motivational interviewing to treat overweight …...pediatrics volume 137 , number 1 , january 2016...

ARTICLEPEDIATRICS Volume 137 , number 1 , January 2016 :e 20151979

Motivational Interviewing to Treat Overweight Children: 24-Month Follow-Up of a Randomized Controlled TrialSerena Broccoli, PhD,a,b Anna Maria Davoli, MD,c Laura Bonvicini, MSc,a,b Alessandra Fabbri, MD,d Elena Ferrari, MD,c Gino Montagna, MD,c Costantino Panza, MD,c Mirco Pinotti, MD,e Simone Storani, PhD,f Marco Tamelli, PhD,f Silvia Candela, MD,a Eletta Bellocchio, MD,e Paolo Giorgi Rossi, PhDa,b

abstractBACKGROUND: Pediatrician-led motivational interviewing can be an effective way of controlling

BMI in overweight children in the short term. Its long-term efficacy is unknown. The

primary aim was to determine whether the short-term (12-month) impact of family

pediatrician-led motivational interviews on the BMI of overweight children could be

sustained in the long term (24 months), in the absence of any other intervention.

METHODS: Children were recruited in 2011 by family pediatricians working in the province of

Reggio Emilia, Italy, and randomly allocated to receive either 5 interviews delivered over

a 12-month period or usual care. Eligible participants were all 4- to 7-year-old overweight

children resident in the province of Reggio Emilia who had been receiving care from the

pediatrician for ≥12 months. The primary outcome of this study was individual variation in

BMI between the baseline visit and the 24-month follow-up, assessed by pediatricians not

blinded to treatment group allocation.

RESULTS: Of 419 eligible families, 372 (89%) participated; 187 children were randomized to

receive intervention and 185 to usual care. Ninety-five percent of the children attended

the 12-month follow-up, and 91% attended the 24-month follow-up. After the 12-month

intervention period, BMI in the intervention group increased less than in the control group

(0.46 and 0.78, respectively; difference −0.32; P = .005). At the 24-month follow-up, the

difference had disappeared (1.52 and 1.56, respectively; difference −0.04; P = .986).

CONCLUSIONS: The intervention lost its effectiveness within 1 year of cessation. Sustainable

boosters are required for weight control and obesity prevention.

aEpidemiology Unit, Azienda Unità Sanitaria Locale, Reggio Emilia, Italy; bArcispedale Santa Maria Nuova, Istituto

di Ricovero e Cura a Carattere Scientifi co - IRCCS, Reggio Emilia, Italy; cPrimary Care Pediatrician, dPublic

Health Nutrition Unit, and ePrimary Health Care, Local Health Authority, Reggio Emilia, Italy; fPromotion Health

Researchers, League Against Cancer, Reggio Emilia, Italy

Drs Broccoli, Fabbri, Ferrari, Montagna, Panza, and Candela and Ms Bonvicini contributed to study

design; Dr Broccoli and Ms Bonvicini coordinated data collection and carried out the analyses;

Drs Broccoli and Fabbri and Ms Bonvicini supervised data collection; Dr Broccoli drafted the

Methods, Results, and Discussion of the manuscript; Dr Davoli designed the study and coordinated

and supervised the pediatricians; Ms Bonvicini drafted the Introduction of the manuscript;

Drs Davoli, Fabbri, and Candela and Ms Bonvicini reviewed and revised the manuscript; Dr Fabbri

analyzed the results; Drs Ferrari and Montagna coordinated and supervised the recruitment

phase; Dr Ferrari assisted with training pediatricians and carrying out the study; Dr Montagna

assisted with carrying out the study; Drs Montagna (pediatrician component), Pinotti (Local

Health Unit component), and Bellocchio (Local Health Unit component) directed the pediatricians’

involvement; Drs Panza, Pinotti, and Bellocchio contributed to study development; Dr Panza

conducted the preliminary systematic review of the interventions; Drs Storani and Tamelli

contributed to intervention design and trained the pediatricians; Dr Candela developed the study;

Dr Rossi planned the data analysis, drafted the outline of the manuscript, and critically reviewed

and revised the manuscript; and all authors approved the fi nal manuscript as submitted.

To cite: Broccoli S, Davoli AM, Bonvicini L, et al. Motivational

Interviewing to Treat Overweight Children: 24-Month

Follow-Up of a Randomized Controlled Trial. Pediatrics.

2016;137(1):e20151979

WHAT’S KNOWN ON THIS SUBJECT: Childhood

obesity can seriously affect health outcomes.

Motivational interviewing in primary care has been

shown to be effective in BMI control, but previous

studies measured its effi cacy only just after the

intervention ended. There are no available long-term

follow-up data.

WHAT THIS STUDY ADDS: Despite very encouraging

initial results, 12 months after intervention ended,

children who received motivational interviewing lost

all the advantage in terms of BMI, compared with the

control group.

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BROCCOLI et al

The number of overweight or obese

children (0–5 years old) increased

from 31 million globally in 1990

to 44 million in 2012.1 Childhood

obesity is associated with a higher

chance of obesity, premature death,

and disability in adulthood. More

than 60% of children who are

overweight before puberty will

be overweight in early adulthood,

reducing the average age at which

noncommunicable diseases become

apparent and greatly increasing the

burden on health services, which

have to provide treatment during

much of their adult lives.2 At present,

there is lack of consensus worldwide

on which approaches and which

combinations of interventions

are likely to be most effective at

preventing childhood obesity in

different contexts and societies.3

Motivational interviewing (MIs)

has been applied to pediatric

populations over the last 2 decades4,5

and is recommended6 and widely

used7 for weight control and

obesity prevention. Nevertheless,

published studies8–13 evaluating

the influence of MI or behavioral

counseling on the BMI of overweight

children showed inconsistent

results. Although individual studies

reported mixed success, a recent

meta-analysis demonstrated that

behavioral strategies to improve

diet and physical activity (PA) were

efficacious in reducing children’s

BMI.14 In 2011, we conducted a

randomized trial in the province

of Reggio Emilia, Italy, to evaluate

the efficacy of pediatrician-led MI

in controlling BMI in overweight

children aged 4 to 7 years.15 Our

study concluded that MI was effective

at controlling the BMI of overweight

children (BMI percentile ≥85th and

<95th) in the short term. Despite

the encouraging short-term effect,

evidence of long-term efficacy is

required.16,17

In this article, we report the results of

the follow-up conducted 12 months

after the end of intervention.

METHODS

Study Design and Setting

The study is an individually

randomized controlled trial and

was conducted in the province of

Reggio Emilia, Italy, from 2011 to

2013. Reggio Emilia had a resident

population of 530 543 on January 1,

2011; 15.2% were children aged 0 to

14 years18 under the care of 82 public

health service family pediatricians.

In 2010, the estimated prevalence of

overweight children was 22%.19 The

methods used to conduct this trial

are published elsewhere15 and are

described briefly below.

Participants

Children were recruited through

their family pediatrician. A

maximum of 12 overweight children

(based on a previous survey) per

participating pediatrician were

randomly selected for eligibility

assessment. During a baseline visit

for eligibility assessment, the family

pediatrician proposed the study and

gave consent forms to parents of

eligible children. Eligible participants

were all overweight children (BMI

percentile ≥85th and <95th)20 aged

between 4 and 7 years, resident in

the province of Reggio Emilia, and

under the care of said pediatrician

for ≥12 months. Exclusion criteria

were metabolic pathologic conditions

and all pathologic conditions related

to obesity and being overweight.

Moreover, those families who did

not consider their children being

overweight to be an issue and

were not interested in the negative

consequences or advice on how

to lose weight (families in the

“precontemplation stage”) were also

excluded. Recruitment took place

from June to August 2011.

Randomization

The epidemiology unit used the

RALLOC package of Stata software to

randomly allocate eligible children

whose parents signed the informed

consent form to an intervention or

control group.21 The allocation rule

depended on the number of eligible

children. Pediatricians were not

blinded to the group allocation.

Intervention

The intervention consisted of 5 MIs

delivered at 1, 4, 7, and 12 months

after the baseline visit. Before

enrollment began, all pediatricians

attended a 4-hour training course on

how to accurately measure weight

and height and how to calculate the

BMI percentile, as well as a 20-hour

training course on MI conducted

by specialist psychologists from

“Luoghi di Prevenzione,” the regional

reference center for training in health

promotion.22 As previously reported,

94% of participants allocated to the

intervention group completed all 5

MIs.15 The intervention was based

on the transtheoretical model of

addiction and behavioral change.23

The child and parents always had

to leave the meeting having agreed

on 2 clearly defined and achievable

objectives (1 concerning food and

1 concerning PA improvements).

During each subsequent interview,

the extent to which the objectives

set at the previous meeting had

been achieved was assessed. The

objectives were then reinforced or

redefined and recorded accordingly.

Participants who were randomly

assigned to the control group

attended the baseline and 12-month

visits. They received a booklet with

the main information on obesity

prevention (eg, opportunistic

healthy diet recommendations if the

pediatrician was seeing the child for

other reasons). All children were

invited to attend a follow-up visit

24 months after the baseline, eg, 12

months after the intervention had

finished.

Data Collection and Outcomes

Baseline and follow-up data

were collected by means of a

corporate Intranet Web form

customized for the trial, compiled

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PEDIATRICS Volume 137 , number 1 , January 2016

by the pediatrician. Dietary and

PA habits were assessed using the

questionnaire from the Italian survey

on childhood obesity “OKKIO alla

Salute,”24 conducted periodically

by Italy’s National Institute of

Health and part of the World Health

Organization European region

Childhood Obesity Surveillance

Initiative25; some items were

supplemented with regional food

names. Sociodemographic data on

the parents and child was collected

at the baseline visit. Weight, height,

BMI, BMI percentile, and dietary and

PA habits were assessed at baseline

and at 12- and 24-month follow-up

visits. The primary outcome of the

study was the individual BMI score

variation (Δ0–24BMI) between the

baseline and 24-month follow-up

visits (long-term effect).26 The

secondary outcomes were the

percentage of positive changes in

parent-reported dietary behavior

and PA between the baseline and

24-month follow-up visits.

Sample Size

The study was sized to detect a

between-group difference in ΔBMI

of ≥0.5 with a 5% significance level

and a power of 90%, assuming an SD

of ΔBMI of 1. The smallest possible

sample size was 85 children per

group. Considering a 30% dropout

rate, recruitment of at least 110

children per arm was planned.

Statistical Analysis

Data analysis was performed using

Stata statistical software, version

13.0. Primary objective analysis

was conducted on the basis of an

intention-to-treat approach, replacing

missing Δ0–24BMI values with the

mean BMI variation observed in

the control group. All inferential

analyses were performed using

weights to balance allocation within

strata.15 Mean Δ0–24BMI, Δ0–24BMI

z-score, and relative 95% confidence

interval (CI) were presented by

study group. Δ0–24BMI, Δ0–12BMI

(short-term effect), and Δ12–24BMI

(rebound effect) in the intervention

and control groups were compared

using nonparametric Wilcoxon–

Mann–Whitney test because

the outcome was not normally

distributed. Multilevel linear models

were developed to measure the

influence of pediatricians on long-

term effectiveness. Two multilevel

models were specified: random

intercept and random intercept

and slopes. The intrapediatrician

correlation coefficient was reported

with the likelihood ratio test to

compare models. Post hoc subgroup

analyses were performed by gender,

age at baseline (<6 or ≥6 years),

and mother’s level of education

(<13, 13, or >13 years of schooling).

Changes in PA and dietary habits in

intervention and control groups were

compared using Wilcoxon rank sum

test.

We performed a formal mediation

analysis to distinguish the direct

effect of the intervention on BMI

and its mediated effect through diet

and physical activity. The analysis

comprised 3 steps: (1) checking the

association between the intervention

and the putative mediator; (2)

checking the association between

the putative mediator and BMI;

and (3) calculating the mediation

percentage, eg, the proportion of the

intervention effect on BMI mediated

by the mediator. Twelve-month

variations were considered, eg, 0–12

and 12–24, and the intervention was

categorized in 3 ways: untreated,

treatment period, and posttreatment.

This analysis included all the dietary

and PA variables associated with the

BMI z-score (P < .05). Separate age-

adjusted models were developed for

each putative mediator.

RESULTS

Four hundred nineteen parents were

asked to consent to their child’s

participation in the study, and 372

signed the informed consent form;

187 were randomized to intervention

and 185 to usual care. The

intervention and usual care groups

had similar baseline characteristics

(gender, breastfeeding, overweight

before the age of 5 years, gestational

age at birth, small for gestational

age, parent level of education, parent

weight, baseline age, and baseline

BMI).15

Participation in 12-Month Follow-Up

Of the 187 participants in the

intervention group, 167 (89.3%)

attended the 24-month follow-up

visit, as did 170 (91.9%) of the 185

participants in the usual care group.

One treated child was excluded

from the analysis because, during a

follow-up assessment, a pediatrician

noted that the weight and height

measurements had been recorded

incorrectly at the baseline visit

and, on the basis of the up-to-date

information, the child was not eligible

to take part27 (Fig 1). There were no

differences in baseline characteristics

between participants who provided

data at the 12-month follow-up and

those who did not. At 24 months,

a greater proportion of children

with Italian-born parents attended

the follow-up visit compared with

children with ≥1 immigrant parent

(94% vs 62%; P < .001).

Effect on BMI

At the 24-month follow-up, there

were no significant differences

between groups in terms of the

Δ0–24BMI score: −0.04 (95%

CI −0.36 to 0.28). During the

postintervention period (between

the 12- and 24-month follow-ups),

mean BMI increased more among

children in the intervention group

(Δ12–24BMI 1.06% [95% CI 0.90 to

1.22]) than in the usual care group

(Δ12–24BMI 0.78% [95% CI 0.59 to

0.97]) (rebound effect). (Table 1)

The proportion of pediatrician-level

variance on the overall variation

in Δ0–24BMI (intraclass correlation

coefficient) was 7.2%. Mean Δ0–24BMI


BROCCOLI et al

in the control group differed

significantly across pediatricians

(likelihood ratio test, random-

intercept versus linear-regression

model: χ2[2] = 7.72, P = .0211). The

lack of long-term effectiveness of

MI did not vary significantly across

pediatricians (likelihood ratio test,

random-intercept and slope versus

random-intercept model: χ2[2] =

0.30, P = .8602). The results were

similar if only the rebound effect was

taken into consideration.

Subgroup Analysis

The long-term effect was similar by

gender (interaction test P = .332),

although breaking down the results

into short-term and rebound effects

showed that the short-term effect

was stronger in girls (Δ0–12BMI

−0.54 and 0.0 for females and males,

respectively, interaction test P = .053),

as was the rebound effect (Δ12–24BMI

0.36 and 0.16 for females and males,

interaction test P = .421) (Table 2; Fig

2). There was no interaction between

age and intervention (interaction

test P = .333) (Table 2). MI had a

positive long-term effect on Δ0–24BMI

in children whose mother had a high

(Δ0–24BMI −0.73% [95%CI −1.65 to

0.18]) or medium (Δ0–24BMI −0.31%

[95% CI −0.74 to 0.13]) level of

education, whereas it had a negative

long-term effect in children whose

mother had a low level of education

(Δ0–24BMI 0.66% [95% CI 0.08 to

1.23) (interaction test P = .008). The

same results were observed in the

short term (Table 2; Fig 3).

Effect of Diet and PA on BMI Changes

Several improvements in behaviors

were evident in the MI group in

the short term,15 but almost no

improvements were observed during

the follow-up period. As a result, the

long-term effect showed only modest

improvements in nonorganized PA

(P = .072), fruit intake (P = .070), and

the consumption of sweet snacks/

candies (P = .066), desserts (P =

.047), and sweetened drinks (P =

.004) (Table 3). The only 2 exceptions

were having breakfast and eating

fruit, for which improvements were

observed during both periods (Table

3), but they were not associated

with BMI. Only parent-reported

nonorganized PA and vegetable and

dessert consumption were associated

with BMI variations (Table 4). The

mediation analysis showed that the

increase in nonorganized PA and the

decrease in dessert consumption

accounted for 7.7% (95% CI 4.5 to

22.7) and 8.6% (95% CI 5.0 to 27.7),

respectively, of the total effect of the

intervention on the BMI z-score.

DISCUSSION

In 2013, we published the positive

results of MI intervention conducted

by pediatricians in an Italian

province.15 We concluded that

intervention was effective in the

short term, although the results

were less convincing for boys

and for children whose mothers

had low levels of education. Our

positive conclusions have now been

dampened by the follow-up results

for the children enrolled in the trial,

as reported in this article: 12 months

after the end of the intervention,

the advantages observed in the

intervention group had almost

disappeared and, in fact, the rebound

effect in the intervention group

produced a greater increase in BMI

than in the control group.

Gender and mother’s level of

education were found to play an

4

FIGURE 1Flow chart of study participants. aDuring follow-up assessment, a pediatrician reported an error in the weight and height measurement at baseline. According to update information, the child was not eligible and we decided to exclude her from the “intention to treat” population.



important role in determining

the outcome. Whereas benefits

disappeared after the 12-month

follow-up visit for children whose

mothers had spent >13 years at

school, the effects of intervention

5

seem counterproductive in the long

term for children whose mothers

had received <13 years of education.

The child’s gender also modified the

effect of our proposed MI, but unlike

the low level of maternal education,

the lack of an effect in boys was

not followed by a rebound effect,

meaning that the intervention does

not help boys, but also that it does

not harm them.

Our results are consistent with

previous results in adults,28–30 where

the success of initial motivational

interviewing in BMI control and

changing diet and levels of PA is

often followed by a lack of long-

term efficacy. A relapse once MI has

been discontinued is considered

an intrinsic characteristic of these

interventions.31,32

Nevertheless, 2 more recent studies

reported the effect of MI over the

course of 24 months.12,13 In Resnicow

et al, the study population and the

intervention were quite similar to

those of our own study, although

only 4 interviews were distributed

throughout the entire 24-month

period and 1 of the intervention

groups included a dietitian

interview.12 The study observed a

larger δ in BMI variation between

the intervention group and the usual

care group than we observed during

TABLE 1 BMI Score, BMI Z-Score, BMI Percentile, Δ0–24BMI (Primary Outcome, Long-term Effect),

Δ0–12BMI (Short-term Effect), and Δ12–24BMI (Rebound Effect) by Study Group

Outcome Intervention Usual Care Between-Group

Difference in ΔBMI Score

and z-Score

Baseline

BMI score 18.27 (18.16 to 18.38) 18.21 (18.10 to 18.33)

BMI z-score 1.35 (1.32 to 1.38) 1.35 (1.32 to 1.37)

BMI percentile 90.96 (90.53 to 91.38) 90.88 (90.46 to 91.30)

12-mo follow-up

BMI score 18.73 (18.51 to 18.96) 18.99 (18.78 to 19.21)

BMI z-score 1.23 (1.17 to 1.30) 1.34 (1.28 to 1.40)


24-mo follow-up

BMI score 19.79 (19.53 to 20.05) 19.77 (19.51 to 20.04)

BMI z-score 1.29 (1.22 to 1.37) 1.31 (1.25 to 1.38)


Long-term effect

Δ0–24BMI score 1.52 (1.29 to 1.75) 1.56 (1.33 to 1.79) −0.04 (−0.36 to 0.28)*

Δ0–24BMI z-score −0.05 (−0.12 to 0.01) −0.03 (−0.09 to 0.02) −0.02 (−0.11 to 0.07)

Short-term effect

Δ0–12BMI score 0.46 (0.27 to 0.65) 0.78 (0.61 to 0.96) −0.32 (−0.57 to −0.06)**

Δ0–12BMI z-score −0.12 (−0.17 to −0.06) −0.01 (−0.06 to 0.04) −0.11 (−0.18 to −0.03)

Rebound effect

Δ12–24BMI score 1.06 (0.90 to 1.22) 0.78 (0.59 to 0.97) 0.28 (0.03 to 0.52)***

Δ12–24BMI z-score 0.06 (0.02 to 0.10) −0.03 (−0.08 to 0.02) 0.09 (0.02 to 0.15)

Data are expressed as mean (95% CI).

Wilcoxon–Mann–Whitney test: *P = .986, **P = .005; ***P = .011.

TABLE 2 Δ0–24BMI Score, Δ0–12BMI Score, and Δ12–24BMI Score by Gender, Children’s Age, Maternal Level of Education, and Study Group

Characteristic Long-Term Effect (Δ0–24BMI Score) Short-Term Effect (Δ0–12BMI Score) Rebound Effect (Δ12–24BMI Score)

Intervention Usual Care Pa Intervention Usual

Care

Pa Intervention Usual

Care

Pa

Gender .332 .053 .421

Male 1.69 (1.30 to

2.08)

1.53 (1.14 to

1.92)

0.77 (0.46 to 1.07) 0.77 (0.44

to 1.10)

0.92 (0.68 to 1.17) 0.76 (0.50

to 1.02)

Female 1.40 (1.12 to

1.68)

1.58 (1.28 to

1.87)

0.25 (0.02 to 0.48) 0.79 (0.56

to 0.99)

1.15 (0.94 to 1.37) 0.79 (0.54

to 1.04)

Age, y .333 .843 .291

<6 1.74 (1.34 to

2.15)

1.55 (1.15 to

1.94)

0.46 (0.13 to 0.78) 0.74 (0.42

to 1.06)

1.29 (0.95 to 1.62) 0.81 (0.52

to 109)

≥6 1.43 (1.15 to

1.70)

1.57 (1.28 to

1.86)

0.46 (0.23 to 0.69) 0.80 (0.59

to 1.01)

0.96 (0.78 to 1.14) 0.77 (0.52

to 1.01)

Mother’s level of

education, y of

school

.008 .004 .774

<13 2.04 (1.65 to

2.42)

1.38 (0.95 to

1.81)

0.86 (049 to 1.23) 0.61 (0.32

to 0.91)

1.18 (0.88 to 1.47) 0.77 (0.42

to 1.11)

13 1.35 (1.04 to

1.65)

1.65 (1.34 to

1.97)

0.34 (0.13 to 0.56) 0.86 (0.61

to 1.11)

1.00 (0.77 to 1.23) 0.79 (0.53

to 1.06)

>13 0.77 (0.13 to

1.40)

1.5 (0.81 to

2.20)

−0.2 (−0.71 to 0.30) 0.84 (0.31

to 1.36)

0.97 (0.58 to 1.36) 0.67 (0.24

to 1.10)

Data are expressed as mean (95% CI).a Interaction test.


BROCCOLI et al

our 12-month intervention period.

This larger effect could be because

the study analyzed only children

who were not lost during follow-up,

the longer period of intervention,

the involvement of paid volunteer

pediatricians, or the inclusion of

dietitians in the intervention. On the

other hand, the authors only report

data at the end of intervention, which

is not comparable with our 24-month

follow-up data and, to date, it is not

known whether a rebound would

also occur after the 24-month period

covered by their study.

Taylor et al reported positive

long-term results of family-based

intervention addressing overweight

and obese children aged 4 to 8

years.13 This intervention is tailored

to the needs of each family and

consists of frequent, low-dose contact

over a 2-year period (total contact

time was 6 to 7 hours per family).

The Δ0–24BMI z-score difference

between the intervention and control

group was −0.12 (95% CI −0.20

to −0.04), which is consistent with

our findings after 12 months of

intervention. The long-term efficacy

after the intervention has not been

investigated.

It is encouraging that 3 trials12,13,15

conducted in very different contexts

and health systems showed

improvements after MI interventions

delivered in a primary care setting, at

least for as long as the intervention

was still in progress, in line with

results of a recent systematic

review.14

The family, particularly the mother,

also proved to be an important

effect modifier for the long-term

maintenance of the effects in other

studies.33,34 In our setting, some

pediatricians reported that the

motivational diagnosis was more

difficult and less accurate when

the mother had a lower level of

education. The potentially harmful

effect of MI when administered

in precontemplative subjects has

been postulated in theory and also

observed in experimental settings.35

The observed results stress the

need for an accurate motivational

diagnosis in all families.

In our population, the changes in BMI

in the intervention and control group

were consistent with the changes

in diet and physical activity at the

end of the intervention.15 During

the follow-up period, the changes

in parent-reported diet and PA no

longer showed any benefit in the

intervention group compared with

the control group. We conducted

an analysis trying to understand

which dietary and PA changes played

the biggest part in the mechanism

leading to BMI reduction after

intervention and BMI rebound

after intervention cessation. Only 2

behavioral factors were found to be

weak mediators of the intervention

effect on BMI: nonorganized PA and

dessert consumption.

Strengths and Limitations

The follow-up visit was well

attended, and there was good

compliance with treatment, with

positive feedback from families and

children. The study maintained a

good level of statistical power, at

least for the primary endpoint.

One of the main limitations of the

follow-up study was the risk of

6

FIGURE 2BMI z-score trajectories by gender and study group.



randomized group contamination

after the end of the intervention.

In fact, the study protocol

recommended offering usual care

only once the intervention was over,

to both treated children and those

from the control group. However,

it cannot be ruled out that the

pediatricians may have adopted a

different approach to usual care

after training and experience with

MI techniques developed during the

trial. Moreover, we did not ask them

to record contact with participants

during the follow-up period.

Interestingly, we observed a rebound

effect during the follow-up period

rather than an improvement in the

control group, as would be expected

in the case of contamination. What

is more, the rebound effect did

not differ between pediatricians,

suggesting an effect independent

from the pediatrician’s approach.

Another limitation of the study is that

parent-reported dietary habits may

be strongly affected by desirability

bias, and the bias may be stronger for

those who received the intervention

with 4 MIs in which they had to agree

on dietary habit and PA targets than

for families in the control group. This

bias can lead to an overestimation

relating to the effect of intervention

on dietary and PA habits and could

also explain why at 24 months we

still observed an effect on some

lifestyle habits but not on BMI.

Nevertheless, desirability bias does

not have an excessive influence on

the results of the mediation analysis,

where we compare the relative

influence of different behavioral

variables in both groups during the

intervention and follow-up periods.

Considerations on Designing Effective Long-Term Intervention

According to the review by Janicke et

al14 and the experiences reported by

Resnicow et al12 and Taylor et al,13 it

seems that longer intervention with

fewer interviews per year or with

more shorter interviews tailored to

the needs of each family could at least

maintain the effectiveness of MI for

24 months, with similar consumption

of resources.

Some studies in the child obesity

literature have reported the

positive long-term effects of

different individual interventions:

long-term interventions, including

boosters, based on family-based

behavioral intervention in primary

care36; parent-centered dietary-

modification programs combined

with child-centered physical-activity

skill-development programs37;

or structured outpatient training

programs consisting of physical

exercise, nutritional education, and

behavioral therapy.38,39

Wilfley et al40 demonstrated that

maintenance treatments based

on behavioral skills and social

facilitation improve the long-term

efficacy of weight loss treatments.

Researchers have underlined

the need for strong maintenance

treatments to sustain effects after

weight loss treatment.34 We are

therefore planning MI reinforcement

for the treated children, while

continuing to monitor the control

group.

The intervention proposed in

this trial has been designed to be

sustainable and feasible when

scaled up to the entire population

of overweight 5-year-olds in the

7

FIGURE 3BMI z-score trajectories by maternal level of education and study group.


BROCCOLI et al

province of Reggio Emilia.15 A longer

intervention risks disrupting the

sustainability of the intervention.

The cost-effectiveness and cost

opportunity of longer individual

interventions should be compared

with the costs of community-based

interventions.

Finally, it must be considered that

even interventions proven to be

effective in some groups can be

completely ineffective or damaging in

others, highlighting the importance of

considering pretreatment variables

(BMI at baseline, family support)

as modifiers of the effectiveness of

different strategies.34 This was the

case for our intervention, which had

no effect on boys or children whose

mothers had low levels of education.

CONCLUSIONS

The long-term effectiveness of MI in

BMI control cannot be inferred on

the basis of immediate effectiveness.

There is a need for sustainable and

effective boosters and maintenance

strategies for MI interventions.

ACKNOWLEDGMENTS

We are grateful to all pediatricians

working in the Province of Reggio

Emilia for their participation in this

project. Their work and attention to

details contributed to the success of

the study. We wish to thank Paola

Albertini (Local Health Authority,

Reggio Emilia) for her support

and assistance in providing the

customized corporate Intranet Web

form for the trial.

ABBREVIATIONS

CI: confidence interval

MI: motivational interviewing

PA: physical activity

8

TABL

E 3

Per

cen

t of

Ch

ange

s (P

osit

ive

or N

egat

ive)

in P

aren

t-R

epor

ted

PA

and

Die

tary

Hab

its

by

Stu

dy

Gro

up

an

d P

erio

d

Hab

it

0- t

o 12

-mo

Ch

ange

s, %

12- t

o 24

-mo

Ch

ange

s, %

0- t

o 24

-mo

Ch

ange

s, %

Inte

rven

tion

Usu

al C

are

Pa

Inte

rven

tion

Usu

al C

are

Pa

Inte

rven

tion

Usu

al C

are

Pa

nN

egP

osn

Neg

Pos

nN

egP

osn

Neg

Pos

nN

egP

osn

Neg

Pos

PA

hab

its

O

rgan

ized

PAb

174

8.6

22.4

175

8.6

22.3

.964

167

16.8

20.4

161

17.4

19.3

.860

168

7.7

24.4

157

11.5

24.2

.655

N

onor

gan

ized

PAb

174

12.6

35.1

175

24.0

25.1

.007

167

34.7

15.6

160

36.3

18.1

.725

168

26.2

29.8

156

35.3

23.1

.072

S

cree

n t

imec

174

8.0

19.5

174

11.5

13.2

.056

168

14.9

8.9

161

13.0

9.3

.640

169

16.0

23.7

156

16.0

13.5

.103

Die

tary

hab

its

H

avin

g b

reak

fast

b17

38.

116

.817

28.

111

.0.2

3316

26.

89.

915

69.

09.

0.4

9816

43.

017

.715

69.

610

.9.0

09

Ve

geta

ble

sb17

418

.437

.917

417

.224

.1.0

7316

835

.125

.016

126

.724

.8.3

9916

928

.435

.515

726

.129

.3.6

43

Ve

geta

l sou

pb

174

7.5

17.8

175

12.6

10.9

.023

168

17.9

13.7

161

11.8

14.3

.237

169

10.1

17.2

157

12.7

16.6

.589

Fr

uit

b17

417

.235

.117

518

.930

.3.4

3216

834

.523

.816

137

.319

.3.5

4916

923

.129

.615

731

.219

.7.0

70

S

wee

t sn

acks

/

can

die

sc17

310

.453

.817

520

.633

.7<

.001

167

30.5

22.2

160

22.5

32.5

.033

169

11.2

49.7

156

21.2

44.9

.066

D

esse

rtsc

174

12.6

36.2

175

19.4

26.3

.012

168

22.0

19.6

161

21.7

23.6

.639

169

14.8

37.3

157

20.4

28.7

.047

S

alty

sn

acks

c17

410

.326

.417

514

.920

.0.0

8116

719

.213

.816

116

.118

.0.2

2416

814

.926

.215

715

.926

.8.9

17

Fr

ied

foo

dc

174

9.8

23.0

175

12.0

14.3

.050

168

15.5

11.9

161

14.9

11.2

.992

169

10.7

18.9

157

12.7

12.1

.110

S

wee

ten

ed d

rin

ksc

174

8.0

46.0

175

17.1

32.0

<.0

0116

822

.622

.016

021

.922

.5.9

3716

911

.250

.315

617

.938

.5.0

04

Neg

, neg

ativ

e; P

os, p

osit

ive.

a P

rob

abili

ty t

hat

th

e d

irec

tion

of

chan

ges

is n

ot d

iffe

ren

t in

2 g

rou

ps

by

Wilc

oxon

ran

k su

m t

est

com

par

ing

the

ran

k of

ch

ange

s ac

cord

ing

to t

he

cate

gori

es (

0, 1

–3,

4–

5, >

5, 1

/day

, mor

e) a

nsw

ered

in t

he

qu

esti

onn

aire

.b In

crea

ses

in t

ime

spen

t or

con

sum

pti

on a

re c

onsi

der

ed p

osit

ive

chan

ges.

c D

ecre

ases

in t

ime

spen

t or

con

sum

pti

on a

re c

onsi

der

ed p

osit

ive

chan

ges.



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DOI: 10.1542/peds.2015-1979

Accepted for publication Oct 15, 2015

Address correspondence to Serena Broccoli, Epidemiology Unit, Local Health Authority of Reggio Emilia, via Amendola 2, Reggio Emilia, Italy. E-mail: serena.

[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.

TABLE 4 Multivariate Linear Regression Models of ∆Habit on ∆BMI Z-Score, Total and Stratifi ed by Study Group and Period

Habit n Total Modela Stratifi ed Modelsb

Habits Variation P for

Interaction

Usual Care MI (0–12) MI (12–24)

βc P βc P βc P βc P

PA habits

Organized PAd 354 −0.003 .861 .875

Nonorganized PAd 354 −0.018 .050 .748

Screen timee 354 −0.042 .093 .586

Dietary habits

Having breakfastd 349 −0.001 .976 .164 −0.019 .428 0.006 .840 0.058 .078

Vegetablesd 354 −0.031 .001 .027 −0.003 .834 −0.053 .001 −0.052 <.001

Vegetal soupd 354 −0.022 .210 .936

Fruitd 354 −0.012 .159 .040 0.013 .409 −0.022 .235 −0.038 .004

Sweet snacks/candiese 353 0.014 .138 .726

Dessertse 354 0.027 .041 .144 0.030 .167 0.001 .970 0.056 .002

Salty snackse 354 0.034 .105 .204

Fried foode 354 0.020 .432 .370

Sweetened drinkse 354 0.003 .818 .215

a Linear regression models: ∆BMI z-score = ∆Habit + group + age + interaction(∆Habit × group).b Linear regression models by study group: ∆BMI z-score = ∆Habit + age; only reported if P for interaction <.2.c Negative coeffi cients correspond to a decrease in BMI and positive coeffi cients correspond to an increase in BMI.d Increases in time spent or consumption are considered behavioral improvements.e Increases in time spent or consumption are considered behavioral worsening.


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DOI: 10.1542/peds.2015-1979 originally published online December 23, 2015; 2016;137;Pediatrics

Tamelli, Silvia Candela, Eletta Bellocchio and Paolo Giorgi RossiFerrari, Gino Montagna, Costantino Panza, Mirco Pinotti, Simone Storani, Marco Serena Broccoli, Anna Maria Davoli, Laura Bonvicini, Alessandra Fabbri, Elena

of a Randomized Controlled TrialMotivational Interviewing to Treat Overweight Children: 24-Month Follow-Up

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