ArticlePDF Available

See How They Grow: Testing the feasibility of a mobile app to support parents’ understanding of child growth charts


Abstract and Figures

Background Mobile devices provide new opportunities for the prevention of overweight and obesity in children. We aimed to co-create and test an app that offered comprehensible feedback to parents on their child’s growth and delivered a suite of age-specific information about nutrition and activity. Methods A two-phased approach was used to co-create the digital growth tool—See How They Grow—and test its feasibility. Phase one used focus groups (parents and professionals such as paediatricians and midwives) and a national on-line survey to gather requirements and build the app. Phase two involved testing the app over 12-weeks, with parents or carers of children aged ≤ 2-years. All research activities were undertaken exclusively through the app, and participants were recruited using social media and hard copy materials given to patents at a child health visit. Findings Four focus groups and 101 responses to the national survey informed the features and functions to include in the final app. Two hundred and twenty-five participants downloaded the app, resulting in 208 eligible participants. Non-Māori/Non-Pacific (78%) and Māori (14%) had the highest downloads. Fifty-four per cent of participants were parents of children under 6-months. These participants were more likely to regularly use the app than those with children older than 6-months (64% vs 36%, P = 0 . 011) . Over half of the participants entered three measures (n = 101, 48%). Of those that completed the follow-up survey (n = 101, 48%), 72 reported that the app helped them better understand how to interpret growth charts. Conclusion The app was acceptable and with minor modifications, has the potential to be an effective tool to support parents understanding of growth trajectories for their children. A larger trial is needed to evaluate if the app can have a measurable impact on increasing knowledge and behaviour, and therefore on preventing childhood overweight and obesity.
Content may be subject to copyright.
See How They Grow: Testing the feasibility of
a mobile app to support parents’
understanding of child growth charts
Gayl HumphreyID
*, Rosie Dobson
, Varsha Parag
, Marion Hiemstra
, Stephen Howie
Samantha Marsh
, Susan Morton
, Dylan Mordaunt
, Angela Wadham
, Chris Bullen
1National Institute for Health Innovation, Faculty of Medical and Health Science, University of Auckland,
Auckland, New Zealand, 2Plunket, National Education, Auckland, New Zealand, 3Department of
Paediatrics, Child and Youth Health, Faculty of Medical and Health Science, University of Auckland,
Auckland, New Zealand, 4Growing Up in New Zealand, Faculty of Medical and Health Science, University of
Auckland, Auckland, New Zealand, 5University of Adelaide, South Australia, Australia, 6Flinders University,
South Australia, Australia
Mobile devices provide new opportunities for the prevention of overweight and obesity in
children. We aimed to co-create and test an app that offered comprehensible feedback to
parents on their child’s growth and delivered a suite of age-specific information about nutri-
tion and activity.
A two-phased approach was used to co-create the digital growth tool—See How They Grow
—and test its feasibility. Phase one used focus groups (parents and professionals such as
paediatricians and midwives) and a national on-line survey to gather requirements and build
the app. Phase two involved testing the app over 12-weeks, with parents or carers of chil-
dren aged 2-years. All research activities were undertaken exclusively through the app,
and participants were recruited using social media and hard copy materials given to patents
at a child health visit.
Four focus groups and 101 responses to the national survey informed the features and func-
tions to include in the final app. Two hundred and twenty-five participants downloaded the
app, resulting in 208 eligible participants. Non-Māori/Non-Pacific (78%) and Māori (14%)
had the highest downloads. Fifty-four per cent of participants were parents of children under
6-months. These participants were more likely to regularly use the app than those with chil-
dren older than 6-months (64% vs 36%, P = 0.011). Over half of the participants entered
three measures (n = 101, 48%). Of those that completed the follow-up survey (n = 101,
48%), 72 reported that the app helped them better understand how to interpret growth
PLOS ONE | February 19, 2021 1 / 19
Citation: Humphrey G, Dobson R, Parag V,
Hiemstra M, Howie S, Marsh S, et al. (2021) See
How They Grow: Testing the feasibility of a mobile
app to support parents’ understanding of child
growth charts. PLoS ONE 16(2): e0246045. https://
Editor: Vijayaprasad Gopichandran, ESIC Medical
Received: April 27, 2020
Accepted: January 11, 2021
Published: February 19, 2021
Copyright: ©2021 Humphrey et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data underlying this
study cannot be made publicly available due to
restrictions imposed by the ethical approval
obtained to conduct the study. Additionally, the
National Ethics Advisory Committee Standards
quality-improvement/part-two/12-health) has set
some clear guidelines for data which involve Māori
(indigenous population) and which our study
included. The standards are guided by Te Mana
Raraunga (
The app was acceptable and with minor modifications, has the potential to be an effective
tool to support parents understanding of growth trajectories for their children. A larger trial is
needed to evaluate if the app can have a measurable impact on increasing knowledge and
behaviour, and therefore on preventing childhood overweight and obesity.
Growth during the first few years of life plays an important role in setting body mass index
(BMI) trajectories into childhood and adulthood [1,2]. Once obesity is established, it is diffi-
cult to reverse [3]. In a recent Cochrane Review of interventions to prevent childhood obesity
for children aged 0–5 years, there was moderate evidence from 16 randomised control trials
that diet and exercise combined are effective on reducing zBMI scores, although these reduc-
tions were small [4]. For example, evidence from sixteen randomised control trials that com-
bined diet and physical activity interventions for 0-5-year olds, compared with controls,
reduced zBMI (mean difference 0.07 kg/m
, 95% confidence interval (CI) 0.14 to 0.01).
There was little impact on zBMI for individual diet or physical activity interventions (Diet:
mean difference 0.14, 95% CI 0.32 to 0.04; Physical activity; mean difference 0.01, 95% CI
0.10 to 0.13). A zBMI change of at least -0.25 for a clinically significant impact has been sug-
gested [5].
While these studies do not account for the role of parental perception of their child’s
weight, a recent meta-analysis exploring parental underestimates of child weight found that
50.7% (95% confidence interval 31.1%–70.2%) of parents underestimate their overweight/
obese children’s weight [6]. Other studies have also reported similar findings [710]. The
increase in children’s weight at a population level is clearly illustrated in a recent report, where
the median child weight now falls on the 67
centile rather than the 50
centile (1990 baseline)
Being overweight or obese in childhood is reported as a notable factor for being overweight
or obese in adolescence and adulthood [12]. Overweight and obesity have an impact across the
life course through the development of long term conditions and contributing to mental ill-
health. The economic (personal and societal) costs are also immense [13].
When parents perceive childhood overweight and obesity as a concern, they also exhibit an
inability to internalise this into their own lives [14]. Factors that contribute to parental failure
to recognise or perceive overweight in their children are complex and multifaceted. They
include parental beliefs and values of body weight, their own body weight [15], socio-eco-
nomic factors [16], environmental factors [17] and societal normalisation of overweight [18,
19]. This normalisation of overweight and obesity within society is likely to be a contributing
factor in the increase of childhood overweight and obesity [7,1921].
Effective, evidence-based interventions that focus on how best to support parents to under-
stand their child’s growth and the influence the early years have on unhealthy weight gain is
sparse [22,23], with many studies and reports concluding that more research is needed [5,24].
Growth charts enable children’s growth to be assessed by comparing them with a normal
range for other children of the same age and gender, relative to a reference population. The
serial measurements of height, weight and head circumference, taken as a child ages, are sensi-
tive measures of their general health [24]. In many countries, growth charts are part of the par-
ent-owned or parent-held child wellbeing books [25,26] and used to provide access to
See How They Grow
PLOS ONE | February 19, 2021 2 / 19
), which outlines the importance of Indigenous
sovereignty of data. All requests for de-identified
individual participant data or study documents will
be considered, where the proposed use aligns with
the ethical approval for the study, aligns with
public-good purposes, does not conflict with other
requests, or planned use by the Study Steering
Committee, and the requestor is willing to sign a
data access agreement and has obtained relevant
ethical approvals. Contact will be via Director of the
National Institute for Health Innovation or the National
Ethics Advisory Committee
Information for the See How They Grow app can be
found at the GitHub link,
Funding: This study was funded through a
contestable funding round by New Zealand Cure
Kids (, the National Science
Challenge: a Better Start (https://www.abetterstart.
nz/) and Precision Driven Health (https://, grant reference 7006.
The funders had no influence on the design,
implementation, interpretation or reporting of the
study findings.
Competing interests: This study was funded
through a contestable funding round by Cure Kids
(, the National Science
Challenge: a Better Start (https://www.abetterstart.
nz/) and Precision Driven Health (PDH) (https://, grant reference 7006.
PDH is a commercial entity. The funders including
PDH, had no influence on the design,
implementation, interpretation or reporting of the
study findings. The involvement of PDH, does not
alter our adherence to PLOS ONE policies on
sharing data and materials.
understandable, evidence-based information to parents about their child’s growth compared
to population norms [27]. Growth charts can play an important role in helping parents, at a
glance, view the growth of their child. Despite this, parental understanding of the meaning of
growth charts relative to their child(ren) is variable [28,29].
However, some health professionals report that they often don’t use these charts with
parents [30]. The reasons given include that parents have low health literacy [31]; parents find
growth charts confusing [32] and misunderstand the meaning of chart percentiles (including
the misperception that a high percentile is a sign of robustness), and confusion about popula-
tion norms and how that applies to their child [28,3336]. Health professionals have also
reported that the hand-held record book is often forgotten or lost, reducing its value for
parents [37]. Notwithstanding this, parents do have a desire to hear if their child was at risk of
obesity [38].
The rapid growth in mobile technology and in particular, the ubiquity of the smartphone is
an opportunity to overcome such issues by digitising the hand-held growth record. A plethora
of child growth labelled apps are available on the App Stores. Yet little evidence exists of the
effectiveness of these apps in the prevention of childhood overweight and obesity [39].
Child health in the New Zealand context
Child health in New Zealand is packaged as a comprehensive programme called Well-Child/
Tamariki Ora. The Tamariki Ora/Well-Child programme is a comprehensive package of uni-
versal health services offered free to all New Zealand families/whānau for children from birth
to 5 years. There are 13 planned health check contacts, with 11 checks provided between birth
and 18-months. Several organisations specifically provide well-child services nationally, for
example, Plunket ( while other organisations are location-specific
[40] or are Māori (indigenous population), providers. Services are provided by several health
professions including midwifery, obstetrics, paediatrics, general practitioners and registered
nurses. These services are often supported by kaiāwhina (community health workers) and
vision and hearing technicians [26] and delivered in a variety of settings such as primary care
settings, Marae (a place where for Māori communities gather and share) and the home. A Well
Child/Tamariki Ora Health book is also produced and is given to all parents of new babies, to
use as a hand-held record of health visits and immunisations. The Well Child / Tamariki Ora
Health book also includes information for parents on milestones, safety and illnesses [41].
In this study, we aimed to co-create a child growth monitoring app and test its utility and
feasibility. The focus on children from birth to two years was chosen because excessive weight
gain in the first 100 weeks of life may be an important marker for the onset of overweight by
school age and into adulthood [42,43], and because of the high number of planned and free
Well-Child/Tamariki Ora health visits in the first 24 months of life in New Zealand [26].
Study objectives
This study aimed to co-create, develop and test the utility and feasibility of a smartphone-
based digital child growth application (app) for use by New Zealand parents and caregivers
with children aged 0–24 months.
Co-creation and development of the app
Between January—May 2018, an iterative process was used to gather detailed requirements for
the app. This process involved a national on-line survey and focus groups of parents and/or
See How They Grow
PLOS ONE | February 19, 2021 3 / 19
caregivers with children under the age of 5 years, and interviews with health professionals
(midwife, paediatrician, nurses) working in child health services. Recruitment processes for
the survey used social media advertising (via FaceBook posts, Google Ads), using title tags
such as “well-child providers”, and meta description tags such as “childhood development
milestones.” Health provider and parent focus group participants were identified and invited
to participate through existing networks.
Informed consent was obtained from all participants. Parent participants were asked what
they knew about growth charts and the tools they used to help monitor their children’s growth.
Health professionals were asked how they used growth charts with parents and barriers to
their use. All participants were asked about what features and functions they would want in a
digital growth chart tool. The final app features and functions would be pragmatically deter-
mined based on study time-frames and the technical difficulty and time need to build a partic-
ular feature.
To accompany the app and provide positive framed content and activity related prompts,
within app notifications were used. App notifications are messages that display outside of the
app and are used to communicate information (support knowledge development) and
reminders (to act or do something) to the user. The type and content of the See How They
Grow app (SHTG) notifications were underpinned by behaviour change theory [44,45], and
mHealth engagement research [46]. A programme logic was used to determine the type and
frequency of these notifications.
In addition to routinely programmed information and activity notifications, specific action
notification messages about entering a measurement were sent at 3-days and 14-days if no
new measure had been added, and a third notification was sent if it was longer than 6-weeks
since the last data entry. If participants had entered a measurement, they would not receive
these message types.
The app was built for both Apple and Android operating systems using Ionic (https:// and was made available for download from February–August 2019.
Ethics approvals were obtained from the Auckland University Human Participants Ethics
Committee; Formative Study Reference 020166 and Feasibility Study Reference 022248. The
feasibility study was registered on the Australia New Zealand Clinical Trials Registry
(ANZCTR) reference ACTRN12619000905167.
Feasibility and utility assessment
Participants were eligible to take part in the study if they were, 1) adults aged 18-years and
over, 2) lived in NZ, 3) had a child or children under the age of 2-years, 4) had access and use
of a smartphone capable of downloading the app and 5) were able to read and understand
English. Eligible participants provided informed consent (agreement to complete a baseline
and 12-week follow-up questionnaire, to enter at least three measures during the 12-week
study period and for their app use data to be collected during and post-12-weeks (until the last
participant had completed 12-weeks). All study procedures and data collection were managed
through the app. Engagement with the app was measured using the date of last activity, fre-
quency of use and self-reported feedback on utility, barriers, enablers and improvements (see
Table 1). Underpinned by the broader app engagement literature, the engagement categories
were explicitly designed for this study. The app had both off-line and on-line capability.
Recruitment promotion strategies included inviting participants from the co-creation phase,
social media posts using keywords and meta tags such as “Māori well-child services”,
See How They Grow
PLOS ONE | February 19, 2021 4 / 19
“traditional Māori parenting” or child immunisations”; paid digital advertising, a study web-
site, promotion through health provider networks and hard-copy flyers placed in new baby
As a feasibility study, no sample size calculation was performed. All statistical analyses used
SASwith descriptive statistics used to analyse participant characteristics, app engagement
and utilisation variables and Chi-square and Fisher Exact Tests used to compare differences.
We did not include invited users in these analyses due to the low numbers (n = 4). For analyses
participants were grouped into age bands (18–19; 20–24; 25–29; 30–34; 35–39; 40–44, and 45–
Co-creation and development of the See How They Grow app
One hundred and ten parents or caregivers responded to the requirements survey with 101
included in the final analyses. Nine participants were excluded as they provided no or limited
information. The majority were NZ European ethnic group with only 8% reporting as Māori.
Participants were between 20 and 39 years. Fifty-one per cent reported that their youngest
child was 12-months. Of the four focus groups, three comprised mainly of Māori and Pasi-
fika parents, while the fourth group was mainly composed of NZ European parents and care-
givers. Focus group numbers ranged from 5 to 8 participants. Most participants were women.
Face-to-face interviews involved two midwives, a well-child nurse educator and a paediatrician
and seven other well-child health providers provided email comments.
Table 1. Schedule of baseline and follow-up data collection.
Description Baseline 12-weeks
E-informed consent
Age, sex, and ethnicity of adult users
Age and sex of the child
Home Region
Baseline Survey
Knowledge of growth charts
Use of growth charts
Follow up Survey
Reported changes in knowledge of growth charts
Reported changes in activities
Reported changes in foods offered
Perception of usefulness and utility
Best features
App engagement
• Not engaged No activity beyond day 1
• Somewhat engaged No activity past 30 days
• Mostly Engaged No activity past 60 days
• Actively Engaged Activity throughout 90 days
• Very Engaged Activity post 90 days
Frequency of app use Continuous throughout the study
Type of app use Continuous throughout the study
Feedback At any time throughout the app
See How They Grow
PLOS ONE | February 19, 2021 5 / 19
All parents or caregivers reported that knowing how their children were growing was
important. Discussions of their child’s growth needed to be grounded in the broader context
of their culture and social situation. The influence of culture was intricately woven into how
they, as parents make decisions about their child(ren). Therefore, any tool needs to have an
element of understanding about the norms, values, ideas and behaviours that may be deeply
rooted in a particular culture and form part of daily living, to be successful. Foods and activi-
ties that were culturally relevant, were suggested as simple ways to incorporate some cultural
nuances into the SHTG app.
However, when growth was discussed in the context of mapping measures onto growth
charts, focus group participants, and 72% (n = 73) of survey participants reported that they
thought the growth charts were more a tool for the health professional than for them.
Health professionals all reported they used growth charts with the parents or caregivers
they worked with, but that parental understanding of the purpose of growth charts was vari-
able. Most noted that it was common for parents to forget to bring their hand-held book to the
visit and consequently, the utility of the growth chart in the book was suboptimal due to a lack
of measures entered.
All participant groups (parent and health professional) remarked that an electronic tool
would be a useful addition to the well-child space. And that it was important that any digital
tool helped to reinforce New Zealand-oriented information and evidence, and supported the
relationships between parents and well-child providers and other health professionals, rather
than be perceived as replacing them.
Parent or caregivers reported that they should be able to add their self-collected measures
onto a digital chart. This capability elicited a cautionary reaction from the health professional
participants, as they were concerned that parents might become obsessive about growth indi-
cators as a singular measure of wellbeing. They also remarked that it would be important to
convey that a self-measurement was different from one performed and recorded by a health
professional and not confuse interpretations.
Three main themes emerged to describe barriers to digital tool use,
1. Functional (such as internet access, access to smart devices),
2. Acceptability and Utility (such as information relevancy, ease of use and cultural relevancy),
3. Systems (e.g. privacy and security, information ownership).
Determining and shaping the features and functions to include in the See
How They Grow app
Identified functions and features were converted into use-case stories, and these were mapped
to underpinning conceptual knowledge and behavioural change themes. Table 2 presents the
findings from this process, and Fig 1 provides a snapshot of images from the final SHTG app
design. There were over 50 screens with which the participant could navigate and interact.
Tables 3and 4present a sample of the notification message types sent to participants. The
words in brackets such as [HI] or [FIRSTNAME] are tokens, and they are used to personalise
the message. For example, the participant registered their First Name as Moana and ethnicity
as Māori; therefore, the bracketed words would be replaced with Kia or Moana.
Feasibility study
A total of 225 people from across NZ downloaded the app. Of these, 17 (7.5%) were excluded as no
data were entered, and 208 (92.4%) participants were eligible. Of the eligible participants, eight (4%)
See How They Grow
PLOS ONE | February 19, 2021 6 / 19
registered two children, while all others registered one child. Of the 208 eligible participants, 101
(49.5%) completed the follow-up survey (Table 5). The majority of participants (191, 92%) reported
that they were female (P = 0.008) and the mother of the child (193, 93%); were mostly European
(78%). Māori participants were significantly less likely to complete the follow-up survey (baseline
14% were Maori, Follow-up 8%: p = 0.010). There was no difference in participants by age at base-
line and follow up were similar (p0.227). At baseline, the mean child age was 7.8 months (median
5.7 months, SD 6.4 months). There were more participants with children aged 6-months com-
pleting the follow-up survey compared with those aged 6-months (p0.001).
Measurement data entered
Almost half (100; 48%) entered measures (weight, height, head circumference) on three occa-
sions or more, with 25% (n = 52) adding two measurements. Weight and height were the most
Table 2. See How They Grow app design features and functions.
Key Features Identified Purpose/ Intent of
Features and Behaviour
Use Case Examples Final Features and Functions adopted in
See How They Grow
“Ability to upload photos.To share experiences,
memories and have
reinforcement (BCT 3.1)
• To be able to add my comments or photos
and have others (privately or publicly) view
and comment so that it is more than just
• Create a longitudinal experience or history
makes it difficult to stop interacting and
difficult to delete, so I keep using it, and it is
• Invite Others,
• Create Memories,
• Share Memories,
• Enable others to like/comment on
• Add photos
“Share experiences.SOCIAL /
“Share growth.
“Positive quotes/
comments—sometimes new
mums just need to hear they
are doing a good job!
To be rewarded, receive
feedback, feel positive
(BCT 10.3)
• To add or interact within the app and
receive “unanticipated” rewards or
appreciation that help reinforce behaviour
• Having information in smaller sections
and showing easy actions to promote a sense
of capability and then skill development
linked to my child(ren) age and stage of
• Messages (notifications) that pop-up when
you complete an activity in the app such as a
child’s measure or tick an immunisation
with a positive message
• Informative and timely messages
• Likes and comments on Memories Page
• Action prompts regarding measurements
are about seeing a pattern and learning
from each step, so if a change is considered
important, then it seems more achievable.
“Clearer indication of what
weight is healthy (e.g.
percentile range) at what
Supports changes (BCT
2.2) Investment
• Real-time notification messages to
reinforce activities and help to interpret or
reinforce the activity
• To have new or novel information
presented either through news type feeds or
notifications or links out if needed to read
• Attracting the attention of the user is about
relevance and supports the usefulness of the
• Supports idea for how to change
• Managing their child measures
• Map measurements to their child and
present back what that measure means.
• Additional measurements entered
prompts new information notifications
• Resources and Tips sections,
“See change and understand
it.” “Child first aid.
New information and
“Tips around keeping your
child healthy.
“Family support and
“Family groups can be
“Self-entered data on other
“Reminders for important
Personalise SKILLS /
• Being able to see data in a way that is
relevant to me and minimises unhelpful/
inappropriate comparisons
• Creates achievements and motivations to
keep me engaged
• Integrated with my own calendar,
reminders of important events/activities like
immunisations, so I have it all in one place.
• Can add other information important to
me and my child(ren), like feeds or nappy
change or sleep.
• Select avatars or add photos self and
• Ability to create a whanau group to share
the child(ren) journey and information
• Calendar links to immunisation timelines.
• Event entry within the app that links to
the phone “native” calendar to minimise
duplication and create synergy with
common phone activity.
Interpretation and
See How They Grow
PLOS ONE | February 19, 2021 7 / 19
frequently entered of all measure types (70% and 66% respectively). Ninety per cent of all mea-
surements were recorded as being from a Health Professional with 70% of these being for
children 6-months.
Fig 1. Images of some of the elements of the See How They Grow app.
See How They Grow
PLOS ONE | February 19, 2021 8 / 19
Date of last activity. Five categories from not engaged (no activity after day one) to very
engaged (activity ongoing after 12-weeks) were used to categorise participant app use data.
Forty-four per cent (n = 91/208) of baseline participants were engaged up to or over the
12-weeks. This group were more likely to complete the 12-week survey. There were no differ-
ences in the ages of participants and their last activity (Chi-squared p = 0.494). However, par-
ticipants who identified as Māori were more likely to have no app activity recorded after 60
days (Fisher Exact Test p = 0.017) compared to Non-Māori (Table 6).
Navigation and exploring screens. We found no difference in the number of screens nav-
igated to and viewed, irrespective of when the last app activity was (see Table 7).
The role of notifications in supporting app activity and engagement. Measurements
entered after a reminder notification was received, are presented in Fig 2. The day three
reminders were sent to 87 participants prompting 42 new measurements entered on the same
day or the next. The day 14 reminder was sent to 76 participants, and 24 new measurements
were added. The week six reminder was sent to 65 participants, and 28 new measurements
were entered.
Table 3. Examples of the routine within app notifications.
Routine messages
using programme logic
Message Type Timing
Message content
Welcome 0 Welcome to the SHTG study. Over the next 3-months, you will be part of a
study designed to test out our new app. Thank you for taking part.
Welcome #2 regarding study contact details 2 [HI] [FIRSTNAME], thanks for taking part in the SHTG study. If you need to
contact us, you can call us on 0800 3676444 or email us on
Reminder about app functions–Resources 5 We hope you are enjoying the SHTG app. Did you know that the app has
information about services and events relevant to your child as well as tips to
support your child’s healthy growth?
Reminder about app functions—
immunisations & re flagging immunisations
complete in the app
9 Has [CHILDNAME] had [HIS/HER] latest immunisations? If so, you can
update this in the app. Go to immunisations and tick the ones [HE/SHE] has
About growth tracking 10 Remember, a growth chart isn’t a test that children can pass or fail, and there
isn’t a centile that he or she must reach to be healthy.
Table 4. Examples of data entry dependent messages.
Data Entry
Dependant Messages
Message Description Message content Mapped to
First measure entered after registration Thanks for entering in a weight measurement for [CHILDNAME].
It looks like [HE/SHE] is in the [CENTILE#] centile for weight.
New measurement entered by professional Thanks for entering in a new weight measurement for
[CHILDNAME]. It looks like [HE/SHE] is continuing to grow along
the same centile for weight which is great.
Decrease in weight (across one centile band)
compared with the previous measure–Non-
professional gathered measure
Thanks for entering in a new weight measurement for
[CHILDNAME]. It looks like the rate that [HE/SHE] is growing
might have decreased. This is usually ok, but it is a good idea to talk
to your doctor or nurse if you are concerned. Also, look at TIPS
for more information on centiles.
Message if there is a change across once centile
bands up or down.
[HI], you can expect to see [CHILDNAME] growth line stay roughly
in the same area of the chart as [HE/SHE] grows, but it probably
won’t follow a centile line exactly. It’s perfectly normal for [HIS/
HER] growth line to move between centiles occasionally.
See How They Grow
PLOS ONE | February 19, 2021 9 / 19
Improved growth chart interpretation and understanding
At baseline, the majority of participants responded that they were aware of the Well Child
Tamariki Ora book (201; 97%) and 96% (192) reported finding them useful for tracking their
Table 5. Characteristics of participants at baseline and follow-up.
Baseline 12-week Follow up Survey
N % N % Chisq p
Total 208 - 101 -
Relationship to child
Father 13 6 4 4
Friend 1 0 0
Mother 193 93 98 95
Uncle 1 0 1 1
Ethnicity (note more than one can be selected)
European 163 78 84 82 0.337
Maori 30 14 8 8 0.010
Asian 7 3 5 5
Pacific 11 5 4 4
Chinese 6 3 5 5
Indian 9 4 3 3
Other 16 8 8 8
Gender 0.008
Female 191 92 98 95
Male 17 8 5 5
Collapsed Age Groups 0.227
18–19 3 1 2 2
20–24 16 8 9 9
25–29 53 25 25 24
30–34 65 31 38 37
35–39 61 29 26 25
40–44 9 4 3 3
45–49 1 0 0
Auckland 97 47 52 50
Bay of Plenty 13 6 8 8
Canterbury 13 6 8 8
Hawke’s Bay 3 1 2 2
Manawatu-Wanganui 6 3 3 3
Northland 21 10 8 8
Otago 4 2 1 1
Southland 2 1 0
Taranaki 2 1 2 2
Tasman 1 0 1 1
Waikato 21 10 5 5
Wellington 23 11 12 12
West Coast 2 1 1 1
Chi-squared P value comparing participants that completed the follow-up survey to those that didn’t.
See How They Grow
PLOS ONE | February 19, 2021 10 / 19
child’s growth. One-third reported that they understood the information conveyed by growth
charts only "a little" (68, 34%) while 65% (130) reported understanding them clearly and 1%
reporting not understanding them at all. The majority of participants (173; 86%) reported that
the growth charts were relevant to their child. Of the 101 (48.5%) participants who completed
the 12-week survey, 72 (70%) agreed that the app helped them to understand and interpret
growth charts better. All except one participant reported that they found that the visual presen-
tation and the interpretive information provided after measurements were entered were infor-
mative and relevant to their child. The following quotes illustrate how some participants found
the app useful:
It gives the caregiver a visual representation to see how their child is growing, and it sends use-
ful notifications.
Parent of under 6-month child
Because we could show the GP + the paediatrician when we had to see them.The DHB [Dis-
trict Health Board] Paediatrician had paper and had to replicate the plots,and the GP only
entered the measurement she took,which didn’t show a trend.We had the birth weight,plun-
ket weights,nurse weights,paediatrician AND GP weights on the graph.
Parent of under 6-month child
Four participants reported that they did not find SHTG useful overall. "Potential privacy
issues" was the only free-text comment documented to explain this response.
Twenty-one per cent (22) reported that they had learnt something new at the end of the
12-week study. Of these, 19 were 34-years of age, and 17 had children 6-months of age,
Table 6. Participant last app activity by engagement category.
Baseline 12-week Follow-up
N % N %
Engagement Categories Total 208 - 101
1 Not engaged No activity beyond day 1 48 23 8 8
2 Somewhat engaged No activity past 30 days 38 18 6 6
3 Mostly Engaged No activity past 60 days 31 15 15 15
4 Actively Engaged Activity up to 90 days 25 12 17 17
5 Very Engaged Activity past 90 days 66 32 55 54
Table 7. The number of screen views by last activity engagement categories.
Screen Views
Engagement Category N (%) mean sd median lower IQRupper IQRmin max
1 Not engaged No activity beyond day 1 48 (23) 22.3 12.7 20 12 34 2 47
2 Somewhat engaged No activity past 30 days 38 (18) 32 32.5 23.5 17.5 30.5 13 141
3 Mostly Engaged No activity past 60 days 31 (15) 22.8 22.6 15 8 34.5 2 90
4 Actively Engaged Activity up to 90 days 25 (12) 14.1 8.9 13 9 17 1 41
5 Very Engaged Activity past 90 days 66 (32) 33.5 29.6 28 15 41 2 138
Interquartile range
See How They Grow
PLOS ONE | February 19, 2021 11 / 19
with the majority of responses being from European ethnicity (18). Box 1 presents the main
themes where participants reported gaining new knowledge or understanding.
Food and activity knowledge and behaviour changes
There were 81 eligible participants after excluding responses from parents who signalled that
they were still exclusively breastfeeding (17/101) and those that did not respond to this
Fig 2. Notifications and corresponding app activity.
Box 1. What was something new that I learnt while using the SHTG
Knowledge Related to their Child Understanding
General Knowledge of Growth Charts
• How well they are growing (hopefully)
and when to be concerned
• I read extra about growth charts to feel
better than my child was dropping
percentile points. I spoke with my Plunket
nurse who reassured me that my child’s
growth, while not following the line, was
still tracking very well
• If my son is on average growth.
• That it’s not a test that a child must pass.
Each child is different and they don’t have
to be a certain centile to be healthy
• That a general consistent growth is
healthy! And I would imagine it would be
really good to notice weight loss due to
illness or allergy
• It was interesting to see where his
different measurements sat on the
• When to be concerned & contact a health
• That so long as your child follows their
own line and keeps making progress they
are on the right track
• What the
percentiles meant
• Differences in
• How the
percentiles work
• Why they are
• The way
percentiles work
• How Plunket nurses use a growth chart
• That it’s an accumulation of the data set/
trend of growth that matters not a single
point that really matters.
• That they just have to follow one line not
try to get to 100%
• That growth charts include height and
head as well as weight
• The data used to formulate the growth
• I really liked the blub below the chart
explaining that all babies grow differently
See How They Grow
PLOS ONE | February 19, 2021 12 / 19
question (3). Of these 81 participants, the majority (71) reported: "No—SHTG did not change
what foods they provided," whereas 10 participants reported either "yes" (n = 6) or "a little"
(n = 4) to making a change to the foods they offered. “Offered more variety”, “amount of food
offered”, and “new ideas of what to offer” were the typical responses.
Eleven participants reported "yes" (n = 8) or "a little" (n = 3) to increasing the activities they
and their child were doing. The remaining participants either did not answer this question
(n = 13) or reported No (n = 77). "More tummy time" and "more playtime" were the common
free-text responses with one participant remarking that SHTG "Helped me figure out more sim-
ple yet effective activities to do with my baby".
Other features and functions used
Sharing the app was highlighted as an important feature in the co-creation phase, and 41
(20%) participants sent a share app invitation. All were parents or caregivers of children aged
under 12-months. Being the Partner of the invitee, was the main category for whom was
invited (n = 17), followed by Grandparents (n = 3), Sisters/Brothers, Aunties/Uncles, extended
whānau/family and Other, were all equally mentioned (n = 2 respectively). Only three people
accepted the invitation. Despite the low use of this feature, 74% (n = 76) of survey respondents,
reported that this is an important feature.
Adding self-measurements and reported positively by 95%, followed by the resources and tips
screens (40%). The link to services and activities and adding photos were the third and fourth liked
features, 17% and 12% respectively. Interestingly, 23% liked the notification messages while almost
the same proportion (25%) did not. This latter group were more likely to have their last activity
recorded at 0–30 days, whereas the former group were actively using the app for the full 12-weeks.
Technical issues
Sixty-two per cent of participants (n = 64) reported that they had no technical problems with
the app. Not being able to delete a measure or zoom in on the charts were reported as prob-
lems, albeit these capabilities had not been designed into the app.
Acceptability feedback and new features
The majority of participants (87%; 88) reported that they would recommend the app to others. One
hundred participants (97%) said that the app was culturally appropriate and that the role of culture
needed to be woven into the information provided, as illustrated in the following response.
There are lots of cultural difference for Chinese mums during the first month after the birth of
baby, including food, diet, beliefs.
Parent of under 6-month child
The majority (86%, 87) recommended that the app should include monitoring for children
over 24-months old.
While participant comments overall were positive, some participants (7) did not find the
app useful, appealing or easy to use for example one participant’s remarked,
The app is just a bit clunky to use. If it was easier to operate or automatically updated, then
the app would be awesome!
Parent with a 6-12-month child.
See How They Grow
PLOS ONE | February 19, 2021 13 / 19
A range of suggestions for new features and general app improvements are illustrated in
Box 2.
Finally, the importance of being able to share the information with a family doctor or health
provider was highlighted, with one participant commenting on the importance of this feature
for them.
Ability to email to GP [General Practitioner], so the export says where the data is from and
its provenance.I had to explain to the GP who had entered the data incorrectly into her Med-
Tech chart,and said ’growth looks normal’ THEN I showed her the app and my growth chart
and then my interpretation and she suddenly realised she’d inputted her data incorrectly.
Parent with a 6-12-month child.
The findings from this feasibility study highlight that the digitalisation of growth charts and
embedding these within a mobile app is an acceptable and potentially preferable modality for
parents and carers to capture and monitor their child’s growth. This finding was most appar-
ent amongst parents with children under 6- months. While not a panacea, increasingly
research is finding that a variety of interventions in the prenatal and early infancy period.
Findings reported were improvement in positive parental health practises, such as longer
breastfeeding, later introduction of solids and increased child activity, all having a positive
impact on early obesity compared to control groups [23].
The lower engagement by Māori compared to non-Māori suggests that SHTG was missing
something that could have kept this cohort engaged longer. The role of culture in child-rearing
and perceptions of growth and development needs to be better understood and interventions
designed that account for these differences, otherwise, it is likely they could increase the dis-
parity [4749].
The majority of app activity was related to entering measures. When measures were
entered, the data were overwhelmingly labelled as being from health professionals; reinforcing
the importance parents placed on this trusted information. Importantly, we found knowledge
and understanding of growth measures increased over the study period. Still, the impact for
Māori was muted due to the lower SHTG engagement over time, similarly for Pasifika popula-
tions who were under-represented as participants overall; despite these groups being well rep-
resented in the co-creation phase. Future research which includes interviews or focus groups
with participants at the end of these feasibility studies, would help provide more contextualisa-
tion and understanding of the usage data than survey tools can.
The impact of personalised and tailored messages have been well researched for text mes-
saging interventions [4951]. Similar in principle to text messaging are app notification
Box 2. New features and functions
Having tick boxes for when immunisations are completed.
Being able to zoom into the graphs.
Having multimedia options such as videos on what to expect at milestones.
Being able to delete incorrect entries easier.
Feed and sleep tracking.
More ideas for activities.
Have more information on how to stimulate motor development and cognitive abilities.
See How They Grow
PLOS ONE | February 19, 2021 14 / 19
messages. The added advantage of app notification messages is that they can also guide the
end-user to further information within the app or to record an activity. While text messaging
can also direct users to further information, it is difficult to measure unless participants
actively report their behaviour. With app notifications, measuring responses is much simpler,
as the receipt of the notification and response can be captured and counted. We found that for
some, the notification feature was annoying, but some notifications did trigger the corre-
sponding activity suggesting that they can be useful tools to engage end-users. Other studies
have found similar outcomes, for example, Freyne et al. [52] report in their study on partial
meal replacement that participants with access to self-monitoring tools and notifications
accessed the app more than those with program information only. They also report that activ-
ity in the app was almost double around the time(s) notifications were sent compared to other
There was no notable impact on any behaviour changes regarding nutrition or activity by
participants that may have been influenced by using the app. However, the positive direction
of notification and action suggests that further development of the app to include a compre-
hensive adaptive and responsive element underpinned by theory could influence both knowl-
edge and behaviour.
Further understanding of the reasons for family or friends not accepting invites to use the
app, is needed. The main areas for improvement were minor functional improvements and
some feature enhancements suggesting that the co-creation phase had identified many of the
desirable features and functions. Furthermore, the additional features suggested were not
unexpected, for example, to expand it to encompass a more extensive age range, at minimum
0–5 five-year-olds, and to have more interactive elements and tailored age-appropriate multi-
media tools. These latter two attributes are commonly available in more commercially avail-
able applications (for example in travel and banking mobile applications).
The low Māori and Pasifika engagement suggests that exploring the cultural aspects and
contextual understandings of parenting are essential to ensure that the final product supports
and encourages engagement beyond the first few interactions.
Overall, the SHTG feasibility study found that a digital growth chart and mHealth interven-
tion has the potential to be acceptable and useful for supporting parents in their knowledge of
growth charts and by encouraging them to actively monitor their child’s growth. This finding
aligns with other results that have used digital tools in the prevention of obesity in older chil-
dren. While not specific to the use of growth charts, a systematic review of studies using wire-
less and mobile technologies to prevent and treat paediatric obesity, report that several of the
studies included in the review describe increases in physical activity, increased fruit and vege-
table intake and improved self-monitoring [39].
The See How They Grow app was found to be acceptable, feasible and easy to use by the major-
ity of the participants. While this study was not powered to detect changes or to measure the
impact of the app on knowledge and behaviour change, the findings suggest that further devel-
opment is worth pursuing. Despite high participation in the co-creation phase, the low uptake
of indigenous and Pasifika populations is a notable limitation to the findings. Critical next
steps will be to include more depth, culturally appropriate and relevant content to meet the
diverse needs of all population groups. Similarly, understanding the reasons for the higher dis-
engagement amongst some population groups is also needed. This should be followed by more
research that explores the impact of digital growth and mHealth tools on reducing childhood
overweight and obesity.
See How They Grow
PLOS ONE | February 19, 2021 15 / 19
Supporting information
S1 File.
The authors would like to thank the parents and health professionals who participated in the
co-creation and development study and helped shape the app, as well as the parents who took
part. We also wish to thank the project and the technical team.
Author Contributions
Conceptualization: Gayl Humphrey, Chris Bullen.
Formal analysis: Gayl Humphrey, Varsha Parag.
Funding acquisition: Gayl Humphrey, Chris Bullen.
Investigation: Marion Hiemstra.
Methodology: Gayl Humphrey, Rosie Dobson, Stephen Howie, Samantha Marsh, Susan Mor-
ton, Dylan Mordaunt, Chris Bullen.
Project administration: Angela Wadham.
Supervision: Gayl Humphrey.
Validation: Gayl Humphrey, Varsha Parag.
Visualization: Gayl Humphrey, Samantha Marsh.
Writing – original draft: Gayl Humphrey.
Writing – review & editing: Gayl Humphrey, Rosie Dobson, Varsha Parag, Marion Hiemstra,
Stephen Howie, Samantha Marsh, Susan Morton, Dylan Mordaunt, Chris Bullen.
1. Denney-Wilson E, Laws R, Russell CG, Ong KL, Taki S, Elliot R, et al. Preventing obesity in infants: the
Growing healthy feasibility trial protocol. BMJ Open. 2015; 5(11):e009258.
bmjopen-2015-009258 PMID: 26621519
2. Pryor LE, Tremblay RE, Boivin M, Touchette E, Dubois L, Genolini C, et al. Developmental Trajectories
of Body Mass Index in Early Childhood and Their Risk Factors: An 8-Year Longitudinal Study. JAMA
Pediatrics. 2011; 165(10):906–12.
3. Mead E, Brown T, Rees K, Azevedo LB, Whittaker V, Jones D, et al. Diet, physical activity and beha-
vioural interventions for the treatment of overweight or obese children from the age of 6 to 11 years.
Cochrane Database of Systematic Reviews. 2017(6).
PMID: 28639319
4. Brown T, Moore THM, Hooper L, Gao Y, Zayegh A, Ijaz S, et al. Interventions for preventing obesity in
children. Cochrane Database of Systematic Reviews. 2019(7).
CD001871.pub4 PMID: 31332776
5. Hankey C, editor. Advanced Nutrition and Dietetics in Obesity: John Wiley & Sons Ltd.; 2018.
6. Lundahl A, Kidwell KM, Nelson TD. Parental Underestimates of Child Weight: A Meta-analysis. Pediat-
rics. 2014; 133(3):e689–e703. PMID: 24488736
7. Jeffery AN, Metcalf BS, Hosking J, Mostazir MB, Voss LD, Wilkin TJ. Awareness of body weight by
mothers and their children: repeated measures in a single cohort (EarlyBird 64). Child Care Health Dev.
2015; 41(3):434–42. PMID: 24912623
8. Musaad SMA, Donovan SM, Fiese BH. Parental perception of child weight in the first two years-of-life: a
potential link between infant feeding and preschoolers’ diet. Appetite. 2015; 91:90–100.
10.1016/j.appet.2015.03.029 PMID: 25843938
See How They Grow
PLOS ONE | February 19, 2021 16 / 19
9. McKee C, Long L, Southward LH, Walker B, McCown J. The Role of Parental Misperception of Child’s
Body Weight in Childhood Obesity. Journal of Pediatric Nursing. 2016; 31(2):196–203.
10.1016/j.pedn.2015.10.003 PMID: 26521022
10. Manios Y, Moschonis G, Karatzi K, Androutsos O, Chinapaw M, Moreno LA, et al. Large proportions of
overweight and obese children, as well as their parents, underestimate children’s weight status across
Europe. The ENERGY (EuropeaN Energy balance Research to prevent excessive weight Gain among
Youth) project. Public Health Nutrition. 2015; 18(12):2183–90.
S136898001400305X PMID: 25650819
11. Public Health England. Patterns and trends in child obesity: a presentation of the latest data on child
obesity. 2017.
12. de Onis M, Blossner M, Borghi E. Global prevalence and trends of overweight and obesity among pre-
school children. American Journal of Clinical Nutrition. 2010; 92(5):1257–64.
ajcn.2010.29786 PMID: 20861173
13. Malik VS, Willett WC, Hu FB. Global obesity: trends, risk factors and policy implications. Nat Rev Endo-
crinol. 2013; 9.
14. Appleton J, Fowler C, Brown N. Parents’ views on childhood obesity: qualitative analysis of discussion
board postings. Contemporary Nurse. 2017; 53(4):410–20.
1358650 PMID: 28728473
15. Wang Y, Min J, Khuri J, Li M. A Systematic Examination of the Association between Parental and Child
Obesity across Countries. Adv Nutr. 2017; 8(3):436–48. PMID:
16. Templin T, Cravo Oliveira Hashiguchi T, Thomson B, Dieleman J, Bendavid E. The overweight and obe-
sity transition from the wealthy to the poor in low- and middle-income countries: A survey of household
data from 103 countries. PLOS Medicine. 2019; 16(11):e1002968.
pmed.1002968 PMID: 31774821
17. Nnyanzi LA, Summerbell CD, Ells L, Shucksmith J. Parental response to a letter reporting child over-
weight measured as part of a routine national programme in England: results from interviews with
parents. BMC public health. 2016; 16:846–. PMID:
18. Miller JC, Grant AM, Drummond BF, Williams SM, Taylor RW, Goulding A. DXA Measurements Con-
firm that Parental Perceptions of Elevated Adiposity in Young Children are Poor. Obesity. 2007; 15
(1):165–. PMID: 17228044
19. Burke MA, Heiland FW, Nadler CM. From “Overweight” to “About Right”: Evidence of a Generational
Shift in Body Weight Norms. Obesity. 2010; 18(6):1226–34.
PMID: 19875997
20. Parkinson KN, Reilly JJ, Basterfield L, Reilly JK, Janssen X, Jones AR, et al. Mothers’ perceptions of
child weight status and the subsequent weight gain of their children: a population-based longitudinal
study. International Journal of Obesity. 2017; 41(5):801–6. PMID:
21. Pryke R. Childhood obesity: running from this crisis of ‘normalisation’ won’t work. British Journal of Gen-
eral Practice. 2018; 68(673):358–9. PMID: 30049753
22. Baur LA, Garnett SP. Early childhood—a critical period for obesity prevention. Nature Reviews Endocri-
nology. 2019; 15(1):5–6.
23. Blake-Lamb TL, Locks LM, Perkins ME, Woo Baidal JA, Cheng ER, Taveras EM. Interventions for
Childhood Obesity in the First 1,000 Days A Systematic Review. American Journal of Preventive Medi-
cine. 2016; 50(6):780–9. PMID: 26916260
24. World Health Organisation. Report of the Commission on Ending Childhood Obesity. WHO; 2017. Con-
tract No.: WHO/NMH/PND/ECHO/17.1.
25. Webster J, Forbes K, Foster S, Thomas I, Griffin A, Timms H. Sharing Antenatal Care: Client Satisfac-
tion and Use of the ‘Patient-held Record’. Australian and New Zealand Journal of Obstetrics and Gynae-
cology. 1996; 36(1):11–4. PMID: 8775241
26. Ministry of Health. Well Child Tamariki Ora visits 2015 [updated 30 June 2015. Available from: https://
27. TechNet-21. TechNet-21 [Available from: https://www.technet-21.
See How They Grow
PLOS ONE | February 19, 2021 17 / 19
28. Ben-Joseph EP, Dowshen SA, Izenberg N. Do Parents Understand Growth Charts? A National, Inter-
net-Based Survey. Pediatrics. 2009; 124(4):1100–9. PMID:
29. Ansari Z. How to Design Effective Child Growth Apps, Not Just Attractive Ones: Evaluating the design
of child growth chart apps and investigating end-user design preferences and effective design for an
app-based child growth chart: Masters Thesis: University of Auckland; 2018.
30. Lakshman R, Landsbaugh JR, Schiff A, Cohn S, Griffin S, Ong KK. Developing a programme for healthy
growth and nutrition during infancy: understanding user perspectives. Child Care Hlth Dev. 2012; 38
(5):675–82. PMID: 21752063
31. Kickbusch I, Walt S, Maog D. Navigating Health: The role of health literacy. 2005 [Available from: http://
32. Sachs M, Sharp L, Bedford H, Wright CM. ’Now I understand’: consulting parents on chart design and
parental information for the UK-WHO child growth charts. Child Care Hlth Dev. 2012; 38(3):435–40. PMID: 21668464
33. Laraway KA, Birch LL, Shaffer ML, Paul IM. Parent perception of healthy infant and toddler growth. Clin
Pediatr. 2010; 49(4):343–9. PMID: 19745095
34. Valencia AC, Thomson CA, Duncan B, Arthur A. Evaluating Latino WIC Mothers’ Perceptions of Infant’s
Healthy Growth: A Formative Assessment. Matern Child Hlth J. 2016; 20(3):525–33.
1007/s10995-015-1850-7 PMID: 26530036
35. Ben-Joseph EP, Dowshen SA, Izenberg N. Public understanding of growth charts: A review of the litera-
ture. Patient Educ Couns. 2007; 65(3):288–95. PMID:
36. Jones AR, Parkinson KN, Drewett RF, Hyland RM, Pearce MS, Adamson AJ, et al. Parental percep-
tions of weight status in children: the Gateshead Millennium Study. International journal of obesity
(2005). 2011; 35(7):953–62. PMID: 21673651
37. World Health Organisation. WHO recommendations on home-based records for maternal, newborn
and child health. Geneva: World Health Organization; 2018.
38. Butler E
´M, Derraik JGB, Glover M, Morton SMB, Tautolo E-S, Taylor RW, et al. Acceptability of early
childhood obesity prediction models to New Zealand families. PLOS ONE. 2019; 14(12):e0225212. PMID: 31790443
39. Turner T, Spruijt-Metz D, Wen CKF, Hingle MD. Prevention and treatment of pediatric obesity using
mobile and wireless technologies: a systematic review. Pediatric Obesity. 2015; 10(6):403–9. https:// PMID: 25641770
40. Ministry of Health. Find a Well Child Tamariki Ora provider
child-tamariki-ora-provider2020 [updated 29 September 2016 Available from:
41. Ministry of Health. Well Child Tamariki Ora My Health Book.
resource-files/HE7012_Well%20Child%20Tamariki%20Ora_0.pdf: Ministry of Health 2010 (revised
42. Glavin K, Roelants M, Strand BH, Ju
´usson PB, Lie KK, Helseth S, et al. Important periods of weight
development in childhood: a population-based longitudinal study. BMC Public Health. 2014; 14(1):160. PMID: 24524269
43. Baird J, Fisher D, Lucas P, Kleijnen J, Roberts H, Law C. Being big or growing fast: systematic review of
size and growth in infancy and later obesity. BMJ. 2005; 331(7522):929.
38586.411273.E0 PMID: 16227306
44. Michie S, Ashford S, Sniehotta F, Dombrowski S, Bishop A, French D. A refined taxonomy of behaviour
change techniques to help people change their physical activity and healthy eating behaviours: the
CALO-RE taxonomy. Psychol Health. 2011; 26:1479–98.
540664 PMID: 21678185
45. Michie S, West R, Campbell R, Brown J, Gainford H. ABC of behaviour change theories. Great Britain:
Silverback Publishing 2014.
46. Taki S, Lymer S, Russell CG, Campbell K, Laws R, Ong KL, et al. Assessing User Engagement of an
mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement
Index. JMIR Mhealth Uhealth. 2017; 5(6):e89. PMID: 28663164
47. Shackleton N, Derraik JGB, Audas R, Taylor RW, Glover M, Morton SMB, et al. Decomposing ethnic
differences in body mass index and obesity rates among New Zealand pre-schoolers. International
See How They Grow
PLOS ONE | February 19, 2021 18 / 19
Journal of Obesity. 2019; 43(10):1951–60. PMID:
48. Glover M, Wong SF, Fa’alili-Fidow J, Derraik JGB, Taylor RW, Morton SMB, et al. Ranked Importance
of Childhood Obesity Determinants: Parents’ Views across Ethnicities in New Zealand. Nutrients. 2019;
11(9):2145. PMID: 31500336
49. Dobson R, Whittaker R, Bartley H, Connor A, Chen R, Ross M, et al. Development of a Culturally Tai-
lored Text Message Maternal Health Program: TextMATCH. JMIR Mhealth Uhealth. 2017; 5(4):e49. PMID: 28428159
50. Abroms LC, Whittaker R, Free C, Mendel Van Alstyne J, Schindler-Ruwisch JM. Developing and Pre-
testing a Text Messaging Program for Health Behavior Change: Recommended Steps. JMIR Mhealth
Uhealth. 2015; 3(4):e107. PMID: 26690917
51. Gallegos D, Russell-Bennett R, Previte J, Parkinson J. Can a text message a week improve breastfeed-
ing? Bmc Pregnancy and Childbirth. 2014; 14. PMID:
52. Freyne J, Yin J, Brindal E, Hendrie GA, Berkovsky S, Noakes M. Push Notifications in Diet Apps: Influ-
encing Engagement Times and Tasks. International Journal of Human–Computer Interaction. 2017.
See How They Grow
PLOS ONE | February 19, 2021 19 / 19
... This will allow the child to be monitored by comparing their growth with a normal range for other children of the same age and gender, relative to a reference population. 7 Advances in knowledge and technology have created opportunities to help monitor the treatment progress of a child with poor nutritional status. Since child care is part of health service in that provided by the government and recorded, the use of technology to monitor child growth and development is needed. ...
Full-text available
Advances in knowledge and technology have created opportunities to help monitor child growth. Thus, we conducted a systematic review to determine if the use of mobile apps resulted in improved growth outcomes for children. We include articles published related to children's growth with poor nutritional status. The relevant articles were searched from PubMed, ScienceDirect, Scopus, ProQuest, and Google Scholar. Twelve studies were identified, which is the use of the mobile app to monitor growth in undernutrition and obesity in children. Six studies found that the use of mobile apps improved undernutrition child growth and improved parents’ and/or front health workers’ knowledge to prevent, treat, and monitor children with undernutrition. Six studies stated that the use of mobile app helps overweight/obese children lose weight and motivate them to achieve ideal body weight. Mobile apps for monitoring the growth of children with various standards are likely a promising means for early detection of growth failure and guiding overweight/obese children in gaining normal weight. Studies with large sample sizes and long-term interventions and follow-ups are needed to help assess the effectiveness of mobile app intervention programs and their impact on multiple growth outcomes more comprehensively and accurately.
... Moreover, if a child or infant has a disease that impacts their motor function or attention span, measurement may be greatly hindered, especially for those parents who are not trained in the proper measurement procedures. Parents are deeply invested in their infant's growth, often citing actual numbers for weight and length to friends and family as a proxy for their child's health (14,15). Thus, it is expected that parental interest and participation in learning and utilizing remote methods will be high. ...
Child growth measurements are critical vital signs to track, with every individual child growth curve potentially revealing a story about a child's health and well‐being. Simply put, every baby born requires basic building blocks to grow and thrive: proper nutrition, love and care, and medical health. To ensure that every child who is missing one of these vital aspects is identified, growth is traditionally measured at birth and each well‐child visit. While the blue and pink growth curves appear omnipresent in pediatric clinics, it is surprising to realize that their use only became standard of care in 1977 when the National Center for Health Statistics (NCHS) adopted the growth curve as a clinical tool for health. Behind this practice lies a socioeconomically, culturally, and politically complex interplay of individuals and institutions around the world. In this review, we highlight the often forgotten past, current state of practice, and future potential of this powerful clinical tool: the growth reference chart, with a particular focus on clinical genetics practice. The goal of this article is to understand ongoing work in the field of anthropometry (the scientific study of human measurements) and its direct impact on modern pediatric and genetic patient care.
Background Following increases in smartphone access, more parents seek parenting advice through internet sources, including blogs, web-based forums, or mobile apps. However, identifying quality apps (ones that respond to the diverse experiences of families) for guidance on child development can be challenging. Objective This review of mobile health apps aimed to document the landscape, design, and content of apps in the United States available to parents as they promote their child’s developmental health. Methods To understand the availability and quality of apps for early childhood health promotion, we completed a content analysis of apps in 2 major app stores (Google Play and Apple App stores). Results We found that most apps do not provide tailored experiences to parents, including cultural considerations, and instead promote generic guidance that may be useful to parents in some contexts. We discuss the need for an evaluative framework to assess apps aimed to support parents on child development topics. Conclusions Future work is needed on how to support designers in this area, specifically related to avoiding potential burdens on users and providing culturally informed and equity-driven experiences.
Objective To assess the functionality and feasibility of the GROWIN app for promoting early detection of growth disorders in childhood, supporting early interventions, and improving children’s lifestyle by analyzing data collected over 3 years (2018–2020). Methods We retrospectively assessed the growth parameters (height, weight, body mass index [BMI], abdominal circumference) entered by users (caregivers/parents) in the GROWIN app. We also analyzed the potential health problems detected and the messages/recommendations the app showed. Finally, we assessed the possible impact/benefit of the app on the growth of the children. Results A total of 21 633 users (Spanish [65%], Latin American [30%], and others [5%]) entered 10.5 ± 8.3 measurements (0–15 y old). 1200 recommendations were for low height and 550 for low weight. 1250 improved their measurements. A specialist review was recommended in 500 patients due to low height. 2567 nutrition tests were run. All children with obesity (n = 855, BMI: 27.8 kg/m2 [2.25 SD]) completed the initial test with a follow-up of ≥1 year. Initial results (score: 8.1) showed poor eating habits (fast food, commercially baked goods, candy, etc.), with >90% not having breakfast. After 3–6 months, BMI decreased ≥1 point, and test scores increased ≥2 points. This benefit was maintained beyond 1 year and was correlated with an improvement in BMI (r = −.65, P = .01). Discussion/Conclusions The GROWIN app represents an innovative automated solution for families to monitor growth. It allows the early detection of abnormal growth indicators during childhood and adolescence, promoting early interventions. Additionally, in children with obesity, an improvement in healthy nutritional habits and a decrease in BMI were observed.
Full-text available
Objective While prediction models can estimate an infant’s risk of developing obesity at a later point in early childhood, caregiver receptiveness to such information is largely unknown. We aimed to assess the acceptability of these models to New Zealand caregivers. Methods An anonymous questionnaire was distributed online. The questionnaire consisted of multiple choice and Likert scale questions. Respondents were parents, caregivers, and grandparents of children aged ≤5 years. Results 1,934 questionnaires were analysed. Responses were received from caregivers of various ethnicities and levels of education. Nearly two-thirds (62.1%) of respondents would “definitely” or “probably” want to hear if their infant was at risk of early childhood obesity, although “worried” (77.0%) and “upset” (53.0%) were the most frequently anticipated responses to such information. With lower mean scores reflecting higher levels of acceptance, grandparents (mean score = 1.67) were more receptive than parents (2.10; p = 0.0002) and other caregivers (2.13; p = 0.021); males (1.83) were more receptive than females (2.11; p = 0.005); and Asian respondents (1.68) were more receptive than those of European (2.05; p = 0.003), Māori (2.11; p = 0.002), or Pacific (2.03; p = 0.042) ethnicities. There were no differences in acceptance according to socioeconomic status, levels of education, or other ethnicities. Conclusions Almost two-thirds of respondents were receptive to communication regarding their infant’s risk of childhood obesity. While our results must be interpreted with some caution due to their hypothetical nature, findings suggest that if delivered in a sensitive manner to minimise caregiver distress, early childhood obesity risk prediction could be a useful tool to inform interventions to reduce childhood obesity in New Zealand.
Full-text available
Background In high-income countries, obesity prevalence (body mass index greater than or equal to 30 kg/m²) is highest among the poor, while overweight (body mass index greater than or equal to 25 kg/m²) is prevalent across all wealth groups. In contrast, in low-income countries, the prevalence of overweight and obesity is higher among wealthier individuals than among poorer individuals. We characterize the transition of overweight and obesity from wealthier to poorer populations as countries develop, and project the burden of overweight and obesity among the poor for 103 countries. Methods and findings Our sample used 182 Demographic and Health Surveys and World Health Surveys (n = 2.24 million respondents) from 1995 to 2016. We created a standard wealth index using household assets common among all surveys and linked national wealth by country and year identifiers. We then estimated the changing probability of overweight and obesity across every wealth decile as countries’ per capita gross domestic product (GDP) rises using logistic and linear fixed-effect regression models. We found that obesity rates among the wealthiest decile were relatively stable with increasing national wealth, and the changing gradient was largely due to increasing obesity prevalence among poorer populations (3.5% [95% uncertainty interval: 0.0%–8.3%] to 14.3% [9.7%–19.0%]). Overweight prevalence among the richest (45.0% [35.6%–54.4%]) and the poorest (45.5% [35.9%–55.0%]) were roughly equal in high-income settings. At $8,000 GDP per capita, the adjusted probability of being obese was no longer highest in the richest decile, and the same was true of overweight at $10,000. Above $25,000, individuals in the richest decile were less likely than those in the poorest decile to be obese, and the same was true of overweight at $50,000. We then projected overweight and obesity rates by wealth decile to 2040 for all countries to quantify the expected rise in prevalence in the relatively poor. Our projections indicated that, if past trends continued, the number of people who are poor and overweight will increase in our study countries by a median 84.4% (range 3.54%–383.4%), most prominently in low-income countries. The main limitations of this study included the inclusion of cross-sectional, self-reported data, possible reverse causality of overweight and obesity on wealth, and the lack of physical activity and food price data. Conclusions Our findings indicate that as countries develop economically, overweight prevalence increased substantially among the poorest and stayed mostly unchanged among the wealthiest. The relative poor in upper- and lower-middle income countries may have the greatest burden, indicating important planning and targeting needs for national health programs.
Full-text available
Māori, Pacific, Indian, and New Zealand European pre-school children’s caregivers’ views on determinants of childhood obesity are needed to inform strategies that will reduce disparities in prevalence. Nineteen focus groups were conducted to explore the relative influence of factors contributing to body weight in children. Predetermined and participant-suggested factors were ranked. Discussion data were inductively analysed. The cost of healthy foods was the highest ranked factor across all groups. Ranked similarly were ease of access to takeaways and lack of time for food preparation. Cultural factors followed by screen time induced sedentariness in children and lack of time to ensure children exercised was next. Participant-raised factors included lack of familial, social, and health promotion support, and others’ behaviour and attitudes negatively impacting what children ate. All groups rejected stereotyping that blamed culture for higher obesity rates. Compared to the Māori and NZ European groups, the Pacific Island and Indian participants spoke of losing culture, missing extended family support, and not having access to culturally appropriate nutrition education or social support and services. Public health policies need to mitigate the negative effects of economic deprivation on food insecurity. Complementary interventions that increase access to healthier meal choices more often are needed.
Full-text available
Objective: To determine the extent to which ethnic differences in BMI Z-scores and obesity rates could be explained by the differential distribution of demographic (e.g. age), familial (e.g. family income), area (e.g. area deprivation), parental (e.g. immigration status), and birth (e.g. gestational age) characteristics across ethnic groups. Methods: We used data on 4-year-old children born in New Zealand who attended the B4 School Check between the fiscal years of 2010/2011 to 2015/2016, who were resident in the country when the 2013 census was completed (n = 253,260). We implemented an Oaxaca-Blinder decomposition to explain differences in BMI Z-score and obesity between Māori (n = 63,061) and European (n = 139,546) children, and Pacific (n = 21,527) and European children. Results: Overall, 15.2% of the children were obese and mean BMI Z-score was 0.66 (SD = 1.04). The Oaxaca-Blinder decomposition demonstrated that the difference in obesity rates between Māori and European children would halve if Māori children experienced the same familial and area level conditions as Europeans. If Pacific children had the same characteristics as European children, differences in obesity rates would reduce by approximately one third, but differences in mean BMI Z-scores would only reduce by 16.1%. Conclusion: The differential distribution of familial, parental, area, and birth characteristics across ethnic groups explain a substantial percentage of the ethnic differences in obesity, especially for Māori compared to European children. However, marked disparities remain.
Background: Prevention of childhood obesity is an international public health priority given the significant impact of obesity on acute and chronic diseases, general health, development and well-being. The international evidence base for strategies to prevent obesity is very large and is accumulating rapidly. This is an update of a previous review. Objectives: To determine the effectiveness of a range of interventions that include diet or physical activity components, or both, designed to prevent obesity in children. Search methods: We searched CENTRAL, MEDLINE, Embase, PsychINFO and CINAHL in June 2015. We re-ran the search from June 2015 to January 2018 and included a search of trial registers. Selection criteria: Randomised controlled trials (RCTs) of diet or physical activity interventions, or combined diet and physical activity interventions, for preventing overweight or obesity in children (0-17 years) that reported outcomes at a minimum of 12 weeks from baseline. Data collection and analysis: Two authors independently extracted data, assessed risk-of-bias and evaluated overall certainty of the evidence using GRADE. We extracted data on adiposity outcomes, sociodemographic characteristics, adverse events, intervention process and costs. We meta-analysed data as guided by the Cochrane Handbook for Systematic Reviews of Interventions and presented separate meta-analyses by age group for child 0 to 5 years, 6 to 12 years, and 13 to 18 years for zBMI and BMI. Main results: We included 153 RCTs, mostly from the USA or Europe. Thirteen studies were based in upper-middle-income countries (UMIC: Brazil, Ecuador, Lebanon, Mexico, Thailand, Turkey, US-Mexico border), and one was based in a lower middle-income country (LMIC: Egypt). The majority (85) targeted children aged 6 to 12 years.Children aged 0-5 years: There is moderate-certainty evidence from 16 RCTs (n = 6261) that diet combined with physical activity interventions, compared with control, reduced BMI (mean difference (MD) -0.07 kg/m2, 95% confidence interval (CI) -0.14 to -0.01), and had a similar effect (11 RCTs, n = 5536) on zBMI (MD -0.11, 95% CI -0.21 to 0.01). Neither diet (moderate-certainty evidence) nor physical activity interventions alone (high-certainty evidence) compared with control reduced BMI (physical activity alone: MD -0.22 kg/m2, 95% CI -0.44 to 0.01) or zBMI (diet alone: MD -0.14, 95% CI -0.32 to 0.04; physical activity alone: MD 0.01, 95% CI -0.10 to 0.13) in children aged 0-5 years.Children aged 6 to 12 years: There is moderate-certainty evidence from 14 RCTs (n = 16,410) that physical activity interventions, compared with control, reduced BMI (MD -0.10 kg/m2, 95% CI -0.14 to -0.05). However, there is moderate-certainty evidence that they had little or no effect on zBMI (MD -0.02, 95% CI -0.06 to 0.02). There is low-certainty evidence from 20 RCTs (n = 24,043) that diet combined with physical activity interventions, compared with control, reduced zBMI (MD -0.05 kg/m2, 95% CI -0.10 to -0.01). There is high-certainty evidence that diet interventions, compared with control, had little impact on zBMI (MD -0.03, 95% CI -0.06 to 0.01) or BMI (-0.02 kg/m2, 95% CI -0.11 to 0.06).Children aged 13 to 18 years: There is very low-certainty evidence that physical activity interventions, compared with control reduced BMI (MD -1.53 kg/m2, 95% CI -2.67 to -0.39; 4 RCTs; n = 720); and low-certainty evidence for a reduction in zBMI (MD -0.2, 95% CI -0.3 to -0.1; 1 RCT; n = 100). There is low-certainty evidence from eight RCTs (n = 16,583) that diet combined with physical activity interventions, compared with control, had no effect on BMI (MD -0.02 kg/m2, 95% CI -0.10 to 0.05); or zBMI (MD 0.01, 95% CI -0.05 to 0.07; 6 RCTs; n = 16,543). Evidence from two RCTs (low-certainty evidence; n = 294) found no effect of diet interventions on BMI.Direct comparisons of interventions: Two RCTs reported data directly comparing diet with either physical activity or diet combined with physical activity interventions for children aged 6 to 12 years and reported no differences.Heterogeneity was apparent in the results from all three age groups, which could not be entirely explained by setting or duration of the interventions. Where reported, interventions did not appear to result in adverse effects (16 RCTs) or increase health inequalities (gender: 30 RCTs; socioeconomic status: 18 RCTs), although relatively few studies examined these factors.Re-running the searches in January 2018 identified 315 records with potential relevance to this review, which will be synthesised in the next update. Authors' conclusions: Interventions that include diet combined with physical activity interventions can reduce the risk of obesity (zBMI and BMI) in young children aged 0 to 5 years. There is weaker evidence from a single study that dietary interventions may be beneficial.However, interventions that focus only on physical activity do not appear to be effective in children of this age. In contrast, interventions that only focus on physical activity can reduce the risk of obesity (BMI) in children aged 6 to 12 years, and adolescents aged 13 to 18 years. In these age groups, there is no evidence that interventions that only focus on diet are effective, and some evidence that diet combined with physical activity interventions may be effective. Importantly, this updated review also suggests that interventions to prevent childhood obesity do not appear to result in adverse effects or health inequalities.The review will not be updated in its current form. To manage the growth in RCTs of child obesity prevention interventions, in future, this review will be split into three separate reviews based on child age.
In a longitudinal study that tracked BMI from early life, most children with obesity at age 3 years had overweight or obesity by adolescence. Of adolescents with obesity, ~50% were affected by overweight or obesity from age 5 years onwards, and the most rapid increase in BMI had occurred between 2 and 6 years of age.
Background: Childhood obesity is an increasing concern for parents and health professionals alike. Parents' perception of obesity as a current health issue for their children is important for the everyday parenting and health choices parents make. As parents are frequently going online to seek and exchange information about parenting and child health, asynchronous online discussion forums provide an opportunity to investigate their perceptions and concerns. Understanding parents' perceptions, beliefs and attitudes is important in any childhood obesity prevention and intervention. Aim: To explore parents' perceptions, perspectives and concerns regarding childhood obesity expressed on asynchronous online discussion forums. Methods: A qualitative descriptive approach using template analysis to analyse a novel data collection strategy of 34 purposefully sampled threads from two Australian-based asynchronous online discussion forums. Results: Parents on the discussion forum displayed an understanding of childhood obesity as a public health concern, the discussion incorporated issues such as providing a healthy diet and lifestyle for children. Parents shared their own opinions and experiences that challenged or conceded to the status quo of the discussion. Parents discussed the role of health professionals in obesity prevention. There were varied opinions on the relevance of health professionals, particularly nurses, monitoring of growth and risk of obesity. Conclusion: This exploratory study highlights that parents perceive childhood obesity as an important public health concern, and that they understand the key public health messages of prevention and intervention. Yet, for many it is difficult to successfully implement these messages into their everyday lives. Health professionals need to play a key role in providing non-judgemental, innovative support and advice to parents to successfully implement prevention and intervention strategies.
Background Childhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics. Objective The aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement. Methods The Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ≤3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant’s date of birth. ResultsThe overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including “app only” users or “both” app and email users (P