• No results found

A Parent-Focused Intervention to Reduce Infant Obesity Risk Behaviors: A Randomized Trial

N/A
N/A
Protected

Academic year: 2020

Share "A Parent-Focused Intervention to Reduce Infant Obesity Risk Behaviors: A Randomized Trial"

Copied!
11
0
0

Loading.... (view fulltext now)

Full text

(1)

A Parent-Focused Intervention to Reduce Infant Obesity

Risk Behaviors: A Randomized Trial

WHAT’S KNOWN ON THIS SUBJECT: While obesity-promoting eating, sedentary and physical activity behaviors, and increased prevalence of adiposity are evident from early life, few

high-quality studies have evaluated interventions that seek to influence

the development of these behaviors in very early childhood.

WHAT THIS STUDY ADDS: This study highlights the receptivity of

first-time parents to interventions focused on their new infant’s

eating and active play and provides evidence of effectiveness on some obesity-promoting behaviors in very early childhood.

abstract

OBJECTIVE:To assess the effectiveness of a parent-focused intervention on infants’obesity-risk behaviors and BMI.

METHODS: This cluster randomized controlled trial recruited 542 parents and their infants (mean age 3.8 months at baseline) from 62first-time parent groups. Parents were offered six 2-hour dietitian-delivered sessions over 15 months focusing on parental knowledge, skills, and social support around infant feeding, diet, physical activity, and television viewing. Control group parents received 6 newsletters on nonobesity-focused themes; all parents received usual care from child health nurses. The primary outcomes of interest were child diet (3 3 24-hour diet recalls), child physical activity (accelerometry), and child TV viewing (parent report). Secondary outcomes included BMI z-scores (measured). Data were collected when children were 4, 9, and 20 months of age.

RESULTS:Unadjusted analyses showed that, compared with controls, intervention group children consumed fewer grams of noncore drinks (mean difference =–4.45; 95% confidence interval [CI]:–7.92 to–0.99; P= .01) and were less likely to consume any noncore drinks (odds ratio = 0.48; 95% CI: 0.24 to 0.95;P= .034) midintervention (mean age 9 months). At intervention conclusion (mean age 19.8 months), in-tervention group children consumed fewer grams of sweet snacks (mean difference =–3.69; 95% CI:–6.41 to–0.96;P= .008) and viewed fewer daily minutes of television (mean difference =–15.97: 95% CI:

–25.97 to –5.96; P = .002). There was little statistical evidence of differences in fruit, vegetable, savory snack, or water consumption or in BMI z-scores or physical activity.

CONCLUSIONS:This intervention resulted in reductions in sweet snack consumption and television viewing in 20-month-old children.Pediatrics 2013;131:652–660

AUTHORS:Karen J. Campbell, PhD,aSandrine Lioret, PhD,a Sarah A. McNaughton, PhD,aDavid A. Crawford, PhD,aJo Salmon, PhD,aKylie Ball, PhD,aZoe McCallum, PhD,bBibi E. Gerner, MPH,cAlison C. Spence, PhD,aAdrian J. Cameron, PhD,aJill A. Hnatiuk, MSc,aObioha C. Ukoumunne, PhD,d Lisa Gold, PhD,eGavin Abbott, PhD,aand Kylie D. Hesketh, PhDa

aCentre for Physical Activity and Nutrition Research, andeDeakin

Health Economics, Deakin University, Burwood, Australia;

bDepartment of Paediatrics, The University of Melbourne,

Melbourne, Australia;cCentre for Community Child Health, Royal

Children’s Hospital, Parkville, Australia; anddPenninsula

Collaboration for Leadership in Applied Health Research and Care, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, United Kingdom

KEY WORDS

RCT, obesity prevention, infant, diet, physical activity, TV viewing

ABBREVIATIONS

CI—confidence interval

ICC—intraclass correlation coefficient LGA—local government areas MCH—Maternal and Child Health RCT—randomized controlled trial SEP—socioeconomic position zBMI—BMI z-scores

(2)

Worldwide, 43 million children aged 0 to 5 years are overweight or obese, rep-resenting a relative increase in preva-lence of 60% since 1990.1 A recent analysis of the impact of early childhood weight on later adiposity confirms that body size in children as young as 5 to 6 months and weight gain from 0 to 2 years are consistently predictive of high subsequent body size at age 13 years.2 Furthermore, upward crossing of major weight-for-length percentiles in thefirst 6 months of life is associated with high obesity rates at ages 5 and 10 years.3 Prevention of excess weight gain in early life is clearly of paramount im-portance, yet the most recent Cochrane Review of childhood obesity prevention4 identified just 1 rigorous obesity pre-vention study in early childhood.5Given the opportunity for early prevention, an urgent need exists for studies demon-strating the potential of interventions to reduce obesity-risk behaviors.

Parents are a key influence on the development and maintenance of children’s behaviors and are a rational target for child obesity prevention.6 They are also an attractive target given their focus on child health. In Victoria, Australia, parents access health serv-ices an average of 35 times in thefirst year of their child’s life, primarily to seek advice on promoting health and wellness.7 The free, universal service provided by Maternal and Child Health (MCH) nurses engages ∼66% of all first-time parents in groups that run over a 6- to 8-week period in thefirst 3 months of a child’s life. Most of these MCH nurse-initiated groups sub-sequently become independent social groups that continue to meet (fort-nightly on average) for∼18 months.8 These preexisting social groups pro-vide an opportunity for the promotion of knowledge, skills, and strategies likely to promote healthy child behav-iors and present an ideal setting for obesity prevention interventions.

In this cluster randomized controlled trial (RCT), we tested the hypotheses that an obesity prevention intervention delivered to first-time parents in pre-existing social groups would improve aspects of child diet, increase child time spent physically active, and reduce child television viewing time. We also hypothesized that child BMI z-score would be lower in the intervention group children. This study is reported by using the framework of the CONSORT statement for parallel group random-ized trials.9

METHODS

Study Design

This study was a cluster RCT with bal-anced (1:1) randomization. Fourteen local government areas (LGAs) were randomly selected from the 28 eligible LGAs located within a 60-km radius of the research center, situated within the major metropolitan city of Melbourne, Australia (population 4 million). One eligible LGA declined to participate and was replaced with the next LGA on the randomly ordered list. Good represen-tation of LGAs across socioeconomic groups was achieved, with an area-level index of relative disadvantage10 in-dicating selection of 3 LGAs in each of the lowest and highest tertile-defined categories and eight classified as middle category.

Sample Selection

Fifty percent of eligible first-time parents’ groups (rounded to next even number) within each LGA were randomly selected (62/103 groups) and approached by research staff for recruitment during 1 of the standard nurse-facilitated group sessions. In-dividual parents were eligible to par-ticipate if they gave informed written consent, were first-time parents, and were able to communicate in English. Parent groups were eligible if $8 parents enrolled or $6 parents

en-rolled in areas of low socioeconomic position (SEP) because mothers in areas of low SEP are less likely to at-tend first-time parent groups. When first-time parents’ groups declined to participate, another randomly selected group was approached.

Randomization of first-time parents’ groups (clusters) occurred after re-cruitment to avoid selection bias.11 Randomization (stratified by LGA) was conducted by an independent statisti-cian. Although parents were not blinded to allocation, they were not informed of the study aims or hypotheses. Staff measuring height and weight were not blinded to intervention status because they also delivered the intervention. All dietary recalls, data entry, and analyses were conducted with staff blinded to participant’s group allocation.

Intervention

Intervention Arm

Intervention delivery occurred from June 2008 to February 2010 with in-dividual groups involved for 15 months. The dietitian-delivered intervention comprised six 2-hour sessions delivered quarterly during thefirst-time parents’ group regular meeting. Data collection occurred at child age 4 months (base-line) and when children were 9 months of age (midintervention) and 20 months of age (postintervention). If parents did not attend these sessions, data collec-tion occurred in the home. Program (intervention) materials were sent to nonattending parents, and a researcher telephoned to invite questions. Program fidelity was audited via checklists by researchers attending but not de-livering the intervention.

(3)

presented before the associated child developmental phase. Social cognitive theory14guided program development, incorporating a range of delivery modes and educational strategies in-cluding group discussion and peer support, as well as exploration of facilitators and barriers to uptake of key messages. Intervention materials incorporated 6 purpose-designed key messages (for example, “Color Every Meal With Fruit and Veg,” “Eat Together, Play Together,” “Off and Running”) within a purpose-designed DVD and written materials. A newsletter rein-forcing key messages was sent to participants between sessions.

Control Group

Control parents received usual care from their MCH nurse, who may have provided lifestyle advice. The provision of this information was not assessed. Researchers met with control families in theirfirst-time parent groups at the 3 data collection occasions. For those no longer attending groups, data collec-tion occurred in the home. Control families received 6 newsletters re-garding unrelated aspects of child health or development (eg, literacy, common illnesses). Intervention and control group families’ participation was acknowledged by small gifts (maximum value AUD$15.00) on receipt of completed questionnaires.

Measures

24-Hour Dietary Recall

Child’s dietary intake (3 days, includ-ing 1 weekend day) was assessed by trained nutritionists at mid- and postintervention data collections by telephone-administered multipass 24-hour recall with parents.15To limit bias of responses, calls were unscheduled when possible (96% of all calls). When necessary (4% of recalls), food diaries were completed by other carers during child-care periods on scheduled days.

These were then used by parents to provide detailed 24-hour recalls. Coding of the dietary data involved matching each food/beverage item to an appropriate nutrient composition and quantity, using the 2007 Australian Food and Nutrient Database (AUSNUT) Database.16Data were checked for ac-curacy by dietitian review of interviews following coding.

Participants with ,2 days of dietary recall were excluded from analyses (n= 53 midintervention; n = 82 post-intervention). Average daily intakes of fruits (excluding juice), vegetables (ex-cluding potatoes), noncore sweet foods (eg, chocolate, candy, cakes), noncore savory foods (eg, crisps, savory bis-cuits), noncore drinks (ie, fruit juice, soft drinks), and water were calculated.

Objectively Assessed Physical Activity

Physical activity was assessed by using ActiGraph accelerometers (Model GT1M, Pensacola, FL) postintervention.17 Chil-dren wore the accelerometer over the right hip for 8 days, removing only for sleeping and bathing. Those with $4 days of valid data were included in analyses (n= 286) as 4 days of data ($7.4 hours per day) in this sample provided an acceptable reliability esti-mate (intraclass correlation coefficient [ICC] .0.70) for light- to vigorous-intensity physical activity.18 Light-, moderate-, and vigorous-intensity phy-sical activity was examined together as the intervention promoted participa-tion in physical activity of any intensity.

Television Viewing Time

At each time point, parents completed a questionnaire assessing infant time spent watching television on a typical day. A 2-week test-retest assessment of question reliability in a separate (unrelated) sample of 60 mothers of 9-month-old infants and 51 mothers of 18-month-old infants showed good re-liability (ICC = 0.86, 95% confidence

interval [CI] 0.77 to 0.91 and ICC = 0.84, 95% CI 0.72 to 0.90, respectively).

BMI

Children’s height/length and weight without clothes were measured by trained staff at each time point. Height/ length was measured to 0.1 cm using a calibrated measuring mat (Seca 210, Seca Deutschland, Germany) or porta-ble stadiometer (Invicta IPO955, Oadby, Leicester). Weight was measured to 10 g using calibrated infant digital scales (Tanita 1582, Tokyo, Japan). The aver-age of 2 measures was used in analy-ses. BMI (kg/m2) and BMI z-scores (zBMI) were calculated by using World Health Organization gender-specific BMI-for-age growth charts.19

Process Evaluation

Process evaluation was undertaken at each session and sought participants’ feedback on the usefulness and rele-vance of the program (ie,“How useful was the session overall?” and “How relevant was this session to you and your family?”). Respondents were asked to rate on a 4-point scale from

“not at all useful/relevant” to “very useful/relevant.”

Economic Analysis

Resources used for dietitian training, coordination, and session delivery were recorded prospectively via time-use logs kept by the research team. Resources were valued in 2010 Australian dollars by using existing national estimates for labor time and travel and market prices for venue and materials.

Sample Size and Power

(4)

consumption (considered the mini-mum meaningful change required) was the largest of those calculated and was therefore the final study sample size. Three days of food data on 18-month-old Australian children in-dicated that they ate 32 g (SD 15 g) of vegetables (not including potato) per day. For 80% power at the 5% level of significance, the total number of par-ticipants required for a trial that ran-domizes individuals is 112 (56 in each arm) to detect a vegetable increase from 32 to 40 g. To account for within-parent group clustering the sample size was increased by the design effect/ inflation factor of 2.8, based on assumptions of each cluster consisting of∼10 people and a conservatively high intracluster (intraparent group) corre-lation coefficient of 0.2.21This meant the study required 160 participants (10 participants from each of 16 parent group clusters) in each trial arm (ie, 320 participants). To our knowledge, this is thefirst study to recruit and deliver an intervention using first-time parent groups, and thus there were no data available to inform estimates of in-dividual or cluster attrition. In light of this, and the likelihood that missing data at any time point would further strain our usable sample size, we con-servatively oversampled with a view to doubling the number of clusters in each study arm (ie, 64 parent group clusters).

Statistical Analysis

All analyses were conducted on an intention-to-treat basis with partic-ipants analyzed according to the trial arm to which they were randomized where they provided outcome data (ie, analysis of completers). Physical ac-tivity was assessed at postintervention only, and wear-time was adjusted for in analyses.

Random effects linear regression mod-els, estimated using maximum likeli-hood, werefitted to compare continuous

outcomes between the trial arms, specifying parent groups as random effects to take account of clustering. Because some of the outcomes were highly skewed, the nonparametric bootstrap method22was used to vali-date the CIs, based on 2000 resamples, for all linear regression models. Be-cause the CIs were virtually the same, we report the model-based confidence intervals and Pvalues. For those out-comes in which a large proportion of children (particularly in the mid-intervention data) had scores of zero due to their young age (noncore drinks, sweet snacks, savory snacks, and TV viewing), additional analyses were conducted to assess the effect of the intervention on the odds of consuming/ viewing any compared with none. Mar-ginal logistic regression models, esti-mated using generalized estimating equations with information sandwich (“robust”) standard errors, werefitted to compare these binary outcomes be-tween the trial arms, taking account of clustering. An exchangeable correlation structure was specified for these anal-yses. Because baseline child BMI data were available, models of the zBMI out-come adjusted for the baseline values. Additional multivariable models were fitted controlling for known predictors of the outcomes studied (ie, mothers’ education level [in all models]; mothers’ prepregnancy BMI [for zBMI outcomes]; child age [for TV viewing or physical activity outcomes]; child gender [for physical activity outcome]; and child energy intake [for dietary outcomes]). Analyses were conducted by using Stata software (Release 12; StataCorpLP, Col-lege Station, TX).

RESULTS

Participant recruitment and retention are detailed in Fig 1. Final recruitment of 62 parent group clusters resulted in a sample of 542 children. Although Ta-ble 1 shows no marked differences in

baseline characteristics between trial arms, participating parents excluded from midintervention analyses due to missing data and loss to follow-up were more likely at baseline to have low levels of maternal education (57.5% vs 36.1%). There were no marked dif-ferences between parents excluded from postintervention analyses and those not excluded. There was also no difference in the proportions of intervention and control group participants excluded from analysis at either time point.

Table 2 shows that, at midintervention assessment (parents having attended 2 of the 6 sessions; child mean age 9 months [SD 1.1]), intervention group children had lower noncore-drink in-take than control group children. This difference remained when results were adjusted for prognostic factors. Intervention group children also had lower sweet snack intakes in the ad-justed analyses.

Differences in outcomes between the intervention and control arms at study completion (mean child age 19.8 months [SD 2.7]) are also presented in Table 2. Intervention group children had lower intake of sweet snacks and lower tele-vision viewing time compared with those in the control group. These dif-ferences remained after adjustment.

Table 3 shows the effects of the in-tervention on the odds that children consumed any noncore food/drinks and watched any television. Interven-tion group children had reduced odds of consuming noncore drinks mid-intervention, and this association re-mained when adjusting for covariates. No other intervention effects were found for children’s likelihood of con-suming noncore food/drinks or view-ing television at either time point.

(5)

consistently reported high levels of program usefulness and relevance (Table 4).

The total estimated cost of delivering the program, based on the costs of the intervention adjusted for the fact that

a trial setting sees an artificially small number of families included relative to the workforce employed, was approxi-mately AUD $500 per family.

DISCUSSION

This study provides evidence that sweet snack consumption and television viewing time in young children can be reduced by a relatively low-dose23 group-level intervention focused on parent knowledge and skills. At the conclusion of the program, interven-tion group children were watching

∼25% less television and consuming

∼25% fewer sweet snacks than con-trols. Although small changes to diet and sedentary behaviors may confer health benefits from a very early age

TABLE 1 Baseline (T1) Characteristics of 542 First-Time Mothers and Infants According to Treatment Arm

All Control Intervention Sample size 542 271 271 Children

Age at baseline (mo), mean (SD) 3.9 (1.6) 3.9 (1.6) 3.9 (1.6) Male (%) 52.6 53.5 51.7 zBMI, mean (SD) 20.5 (1.0) 20.5 (1.0) 20.4 (1.1) Ever breastfed (%) 96.6 96.6 96.6 Mothers

Age at baseline (y), mean (SD) 32.3 (4.3) 32.1 (4.4) 32.5 (4.2) BMI before pregnancy, mean (SD) 24.5 (5.2) 24.3 (4.9) 24.6 (5.6) Education level, %

Low (completed up tofinal year of secondary school) 21.1 20.3 22.0 Intermediate (completed trade/certificate postsecondary

school)

24.7 22.9 26.5 High (completed university degree or beyond) 54.2 56.8 51.5 Born in Australia (%) 79.1 79.7 78.4 English is main language spoken at home (%) 93.8 93.6 93.9

T1, baseline data. FIGURE 1

(6)

and across the life course,24we have not shown any impact on growth at 20 months. It is possible that the differ-ences in behaviors observed in this study were not sufficiently large, or the exposure not of sufficient duration, to effect group differences in zBMI. It is feasible, however, given the small dif-ferences in energy balance (30–45 calories/day) estimated to promote weight gain over time,24,25 that main-tenance of the magnitude of the dif-ferences in these behaviors may have a positive impact on weight trajecto-ries in the longer term.

There were no significant differences between groups at program conclusion for other dietary outcomes (fruit, veg-etables, water, noncore savory snacks). Indeed, fruit and vegetable intakes in this young group were relatively high, whereas noncore savory snack con-sumption was relatively low, and this may limit the capacity to show effect. Importantly, however, vegetable intake decreased, whereas noncore savory snack consumption increased in both arms between 9 and 20 months. This is consistent with reported trajectories across childhood and adolescence in Australia.26 It is possible that more favorable trajectories of consumption may be observed in the intervention arm over the longer term. Follow-up of this cohort to age 5 concludes in 2013.

There were also no differences between the trial arms in physical activity. We previously reported that parents of young children believe children are naturally active,27and thus it is plau-sible that parents were not receptive to messages to increase their child’s physical activity (active play). It is also possible that we need to design more focused interventions for physical ac-tivity in this age group. This area requires additional examination.

To the authors’knowledge, this study is unique in its focus on community-based delivery of an intervention targeting

(7)

first-time mothers of infants before weaning and adds important support to a growing body of evidence endors-ing a focus on early childhood. This focus for obesity prevention has gained considerable currency since this study commenced, with 1 published pro-tocol,282 pilot studies,29,30 and 1 addi-tional RCT published.31 Data from the pilots and RCTs, targeting mothers and infants,6 months of age, provide ad-ditional support for a focus on early childhood with 1 nonrandomized trial (n= 80) reporting a trend to reductions

in television viewing30and another (n= 110) reporting a lower weight-for-length percentile in intervention chil-dren (P= .009).29Wens RCT intensive home visiting intervention31 targeting families living in socially and econom-ically disadvantaged areas of Sydney, Australia (n= 667), reports lower BMI and improvements in some obesity-promoting behaviors at age 2 years in the intervention compared with the control group. The previously men-tioned RCT in a Cochrane review4(n= 43) reported evidence of lower child

energy intake in the intervention group (P= .06) but no differences in BMI.5

Generalizability and Implications

The high levels of participation ob-served in this trial, in terms of re-cruitment, retention, and program attendance, likely reflect parents’ re-ceptivity and desire to learn at this time in child’s development. This is also reflected in positive ratings of program usefulness and relevance.

The public health utility of this inter-vention is important to acknowledge. This was a low-dose, low-cost interven-tion that could feasibly be translated to real-world settings. The economic cost of the delivery of programs needs to be considered in terms of the overall impact of interventions. In this intervention, we have also demonstrated improvements in intervention group mothers’dietary patterns32and improvements in paren-tal knowledge and attitudes regarding television viewing, active play, and di-et.33The utility of the program is based on the impact on the family as a whole and over time. The “ripple” effects of this intervention on other family mem-bers and over time will be the subject of future studies.

TABLE 3 Effects of the Intervention on Prevalence of Any (Versus None) Non-core Food and Drink Consumption and Television Viewing

% Consumers/ Viewers Effects of the Intervention Effects of the Intervention Accounting for Covariates Control Intervention OR (95% CI)a P OR (95% CI)b P

Midintervention (mean child age 9 [1.1] mo)

Noncore drink intake 13.8 7.1 0.48 (0.24–0.95) .03 0.46 (0.23–0.93) .03 Sweet snack intake 29.0 26.3 0.78 (0.48–1.27) .32 0.73 (0.46–1.17) .19 Savory snack intake 22.3 20.1 0.81 (0.52–1.25) .33 0.78 (0.51–1.22) .28 Television viewing 72.9 68.8 0.81 (0.53–1.24) .33 0.79 (0.53–1.18) .25 Postintervention (mean child age 19.8 [2.2] mo)

Noncore drink intake 27.3 22.5 0.81 (0.51–1.30) .38 0.81 (0.51–1.28) .37 Sweet snack intake 76.3 70.7 0.69 (0.43–1.10) .12 0.69 (0.43–1.10) .12 Savoury snack intake 52.0 58.6 1.25 (0.87–1.81) .23 1.24 (0.86–1.79) .25 Television viewing 80.7 80.5 1.01 (0.61–1.65) .97 0.91 (0.56–1.46) .69

aMarginal logistic regression models, estimated using generalized estimating equations with information sandwich (robust) standard errors, weretted to compare binary outcomes between the trial arms, taking account of clustering. An exchangeable correlation structure was specified for these analyses.

bMarginal logistic regression models, estimated using generalized estimating equations with information sandwich (robust) standard errors, weretted to compare binary outcomes between the trial arms, taking account of clustering. An exchangeable correlation structure was specified for these analyses. The dietary intake models adjusted for mothers’education level and child’s overall energy intake. The television-viewing model adjusted for mothers’education level and child’s age.

TABLE 4 Average Perceived Group Session Usefulness and Relevance

Question Rating Average Over Sessions 1 to 6a

How useful was the session overall? Not at all useful 0.6% A little useful 11.2% Quite useful 45.5% Very useful 42.8% How useful was the information your group leader talked about? Not at all useful 0.4% A little useful 10.7% Quite useful 43.6% Very useful 45.4% How useful was the information other parents in your group

talked about?

Not at all useful 0.6% A little useful 17.3% Quite useful 45.9% Very useful 36.3% How relevant was this session to you and your family? Not at all relevant 0.6% A little relevant 11.3% Quite relevant 47.7% Very relevant 40.5%

(8)

Strengths and Limitations

Study strengths are the RCT design and use of gold standard measures for diet and physical activity, but it is important to acknowledge that intervention-arm parents may have been more likely to provide socially desirable responses when recalling diet (and television viewing), although one might expect such error to occur for all self-reported outcomes. Women from all socioeco-nomic strata were recruited; however, high SEP women (as assessed by edu-cation) were overrepresented. Low SEP women were no more likely to drop out of the study yet were more likely to have missing data at midintervention, but not postintervention. This may have

implications for generalizability of midinterventionfindings.

The analysis of data across early child-hood is challenging because measures of our primary outcomes are not mean-ingful at baseline; for example, at 3 months of age, our cohort consumes only breast or formula milk and is not yet physically active. This may be considered as a limitation in our analyses. It reflects, however, the fact that ourcohort is almost identical on measures of diet and activity at baseline (having a baseline of zero).

CONCLUSIONS

A low-dose intervention targetingfi rst-time parents in preexisting social

groups resulted in reductions in 9-month-old children’s noncore drink consumption and 20-month-old child-ren’s sweet snack consumption and television viewing time. It is important to assess whether these effects are lost, maintained, or magnified over time.

ACKNOWLEDGMENTS

We thank all participating local govern-ment regions for their encouragegovern-ment, advice, and access to Maternal and Child Health nurses. We are grateful to the participating families and nurses for their enthusiastic commitment to the study and in particular to the InFANT research team and student volunteers.

REFERENCES

1. de Onis M, Blössner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr. 2010;92(5):1257–1264

2. Stocks T, Renders CM, Bulk-Bunschoten AMW, Hirasing RA, van Buuren S, Seidell JC. Body size and growth in 0- to 4-year-old children and the relation to body size in primary school age.Obes Rev. 2011;12(8):637–652

3. Taveras EM, Rifas-Shiman SL, Sherry B, et al. Crossing growth percentiles in in-fancy and risk of obesity in childhood.Arch Pediatr Adolesc Med. 2011;165(11):993–998

4. Waters E, de Silva-Sanigorski A, Hall B, et al. Interventions for preventing obesity in children. Cochrane Database Syst Rev. 2011;(12):CD001871

5. Harvey-Berino J, Rourke J. Obesity pre-vention in preschool Native-American chil-dren: a pilot study using home visiting.

Obes Res. 2003;11(5):606–611

6. Brotman LM, Dawson-McClure S, Huang KY, et al. Early childhood family intervention and long-term obesity prevention among high-risk minority youth. Pediatrics. 2012; 129(3). Available at: www.pediatrics.org/ cgi/content/full/129/3/e621

7. Goldfeld SR, Wright M, Oberklaid F. Parents, infants and health care: utilization of health services in the first 12 months of life. J Paediatr Child Health. 2003;39(4): 249–253

8. Scott D, Brady S, Glynn P. New mother groups as a social network intervention: consumer and maternal and child health

nurse perspectives.Aust J Adv Nurs. 2001; 18(4):23–29

9. Moher D, Hopewell S, Schulz KF, et al; Consolidated Standards of Reporting Trials Group. CONSORT 2010 Explanation and Elaboration: Updated guidelines for reporting parallel group randomised trials. J Clin Epidemiol. 2010;63(8):e1– e37

10. Australian Bureau of Statistics. Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia. 2008. Available at: www.abs.gov.au/AUSSTATS/ abs@.nsf/DetailsPage/2033.0.55.0012006? OpenDocument. Accessed January 10, 2012

11. Eldridge S, Kerry S, Torgerson DJ. Bias in identifying and recruiting participants in cluster randomised trials: what can be done?BMJ. 2009;339:b4006

12. Campbell K, Hesketh K, Crawford D, Salmon J, Ball K, McCallum Z. The Infant Feeding Activity and Nutrition Trial (INFANT) an early intervention to prevent childhood obesity: cluster-randomised controlled trial. BMC Public Health. 2008;8(1):103

13. Nelson CS, Wissow LS, Cheng TL. Effective-ness of anticipatory guidance: recent developments. Curr Opin Pediatr. 2003;15 (6):630–635

14. Bandura A.Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall; 1986

15. Blanton CA, Moshfegh AJ, Baer DJ, Kretsch MJ. The USDA Automated Multiple-Pass Method accurately estimates group total

energy and nutrient intake.J Nutr. 2006;136 (10):2594–2599

16. Food Standards Australia New Zealand. AUSNUT 2007. Australian Food, Supplement and Nutrient Database for Estimation of Pop-ulation Nutrient Intakes. Canberra, Australia: Food Standards Australia New Zealand; 2008

17. Van Cauwenberghe E, Gubbels J, De Bourdeaudhuij I, Cardon G. Feasibility and validity of accelerometer measurements to assess physical activity in toddlers. Int J Behav Nutr Phys Act. 2011;8:67

18. Hnatiuk J, Ridgers N, Salmon J, Campbell K, McCallum Z, Hesketh K. Physical activity lev-els and patterns of 19 month old children.

Med Sci Sports Exerc.2012;44(9):1715–1720

19. WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: Length/Height-for-Age, for-Age, Weight-for-Length, Weight-for-Height and Body Mass Index-for-Age: Methods and Development. Geneva, Switzerland: World Health Organiza-tion; 2006

20. Webb KL, Lahti-Koski M, Rutishauser I, et al; CAPS Team. Consumption of “extra” foods (energy-dense, nutrient-poor) among chil-dren aged 16–24 months from western Sydney, Australia.Public Health Nutr. 2006;9 (8):1035–1044

21. Kish L. Sampling organizations and groups of unequal sizes. Am Sociol Rev. 1965;30: 564–572

(9)

23. Hesketh KD, Campbell KJ. Interventions to prevent obesity in 0–5 year olds: an updated systematic review of the literature. Obe-sity (Silver Spring). 2010;18(suppl 1):S27– S35

24. Hall KD, Sacks G, Chandramohan D, et al. Quantification of the effect of energy im-balance on bodyweight. Lancet. 2011; 378 (9793):826–837

25. Wang YC, Orleans CT, Gortmaker SL. Reach-ing the healthy people goals for reducReach-ing childhood obesity: closing the energy gap.

Am J Prev Med. 2012;42(5):437–444

26. 2007 Australian National Children’s Nutri-tion and Physical Activity Survey—Main Findings. Canberra, Australia: Common-wealth Scientific Industrial Research Or-ganisation (CSIRO), Preventative Health National Research Flagship, University of South Australia, 2008

27. Hesketh K, Campbell K. What can mothers’ expectations about young children’s phy-sical activity and sedentary behaviour tell us about intervention needs? J Sports Sci Med. 2007;10(6, December [suppl]): S131

28. Daniels LA, Magarey A, Battistutta D, et al. The NOURISH randomised control trial: positive feeding practices and food pref-erences in early childhood—a primary prevention program for childhood obesity.

BMC Public Health. 2009;9(1):387

29. Paul IM, Savage JS, Anzman SL, et al. Preventing obesity during infancy: a pilot study.Obesity (Silver Spring). 2011;19(2): 353–361

30. Taveras EM, Blackburn K, Gillman MW, et al. First steps for mommy and me: a pilot in-tervention to improve nutrition and physi-cal activity behaviors of postpartum

mothers and their infants. Matern Child Health J. 2011;15(8):1217–1227

31. Wen LM, Baur LA, Simpson JM, Rissel C, Wardle K, Flood VM. Effectiveness of home based early intervention on children’s BMI at age 2: randomised controlled trial.BMJ. 2012;344:e3732

32. Lioret S, Campbell KJ, Crawford D, Spence AC, Hesketh K, McNaughton SA. A parent focussed child obesity prevention inter-vention improves some mother obesity risk behaviours: The Melbourne InFANT program.

Int J Behav Nutr Phys Act.2012;28(9):100

33. Spence A, Campbell K, Hesketh K, Crawford D. Maternal feeding knowledge and feed-ing practices: Results of the Melbourne InFANT Program, a cluster-randomised controlled trial to promote optimal feed-ing and nutrition from 3–18 months of age.Nutr Diet.2010;67(suppl 1):15–16

(Continued fromfirst page)

Dr Campbell (lead chief investigator) conceptualized, designed, and managed all aspects of the study and analyses; wrote the initial manuscript; and revised the

final manuscript. Dr Lioret undertook and managed the majority of data analyses. Dr McNaughton (associate investigator) designed and managed all dietary data collection and analyses. Dr Crawford (chief investigator) was involved in the conceptualization, design, and management of all aspects of the study and analyses. Dr Salmon (chief investigator) was involved in the conceptualization, design and management of all aspects of the study and analyses. Dr Ball (chief investigator) was involved in the conceptualization, design, and management of all aspects of the study and analyses. Dr McCallum (chief investigator) was involved in the conceptualization, design, and management of all aspects of the study and analyses Ms Gerner (project manager) oversaw day-to-day management of recruitment, intervention delivery, and data collection and management. Dr Spence (PhD student) participated in day-to-day management of recruitment, intervention delivery, data collection and management. Dr Cameron provided expert input to data analyses. Ms Hnatiuk (PhD Student) undertook all management of physical activity related data. Dr Ukoumunne provided expert statistical advice into study reporting and analyses. Dr Gold (associate investigator) developed and analyzed measures used in the economic assessment of the study. Dr Abbott undertook the bootstrapping analyses for this manuscript and provided substantive input into manuscript writing. Dr Hesketh (lead chief investigator 2) conceptualized, designed, and managed all aspects of the study and analyses. All authors critically reviewed the manuscript and approved thefinal manuscript as submitted

This trial has been registered with the ISRCTN Register (http://isrctn.org) (identifier ISRCTN81847050).

Ethical approval for this study was obtained from the Deakin University Human Research Ethics Committee (ID number: EC 175-2007) and by the Victorian Office for Children (Ref: CDF/07/1138).

www.pediatrics.org/cgi/doi/10.1542/peds.2012-2576 doi:10.1542/peds.2012-2576

Accepted for publication Nov 27, 2012

Address correspondence to Karen Campbell, PhD, Centre for Physical Activity and Nutrition Research, Deakin University, 221 Burwood Highway, Burwood 3125, Australia. E-mail: karen.campbell@deakin.edu.au

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275). Copyright © 2013 by the American Academy of Pediatrics

FINANCIAL DISCLOSURE:Drs Campbell and Crawford are supported by fellowships from the Victorian Health Promotion Foundation; Dr Hesketh is supported by a National Heart Foundation of Australia Career Development Award; Dr Lioret is supported by a Deakin University Alfred Deakin Postdoctoral Fellowship; Dr McNaughton is supported by an Australian Research Council Future Fellowship; Dr Cameron is supported by a fellowship from the Australian National Health and Medical Research Council; Dr Ball is supported by a Senior Research Fellowship from the National Health and Medical Research Council. Dr Salmon is supported by a National Health and Medical Research Council Principal Research Fellowship (APP1026216); Dr Ukoumunne is supported by the UK National Institute for Health Research funded Peninsula Collaboration for Leadership in Applied Health Research and Care; Ms Hnatiuk is supported by a Deakin International Postgraduate Research Scholarship; the other authors have indicated they have nofinancial relationships relevant to this article to disclose.

(10)

DOI: 10.1542/peds.2012-2576 originally published online March 4, 2013;

2013;131;652

Pediatrics

D. Hesketh

Cameron, Jill A. Hnatiuk, Obioha C. Ukoumunne, Lisa Gold, Gavin Abbott and Kylie

Salmon, Kylie Ball, Zoe McCallum, Bibi E. Gerner, Alison C. Spence, Adrian J.

Services

Updated Information &

http://pediatrics.aappublications.org/content/131/4/652

including high resolution figures, can be found at:

References

http://pediatrics.aappublications.org/content/131/4/652#BIBL

This article cites 21 articles, 4 of which you can access for free at:

Subspecialty Collections

http://www.aappublications.org/cgi/collection/obesity_new_sub

Obesity

following collection(s):

This article, along with others on similar topics, appears in the

Permissions & Licensing

http://www.aappublications.org/site/misc/Permissions.xhtml

in its entirety can be found online at:

Information about reproducing this article in parts (figures, tables) or

Reprints

http://www.aappublications.org/site/misc/reprints.xhtml

(11)

DOI: 10.1542/peds.2012-2576 originally published online March 4, 2013;

2013;131;652

Pediatrics

D. Hesketh

Cameron, Jill A. Hnatiuk, Obioha C. Ukoumunne, Lisa Gold, Gavin Abbott and Kylie

Salmon, Kylie Ball, Zoe McCallum, Bibi E. Gerner, Alison C. Spence, Adrian J.

Karen J. Campbell, Sandrine Lioret, Sarah A. McNaughton, David A. Crawford, Jo

Randomized Trial

A Parent-Focused Intervention to Reduce Infant Obesity Risk Behaviors: A

http://pediatrics.aappublications.org/content/131/4/652

located on the World Wide Web at:

The online version of this article, along with updated information and services, is

by the American Academy of Pediatrics. All rights reserved. Print ISSN: 1073-0397.

Figure

FIGURE 1Participant recruitment and retention details. T2, mid intervention; T3, post intervention.
TABLE 2 Distribution of the Outcomes and Assessment of the Effects of the Intervention on These Outcomes at Mid- (T2) and Postintervention (T3)
TABLE 3 Effects of the Intervention on Prevalence of Any (Versus None) Non-core Food and Drink Consumption and Television Viewing

References

Related documents

The studies on phenological and phenomenological changes have been conducted in annuals and biennials subsequent to acclimatization at different altitudes (Woodward

To determine the zinc bioaccumulation properties of yeast isolates using kinetic equations, experiments were conducted with various concentration of zinc(II) ions (10- 50 mg/L)

Acid Peptic Disease is vast termi- nology it cannot be correlated with Amla- pitta but the etiology and symptoms of non ulcer Acid Peptic diseases like gastritis is

higher the growth rate of companies, higher will be debt in their capital structure.The collateral value of assets, growth, liquid assets, size, asset structure

The results showed that molecular subtype, LVI, mass descriptors (size, margin, microcalci fi cation and blood fl ow signal) and LN descriptors (shape, cortical thick- ness and LSR)

We also conducted the study to explore the characteristics of the relationship of individual social activity and cognitive func- tion, with a view to find which types

Predicting musculoskeletal injury in National Collegiate Athletic Association division II athletes from asymmetries and individual-test versus composite Functional Move- ment