preferred fewer
SMS messages. Almost all participants reported that the text
messages were comprehensible (98.7%, 379/384). Participants
were also asked whether the text messages were helpful, and
296 out of 384 (77.1%) agreed with this. Three out of 4
participants (73.2%, 281/384) indicated that they perceived the
text messages as individually tailored to them.
Figure 4 presents additional evaluations of the program and
specific program elements. The program overall was evaluated
as “very good” or “good” by 94.6% of the participants. Out of
the specific program elements, the competition for prizes, the
quiz questions, and the text messages in general received the
best evaluations, with >90% of participants rating them “good”
or “very good.” The picture and message contests received the
poorest ratings (56.8% “good” or “very good”).
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Figure 4. Evaluation of the program and specific program elements by program participants (n=384). Values are presented for percentages >5%.
Program Effectiveness
Life Skills
Pre-post comparisons of the variables addressing life skills are
displayed in Table 2. The GEE analyses revealed a statistically
significant decrease in perceived stress (odds ratio, OR 0.93,
95% CI 0.87-0.99; P=.03). Meanwhile, statistically significant
increases were obtained for the items addressing the
self-management skills seeking social support (OR 1.18, 95%
CI 1.05-1.33; P=.008) and palliative emotion regulation (OR
1.13, 95% CI 1.01-1.28; P=.04), as well as for the scale
addressing social skills (OR 1.07, 95% CI 1.00-1.13; P=.04).
Substance Use
Pre-post comparisons of substance use prevalence rates are
displayed in Table 3. Concerning alcohol use, GEE analyses
revealed a statistically significant decrease in the percentage of
persons with at-risk alcohol use from the baseline assessment
to the follow-up assessment (OR 0.70, 95% CI 0.53-0.93;
P=.01). No significant pre-post differences were obtained in
the percentage of persons using cannabis or smoking cigarettes.
Table 2. Pre-post comparisons of variables addressing life skills.
Pvalue
ORb(95% CI)c
(N=877)
Post
mean (SD)
Pre
mean (SDa)
Variable
.030.93 (0.87-0.99)2.4 (0.7)2.5 (0.7)
Perceived stress (PSSd, range 1-5)
Self-management skills (range 1-5)
.0081.18 (1.05-1.33)3.2 (1.2)3.0 (1.2)Seeking social support
.230.93 (0.83-1.04)3.3 (1.0)3.4 (1.0)Problem-solving
.161.09 (0.97-1.23)2.5 (1.1)2.4 (1.0)Avoidant coping
.041.13 (1.01-1.28)3.4 (1.1)3.2 (1.2)Palliative emotion regulation
.810.99 (0.87-1.11)2.6 (1.2)2.7 (1.2)Anger-related emotion regulation
.041.07 (1.00-1.13)3.9 (0.6)3.8 (0.6)Social skills (range 1-5)
aSD: standard deviation.
bOR: odds ratio.
cLinear generalized estimation equation (GEE) models, with time variable (baseline vs follow-up assessment) as the predictor, adjusted for attrition
bias.
dPSS: Perceived Stress Scale.
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Table 3. Pre-post comparisons of variables addressing substance use.
Pvalue
ORa(95% CI)b
(N=877)
Post
n (%)
Pre
n (%)
Variable
.010.70 (0.53-0.93)65 (15.5)85 (20.2)
At-risk alcohol use, AUDIT-Cc(N=420)
.760.94 (0.65-1.36)31 (7.9)33 (8.4)Tobacco smoking, previous 30 days (N=392)
.540.91 (0.67-1.24)40 (9.5)44 (10.5)Cannabis use, previous 6 months (N=419)
aOR: odds ratio.
bLogistic generalized estimation equation (GEE) models, with time variable (baseline vs follow-up assessment) as predictor, adjusted for attrition bias.
cAUDIT-C: Alcohol Use Disorders Identification Test-C.
Discussion
Principal Findings
Within this study, we tested the acceptability and explored the
potential effectiveness of a newly developed mobile
phone–based life skills training program for substance use
prevention among adolescents. The study revealed three main
findings: (1) concerning participation, a large proportion of the
eligible adolescents who were invited for program and study
participation in the setting of a school classroom, participated;
(2) concerning program use, the majority of program participants
completed the entire program and engaged in program activities;
however, regular program use could be improved; and (3)
concerning program effectiveness, the initial results derived
from this pre-post comparison revealed statistically significant
increases in the life skills addressed, a decline in at-risk alcohol
use, and stable prevalence rates for tobacco and cannabis use.
The proactive invitation for program and study participation in
the school setting, in combination with the offer of a
low-threshold mobile phone–based intervention, permitted us
to reach 4 out of 5 adolescents for participation in the life skills
program ready4life. Given the program duration of 6 months
and that program participants needed to indicate their mobile
phone number, this high participation rate is particularly
remarkable and was even higher than for substance-specific
mobile phone–based programs conducted in the same setting
and using similar recruitment procedures; between 50% and
75% participated in comparable programs to support smoking
cessation [25,26], whereas 75% participated in comparable
programs to reduce problem drinking [39,40]. Beyond proactive
recruitment in the school setting and during school hours, the
following reasons might have contributed to the high
participation rate we observed: (1) adolescents were invited by
an institution independent of their school and teacher
(anonymity); (2) the mobile phone–based program was flexible
for use at any time and in any place, and withdrawal from the
program was permitted at any time; (3) program participation
and use were associated with participation in a friendly
competition with the chance to win one of several attractive
prizes; and (4) the program contents were developed specifically
for adolescents during their vocational training.
Participation in the program was lower in male adolescents and
among those reporting an immigrant background. These findings
should be considered for program optimization, for example,
by highlighting the relevance of this program for these
subgroups or by emphasizing interesting program elements,
focusing particularly on these target groups, during program
presentations in school classrooms.
Overall acceptance of the intervention was good. Nearly all
program participants (95%) stayed logged in until the end of
the program, which lasted 6 months. The SMS messages were
read by almost all program participants (94%), and 3 out of 4
participants reported that they were helpful and perceived the
text messages as individually tailored to them. However, 15%
failed to engage in any of the 39 program activities, and 52%
engaged in fewer than half of the possible activities. On the
basis of this finding, there is clearly room for improvement in
terms of active program engagement, particularly concerning
the picture and message contests, which received the poorest
ratings among all program elements. The poor rating for this
highly interactive element might be because of the limitations
of mobile phone texting to receive and send pictures, which
could be implemented more elegantly within a chat-based native
mobile phone app.
The results concerning the initial effectiveness of this program
derived from a pre-post investigation are promising. Data
revealed a decrease in perceived stress, an increase in social
skills, and increases in two out of the three desirable
self-management strategies (seeking social support and palliative
emotion regulation), whereas no changes were observed in less
desirable self-management strategies (avoidant coping and
anger-related emotion regulation). The proportion of adolescents
with at-risk alcohol use was reduced by a quarter from baseline
assessment to follow-up, whereas no significant changes were
obtained in the prevalence of tobacco and cannabis use. On the
basis of typically increasing levels and frequency of substance
use in adolescence and early adulthood [41], these stable or
decreasing prevalence rates might be attributable to program
participation. However, no final conclusions on program
effectiveness should be drawn from this study, as we could not
control other factors such as fluctuations that might have
occurred over the course of the year.
Limitations
Beyond the limitations associated with the pre-post study design,
some other study limitations should be mentioned. First, the
results are restricted to adolescents without regular cigarette
use, as only they were deemed eligible to participate in this
general mobile phone–based life skills training. Second, only
50% of the study participants completed the follow-up
assessment, which might have biased evaluations of the program
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and the results on efficacy, even though we controlled for
attrition bias in our GEE models. Third, since the study focused
on program appropriateness, meaning that we wanted it to be
relevant to actual prevention practices, we restricted our outcome
assessments to either short forms of, or single items extracted
from more extensive validated instruments; furthermore all
outcomes were self-reported.
Conclusions and Outlook
This is the first study to test a comprehensive life skills training
program for substance use prevention delivered by a mobile
phone among adolescents. Our results suggest that this program,
which delivers individualized messages and interactive activities
integrated within a friendly competition, is both appropriate and
promising in its effectiveness. Moreover, this intervention could
be easily and economically implemented. On the basis of these
initial positive results, a reasonable next step would be to test
the efficacy of this program within a controlled trial.
Beyond testing the efficacy of digital-delivered life skills
training programs, the examination of moderators and mediators
of life skills trainings outcome in general remains an interesting
question, which should be addressed in future studies.
Concerning moderators, it would be of particular interest to
examine whether individuals with higher levels of substance
use could also benefit from life skills training programs and to
compare the effectiveness of these general life skills training
programs with substance-specific interventions, for example,
mobile phone–based programs for adolescents already
consuming specific substances such as tobacco or alcohol
[20-22]. Results from the IPSY program, conducted in young
adolescents from Germany [13], indicated that this school-based
and face-to-face delivered life skills training was ineffective for
adolescents who are on a problematic developmental pathway
of alcohol use. The authors conclude that this subgroup might
be in need of an earlier, more intensive and tailored treatment
compared with IPSY. In contrast, findings from the ALERT
Plus project delivered in seventh and eighth grade, with booster
lessons in the ninth grade, showed that curricula during high
school can also be effective with at-risk youth, who are
particularly likely to escalate drug use and experience
drug-related harms [42].
Concerning mediators, it would be of particular interest to test
which of the life skills addressed and successfully modified in
turn might prevent or decrease problematic substance use, for
example, results from the ALERT Plus project showed that
program-induced changes in perceived social influences, one’s
ability to resist those influences, and beliefs about the
consequences of drug use mediated the effects on drug use [42].
Another interesting topic which should be addressed in future
studies concerns potential spillover effects of general life skills
training programs on other mental health conditions. As
substance use and other mental disorders, for example, alcohol
use disorders and depression, do not emerge as single
impairments but rather co-occur [1,43], effects of these programs
on further mental health–related outcomes should also be
investigated. Concerning the comparison of substance-specific
interventions and more general life skills training programs,
one could assume that the latter might have an impact on a wider
spectrum of mental health conditions.
Acknowledgments
Funding for this study was provided by the Lung Association. The authors would like to thank all the involved prevention
specialists within the Cantonal Lung Leagues in Aargau, Basel-Land, Basel-Stadt, Berne, Vaud, and St Gallen. Finally, they
would like to thank all the vocational school teachers and students who supported and participated in the project.
Conflicts of Interest
Single authors (SH, RPC, and AF) were also involved in the development of the intervention. The funding institution did not
influence the design and conduct of the study; the management, analysis, or interpretation of data; or the preparation, review, or
approval of the manuscript.
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Abbreviations
AUDIT-C: Alcohol Use Disorder Identification Test-C
GEE: generalized estimating equation
IPSY: Information + Psychosocial Competence = Protection
OR: odds ratio
PSS: Perceived Stress Scale
SD: standard deviation
SMS: short message service
WHO: World Health Organization
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Edited by G Eysenbach; submitted 14.07.17; peer-reviewed by R Schwarzer, B Suffoletto; comments to author 02.08.17; revised
version received 10.08.17; accepted 10.08.17; published 04.10.17
Please cite as:
Haug S, Paz Castro R, Meyer C, Filler A, Kowatsch T, Schaub MP
A Mobile Phone-Based Life Skills Training Program for Substance Use Prevention Among Adolescents: Pre-Post Study on the
Acceptance and Potential Effectiveness of the Program, Ready4life
JMIR Mhealth Uhealth 2017;5(10):e143
URL: https://mhealth.jmir.org/2017/10/e143/
doi:10.2196/mhealth.8474
PMID:
©Severin Haug, Raquel Paz Castro, Christian Meyer, Andreas Filler, Tobias Kowatsch, Michael P Schaub. Originally published
in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.10.2017. 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 mhealth and uhealth, is properly
cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright
and license information must be included.
JMIR Mhealth Uhealth 2017 | vol. 5 | iss. 10 | e143 | p.14https://mhealth.jmir.org/2017/10/e143/
(page number not for citation purposes)
Haug et alJMIR MHEALTH AND UHEALTH
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