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Citation: de Sousa, D.; Fogel, A.;
Azevedo, J.; Padrão, P. The
Effectiveness of Web-Based
Interventions to Promote Health
Behaviour Change in Adolescents: A
Systematic Review. Nutrients 2022,14,
1258. https://doi.org/10.3390/
nu14061258
Academic Editor: Carlos
Vasconcelos
Received: 30 January 2022
Accepted: 10 March 2022
Published: 16 March 2022
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nutrients
Systematic Review
The Effectiveness of Web-Based Interventions to Promote
Health Behaviour Change in Adolescents: A Systematic Review
Daniela de Sousa 1,2, Adriana Fogel 1,2 , JoséAzevedo 1,2,3 and Patrícia Padrão1,2,4,*
1EPIUnit—Instituto de Saúde Pública, Universidade do Porto, 4050-600 Porto, Portugal;
daniela.sousa@ispup.up.pt (D.d.S.); adriana.fogel@gmail.com (A.F.); azevedo@letras.up.pt (J.A.)
2Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR),
4050-600 Porto, Portugal
3Faculdade de Letras, Universidade do Porto, 4150-564 Porto, Portugal
4Faculdade de Ciências da Nutrição e Alimentação, Universidade do Porto, 4150-180 Porto, Portugal
*Correspondence: patriciapadrao@fcna.up.pt; Tel.: +351-22-5074320
Abstract:
Although web-based interventions are attractive to researchers and users, the evidence
about their effectiveness in the promotion of health behaviour change is still limited. Our aim
was to review the effectiveness of web-based interventions used in health behavioural change in
adolescents regarding physical activity, eating habits, tobacco and alcohol use, sexual behaviour,
and quality of sleep. Studies published from 2016 till the search was run (May-to-June 2021) were
included if they were experimental or quasi-experimental studies, pre-post-test studies, clinical
trials, or randomized controlled trials evaluating the effectiveness of web-based intervention in
promoting behaviour change in adolescents regarding those health behaviours. The risk of bias
assessment was performed by using the Effective Public Health Practice Project (EPHPP)—Quality
Assessment Tool for Quantitative Studies. Fourteen studies were included. Most were in a school
setting, non-probabilistic and relatively small samples. All had a short length of follow-up and were
theory driven. Thirteen showed significant positive findings to support web-based interventions’
effectiveness in promoting health behaviour change among adolescents but were classified as low
evidence quality. Although this review shows that web-based interventions may contribute to health
behaviour change among adolescents, these findings rely on low-quality evidence, so it is urgent to
test these interventions in larger controlled trials with long-term maintenance.
Keywords:
systematic review; web-based intervention; health behaviour; behaviour change;
adolescents
1. Introduction
A major concern to public health researchers is lifestyle behaviours. Risky behaviours,
such as tobacco and alcohol use, unhealthy food habits, physical inactivity, risky sexual
practices, and insufficient sleep duration, play a significant role in many of the leading
causes of death worldwide [
1
]. According to data from 2015, 70% of all preventable deaths
from non-communicable diseases in adults are related to lifestyle risk factors adopted
during adolescence [2].
Adolescence is a critical period since many unhealthy habits and risky behaviours
begin at this age. However, it is also a window of opportunity for the development of
health-protective behaviours since health-related habits adopted at this age tend to persist
into adulthood [
3
]. For this reason, it is suggested that public health interventions aimed to
prevent or stop risky behaviours should target this life period [
4
], knowing that improving
adolescents’ health now is also ensuring a better future for the next generations [5,6].
A positive aspect for health promotion is that, since behaviours are modifiable
health-related
variables, the threat they represent is greatly preventable [
7
–
9
] and even
minor changes in human behaviours can improve the overall population’s health [
1
].
However, the drawback is that behaviour change is a very complex and iterative process,
Nutrients 2022,14, 1258. https://doi.org/10.3390/nu14061258 https://www.mdpi.com/journal/nutrients
Nutrients 2022,14, 1258 2 of 24
in which even individuals who are aware of better health practices still fall short in
adjusting their behaviour [10].
Therefore, focusing on achieving and maintaining successful behavioural change
in individuals and communities is a key question for health research [
10
]. Over the
years,
theories
, models, and techniques have been suggested to understand and predict
behaviour [11]
, contributing to the support of planners in handling challenges in the re-
search concept, implementation, and evaluation to improve the effectiveness of behavioural
interventions [12].
Until this moment, there has been no consensus on the key role that these behavioural
interventions can play in population-level health. However, there is already agreement
that understanding theories of behaviour change is an essential element of successful
health-related interventions [13].
The relationship between the healthcare provider and the patient is now much more
different than it was before, and at the end of the 20th century, researchers and health
professionals began to realize how important shared decision-making is in healthcare
service. Additionally, a growing interest in participatory approaches to health promotion
has been observed, especially in interventions targeting children and adolescents. So, as the
medical paradigm was changing, digital technologies able to raise patient empowerment
were also becoming more readily available [14].
Thus, digital health promotion interventions, especially internet-based technologies,
have been suggested as important tools to improve individuals’ health and the quality of
healthcare services and to reduce health inequalities due to their large-scale availability.
Although there is a lack of robust evidence to support the effectiveness of web-based inter-
ventions, it seems to be a promising approach to support behavioural health change [
15
],
which is becoming increasingly attractive to researchers [16].
The interest in using web-based interventions in the health field is still growing. The
number of publications reveals this interest since this amount increased from 770 in 2016 to
1464 in 2020. In this same period, there was an increase of 683% in Pubmed/MEDLINE
publication results for “health AND web-based intervention” (Figure 1).
Figure 1.
Self-elaborated graph of trends by year of publication for search terms “health AND
web-based intervention” from PubMed/MEDLINE data on 26 August 2021.
Nutrients 2022,14, 1258 3 of 24
Although web-based interventions may be attractive to both users and researchers,
it is crucial to summarize evidence about their effectiveness. It is essential to identify
which are the delivery modes and the behavioural change theories and techniques most
frequently used to promote positive health behavioural change and its maintenance in the
long term [13].
Since there is a wide range of digital technologies (e.g., social media, telemedicine, data
analytics, artificial intelligence, personalized medicine, wearables, mobile apps, electronic
medical records, web-resources for health education, among others) and each of them
has unique capabilities and specificities [
17
], characterizing each one individually will
help researchers construct more robust evidence to better explain their impact on different
health outcomes.
So, it is valuable to summarize the recent findings concerning the effectiveness
of web-based interventions in adolescents’ health, especially those more critical to the
10–24-year-old
age range, namely physical activity, eating, smoking, alcohol use, sexual
behaviour, and quality of sleep. Additionally, it is important to evaluate how these inter-
ventions are modelled and used to support and maintain behavioural health change in this
population. The main objective of this study is to respond to this need by systematically
reviewing the literature published in the last 5 years.
2. Methods
2.1. Data Sources
This systematic review was prepared in accordance with the Preferred Reporting
Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [
18
]. The review
protocol was registered on the International Prospective Register of Systematic Reviews
(PROSPERO) (registration number CRD42021275508).
The electronic databases of PubMed/MEDLINE, Scopus, APA Journals, Web of Science
and SAGE Journal were searched from May to June 2021. The search terms used were
organized in three main sections: population, intervention, and health outcomes, each
illustrated by keywords and synonyms. All the terms used in the search strategy were
connected by the Boolean operator AND, between each main section, and OR, within each
section, as detailed in Table S1. We set the alerts for each database to reach new added
results. Additional articles were identified from the reference list of retrieved articles by
applying a reverse snowballing search.
2.2. Inclusion and Exclusion Criteria
Studies were eligible for inclusion if their full text was published in scientific journals
in English, Portuguese, or Spanish. Only studies published from 2016 till June of 2021 were
included, considering the wide range of digital technologies [
17
], the raising of patient
empowerment and their relationship with technology [
14
], as well as the exponential and
rapid development of digital technology in this last five-year period [19].
Considering the PICO-S framework (population, intervention, comparison, outcomes,
study design), we defined the other inclusion and exclusion criteria that we described
below. Articles that cover all inclusion criteria were considered in this systematic review.
2.2.1. Participants/Population
Inclusion Criteria
The population included only adolescents according to the definition used by
Sawyer et al., (2018),
which corresponds to people between 10 and 24 years old [
20
]
and who have participated in web-based health interventions.
Exclusion Criteria
Interventions targeted not directly to participants aged 10 to 24 years old but targeted
their parents, educators, or healthcare professionals were excluded. Interventions targeting
multiple ages were excluded if it was not possible to isolate the desired target age group.
Nutrients 2022,14, 1258 4 of 24
Specific subgroups such as disabled people or people with irreversible clinical conditions
or major chronic diseases were excluded since the review focused on general adolescents.
2.2.2. Intervention/Exposure
Inclusion Criteria
In our review, we considered the definition described by Barak et al., (2009), which
mentions that a web-based intervention is “a primarily self-guided intervention programme
that is executed using a prescriptive online programme operated through a website and
used by consumers seeking health and mental-health related assistance. The intervention
programme itself attempts to create positive change and or improve/enhance knowledge,
awareness, and understanding via the provision of sound health-related material and use
of interactive Web-based components” [
21
]. We considered web-based interventions devel-
oped for health promotion to improve and/or maintain positive health behaviours. To be in-
cluded, the web-based intervention can be a stand-alone intervention or a multicomponent
intervention where the use of web-based resources is one of the
intervention’s components.
Exclusion Criteria
We excluded articles if they do not report a web-based intervention, if the intervention
does not aim to reduce lifestyle risk factors (non-health promotion interventions) or if the
interventions aim to improve disease screening or to control major chronic diseases.
2.2.3. Comparator(s)/Control
Inclusion Criteria
Studies that compare the proposed intervention/exposure to another intervention
or a non-intervention group, as well as studies with a pre-test and post-test design, were
considered in this review.
Exclusion Criteria
Studies with no control group and only with a post-test design were excluded.
2.2.4. Outcome(s)
Inclusion Criteria
Regarding the main objective of this systematic review, we included studies if they
analysed the effectiveness of the intervention to promote desired behavioural change
using quantitative or mixed methods. This may include outcomes such as the extent and
maintenance of behaviour changes, risk reduction, cognitions and attitudes, behavioural
intention, subjective norm, self-efficacy, perceived behavioural control and pre-conditions
for practising and maintaining the desired behaviours (regarding physical activity, eating
habits and weight control, smoking, alcohol use, sexual behaviour, and quality of sleep).
Secondary outcomes such as adoption and adherence rates of the web-based interven-
tion, patient-reported experience, feasibility and usability assessments, and coherence of
the technology with behavioural change techniques will also be analysed when available.
Exclusion Criteria
Articles only evaluating the effectiveness of the intervention by qualitative methods
were excluded, as well as those that did not have a measurement of the effectiveness.
Additionally, studies not evaluating health outcomes related at least to one of these health
behaviours were excluded: physical activity, eating habits and weight control, smoking,
alcohol use, sexual behaviour, and quality of sleep.
Nutrients 2022,14, 1258 5 of 24
2.2.5. Study Design
Inclusion Criteria
The final set of included studies was limited to quantitative or mixed methods studies
as experimental studies, quasi-experimental studies, before-and-after studies/pre-post-test
studies, clinical trials, and randomized control trials.
Exclusion Criteria
Other types of publications, such as case studies, systematic reviews, meta-analyses,
case reports and series, ideas, editorials, opinions, study protocols and studies using only
qualitative methods were not included.
2.3. Data Extraction
The main author (DS) performed a search of the electronic databases. The articles found
by databases search, after applying the filters, were imported into Endnote TM20 software and
the duplicate records were removed by automation tools and manual search. Early screening
by titles and abstracts was performed by one author (DS) based on the aim of the study and the
eligibility criteria. Those articles identified as being potentially eligible were fully examined
by two researchers (DS and AF) separately to make sure they met the inclusion criteria. In
case of discrepancies, the decision was discussed and deliberated by both reviewers. If the
disagreement persisted, it was solved by two other authors (PP and JA).
The articles that met the specified inclusion criteria had their data extracted by the
main reviewer (DS) and validated by a second reviewer (AF) using a table developed in
Microsoft®Excel by the study team (Table S2).
Two researchers (DS and AF) independently performed the risk of bias assessment for
all included studies using the Effective Public Health Practice Project—Quality Assessment
Tool for Quantitative Studies (EPHPP), which has been validated for use in public health
research [
22
]. Even though we considered several tools, we chose the Effective Public
Health Practice Project (EPHPP) tool because it was a validated tool that could be used
across multiple study designs and had been developed to be used in systematic reviews
about effectiveness to questions related to public health programs [22,23].
The global rating for each article was assessed by evaluation as weak, moderate, or
strong regarding six domain ratings: selection bias, study design, confounders, blinding,
data collection methods, withdrawals, and drop-outs, according to a standardized guide
and dictionary (Table 1). Those with no weak ratings and at least four strong ratings
were considered strong. Those with less than four strong ratings and one weak rating
were considered moderate. Finally, those with two or more weak ratings were considered
weak. Two other domains were included in the assessment, but they were not included in
the overall score (the integrity of the intervention and analysis) [
23
]. After classifying all
dimensions, both reviewers (DS and AF) discussed and compared their assessments. When
discrepancies occurred, the reason was identified as oversight, differences in interpretation
of criteria or differences in interpretation of the study. After discussion, both researchers
agreed on a final decision.
Nutrients 2022,14, 1258 6 of 24
Table 1.
Quality assessment components and ratings for EPHPP instruments reproduced from
Thomas BH, Ciliska D, Dobbins M, Micucci S. A process for systematically reviewing the literature:
providing the research evidence for public health nursing interventions. Worldviews Evid Based
Nurs. 2004. [
23
], with permission from John Wiley and Sons, Copyright
©
2004 (License number
5266150531479 obtained on 11 March 2022).
Components Strong Moderate Weak
Selection bias
Very likely to be
representative of the target
population and greater than
80% participation rate
Somewhat likely to be
representative of the target
population and 60–79%
participation rate
All other responses or not
stated
Study design RCT and CCT
Cohort analytic, case–control,
cohort. Or an interrupted time
series
All other designs or not stated
Confounders Controlled for at least 80% of
confounders
Controlled for 60–79% of
confounders
Confounders not controlled
for or not stated
Blinding
Blinding of outcome assessor
and study participants to
intervention status and/or
research question
Blinding of either outcome
assessor or study participants
Outcome assessor and study
participants are aware of
intervention status and/or
research question
Data collection methods Tools are valid and reliable Tools are valid but reliability is
not described
No evidence of validity or
reliability
Withdrawals and drop-outs Follow up rate >80% of
participants
Follow-up rate of 60–79% of
participants
Follow-up rate of <60% of
participants or withdrawals
and drop-outs not described
2.4. Data Synthesis
Considering the broad health behaviours included in our research question, substan-
tial heterogeneity between studies was found regarding their aims, methods and reported
outcomes. Thus, we decided to perform a qualitative synthesis to summarize the extracted
data rather than perform a meta-analysis. By doing so, we intended to systematically review
web-based interventions related to health behaviour change to interpret the results and
draw conclusions about their effectiveness, feasibility, usability, and use of behaviour chang-
ing techniques. Furthermore, we also identified the limitations and proposed directions for
future research.
3. Results
Table 2encompasses the summary of narrative synthesis, and it includes authors,
publication year, country/region, setting of the study, study design, health outcomes and
main findings. More detailed information is available in Tables S3–S5.
3.1. Study Selection
As described in the PRISMA flowchart (Figure 2), 449 results were found through
the search in the five electronic databases. Of those, 189 results were marked as ineligible
by automated tools of the databases, the remaining articles were imported into Endnote
TM20 software, and 77 results were found to be duplicate records. In total, 266 results were
removed before the screening. One of the authors (DS) screened the title and abstracts of
183 studies and, based on the purpose of the study and the inclusion and exclusion criteria,
DS identified 65 studies sought for retrieval. The full texts of those potentially eligible
studies were independently assessed by two reviewers (DS and AF) using the inclusion
and exclusion criteria. In cases of discrepancies, the decision was discussed and deliberated
by both reviewers. If the disagreement persisted, it was solved by the two other authors
(PP and JA).
Nutrients 2022,14, 1258 8 of 24
The reference list of retrieved articles was searched, which resulted in 11 articles
being added to be assessed for eligibility, two others were identified from databases’ alerts
and three more were found by searching the intervention names of the articles excluded
because they did not evaluate the effectiveness of the intervention. In total, 16 records were
identified via other methods, and their full texts were compared with our eligibility criteria;
of those, 12 were excluded.
In total, 18 records met all the inclusion criteria, describing a total of 14 different
interventions. We found five records with the same main author describing the same
intervention (The eCHECKUP TO GO) [
24
–
28
], so prevent duplicate studies that might
lead to biased results, we assessed the time of recruitment, the sample size, and the
time of follow-up [
29
]. The decision was to include in the narrative synthesis the work
of
Doumas, D. M. et al., (2021)
since it had the best combination of the longest time of
follow-up (6 months) with the greatest sample size (n= 311) [28].
3.2. Description of the Studies
From among the 14 included studies, 6 were conducted in the United States of
America (42.9%) [
28
,
30
–
34
], 3 in European countries (21.4%) [
35
–
37
], 3 in Asian coun-
tries
(21.4%) [38–40]
and 2 in Mexico (14.3%) [
41
,
42
]. Of those, 10 were implemented in an
educational setting (71.4%) [28,30,32,33,37–42].
Concerning study design, seven studies were randomized controlled trials (50.0%)
[28,32,33,35–37,40]
, four were quasi-experimental studies
(28.6%) [38,39,41,42]
and three
had a pre and post-test design (21.4%) [30,31,34].
Regarding the desired behaviour change, four interventions intended to promote
physical activity (28.6%) [
30
,
31
,
38
,
39
], one aimed to modify physical activity simultane-
ously with fruit and vegetable consumption (7.1%) [
40
], four were related to alcohol use
(28.6%) [
28
,
34
,
35
,
37
], four tried to prevent risky sexual behaviours (28.6%) [
33
,
36
,
41
,
42
] and
one proposed preventing tobacco use (7.1%) [
32
]. None of the sleep hygiene interventions
records was found to meet the inclusion criteria.
Nutrients 2022,14, 1258 9 of 24
Table 2. Summary of narrative synthesis of included studies.
Author, Year,
Country Setting Study Design Participants Web-Based Intervention Health Outcomes of Interest Main Findings
Wilson, M. et al.,
(2017) [30]
USA, North-western
United States
School
One-group
pre-/post-test design
(pre-experimental)
N= 20 students
(convenience sample)
Mean age of 16.8 years.
Multicomponent Intervention:
wearable digital tracking
device using an
Internet-based platform +
group physical activities +
nutrition group
education/individual
counselling session on healthy
eating + weekly
goal-setting sessions.
Measured at baseline and
post-intervention:
BMI calculation.
Blood glucose level.
Blood pressure and pulse
measurements.
Fitness and cardiovascular fitness.
Cognitive and affective variables
related to health behaviours.
Adolescents’ physical activity (PA)
and healthy eating.
Self-efficacy for PA and healthy
eating.
Self-determination.
Screen time.
Participants showed improvements from
pre-test to post-test in health and fitness
markers (positive changes in weight, fitness,
and cardiovascular measurements) and
improved motivation toward PA and reduced
screen time.
Larsen, B. et al.,
(2018) [31]
USA (San Diego, CA)
Hispanic
community
Pre/post-test design
(Single-arm pilot
trial)
N= 21 Latina adolescents
Mean age of 14.7 years.
Website mobile phone
friendly (tailored
Internet-delivered activity
manuals, computer-expert
system tailored reports,
activity tip sheets, and a guide
of local activity resources)
Measured at baseline and
follow-up (12 weeks): PA by 7-day
physical activity recall (PAR)
interview and ActiGraph GT3X+
accelerometers.
Results from the 7-day PAR showed that
positive changes in PA at 12 weeks were seen
not just in quantity but also in type. The usage
of validated self-report measures showed to be
better than accelerometers among this
population since there are some activities in
which the accelerometer may not be worn or
that were not well measured by the
accelerometer.
Huang, S. J. et al.,
(2019) [39]
Taiwan, Taipei City
School Quasi-experimental
(Three-armed)
Ninitial = 617 students
Mean age of 11.4 years.
Two experimental groups:
One using a web-based
exercise program applying a
self-management strategy
combined with geographical
information system (GIS)
mapping function and using a
narrative animated cartoon.
The other was
knowledge-only using only
the animated story.
Measured at baseline,
immediately post and 3-month
follow-up:
PA by the Chinese version of the
Child/Adolescent Activity Log.
Exercise-related self-efficacy using
a 5-item
Exercise-Related-Self-Efficacy
Scale.
Perceived benefit of PA using a
self-developed 7-item
Perceived-Benefit-of-Exercise
Scale.
This intervention using self-management
strategy + GIS mapping function was effective
in producing small but significant increases in
school children’s self-efficacy and PA.
The perceived benefit and self-efficacy of
regular PA might have partly affected the
participants’ PA levels because the self-efficacy
factor was always higher for both
experimental groups than for the control at the
post-test and follow-up; it was also higher for
the self-management group than for the
knowledge-only group. The intervention was
more effective for male students than females.
Nutrients 2022,14, 1258 10 of 24
Table 2. Cont.
Author, Year,
Country Setting Study Design Participants Web-Based Intervention Health Outcomes of Interest Main Findings
Pirzadeh, A. et al.,
(2020) [38]
Iran, Isfahan
University Quasi-experimental
N= 278 high school
students
Mean age is not described.
Two web-based intervention
groups.
One group received education
through a website with
tailored education strategies
based on TTM.
The second group only
received general education by
the same website but without
tailored materials.
Measured before intervention and
6 months after:
Stage of exercise behaviour
change questionnaire.
Processes of change questionnaire.
Decision-making balance
questionnaire.
Exercise self-efficacy scale.
International PA questionnaire
short form.
Education on PA based on the website can be
effective. The percentage of students with low,
moderate, and severe levels of physical activity
in the two intervention groups has increased
significantly after the intervention.
Participants showed significant progress
during stages of change post-intervention and
changes were greater in the group who was
trained by the TTM.
Duan, Y. P. et al.,
(2017) [40]
China, Central
Region
University Randomized
controlled trial
Ninitial = 493
undergraduate students
Npost-intervention = 337
N1-month follow-up = 142
Mean age of 19.2 years.
Web-based intervention
modules target
social–cognitive indicators for
health behaviour change for
Physical Activity and Fruit
and Vegetable Intake (FVI)
(information about risks and
benefits, motivating
intentions to change,
identification of barriers, goal
setting, development of action
plans, coping plans and social
support, providing tailored
normative feedback).
PA by Chinese short version of the
International Physical Activity
Questionnaire (IPAQ-C).
FVI in the past 7 days.
Stages of behavioural change for
PA and FVI.
Social-cognitive indicators of
behaviour change: positive and
negative outcome expectancies for
PA and FVI; self-efficacy for PA
and FVI; action planning; coping
planning; social support;
intentions for PA and FVI;
habit scale.
Students in the intervention group reported
more FVI over time. Average FVI for the
intervention group were all greater than the
recommended amounts at the end of the
8-week intervention and the 1-month
follow-up.
In terms of PA behaviour, there was no
significant interaction effect.
Positive results on stage progression for the PA
and FVI.
All 6 tests revealed significant treatment effects
on motivational, volitional, and distal
indicators of PA and FVI over time.
Khalil, G. E. et al.,
(2017) [32]
USA, Texas, Houston
School
Randomized
controlled trial (2-arm
single-blinded)
N= 101 adolescents
Mean age of 13.4 years.
Two web-based intervention
groups: One features
interactivity and
entertainment to engage
adolescent users (text,
animations, videos,
task-oriented activities,
two-dimensional environment
to explore health information
and make a virtual character).
The second included the same
health information but
without any features of
interactivity or entertainment.
Measured at baseline and
follow-up:
Intention to smoke using items
adapted from the susceptibility to
smoke scale.
The more participants considered intervention
interactive and entertaining, the more they
were probably going to show a reduction in
their intention to smoke. Perceived
interactivity had a more grounded relationship
with the reduction in intention to smoke than
perceived entertainment.
Nutrients 2022,14, 1258 11 of 24
Table 2. Cont.
Author, Year,
Country Setting Study Design Participants Web-Based Intervention Health Outcomes of Interest Main Findings
Castillo-Arcos Ldel,
C. et al., (2016) [42]
Mexico, Urban
Mexico
School
Quasi-experimental
(single-stage cluster
sampling)
N= 193 participants
Mean age of 15.8 years.
Multicomponent intervention:
6 online sessions + 2
face-to-face
activities aimed at increasing
levels of social competence
and resilience about sexual
behaviours.
Measured pre-and
post-intervention:
Self-reported risky sexual
behaviours (defined as
self-reporting unprotected sex,
multiple concurrent sexual
partners, and alcohol or drug use
during sex).
Resilience to risky sexual
behaviour (defined as the ability
to identify and practice strategies
to avoid risky sexual behaviour).
The intervention was independently
associated with improved self-reported
resilience to risky sexual behaviours though
not with a significant reduction in those
behaviours in multivariate analyses.
Participant age mediated the effect of the
intervention on resilience, influencing the
effectiveness of the intervention.
Doubova, S. V. et al.,
(2017) [41]
Mexico, Mexico City
School Quasi-experimental
(field trial)
N= 833 adolescents
Mean age is not described.
Multicomponent intervention:
Educational sessions through
a website displayed by two
central characters + class
discussions
Main topics: dating, courtship,
sexual relationships,
misconceptions and myths
about gender roles and sexual
relationships, partner abuse,
STIs, early pregnancy,
self-esteem, safe sex, use of
condoms and condom
negotiation.
Measured at baseline, at the end
of the four educational sessions
(first month), and the end of the
follow-up period (fourth month):
Knowledge of STIs.
Multidimensional Condom
Attitudes Scale measuring
attitudes regarding condom use.
Self-efficacy toward consistent
condom use.
The intervention had a positive effect on
improving adolescents’ knowledge of STIs,
attitudes and self-efficacy toward consistent
condom use. In the intervention group, the
average knowledge of STIs increased by 30
points compared to the control group. An
increase in positive attitudes and self-efficacy
toward consistent condom use was also
observed more often in the intervention group.
Brown, K. E. et al.,
(2018) [36]
United Kingdom
(UK), Midlands
Clinical
(sexual
health
service)
Pilot randomized
controlled trial
(two-armed
parallel-group)
Ninitial = 88 integrated
sexual health service
attendees
Nfollow-up = 67
Mean age of 20.0 years.
Multicomponent intervention:
brief tailored web-based
programme + paper-based
action planning card.
Content about contraceptive
pills and/or condoms use
using characters with audio to
take the user through the
process of identifying
environmental cues to key
target behaviours and
planning to perform those
behaviours when the
environmental cue is present.
Measured at baseline and 3-month
follow-up:
Self-reported contraceptive pill or
condom “mishaps” in the past 3
months.
Contraceptive pill or condom use
intention.
Attitude toward contraceptive pill
or condom use.
Perceived behavioural control
over pill or condom use.
Subjective norm relating to pill or
condom use.
Trait self-control.
The intervention supported pill and condom
users to produce quality plans since potential
improvements were identified. Bivariate
correlations suggest that perceived
behavioural control may have a role over
method use within intervention content.
Additionally, having greater levels of trait
self-control may negatively affect plan quality.
The study suggests early indications that the
intervention could reduce the number of
mishaps of intervention participants.
Nutrients 2022,14, 1258 12 of 24
Table 2. Cont.
Author, Year,
Country Setting Study Design Participants Web-Based Intervention Health Outcomes of Interest Main Findings
Widman, L. et al.,
(2018) [33]
USA, South-eastern
School Randomized
Controlled Trial
N= 222 tenth-grade girls
Mean age of 15.2 years.
Interactive, skills-focused
web-based intervention.
The intervention includes 5
modules about safer sex
motivation, HIV and other
STDs, sexual norms and
attitudes, safer sex
self-efficacy, sexual
communication skills that can
be completed on a computer,
tablet, or smartphone device.
Each module used audio and
video clips, tips from other
adolescents, interactive games
and quizzes, infographics,
and skill-building exercises
with self-feedback given in
real-time).
Measured at pre-test, post-test and
4-month follow-up:
Behavioural assessment of sexual
assertiveness skills (at refusing
unwanted sexual activity and
negotiating condom use).
Self-reported sexual assertiveness
by Multidimensional Sexual Self
Concept Scale.
Knowledge regarding HIV and
other STDs.
Intentions to use condoms and to
communicate about sex
with items from the AIDS Risk
Behaviour Assessment.
Sexual Self-Efficacy from
self-efficacy for HIV
prevention scale.
Immediately post-test, the intervention group
showed better sexual assertiveness skills
measured with a behavioural task, higher
self-reported assertiveness, intentions to
communicate about sexual health, knowledge
regarding HIV and other STDs, safer sex
norms and attitudes, and condom self-efficacy
compared with the control condition. At a
4-month follow-up, group differences
remained in knowledge regarding HIV and
other STDs, condom attitudes, and condom
self-efficacy.
Arnaud, N. et al.,
(2016) [35]
European countries
(Sweden, Germany,
Belgium, and the
Czech Republic)
Online
Randomized
controlled trial
(Two-armed
multisite)
Ninitial = 1449 adolescents
(Convenience sample)
Nfollow-up = 211
Mean age of 16.8 years.
Interactive web-based system
to generate individually
tailored content. Generated
information in small units
using text and graphics and
referred to previous
participants’ statements.
Measured at baseline and 3-month
follow-up:
Self-reported drinking index
(drinking frequency, frequency of
binge drinking, and typical
quantity of drinks) using
AUDIT-C screening tool.
Self-reported risky drinking as measured by a
drinking index was significantly reduced for
participants in the intervention group.
Statistically significant mean differences at
follow-up in favour of the intervention were
found for drinking frequency and binge
drinking frequency but not for quantity when
missing follow-up data were not imputed. In
contrast, analyses using an EM-imputed
dataset revealed drinking quantity as the only
significant secondary effect.
Nutrients 2022,14, 1258 13 of 24
Table 2. Cont.
Author, Year,
Country Setting Study Design Participants Web-Based Intervention Health Outcomes of Interest Main Findings
Norman, P. et al.,
(2018) [37]
UK, large city
University
Randomized
controlled trial
(full-factorial design)
Ninitial = 2,951 students
before starting university
Npost-intervention = 2681
Mean age of 18.8 years.
Brief online intervention
combining self-affirmation x
TPB-based messages x
implementation intentions in
a factorial design.
Measured at baseline, 1-week,
1-month and 6-month follow-up:
Self-reported alcohol intake (total
number of units consumed and
number of binge drinking
sessions/week).
Hazardous and harmful patterns
of alcohol use from 10-item
AUDIT (only at 6-month
follow-up).
Cognitions about binge drinking
(intention, affective attitude,
cognitive attitude, subjective
norms, descriptive norms, and
perceived control) and extent of
endorsement for the beliefs
(Engaging in binge drinking at
university would be fun; engaging
in binge drinking at university
would have a negative impact on
my studies; my friends engaging
in binge drinking would make my
binge drinking at university
more likely).
TPB-based messages had significant effects on
reducing the quantity of alcohol consumed,
frequency of binge drinking and harmful
patterns of alcohol use over the first 6 months
at university. Its effects did not diminish over
time. The messages also had significant
positive effects on intentions to binge drink,
cognitive attitudes, subjective norms,
descriptive norms, and self-efficacy, although
some effects weakened over time. The effects
on the quantity of alcohol and frequency of
binge drinking were mediated by TPB
variables with significant indirect effects
through intention and self-efficacy. The effect
sizes for the TPB-based messages on the
quantity of alcohol consumed (d = 0.20) and
the frequency of binge drinking (d = 0.17) were
small.
Messages were sufficiently relevant and
persuasive to produce changes in behaviour
without the need to form if-then plans.
Non-significant effects were found for
self-affirmation and forming implementation
intentions.
Coughlin, L. N. et al.,
(2021) [34]
USA, Michigan
Online Pre/post-test design
(Pilot study)
N= 39 participants
Mean age of 20.7 years.
Mobile intervention with
tailored messages and tips,
inspirational images to
reinforce content, web links to
articles, or other web-based
resources, based on users’
responses to daily and weekly
surveys.
The intervention included
gamification through a virtual
aquarium environment.
Measured at baseline and 1-month
follow up:
Concerning alcohol use (quantity
and frequency of use,
consequences of use, intention,
importance confidence of change,
perceived risk, reasons for use,
and past month driving under
influence of use).
Participants’ substance use declined over time,
and those reporting using the app more often
reported less substance use (including fewer
days drinking alcohol, binge drinking, fewer
consequences of use and episodes of driving
after drinking) at the 1-month follow-up than
those who reported using the app less often.
Nutrients 2022,14, 1258 14 of 24
Table 2. Cont.
Author, Year,
Country Setting Study Design Participants Web-Based Intervention Health Outcomes of Interest Main Findings
Doumas, D. M. et al.,
(2021) [28]
USA, Northwest
region
School Randomized
controlled trial
N= 311 high school seniors
Mean age of 17.1 years old.
Online personalized
normative feedback
intervention via text, graphs,
and video recordings. The
program is intended to reduce
risk factors for alcohol use
and increase protective
behaviours.
Measured at baseline, 30-day and
6-month follow-up:
Weekly drinking quantity.
Estimated peak blood alcohol
concentration (eBAC).
Self-reported peak alcohol volume.
Classification of High-Risk vs.
Low-Risk drinkers by participants’
report on the frequency of binge
drinking in the past month.
The intervention effects were moderated by
risk status, such that high-risk students in the
intervention condition reported a greater
reduction in alcohol use relative to students in
the control condition.
For weekly drinking quantity, intervention
effects were limited to the baseline to 30-day
follow-up period. Among high-risk students
was found a significant decrease in weekly
drinking in the intervention condition.
However, intervention effects from baseline to
the 6-month follow-up were not significant
since the control condition also reported
significant decreases in weekly drinking.
For eBAC, intervention effects were evident at
the 30-day follow-up and were sustained at the
6-month follow-up. Specifically, among
high-risk students, we found a significant
decrease in eBAC relative at the 30-day and
6-month follow-up. It is unclear why sustained
intervention effects were found for eBAC but
not for weekly drinking.
Non-significant intervention effects for
low-risk drinkers.
Nutrients 2022,14, 1258 15 of 24
3.3. Recruitment and Participants
Our review encompasses data from 7616 participants, with 10 included studies having rela-
tively small size samples (
≤
150 subjects per study’s condition or
control) [28,30–34,36,38,39,42]
.
Sample sizes ranged from 20 subjects in the published work from Wilson, M. et al., (2017) [
30
] to
almost 3000 participants in the study from Norman, P. et al., (2018) [37].
Data included in our review were collected from participants aged 10 to 24 years
old, with an average mean age of 16.7, from all studies, except two of them who did not
indicate the mean age of subjects [
38
,
41
]. The majority of the studies included older adoles-
cents (aged > 14 years old) [
28
,
30
,
33
,
35
,
37
,
38
,
41
,
42
], only one was focused only on younger
adolescents [39]
, two also encompassed younger adolescents (aged
≤
14 years old) alongside
older ones [31,32], and three also analysed emerging adults (aged < 25 years old) [34,36,40].
Concerning ethnic background, most interventions were tested in Caucasian par-
ticipants [
28
,
30
,
33
–
37
], three focused on Asian participants [
38
–
40
], another three were
implemented in Hispanic participants [
31
,
41
,
42
] and one mostly included both Hispanic
(43.6%) and Afro-American participants (41.6%) [32].
There were more females than males in most studies [
28
,
30
,
34
,
37
,
40
–
42
], with two
of the included studies only targeting girls [
31
,
33
], yet none were targeted only to male
participants. In three studies, slightly more than half of the sample were men [
32
,
35
,
39
].
No information about sex representativity was available in Brown, K. E. et al., (2018) [
36
]
and Pirzadeh, A. et al., (2020) [38].
In most of the studies, recruitment was achieved through educational institutions
using institution-wide announcements, information sessions in lectures, classrooms, or
after-school program meetings, and by sending emails and letters to participants and
parents when applied [
28
,
30
,
32
,
33
,
37
–
42
]. Social media advertisements [
34
,
35
], announce-
ments in health promotion sites, open access to the intervention’s website landing page [
35
],
printed promotion materials distributed in public areas (such as schools, cafes, bars,
stores, youth meetings and health-focused community events) [
31
,
35
], referencing by
other
participants [31]
and using a brief verbal introduction and printed material presented
by the staff to clinical attendees [
36
] were other recruitment strategies identified in the
included studies.
These recruitment strategies resulted in non-probabilistic samples in all these trials, so
results may not be generalized to out-of-sample contexts.
In eight studies, financial rewards, gift cards, giveaway items and prize draws were
used as incentives for retention [28,30,32–35,37,42].
Only three studies had 6 months of follow up [
28
,
37
,
38
]. The others had a shorter
length of study follow-up (<6 months) [
31
,
33
–
36
,
39
–
41
] and three of them did not include
follow-up measurements aside from the moment immediately post-intervention [
30
,
32
,
42
].
3.4. Web-Based Interventions
A variety of web-based interventions were evaluated in the included articles, from
brief online interventions based on text messaging delivered through e-mail with multi-
media content links [
37
] or wearable digital tracking devices to record data and provide
feedback on progress using an internet-based platform [
30
], to websites using narrative
and animation to deliver content and challenges into a real-life context combined with a
geographical information system to record progress [39].
Nearly half of them were at least somewhat tailored [
33
–
36
,
38
,
40
] and the degree of
customization was also variable, ranging from interactive systems designed to generate
individually tailored content matching participants’ response choices [
35
,
36
] to interven-
tion elements that were consistent with the participants’ level of motivation/readiness
to change and personalized reports on the participants’ progress [
29
] according to their
questionnaire responses.
From among our 14 included articles, 10 interventions were exclusively internet-
based and used the web to deliver all intervention components including the online data
collection [
26
,
29
,
32
–
34
,
36
–
39
]. Most were delivered through a website and one of them was
Nutrients 2022,14, 1258 16 of 24
presented as a mobile application to create a gamification environment, a data collection
field and shared affiliation links to other web-based resources [
30
]. However, in the other
four studies, the web-based component was merely one element of a multicomponent
intervention, such as using a wearable digital tracking device to record progress in an
internet-based platform combined with workshops, lectures, and goal setting counselling
face-to-face with professionals [
31
] or a brief tailored web-based programme with paper-
based action planning cards [
35
], or to support online educational sessions with face-to-face
sessions [41] or class discussions [40].
All our identified trials were health promotion interventions and included content
to promote behavioural change on a range of topics, such as dietary patterns and healthy
eating, physical activity, alcohol and tobacco use, and sexual behaviour.
3.5. Behaviour Change Theories and Techniques
All the included studies were theory-based interventions. Some of them were con-
structed based on only one theory or model, such as the Transtheoretical Model/Stages
of Change [
37
], Operant Conditioning Theory [
30
], Information–Motivation–Behavioural
Skills Model [
40
], Conceptual Framework of Adolescent Sexual Resilience [
41
], Theory of
Motivational Interviewing [
34
], Health Action Process Approach [
39
], and the Self-efficacy
Theory as a subset of the Social Cognitive Theory [
31
]. In contrast, other studies relied on
more than one theoretical model, combining, namely: the Social Cognitive Theory with
the Transtheoretical/Stages of Change Model [
29
], the Experiential Learning Theory and
the Extended Elaboration Likelihood Model [
32
], the Theory of Planned Behaviour with
the Health Action Process Approach [
35
] or with Self-affirmation and Implementation
Intentions [
36
], the Social Cognitive Theory and the Health Belief Model [
38
], the Social
Norming Theory with Motivational Enhancement models [
26
], as well as the Reasoned
Action Model and Fuzzy Trace Theory with multiple others psychological and health
behaviour change techniques [33].
3.6. Effectiveness of the Web-Based Interventions
Among the 14 included studies, three used differences between pre and post-test as-
sessment [
30
,
31
,
34
] to document their effectiveness, while the remaining 11 based their find-
ings on differences from intervention group to control groups, using active and non-active-
control groups. Namely, six studies used a control group as
assessment-only [28,35–38,40]
;
three studies used a non-web-based educational intervention as the control group, with one
regarding the study outcome [
41
], while the other two were about generic health themes
other than the one being studied [
39
,
42
]; the last two were web-based interventions, where
one was about an unrelated health theme [
33
] and the other used a website with only
written content and without interactivity or entertainment features [32].
Thirteen of the fourteen studied interventions revealed significant positive findings
that support web-based intervention effectiveness in promoting health behaviour change,
namely in improving motivation [
30
] and the practice of physical activity [
31
,
38
,
39
] as well
as positive changes in weight, fitness and cardiovascular measurements [
30
]; in decreasing
self-reported problematic alcohol use [
28
,
34
,
35
,
37
] and alcohol-related consequences [
34
];
in improving sex norms and attitudes, self-efficacy, self-reported sexual assertiveness
skills, intentions to communicate about sexual health, knowledge concerning to sexually
transmitted diseases and condom use [
33
,
41
] and to mitigate the numbers of mishaps in pill
and condom use [
36
]; in reducing intention to smoke in non-smokers [
32
]; and in increasing
fruit and vegetable intake [40].
Although the study from Castillo-Arcos Ldel, C. et al., (2016) had observed a crude
reduction in risky sexual behaviours in the intervention group, they were not able to show
a significant reduction in those behaviours using multivariate analyses since unexpected
effects in pre and post-test scores occurred in the control group. It is important to note
that the control group was subjected to the visualization of an educational video aimed to
Nutrients 2022,14, 1258 17 of 24
improve general health status, focusing on unhealthy food habits, mental health disorders,
drug use, violence, and accidents [42].
3.7. Other Outcomes
The most frequent non-health-related outcome measured in the included studies was
acceptability [30,31,34,36,39,41]
, but feasibility [
30
,
31
,
36
], engagement [
32
,
34
],
adherence [30,31]
and usability [30] were also evaluated in some studies.
The main aim of some of the studies was even to test feasibility and acceptability,
being the evaluation of potential efficacy, a secondary objective given the pilot nature of
those trials [30,31,34,36].
In these studies, the interventions overall proved to have reasonable levels of acceptabil-
ity (ranging from moderate [
31
,
36
] to good [
30
,
34
,
39
,
41
]) and good
feasibility [30,31,36,42]
.
As positive features, web-based interventions were classified by participants as easy to
use [
34
], interactive and entertaining [
32
]. The time demanded to accomplish proposed
activities [
41
], technical problems [
34
] and high drop-out rates [
35
,
40
] were negative aspects
of some of these interventions.
3.8. Risk of Bias Assessment
The critical appraisal of individual studies performed using the Effective Public
Health Practice Project—Quality Assessment Tool for Quantitative Studies (EPHPP) [
23
] for
selection bias
, study design, confounders, blinding, data collection methods, withdrawals
and drop-outs are described in Table 3. Overall, the EPHPP tool showed the low quality of
study methodology since 12 studies were classified as weak [
28
,
30
,
31
,
33
–
40
,
42
] and two
as moderate [32,41].
Nutrients 2022,14, 1258 18 of 24
Table 3. Risk of Bias Assessment—EPHPP Assessment Tool for Quantitative Studies.
Author, Year
Section Rating
Global Rating
Selection Bias Study Design Confounders Blinding Data Collection
Methods
Withdrawals and
Drop-Outs
Doumas, D. M. et al., (2021) [28] WEAK STRONG STRONG WEAK STRONG MODERATE WEAK
Wilson, M. et al., (2017) [30] WEAK MODERATE STRONG WEAK STRONG MODERATE WEAK
Larsen, B. et al., (2018) [31] WEAK MODERATE STRONG WEAK STRONG STRONG WEAK
Khalil, G. E. et al., (2017) [32] WEAK STRONG STRONG WEAK WEAK NOT APPLICABLE WEAK
Widman, L. et al., (2018) [33] MODERATE STRONG STRONG WEAK WEAK STRONG WEAK
Coughlin, L. N. et al., (2021) [
34
]
WEAK MODERATE STRONG WEAK STRONG STRONG WEAK
Arnaud, N. et al., (2016) [35] WEAK STRONG STRONG WEAK STRONG WEAK WEAK
Brown, K. E. et al., (2018) [36] MODERATE STRONG STRONG WEAK STRONG STRONG MODERATE
Norman, P. et al., (2018) [37] WEAK STRONG STRONG WEAK STRONG WEAK WEAK
Pirzadeh, A. et al., (2020) [38] WEAK STRONG WEAK WEAK STRONG STRONG WEAK
Huang, S. J. et al., (2019) [39] MODERATE STRONG STRONG WEAK MODERATE WEAK WEAK
Duan, Y. P. et al., (2017) [40] MODERATE STRONG WEAK WEAK STRONG WEAK WEAK
Doubova, S. V. et al., (2017) [41] WEAK STRONG STRONG MODERATE STRONG STRONG MODERATE
Castillo-Arcos Ldel, C. et al.,
(2016) [42]WEAK STRONG STRONG WEAK WEAK MODERATE WEAK
Nutrients 2022,14, 1258 19 of 24
4. Discussion
4.1. Summary of Findings
Most previous systematic reviews about digital health interventions are limited to
the self-management of clinical conditions or symptoms instead of focusing on health
promotion [
43
–
47
] or try to understand only one major health outcome change such as
nutrition-related behaviours [
48
–
51
], sedentary behaviours [
52
] and physical activity [
53
],
depression and mental health [
54
], alcohol-related problems [
55
] and their target population
is other than adolescents such as adults [56] and older adults [57].
Although one previous review and meta-analysis performed by Wantland, D. J. et al.,
(2004) had found substantial evidence that the use of web-based interventions could
improve knowledge and/or behavioural change outcomes when compared with non-web-
based interventions, that one focused on the general population [58].
Since we hypothesized that its effectiveness could be more relevant in a very digital-
skilled population, such as young people, our review intended to evaluate the effectiveness
of web-based interventions in health behaviour change in adolescents. Moreover, due to
the rapid growth of the technological field, an update focused on the most recent literature
was justified.
As well as the work from Wantland, D. J. et al., (2004) [
58
], our systematic review
also showed positive effects of internet-based interventions to achieve health behaviour
change, including increased motivation [
30
] and physical activity level [
30
,
31
,
38
,
39
], de-
creased harmful alcohol use and its consequences [
28
,
34
,
35
,
37
], improved attitudes, self-
efficacy, assertiveness skills, intentions to communicate and knowledge concerning to
sexual
behaviours [33,36,41]
, decreased intention to smoke [
32
], and increased fruit and
vegetable consumption [40].
However, these findings relied mostly on small sample sizes [
28
,
30
–
34
,
36
,
38
,
39
,
42
],
non-probabilistic samples, and studies with a lower length of follow-up, very context-
specific [
28
,
30
–
42
], which limits the generalizability of the results, as already described in
the literature [59].
In addition, although all studies analysed presented statistically significant differences
between groups (control vs. intervention or pre-test vs. post-test), not all evaluated
the intervention effect size [
31
,
36
] and when they do, different analytic estimators were
used, compromising the quantitative summary and interpretation of the dimension of
the differences.
In addition to the widely used statistical significance, the use of effect size for each
outcome should also be promoted, as it would allow for more detailed reading and inter-
pretation of results [60].
Although our review suggests that web-based interventions are a promising ap-
proach to achieve health behaviour change, robust evidence from larger randomized
controlled trials, from the population’s representative samples, with proven relevant effects,
is still needed.
A wide range of web-based interventions was found. It is worth noticing that the more
interactive and entertaining the intervention was, the higher was the participants’ retention in the
study. It also increases the intervention’s acceptability and
feasibility [32]
. Overall, web-based
interventions seem to have moderate to good acceptability and feasibility [30,31,34,36,39,41,42].
In contrast to the previous literature [
61
], we found that researchers are largely devel-
oping interventions based on theoretical frameworks and models, which has been shown
to improve an intervention’s effectiveness [
62
]. Critical points to the quality of evidence
seem to be mainly related to sampling issues, representativeness of populations and ab-
sence of blinding. We identified that some important information, which is required by
risk assessment tools, is sometimes non-available or unclear. The previous literature also
underlines that researchers should include more detailed descriptions of their web-based
interventions to achieve improved research designs [
63
]. Therefore, examining in advance
all the domains evaluated in these tools may help researchers to conduct more robust
methodological studies with a higher quality of evidence.
Nutrients 2022,14, 1258 20 of 24
Even though web-based interventions seem to be a promising approach in health
behaviour change with positive acceptability among adolescents, robust evidence is still
lacking. We keep making the same errors as in the past since we still lack results from
larger randomized controlled trials from high-quality papers (lower risk of bias), with
representative samples and testing the long-term maintenance of these health behaviour
changes (time of follow-up > 6 months). These limitations are known by most of the
authors, who refer to them in their papers [
28
,
30
–
35
,
39
–
41
]. Nonetheless, it is crucial to
identify why they keep being reported repeatedly and, moreover, try to overcome them.
It was also frequent that studies were being classified as pilot projects, highlighting the
need to study the effectiveness of the intervention in other studies, but never publish-
ing those randomized controlled trials with the sample size needed for the effect size
wanted. This may partially be explained by the lack of financial resources as well as time
availability. Planning, developing, ensuring internal testing and usability testing is very
time-demanding and costly research since the development of a web-based intervention
is often a back and forward process [
15
]. With limited funding and restricted time to
accomplish the intervention, most researchers will fail in providing robust evidence. We
suggest that more than developing new web-based interventions, researchers should unify
their strengths and resources to largely test the existing intervention in different cultural
contexts within different populations.
4.2. Limitations of This Review
The present review is intended to summarize the most relevant evidence available to
assess the effectiveness of web-based interventions to promote health behaviour change in
adolescents. We acknowledge that our selection of 2016 as the oldest reference comports
the risk of excluding previous robust evidence. However, the recent global increment of
the importance of the web in our daily life and the emergence of other digital innovative
technologies justify our focus.
It is also a fact that in the last two years, the resources and efforts of the worldwide
scientific community have focused on the issue of COVID-19, mitigating the investment in
health-promotion interventions for children and adolescents, since the educational context
where these interventions were often implemented has undergone critical adaptations.
This may partially justify the reduced number of included papers despite the growing
trend observed during the last years. Nevertheless, the mandatory lockdown reinforced
the need to invest in technological health promotion strategies that may be implemented at
a distance, large scale and with almost the same financial and human resources.
We are aware that, due to time restrictions, we left out other databases relevant to this
topic, such as EMBASE, ERIC, B-on, and Emerald, among others. It has been suggested
that piloting a sample of records through every review’s step, such as producing a “mini”
review, could be used to effectively change the criteria in the data extraction table to ensure
that the full review would include the most useful and relevant information, removing the
need to re-visit the papers at a later phase [
58
]. Thus, we can consider this work as a “mini”
systematic review since an update should be performed by running the search query in
other relevant databases. In addition, searching in grey literature, which was not included
in this review, could expand the number of eligible publications. Even though publication
bias has been largely documented in the literature, it seems that this bias is increasing. It
could be explained by the higher competition between researchers that tend to publish
positive results rather than negative ones [
59
]. For this reason, searching the grey literature
would give us a more realistic summary of evidence.
The main limitations of the present review include the combination of results from
well-designed and less rigorously designed studies, their heterogeneity of studies in terms
of setting, interventions, methods and outcome measures, and the lack of included records
on sleep hygiene. Additionally, the absence of analysis by subgroups of health outcomes
and active components of the behaviour change intervention may be considered a limitation.
Nutrients 2022,14, 1258 21 of 24
However, the low number of studies that used each health area and each behaviour change
technique did not allow this analysis to be performed.
In addition to these limitations, to our knowledge, this is the first systematic review
summarizing the effectiveness of web-based interventions in promoting a wide range of
health behaviour changes focused specifically on adolescents. Important findings were
highlighted to help researchers to reach high-quality evidence in the development and
evaluation of web-based interventions. Authors should discuss the results and how they
can be interpreted from the perspective of previous studies and of the working hypotheses.
The findings and their implications should be discussed in the broadest context possible.
Future research directions may also be highlighted.
5. Conclusions
Our findings support that web-based interventions significantly contribute to achiev-
ing health behaviour change among adolescents, regarding physical activity, eating habits,
tobacco and alcohol use and sexual behaviour, with reasonable levels of acceptability
and feasibility. Additionally, more evidence is needed to prove their effectiveness in
long-term
maintenance, since there are few studies with follow-up assessments longer than
6 months. As shown by the critical assessment of the risk of bias, these findings are of
low-quality evidence, so it is urgent to test these web-based interventions in larger ran-
domized controlled trials, within probabilistic samples, ideally in single or double-blinded
design and testing the long-term maintenance of these health behaviour changes (time of
follow-up > 6 months).
Supplementary Materials:
The following are available online at https://www.mdpi.com/article/
10.3390/nu14061258/s1, Table S1: Search terms. Table S2: Excel spreadsheet with extracted data
from included studies. Table S3: Data extracted about Recruitment and Participants. Table S4: Data
extracted about web-based intervention and behaviour change theories. Table S5: Data extracted
about secondary outcomes.
Author Contributions:
D.d.S. was responsible for the first draft of this manuscript and this systematic
review’s protocol. D.d.S. and A.F. performed the full-text screening against eligibility criteria, risk
of bias (quality) assessment and data extraction. P.P. and J.A. contributed to this study protocol
conception and design. All the authors critically revised the manuscript and gave their final approval.
All authors have read and agreed to the published version of the manuscript.
Funding:
This review was funded by national funds through the FCT—Foundation for Science
and Technology, I.P., under the project UIDB/04750/2020. Additionally, an individual PhD grant
attributed to AF (2021.08877.BD) was funded by FCT—Fundação para a Ciência e a Tecnologia and
the European Social Fund (ESF).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the
design, analysis or writing of this paper.
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