REVIEW ARTICLE
Health Information Technology in Screening and
Treatment of Child Obesity: A Systematic Review
AUTHORS: Anna Jo Smith, MPH, MSc,a,b Áine Skow, JD,
MSc,a Joann Bodurtha, MD, MPH,c and Sanjay Kinra, MD,
PhDa
aDepartment
of Non-communicable Disease Epidemiology,
London School of Hygiene and Tropical Medicine, London, United
Kingdom; bHarvard Medical School, Boston, Massachusetts; and
cMcKusick-Nathans Institute of Genetic Medicine and Department
of Pediatrics, Johns Hopkins School of Medicine, Baltimore,
Maryland
KEY WORDS
review, child, obesity, BMI, information technology, electronic
health records, telemedicine, text messaging, decision support
systems
ABBREVIATIONS
CI—confidence interval
EHR—electronic health records
IT—information technology
RCT—randomized controlled trial
Ms Smith conceptualized and designed the study, carried out
analysis and interpretation of data, drafted the original
manuscript, and approved the final manuscript as submitted;
Ms Skow assisted with the design of the study, carried out
analysis and interpretation of data, critically revised article
drafts, and approved the final manuscript as submitted; Dr
Bodurtha assisted with the conceptualization of the study and
data acquisition, critically revised article drafts, and approved
the final manuscript as submitted; and Dr Kinra assisted with
the design of the study, critically revised article drafts, and
approved the final manuscript as submitted.
www.pediatrics.org/cgi/doi/10.1542/peds.2012-2011
doi:10.1542/peds.2012-2011
Accepted for publication Oct 17, 2012
Address correspondence to Anna Jo Smith, MPH, Harvard Medical
School, 260 Longwood Ave., Boston, MA 02115. E-mail:
[email protected]
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
abstract
BACKGROUND AND OBJECTIVES: Childhood obesity is a major problem in the United States, yet screening and treatment are often inaccessible or ineffective. Health information technology (IT) may
improve the quality, efficiency, and reach of chronic disease management. The objective of this study was to review the effect of health IT
(electronic health records [EHRs], telemedicine, text message or telephone support) on patient outcomes and care processes in pediatric
obesity management.
METHODS: Medline, Embase, and the Cochrane Registry of Controlled
Trials were searched from January 2006 to April 2012. Controlled trials,
before-and-after studies, and cross-sectional studies were included if
they used IT to deliver obesity screening or treatment to children aged
2 to 18 and reported impact on patient outcomes (BMI, dietary or
physical activity behavior change) or care processes (BMI screening,
comorbidity testing, diet, or physical activity counseling). Two independent
reviewers extracted data and assessed trial quality.
RESULTS: Thirteen studies met inclusion criteria. EHR use was associated with increased BMI screening rates in 5 of 8 studies. Telemedicine counseling was associated with changes in BMI percentile similar
to that of in-person counseling and improved treatment access in 2
studies. Text message or telephone support was associated with
weight loss maintenance in 1 of 3 studies.
CONCLUSIONS: To date, health IT interventions have improved access
to obesity treatment and rates of screening. However, the impact on
weight loss and other health outcomes remains understudied and inconsistent. More interactive and time-intensive interventions may
enhance health IT’s clinical effectiveness in chronic disease management. Pediatrics 2013;131:1–9
Copyright © 2013 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE: The authors have indicated they have
no financial relationships relevant to this article to disclose.
FUNDING: No external funding.
PEDIATRICS Volume 131, Number 3, March 2013
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More than one-third of children in the
United States are now overweight or
obese.1 However, childhood obesity
remains underrecognized and undertreated. Of children who see a physician, surveys indicate that less than
half of them receive BMI screening and/
or preventive counseling about diet and
physical activity as recommended by
the American Academy of Pediatrics.2–4
A minority of overweight and obese
adolescents are referred to dietitians or
behavioral counseling for treatment,
and adherence to treatment is often
poor.3,5–7 Additionally, many weight
management programs are concentrated in academic medical centers,
typically located far from the medically
underserved rural and urban areas
where obesity is more common.8,9
Information technology (IT) has the
potential to improve the quality, consistency, and availability of health
care.10 Common forms of health IT are
electronic health records (EHRs), computerized decision support systems,
telemedicine, and text message or
telephone support. In adults, EHR
adoption and telemedicine have been
associated with small improvements in
physician behavior (BMI screening) and
patient outcomes (weight loss), respectively.11,12 In pediatrics, the 2009 US
Congressional Act, Health Information
Technology for Economic and Clinical
Health, is rapidly expanding the use of
electronic health records and telemedicine. This act includes specific
incentives for IT-facilitated delivery of
BMI screening and counseling on diet
and physical activity, such as computerized growth charts.13,14 However, little
is known about the clinical effectiveness
of IT for pediatric obesity management.
Despite some success in adults, health
IT may be complicated by the lack of
pediatric-specific EHR systems and the
different stages of child development
and family involvement. Pediatric IT interventions for other chronic diseases
2
have reported issues with age-appropriate
messaging, pediatrician uptake, and
minor confidentiality.15–17
Developing a clinical evidence base for
health IT is an important step for delivering effective chronic disease
management and leveraging new federal funding for IT. We performed
a systematic review of the impact of
health IT on patient outcomes and care
processes in childhood obesity management.
METHODS
Search Strategy
Given rapid advancements in technology, the literature review was limited
to English-language articles from 2006
to April 2012. Three databases were
searched: Medline, Embase, and the
Cochrane Registry of Controlled Trials.
Table 1 shows search terms used for
Medline, which were modified where
necessary for Embase and Cochrane
Registry of Controlled Trials. Terms for
“child,” “obesity,” and “IT” were adapted from previous reviews of obesity
prevention, mobile technologies, and
IT.18–20 Reference lists of included articles were also searched.
Inclusion/Exclusion Criteria
Randomized controlled trials (RCTs)
and nonrandomized controlled trials,
before-and-after studies, and crosssectional studies were considered.
Studies were included if they (1) used IT
todeliverobesityscreeningor treatment
in children 2 to 18 years of age; (2) were
clinic based or involved data communication with child health professionals;
and (3) reported measures of clinical
effectiveness (BMI, dietary or physical
activity behavior change, treatment
adherence) or care processes (BMI
screening, comorbidity testing, counseling on diet or physical activity).
Studies of obesity treatment outside
clinical care, such as internet-only or
SMITH et al
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videogame-based interventions, and
studies without a comparison group
were excluded. Telemedicine was defined as the remote diagnosis and/
or treatment of patients using IT.21
Two independent reviewers screened
all abstracts and titles for initial assessment of eligibility. After abstract
screening, both reviewers reviewed fulltext articles to make final inclusion/
exclusion decisions. Disagreements
TABLE 1 Search Terms in Medline
1. exp child/
2. exp adolescent/
3. juvenile.mp
4. child$.mp
5. teen$.mp or adolescen$.mp./
6. exp pediatrics/ or P?ediatric$/
7. child$.af or adolescen$.af.
8. (boys or girls or youth or youths).af.
9. (teenage$ or young people or
young person or young adult$).af.
10. or/1-10
11. exp obesity/
12. exp wt gain/
13. exp wt loss/
14. exp body wt/
15. exp BMI/
16. obes$.af.
17. (wt gain or wt loss).af.
18. exp body wt changes/
19. (overweight or over wt or overeat$
or overeat$).af.
20. wt change$.af.
21. (BMI or BMI or body-mass-index
or BMI-for-age or wt-for-height).af.
22. or/11-21
23. exp medical informatics/ or exp
medical informatics applications/p
24. exp public health informatics/
25. exp educational technology/
26. exp telemedicine/
27. exp cellular phone/
28. exp text messaging/
29. exp computer-assisted therapy/
30. exp user-computer interface/
31. exp personal digital assistant/
or computer, handheld/
32. exp electronic medical record/
33. electronic health record$.mp
or personal health record$.mp
34. exp Diagnosis, Computer-Assisted/
35. exp Decision Support Systems,
Clinical/ or exp Decision Support
Systems, Management/
36. or/23-34
37. 10 and 22 and 35
38. limit 36 to years 2006 to current and
English-language and human
REVIEW ARTICLE
regarding eligibility were resolved by
consensus of the reviewers.
RESULTS
Methodological Quality Assessment
Description of Studies
Quality Assessment
Of 2747 abstracts screened, 13 studies
were eligible for inclusion as shown in
Fig 1. All studies were published after
2009. Nine involved EHRs or computerized decision support, 2 used telemedicine, and 3 used text message or
telephone support. Eleven studies were
from the United States, 1 was from
Australia,23 and 1 was from Canada.24
For the 5 treatment studies reporting
patient outcomes, sample sizes varied
from 17 to 475 participants at 1 to 10
practice sites.25,26 Three treatment
studies focused on obese children with
mean ages of 8 to 12 years,25,27,28 1
focused on obese children 2 to 6.9
years old,26 and 1 focused on overweight adolescents 13 to 16 years
old.23 Of the eight studies on care
processes, sample sizes ranged from
152 to 60 711 patients.24,29 No articles
discussed intervention costs or costeffectiveness.
Of the 13 included studies, 4 were RCTs,
including1cluster-randomizedtrial.23,25,26,28
Five were before-and-after studies performed by using chart reviews of EHR
data,27,29–32 and 4 were cross-sectional
studies.24,33–35 The trials reported participation rates ranging from 61% to
100%, and 3 of 4 reported no baseline
differences considered relevant to study
outcomes. For the 3 surveys, response
rates ranged from 36.9%24 to 62%.34
All RCTs and before-and-after studies
reported objective outcomes, although
only 1 study used blinded outcome assessors.25 One RCT had 1-year attrition of
,10%,23,26 whereas the remaining trials
had attrition of 18%, 18%, and 30%, respectively.23,25,28 On the 10-point quality
scale, the mean score was 3.7 (SD 2.8),
with a range of 1 to 10 (Tables 2 and 3).
Reviewers assessed study quality by
using a tool developed for health IT
studies.20,22 Studies were scored for
methodological quality on a 10-point
scale, including the method of allocation to study group (random: 2, quasirandom: 1, concurrent or no controls:
0), unit of allocation (cluster: 2, health
professional: 1, patient or not applicable: 0), baseline differences between
groups that may be linked to study
outcomes (no baseline differences or
differences adjusted with appropriate
statistical methods: 2, baseline differences reported and no statistical adjustment: 1, baseline characteristics
not reported: 0), outcome objectivity
(objective outcomes or subjective
outcomes with blinding: 2, validated
subjective outcome: 1, or subjective
outcomes poorly defined: 0), and attrition (,10%: 2, 10–20%: 1, .20% or not
applicable: 0).20 On this scale, the
maximum score for a before-and-after
study was 4, and the maximum score
for a cross-sectional study was 3 because of the lack of concurrent controls
and group allocation. Both reviewers
assessed quality independently, and
disagreements were resolved by consensus.
EHRs
One study examined the impact of EHRs
on patient outcomes (Table 2), and 8
Data Extraction and Synthesis
For a given study, we extracted all
outcomes relating to patient outcomes
or care processes. Both reviewers used
a standardized form to extract data
independently; data were compared for
concordance. Random-effects metaanalysis was performed by using
Stata (version 12; StataCorp, College
Station, TX) when two or more similarly
designed studies reported comparable
outcomes. Results are presented by IT
application: electronic health records,
telemedicine, and text message or
telephone support.
FIGURE 1
Study selection.
PEDIATRICS Volume 131, Number 3, March 2013
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studies evaluated care processes
(Table 3). Impacts on care processes
were inconsistent but indicated that
EHRs have the potential to improve
physician documentation of child BMI
and comorbidity screening.
Taveras et al26 randomized obese children to enhanced weight management,
including EHRs with decision support,
increased insurance reimbursement,
telephone support, and motivational interviewing, or usual care. At the 1-year
follow-up, Taveras et al reported no significant differences in BMI (20.21; 95%
confidence interval [CI], 20.50 to 0.07; P =
.15) or BMI z-score (20.05 unit; 95% CI, 2
0.14 to 0.04; P = .28) between groups, and
a slightly greater decrease in television
viewing (20.36 h/day; 95% CI, 20.64 to 2
0.09; P = .01) and fast food intake (20.16
servings/week; 95% CI, 20.33 to 0.01; P =
.07) in children randomized to enhanced
weight management.
in a school-based health center and
reported significant increases in documentation of BMI and blood pressure.
The year after implementation, 76% of
children had a BMI percentile documented and 35% had a blood pressure
percentile documented compared with
31% and 1% before, respectively (P ,
.01 for both).
(P , .001). Counseling rates for physical activity increased from 9% to 32%
(P , .001), and counseling on nutrition
increased from 38.3% to 69.2% (P ,
.001). Because the EHR did not include
decision support, the study authors
suggested that changes were caused
by undocumented discussion of weight
concerns.32,36
Benson et al29 reported no change in
diagnosis of overweight, obesity, or
severe obesity after the introduction of
a BMI flag in EHRs that displayed, in red,
BMIs $85th or #10th percentile. During the 4-year period of study, overweight children were less likely to be
diagnosed than obese children (P for
trend , .001).
Keehbauch et al31 compared the effects
of an EHR upgrade, from basic BMI
calculation to calculation of an ageand gender-specific BMI percentile, on
rates of documenting weight status,
screening for lipids, and providing nutrition and physical activity counseling.31 The 2 study sites both upgraded
their EHRs, but 1 site received extra
training on how to document and treat
pediatric obesity using BMI percentiles. One year after the upgrade, documentation of children as overweight
or obese increased from 24.2% to 33.6%
of children whose BMI was .85th percentile (no P value given). Diet and
Shaikh et al32 found no change in documentation of BMI, weight, or height
after the addition of an automatic BMI
calculator to EHRs. Children with higher
BMI percentiles and older children were
more likely to be screened for BMI
before and after EHR implementation
Gance-Cleveland et al30 introduced a
computerized decision support system
TABLE 2 Studies of the Impact of Health IT on Patient Outcomes in Childhood Obesity Treatment
Source
Telemedicine
Davis et al 2011
Irby et al 2012
Study
Design (n)
Taveras et al 2011
Intervention/Change
RCT (17)
5
Group counseling treatment via telemedicine
Before-and-after
study (294)
3
Individual counseling via telemedicine
6
Automated telephone messages to parents
after group treatment
Text message and telephone support
Estabrooks et al 2009
RCT (220)
Kornman et al 2010 and
Nguyen et al 2012
Quality
Score
RCT (151)
RCT (475)
8
10
Text messages, emails, and phone
messages during group treatment
Multicomponent obesity treatment including
motivational phone calls, a television monitoring
device, and EHRs with clinical decision support
Patient Outcome(s)
BMI percentile
Nutrition
Physical activity
BMI z-score
Nob
No
No
Nob
BMI z-score
Yesc
Nutrition
Physical activity
BMI
No
No
No
BMI z-score
Waist circumference
Nutrition
Physical activity
BMI
No
No
No
No
No
BMI z-score
Nutrition
Reduced television time
No
No
Yes
Improvement was defined by conventional levels of statistical significance (P , .05).
Lack of improvement in patient outcomes for a telemedicine intervention indicates that the intervention was comparable to in-person care.
c Only for children of parents who completed a majority of counseling calls.
a
b
4
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Improvement in Patient
Outcome(s)a
REVIEW ARTICLE
TABLE 3 Studies of the Impact of Health IT on Care Processes in Childhood Obesity Management
Source
Study Design (n)
Quality Score
Electronic health records
Adhikari et al 2011
Survey of pediatricians (355)
Benson et al 2009
Before-and-after study (60 711)
2
Keehbauch et al 2012
Before-and-after study (2340)
3
Klein et al 2010
Piccinini-Vallis 2011
Sesselberg et al 2010
Shaihk et al 2011
Computerized decision
support
Gance-Cleveland et al
2010
a
2
Intervention/Change
EHRs
EHR function that displays
in red BMIs $85th or #10th
percentile
EHR upgrade that calculates
and plots BMI percentile +
education at 1 site
Care Process Outcome(s)
Improvement in
Care Process
Outcome(s)a
BMI screening at well child
visits
Blood pressure screening
Lipid screening
Glucose screening
Documentation of weight status
Yes
No
No
No
No
Documentation of weight status
Yes
No
Yes
No
Yes
Survey of pediatricians (677)
Survey of general
practitioners (152)
Survey of family
physicians (445)
Before-and-after (550)
1
1
EHRs
EHRs
Lipid screening
Nutrition and physical activity
counseling
Computation of BMI percentile
Computation of BMI percentile
1
EHRs
Computation of BMI percentile
Yes
2
EHR that displays and plots BMI
Documentation of BMI
Documentation of weight status
No
No
Before-and-after study (201)
4
Computerized decision support
including calculation of BMI
and BMI percentile
Documentation of BMI
Yes
Documentation of BMI
percentile
Yes
Improvement was defined by conventional levels of statistical significance (P , .05).
exercise counseling rates for these
children also improved. Rates of weight
status documentation and lipid assessment were significantly higher at the
site that received education (P , .05 for
both). The EHR upgrade did not affect
initiation of obesity treatment, which
remained low at all sites (P = .36).
Four surveys of pediatricians and primary care providers reported that
pediatricians with EHR access reported
higher rates of BMI screening than
those without EHRs.24,33–35 In a random
effects meta-analysis of the 3 surveys
reporting percentage of children
screened (Fig 2), the odds of BMI
screening were more than twice as
high among pediatricians with EHRs
compared with those without (pooled
odds ratio: 2.53; 95% CI: 1.39–4.62).33–35
However, overall screening rates
remained low, ranging from 39% to
70%. In Klein et al34 and Adhikari et al,33
availability of clinic resources for weight
management, such as a dietitian or
group counseling, appeared to be greater
predictors of BMI screening than access
to EHRs. Adhikari et al found no difference between respondents with and
without EHRs with respect to rates of
blood pressure, lipid, and glucose testing for overweight or obese children.
Telemedicine
Two studies (1 RCT and 1 before-andafter study) evaluated patient outcomes of obesity counseling delivered
via telemedicine.25,27 Davis et al25 found
no significant difference in BMI percentile, nutrition, or physical activity
behavior in children whose parents
attended 4 sessions of telemedicine
group counseling at a school health
center compared with children whose
PEDIATRICS Volume 131, Number 3, March 2013
families attended 1 in-person counseling visit. At 1 year, neither group
showed any change from baseline.
However, parents reported high levels
of satisfaction with telemedicine, primarily because it minimized health
care–related travel, and counseling
attendance was 100%.
Irby et al27 found reductions in BMI
percentile for children enrolled in
a weight management program where
families were seen via telemedicine or
in person every 2 to 4 weeks; the difference between groups was not significant. Sixty-four percent of children
seen via telemedicine decreased BMI
percentile compared with 69% of children seen in person at 1 year. Enrollment of rural families increased, and
treatment attrition was similar in both
groups (30% for telemedicine versus
32% for in person; no P value given).
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computerized decision support showed
the largest magnitude of improvement
in BMI and comorbidity screening rates;
this is consistent with other studies
suggesting that interactive EHRs are
more effective at changing physician
behavior.30,40 Other EHRs in this review
involved passive notification of weight
status, such as color-coding BMIs over
the 85th percentile or displaying BMI
among a child’s vital signs, which
physicians may not notice.32
FIGURE 2
Odds ratios for childhood BMI screening comparing users of EHRs to nonusers of EHRs.33,34,35
Text Message and Telephone
Support
Three studies examined effects of text
message and telephone support on BMI
and other clinical outcomes.23,28 Findings suggest that phone-based support
may affect change, but only if it is high
intensity.
Estabrooks et al28 reported that children whose families completed group
counseling sessions plus an additional
6 to 10 maintenance sessions using
automated telephone counseling experienced larger decreases in BMI
z-score at 1 year than children whose
families received group counseling
alone. No improvement from baseline
to 1 year was seen in children whose
families completed only 0 to 5 sessions
(10 were offered to all in the intervention group), which the study authors
attributed to low parental motivation
and insufficient skill building.
Nguyen et al and Kornman et al23,37
compared adolescents who received
group counseling followed by text
message, telephone, or e-mail every
other week to adolescents who received group counseling alone and
found no difference in mean changes in
BMI, waist circumference, or blood
6
pressure at 1 year. In a process evaluation of the trial, low adolescent engagement was reported: adolescents
responded to ,22% of the messages
marked “please reply.”37
Taveras et al26 reported no difference
in BMI or BMI z-score between children
who received enhanced weight management, including 3 15-minute phone
calls, and usual care at 1 year. Although
3 telephone calls and 3 clinic visits
were offered to all intervention participants, less than half of families
completed $2 calls or visits.
DISCUSSION
Assessment of the Literature
Regardless of the EHR technology used,
the American Academy of Pediatrics’s
recommendations for universal BMI,
blood pressure, and lipid screening
were not met in any of the included
studies.4 Making clinic resources
available for weight management or
continuing medical education on pediatric obesity may enhance rates of BMI
documentation and lifestyle counseling
to a greater extent than EHR use.31,33,34
Counseling delivered via telemedicine
had comparable clinical outcomes to inperson counseling and improved rural
families’ access to obesity treatment.
One trial’s null findings may be because
of the fact that the frequency of counseling sessions was less than that
recommended by clinical guidelines.4,25
Given the US government’s investment
in the telemedicine infrastructure, telemedicine is likely an effective way to
expand access to obesity treatment.
Telemedicine may be cost-effective if it
minimizes travel costs to families and/
or promotes earlier initiation of effective treatment.
Improving pediatric obesity screening
and treatment is vital to addressing the
US obesity epidemic and associated
burdens of chronic disease and health
care costs.38,39 As far as we are aware,
this is the first review to evaluate the effect of using health IT in pediatric obesity
management. This review illustrates that,
although health IT may improve screening rates and access to treatment, it has
had little to no impact on behavior
change and weight loss thus far.
Text message– or telephone-based support showed some positive effects on
weight maintenance in young children
but not in adolescents. Greater frequency of contact may increase impact.
However, as some contact is patient initiated, this association may be noncausal and reflect differential patient
motivation.
EHRs have had small positive effects on
care processes. Of included studies,
Strengths of this review include the
comprehensive search strategy and
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REVIEW ARTICLE
inclusion of recent evidence. Including
a range of study designs is important for
understanding health IT, as randomization may not be feasible in single practicebased studies or with widespread EHR
adoption.41 However, it is difficult to infer
causality from before-and-after and
cross-sectional studies, given potential
confounding from secular trends and
lack of concurrent controls. Moreover,
the majority of included studies were
small and/or of poor methodological
quality, indicating a need for further
higher-quality research. The review’s
search strategy identified 7 ongoing
RCTs on health IT that will provide more
evidence on patient outcomes.17,23,42–46
The review has several limitations. We
limited the search strategy to medical
databases and English-language articles and did not contact study authors
for unpublished data. Reliance on EHR
documentation may under- or overestimate screening and counseling
rates, depending on ease of EHR data
entry and provider incentives to document screening and counseling provided.36 Information on IT implementation
(staff trainings, provider satisfaction,
cost, and computer systems) is important to generalizability and costeffectiveness but was poorly described
in most studies. Meta-analysis was
possible for only 1 outcome, given the
heterogeneity of included study designs
and outcomes measured.
Future Directions
The US government will invest more
than $29 billion in health IT by 2020.13
Despite this, very few studies have
demonstrated positive effects of health
IT on patient outcomes.10 Notably, in
this review, the majority of included
studies did not measure patient outcomes, but rather evaluated impacts
on care processes. This focus was expected: EHRs are designed primarily to
assist clinician decision-making and
record-keeping. Nonetheless, improvements in care processes may lead to
positive behavioral changes. For example, improving BMI screening rates
in children will lead to clinicians and
parents being informed when a child is
overweight or obese. Parents who
recognize their child is overweight and
are aware of the associated health
risks are more likely to make lifestyle
changes.47 Further research is needed
to understand how improved quality
and consistency of care influences patient outcomes.
On the other hand, some interventions,
such as text message support, have not
been associated with improved patient
outcomes thus far. Because these
applications are new, their design and
implementation have room for improvement. IT interventions that have
been effective for other conditions
could be trialed for childhood obesity.
Text message reminders increase attendance for pediatric appointments
and could reduce obesity treatment
dropout.48–50 Personalized daily text
messages have been shown to increase
smoking cessation in young adults;
messages about diet and nutrition might
augment group counseling if delivered
at a similar intensity.51,52 Pop-up alerts
for BMI above healthy thresholds have
been used effectively in adult weight
management.53 Although the review
found no studies on electronic prescribing, the use of drugs, such as orlistat and statins, is increasing in
childhood obesity treatment.4,54 EHRs
that integrate prescribing and decision
support could play an important role in
ensuring safe prescribing and appropriate treatment.16 To maximize accessibility, telemedicine could be used to
deliver obesity screening or treatment
in children’s homes. Many of these
strategies, if successful, could be expanded to management of other pediatric chronic diseases.
CONCLUSIONS
In childhood obesity, studies show
promise for telemedicine to expand
treatment access and for EHRs to assist
physicians in adhering to clinical
guidelines for screening. To date, the
effect on weight loss and behavior
change remains understudied and inconsistent. Further research is necessary to evaluate if changes in care
processes affect clinical outcomes and
if additional IT applications could enhance the quality and availability of
screening and treatment. Understanding the benefits, and limits, of health IT
will enable pediatricians to design and
implement IT programs that fit their
patients’ needs and maximize its value
in pediatric obesity management.
ACKNOWLEDGMENTS
We thank Thomas Smith, MD, from
Johns Hopkins School of Medicine for
providing comments on a draft manuscript. Ms Smith thanks the Marshall
Aid Commemoration Commission for
her studentship.
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Health Information Technology in Screening and Treatment of Child Obesity: A
Systematic Review
Anna Jo Smith, Áine Skow, Joann Bodurtha and Sanjay Kinra
Pediatrics; originally published online February 4, 2013;
DOI: 10.1542/peds.2012-2011
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PEDIATRICS is the official journal of the American Academy of Pediatrics. A monthly
publication, it has been published continuously since 1948. PEDIATRICS is owned, published,
and trademarked by the American Academy of Pediatrics, 141 Northwest Point Boulevard, Elk
Grove Village, Illinois, 60007. Copyright © 2013 by the American Academy of Pediatrics. All
rights reserved. Print ISSN: 0031-4005. Online ISSN: 1098-4275.
Downloaded from by guest on June 18, 2017
Health Information Technology in Screening and Treatment of Child Obesity: A
Systematic Review
Anna Jo Smith, Áine Skow, Joann Bodurtha and Sanjay Kinra
Pediatrics; originally published online February 4, 2013;
DOI: 10.1542/peds.2012-2011
The online version of this article, along with updated information and services, is
located on the World Wide Web at:
/content/early/2013/01/29/peds.2012-2011
PEDIATRICS is the official journal of the American Academy of Pediatrics. A monthly
publication, it has been published continuously since 1948. PEDIATRICS is owned,
published, and trademarked by the American Academy of Pediatrics, 141 Northwest Point
Boulevard, Elk Grove Village, Illinois, 60007. Copyright © 2013 by the American Academy
of Pediatrics. All rights reserved. Print ISSN: 0031-4005. Online ISSN: 1098-4275.
Downloaded from by guest on June 18, 2017