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A Rapid Review of the Impact of Family-Based Digital Interventions for Obesity Prevention and Treatment on Obesity-Related Outcomes in Primary School-Aged Children

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  • Health and Wellbeing Queensland

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Virtual delivery of obesity prevention and treatment programs may be effective for supporting children and families to adopt healthy lifestyle changes while enhancing program accessibility. This rapid review aimed to summarize the impact of family-based digital interventions for childhood obesity prevention and treatment. Four databases were searched up to February 2021 for trials of interactive digital programs aimed to prevent and/or treat obesity in children aged 5–12 years and reported diet, physical activity, sedentary behavior, sleep, or weight-related outcomes in children. A total of 23 publications (from 18 interventions) were included. Behavior change theories were used in 13 interventions with “Social Cognitive Theory” applied most frequently (n = 9). Interventions included websites (n = 11), text messaging (n = 5), video gaming (n = 2), Facebook (n = 3), and/or mobile applications (n = 2). Studies reported changes in body mass index (BMI; n = 11 studies), diet (n = 11), physical activity (n = 10), screen time (n = 6), and/or sleep (n = 1). Significant improvements were reported for diet (n = 5) or physical activity (n = 4). Two of the six interventions were effective in reducing screen time. Digital interventions have shown modest improvements in child BMI and significant effectiveness in diet and physical activity, with emerging evidence supporting the use of social media and video gaming to enhance program delivery.
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Citation: Chai, L.K.; Farletti, R.; Fathi,
L.; Littlewood, R. A Rapid Review of
the Impact of Family-Based Digital
Interventions for Obesity Prevention
and Treatment on Obesity-Related
Outcomes in Primary School-Aged
Children. Nutrients 2022,14, 4837.
https://doi.org/10.3390/nu14224837
Academic Editor: Rosa Casas
Received: 21 October 2022
Accepted: 13 November 2022
Published: 15 November 2022
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nutrients
Review
A Rapid Review of the Impact of Family-Based Digital
Interventions for Obesity Prevention and Treatment on
Obesity-Related Outcomes in Primary School-Aged Children
Li Kheng Chai 1, 2, * , Rebecca Farletti 1, Leila Fathi 2and Robyn Littlewood 1,2
1Health and Wellbeing Queensland, Queensland Government, Brisbane, QLD 4064, Australia
2
School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
*Correspondence: likheng.chai@uq.edu.au
Abstract:
Virtual delivery of obesity prevention and treatment programs may be effective for support-
ing children and families to adopt healthy lifestyle changes while enhancing program accessibility.
This rapid review aimed to summarize the impact of family-based digital interventions for childhood
obesity prevention and treatment. Four databases were searched up to February 2021 for trials of
interactive digital programs aimed to prevent and/or treat obesity in children aged 5–12 years and
reported diet, physical activity, sedentary behavior, sleep, or weight-related outcomes in children. A
total of 23 publications (from 18 interventions) were included. Behavior change theories were used
in 13 interventions with “Social Cognitive Theory” applied most frequently (n= 9). Interventions
included websites (n= 11), text messaging (n= 5), video gaming (n= 2), Facebook (n= 3), and/or
mobile applications (n= 2). Studies reported changes in body mass index (BMI; n= 11 studies),
diet (n= 11), physical activity (n= 10), screen time (n= 6), and/or sleep (n= 1). Significant improve-
ments were reported for diet (n= 5) or physical activity (n= 4). Two of the six interventions were
effective in reducing screen time. Digital interventions have shown modest improvements in child
BMI and significant effectiveness in diet and physical activity, with emerging evidence supporting
the use of social media and video gaming to enhance program delivery.
Keywords: children; family; obesity; virtual; web-based; program; review
1. Introduction
Childhood obesity is a global public health challenge of the 21st century and has been
designated as one of the five key policy priorities of the World Obesity Federation. Evidence
suggests that obesity is linked with an increased risk of complications and mortality from
coronavirus disease 2019 (COVID-19), which further adds to global health concerns [
1
].
Moreover, the need for self-isolation and/or quarantine during the COVID-19 pandemic is
prompting many to rely on processed food with a longer shelf life (instead of fresh produce).
Considering this, the ongoing burden of the pandemic threatens the risk of excessive weight
gain. A report published by the Centers for Disease Control and Prevention [
2
] indicated
that the body mass index (BMI) increment rates almost doubled between pre-pandemic
and pandemic periods in children aged 2–19 years across all BMI categories, except those
who were underweight. These findings highlighted the importance of preventing ex-
cess weight gain in children during and following the pandemic, as well as in the events
of future public health emergencies, by increasing access to programs that promote
healthy behaviors [2].
Conventional family-based obesity interventions are effective in promoting health
behavior changes for children. These programs usually engage at least one parent and the
child to encourage healthy behavior change by coaching parents in weekly, group-based
sessions which focused on goal-setting, problem-solving, monitoring, and role-modelling
Nutrients 2022,14, 4837. https://doi.org/10.3390/nu14224837 https://www.mdpi.com/journal/nutrients
Nutrients 2022,14, 4837 2 of 19
healthy behaviors. However, the common barriers faced by participants include geo-
graphical distance, ease of access, time constraints, and weight-related stigma. Research
suggests that web-based programs may be effective in supporting children and families to
adopt healthy lifestyle changes while enhancing program accessibility [
3
]. COVID-19
has led to an increase in using online digital platforms for remote work, education,
and healthcare [
4
]. Globally, there is a commitment to the development and implementation
of digital health technologies for the detection, prevention, and treatment of disease, and the
promotion of health and wellbeing. These technologies often include computers, websites,
mobile applications (apps), short message services (SMS), digital games, telehealth, and
wearables/monitoring devices. The potential scalable reach of these technologies provides
a unique opportunity for the implementation of population-wide behavioral interventions.
The current literature has broadly investigated the acceptability and efficacy of
technology-based interventions to improve clinical outcomes for children and adolescents [
5
]
and its impact to facilitate communication between caregivers and health professionals
in the clinical setting [
6
]. However, limited reviews of the literature to date have investi-
gated the effectiveness of family-based digital interventions across various health outcomes
in the context of obesity prevention or treatment for children. Due to the heterogeneity
and differing methodologies employed by studies investigating the effectiveness of dig-
ital interventions to improve health outcomes [
7
], as well as the dynamic development
and evolvement of digital technologies and their various applications, a rapid review
methodology would be more appropriate than a thorough systematic literature review
or meta-analysis [
8
]. Rapid reviews are useful in fields where change is ongoing, such as
the development and advancement of digital technologies. This review utilized a simpli-
fied methodology based on a systematic literature review process to produce synthesized
knowledge in a time-efficient manner [
9
]. The aim of this rapid review is to summarize
the impact of family-based digital interventions for obesity prevention and treatment in
children, with a specific focus on diet, physical activity, sedentary behavior, sleep, and
weight-related outcomes in primary school-aged children.
2. Materials and Methods
2.1. Search Strategy
Peer-reviewed journal articles were searched in February 2021 in four electronic
databases, including MEDLINE (1946 to present), CINAHL (1981 to present), PsycINFO
(1806 to present), and Cochrane Library (including Cochrane Reviews, the Database of
Abstracts of Reviews of Effectiveness, the Health Technology Assessment database). Hand-
searching of relevant outcome papers of eligible published protocols and the reference lists
of relevant systematic reviews were performed to identify other potentially eligible studies.
No language or geographical region restrictions were imposed. Unpublished studies or
grey literature publications (conference proceedings, blogs, theses, and dissertations) were
excluded from this review.
A list of search terms was developed based on previous reviews [
10
,
11
], presented in
Supplementary Table S1 (see supplementary materials). In brief, the search terms and com-
binations used were as follows: Family
(family OR parent OR carer OR mother OR father)
AND child (child OR children OR young person NOT infant) AND digital intervention
(online program OR digital tool OR multimedia OR app OR website OR eHealth OR mHealth)
AND interactive component (gaming OR music OR animation OR interactive) AND obesity-
related outcomes (body mass index OR waist circumference OR nutrition OR physical
activity OR sleep).
Web searching was performed in February 2021 to identify digital interventions
for obesity prevention and treatment in children, which may not have been indexed or
published in the databases searched. The Cochrane Handbook for Systematic Reviews
of Interventions [
12
] recommended that web searching should use a combination of search
engines and websites to ensure a wide range of sources are identified and searched in depth.
Google Search and Google Scholar were used to search for websites of digital interventions,
Nutrients 2022,14, 4837 3 of 19
online programs that were known to the researchers, and associated publications that
may be relevant to this review. Due to the basic search interfaces of the search engines,
a combination of simplified and natural language key terms, based on the search terms
used for searching bibliographic databases (child obesity online prevention program, an
online healthy lifestyle program for families), were used. The search was limited to the first
30 hits sorted by relevance.
2.2. Inclusion and Exclusion Criteria
2.2.1. Study Design
The review included studies with a parallel control group, including randomized con-
trolled trials (RCTs) and cluster RCTs; quasi-RCTs and cluster quasi-RCTs; controlled before
and after studies (CBAs) and cluster CBAs; controlled time series designs. Observational
studies, such as longitudinal cohort studies and cross-sectional studies were excluded.
Studies could be published in any language or geographic region. Only studies that:
(1) compare an online/digital intervention with no intervention or “usual practice” control
group, or (2) compare two or more online/digital interventions which aim to improve
obesity-related outcomes in children were included. Studies without a comparison arm
were excluded.
2.2.2. Participants
Eligible studies involved children who were aged 5–12 years and generally healthy,
and/or their parent(s) (i.e., carers, caregivers, guardians). Participants could be grouped
as follows: children and parents, whole family, parents only, or child only. Studies that
involved children aged below five years, pre-natal or ante-natal, infants, those aged above
12 years (adolescents), or those with chronic health conditions, were excluded.
2.2.3. Interventions
The conditions of interest were interactive digital programs for obesity prevention
and treatment interventions in children targeting diet, physical activity, sedentary behavior,
sleep, or weight-related measures. Any prevention and/or treatment intervention that
aimed to improve obesity-related outcomes in children and was delivered to participants
using a digital and/or an online platform as a standalone intervention or in combination
with a non-digital/online component was included. The platform may include websites,
mobile apps, SMS, and other online mediums. Interventions were excluded if they were
telephone-based or included phone coaching or in-person face-to-face interventions with-
out a digital/online component. The digital component of interest was the use of interactive
multimedia content in the interventions for visual storytelling and/or play-based learn-
ing. Examples include the use of games, gamification, music, and/or animation. Given
the novelty of this research area, studies were included where authors reported the use
of a digital and/or online platform, and a standard data extraction template was used
to collect information related to the use of interactive multimedia content included in
the interventions.
2.2.4. Outcomes
The main outcomes were any measure of the change in a child’s weight, BMI, waist
circumference, diet, physical activity, sedentary behavior, screen time, and/or sleep from
baseline to the last available follow-up, to assess intervention impact on child obesity-
related anthropometric and/or behavioral health outcomes. Most of the outcome variables
were continuous data, such as BMI and waist circumference, and were expressed as mean
difference and standard deviation, and/or confidence intervals. Where categorical outcome
data were reported, for example, whether participants improved dietary habits by eating
breakfast every day, the effect measures were expressed as the proportion of participants
who ate breakfast every day in the intervention group vs the control group.
Nutrients 2022,14, 4837 4 of 19
2.2.5. Settings
Studies undertaken in family-based or home-based settings were included. School-
based and hospital-based studies and experiments in a controlled environment
were excluded.
2.3. Study Selection
Two review authors (Leila Fathi and either Rebecca Farletti or Li Kheng Chai) screened
abstracts and titles independently for eligible studies. Review authors were not blinded to
author or journal information. The screening was performed using a standardized screening
tool developed by the author team experienced in conducting systematic reviews based on
the Cochrane Handbook for Systematic Reviews of Interventions [
12
], with reference to
the Rapid Reviews to Strengthen Health Policy and Systems: a Practical Guide published
by World Health Organization (WHO) [
13
]. The full texts of potentially eligible studies
were retrieved for further screening. Conflicts between review authors regarding study
eligibility were resolved by a third review author.
2.4. Data Extraction
Data from the included trials were extracted and checked by two review authors
(Leila Fathi and Rebecca Farletti). Conflicts between review authors regarding extracted
data were resolved by consensus and via a third review author where required.
Extracted data included study characteristics (aims and objectives, study design, number
of experimental conditions, targeted participants demographic characteristics, country,
intervention description and duration, behavior change theory/techniques used, and over-
all conclusion); study outcomes, including the data collection method, the validity of
instruments/measures used, effect size and measures of outcome variability; and source(s)
of research funding and potential conflicts of interest.
2.5. Quality Appraisal
Critical appraisal was performed by two review authors (Li Kheng Chai and
Robyn Littlewood) independently using the Joanna Briggs Institute Critical Appraisal
Tools, including Checklist for Randomized Controlled Trials [
14
]. The critical appraisal
tool that has previously been used by the author team in other systematic reviews, was
adapted for use in this review and was piloted before use. A risk of bias classification
(“high”, “low” or “unclear”) was assigned for each of the following study characteristics:
sequence generation, allocation concealment, blinding of participants and personnel, blind-
ing of outcome assessors, incomplete outcome data, selective outcome reporting, outcome
measure reliability, trial design, and “other” potential sources of bias. Additionally, a
criterion for “potential confounding” was included for the assessment of the risk of bias
in non-randomized trial designs. An overall risk of bias classification was assigned to
each study, giving consideration to all such study characteristics. Conflicts between review
authors regarding the methodological quality of studies were resolved by consensus and
via a third review author where required.
2.6. Data Synthesis
It was anticipated that differences in measures and study outcomes reported in the
included studies may preclude the use of summary statistics to describe treatment ef-
fects and also necessitate a narrative synthesis. However, whenever possible, the rele-
vant outcomes were synthesized and the effect measures were summarized including the
child’s weight, BMI, waist circumference, and other anthropometry outcomes; diet, physical
activity, sedentary behavior, sleep, and other obesity-related behavioral outcomes.
Nutrients 2022,14, 4837 5 of 19
3. Results
3.1. Study Selection
The study selection process is presented in an adapted Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [
15
] presented in Figure 1.
The database searches identified 362 records for the title and abstract screening against the
inclusion criteria, followed by 40 full texts retrieved for further screening, which resulted in
the final inclusion of 23 publications (from 18 interventions) in this review. The majority of
the 17 excluded publications were primary studies with irrelevant study designs (n= 11). A
list of excluded studies and reasons for exclusion are presented in
Supplementary Table S2
(see supplementary materials).
Nutrients 2022, 14, x FOR PEER REVIEW 5 of 19
and also necessitate a narrative synthesis. However, whenever possible, the relevant out-
comes were synthesized and the effect measures were summarized including the child’s
weight, BMI, waist circumference, and other anthropometry outcomes; diet, physical ac-
tivity, sedentary behavior, sleep, and other obesity-related behavioral outcomes.
3. Results
3.1. Study Selection
The study selection process is presented in an adapted Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [15] presented in Figure
1. The database searches identified 362 records for the title and abstract screening against
the inclusion criteria, followed by 40 full texts retrieved for further screening, which re-
sulted in the final inclusion of 23 publications (from 18 interventions) in this review. The
majority of the 17 excluded publications were primary studies with irrelevant study de-
signs (n = 11). A list of excluded studies and reasons for exclusion are presented in Sup-
plementary Table S2 (see supplementary materials).
Figure 1. PRISMA 2009 Flow Diagram.
Web searching identified five additional digital interventions for obesity prevention
and treatment in Australian children. Brief descriptions and characteristics of these inter-
ventions are presented in Supplementary Table S3 (see supplementary materials). The in-
terventions predominantly used websites, telehealth coaching, and mobile apps as digital
Figure 1. PRISMA 2009 Flow Diagram.
Web searching identified five additional digital interventions for obesity prevention
and treatment in Australian children. Brief descriptions and characteristics of these in-
terventions are presented in Supplementary Table S3 (see supplementary materials). The
interventions predominantly used websites, telehealth coaching, and mobile apps as digital
delivery methods. Only one of these interventions, i.e., the Kurbo app by WW International
(formerly known as Weight Watchers International) has published evaluation findings;
however, the study was a retrospective cohort study without a comparison arm [
16
]. The
searches did not retrieve any other peer-reviewed publications which reported the ac-
ceptability, usability, or effectiveness of these interventions on obesity-related outcomes.
As such, these interventions were excluded from the synthesis.
Nutrients 2022,14, 4837 6 of 19
3.2. Quality Appraisal
Table 1presents the results of the quality appraisal for the 18 interventions [
17
34
]
included in the review. Overall, the majority of the studies were of moderate to high quality
(i.e., meeting at least nine of the thirteen criteria). The studies have reported an adequate
randomization procedure (n= 13) and concealment of group allocation (n= 11), while the
two studies did not describe the randomization procedure, nor the allocation concealment.
Blinding of participants was not reported in eight studies and deemed not possible in
four studies due to the nature of the intervention study design (e.g., waitlist control).
Only six studies were rated low in risk of bias related to blinding of participants to
treatment assignment.
Table 1. Quality appraisal.
Author (Year) Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13
Ahmad (2018) [17] Y Y Y Y NA Y Y Y Y Y Y Y Y
Bakirci-Taylor (2019) [18] Y Y Y N Y U Y Y N Y Y Y Y
Baranowski (2003) [19] Y Y N U NA U N U U Y Y Y Y
Chai (2021) [20] Y Y Y U Y Y Y Y Y Y Y Y Y
Cullen (2017) [21] Y U Y U NA U Y Y N Y Y Y Y
De Lepeleere (2017) [22] N N Y NA NA Y Y Y N Y Y Y N
Jake-Schoffman (2018) [
23
]
U Y U U U Y Y N U Y Y Y Y
Johansson (2020) [24] Y Y Y N N N Y Y Y Y Y Y Y
Knowlden (2015) [25] Y U Y Y U Y Y Y N Y Y Y Y
Maddison (2014) [26] Y Y Y NA NA U Y U Y Y Y Y Y
Morgan (2019) [27] Y Y U NA NA N Y Y Y Y Y Y Y
Perdew (2021) [28] N N Y NA NA N Y U Y Y Y Y N
Rangelov (2018) [29] Y U Y U Y U Y Y U Y Y Y Y
Thompson (2015) [30] Y Y Y Y Y Y Y Y U Y Y Y Y
Trost (2021) [31] Y Y Y U Y Y Y Y Y Y Y Y Y
Trost (2014) [32] Y U Y U N N Y Y Y Y Y Y Y
Wald (2018) [33] U Y Y U NA U U Y Y Y U Y Y
Williamson (2005) [34] U U Y Y NA N Y Y Y Y Y Y Y
Y: Yes, N: No, U: Unclear, NA: Not Applicable. Q1. Was true randomization used for the assignment of participants
to treatment groups? Q2. Was allocation to treatment groups concealed? Q3. Were treatment groups similar at the
baseline? Q4. Were participants blind to the treatment assignment? Q5. Were those delivering treatment blind
to treatment assignment? Q6. Were outcome assessors blind to the treatment assignment? Q7. Were treatment
groups treated identically other than the intervention of interest? Q8. Was the follow-up complete and if not,
were differences between groups in terms of their follow-up adequately described and analyzed? Q9. Were
participants analyzed in the groups to which they were randomized? Q10. Were outcomes measured in the same
way for treatment groups? Q11. Were outcomes measured in a reliable way? Q12. Was appropriate statistical
analysis used? Q13. Was the trial design appropriate, and was any deviation from the standard RCT design
(individual randomization, parallel groups) accounted for in the conduct and analysis of the trial?
Blinding of intervention providers was not applicable to three studies where the in-
terventions were web-based modules accessible by participants independently without
needing an intervention provider. Additionally, blinding of intervention providers was
not possible in six studies due to the nature of the study design (e.g., waitlist control
commenced intervention at a later time). The blinding of outcome assessors was mixed
in the included studies, where five studies were rated a high risk of bias and six stud-
ies were rated as unclear. In studies where BMI was reported, actual height and weight
measurements were recorded by research staff using standardized protocols. However,
one included study, conducted by Rangelov et al., used parent-reported child height and
weight and presented outcomes as a percentage of weight categories (e.g., % healthy weight,
% overweight/obesity) instead of BMI changes. All studies used a validated instrument
tool when measuring weight, physical activity, diet, sedentary behavior, and/or sleep.
The intention-to-treat statistical approach was applied in 11 studies using the last obser-
vation carried forward or multiple imputation method, including two studies that had
22% and 59% drop out rates, respectively.
Nutrients 2022,14, 4837 7 of 19
3.3. Study Characteristics
All studies included parent-child dyads with the majority of children (n= 1693 of 2053)
aged between 9–11 years. One study specifically focused on mothers [
25
], while another
focused on fathers [
27
]. Among the included interventions, 10 were “treatment focused”
that targeted children who were overweight or have obesity, and the remaining eight
were “prevention focused that targeted children in all weight categories, including one in-
tervention that targeted children in the healthy weight category. Most of the studies (n= 13)
were published in the last five years. The studies were conducted in eight countries
including Belgium, Malaysia, Sweden, the USA, New Zealand, Australia, Canada, and
Switzerland. Overall, the study period ranged between 4 and 20 weeks in duration, with
the longest follow-up being between two months and two years from baseline. A total
of 14 studies reported
20% dropout rate at follow-up, and the remaining four studies
had a dropout rate that ranged between 22% to 59%. Demographic characteristics of
families included in the studies and a summary of interventions are presented in Table 2.
Further details on study aims, participant characteristics, attrition rates, intervention use, and
theoretical framework are presented in Supplementary Table S4 (see supplementary materials).
Table 2. Summary of intervention studies and key findings.
Author
(Year)
Country
Prevention/
Treatment aStudy
Design
Participant
Characteristics
Intervention Duration
and Intensity Digital Components Overall Findings b
Ahmad
(2018)
Malaysia
[17]
Treatment RCT
134 parent-child
dyads. Children aged
7–10 years. All
participants were
Malay females.
4 weeks of weekly
training for parents to
change child behavior
and 3 months of weekly
booster which consisted
of weekly one-hour
sessions using a
WhatsApp group.
nFace-to-face session
(week 1 and 4),
subsequently uploaded
to Facebook
nSessions on Facebook
Group (week 2 and 3)
nWhatsApp group for
information sharing
BMI z-scores were
significantly reduced in the
intervention group
compared to the wait-list
group for all the children
at 6-month post-training.
For waist circumference
percentile and body fat
percentage, the
intervention group
experienced a significant
reduction compared to the
wait-list group, within the
obese subgroup, and within
the overweight subgroup.
Bakirci-
Taylor
(2019)
USA [18]
Prevention RCT
30 parent-child dyads.
Children were aged
3–8 years. All parents
were female, married,
and predominantly
Caucasian. Over 70%
had at least a
bachelor’s degree and
40% reported incomes
of $75,000.
10 weeks of the mobile
Jump2Health
intervention which
included 3 components:
a mobile website
(Jump2Health), social
media (Facebook page),
and short message
service or text messages.
The Facebook page
provided information
that was unavailable on
the mobile Jump2Health
website, but it also
Mentioned and reinforced
information and text
found on the website and
promoted linked
resources on the website.
nWebsite (main content)
nFacebook page
(additional content)
nMobile text messages
(about FV consumption)
Skin carotenoids of both
children and parents
showed significant Week x
Treatment interactions in the
INT group compared with
CON (p< 0.001) indicating
increased veg intake.
Baranowski
(2003)
USA [19]
Prevention RCT
35 parent-child dyads.
Children aged 8 years
with a BMI in the 50th
percentile for age and
gender specific BMI.
All participants were
female African
Americans. Majority
had a household
income of > $40,000,
college graduate, or
higher education.
4-week summer day
camp, followed by an
8-week home Internet
intervention for the girls
and their parents which
included weekly behav-
ioral/environmental foci.
The treatment camp
blended usual camp
activities with activities
specially designed
for GEMS-FFFP.
nSummer day camp
(4 weeks in duration)
nWebsite (new content
weekly for 8 weeks)
nWeekly email and phone
reminders to log on
There were no significant
changes in BMI, waist
circumference, dietary
intake, and physical
activity level.
Nutrients 2022,14, 4837 8 of 19
Table 2. Cont.
Author
(Year)
Country
Prevention/
Treatment aStudy
Design
Participant
Characteristics
Intervention Duration
and Intensity Digital Components Overall Findings b
Chai
(2021)
Australia
[20]
Treatment RCT
46 parent-child dyads.
Children (mean age
9 years) were
predominantly male,
overweight/obese
and resided with both
biological parents.
Most parents
(mean age 41 years)
were female, of
middle SES, living in
major cities,
overweight/obese,
and attained
certificate/diploma or
postgraduate degree.
Both INT 1 and 2 groups
received two telehealth
consultations delivered
by a dietitian, 12 weeks
access to a nutrition
website and a private
Facebook group. INT 2
group received
additional text messages.
nWebsite (new content
weekly for 12 weeks).
nTelehealth dietitian
(2 appointments)
nFacebook group
nMobile text messages
(various frequency
for 12 weeks
Percentage energy from
EDNP food was reduced
and percentage energy
from nutrient-rich core
food was increased in
Telehealth+SMS when
compared to CON.
Cullen
(2017)
USA [21]
Prevention RCT
126 parent-child
dyads. Children aged
8–12 years. All
participants were
African American.
Majority of parents
were female,
aged < 40 years,
college graduate or
higher education, and
have less than
two children.
Approximately 8 weeks
web-based program for
African American
families that was
designed to promote
healthy home food
environments, positive
parental behaviors
related to improving
dietary behaviors of
family members, and
goal setting.
nWebsite (narrated
graphic story viewing).
nEight stories follow the
Johnson family (an
African American family
with two 8- to
12-year-old children) as
they try to develop
healthier eating habits.
nAfter viewing the weekly
story, the parents had a
challenge (goal) to
complete during the next
week and viewed a
family food problem. Tip
sheets targeting the
session content and
recipes could be
downloaded from
the website.
Home availability of juice
(p< 0.05), vegetables
(p< 0.01), and
low-fat/fat-free foods
(p< 0.05) were significantly
higher in INT at 2 months.
Parent menu planning
skills were significantly
higher in INT at 6 months.
Both INT and CON groups
showed significant
increases in home
juice/fruit availability,
parent modelling, food
preparation practices, and
menu planning, and a
significant decrease in
home sugar-sweetened
beverage availability
(all p< 0.05).
De
Lepeleere
(2017)
Belgium
[22]
Prevention
Quasi-
experimental
controlled
trial
135 parents of a
primary school-aged
child. Majority of
parents were female
and from a
medium-high SES.
4-week access to website
(health promoting
videos); content
delivered weekly over
four weeks; contact time
~2 min per video
(22 videos)
nWebsite (online videos
followed by narrator
explanation)
n22 short videos
(2 min each) showing a
difficult child-parent
scenario followed by an
appropriate reaction of
the parent, then a
narrator explains the
parenting practices
showed
in the video.
Most significant
intervention effects were
found for more complex
parenting practices (e.g., an
increase in motivating the
child to eat fruit).
Subgroup analyses showed
that the intervention had
more effect on the actual
parenting practices related
to PA, screen-time, and
healthy diet in parents of
older children (10–12 years
old), whereas intervention
effects on parental
self-efficacy related to
those behaviors were
stronger in parents of
younger children
(6–9 years).
Nutrients 2022,14, 4837 9 of 19
Table 2. Cont.
Author
(Year)
Country
Prevention/
Treatment aStudy
Design
Participant
Characteristics
Intervention Duration
and Intensity Digital Components Overall Findings b
Jake-
Schoffman
(2018)
USA [23]
Prevention RCT
33 parent-child dyads.
Majority of parents
were female,
Caucasian, college
graduates, and have
obesity. Majority of
children were female,
Caucasian, aged 11
years, and have a
healthy weight.
Dyads were asked to
self-monitor using a
mobile responsive
design website made for
the intervention for
12 weeks.
nMobile website with
messaging function and
features such as
side-by-side graphs to
show the daily progress
of parents and children
toward study goals, and
a messaging feature
where parents and
children could send
messages of support and
encouragement to one
another to help reinforce
behavioral goals.
nWebsites included
sections directed to
parents, separate
sections for children, and
a section for the family,
to encourage
collaboration.
nEmail newsletter
There were no significant
Group ×Time ×Parent or
Group ×Time effects on
any of the intervention
outcomes: minutes of
MVPA (accelerometer),
daily steps (pedometer),
servings of fruit,
vegetables, fast food,
and SSBs.
Johansson
(2020)
Sweden
[24]
Treatment RCT
28 children aged
5–12 years who have
obesity according to
the International
Obesity Task Force
(IOTF) with parents
who speak Swedish.
6 months daily
self-monitoring of
weight recorded via a
mobile app used by
parents, a website in
which clinicians could
track treatment progress,
prespecified treatment
goals for change in
degree of obesity shown
in the app and on the
website, and text
message interactions
between clinicians and
parents. In addition to
the mHealth approach,
the intervention group
received standard care
(clinical appointment).
nDaily weighing at home
on scales with no
displays to indicate
weight and data were
transferred to the mobile
app (for families) and to
the clinic’s
interface (website).
nThe clinicians were
instructed to check the
participants’ weight
charts on the clinic’s
interface at least weekly
and give feedback via
text messages.
nA wrist-worn activity
monitor was connected
to a gamified app which
prompted for physical
activity to generate
rewards that were
displayed in the app.
At 6 months the
intervention group had a
greater reduction in
standardized BMI than
standard care.
Knowlden
(2015)
USA [25]
Prevention RCT
57 mothers with
children aged
4–6 years. Mothers
(mean age of 36 years)
were predominantly
Caucasian, married,
unem-
ployed/homemakers.
Children (mean age of
5 years) were
primarily Caucasian,
male, with a mean age
of 5 years.
4 weekly audiovisual
presentations (30 min
per session) via website
and 1 booster session.
nEach of the five online
sessions included a 10-
to-15-min audiovisual
presentation, an
interactive online
worksheet, and a
discussion board post
designed to increase
knowledge supplemented
each module.
The EMPOWER arm of the
trial resulted in an overall
increase of 1.680 daily cups
of fruits and vegetables
consumed by children,
relative to the comparison
group (p< 0.001,
95% confidence
interval. Web-based
maternal-facilitated
interventions can induce
sustained effects on
child behaviors.
Nutrients 2022,14, 4837 10 of 19
Table 2. Cont.
Author
(Year)
Country
Prevention/
Treatment aStudy
Design
Participant
Characteristics
Intervention Duration
and Intensity Digital Components Overall Findings b
Maddison
(2014)
New
Zealand
[26]
Treatment RCT
251 parent-child
dyads. Children
(mean age of 11 years)
were predominantly
male and of
Pacific origin
Delivered over 20 weeks,
consisting of a
face-to-face meeting
with the
parent/caregiver and
the child to deliver
intervention content; TV
monitoring device;
monthly newsletters.
nInitialface-to-face session
nWebsite (health
information and links to
community-based
activity programs)
nTime Machine TV
monitoring device
(2 devices per family)
n35]Activity pack for
children
nThe Time Machine was
connected to a TV, or
other media device
(e.g., DVD player, video
game console), but it was
not possible to connect
the device to a computer.
Each Time Machine
came with 30 tokens,
with each token allowing
30 min of viewing time;
however, caregivers
were able to allocate
these as they chose.
There was no significant
difference in change of
BMI z-scores between the
intervention and control
groups, although a
favorable trend was
observed (0.016;
95% CI: 0.084, 0.051;
p= 0.64).
There were also no
significant differences in
secondary outcomes,
except for a trend towards
increased children’s
moderate intensity
physical activity in the
intervention group
(24.3 min/d; 95% CI: 0.94,
49.51; p= 0.06).
Morgan
(2019)
Australia
[27]
Prevention RCT
153 father-child dyads.
Children were aged
412 years. Most
fathers were
employed, born in
Australia, and were
married or living with
a partner (99%).
Families were
represented from most
socio-economic areas.
90-min group sessions
weekly for 8 weeks that
included educational
and practical
components. They were
provided with a
web-based app at the
conclusion of the
program for long-term
maintenance.
n8 weekly group sessions
(90 min)
nPrinted resources
nApp: To encourage
long-term behavior
maintenance, families
were provided with
access to a web-based
app at the conclusion of
the program, which
included a variety of fun
physical activities for
daughters and fathers to
complete and track
together weekly.
ITT analyses revealed
favorable group-by-time
effects for physical activity
in daughters (p= 0.02,
d = 0.4) and fathers
(p< 0.001, d = 0.7) at
9 months. At
postintervention and
follow-up, significant
effects (p< 0.05) were also
identified for daughters’
fundamental movement
skills competence
(objective: d = 1.1–1.2;
perceived: d = 0.4–0.6), a
range of fathers’ physical
activity parenting practices
(d = 0.3–0.8), and
screen-time for daughters
(d = 0.5–0.8) and fathers
(d = 0.4–0.6,
postintervention only).
Program satisfaction and
attendance were very high.
Perdew
(2021)
Canada
[28]
Treatment
Quasi-
experimental
design
71 parent-child dyads.
Children were aged
812 years; at or
above 85th percentile
for BMI for age
and sex.
10 weekly face-to-face 90
min sessions, four
community-based
activities (i.e., family
grocery store tour), and
an interactive web-portal.
n10 weekly face-to-face
90-min sessions,
nFour community-based
activities (i.e., family
grocery store tour), and
nFollowing the in-person
sessions, 10 weekly
online interactive lessons
were made available to
the families using a
web portal.
Children’s BMI z-scores
were not significantly
changed. The intervention
group significantly
improved their days of
moderate-to-vigorous
physical activity relative to
control; however, child
dietary behaviors were not
significantly changed.
Relative to control,
intervention group showed
significant improvements
in physical activity.
Nutrients 2022,14, 4837 11 of 19
Table 2. Cont.
Author
(Year)
Country
Prevention/
Treatment aStudy
Design
Participant
Characteristics
Intervention Duration
and Intensity Digital Components Overall Findings b
Rangelov
(2018)
Switzer-
land
[29]
Prevention RCT
608 parent-child
dyads. Children
(mean age of 8.5 years)
were in the first two
years of secondary
school and about
equal proportion of
boys and girls.
8 weeks access to
website (parents) and a
personalized and
tailored letter by post
(children). The emails
(INT 1) and mobile text
messages (INT 2) were
used as weekly
reminders to prompt
parents to visit the
Website. The email also
provided a short
summary of the
weekly theme.
nWebsite (weekly
nutrition content and a
forum for participants
discussion together and
with a dietitian)
nEmails and mobile text
messages were sent as
weekly reminders to
prompt parents to visit
the website.
Overall, the intervention
effects were not
significantly different
across groups. Children
increased their daily
consumption of fruit and
decreased that of sweets
regardless of the group
they were assigned.
Thompson
(2015)
USA [30]
Prevention RCT
400 parent-child
dyads. Children were
in 4th or 5th grade
(around 9–11 years).
Almost evenly
distributed by gender
(female, 52.7%) and
were of diverse
ethnicity
(White-36.8%,
Hispanic 27.4%,
African American
26.4%). Parents were
mostly female (96.3%),
White (40.3%),
married (77.5%), and
40–59 years old (55.3%).
Highest level of
household education
was predominately
post-graduate study
(36.7%), and average
household income
was >$61,000 (57.6%).
All INT groups played
the 10-episode (1 h each)
online videogame. The
groups varied only in
type of implementation
intention created after
setting a goal to eat FV,
including the use of an
action plan (specifying
actions), or a coping
plan (identifying
barriers), or both action
and coping plans.
n10 episodes of online
video game
n10 newsletters
(for parents)
n10 installment
(parents only) website
(the parent intervention
was connected to the
child intervention.)
A significant
group-by-time interaction
for FV intake (p< 0.001)
was found in only the
Action group, which had
significant increases in FV
intake at post 1 (p< 0.0001)
and post 2 (p< 0.0001). No
other significant
interactions
were observed.
Trost
(2014)
USA [32]
Treatment RCT
75 parent-child dyads.
Children aged
8–12 years, had a BMI
greater than the 85th
percentile for sex and
age and
predominantly were
female and White.
Parents were college
graduates or
postgraduates.
INT group received
hardware consisting of a
game console and
motion capture device
and 1 active game at
their second treatment
session and a second
game in week 9 of
the program.
nVideo games using a
game console and
motion capture device
with 2 active sports
games (at second
treatment and week 9).
nINT program is 16
weekly group sessions.
Participants in the program
and active gaming group
exhibited significant
increases in MVPA at week
16 (p< 0.05). In the
program-only group, a
declineor no change was
observed in the
moderate-to-vigorous and
vigorous physical activity.
Participants in both groups
exhibited significant
reductions in percentage
overweightand BMI z scores
at week 16. However, the
program and active gaming
group exhibited significantly
greater reductions in
percentage overweight.
Nutrients 2022,14, 4837 12 of 19
Table 2. Cont.
Author
(Year)
Country
Prevention/
Treatment aStudy
Design
Participant
Characteristics
Intervention Duration
and Intensity Digital Components Overall Findings b
Wald
(2018)
USA [33]
Treatment RCT
73 parent-child dyads.
Children (mean
age 5 years) were
predominantly
non-Hispanic and
overweight/obese.
Mothers were
predominantly aged
< 40 years, college
graduate or higher
education,
and married.
The INT was composed
of 6-in-person group
sessions and a
customized website over
12 months.
n6 weekly group
counseling sessions
n1-year access to Website
with weekly update
including health topics
related to nutrition and
physical activity, local
resources for current
activities for children
and families, personal
stories that emphasized
authoritative parenting,
interactive discussion
group, and Ask
the Expert.
nParents were encouraged
to share their triumphs
and challenges so that all
might benefit.
Among children with
12-month visits, BMI
z-scores decreased from
baseline to 12 months in
both the control and
intervention arms;
however, the mean
reductions were not
significantly different
between the control and
intervention groups
(p= 0.7492). The percent of
children who reduced their
screen time by 15% did
not differ significantly
between the intervention
and control groups.
Williamson
(2005)
USA [34]
Treatment RCT 57 African American
girls aged 11–15 years.
Interactive website and
4 face-to-face sessions of
behavior modification
over 12 weeks focused
on goal setting,
behavioral contracting,
monitoring of progress,
and problem-solving.
Participant-initiated
weekly emails
with counsellor.
n4 face-to-face counselling
sessions during the first
12 weeks of program.
The intervention used a
family-oriented format,
i.e., a program that
invited the parents, the
child, and other
members of the family to
be involved using
mutual problem-solving
and behavioral
contracting.
nInteractive website
components included
interactive graph to track
exercise, interactive food
monitoring worksheets
with instant feedback on
food choices,
problem-solving
worksheet (online), and a
quiz followed every
weekly lesson with
instant feedback
provided.
nOnline counseling
included weekly email
communication with
counsellor for feedback
on program components
(e.g., quizzes, lessons,
weight graphs,
goal setting,
clinic appointments).
Participants in the
intervention group lost
significantly (p< 0.05)
more body fat
(1.12 ±0.47 SE) than the
control group 0.43 ±0.47 SE).
There was a significant
difference in BMI change
between groups
(intervention 0.19 ±0.24 SE,
< 0.05,
control +0.65 ±0.23 SE,
p< 0.05).
Participants in the
intervention group
significantly reduced fat
intake compared with
control group (FFQ)
(145.67 ±37.67 SE,
p< 0.05)
RCT: Randomized Controlled Trial; INT: intervention group; CON: control or comparison group;
BMI: Body Mass Index; N/A: not applicable; NR: not reported; NS: not significant; FJV: fruit, juice, and vegetables;
PA: physical activity; EDNP: energy dense nutrient poor; SSB: sugar-sweetened beverages; FV: fruit and veg-
etables; MVPA: moderate to vigorous physical activity; ITT: intention-to-treat.
a
Prevention studies had normal
weight as participant inclusion criteria or no weight criteria specified. Treatment studies had overweight or
obesity status criteria as participant inclusion criteria.
b
Between-group differences. Note: Refer to supplementary
materials
Supplementary Tables S4 and S5
for further details on study aims, participant characteristics, attrition
rates, intervention use, theoretical framework, and outcomes related to anthropometry, dietary intake, physical
activity, sedentary behaviour, screen time, and sleep.
3.4. Theoretical Framework
Behavior change theories (BCT) were used in 13 interventions with Social Cognitive Theory”
(SCT) applied most frequently (n= 9) [
17
19
,
22
,
23
,
25
27
,
30
]. The SCT theorizes behavioral
outcomes (e.g., child behavior change) as a reciprocal interaction between the person
(e.g., child) and environmental factors (e.g., home and family unit) [
17
]. Interventions
Nutrients 2022,14, 4837 13 of 19
focused on elements of behavior capability in parents, such as self-monitoring, goal setting,
self-efficacy, problem-solving, relapse prevention, and stimulus control [
17
,
18
,
23
]. Similarly,
a study by Knowlden et al. focused on the environment, emotional coping, expectations,
self-control, and self-efficacy of parents [
25
]. Other interventions also focused on creating
a shift in attitudes toward healthy behavior among parents [
22
] or the family unit [
23
].
Ahmad et al. encouraged parents to practice authoritative parenting skills, consistent with
Wald et al., and focus on self-efficacy of the child’s healthy behaviors in addition to the
elements of behavior capability in parents [17,33].
Nine studies incorporated one or more other behavior change theories, some in
conjunction with SCT, when designing the intervention [
22
,
23
,
26
,
27
,
30
]. For example,
Jake et al. incorporated elements of Family Systems Theory with SCT and conceptualized
parent–child relationships in the context of reciprocal interactions [
23
]. Morgan et al.
and Wald et al. incorporated Self-Determination Theory, which suggests that three basic
psychological needs (autonomy, competency, and relatedness) must be satisfied to foster
well-being [
27
,
33
]. Chai et al. and Perdew et al. used a different behavior change framework
underpinned by constructs similar to SCT: attitude, motivation, self-efficacy, opportunity,
behavioral regulation, identity, and habit [
20
]. Rangelov et al. used the Social Marketing
Framework incorporating all six elements of the marketing mix: product, place, price,
promotion, policy, and partnership.
The use of BCT was not reported in five studies [
21
,
24
,
31
,
32
,
34
]; however, two of the
studies [
32
,
34
] described intervention strategies that were aligned with SCT, including
goal setting, reinforcement, modeling, changing the home environment, problem-solving,
and/or behavioral contracting.
3.5. Intervention Components and Usage
Interventions included in this rapid review facilitated delivery via websites
(n= 11) [
18
,
20
,
21
,
23
,
24
,
26
,
28
30
,
33
,
34
], text messaging (n= 5) [
18
,
20
,
24
,
29
,
31
], Facebook
(n= 3) [
17
,
18
,
20
], mobile apps (n= 2) [
24
,
31
] and/or video gaming (n= 2) [
30
,
32
]. Web-
sites were used for providing additional resources to complement the in-person group
sessions on topics, such as healthy living, physical activity, and recipe ideas [28,34]. Some
intervention websites included an online discussion forum [
25
,
28
,
29
,
33
] to engage par-
ents in sharing their challenges and successes while offering a forum for parents to ask
questions [
33
]. Websites were also used for goal setting and self-monitoring, where fam-
ilies may log activities and track progress with an interactive graph [
23
,
34
]. Jake et al.
reported high adherence to self-monitoring protocols with parents and children using
the website for step and food logs [
23
]. A study by Williamson et al. provided instant
feedback after parents completed online worksheets and quizzes involving self-reported
health behavior [34].
Intervention websites provided content in the form of short videos [
22
], audiovisual
presentations [
25
], or narrated graphic stories [
21
]. Studies reported moderate to high
utilization of websites, ranging from around 86% to 93% of participants accessing the
content [
21
23
], except for Rangelov et al. who reported 39% to 46% of participants visiting
the website despite weekly email and SMS reminders. Evidence suggests that websites with
interactive features have higher engagement rates compared to those with non-interactive
content (e.g., texts and pictures) [
34
], which may explain the lower website usage rates in
Rangelov et al.
An emerging number of studies have used apps and/or social media platforms to
boost participant engagement with the intervention content. Two interventions used
a combination of website, social media platforms, and text messaging which achieved
high utilization rates with 77% to 86% of families accessing the website regularly [
18
,
20
].
Tuesdays had the highest number of website visits and 16:00 was the most popular time on
most days [
20
]. Many parents also preferred a weekly text message on earlier days of the
week, such as Monday (40%) and Tuesday (36%) [
18
]. To encourage long-term behavior
maintenance, a study provided families with access to an app at the conclusion of the
Nutrients 2022,14, 4837 14 of 19
in-person group program, which included a variety of fun physical activities for families to
complete and track together weekly. The app was used by 83% of families [
27
]. Another
study [
31
] reported that 71% of families used the app at least two times per week, including
35% who used it at least three times a week. Studies reported higher rates of participation
in WhatsApp and/or Facebook sessions compared to in-person sessions and websites [
18
];
indicating that a social media approach may enhance participation. Some parents also
noted interaction among Facebook participants as an important feature [18].
Other interventions have used technological devices including a screentime moni-
toring device to help budget family media use (but 46% of parents reported never us-
ing the device to budget their child’s television or computer use) [
26
], and a Bluetooth
weighing scale with no displays to indicate weight readings together, with a wrist-worn
activity monitor that was connected to a gamified app that encourages physical activity
(in which 80% of children participated) [
24
]. Two other interventions incorporated gamifi-
cation in the form of video games and reported 76% to 91% participation rates [32].
3.6. Intervention Effects
A summary of intervention effects on child outcomes, including anthropometrics,
dietary intake, physical activity level, sedentary behavior, screen time, and sleep, are
presented in Table S5 (see supplementary materials). Overall, studies reported changes
in BMI (n= 11 studies) [
17
20
,
24
,
26
28
,
32
34
], dietary intake (n= 11) [
18
23
,
25
,
28
30
,
34
],
physical activity (n= 10) [
19
,
20
,
22
,
23
,
25
28
,
31
,
32
] and/or screen time (n= 6) in
children [
22
,
25
28
,
33
]. Significant improvements were reported for the dietary intake
(n= 5) [
18
,
20
,
25
,
29
,
30
] or physical activity levels (n= 4) of children [
25
,
27
,
28
,
32
]. Two [
25
,
27
]
of six interventions were effective in reducing screen time. Overall, the attrition rates of
included studies ranged between 9% and 59%, at the longest follow-up.
3.6.1. Anthropometry
Studies predominantly reported anthropometric outcomes in BMI, BMI z-scores (zBMI),
waist circumference, and body fat percentage. Intervention effects on child anthropometry
reported in the included studies were mixed. Eight studies (n= 587 families) [
18
20
,
26
28
,
33
,
34
]
reported that the changes in BMI, zBMI, and/or waist circumference were not significant.
Only three studies (n= 207) [
17
,
24
,
32
] reported that BMI z-scores were significantly reduced
in the intervention group compared to the control group and all these three studies were
‘treatment focused’ with one of these studies (n= 122) [
17
] also reported significant decreases
in waist circumference percentile and body fat percentage in the intervention group.
3.6.2. Dietary Intakes
Overall, 11 studies have reported dietary outcome measures via food group consump-
tion and total energy intake. Fruit and/or vegetable intakes were the most frequently re-
ported in the studies [
18
23
,
25
,
28
30
], followed by sugar-sweetened
beverages (SSB) [
19
,
21
23
,
25
,
28
,
29
]. Studies reported food and/or drink intakes in varying
units, including frequency per day, frequency per week, portions per week, or standard
serves per week. Mixed findings were reported on child dietary behavior where six studies
(n= 313) [
19
,
21
23
,
28
,
34
] reported that the changes in fruit, vegetable, and/or SSB in-
takes were not significant compared to five studies that reported significant improvements.
Intervention groups in four studies (n= 533) [
18
,
20
,
25
,
30
] showed a significant increase
in healthy foods/food group consumption post-intervention with a decrease in SSB or
discretionary food (e.g., fast food) intake. One study (n= 608) [
29
] found that families who
received an SMS intervention had reported improved dietary intake for vegetables, but not
for fruit, sweets, SSB, and water.
3.6.3. Physical Activity
Among the 10 studies which measured physical activity, the six studies
(n= 433) [
19
,
20
,
22
,
23
,
26
,
31
] reporting intervention effects were not significant when com-
Nutrients 2022,14, 4837 15 of 19
paring the physical activity scores, step counts, or minutes of active transportation between
groups. Four studies (n= 297) [
25
,
27
,
28
,
32
] reported a significant increase in physical
activity levels or step counts post-intervention.
3.6.4. Screen Time/Sedentary Behavior
Two of the six studies [
22
,
25
28
,
33
] that reported child screen time showed a significant
intervention effect [
25
,
27
] on decreasing screen time at post-intervention and follow-up.
The two studies (n= 175) [
25
,
27
] were delivered as a knowledge-based healthy lifestyle
intervention focusing on one or more changed behaviors and they each included a weekly
group session and web-based materials. While two other studies (n= 92) [
28
,
33
] used
similar intervention modalities (i.e., weekly group sessions and websites), the changes in
screen time were not significant.
3.6.5. Sleep
Sleep outcome was reported in only one study [
26
] that focused on reducing leisure
time screen-based sedentary behavior in children (n= 238) aged nine to 12 years, delivered
over 20 weeks. A key focus was to train the primary caregivers to initiate change in the
home environment, implementing behavior change strategies with an aim of facilitating
behavioral change in the child. However, the changes in sleep duration (minutes/day)
between groups from baseline to six months follow-up were not significant. This lack
of effect may be explained by the bias associated with self-report measures and/or poor
compliance, where 46% of parents reported never using the screen time monitoring device
to budget their child’s television or computer use [26].
4. Discussion
The COVID-19 pandemic has impelled the potential of using online platforms for
day-to-day tasks, such as remote work, videoconferencing, telehealth, and food ordering
services. Online platforms and digital solutions are no longer unfamiliar to many and
have increasingly demonstrated the potential for use in health promotion and prevention.
This rapid review aimed to summarize the impact of family-based digital interventions
for obesity prevention and treatment, with a focus on diet, physical activity, sedentary
behavior, sleep, and/or weight-related outcomes in primary school-aged children.
Digital intervention for family-based childhood obesity prevention and treatment
is an emerging research area with increased publications over the last five years. The
interventions have shown promising preliminary results with modest improvement in child
BMI, diet, and physical activity. Similar findings were reported in a systematic review [
35
]
that examined the effectiveness of mobile apps aimed at obesity prevention in children aged
eight to 12 years and suggested that apps for health behavior promotion interventions may
potentially improve health behaviors among children, but their effectiveness in improving
anthropometric measures remains unclear. When comparing the results between studies, it
is important to consider the various quantifying units used in measuring dietary intake
and physical activity. The lack of consistency in measuring and reporting approaches in
behavioral studies such as these has hindered further analysis to be conducted to make a
comprehensive review.
A scoping review [
36
] of E&M Health interventions (defined as the use of information
and communications technology, especially Internet (web), text messages with SMS, smart-
phone apps, and social media to monitor,
improve or enable health behavior or health care)
for childhood obesity reported that weight, physical activity, and diet were frequently as-
sessed, while limited reviews have focused on sedentary behavior. The current rapid
review included sedentary behavior and found six studies that reported child screen time,
in which two of the studies showed significant screen time reduction after the intervention.
However, the evidence around screen time and sleep remains scarce. Only one study [
26
]
included in the current review has reported sleep outcomes but the between-group differ-
ences were not significant. Similarly, a systematic review identified that only eight trials
Nutrients 2022,14, 4837 16 of 19
included a sleep component in obesity prevention interventions and that evidence on sleep
intervention research is limited. Thus, the potential impact of sleep on childhood obesity
remains unclear [11].
Interventions included in this review used websites, text messaging, Facebook, mobile
apps, and/or video games, incorporating digital interactive elements and/or gamification.
Research suggests that virtual delivery utilizing gamification can be effective in increasing
the acceptability and accessibility of health interventions, thus, more readily supporting
children and families to adopt healthy lifestyle changes [
5
]. A systematic review found that
gamification, behavioral monitoring, and goal setting were common features of mobile apps
aimed at childhood obesity prevention [
35
]. However, the reporting of process indicators,
such as attendance tracking, participation, and retention rates were limited [
35
]. This rapid
review found digital approaches are acceptable and positively received by families, with
high participation rates.
There was emerging evidence of the use of social media and gamification to support
program delivery. In line with established theories of intrinsic motivation [
37
], interactive
digital components can employ motivational features such as goal setting, and real-time
feedback through interface elements such as point scores, badges, levels, challenges, so-
cial support through leaderboards, teams and communication functions, and autonomy
through customizable profiles and user choice in goals and activities [
38
,
39
]. Another
study found that interventions using gamification to target nutrition behaviors among
children and adolescents increased program adherence, knowledge, self-efficacy, and
nutrition-related health behavior in the short term [39]. Similarly, interactive social media
interventions reported improvements in overall wellbeing and were effective in improv-
ing physical activity among adults [
40
]. However, the effectiveness, dose of meaningful
engagement (e.g., click-through rates, posts, comments), and sustainability of these novel
approaches require further studies, given the limited existing evidence [41].
Despite several digital interventions for childhood obesity that were accessible in
online marketplaces to the wider public in Australia, only one program targeted prevention
while the remaining four programs focused on weight loss treatment or weight manage-
ment. It is unclear whether the market programs have peer-reviewed evidence or rigorous
evaluation to support their usability, acceptability, or effectiveness. This raises concerns
regarding the quality and credibility of readily accessible programs on the internet. The
limited evaluation findings of existing programs present a missed opportunity to advance
the evidence base around digital interventions for childhood obesity, exchange learnings
between peers, and inform future interventions. Thus, there is a research-practice gap
that limits the availability of evidence-based childhood obesity prevention and treatment
programs in the public domain, and more support is needed to enhance the translation of
effective interventions into population health benefits.
A limitation of the current rapid review was the inclusion of just four electronic
databases in the search strategy, unlike systematic reviews which usually search more
databases. The search terms around digital and interactive interventions used in this rapid
review were broad and less exhaustive, however, the lack of standard definition in this
emerging field has contributed to the complication. Despite the rapid review design, the
methodological rigor was upheld by including the quality appraisal of the included studies
and the use of two independent reviewers across the screening phases, while applying the
WHO practical guide for rapid reviews [
13
]. A strength of the current rapid review was the
web searching of marketplace digital interventions for obesity prevention and treatment
in children which may not have been indexed or published in the databases searched.
The current rapid review also included sedentary behavior and sleep which were rarely
included or reported in reviews. The rapid review also reported on the digital components
of interventions and process indicators, such as intervention usage and engagement rates
where available, instead of focusing solely on the efficacy results regarding behavior change
which were commonly reported in other reviews.
Nutrients 2022,14, 4837 17 of 19
Australia has committed to the WHO global target to halt the rise in obesity and being
overweight, and recently released the National Obesity Strategy—a 10-year framework
for action to prevent, reduce, and treat obesity and being overweight in Australia. The
strategy has been agreed upon by Australian State and Territory Governments and has
advocated for early intervention services to consider various delivery modes, including
telehealth and other digital technology, that are affordable and accessible for all. For exam-
ple, the Queensland Government has established a dedicated prevention agency, Health
and Wellbeing Queensland, which will lead the statewide implementation of the National
Obesity Strategy and drive innovative solutions to combat obesity. Evidence presented in
the current rapid review supported the recommendations for virtual intervention delivery
utilizing digital approaches, interactive elements, and/or gamification. However, more
research is warranted to better understand how virtual delivery may impact screen time
and sedentary behavior, the optimal dose and engagement for health behavior change, and
whether educational screen time and discretionary screen time contribute to health and
behavior outcomes differently. Future research assessing the effectiveness of digital inter-
ventions on improving sleep is needed, considering the limited research despite evidence
suggesting sleep is associated with overweight and obesity in children [11].
5. Conclusions
In conclusion, family-based digital interventions for childhood obesity prevention
and treatment were acceptable and well-received by families. The findings of the review
suggest that digital interventions have shown some improvements in child BMI, diet, and
physical activity, however, statistically significant results were mixed. The evidence around
screen time and sleep remains scarce due to limited studies available. There is emerging
evidence of the use of social media and gamification in digital interventions. However,
further research is warranted to understand its long-term effectiveness and sustainability,
and whether a blend of virtual and in-person approaches could be the recommended
best practice for the future. Such approaches must consider the need for prevention and
treatment interventions to be adaptable in response to unforeseen crises, such as a global
pandemic.
Supplementary Materials:
The following supporting information can be downloaded at
https://www.mdpi.com/article/10.3390/nu14224837/s1: Table S1: Search terms; Table S2:
List of excluded studies and reasons for exclusion; Table S3: Interventions available on marketplace
(identified from web searching); Table S4: Characteristics of included studies; Table S5: Intervention
effects on child outcomes.
Author Contributions:
Conceptualization, L.K.C. and R.F.; methodology, L.K.C. and R.F.; data
collection and extraction, L.K.C., R.F., and L.F.; data analysis, L.K.C. and R.L.; writing—original draft
preparation, L.K.C.; writing—review and editing, R.F., L.F., R.L. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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We included studies comparing an interactive social media intervention alone or as a component of a multi-component intervention with either a non-interactive social media control or an active but less-interactive social media comparator (e.g. a moderated versus an unmoderated discussion group). Our main outcomes were health behaviours (e.g. physical activity), body function outcomes (e.g. blood glucose), psychological health outcomes (e.g. depression), well-being, and adverse events. Our secondary outcomes were process outcomes important for behaviour change and included knowledge, attitudes, intention and motivation, perceived susceptibility, self-efficacy, and social support. Data collection and analysis: We used a pre-tested data extraction form and collected data independently, in duplicate. Because we aimed to assess broad outcomes, we extracted only one outcome per main and secondary outcome categories prioritised by those that were the primary outcome as reported by the study authors, used in a sample size calculation, and patient-important. Main results: We included 88 studies (871,378 participants), of which 84 were RCTs, three were CBAs and one was an ITS. The majority of the studies were conducted in the USA (54%). In total, 86% were conducted in high-income countries and the remaining 14% in upper middle-income countries. The most commonly used social media platform was Facebook (39%) with few studies utilising other platforms such as WeChat, Twitter, WhatsApp, and Google Hangouts. Many studies (48%) used web-based communities or apps that mimic functions of these well-known social media platforms. We compared studies assessing interactive social media interventions with non-interactive social media interventions, which included paper-based or in-person interventions or no intervention. We only reported the RCT results in our 'Summary of findings' table. We found a range of effects on health behaviours, such as breastfeeding, condom use, diet quality, medication adherence, medical screening and testing, physical activity, tobacco use, and vaccination. For example, these interventions may increase physical activity and medical screening tests but there was little to no effect for other health behaviours, such as improved diet or reduced tobacco use (20,139 participants in 54 RCTs). For body function outcomes, interactive social media interventions may result in small but important positive effects, such as a small but important positive effect on weight loss and a small but important reduction in resting heart rate (4521 participants in 30 RCTs). Interactive social media may improve overall well-being (standardised mean difference (SMD) 0.46, 95% confidence interval (CI) 0.14 to 0.79, moderate effect, low-certainty evidence) demonstrated by an increase of 3.77 points on a general well-being scale (from 1.15 to 6.48 points higher) where scores range from 14 to 70 (3792 participants in 16 studies). We found no difference in effect on psychological outcomes (depression and distress) representing a difference of 0.1 points on a standard scale in which scores range from 0 to 63 points (SMD -0.01, 95% CI -0.14 to 0.12, low-certainty evidence, 2070 participants in 12 RCTs). We also compared studies assessing interactive social media interventions with those with an active but less interactive social media control (11 studies). Four RCTs (1523 participants) that reported on physical activity found an improvement demonstrated by an increase of 28 minutes of moderate-to-vigorous physical activity per week (from 10 to 47 minutes more, SMD 0.35, 95% CI 0.12 to 0.59, small effect, very low-certainty evidence). Two studies found little to no difference in well-being for those in the intervention and control groups (SMD 0.02, 95% CI -0.08 to 0.13, small effect, low-certainty evidence), demonstrated by a mean change of 0.4 points on a scale with a range of 0 to 100. Adverse events related to the social media component of the interventions, such as privacy issues, were not reported in any of our included studies. We were unable to conduct planned subgroup analyses related to health equity as only four studies reported relevant data. Authors' conclusions: This review combined data for a variety of outcomes and found that social media interventions that aim to increase physical activity may be effective and social media interventions may improve well-being. While we assessed many other outcomes, there were too few studies to compare or, where there were studies, the evidence was uncertain. None of our included studies reported adverse effects related to the social media component of the intervention. Future studies should assess adverse events related to the interactive social media component and should report on population characteristics to increase our understanding of the potential effect of these interventions on reducing health inequities.
Article
Background: The purpose of this study was to evaluate the effectiveness of a 10-week blended family-based childhood obesity management program, relative to a wait-list control, in improving child body mass index (BMI) z-scores, child lifestyle behaviors, parental support for healthy eating and physical activity, and self-regulation for healthy eating and physical activity support. Methods: This study was registered as a randomized wait-listed controlled trial; however, due to low recruitment and program delivery logistics, this study transitioned into a quasi-experimental design. Families with children 8-12 years of age with a BMI ≥85th percentile for age and sex were recruited (October 2018 to March 2019) in British Columbia, Canada. The intervention provided families 10 weeks of in-person and online support on improving lifestyle behaviors. Results: Children's BMI z-scores were not significantly changed. Intervention group significantly improved their days of moderate-to-vigorous physical activity relative to control (0.75 ± 1.5; p < 0.01; ηp2 = 0.15); however, child dietary behaviors were not significantly changed. Relative to control, intervention group showed significant improvements in parental support for healthy eating (0.13 ± 0.36; p < 0.05; ηp2 = 0.06) and physical activity (1.0 ± 1.6; p < 0.05; ηp2 = 0.09) and self-regulation for healthy eating (2.0 ± 3.5; p < 0.01; ηp2 = 0.11) and physical activity support (2.0 ± 3.2; p < 0.05; ηp2 = 0.28). Conclusions: Preliminary evidence showed that the intervention was not effective in improving child BMI z-scores, but it was effective in promoting children's physical activity and parents' support behaviors. A longer study period may be required to change BMI z-scores. Clinical Trial Registration number: NCT03643341.
Article
Objective: Fundamental movement skills (FMS) are the foundational building blocks for lifetime participation in physical activity (PA). Programmes to promote FMS development have been primarily delivered in childcare settings. No studies have evaluated the effectiveness of an interactive digital application, designed to be co-used by parent and child, to increase FMS proficiency in preschool-aged children. Therefore, the purpose of this study was to evaluate the effectiveness of the Moovosity™ programme, a novel digital application to increase FMS proficiency in 3- to 6-year-old children. Methods: A randomized controlled trial (RCT) was conducted involving 34 parent-child dyads randomly assigned to either the 8-week intervention (n = 17) or wait-list control (n = 17) condition. FMS proficiency, PA, and parental support for PA was assessed at baseline and 8-weeks. Intervention participants were given free access to the app over a period of 8 weeks. Wait-listed controls were given access after the 8-week period. Group differences for pre to post changes in outcomes were tested for significance using general linear mixed models. Results: There was a significant group by time interaction for object control skills (F1,32 = 10.81, P = 0.003). Intervention children exhibited significant improvements in object control skills, while children in the wait-list control group exhibited a modest decline. Intervention children also exhibited improvements in locomotor skills, while wait-listed controls exhibited minimal change; however, the group by time interaction fell outside the 0.05 level of significance (F1,32 = 3.15, P = 0.09). There were no significant intervention effects observed for child PA (F1,32 = 0.03, P = 0.86) and parental support for PA (F1,32 = 0.84, P = 0.37). Conclusions: An 8-week exposure to a digital application to promote motor competence within a family environment was effective in improving FMS proficiency in preschool-aged children. The results warrant further investigation in larger trials.