Brief Primary Care Obesity Interventions: A Meta-analysis · 9/8/2016 · PEDIATRICS Volume 138 , number 4 , October 2016 :e 20160149 REVIEW ARTICLE Brief Primary Care Obesity Interventions:

REVIEW ARTICLEPEDIATRICS Volume 138 , number 4 , October 2016 :e 20160149

Brief Primary Care Obesity Interventions: A Meta-analysisLeslie A. Sim, PhD, a Jocelyn Lebow, PhD, a, b Zhen Wang, PhD, c Afton Koball, PhD, d M. Hassan Murad, MDc

abstractCONTEXT: Although practice guidelines suggest that primary care providers working with

children and adolescents incorporate BMI surveillance and counseling into routine practice,

the evidence base for this practice is unclear.

OBJECTIVE: To determine the effect of brief, primary care interventions for pediatric weight

management on BMI.

DATA SOURCES: Medline, CENTRAL, Embase, PsycInfo, and CINAHL were searched for relevant

publications from January 1976 to March 2016 and cross-referenced with published studies.

STUDY SELECTION: Eligible studies were randomized controlled trials and quasi-experimental

studies that compared the effect of office-based primary care weight management

interventions to any control intervention on percent BMI or BMI z scores in children aged 2

to 18 years.

DATA EXTRACTION: Two reviewers independently screened sources, extracted data on

participant, intervention, and study characteristics, z-BMI/percent BMI, harms, and study

quality using the Cochrane and Newcastle-Ottawa risk of bias tools.

RESULTS: A random effects model was used to pool the effect size across eligible 10

randomized controlled trials and 2 quasi-experimental studies. Compared with usual care

or control treatment, brief interventions feasible for primary care were associated with

a significant but small reduction in BMI z score (–0.04, [95% confidence interval, –0.08

to –0.01]; P = .02) and a nonsignificant effect on body satisfaction (standardized mean

difference 0.00, [95% confidence interval, –0.21 to 0.22]; P = .98).

LIMITATIONS: Studies had methodological limitations, follow-up was brief, and adverse effects

were not commonly measured.

CONCLUSIONS: BMI surveillance and counseling has a marginal effect on BMI, highlighting the

need for revised practice guidelines and the development of novel approaches for providers

to address this problem.

Departments of aPsychiatry and Psychology, and cEvidence-Based Practice Center and Center for Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota; bDepartment of

Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, Florida; and dGundersen Lutheran Health System, Department of Behavioral Health, LaCrosse,

Wisconsin

Dr Sim conceptualized and designed the study, extracted the data, and drafted the initial manuscript; Dr Lebow designed the study, extracted the data, and drafted

the initial manuscript; Dr Koball assisted with data extraction, drafted the tables, and revised the manuscript; Dr Wang analyzed the data and critically reviewed and

revised the manuscript; Dr Murad assisted with study design and interpretation and critically reviewed and revised the manuscript; and all authors approved the

fi nal manuscript as submitted.

DOI: 10.1542/peds.2016-0149

Accepted for publication Jun 9, 2016

To cite: Sim LA, Lebow J, Wang Z, et al. Brief Primary Care Obesity Interventions: A Meta-analysis. Pediatrics. 2016;138(4):e20160149

by guest on October 1, 2020www.aappublications.org/newsDownloaded from

SIM et al

Concerns about the rising prevalence

of pediatric obesity, as well as the

associated comorbidities and long-

term medical consequences, have

led to well-publicized public health

initiatives to reduce obesity in

youth. In this effort, primary care

practitioners have been charged

with the task of identifying and

intervening when at-risk young

patients present for a routine

appointment. 1 – 3 Although these

recommendations seem reasonable,

there are some concerning gaps in

the science. In a 2005 comprehensive

literature review, there were no

studies that addressed the key

question of whether screening/

intervention at the level of primary

care for overweight in children and

adolescents improves behavior,

health outcomes, or weight. 2

Moreover, a 2010 systematic

review of obesity interventions

feasible for implementation in a

pediatric primary care setting found

consistently poor quality studies

with the majority showing little to no

change in BMI or other physiologic

measures (eg, lipid levels, glucose

tolerance, blood pressure, physical

fitness measures).4

According to the American Academy

of Pediatrics, the recommendation

regarding screening and behavioral

counseling for children at risk for

obesity is largely extrapolated from

primary care-based prevention

in other areas, such as physician

conversations about smoking

cessation or breastfeeding. 4 Not only

are these behaviors distinct from

weight management, the latter also

differs in that evidence suggests

that physicians’ conversations about

weight loss may have unintended

consequences including increasing

weight-related stigma, dieting

behaviors, consequent binge-eating

and weight gain, as well as risk for

eating disorders. 5 –8 In addition, data

have suggested that perceived weight

stigma, including being weighed

and given feedback about gaining

weight, contributes to adults with

higher BMIs avoiding or delaying

necessary and routine medical

appointments. 9 Similarly, a recent

study has found that overweight and

obese children are more likely to

receive routine medical care in an

emergency department, as opposed

to a primary care setting. 10 Although

no causal mechanism was reported,

the study suggests the potential for

overweight/obese pediatric patients

to underutilize preventative health

care services in a manner comparable

to their adult counterparts. 10

Understanding the balance of

potential benefits and harms is

particularly important in light of

research suggesting the financial

impact of these interventions

to both the family and society

is considerable. 11 – 14 If these

interventions have a marginal

benefit, resources may be better

used developing novel programs or

directed toward interventions that

have a larger impact on children’s

health and well-being. Consequently,

high-quality empirical data regarding

both the benefits and possible

harms of screening and behavioral

counseling for pediatric obesity

prevention conducted within the

primary care setting are needed.

The objective of this study was

to summarize the available

observational and interventional

evidence in a systematic review

and meta-analysis to determine

the effect of typical primary care,

office-based, weight management

interventions (eg, motivational

interviewing, lifestyle modification

education) compared with any

control intervention (eg, usual care,

no intervention, BMI feedback only,

active control treatment) on BMI in

children and adolescents aged

2 to 18 years. Although several

systematic reviews have summarized

primary care interventions for

pediatric weight management, 2, 15

these reviews have included studies

with substantial threats to external

validity, including aspects of

intervention design and delivery that

are not feasible for implementation

in primary care (eg, home visits, 18

session protocols, specialized obesity

treatment, behavioral specialist–

delivered treatment). Because the

goal of this study was to understand

how physicians’ conversations

about children’s weight, as well as

guidance and interventions typically

offered in primary care influence

children’s BMI, we limited our focus

to brief interventions appropriate

and feasible for the average primary

care setting rather than interventions

representative of specialty weight

management services. A second

goal of this study was to examine

potential adverse effects of these

practices.

METHODS

Using an unpublished review

protocol (see Supplemental

Information) that followed PRISMA

(Preferred Reporting Items for

Systematic Reviews and Meta-

Analyses) guidelines, this review

set out to determine the effect of

primary care–level interventions

for weight management on z-BMI

or BMI percentile in children and

adolescents (ages 2–18).

Patient Involvement

Patients, carers, and laypeople

were not systematically involved

in the development of the research

question, study design, or outcome

measures and were not involved in

the implementation of the study.

However, the idea behind this

study originated from adolescents’

experiences with routine primary

care BMI monitoring and healthy

habits coaching during primary

care visits. There are no plans to

disseminate the results of the study

to patients or caregivers but results

will be used to inform primary

care practices related to obesity

prevention.

2 by guest on October 1, 2020www.aappublications.org/newsDownloaded from

PEDIATRICS Volume 138 , number 4 , October 2016

Eligibility Criteria

Eligible studies were randomized

controlled trials (RCTs), quasi-

experimental trials, nonrandomized

trials, and prospective cohort studies

published in any language that

compared the effect of office-based

primary care weight management

interventions (eg, lifestyle

modification education, BMI feedback

and lifestyle counseling, motivational

interviewing/solution-focused

therapy) to any control intervention

(eg, usual care, no intervention,

BMI feedback only, active control

treatment) on BMI in children and

adolescents aged 2 to 18 years.

Studies that were considered eligible

included those where the majority

of the intervention was delivered by

staff members routinely involved

in primary care or by research

assistants supervised by disciplines

routinely involved in primary

care (eg, physicians, physicians-

in-training, nurse practitioners,

physicians assistants, registered

nurses, bachelor’s degree health

educators) as opposed to specialists

or staff members not regularly

involved in primary care (eg,

psychologists, physical therapists,

dieticians). Eligible studies assessed

percent BMI or BMI z scores before

and at the end of and/or after

treatment.

We excluded studies that included

patients presenting for targeted

weight management services

at non-primary care/specialty

clinics (eg weight management

clinics, endocrinology, bariatric

surgery, psychology) and studies

of interventions representative

of specialty weight management

services (eg, family-based behavioral

treatment of obesity, intensive

behavioral treatment of obesity),

or with interventionists that had

specialty training in behavioral

interventions (eg, psychologists,

psychology graduate students) or

interventions that were beyond the

scope of practice for typical primary

care staff members. Dieticians could

be involved as long as a primary

care staff member was the primary

interventionist.

Information Sources and Searches

With input from a methodologist

(M.H.M.) with expertise in conducting

systematic reviews, a reference

librarian (P.J.E.) designed and

conducted the electronic search

strategy. This systematic search

included electronic databases

(Medline, CENTRAL, Embase,

PsycInfo, and CINAHL) from January

1, 1976, to March 25, 2016. We

used a combination of text words

and indexed terms related to

“primary health care, ” BMI, ” “child”

or “adolescent, ” and “intervention”

(see Supplemental Information). To

identify additional candidate studies,

we reviewed the reference section of

each of the eligible primary studies

and of narrative and systematic

reviews.

Study Selection

Working independently and in

duplicate, reviewers (J.L. and L.A.S.)

screened all abstracts and titles.

Reviewers obtained all potentially

eligible studies in full text. Acceptable

chance adjusted agreement

(κ = 0.70) was observed between

the 2 reviewers who determined

the eligibility of full text reports.

Reviewers resolved disagreements

by consensus or arbitration (A.K.).

Data Collection Process

Using a pilot-tested computerized

extraction form, J.L. and L.A.S.,

working in duplicate, abstracted data

describing the patient population

and treatments studied. In the

case of disagreements, the same

2 researchers met to review and

resolve discrepancies for final data

extraction. Authors were contacted to

obtain missing data and to verify the

data as abstracted.

Data Items

Data were abstracted on participant

age, gender, ethnicity, and BMI. We

extracted information on type of

intervention, number and frequency

of in-person sessions, number of

interim phone calls, and duration

of intervention, type of treatment

provider, target of intervention

(parent only versus parent and

child), type of control group (active

control versus usual care/wait-list),

and duration of follow-up.

The outcomes extracted included

percent BMI and BMI z scores

(z-BMI) including end of study and/

or change from baseline values.

Outcomes extracted were those

reported at the longest point of

complete follow-up.

Pairs of reviewers worked

independently to determine the

reported risk of bias of eligible RCTs

using the Cochrane Collaboration

Risk of Bias Tool 16 with acceptable

interrater agreement.

Statistical Analysis

BMI z scores or converted BMI

percentiles to BMI z scores were

extracted from the included studies.

When BMI data were available

without z scores, authors were

contacted to obtain these data. The

summary measure was the weighted

mean difference in change in BMI

from baseline to follow-up between

children and adolescents who were

exposed to primary care weight

management interventions and

those in control conditions. BMI

z scores were then pooled by using

the DerSimonian-Laird random

effect model, and pooled effects and

their 95% confidence intervals 17

were estimated. 18 Inconsistency was

assessed by using the I2 statistic,

which describes the proportion of

the observed overall between-study

variability not due to chance. 17

I2 <25% reflects small inconsistency,

and I2 > 50% reflects large

inconsistency. All statistical analyses


SIM et al

were conducted by using Stata 13.1

(StataCorp, College Station, TX).

Subgroup Analyses

To explain heterogeneity across

studies, a priori hypotheses for

subgroup analyses included

participant age (≤6 vs >6 years),

target of intervention (parent

only, child only, parent and child),

treatment provider (physician/

physician-in-training/nurse

practitioner/physician assistant vs

registered nurse/health educator),

number of in-person sessions (≤4

visits vs >4 visits), interim telephone

contact (none vs telephone sessions),

duration of intervention (≤6 vs

>6 months), control intervention

(active control vs usual care/

wait-list), follow-up (no follow-up

vs follow-up), and study quality

including type of study, blinding of

outcome assessors and extent of loss

to follow-up. We conducted tests for

treatment-subgroup interactions, 19

considering a significant interaction

when P < .05.

RESULTS

Search Results

After screening 800 abstracts, 27

full text articles were identified.

A review of reference sections

identified another 54 studies and

27 full text articles. A review of full

text articles initially identified 15

article reporting on 10 RCTs 20 – 28 and

4 quasi-experimental studies.29 – 32

Three studies were excluded because

of missing outcome data that the

authors were unable to provide. 31– 33

The final analysis included 13 articles

reporting on 10 RCTs 12, 20 – 28, 34 and 2

quasi-experimental studies. 29, 30 All of

the RCTs had missing methodological

quality indicators. We contacted

all of the corresponding authors

(10 authors replied with additional

details). Study selection flow is

depicted in Fig 1.

Study Characteristics

All but 1 of the studies included

children who were in the overweight

to mildly obese weight range. Two

studies recruited both children

and adolescents (ages 4–18 and

7–16 years), yet the mean age of

these participants was under 12.

The majority of studies recruited

preadolescent children with 5 studies

including participants with a mean

age of under 6 years. The majority

of the interventions studied offered

≥4 in-person meetings, and most

included between-session phone calls

from intervention staff members.

All but 2 of the interventions used

motivational interviewing/solution-

focused therapy approaches, and all

but 1 of the interventions delivered

information regarding nutrition

education. In terms of intervention

delivery, 9 of treatments were

delivered by primary care providers

(physicians, physicians-in-training,

nurse practitioners, or physician

assistants), and 3 were delivered

by RN staff members, bachelor’s

degree health educators, and/ or

research assistants who functioned

as a health educator. Only 2 studies

had a follow-up period of ≥1 year;

all others had follow-up periods of

<12 months, including 5 studies that

had no follow-up period at all, and

only evaluated outcomes at end of

treatment. For a description of study

and interventions characteristics, see

Table 1.

Risk of Bias

Overall, the included RCTs had

minimal reporting of methodological

features that protect against bias

( Table 2). Although 60% of the

trials clearly blinded data collectors

and assessors, 1 of the studies

blinded providers and 3 blinded

participants of the interventions.

The median loss to follow-up was

14.15%. These numbers should

be interpreted cautiously, because

half of the RCT studies had no

follow-up period posttreatment.

Only 3 studies measured potential

harms of the intervention (body

image dissatisfaction, quality of life,

perceived appearance).

The quasi-experimental studies were

representative of exposed individuals

4

FIGURE 1PRISMA fl owchart of study selection. Results of the systematic review with PRISMA fl ow of studies for eligibility into the review and meta-analysis. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.


PEDIATRICS Volume 138 , number 4 , October 2016 5

TABLE 1 Characteristics of Study Interventions

Author, Year Patients Description of Intervention Intervention

Intensity

Type of

control

Follow-up

Gillis, 2007 20 27 Israeli children (7–16 y old; mean

age = 10.6; BMI >90th percentile,

child mean BMI percentile = 95.6)

Psychoeducational sessions delivered by physician

in training about healthy habits. Patients were

instructed to keep weekly food diaries monitored

via phone.

2 sessions

over 6 mo

Active

control

None

McCallum, 2007 21 163 Australian children and their

parents (children were 5–10 y

old; mean age = 7.4; BMI ≥85th

percentile; child mean UK BMI

percentile = 94.25)

Brief, solution-focused therapy delivered by PCP

designed to help families set and meet lifestyle

goals. Used print materials including a “Family

Folder” with targeted behavioral change

worksheets.

4 sessions

over 3 mo

Usual

care

12 mo

O’Connor, 2011 22 40 American parent–child dyads

(5–8 y old; mean age = 6.8; BMI

85th–99th percentile; mean BMI

percentile = 95.85)

Allied health staff delivered intervention to improve

communication and parents’ abilities to set and

implement lifestyle goals. Sessions also included

psychoeducation on healthy habits and print

materials.

6 sessions

over 6 mo

Usual

care

None

Schwartz, 2007 29 91 American children and their

parents (3–7 y old; mean age =

4.8; either children with BMI 85th–

95th percentile or children with

BMI ≥50th percentile and parents

BMI ≥30; mean BMI percentile =

83.23)

Minimal: Pediatrician led brief MI session as well

as print and video patient materials. Intensive:

Pediatrician and RD led MI sessions, as well as print

and video patient materials.

1 session

over 1 mo;

2 sessions

over 3 mo

Usual

care

Minimal:

5 mo;

Intensive:

3 mo

Resnicow, 2015 34 645 Australian children and their

parents (2–8 y old; mean age =

5.1; BMI ≥85 ≤97 percentile mean

BMI percentile = 91.9 percentile

PCPs provided brief MI on discrete behaviors assessed

through a patient questionnaire. PCPs provided

positive feedback for healthy behaviors and then

collaboratively identifi ed behaviors that might be

modifi ed. Parents fi lled out self-monitoring logs to

target behavior change.

4 sessions

over 2 y

Usual

care

None

Taveras, 2015 25 549 American parent-child dyads


≥95th percentile; mean BMI

percentile = 96.1. CDS and CDS+

coaching

All arms of the intervention included a CDS system

designed to help providers with management

and tracking of patients. CDS conditions: brief MI

at regularly scheduled pediatric visits and print

materials designed to help families with self-

guided behavior change. CDS + coaching condition:

Pediatric clinicians and health educator delivered

motivational interviewing delivered via phone, text,

or e-mail.

2 sessions

over 9 mo

Usual

care

3 mo

Taveras, 2011 24 475 American parent-child dyads

(2–7 y old; mean age = 4.9;

either children with BMI ≥95th

percentile or children with BMI

85th–95th percentile with 1

overweight parent; mean BMI

percentile = 93.05.

Primary care restructuring including updates to the

electronic medical record to improve decision

making and patient tracking. Patients received MI

and education from PNP on healthy habits, as well

as print and online resources.

4 sessions

over 12 mo

Usual

care

None

Tucker, 2013 30 125 American parent-child dyads


85th–95th percentile; mean BMI

percentile = 90.79).

Brief motivational interviewing, combined with

information about healthy habits. Patients also

received print materials and gifts (eg, pedometers,

jump ropes)

3 sessions

over 6 mo

Usual

care

6 mo

Wake, 2009 12, 28 258 parent-child dyads (5–10 y old;

BMI 85th–99th percentile; mean

age = 7.5; mean BMI percentile

= 97.1).

Brief solution-focused therapy to set and record

lifestyle goals assisted by 16-page patient education

folder targeting lifestyle goals.

4 sessions

over 12 wk

Usual

care

3 y

Wake, 2012 26 118 Australian parent-child dyads


≥ 95th percentile; mean BMI

percentile = 98.5).

Program used a shared care model supported by

shared software system for providers. Pediatrician

and RD delivered MI and information on healthy

habits.

4–8 sessions

over 12 mo

Usual

care

None

Yilmaz, 2014 27 412 Turkish parent-child dyads (2–6

y old; mean age = 3.5; mean BMI

percentile = 42.2).

Subjects received 4 installments of print and CD-based

psychoeducational materials, informed by social

cognitive theory, about harmful effects of screen

time as well as 1 counseling call.

4 educational

mailings

over 2 mo

Usual

care

9 mo

CDS, clinical decision support; MI, motivational interviewing; PCP, primary care provider; PNP, pediatric nurse practitioner; RD, registered dietician.


SIM et al

in the community ( Table 3). Both of

the studies drew their comparison

sample from the same community

as the exposed cohort, yet neither of

the studies controlled for baseline

characteristics. Both of the studies

had >30% of the participants lost

to follow-up. Neither of the studies

examined potential harms of the

intervention.

Meta-analysis

Figure 2 summarizes the results

of meta-analysis of the effects of

interventions feasible for primary

care on BMI over 14 comparisons.

Compared with no treatment, usual

care, or active control treatments,

brief, office-based, primary care–level

interventions for pediatric obesity

were associated with a significant

effect on z-BMI of –0.04, (95%

confidence interval [CI], –0.08 to

–0.01), P < .02; with no inconsistency

across studies (I2 = 0%). This

compares to an average effect size of

family-based behavioral treatments

for pediatric obesity of –0.37, (95%

CI, –0.05 to –0.73). 35

Compared with no treatment, usual

care, or active control treatments,

office-based, primary care


were associated with a nonsignificant

effect on body satisfaction

(standardized mean difference [SMD]

0.00, [95% CI, –0.21 to 0.22]); P = .98,

I2 = 64.1%, child-reported quality

of life (SMD 0.06, [95% CI, –0.12 to

0.24]), P = .53, I2 = 0.0%, parent-

reported quality of life (SMD 0.13,

[95% CI, –0.05 to 0.31]), P = .15,

I2 = 0.0%, and physical appearance/

self-worth (SMD 0.71 [95% CI, –0.17

to 1.58]), P = .55, I2 = 93.5% ( Fig

3). These results suggest that these

interventions are not associated with

harm, at least with regard to these

measures.

Subgroup Analyses

Results of subgroup analyses found

no significant interactions caused

by participant age (≤6 vs >6 years;

P = .44), target of intervention (parent

veruss child versus parent and child;

P = .10, treatment provider (primary

care provider versus nurse/health

educator; P = .82), interim telephone

calls (yes versus no; P = .66), number

of sessions (≤4 visits vs >4 visits;

P = .65), duration of intervention

(≤6 vs >6 months; P = .46), duration

of follow-up (posttreatment versus

follow-up; P = .70), type of study

(RCT versus quasi-experimental;

P = .15), control intervention (active

control versus usual care/wait-list;

P = .07), and study quality including

blinding of outcome assessors (blind

versus not blind; P = .30), and extent

of loss to follow-up (<30% vs ≥30%;

P = .10).

DISCUSSION

This systematic review and meta-

analysis found a marginal effect

for primary care–based early


with regard to BMI reduction. To put

the finding in context, for a 10-year-

old girl with a BMI at the 90th

percentile, the effect is equivalent to

a difference between the intervention

and control groups of 1 kg over

a 0- to 3-year follow-up period.

Moreover, the change in z-BMI found

in this study of –0.04 compares with

an average effect found in studies

of family-based behavioral weight

management treatments of –0.37. 35

Because a BMI z score reduction of

0.5 to 0.6 is needed to be sure of clear

fat reduction and associated health

benefit, 36 the approach examined in

the reviewed studies is considered to

be generally ineffective.

Subgroup analyses, although

underpowered, found no differences

based on participant age; whether

the child or parent participated

in the intervention; the intensity,

duration, or type of intervention;

the type of provider delivering the

intervention; whether telephone

calls were included; or any other

aspect of intervention or study

6

TABL

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Ris

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Bia

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Incl

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and

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linic

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rial

s

Auth

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ear

Allo

cati

on

Con

ceal

men

t

Pat

ien

ts

Blin

d

Pro

vid

ers

Blin

dD

ata

Col

lect

ors

Blin

d

% L

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to

Follo

w-u

p

Inco

mp

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ata

Sel

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utc

ome

Rep

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Adve

rse

Even

tsS

tud

y Fu

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ing

Gill

is, 2

007 20

No

No

No

No

33.3

Yes

No

Not

ass

esse

dU

nfu

nd

ed

McC

allu

m, 2

007 21

Yes

No

No

Yes

10.4

No

No

Asse

ssed

Non

pro

fi t

O’C

onn

or, 2

011 22

Yes

No

No

No

15.0

Yes

No

Not

ass

esse

dN

onp

rofi

t

Sm

all,

2014

23N

ot r

epor

ted

No

No

Not

rep

orte

d38

.3Ye

sN

oN

ot a

sses

sed

Non

pro

fi t

Res

nic

ow, 2

016 34

No

No

No

No

29.0

No

No

Not

ass

esse

dN

onp

rofi

t

Tave

ras,

201

5 25Ye

sYe

sN

oYe

s5.

6N

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ot a

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sed

Non

pro

fi t

Tave

ras,

201

1 24N

/AYe

sN

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3N

oN

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Non

pro

fi t

Wak

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009 12

, 28Ye

sN

oP

arti

alYe

s29

.0N

oN

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sess

edN

onp

rofi

t

Wak

e, 2

013 26

Yes

No

No

Yes

9.3

No

No

Asse

ssed

Non

pro

fi t

Yilm

az, 2

014 27

No

Yes

Yes

Yes

13.3

Yes

No

Not

ass

esse

dN

ot r

epor

ted



design. The included studies were of

variable quality, sample sizes were

small, and follow-up was relatively

brief (ranging from posttreatment

to 3 years). Of concern, only 2

studies followed their sample for

a year or more posttreatment, and

several studies (5 of 10) did not

measure outcomes posttreatment.

This is notable because data

suggest that primary care–based

weight loss programs for adults,

although effective for short periods

of time, have low rates of long-

term participant compliance and

do not result in sustained benefits

after 2 years. 37 Had there been a

meaningful effect size, the lack of

longer follow-up would have been

an important limitation. However,

given the overall lack of efficacy and

tendency found in obesity research

for any BMI effects to typically wash

out over time, additional follow-up

would be unlikely to threaten our

conclusions.

Unfortunately, less than one-third of

included studies measured adverse

effects. In light of this concern, along

with data that demonstrate that

seemingly innocuous public health

campaigns regarding healthy habits

can be perceived to contain inherent

weight stigma by young people, 38, 39

the lack of measurement of potential

harms across the majority of studies

is a considerable oversight. Because

only 2 of the studies recruited

adolescents in their sample and the

mean age of the participants was <12

years, it is unclear how the results

would generalize to an adolescent

population and whether adverse

effects would be more likely in this

group.

A potential harm not measured in

this study is that financial resources

used in implementing these

interventions may be directed away

from other, possibly more beneficial

health care interventions. In light of

the substantial financial cost of these

interventions to the family and to the

society, 12 – 14 the lack of a meaningful

effect of these primary care efforts

in reducing a child’s BMI trajectory

suggests that resources may be

better devoted to other public health

agendas and to the development

and testing of novel approaches to

address this problem in primary care.

Although all studies included BMI

data and several measured other

parameters, data could not be

included for other behavior change

variables (eg, physical activity,

dietary choices, kilocalorie intake)

due to lack of consistency between

assessment measures. Of concern,

despite this study’s focus on

interventions feasible for primary

care, most of the included studies

contained threats to external validity

because they evaluated interventions

considerably more elaborate than

what practice guidelines suggest.

All but 2 of the interventions used

motivational interviewing and

solution-focused techniques. Several

of the interventions implemented

computerized decision support tools

and systems, physician training,

tertiary physician/specialist

consultations, frequent follow-up

appointments, educational materials,

and regular telephone calls. As such,

caution is suggested in generalizing

these results to standard physician

conversations about weight

management with children and their

parents.

Although the primary literature has

limitations, this study has several

notable strengths including a focused

review question, a comprehensive

and systematic literature search,

assessment of the methodological

quality of included studies that

focused on randomized trials and

observational studies, and successful

author contact. Although there

have been previous reviews of

the literature, to our knowledge,

this is the first meta-analysis of

primary care–based interventions

for pediatric obesity. The findings of

this study were similar to previous

systematic reviews that have

7

TABL

E 3

Ris

k of

Bia

s As

sess

men

t of

Incl

ud

ed P

rosp

ecti

ve C

ohor

t S

tud

ies

Auth

or, Y

ear

Rep

rese

nta

tive

of

Exp

osed

Coh

ort

Sel

ecti

on o

f N

onex

pos

ed

Coh

ort

Asce

rtai

nm

ent

of

Exp

osu

re

Com

par

abili

ty o

f

Coh

orts

Ou

tcom

e

Asse

ssm

ent

Ou

tcom

e

Follo

w-u

p

% L

ost

to

Follo

w-u

p

Adve

rse

Even

ts

Tria

l Sp

onso

r

Sch

war

tz, 2

007 23

Rep

rese

nta

tive

Dra

wn

fro

m t

he

sam

e

com

mu

nit

y

Aud

iota

ped

ses

sion

s

to a

sses

s fo

r

inte

rven

tion

fi d

elit

y

Stu

dy

doe

s n

ot m

atch

child

ren

or

con

trol

for

con

fou

nd

ing

vari

able

s

Sta

ff f

rom

offi

ce

mea

sure

d a

nd

reco

rded

hei

ght

and

wei

ght

Inad

equ

ate

33.0

Not

ass

esse

dFo

r p

rofi

t an

d

non

pro

fi t

Tuck

er, 2

013 24

Rep

rese

nta

tive

Dra

wn

fro

m t

he

sam

e

com

mu

nit

y

Aud

iota

ped

ses

sion

s

to a

sses

s fo

r

inte

rven

tion

fi d

elit

y

Stu

dy

doe

s n

ot m

atch

child

ren

or

con

trol

for

con

fou

nd

ing

vari

able

s

Sta

ff f

rom

offi

ce

mea

sure

d a

nd

reco

rded

hei

ght

and

wei

ght

Adeq

uat

e33

.6N

ot a

sses

sed

Non

pro

fi t


SIM et al

found little evidence regarding the

effectiveness of primary care-based

pediatric obesity programs. 2, 4, 40

In adults, a systematic review of

primary care-based behavioral

treatments for obesity found

clinically meaningful weight

outcomes for intensive behavioral

counseling, yet insufficient evidence

to support the feasibility of

integrating targeted and intensive

counseling into standard primary

care practice. 41 In light of these

results, standard practice guidelines

regarding BMI surveillance and

counseling should be revised.

Moreover, novel approaches feasible

for primary care to address pediatric

obesity should be developed and

tested, as opposed to continuing to

pursue programs that do not appear

to have sizeable impacts on the

pediatric population.

CONCLUSIONS

This review suggests that primary

care interventions that incorporate

a systematic approach to addressing

pediatric overweight and obesity (eg,

patient-centered communication,

patient education, regular visits and

phone calls) have only a marginal

effect on reducing pediatric

overweight and obesity in the short

term. Furthermore, the clinical

significance of this finding remains

questionable, and there continue

to be several important knowledge

gaps in primary care prevention and

weight management interventions. It

appears that a paradigm shift might

be indicated, in which novel programs

are designed and tested, potentially

taking into account the evidence

about elements of effective behavioral

weight loss programs in other settings

and about more meaningful markers

of health compared with BMI. Large

methodologically rigorous RCTs

on new approaches implemented

with children and adolescents with

overweight and obesity are needed

to provide evidence as to what

interventions might be effective and

sustainable in treating this population.

Furthermore, it is imperative that

researchers examining interventions

for pediatric obesity in primary care

collect data on potential adverse

effects of interventions, including

increased dieting behaviors, low self-

esteem, perception of weight bias

and stigma, and eating-disordered

cognitions and behaviors, particularly

8

FIGURE 2Random effect meta-analysis (the effect of brief primary care interventions vs usual care or active control on z-BMI). Central vertical line represents no treatment effect. Squares and horizontal lines represent the point estimates and associated confi dence interval (CI) for each study, respectively. Point estimates to the right of the central vertical line refl ect increase in z-BMI. Weights are from random effects analysis.



in adolescents. It is equally important

that financial cost be evaluated, with

regard to both the individual and

society as a whole. For individuals

who are at risk or already affected

by the serious medical complications

and functional impairments related

to pediatric obesity, available data

appear to support referral to a

more intensive behavioral weight

management program run by trained

specialists who can deliver feedback

and counseling about behavior change

over an extended period of time,

during multiple regular visits. 1, 2, 42

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9

ABBREVIATIONS

CI: confidence interval

RCT: randomized controlled trial

SMD: standardized mean

difference

Address for correspondence to Leslie Sim, PhD, Mayo Clinic, 200 First St SW, Rochester, MN 55905. E-mail: [email protected]

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).

Copyright © 2016 by the American Academy of Pediatrics

FINANCIAL DISCLOSURE: The authors have indicated they have no fi nancial relationships relevant to this article to disclose.

FUNDING: No external funding.

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential confl icts of interest to disclose.

COMPANION PAPER: A companion to this article can be found online at www. pediatrics. org/ cgi/ doi/ 10. 1542/ peds. 2016- 2497.

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