The lancet regional health – Western Pacific

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The Lancet Regional Health - Western Paciﬁc

journalhomepage:www.elsevier.com/locate/lanwpc

Research paper

Overweight in the pluri-ethnic adolescent population of New Caledonia: Dietary patterns, sleep duration and screen time

Stéphane Frayon

^a^,^∗

, Guillaume Wattelez

^a

, Emilie Pauﬁque

^a

, Akila Nedjar-Guerre

^a

, Christophe Serra-Mallol

^b

, Olivier Galy

^a

aInterdisciplinary Laboratory for Research in Education, EA 7483, School of Education, University of New Caledonia, BP R4, Avenue James Cook, Noumea Cedex 98851, New Caledonia

bCentre on Work Organizations and Policies (CERTOP), UMR 5044 CNRS, University of Toulouse Jean Jaurès, 5 allées Antonio Machado, 31058 Toulouse cedex 9, France

a rt i c l e i n f o

Article history:

Received 3 July 2020 Revised 19 August 2020 Accepted 2 September 2020

Keywords:

Obesity Melanesian Polynesian Lifestyle Paciﬁc Nutrition Food frequency Ethnicity Westernization Rural Urban

a b s t r a c t

Background: AhighprevalenceofoverweightandobesityhasbeenfoundinadolescentsofNewCaledo- nia and otherPaciﬁcIslandCountries and Territories.AlthoughWesternizationmay contributeto the weight gain in populations ofOceanian, Non-European, Non-Asian ancestry (ONENA), little is known aboutthesociodemographicandlifestylefactorsassociatedwithoverweightintheMelanesianandPoly- nesianadolescentsofNewCaledonia.

Methods: Inthiscross-sectionalstudy,apluri-ethnicsampleofNewCaledonianadolescents(N= 954;

ageM=13.2years)completedasurveytoestimatesleepduration,screentime,anddietarypatternusing afoodfrequencyquestionnaire.Demographicdata(gender,ethnicity,socioeconomicstatus:SES,areaof residence)werecollected,andanthropometricmeasureswereusedtocomputeweightstatus.

Findings: We found a higher risk for being overweight in Melanesian (OR = 1.67) and Polynesian (OR=5.40) adolescentscomparedwithEuropeanadolescents,even aftercontrollingforage,SES,area ofresidence,dietarypattern,sleepdurationand screentime.WealsofoundthatlowSES(OR =3.43) andsleepduration(OR= 0.65perhour) wereindependentlyassociatedwithoverweightstatusinthe EuropeanbutnotinONENAadolescents.

Interpretation: Inthisstudy,themaincontributiontobeingoverweightwasethnicbackground,i.e.being MelanesianorPolynesian.Thehypothesisofageneticinﬂuencethusseemsattractiveandmeritsfurther analyses.

Funding: ThisprojectwasfundedbytheUniversityofNewCaledoniaandtheFondationNestlé France.

ThisisanopenaccessarticleundertheCCBY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Research in context

Evidence before this study: A high prevalence of overweight hasbeen noted inNew Caledonian adolescents, notably in Melanesian and Polynesian adolescents (Oceanian, Non-European,Non-Asianancestry:ONENA).Abetterunder- standingofthe factorsthat inﬂuenceobesity inadolescents is necessary to develop interventions to prevent and man- ageoverweight inthiscrucial periodoflife. Overweight re-

∗ Corresponding author.

E-mail address: sfrayon@ac-noumea.nc (S. Frayon).

sultsfromalong-termenergyimbalancebetweennutritional intake andactivity andseveralfactors havebeen correlated with it, including physical activity, screen time use, sleep deprivation, dietary intake, low socioeconomic status (SES), ethnic background and psychological factors. To date, little is known about the factors associated with overweight in NewCaledoniaadolescents.Previousstudieshaveshownthat themisperception ofweightstatusisfrequent inNewCale- donianoverweight adolescents.Moreover,skippingbreakfast was shown to be correlated withoverweight in New Cale- donian boys. Concerningsociodemographic factors, low SES

https://doi.org/10.1016/j.lanwpc.2020.10 0 025

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andethnicity(being MelanesianorPolynesian)were associ- atedwithahigherriskofoverweight/obesity.

Addedvalueofthisstudy:Inthisstudy,weshowedthat Polynesian, Melanesian and European adolescents living in New Caledonia may differ concerning some of the factors previously correlated withoverweight. Five main foodcon- sumption patterns were identiﬁed: Meats, Fast food, Fruits and vegetables, Sweets, and Dairies and breakfast. Melane- sian and Polynesian adolescents showed some differences in dietary patterns compared with their European counterparts. They also had lower sleeping timesthan their Euro- peancounterparts.MelanesianshadlessscreentimethanEu- ropean and Polynesian adolescents. A higher risk of being overweight wasfound for the Melanesian (OR = 1.67) and Polynesian (OR = 5.40) adolescents compared withthe Eu- ropean adolescents, even aftercontrolling forage, SES,area ofresidence,dietarypattern,sleepdurationandscreentime.

We also found that low SES (OR = 3.43) and sleep duration (OR = 0.65 per hour) were independently associated withoverweight statusinthe Europeanbutnot theONENA adolescents.Lowerconsumptionofdairy foodsandbreakfast wasassociatedwithoverweightinPolynesian andEuropean adolescents, while lower consumption of sweetswascorre- lated with overweight in Melanesian and European adolescents.Higher consumptionof fruitsandvegetableswasalso found in European overweight adolescents compared with non-overweightcounterparts.

Implicationsof all the available evidence: The correlation between the pattern of a lower consumption of dairy foods and breakfast and overweight may reflect the skip- ping breakfastbehaviours previously found in New Caledo- nian adolescents. Higher consumption of fruits and vegetables and lower consumption of sweets may be a strategy usedbysomeoverweightadolescentstocontroltheirweight but this was not found in all ethnic groups. Regardless of thepotentially modifiablebehaviours,specific diet interventionstargetingadolescents maybe usefultoprevent obesity inNewCaledonia.Eventhoughsleepdurationwasonlycor- relatedwith overweight inEuropean adolescents, low sleep duration mayhaveother consequences forwell-being. Thus, interventions targetingsleep duration maybe usefulfor all adolescents. Lastly, in this study and in previous studies, themaincontribution tobeingoverweightwasethnicback- ground, i.e. beingMelanesian orPolynesian. The hypothesis ofa genetic influencethus seems attractive andmerits fur- theranalyses.

1. Introduction

The prevalence of obesity is high in both adults and adoles- centsofthe Pacific Island CountriesandTerritories (PICTs)[1–3]. Asin other countries, overweight inPacific adolescents has been associatedwithgender(being female),sugarsweetened beverage consumption,sedentarybehaviours[4]andsocioculturalinfluences [5,6]. Overweight and obesity have been correlated with several noncommunicablediseases,suchastype2diabetes,cardiovascular events,cancer andhypertension[7–9].Moreover, overweight also seems to be associated with psychological issues like depression [10–13],body dissatisfaction[14,15],disorderedeating behaviours [16], low self-esteem [14,17–21], and insufficient sleep duration [22,23].

New Caledonia is a French archipelago in the South Paciﬁc whose pluri-ethnic population has been affected by obesity and the issues surrounding it. The lifestyles of the New Caledonian populations differ greatly among the main ethnic groups, which aredistinguished both culturallyandsocioeconomically [24].The Melanesians(45%ofthepopulation)weretheﬁrstinhabitantsbe- forethe colonial era. Today,some ofthem still follow the tradi-

tionalPaciﬁc lifestyle, whereas othershavealso takenona more Western lifestyle while still stayingclose to traditional customs.

Other communities of European and Polynesian origins(31% and 12%, respectively) have adopted a frankly Western way of life, even though all Polynesian communities still have different cul- tural practices than Europeans. A high prevalenceof overweight has been noted in New Caledonian adolescents [25,26], with a higherprevalencein MelanesianandPolynesian adolescents [24]. However,todate,littleisknownaboutthefactorsassociatedwith overweightintheseadolescents.

The major cause of overweight/obesity is often a long-term mismatch between energy intake and expenditure [27,28]. This mismatch has become more prominent today in relation with lifestylechanges,whichhavebeenstrikinginpopulationsofOcea- nian, Non-European, Non-Asian (ONENA) ancestry [29]. Indeed, unhealthy eating habits, physical inactivity, screen watching and sleepingpatterns maycontribute tothe currentobesityepidemic in PICTs [30], butthese trends have never been studied in New Caledonianadolescents.

Ithasbeenwelldocumentedthat dietaryenergyintakeisone of the major contributors to positive energy balance in children and adolescents [31]. In PICTs, traditional foods like yams, taro, fish, andindigenousfruits andvegetableshave been replaced by imported sugar, soft drinks,rice, cannedfoods and calorie-dense snackfoods[6,32–35].AddingWesternfoodstothetraditionaldiet can contribute to an increased risk of obesity even in rural areas where lifestyles and diets have remained largely traditional [36].Moregenerally,dietarychangewasobservedinfivePacificIs- landnationsbetween1961 and2000andwasfoundtoconsistof greaterfoodandenergyintakewithanotableincreaseinthecon- sumptionof fattyfoods andmeat [34], whichmay be correlated withtheBMIincreaseobservedinthesameperiod.Peerinfluence andgreaterautonomyaboutfoodchoicesinadolescencemayac- centuatetheunhealthydietsandweightgaininthisgroup[37,38]. Modernization may also contribute to weight gain, as it has meanteasieraccesstotechnologiesliketelevisions(TV),personal computers (PC), smartphones and game consoles. Higher screen time has been linked to higher weight status, probably due to decreased energyexpenditure, morephysical inactivity,increased caloric intake while using screen devices, and increased food- advertisingexposure[39–41].InVanuatu,the increasedmeasures ofadiposity andrisk forobesity were associatedwithownership of electronic devices[42]. A shift in adolescents’ preferences for devices seems to have occurred recently, and the use of smart- phonesandsmallconnecteddevicesisrising [43,44].As aconse- quence,adolescentsowningasmartphonearemorelikelytohave high screentime compared with non-owner adolescents [45]. In thePacific,Maorichildrenwere1.3timesandPacificchildrenwere 1.1timesmorelikelytowatchover2hoftelevisionperdaycom- paredwithnon-Maoriandnon-Pacificchildren[46].Moreover,the associationbetweenhighfatnesslevelsandhighTVwatchingwas foundinMaoriandPacificIslandyouthsinNewZealand[47].

Inadditiontotheputativeeffectofscreentime onweightstatus, screen use was also associated with shorter sleep duration [48].Shortsleepdurationisalsoarisk factorforobesity, andthe relationshipbetweenhighscreentimeexposureandthemorefre- quentconsumptionoffoodshighinfat,freesugarsorsalthasbeen demonstrated [49]. To date,the exact mechanismthrough which sleepdurationaffectsoverweight/obesityisnotcompletelyunder- stood.LittleisknownaboutadolescentsleepdurationintheSouth Paciﬁc population, with most of the studies conducted in New Zealand. Among New Zealanderadults[50,51],children [52], and adolescents[53],theMaoriandPaciﬁc Islandpopulationsshowed shortersleepdurationsthantheEuropeans.

Giventhattheprevalenceofoverweightismuchhigherinthe Polynesian andMelanesian adolescentsofNew Caledonia,we hy-

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pothesizedthatdifferenceswouldbe foundbetweenthesegroups andtheadolescentswithanEuropeanbackground,whoshoweda lowerprevalenceofoverweight.Theaimofthisstudywastolook forputativelinksbetweenoverweight/obesityandsleepduration, dietarypatternsandscreenuseinthepluri-ethnicadolescentpop- ulationofNewCaledonia.

2. Materialsandmethods

The sample for this study consisted in 954 adolescents (488 boys,466girls)recruitedinNewCaledonia.Participantagesranged from10.5to16.1years(M=13.22,SD=1.23).Fulldescriptivein- formationaboutthesampleispresentedinTableS1andTableS2.

EthicsapprovalforthisprojectwasobtainedfromtheConsul- tativeEthicsCommitteeofNewCaledonia (CCE2018-06001) and theprojectwasconductedinaccordancewiththerequirementsof the Declaration of Helsinki. Eight secondary public schools were randomly selected to obtain a representativerepartition between ruralandurbanareas,whichis63% and37%,respectively,inNew Caledonia.Theselectioncriterion wasschoolsize(N>150)toen- suresuﬃcientdatainasingleﬁeld trip.Oneortwoclasseswere then randomly selected in each of fourgrades (levels) by a staff member, for a total of approximately 150 adolescents (6 groups withameanof25studentsperdivision).Weobtained89%ofthe expected data due to parental refusal, no return of the parental agreementorabsenceoftheparticipantsonthedaysofinterven- tion.Participantscompletedtwoanonymousonlinequestionnaires consisting of the measures describedabove and additional mea- suresonbody imageorself-esteem,whichare notreportedhere.

Datawerecollectedovertwoconsecutivedaysinordernottodis- rupt the students’ schedules too much. There were missingdata becausesomeparticipantswereabsent onatleastone ofthetwo consecutivedaysorbecausethey usedawronganonymousnum- ber.Adolescentswithmissingdata(N=108;10.2%)werethenex- cluded fromanalyses (Figure S1). Parents gave informed written consentpriortotheadolescents’participationinthestudy,andall participantsreceived writtendebrieﬁng informationatthe endof thestudy.

In the absence of a validated food frequency questionnaire (FFQ)for New Caledonianadolescents, we useda short FFQpre- viously used for Aboriginal and Torres Strait Islanders [54]. This FFQwasadopted fromanothertool developed inAustraliato as- sess dietaryintakeinchildrenandadolescentsup to16yearsold [55].TheshortquestionsonthisFFQare abletodiscriminatebe- tween differentcategoriesoffoodintake andprovideinformation onrelativeintakes[54].Moreover,thisFFQhasbeenvalidatedina pluri-ethnicpopulationcomposedwithnon-indigenousAustralians (with European background) and Torres Strait Islanders (with a Melanesianbackground),anditwaspreviouslyusedinNewCale- donia[56].Minormodiﬁcations were madebythe researchteam to include foods identiﬁed as important in the Melanesian diet.

Forexample,tubers likecassava,yamsandtaroare consumedin- stead of white potatoes. The FFQ contains 28 questions on food andbeverageintake.Theintakefrequenciesfortheindividualfood items of the FFQ were ﬁrst converted to daily frequency equiv- alents (DFEs), calculated by allocatingproportional values to the originalfrequencycategorieswithreferencetoabasevalueof1.0, equivalentto once a day[57].The scores were calculated asfollows:DFEscoreof0:never;0.07:lessthanonceperweek;0.14:

once per week; 0.21: 1–2 times per week; 0.29: 1–3 times per week; 0.36:2–3timesperweek;0.50:3–4timesper week;0.71:

4–6timesperweek;0.79:5–6timesperweek;1:onceaday;2:2 timesperday;2.5:2–3timesperday;4.5:4–5timesperday;and 6:morethan6timesperday.Thequestionswereaskedontablets distributed by theteam andsometimes computerized inclass so thattheanswerswereautomaticallyrecordedinthedatabase.

The sleep duration was determined with the following four questions:“Whattimedoyoufallasleeponschooldays?”,“What time do you fall asleep on the weekend?”, “What time do you wake up inthe school week?” and “What time do you wakeup ontheweekend?” There were13availablecategoriesforthetime anadolescentmightfall asleep,from“Around9pm orbefore” to

“Around 3 am orlater” with a 30-minute interval betweeneach category.Therewere 15availablecategoriesforthewake-uptime from“Around 5am orbefore” to “Around midday orlater” with a 30-minute interval between each category. Answers were con- vertedtonumericalvaluesbyusingthemedianvalue ofthetime intervalinthecategorizedanswerorbyusing30minbefore(respectively,after) forthe first(respectively, thelast) category.The finalsleepdurationwasthedifferencebetweenthewake-uptime andthefalling-asleep time. Answers aboutsleepduration during theschoolweekandtheweekendwerefirstseparatelyprocessed, and then both factors were combined to get a total sleep dura- tionforthe fullweekasfollows:MeanSleep= 5xSleep(Week- days)+2xSleep(Weekend)dividedby7.

Thescreentime wasdeterminedwiththefollowingtwoques- tions:“Howmuchtime doyouspendonacomputerortableton theweekend?” and“Howmuchtimedoyouspendoncomputeror tabletduringtheschoolday?” Therewerefouravailablecategories fortheamountoftimeanadolescentmightuseacomputer,laptop ortabletscreenonschooldaysfrom“Lessthanonehour” to“More than3h”.Thereweresixavailablecategoriesforthetimeanado- lescentmightuseacomputer,laptoportabletscreenonweekends from“Lessthanonehour” to“Morethan5h”.Answerswerecon- vertedtonumericalvaluesbyusingthemedianvalue ofthetime interval inthe categorizedanswerorby using 30min beforefor the ﬁrst category or 30 min after for the last category. Answers aboutscreentimeduringtheschoolweekandtheweekendwere ﬁrst separately processed, and then both factors were combined toget a total screentime duringthe full week asfollows: Mean screentime use = 5 xscreen use (Weekdays) + 2 xscreen use (Weekend)dividedby7.Thesameprocedurewasusedforsmart- phoneusewithtwoquestions:“Ifyouhaveamobilephone,how longareyouconnectedtoyourphoneduringadayoftheweek?”

and“Ifyou haveamobile phone,how longareyou connectedto yourphoneduringadayoftheweekend?”

Ethnicitywasself-reportedby theadolescentsandcategorized as recommended by the INSERM report on New Caledonia [58], withthe exception that we did not allow participants to choose morethanoneethnicgroup.Participantsfromminorethnicgroups (approximately5%ofthewholesample)werethenclusteredinthe category“Other”.

Socioeconomicstatus(SES)wasindexedonthebasisoftheoc- cupationofthehouseholdreferenceperson(deﬁnedasthehouse- holderwiththehighestincome)usingtheNationalStatisticsSocio- EconomicClassiﬁcation [59]. In thisstudy, three categories were retained: managerialandprofessional occupations (high SES),intermediate occupations(intermediateSES),androutineandman- ualoccupations(lowSES).

Based on data from the last census in New Caledonia [60], areaofresidencewasdeterminedusingaEuropeanstandard[61]. Denselypopulatedareascomprisingatleast50,000inhabitantsin acontinuouszonewithmorethan 500inhabitantsper km² were classifiedasurban. Areaswithmorethan 50,000inhabitants ina continuousareaofover100inhabitantsperkm²orareasadjacent toan urban area were classified assemi-urban. Ruralareaswere definedasthoseareas thatdid notfulfill the conditionsrequired toqualifyasurbanorsemi-urban.

All anthropometric measurements were recorded by a member ofthe research team(trained staff). Heightwas measured to thenearest 0.1 cmusing a portable stadiometer(Leicester Tanita HR001,TanitaCorporation,Tokyo,Japan).Weightwasdetermined

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using a scale (Tanita HA 503,Tanita Corporation) to the nearest 0.1kg,withadolescentsweighed inlightclothing. Bodymass in- dex(BMI)wasthencalculatedbydividingweight(inkilograms)by height(inmeters)squared.

For weight status classifications, three BMI-based definitions were used: fromthe InternationalObesityTask Force: IOTFBMI- basedreferences[62];theWorldHealthOrganization:WHOBMI- basedreferences[63]; andtheFrench BMI-based references[64]. TheBMIz-scores(BMIz)were calculatedusingtheLMS reference valuesfortheIOTFBMI-basedclassification[65],becausethisref- erenceshowedthebestrelationshipwithbodyfatinNewCaledo- nianadolescents[25].

All analyses were conducted using IBM SPSS v.26, with p- values < 0.05 indicating statistical signiﬁcance. For prelimi- nary demographic analysis, gender differences were assessed withindependent-samples t-tests(continuous variables) andchi- squaredtests(categoricalvariables).

The principal component analysis (PCA) was run with all 24 foodgroupsentered asrawfrequencies usingvarimaxorthogonal rotation.Factorswere retained onthe basis ofeigenvalues > 1.0, ascreeplotoftheeigenvalues,andtheinterpretabilityofcompo- nents. Variables with factor loadings≥ 0.4 and≤ -0.4 were con- sideredsignificant whennaming patterns.The patternsproduced were transformed to remove skew andconverted to z-scores, so factorscores had a standardised meanof zero. Data factorability wasassessedusingtheKaiser-Meyer-Olkin(KMO)measureofsam- pling adequacy andBartlett’s test of sphericity. Cronbach’s alpha coefficientwasusedtodeterminetheinternalconsistency.Allpar- ticipantsreceivedascoreforallfivepatterns,withpositivescores indicatinghighconsumptionoffoodsassociatedwiththatpattern and negative scores indicating lower consumption. For example, thehighertheFruits andvegetablesscore,thehigheran individ- ual’s frequencyof consuming fruits, vegetables,tubers, soup and fish.

To determine which factors were significantly associated with each dietary pattern, we computed linear mixed models with each dietary pattern score as the criterion variable. To account for intra-class correlation, school were included and treated as random effects in the linear mixed models with no predictors (nullmodel). Results fromthismodel were usedto calculatethe intra-class correlation coefficient (ICC), which is the proportion of between-school variance to total variance. Second, indepen- dent standardized variables (gender, ethnicity, SES, area of residence,BMIzandage)wereaddedtothemodelsascovariablesand treatedas fixed effects. Ethnicity, gender, area of residence, and SES were previously categorized into groups by creating dummy variables.

Differences inBMIz, sleep duration and screentime were as- sessedusinga4×2analysisofvariance (ANOVA),withethnicity andgender treatedasindependentvariables, andpost-hoc Tukey testsusedto follow-upsigniﬁcantdifferences. Linearmixedmod- elswere theusedtodeterminethefactorssigniﬁcantlyassociated with sleep duration or screen time using the method described above.

Last,weusedmultivariablelogisticregressionanalysestoiden- tifythe factors associated with overweight. The variables in the modelswereage,dietarypatternscores,screenuseandsleepdu- ration for continuous variables and SES (low, intermediate and high),ethnicity(European,Melanesian,PolynesianandOther),gender(boyandgirl)andareaofresidence(ruralandurban)forcate- goricalvariables.

To take into account a possible selection bias because of the missingdatafor theparticipants, we also ransensitivity analysis usingvarying samplesizestoensurethe robustnessoftheanaly- ses.Becausenomeasurehadnomorethan5%ofthedatamissing (FigureS2),wedidnotperformanyimputationmethods.

2.1. Roleoffundingsource

Thefundingsources(UniversityofNewCaledoniaandtheFon- dationNestlé France)hadnoroleinstudydesign,datacollection, dataanalysis,interpretationandwritingofthereport.

3. Results

3.1. Sociodemographicandanthropometriccharacteristics

Thestudypopulationcomprised954adolescents between10.5 and16.1yearsold,with51.2%boys(13.2±1.2years)and48.8%girls (13.3±1.2years).The boysandgirlsdidnotsignificantlydifferre- gardingage,buttheboysweresignificantlytallerandheavierthan thegirls(TableS1).Nosignificantdifferencewasfoundconcerning ethnicity,SESorareaofresidencerepartitionaccordingtogender.

When the data were stratiﬁed by ethnicity, no difference re- gardingageorgender repartition wasfound (Table S2). However, ethnic subgroups differed signiﬁcantly concerning height, weight, SESandareaofresidence.Notably,PolynesianandMelanesianado- lescentsweremorelikelytoliveinaruralareaandhavelowerSES thantheirEuropeancounterparts.

3.2. Overweightprevalence

In our sample, overweight/obesity concerned between 25.4%

(French reference)and35.5%(WHOreference) oftheadolescents, witha32.3%prevalencefortheIOTFreference(Table1).Sensitivity analysisforalltheparticipantswithmeasured heightandweight (N=1011)showednosigniﬁcantvariationinweightstatusrepar- tition(p>0.05usingchi²).Whateverthereference,Polynesianand Melanesian adolescents showedhigher overweight/obesitypreva- lence than adolescents with a European background. Table S3 shows the BMIz average according to ethnicity and gender. The results of a 4 × 2 ANOVA showed no signiﬁcant interaction between ethnicity and gender, F(3, 954) = 2.16, p = 0.091,

η

^p²

< 0.01. However, there was a signiﬁcant main effect of gender [F(1, 954) = 4.86, p = 0.028,

η

p2 < 0.01] and ethnicity [F(3, 954)= 53.01,p<0.001,

η

^p²= 0.14].Tukeytestingindicated that Melanesian and Polynesian adolescents had signiﬁcantly higher BMIz averages than adolescents from European and other ethnic backgrounds(p<0.001inallcases),whileadolescentsfromEuro- peanandotheroriginspresentednodifferenceinBMIzaverage.

3.3. Dietarypattern

Results of PCA yielded KMO=0.877 and the items had suﬃ- ciently highcorrelationsforPCA(Bartlett’stest ofsphericity=

χ

²

(276)=4860,p<0.001).Furthermore,the24itemsusedinthePCA showedgoodconsistency(Cronbach’salpha=0.73).Thenumberof componentsdecideduponwasconfirmedbyanexaminationofthe screeplot,whichshowedaninflectionatthesixthcomponent.PCA identifiedfivedietarypatternsthatexplained47.3%ofthevariance intherespondents’diets(seeTable2).

Thelabelassignedtothecomponentswasbasedonitemswith high loadings within each component andthe interpretability of factors.Theseincluded:(1)Meats,(2)Fastfood,(3)Fruitsandveg- etables, (4) Sweets, and (5) Dairies. It should be noted that the third component, Fruits andvegetables, included fruits and veg- etablesaswellastubers,soupsandﬁsh,anditseemstorepresent thetraditionalONENAdiet.Also,theﬁfthpatternDairiesincluded cheese andyoghurt, aswell asbreakfastcerealwithmilk, andit could thusbe interpreted asDairies andbreakfastpattern. Sensi- tivityanalysiswasconductedforallparticipantswhorespondedto theFFQ(N=1031)andsimilardietarypatternswerefound(Table S4).

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Table 1

Weight status of New Caledonian adolescents stratiﬁed by ethnic background and gender.

Whole sample N = 954

Melanesian N = 429

European N = 368

Polynesian N = 97

Other N = 60

Weight status according to reference used ^∗

Boy N = 214

Girl N = 215

Boy N = 196

Girl N = 172

Boy N = 49

Girl N = 48

Boy N = 29

Girl N = 31

N % N % N % N % N % N % N % N % N %

IOTF ref. U 74 7.8 10 4.7 7 3.3 20 10.2 31 18.0 1 2.0 0 0.0 2 6.9 3 9.7

N 594 62.3 135 63.1 122 56.7 146 74.5 113 65.7 18 36.7 21 43.8 17 58.6 22 71.0

OV 187 19.6 40 18.7 63 29.3 22 11.2 23 13.4 10 20.4 17 35.4 6 20.7 6 19.4

OB 99 10.4 29 13.6 23 10.7 8 4.1 5 2.9 20 40.8 10 20.8 4 13.8 0 0.0

French ref. U 17 1.8 2 0.9 2 0.9 5 2.6 7 4.1 1 2.0 0 0.0 0 0.0 0 0.0

N 695 72.9 149 69.6 146 67.9 163 83.2 143 83.1 18 36.7 28 58.3 20 69.0 28 90.3

OV 242 25.4 63 29.4 67 31.2 28 14.3 22 12.8 30 61.2 20 41.7 9 31.0 3 9.7

WHO ref. U 18 1.9 2 0.9 2 0.9 5 2.6 8 4.7 1 2.0 0 0.0 0 0.0 0 0.0

N 598 62.7 123 57.5 117 54.4 152 77.6 131 76.2 16 32.7 16 33.3 18 62.1 25 80.6

OV 202 21.2 47 22.0 64 29.8 27 13.8 24 14.0 7 14.3 21 43.8 6 20.7 6 19.4

OB 136 14.3 42 19.6 32 14.9 12 6.1 9 5.2 25 51.0 11 22.9 5 17.2 0 0.0

∗ ref: reference, U: underweight, N: normal weight, OV: overweight, OB: obese Table 2

Factor loadings of the food groups in the dietary patterns (principal components) identiﬁed in New Caledonian adolescents.

Principal Component

Meats Fast Food Fruits and vegetables ’ONENA traditional’ Sweets Dairies and breakfast

%variance explained 11.0 10.6 9.3 8.8 7.6

Pork 0.642

Red meat 0.627

Chicken meat 0.588

Canned meat 0.573

Delicatessen 0.458

Pasta & rice 0.456 0.433

Fish 0.452 0.442

Beans 0.404

Fast food (hamburger, pizza, take-away food) 0.716

French fries 0.707

Salty snack/Potato chips 0.618

Egg 0.537

Fruits 0.729

Vegetables 0.712

Tubers 0.600

Soup 0.433

Candy 0.417 0.621

Cake & pastries 0.606

Sugar sweet beverage 0.577

Bread 0.504

Cheese 0.597

Milk 0.577

Breakfast cereal 0.566

Yoghurt 0.558

Table 3

Mixed linear models of dietary patterns and background factors.

Meats Fast Food Fruits and vegetables traditional ONENA diet Sweets Dairies and breakfast

β p β p β p β p β p

Age (years) 0.040 0.216 -0.084 0.008 -0.036 0.230 0.121 0.001 -0.115 < 0.001

Rural area$ -0.028 0.501 0.079 0.089 0.104 0.004 0.035 0.624 -0.080 0.031

Girl † -0.172 < 0.001 -0.040 0.202 -0.007 0.828 0.033 0.299 -0.112 < 0.001 Melanesian ‡ 0.100 0.021 0.161 < 0.001 0.261 < 0.001 0.205 < 0.001 -0.214 < 0.001

Polynesian ‡ 0.061 0.103 0.028 0.442 0.003 0.933 0.098 0.009 -0.088 0.012

Other ‡ 0.034 0.313 0.047 0.152 0.003 0.918 0.012 0.723 -0.018 0.579

Low SES§ 0.040 0.334 0.148 < 0.001 0.039 0.316 0.018 0.657 -0.005 0.904

Mid SES§ -0.001 0.981 -0.015 0.676 -0.004 0.904 0.010 0.769 0.003 0.935

BMIz 0.047 0.178 -0.050 0.139 0.116 < 0.001 -0.116 0.001 -0.072 0.031

ICC 0.001 0.049 0.092 0.039 0.047

ICC: Intra-class correlation coeﬃcient. Reference is: † boy, ‡ European, $ urban area, § high SES.

Mixed linear regressions of dietary patterns and background factors areshownin Table3.In allmixedmodels, theintra-class correlation betweenschools was poor (ICC< 0.1). The ﬁrst pattern,Meats,explained11.0%ofthevariationintheadolescent diets,andhighscoreswere associatedwithethnicbackground(being Melanesian)andgender(being aboy), butnotwithSES, age,

areaofresidenceorBMIz.Thesecondpattern,Fastfood,explained 10.6%ofthevariationintheadolescentdiets,andhighscoreswere associated with younger age, being Melanesian and having low SES. In the third dietary pattern, Fruit and vegetables, the tradi- tionalPaciﬁcdiet,highscoreswereassociatedwithbeingMelane- sian,livinginaruralarea,andhighBMIz.Thescoreforthefourth

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Table 4

Sleep durations for New Caledonian adolescents stratiﬁed by ethnic background and gender.

Ethnicity Gender

Sleep duration on weekdays (hours/day)

Sleep duration on weekends (hours/day)

Average sleep duration (hours/day)

Mean SD Mean SD Mean SD N

Melanesian Boy 8.05 1.48 8.48 2.07 8.17 1.35 214

Girl 8.14 1.22 9.01 1.75 8.39 1.08 215

Total 8.10 1.36 8.74 1.94 8.28 1.23 429

European Boy 8.44 1.16 8.84 2.00 8.55 1.12 196

Girl 8.51 1.23 9.33 1.50 8.74 1.07 172

Total 8.47 1.19 9.07 1.79 8.64 1.10 368

Polynesian Boy 7.69 2.01 7.46 2.20 7.63 1.73 49

Girl 7.94 1.08 8.39 1.93 8.07 1.05 48

Total 7.81 1.62 7.92 2.11 7.84 1.45 97

Other Boy 8.26 1.27 7.91 2.48 8.16 1.40 29

Girl 8.32 1.28 9.10 1.80 8.54 1.15 31

Total 8.29 1.27 8.53 2.22 8.36 1.28 60

Total Boy 8.18 1.43 8.49 2.12 8.27 1.34 488

Girl 8.27 1.23 9.07 1.70 8.50 1.10 466

Total 8.22 1.34 8.77 1.95 8.38 1.23 954

SD: Standard deviation

pattern,Sweets,waspositivelyassociatedwithethnic background (beingMelanesianorPolynesian)andolderageandnegativelyas- sociated with BMIz. The score for the last pattern, Dairies and breakfast,wasnegativelyassociatedwitholderage,ruralarea,be- ingagirl,beingMelanesianorPolynesian,andBMIz,butnotwith SES.

3.4.Sleepduration

Table 4 shows the sleep duration average on weekdays and weekendsaccordingtoethnicityandgender.Theresultsofa4×2 ANOVAforaverage sleepduration showed nosigniﬁcant interaction betweenethnicity andgender, F(3, 954) = 0.52, p = 0.783,

η

p2 < 0.01. However, there were signiﬁcant main effects of ethnicity[F(3,954) = 13.33, p< 0.001,

η

^p² < 0.01] and gender[F(1, 954)= 8.09,p= 0.005,

η

^p² <0.01]. Tukeytestingindicatedthat Polynesianadolescentshadsigniﬁcantlylowersleepdurationthan Europeans(p< 0.001), Melanesians (p= 0.007) andadolescents from other ethnic backgrounds (p = 0.047). Melanesian adoles- centsshowedlowersleepdurationthanEuropeans(p<0.001)but higherthanPolynesianadolescents(p=0.007).Nodifferencewas foundbetweenadolescents fromother originsandMelanesian or European counterparts. Sensitivity analysiswas conductedfor all participantswhorespondedtothispartofthesurvey(N= 1031) andsimilar resultswere found (Table S5), exceptthat no signiﬁ- canteffectsofgenderwerefound(p=0.253).

After controlling for age, SES, area of residence and BMIz in a mixed linear regression (schools ICC = 0.012), the associa- tionbetweensleeptimeaverage andgenderremainedsigniﬁcant (

β

=0.097,p=0.002).Similarly,theethnicityeffectonsleepdu- rationwasconﬁrmedforPolynesians(

β

= -0.149,p<0.001)and Melanesians(

β

=-0.090,p=0.038).Moreover,asigniﬁcantnega- tiverelationshipwasfoundbetweenageandsleepduration(

β

=- 0.126,p < 0.001) and betweenBMIz and sleep duration (

β

= - 0.098,p=0.004).

3.5.Screenuse

Fig.1andTableS6showthescreentime(PC/tabletandsmart- phone) average on weekdays andweekends according to ethnic- ityandgender.Theresultsofa4× 2ANOVAforPC/tabletscreen timeshowednosigniﬁcantinteractionbetweenethnicityandgen- der,F(3,954)=0.94,p=0.418,

η

p2<0.01.Moreover,themainef- fectofgenderwasnotsigniﬁcant,F(1,954)=0.07,p=0.798,

η

^p²

<0.01,butthemaineffectofethnicitywas[F(3,954)=24.72,p<

0.001,

η

^p² <0.01].Tukeytestingindicatedthattheadolescentsof other origins had signiﬁcantly higher screentime than European (p = 0.016), Melanesian (p < 0.001) and Polynesian (p= 0.011) adolescents. Melanesian adolescents showed lower screen time than all the other groups (p < 0.001). No difference was found betweenEuropeanandPolynesianadolescents.Sensitivityanalysis usingahighernumberofadolescents(N=1010)showedthesame results(TableS7).

Concerning smartphone use, the results showed no signiﬁ- cant interaction between ethnicity andgender, F(3, 954) = 0.23, p=0.878,

η

p2 < 0.01.Moreover, themaineffectofethnicity was not signiﬁcant [F(1, 954) = 2.46, p= 0.061,

η

^p² < 0.01], butthe main effect of gender was [F(3, 954) = 7.19, p = 0.023,

η

^p² <

0.01].Student’st-testindicatedthatthegenderdifferencewasonly signiﬁcantbetweenMelanesianboysandgirls.Sensitivityanalysis (Table S7) showeda signiﬁcant effectof gender (p= 0.021) and ethnicity (p = 0.008) with a higher smartphone use for adolescents from other origins compared with Melanesian adolescents (p=0.036).

For total screen time (smartphone + other screen use), the results showed a signiﬁcant main effect of ethnicity, F(3, 954)=12.50,p<0.001,

η

^p²=0.04.However,therewasnosigniﬁ- cantmaineffectofgender[F(1,954)=1.40,p=0.236,

η

^p²<0.01]

andno signiﬁcant interaction betweenethnicity andgender [F(3, 954)= 0.65, p=0.582,

η

^p² < 0.01].Tukeytestingindicated that Melanesian adolescents had signiﬁcantly lower total screentime onaveragethanEuropean(p<0.001)andPolynesian(p=0.005) adolescents and those from other origins (p< 0.001).No differ- enceswerefoundbetweentheotherethnicgroupsofadolescents.

Sensitivityanalysis(TableS7)showedthesameresultsexceptthat Tukey post-hoc analysis indicated that European hadlower total screentimethanadolescentsfromotherorigins(p=0.004).

Whencontrollingforage,SES,areaofresidenceandBMIzina mixedlinearregression(schoolsICC= 0.047),theassociation be- tweentotal screentime andgenderremainednon-signiﬁcantand a positive association wasfound for adolescents from other eth- nicities(

β

=0.067, p=0.038)comparedtoEuropeans. Moreover, apositive correlationwasfound withage(

β

= 0.278,p<0.001) butnocorrelationwasfoundwithSES,livingareaorBMIz.

3.6. Overweightpredictors

Results of the multiple logistic regression examining the relationship between the adolescents’ weight status (being overweight)and the ﬁve identiﬁed dietary patterns,screen time and

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Fig. 1. Total screen time (in hours/day) for New Caledonian adolescents stratiﬁed by ethnic background and gender.

sleepduration, controlledforethnic background,age,gender, SES andareaofresidence,arepresentedinTable5.Forthewholesam- ple, Polynesian and Melanesian adolescents were more likely to be overweight than their European counterparts (OR = 5.40 and OR =1.67, respectively), andlesslikelyto beunderweight (Table S8,OR= 0.07andOR=0.27respectively). Moreover,adolescents withlowerSESweremorelikelytobeoverweight(OR=2.16),but thistrendwasfoundinthewholesampleandtheEuropeanado- lescentsubgrouponly.Melanesian adolescentswithhigherscreen timewerelesslikelytobeoverweight(OR=0.74),andthistrend wasnotfoundintheotherethnicsubgroupsorthewholesample.

In the European adolescent subgroup, highersleep duration was correlatedwithalower riskofbeingoverweight(OR=0.65),and higherriskofbeingunderweight(TableS8,OR=1.55).

Dietary pattern analysisshowedthat the Meats andFast food patternswerenotindependentlyassociatedwithoverweightinthe whole sample or any of the ethnic subgroups. However, adolescents withhigh scoresin the Fruits andvegetables patternwere more likely to be overweight in the European subgroup andthe wholesample(OR=2.06andOR=1.19,respectively).Correlations between the Sweets and the Dairies and breakfast patterns and beingoverweight were found: participantswithhigherscores for theSweetspatternwerelesslikelytobeoverweightinthewhole sample(OR=0.78)andtheEuropean(OR=0.60)andMelanesian (OR=0.81)adolescents.Similarly,participantsscoringhighinthe Dairies andbreakfastpatternwerelesslikelytobe overweightin thewholesample(OR=0.83)andtheEuropean(OR =0.52)and Polynesian(OR =0.49)adolescents.No correlationwasfoundbe- tween dietarypatternsandbeingunderweightinthewhole sam- pleortheEuropeanandMelanesiansubgroup(TableS8).

4. Discussion

Thisstudyinapluri-ethnicpopulationofadolescentslivingin NewCaledoniarevealedﬁvedominantdietarypatterns,withsome

of them found to be associated with overweight. Sleepduration andscreentimewereevaluatedandcorrelationswithweightsta- tuswereexplored. Resultsshowedthat ethnicityandlowSES re- mainedthemainfactorsforbeingoverweightinNewCaledonia.

Toourknowledge,thisistheﬁrststudyexaminingthedietary patternsofNewCaledonianadolescentsfrom10.5to16yearsold.

Fivemainfoodconsumptionpatternswere identified: Meats,Fast food, Fruits andvegetables, Sweets, andDairies and breakfast. In moststudies, an unhealthyWestern dietarypatternhas beenassociated withhigherintakes ofred andprocessed meats,refined grains,sweetsanddesserts,high-fat dairyproducts andfastfood, whilean healthy patternhasbeen found toconsist of higherin- takesoffruits,vegetables,whole grains, fish,poultry,andlow-fat dairy products [66–69]. In our sample, the Meats (including red meatandcannedmeat),FastfoodandSweetspatternsseemedto representunhealthy orWestern-like patterns, whereas the Fruits andvegetablesandDairiesandbreakfastpatternsmayhaverepre- sentedhealthypatterns.

The Fruitsand vegetablespatternwas dense infruits, vegetables,tubers,soupandﬁsh,anditthereforecouldbeconsideredas thetraditionalONENAdiet[70].Inruralareas,highscoresfortra- ditionalONENA Fruits andvegetables patternand low scores for theDairies andbreakfastpatternwereobserved.Thesetrendscan be partly explained by the availability and affordability of these products[56,71].Fruitsandvegetablesarecommonlycultivatedin rural areas by traditional Melanesian family farms or bigger Eu- ropean farms and thus are widely available andaffordable. Food canbepurchasedbutisalsoavailableforsubsistenceconsumption, comprisingsubsistence production plus gifts andexchanges [72]. In contrast, cheese and yoghurt are not produced in rural areas ofNewCaledoniaandmaybeexpensiveandnotalwaysavailable.

Melanesianadolescentsshowedamajor differenceindietarypat- ternscomparedwiththeir Europeancounterparts.Theyconsumed moremeat, fast food, fruits and vegetables andsweets andcon- sumedlessdairy,evenaftercontrollingSES,ageandplaceofres-

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Table5 Multiplelogisticregressionforpredictorsofbeingoverweight. WholesampleN = 954R ²= 0.134MelanesianN = 429R ²= 0.069EuropeanN = 368R ²= 0.165PolynesianN = 97R ²= 0.131 OR(95%CI)pOR(95%CI)pOR(95%CI)pOR(95%CI)p EthnicityMelanesian1.67(1.07–2.61)0.023 Polynesian5.40(3.13–9.30)< 0.001 Other1.75(0.90–3.40)0.101 European1.00 SESLow2.16(1.42–3.30)< 0.0011.38(0.70–2.72)0.3513.43(1.67–7.07)< 0.0011.08(0.29–4.01)0.913 Mid1.39(0.80–2.41)0.2621.02(0.42–2.48)0.9720.77(0.26–2.29)0.6352.49(0.39–15.91)0.334 High1.001.001.001.00 Areaof residenceUrban0.86(0.59–1.26)0.4450.70(0.37–1.33)0.2791.63(0.75–3.51)0.2151.11(0.37–3.35)0.851 Rural1.001.001.001.00 GenderBoy0.87(0.64–1.19)0.3820.66(0.43–1.02)0.0601.05(0.54–2.07)0.8801.40(0.53–3.69)0.499 Girl1.001.001.001.00 Age(years)1.14(1.00–1.29)0.0481.14(0.96–1.36)0.1391.16(0.88–1.53)0.2800.88(0.60–1.29)0.523 Sleepingduration(hours/day)0.93(0.83–1.05)0.2671.07(0.90–1.27)0.4640.65(0.48–0.87)0.0040.97(0.70–1.36)0.866 Screenuse(hours/day)0.88(0.76–1.01)0.0690.74(0.59–0.92)0.0080.96(0.73–1.26)0.7420.86(0.58–1.28)0.462 NutritionPattern1factorscore:Meats1.06(0.91–1.23)0.4341.14(0.94–1.38)0.1781.47(0.97–2.21)0.0670.83(0.52–1.32)0.427 Pattern2factorscore:Fastfood0.93(0.80–1.1090.3880.84(0.69–1.02)0.0721.33(0.77–2.30)0.3050.90(0.55–1.48)0.678 Pattern3factorscore:Fruitsandvegetables1.19(1.02–1.40)0.0321.05(0.85–1.30)0.6572.06(1.36–3.10)0.0011.32(0.81–2.15)0.270 Pattern4factorscore:Sweets0.78(0.66–0.92)0.0040.81(0.66–0.99)0.0400.60(0.38–0.97)0.0361.25(0.75–2.08)0.396 Pattern5factorscore:Dairiesandbreakfast0.83(0.71–0.98)0.0290.97(0.77–1.21)0.7660.52(0.35–0.78)0.0020.49(0.30–0.82)0.006 OR:adjustedoddsratio;CI:conﬁdenceinterval

idence.Thehighconsumption offruitsandvegetablesmayreflect thetraditional Pacificdiet ofthe Melanesianadolescents, butthe highconsumption ofmeats, fastfoodand sweetscomparedwith their Europeancounterparts wassurprisingandneeds furtherin- vestigation. Polynesianadolescents showedsomedifferenceswith theirEuropeancounterparts,withahigherscoreintheSweetspat- ternanda lowerscorefortheDairies andbreakfastpattern.Last, girlsshowedlowerscoresfortheMeats andDairiespatternsthan boys.ItshouldbenotedthatthestudydesignandtheFFQweused didnotallowustoestimatethetotalenergyintakeofthepartici- pants.Itwasdifficulttodeterminewhetheragivenethnic group’s higher consumption in a specific pattern was associated with a higher energyintake orwhether the higher and lower scores in some patterns balanced each other. The Melanesian adolescents’

scores were especiallyhigheron averagein fourpatterns(Meats, Fastfood, Fruits andvegetablesandSweets),whereas they hada lowerscoreonaverageinthelastpattern(Dairies).

OurresultsshowedthattheEuropeanadolescentsscoringhigh inthe Fruitsandvegetablespatternwere morelikely tobe overweight. No causal relationship could be determined because of thecross-sectionalstudydesign;however,an explanationmaybe that theEuropeanadolescents whowere alreadyoverweightmay haveincreasedfruits andvegetablesuptakeordeclarationsofup- take.Thesebehavioursmaybe astrategyto avoidweight gainor to lose weight. In this case, this strategy was not used by Poly- nesian orMelanesian adolescents, because the fruits andvegetables consumption of these subgroups of adolescents wasnot associated withthe overweight odds ratio,possiblybecause ofless weight consciousness, less parental intervention or less motiva- tion to avoid weight gain. Similarly, adolescents scoring high in theSweetspatternwere lesslikelytobeoverweightinthewhole sample andin the Melanesian andEuropean subgroups, but not in thePolynesian subgroup.Once again, thismight be a strategy toavoidweightgain foroverweightadolescents.Anothertrendis thattheEuropeanandPolynesianadolescentswithlowerscoresin theDairiespatternweremorelikelytobeoverweight.Thispattern consistsof cheese andyoghurt butalso milkand breakfastcere- alsand,aspreviouslyexplained,overweightadolescentsmayavoid eatinghigh-fat productslikecheese.Moreover, dairyproducts are nottypicaloftheONENAtraditionaldiet[70].Anotherexplanation isthatthispatternmayreﬂecttherelationshipbetweenbreakfast skippingandoverweight,previously describedinNewCaledonian adolescents[24].Nospeciﬁcdietarypatternswerefoundtobeas- sociatedwithunderweightconditioninoursamples.

We showed that Melanesian and Polynesian adolescents had lowersleepdurations(20 minand40minless, respectively)than their European counterparts, who slept about 8 h and half per night. These results were very similar to those obtained by Gal- land et al. [53], who showed that New Zealand Europeans slept about8hand20minandthatbedtimeswerelaterforMaoriand Pacificadolescents(15and41min,respectively).Wefoundaneg- ative correlation betweenage andsleep duration, and thistrend wasobservedbyotherauthorsinthePacificarea[53].Shortsleep durationshave beenassociated with agreater risk ofdeveloping overweight/obesity and a significant change in BMIz at all ages [23,73,74].However, arecent reportshowed thatthe relationship betweensleeptime andweight statusshould beinterpretedwith caution,ashigherBMImayprecedelesssleep[75],andthuswedo notknowifoneisthecauseoftheotherortheotherwayaround.

In our sample, the association betweensleep duration and over- weightwasonlyfoundintheEuropeansubgroup,withtheoddsof beingoverweightincreasedby50%perhouroflesssleep.Interest- ingly,noassociationwasfoundbetweensleepdurationandweight statusin the Melanesianor Polynesian adolescents. Theseresults areinlinewithobservationsbyReitheretal.[76],inalarge survey(N=30,133)that showedthat therelationshipbetweensleep

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durationandBMIintheU.S.adolescentsmaydifferacrossethnic- itiesandgenders.Theyfoundanegativeassociation betweenBMI andsleepdurationinwhite,HispanicsandAsianboys;nosuchas- sociationinblackboys andwhite,HispanicandAsiangirls;anda positiveassociationinblackgirls[76].

Although screentime showedethnic differences in ourstudy, no correlation between screen time and being overweight was found inthewholesample.Melanesian adolescentsseemtohave lower screen time than their European counterparts. Moreover, Melanesians withhigher screentime were lesslikelyto be overweight thantheEuropeans, butthemagnitudeofthisassociation was weak (OR = 0.74). It should be notedthat moststudies report apositive relationshipbetweenTVwatchingandoverweight [77],butweonlyexaminedPC/tabletandsmartphonescreentime in our survey. Concerning computer use/game time, van Erkis found no evidence fora relationship with BMI/BMIz [77]. More- over, arecentreviewrevealedinsuﬃcientevidence ofan associa- tionbetweenoverallscreentimeornon-televisionscreentimeand greateradiposity[78].

In this study, the main factor for overweight was ethnic background, i.e. being Melanesian (OR = 1.67) or Polynesian (OR = 5.40), which makes attractive the hypothesis of a genetic influence.Recentstudiesabouttherelationship betweentheCRE- BRFgenevariantandBMIgain[79–81],indicatedthatthehypoth- esisofathriftygene shouldbe exploredforpeopleofPolynesian background inNew Caledonia.The absence ofthisvariant inthe MelanesianpopulationofSolomonIslands[81],andtheparticular- ity ofthe gut microbiomein the ruralMelanesian population of PapuaNewGuina[82],suggestanotherpossiblemechanismtoex- plainweightgain.ThehighconsumptionofthetraditionalONENA diet (fruits,vegetables,tubers,fish)oftheMelanesianadolescents inthisstudyandageneticinfluence[82],mighthaveledtoaspe- cificmicrobiomewithhigherenergyextractionfromcarbohydrate- rich diets. The modernization of New Caledonia has led to the higher consumption of processed food (fast foodand sweets) by Melanesianadolescents,whichmightthenhaveledtooverweight becauseofthisspecificmicrobiome.

Anotherpossiblewaytoexplainourresultswastoconsiderthe physical activity (PA) ofthe adolescents. It hasbeen shown that low PAmay becorrelated withoverweight inadolescentpopula- tions[1,38,40,56].PAmaydifferacrossethnicsubgroupsandthus explainthedisparitiesinoverweightprevalence.Indeed,relatively lowPAwasfoundinNewCaledonianMelanesianadolescents[83], witharural/urbandifference[84].Futureinvestigationsareneeded inNewCaledoniatodeterminewhetherPAislowerinMelanesian and Polynesian adolescents compared with their European counterparts.

While our study has strengths, including the anthropometric measurement ofall participants,there arelimitations toconsider when interpretingourfindings. First,the dataarecross-sectional, andthusnocausalrelationshipscouldbe determined.Duetothe sizeofthesample,thesociodemographiccharacteristicsofthepar- ticipants might be slightly different from the total school-going adolescent population. Concerningthe short FFQused, social de- sirabilitybias mayhaveled overweightadolescents to overreport orunderreportfoodconsumption[85].Moreover,thedesignofthe FFQdidnotallowustodeterminethetotalenergyintake.ThisFFQ wasalsovalidatedina specificpopulation inAustralia,soit may notreflectsomeofthespecificitiesoftheNewCaledonianadoles- cent diet. Also, we didnot assessPA, which canhave an impact onweightstatus.Concerningsleeptimeduration,FFQandscreen use,weobtainedself-declarativedataandtheresultsshouldthus be considered withcaution. Despitetheselimitations, thisis the first studyto examine concomitantly the impacts of sleep duration, screentime anddieton overweight ina pluri-ethnicPacific populationofadolescents.

Conclusion

The prevalence of overweight/obesity is high in New Caledo- nian adolescents, particularly Polynesian and Melanesian adolescents. Regardless of the potential modifiable behaviours, specific diet interventionstargetingadolescents maybe usefultoprevent obesityinNewCaledonia.Interventionstodiminishtheconsump- tionoffastfoodandsweetsinMelanesianandPolynesianadoles- centsmayalsobe useful, aswouldinterventionstargetingbreak- fastintakeinPolynesianandEuropeanadolescents.Althoughsleep durationwascorrelatedwithoverweightonlyinEuropeanadoles- cents,low sleepduration mayhaveother consequences forwell- being. Thus, interventionstargetingsleepduration maybe useful foralladolescents.However,ourresultsarecompatiblewithage- netic influenceon weight gain, although research concerning ge- neticvariantand/ormicrobiomeinfluenceremainschallenging.

Contributors

SF and OG conceived and designed the experiment. SF and GWanalysedthedata.SFwrotetheoriginaldraft. Alltheauthors collected the data and reviewed, edited and approved the ﬁnal manuscript.

Datasharingstatement

The datasets collected during the current study are available fromthecorrespondingauthoronreasonablerequest.

DeclarationofCompetingInterest

Theauthorsdeclarethattheyhavenocompetinginterests.

Acknowledgments

Wewouldliketothanktheschoolteachingteamsandadminis- trativestaffsfortheirhelpandsupportinourinvestigations,espe- ciallytheVice-RectoratofNewCaledonia.Wewouldliketothank PaulZongo,Pierre-YvesLeRouxandFabriceWacaliefortheirhelp forcollectingthedata.

Supplementarymaterials

Supplementary material associated with this article can be found,intheonlineversion,atdoi:10.1016/j.lanwpc.2020.100025. Appendix

Résumé

Contexte: Les adolescents de la Nouvelle Calédonie présente une forte prévalence de l’obésité et du surpoids, comme dans beaucoup de Pays et Territoires des Iles du Paciﬁque. Bien que l’occidentalisation puissecontribuer à la prise de poids des populations d’ascendance Océanienne, non Européenne et non Asia- tique(ONENA),peudechosessontconnuessurlesfacteurssocio- démographiques etle mode de vie associésau surpoids chezles adolescentsmélanésiensetpolynésiensdelaNouvelle-Calédonie.

Méthodes: Danscetteétudetransversale,unéchantillonpluri- ethniqued’adolescents néo-calédoniens (N = 954;âge moyen de 13,2 ans) a participé à une enquête pour estimer le temps de sommeil,letemps d’écran etle régimealimentaire. Desdonnées démographiques (sexe, origine ethnique, statut socio-économique (SSE)etzonede résidence)ontété recueilliesetdesmesuresan- thropométriques(tailleetpoids)ontété utiliséespourdéterminer lestatutpondéral.