University of Kentucky
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Theses and Dissertations--Dietetics and Human
Nutrition
Dietetics and Human Nutrition
2017
CHANGES OF PERCENT BODY FAT, WAIST
CIRCUMFERENCE AND FRUIT AND
VEGETABLE INTAKE AMONG DIVISION I
COLLEGIATE FEMALE SOFTBALL PLAYERS
AFTER NUTRITION CURRICULUM PAIRED
WITH TECHNOLOGY
Rachel Thomas
University of Kentucky, [email protected]
Digital Object Identifier: https://doi.org/10.13023/ETD.2017.076
Recommended Citation
Thomas, Rachel, "CHANGES OF PERCENT BODY FAT, WAIST CIRCUMFERENCE AND FRUIT AND VEGETABLE INTAKE
AMONG DIVISION I COLLEGIATE FEMALE SOFTBALL PLAYERS AFTER NUTRITION CURRICULUM PAIRED WITH
TECHNOLOGY" (2017). Theses and Dissertations--Dietetics and Human Nutrition. 52.
http://uknowledge.uky.edu/foodsci_etds/52
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Rachel Thomas, Student
Dr. Janet S. Kurzynske, Major Professor
Dr. Kelly Webber, Director of Graduate Studies
CHANGESOFPERCENTBODYFAT,WAISTCIRCUMFERENCEAND
FRUITANDVEGETABLEINTAKEAMONGDIVISIONICOLLEGIATE
FEMALESOFTBALLPLAYERSAFTERNUTRITIONCURRICULUM
PAIREDWITHTECHNOLOGY
THESIS
Athesissubmittedinpartialfulfillmentofthe
requirementsforthedegreeofMasterofScienceinthe
CollegeofAgriculture,Food,andEnvironmentalSciences
attheUniversityofKentucky
By
RachelRusherThomas
Lexington,Kentucky
Director:Dr.JanetS.Kurzynske,PhD,RD,ExtensionProfessorofDieteticsand
HumanNutrition
Lexington, Kentucky
2017
Copyright© Rachel Rusher Thomas 2017
ABSTRACTOFTHESIS
CHANGESOFPERCENTBODYFAT,WAISTCIRCUMFERENCEAND
FRUITANDVEGETABLEINTAKEAMONGDIVISIONICOLLEGIATE
FEMALESOFTBALLPLAYERSAFTERNUTRITIONCURRICULUM
PAIREDWITHTECHNOLOGY
Previousresearchhasfoundthatthequalityandintakeofcollegiateathletes’
diets does not meet the recommended standards. Little research has been
completedinregardstothedietsofcollegiatefemalesoftballplayers.Thepurpose
ofthisresearchstudywastodetermineifan11‐weeknutritioncurriculumpaired
withatechnologycouldincreasefruitandvegetableconsumptionwhiledecreasing
percent body fat and waist circumference. The sample included 14 female softball
players. Paired t‐test were preformed to compare fruit and vegetable intake, body
fat percentage, and waist circumference pre‐ and post‐intervention. Linear
regressionmodelswereusedtodeterminecorrelationsbetweenchangeinfruitand
vegetableconsumptionandbodyfatpercentage,andchangeinfruitandvegetable
consumption and waist circumference. Results showed that an increase in fruit in
vegetable consumption was associated with a significant increase in waist
circumference (p=0.0328). Of the participants, 43% were freshmen and may be
relatedtofreshmanyearweightgain.Basedonthecurrentfindings,moreresearch
isneededwithstricterprotocols.
KEYWORDS: Fruit, Vegetable, Nutrition Curriculum, College Softball Players, Waist
Circumference,BodyFatPercentage
RachelRusherThomas
April19,2017
CHANGESOFPERCENTBODYFAT,WAISTCIRCUMFERENCEAND
FRUITANDVEGETABLEINTAKEAMONGDIVISIONICOLLEGIATE
FEMALESOFTBALLPLAYERSAFTERNUTRITIONCURRICULUM
PAIREDWITHTECHNOLOGY
By
RachelRusherThomas
JanetKurzynske,PhD,RD
DirectorofThesis
KellyWebber,PhD,MPH,RD,LD
DirectorofGraduateStudies
April19,2017
TABLE OF CONTENTS List of Tables…………………………………………………………………………………………………………………v List of Figures.………………………………………………………………………………………………………………vi Chapter One: Introduction…………………………………………………………………………………………….1 Problem Statement…………………………………………………………………………………………...2 Research Questions……………………………………………………………………………………………2 Hypotheses………………………………………………………………………………………………………..3 Justification……………………………………………………………………………………………………….3 Chapter Two: Literature Review……………………………………………………………………………………4 Determinants of Diet Quality…………………………………………………………………………….5 Meeting Recommendations of Fruit and Vegetable Consumption of Athletes………………………………………………………………………………………………...5 Nutrition Education……………………………………………………………………………….9 National Cancer Institute (NCI) Fruit and Vegetable Screener……………..14 Use of Technology and Tracking Intake………………………………………………..17 Determinants of Body Composition…………………………………………………………………23 Waist Circumference (WC)………………………………………………………………….23 Bod Pod®………………………………………………………………………………………………27 Summary…………………………………………………………………………………………………………30 Chapter Three: Methods……………………………………………………………………………………………..30 Study Design…………………………………………………………………………………………………...30 Participants……………………………………………………………………………………………………..31 Measurements………………………………………………………………………………………………..32 Procedures……………………………………………………………………………………………………...34 Analysis……………………………………………………………………………………………………………37 Chapter Four: Results………………………………………………………………………………………………….37 Demographics………………………………………………………………………………………………...37 Body Composition…………………………………………………………………………………………...41 Total of Fruit and Vegetable Intake………………………………………………………………….42 Fruit Intake………………………………………………………………………………………………………42 Vegetable Intake……………………………………………………………………………………………..43 Correlation Test Between Fruit and Vegetable Intake and Body Fat Percentage……………………………………………………………………………………………………..44 Correlation Test Between Fruit and Vegetable Intake and Waist Circumference………………………………………………………………………………………………..46 Chapter Five: Discussion……………………………………………………………………………………………..47 Limitations……………………………………………………………………………………………………...49 iii
Chapter Six: Conclusion……………………………………………………………………………………………….50 References………………………………………………………………………………………………………………….51 Vita……………………………………………………………………………………………………………………………..54 iv
LISTOFTABLES
Table3.1,Outlineofnutritioncurriculum………………………………………………………….36
Table4.1,CharacteristicsofBodyCompositioninAugust2015………………………….38
Table4.2,CharacteristicsofFruitandVegetableconsumptioninAugust2015…...38
Table4.3,CharacteristicsofBodyCompositioninNovember2015…………………….39
Table4.4,CharacteristicsofFruitandVegetableconsumptionin
November2015……………………………………………………………………………………..40
Table4.5,Pairedt‐test,waistcircumferenceandpercentbodyfat………………………41
Table4.6,Pairedt‐test,combinedfruitandvegetableintake……………………………....42
Table4.7,Pairedt‐test,fruitintake………………………………………………………………….....42
Table4.8,Pairedt‐test,vegetableintake………………………………………………………….....43
Table4.9,Linearregression,changeinbodyfatpercentagevs.changeinfruit
andvegetableintake……….………………………………………………………………………45
Table4.10,Linearregression,changeinwaistcircumferencevs.changeinfruit
andvegetableintake…………………………………………………….…………………………46
v
LISTOFFIGURES
Figure3.1,FruitandVegetableScreenerscoringtool………………………………………....33
Figure4.1,Scatterplotofchangeinbodyfatpercentagevs.changeinfruit
andvegetableintake……………………………………………………………………………….44
Figure4.2,Scatterplotofchangeinwaistcircumferencevs.changeinfruit
andvegetableintake………………………………………………………………...……………..46
vi
CHAPTER1:INTRODUCTION
Manyyoungadultshavepooreatinghabits(Laska,2014).Amongthe
populationofyoungadultsiscollegestudents;particularlystudentathletes.
Researchhasfoundthatfemalecollegiateathletesfailtomeettheirestimated
energyandcarbohydrateneeds,whichcannegativelyimpactbothtrainingand
recoveryalongwithoverallnutritionalstatus(Shriver,2012).Researchershavealso
foundthatcollegiateathletesneedtobetargetedfornutritioneducationasitrelates
tooptimizingperformance(Rosenbloom,2002).
Collegiatefemalesoftballplayerslogmanyhoursoutsideoftheclassrooms
onthefieldsandweightroomstryingtobettertheirperformance.Theperformance
factorforsoftballplayersisnotfocusedonendurance,butisfocusedonstrength
(Mala,2015).Otherfactors,includingqualityofdietplayaroleinoveralllevelof
performance.Therehasbeenalimitedamountofworkcompletedtointervenewith
collegiatefemalesoftballplayers’diet.In2014,theNationalCollegiateAthletic
Associationderegulatedmeals,allowingforathletestomoreappropriatelyrecover
andrefueltheirbodieswithwholefoodsthroughouttheday(Wilfert,2014).This
deregulationwasuniversal,includingbothmaleandfemaleathletes.Littleresearch
hasbeenpublishedonfemaleDivisionIcollegiateathletessincethechanges
(Ludwig,2015).
Thefocusofthisresearchistoidentifyifthereweresignificantchangesin
fruitandvegetableintakeandbodycompositionoffemalecollegiatesoftballplayers
afteran11‐weeknutritioncurriculumwithatechnologycomponentwascompleted
duringpre‐season,afterthede‐regulationofmealsbytheNCAA.
1
PROBLEMSTATEMENT
Manystudieshavebeencompletedtoexaminethedietqualityoffemale
collegiateathletestodetermineiftheyaremeetingminimumnutritionguidelines
(Ludwig,2015;Quatromoni,2008;Rosenbloom,2002;Shiver,2012).Studieshave
alsobeencompletedtoexaminetherelationshipbetweenattendinganutrition
curriculumbasedinterventionanditsimpactonoverallhealthamongcollege
students(Quatromoni,2008;Richards,2006;Murphy,2001;Lua,2012;Kelly,2013;
Plotnikoff,2015;Kicklighter,2010).Thesestudieshavefoundthatcollegeage
studentsareinterestedinnutritioncurriculum,andintakeoffruitsandvegetables
canbeimproved(Quatromoni,2008;Richards,2006;Murphy,2001;Lua,2012;
Kelly,2013;Plotnikoff,2015;Kicklighter,2010).Littleresearchexistsexaminingthe
relationshipbetweenfruitandvegetableintakeandnutritioncurriculumbased
interventionsimpactonpercentbodyfatandwaistcircumferenceamongcollege
athletes,specificallycollegiatefemalesoftballplayers.Thisstudyaimstohelpfill
thisgap.
RESEARCHQUESTIONS
1. Doesan11‐weeknutritioncurriculumdecreasebodyfatpercentagesof
femalecollegiatesoftballplayers?
2. Doesan11‐weeknutritioncurriculumincreaseintakeoffruitsand
vegetablesinfemalecollegiatesoftballplayers?
3. Doeswaist‐circumferencechangeincollegiatefemalesoftballplayers
afteran11‐weeknutritioncurriculum?
2
4. Isthereacorrelationbetweenchangeinfruitandvegetableintakeand
changeinbodyfatpercentageorchangeinwaist‐circumferenceafteran
11‐weeknutritioncurriculumforfemalecollegiatesoftballplayers?
HYPOTHESES
1. An11‐weeknutritioncurriculumwilldecreasethebodyfatpercentagesof
femalecollegiatesoftballplayersasmeasuredbytheBodPod®.
2. An11‐weeknutritioncurriculumwillincreasetheintakeoffruitsand
vegetablesinfemalecollegiatesoftballplayersasmeasuredbyNational
CancerInstitute(NCI)FruitandVegetableScreener.
3. An11‐weeknutritioncurriculumwillresultindecreasewaist‐circumference
asmeasuredbymanualwaist‐circumferencemeasurements.
4. Therewillbeanegativecorrelationbetweenchangeinfruitandvegetable
consumptionandchangeinbodyfatpercentageandchangeinwaist‐
circumferenceafteran11‐weekintervention.
JUSTIFICATION
Researchhasyettobecompletedontheimpactofanutritioncurriculum
pairedwithuseoftechnologyfocusingonfemalecollegiateathletesinimproving
theiroverallfruitandvegetableconsumption.Evidencehasbeenoutlinedbythe
AmericanCollegeofSportsMedicinethatindicatesthatdietqualityimpactsathletic
performance(Thomas,2016).Limitedresearchhasbeendonewithfemale
collegiateathletes,particularlysoftballplayers,andtheuseoftechnologypaired
3
withanutritioncurriculumtoimprovedietquality,particularlyafterthe
deregulationofmeals.Thisderegulationallowsathletesaccesstofruitsand
vegetablesatnoadditionalcost.
CHAPTER2:LITERATUREREVIEW
Whenmostpeoplethinkofcollegiateathletes,theythinkofhighperforming,
lean,andnutritionallysoundathletes.Theaveragecollegiatefemalesoftballplayer
doesnotfitintotheleancategory,yetresemblesthebodycompositionofatypical
Americanofsimilaragerange(Carbuhn,2010).Becausethispopulationwantsto
performattheiroptimalcapacity,meetingtheirindividualnutritionneedsis
important.Multiplestudieshavelookedattheimpactofprovidinganutrition
curriculumandoruseofdietarylogstoimproveandincreaseconsumptionoffruits
andvegetablesamongvariouspopulations,particularlycollegestudents(Lua,2012;
Plotnikoff,2015;Kicklighter,2013;Kelly,2012;Shriver,2013).Currentlythe
researchoncollegiateathletesanddietaryintakeislimited.Researchthathasbeen
completedonfemaleathlete’sdietaryintakehasfocusedoniftheyweremeeting
therecommendeddailyintake,butdidnotfocusonincreasingintakeoffruitsand
vegetables(Shriver,2013).Understandingifprovidingfemalecollegiatesoftball
playerswithanutritioncurriculumcoupledwithatechnologycomponent,and
estimatedneedsbasedonbodycompositionrecommendationsimprovestheirbody
compositionduringpre‐seasonisofinterest.
4
DETERMINANTSOFDIETQUAILTY
MEETINGRECOMMENDATIONSFRUITANDVEGETABLECONSUMPTIONOF
ATHLETES
TheaverageAmericandoesnotmeettherecommendedintakeoffruitsand
vegetables(Shriver,2013).ArecentstudyconductedbyShriver,L.,andcolleagues
focusedontheeatinghabitsoffemalecollegiateathletes,andiftheyareconsuming
enoughfruitsandvegetablestomeetthecurrentsportsnutritionstandards
(Shriver,2013).Themainpurposeofthestudywastoassesstheenergyand
macronutrientintakesoffemalecollegiateathletescomparedtotheminimum
sportsnutritionrecommendations,andtoexploretheeatinghabitsanddietary
patternsinthetargetpopulation(Shriver,2013).Theresearchersrecruited
participantsfromthreedifferentwomen’sathleticteamsbetweenJanuary2009and
May2010(Shriver,2013).Anthropometricmeasurementsandbodycomposition
weretakenoneachoftheparticipants.Theparticipantsalsocompleteda1‐day24‐
hourrecallthatwasadministeredbyatrainedresearchassistant(Shriver,2013).
Afterthe24‐hourrecall,eachoftheparticipantswasprovidedwithinstructionson
howtocompletea3‐dayfoodrecord,andwereaskedtodosoover2‐weekdays,
and1–weekendday,andincludethetypeanddurationofphysicalactivitythey
participatedinduringthosedays(Shriver,2013).Somethingimportanttonoteis
thatduringthetimeofthestudyalltheparticipantswereeitherinlatepreseasonor
earlyseasontraining,participatingintraining6daysperweekfrom1.5to2.5
hours,andwereweightstable(Shriver,2013).
5
Theresearchersfoundthatalmostalloftheathletestheysampledfailedto
meettheirrecommendedenergyintakesfortheirestimatedneeds,withmostof
themfailingtomeettheircarbohydrateneeds(Shriver,2013).Alimitationtothe
presentstudyisthatthesamplesizeisrelativelysmall,andthetimecommitment
neededbytheparticipantswastoodemandingformoretoparticipate.Another
limitationistheydidnotfocusoniftheathletesweremaintaining,gaining,orlosing
weight.Themainfindingofthisstudyisthatfemaleathletesshouldbeevaluatedfor
theirestimatedenergyneeds,andiftheyaremeetingthemornot.
In2014,theNCAAderegulatedthesnackruling,whichallowedforathletic
departmentstoprovidesnacksandmealsaspartofparticipationonasportsteam
(Wilfret,2014).Ludwigwantedtoassesstheconsumptionoffruitsandvegetables
uponDivisionIathletesafterthederegulation.Thepurposeofthestudywasto
determineiffruitandvegetableconsumptionincreased,andwiththeincreaseof
fruitsandvegetablesthatbodyfatpercentagewoulddecreaseafterthederegulation
(Ludwig,2015).Therewasatotalof27,maleandfemalecollegiateathletesincluded
inthestudy,with19beingfootballplayers,andeightvolleyballplayers(Ludwig,
2015).ThebodycompositionusingtheBodPod®andintakeoffruitsandvegetables
usingtheBlockDietaryDatasystems2005FoodFrequencyQuestionnairewas
completedtwice(Ludwig,2015).Thefirsttimewaspre‐deregulation,andthenonce
afterderegulation.Itwasfoundthattherewasnosignificantchangeinfruitor
vegetableintakeforbothvolleyballandfootball(Ludwig,2015).Itwasfound
however,thattheprobabilityofanincreaseinvegetablesrelatedtoadecreasein
bodyfatpercentageforthefootballplayerswassignificant(p=0.0471)(Ludwig).It
6
wasalsofoundthattheprobabilitythattherewasanincreaseinfruitandvegetable
intakerelatedtoadecreaseinbodyfatpercentageinthefootballplayerswas
significant(p=0.0378)aswell(Ludwig,2015).Therewasnosignificantprobability
fortherelationoffruitorvegetableintakeandbodyfatpercentagechangeamong
thevolleyballplayers.Oneofthelimitationstothisstudywasthatitwasasmall,
conveniencesample.Theresearcheralsoreportedthatthetight‐fittingclothingand
swimcapsdifferedamongtheparticipantsduringtheBodPod®measurements,
whichcouldhaveaffectedtheresults(Ludwig,2015).Thestudyconcludedthatthe
increaseinfruitsandvegetablesresultedinadecreaseinbodyfatpercentage
amongcollegiateathletes(Ludwig,2015).
Thelaststudyofinterestfocusedondeterminingthedietandbody
compositionofcollegiateathletes.Theresearcherwantedtodetermineifthere
wereanydietaryexcessesanddeficienciesfoundamongathletes,iftherewasa
relationshipbetweenbodyfatpercentageanddietquality,andiftherewasa
differenceindietqualitybetweenmaleandfemaleathletes(Ireland,2013).Across‐
sectionalstudywascompletedoverayearandahalfperiod,with138collegiate
athletesvolunteeringtoparticipateinthestudy,48beingmaleand90females
(Ireland,2013).BodycompositionwasmeasuredthroughtheuseoftheBodPod®,
whiletheBlock2005FoodFrequencyQuestionnairewasusedtoassessthe
participantsdiets(Ireland,2013).ThedietqualitywascalculatedusingtheBlock
2005FoodFrequencyQuestionnaire,andtheHealthyEatingIndex2005(Ireland,
2013).
7
Alloftheparticipantshadameanbodyfatof17.8;whilethemale
participantshadabodyfatof11.1,andthefemaleparticipantshadabodyfatof
24.1(Ireland,2013).Theaveragedietscorewas51,indicatingathletesdonothave
ahighqualitydietscore(Ireland,2013).Theoverallaveragefruitconsumptionwas
1.7cups;whileformalesitwas1.8cups,andforfemales1.6cups(Ireland,2013).
Theoverallaverageofvegetablesconsumedwas2.4cups;whileformalesitwas3.1
cups,and2.0cupsforfemales(Ireland,2013).Theoverallaverageofdietaryfiber
was20.5grams;whileformalesitwas27.3gramsandforfemales16.8grams
(Ireland,2013).Theoverallaverageofcaloriesfromsolidfats,alcoholicbeverages,
andaddedsugarswas1231.3calories(Ireland,2013).Formales,thecaloriesfrom
solidfats,alcoholicbeverages,andaddedsugarswas1844.6calories,and900.6
caloriesforfemales(Ireland,2013).Theyalsofoundanegativecorrelationbetween
dietscoreandpercentbodyfat,andnocorrelationbetweenBMIanddietscore
(Ireland,2013).Limitationsofthestudywereconveniencesampleandasmall
samplesize.Itwasalsoreportedthattheresearcherwasnotawareiftheathletes
wereinoroutofseasonduringthetimeofassessment.Theoverallfindingfromthe
studywasthatathletesmaynotknowhowtochooseappropriatefooditemstohelp
themachievetheirhighestathleticability.
8
NUTRITIONEDUCATION
Providingnutritioneducationsisaneffectivewayofgivinghealthydietand
nutritioninformationtoparticipants(Lua,2012).Astudywasconductedto
determinetheeffectivenessofacommunity‐basededucationprogramaimedat
changingtheconsumptionrateoffruitsandvegetablesinoverweightandobese
adults.ParticipantshadtohaveaBMIofatleast25kg/m2tobeincludedinthestudy
(WagnerMG,2016).Participantswereexcludediftheywerelessthan18yearsold,
werecurrentsmokers,hadaBMIlowerthan25kg/m2,orhadahistoryofbariatric
surgery(WagnerMG,2016).Thestudyinitiallystartedwithsixty‐seven
participants,withonlyfifty‐fourcompletingthethreephasesofthe14‐weekstudy
(WagnerMG,2016).Theparticipantswereassignedtoacontrolgroup,aneducation
group,orafruitandvegetablegroupthatreceivedtheintervention.
Participantsinthecontrolgroupreceivednointervention,butweregiven
theoptiontoreceiveinformationaboutfruitsandvegetablesattheconclusionof
theintervention(WangerMG,2016).Participantsinboththeeducationandfruits
andvegetablegroupsreceivedinformationweeklyabouttheantioxidantcontentof
fruitsandvegetables,therolesofantioxidantsintheinflammatoryprocess,and
currentrecommendationsforfruitandvegetableconsumption(WangerMG,2016).
Thefruitandvegetablegroupalsoreceivedrecommendationsforincorporating
fruitsandvegetablesintotheircurrentmealplans,andofferedsamplesoffresh,
frozen,orcannedfruitsandvegetablesateachweeklyeducationsession(Wangner
MG,2016).
9
Apre‐andpost‐testweregiventoassessdemographicsandfoodintake
usingathree‐dayfoodlog(WagnerMG,2016).Theinterventionconsistedof10,30‐
minutesessionsthatlastedfor10weeks,foratotalof300minute,where
informationonthebenefitsoffruitsandvegetables,howtoproperlystore,clean,
andpreparethem,andvariousothersfactorswhenincorporatinghealthyeating
intothedietwereaddressed.Attheconclusionofthepost‐test,90.7%(WagnerMG,
2016)oftheparticipantsintheinterventiongroupincreasedtheirintakeoffruits
andvegetables.
Similartoanystudy,limitationstothisstudyshouldbenoted.The
researchersutilizedconveniencesamplingbyfocusingonoverweightandobese
adults.Anotherlimitationnotedisthattheresearchersdidnottakeintoaccount
seasonalavailabilityofvariousfruitsandvegetables(WagnerMG2016),whichmay
havebeenlimitedsincethestudywasduringthefallandwintermonths.Atypical
seasonlasts12weeks,werethestudywasovera14‐weekperiod,andavailability
variesbylocationandclimate.Becausetheparticipantsknewwhatthestudywas
about,areporting,andsocialacceptabilitybiascouldhaveoccurred,causingthe
participantstoincreaseintakeoffruitsandvegetableswithlittleinfluencefromthe
intervention.Lastly,becauseparticipantsdroppedout,thesamplesizeforthethree
groupswasnotequal(WagnerMG,2016).
Overall,theresearchersfoundthatanutritioneducationthateitherdoesor
doesnotprovidefruitsandvegetablestoparticipantstosamplecanbehelpfulin
increasingfruitandvegetableintake.Theresearchersalsofoundthatevenafter
providinganintervention,mostoftheparticipantsdidnotconsumethe
10
recommendedservingsoffruitsandvegetables,eventhoughtheirintakedid
increase(WagnerMG,2016).Thisstudyalsosuggestedthatfurtherresearchshould
bedoneoninterventionsfocusedondecreasingtheintakeofhigh‐energyfoods.
Knowingtheeffectivenessofnutritioninterventionsamongthecollege
studentagepopulationisimportantinunderstandingwhattypeofintervention
worksbestwiththisagegroup.Luaandcolleaguescompletedareviewinorderto
provideasummaryofstudiesontheeffectivenessofnutritioneducation
interventionsusedbycollegestudentsindevelopednations(Lua,2012).Asearch
throughrelevantdatabaseswasconductedforarticlespublishedbetween1990and
2011(Lua,2012).Atbaseline,52articlesweregeneratedwithonly14meetingthe
inclusioncriteria(Lua,2012).Tobeincluded:participantshadtobe18‐24years
old;studydesignwascross‐sectional,exploratory,longitudinal,orrandomized
controltrials;andavailableinfull‐textform(Lua,2012).Studieswereexcludedif
therewerepublishedinanyotherlanguagethanEnglish,werereviews,orabstracts
(Lua,2012).
Theresearchersfoundthestudiesusedoneofthreemethodsofintervention:
web‐basededucation,dietarysupplements,andeducationallectures(Lua,2012).
Todetermineifdietarychangesoccurredinthestudiesreviewed,theresearchers
foundthestudiesreportedtheuseof:foodfrequencyquestionnaires,3‐daydietary
record,and24‐hourrecallquestionnaire(Lua,2012).Itshouldbenotedthatofthe
1668participantsfromall14studies,n=1113oftheparticipantswerefemale,and
manyofthestudieswereconductedintheUnitedStates(Lua,2012).After
conductingthereviewofpreviousresearchstudies,theresearchersconcludedthat
11
significantandbeneficialchangesindietaryhabitsamongcollegestudentsaftera
nutritionintervention;particularlynutritioneducationcombinedwithproviding
supplements(seatangles)isthemosteffectivemethod(Lua,2012).
Limitationstothestudyincludeselectionbiasbasedononlyselectingstudies
inEnglishandfromdevelopednations(Lua,2012).Overallthestudyconcludedthat
providedanutritioneducationtocollegestudentsiseffectiveatimprovingdiet
quality.
Knowingwhichmethodsofdeliveryforprovidinganutritioneducationto
collegestudentsisimportant,butitisalsoimportanttoclarifythedirectionsfor
researchandpractice.Kellyandcolleaguesconductedasystematicreviewofdietary
interventionswithcollegestudents.Theresearchersaimwastofacilitatea
narrativesynthesisofthecurrentliteratureevaluatingnutritionanddietary
interventionsincollegesettings,andtodeterminespecificprogramsandfactors
associatedwithhealthfuldietarychangesamongthestudentpopulationinan
attempttoprovidedirectionforfutureresearchandpractice(Kelly,2013).Atotal
of936abstractswereidentifiedfromtheinitialsearch,withn=14meetingthe
inclusioncriteria(Kelly,2013).Ofthe14includedstudies,n=6wererandomcontrol
trials,n=1wasquasiexperimental,andn=7werenon‐experimental(Kelly,2013).
Theinterventionsusedinthesestudieswerein‐person,online,or
environmental/point‐of‐purchasemessages(Kelly,2013).Theresearchersfound
thatthemostusedtheoreticalapproachwastheSocialCognitiveTheory(Kelly,
2013).Theresearchersalsoconcludedfromtheirfindingsthatinterventions
includingcombinationofface‐to‐face,online,andenvironmentalcomponentscould
12
likelybethemosteffectivemethodofinterventionforcollegestudents(Kelly,
2013).Limitationstothestudyincludedthesamplesizeofstudiesincluded.Overall
thestudysuggeststhatinterventionsinvolvingself‐regulationstrategieshavethe
greatestpotentialofinitiatingdietarychangesamongcollegestudents(Kelly,2013).
Manystudieshavefoundthatnutritioneducationcanimprovethediet
qualityofparticipants,anditisworththetimeandcosttoimplementthe
intervention.ArecentstudyconductedbyPemandcolleaguesfocusedonwhat
factorsinfluencenutritionknowledge,theefficacyofnutritioneducation,fruitand
vegetableintake,energyintake,physicalactivity,BMI,nutritionknowledgeand
attitudesofadultsbeforeandafteranutritioneducationintervention(Pem,2016).
Atbaseline,theparticipantscompletedafoodfrequencyquestionnaire(FFQ)
thatwasadaptedfromtheNutritionalEpidemiologygroupatLeedsUniversitythat
contained67items(Pem,2016).Thenutritionaleducationprogramwaspartially
designedfromadultlearningtheoryandconsistedoffoursteps:assessinglearner
needs,settingeducationalobjectives,choosingandimplementingawiderangeof
methods,andassessingthelearningthatoccurred(Pem,2016).Theadultsinthe
interventiongroupattendedtwo,two‐hoursessionsthatfocusedonlecturing,
handouts,andpicturecollages,withthefinalsessionfocusingonsummarizingthe
topicsdiscussed(Pem,2016).
Thetwelve‐weekinterventionfoundthatatbaselinethecontroland
randomizedinterventiongroupdidnotdifferindietaryintakepatterns.
Confoundingvariableswerecontrolledforinthepost‐interventionassessment,with
resultsshowingthattheinterventiongroupsignificantlyincreasedtheirintakeof
13
fruitsandvegetables,anddecreasedtheirconsumptionofhigh‐energyfoods
comparedtothecontrolgroup(Pem,2016).
Overall,thenutritionalknowledgeincreasedamongtheinterventiongroup.
Theresearchersfoundthatprovidingthenutritionaleducationintervention
resultedinanoverallincreaseinfruits,nutritionalknowledge,attitudestowards
healthiereating,andsignificantdecreaseinhigh‐energyfoods(Pem,2016).There
wereseverallimitationstothisstudy.Nosignificantchangesinanthropometrics
occurredoveratwelve‐weekperiod(Pem,2016).Asecondlimitationwasthatthe
culturalinfluenceonfoodchoiceswasnotconsidered(Pem,2016).Finally,
measuringthefoodfrequencyoverthreedaysdoesnotfullyrepresentusualintake
becauseitdoesnotshowlongtermdietaryhabitswhichcansometimesbe
influencedtofestivalchanges(Pem,2016).Educatingapopulationonproper
nutritioncanimprovetheoveralldietqualityandbeliefsinapopulation,asfoundin
thisstudy.
NationalCancerInstitute(NCI)FRUITANDVEGETABLESCREENER
Determiningintakeofmacronutrientsatthebeginningofaninterventionis
importantinordertodetermineiftheinterventionwassignificantinimprovingdiet
quality.GreeneandcolleaguesconductedastudytodeterminetheuseoftheNCI
(NationalCancerInstitute)FruitandVegetableScreenerwithfiveethnicallyandage
diverseadultpopulations(Greene,2008).Themainfocuswastodeterminethe
correlationbetweenfruitandvegetableintakeestimatesfromthescreener
comparedthemultiple24‐hourrecallsandserumcarotenoidlevels(Greene,2008).
14
Thescreenerwasadministeredatvarioussitesacrossthecountrytopullfrom
differentdemographicpopulations.Thescreenerincludednineteenitemsthatasked
abouttheconsumptionoftendifferentfruitsandvegetablesoveratwelve‐month
period.
TodeterminethevalidityoftheNCIFruitandVegetableScreener,the
screenerwasevaluatedintheEatingatAmerica’sTableStudy(EPI,2001).The
purposeofthestudywastoexaminethecross‐sectionalcapacityofthefruitand
vegetablescreenerand1‐itemmeasuretoestimatethefruitandvegetableservings
relatedtomultiple24‐hourrecallsattwodifferenttimepoints(Peterson,2008).
Thestudyalsofocusedontheperformanceofthetwoscreeningmethodsto
measurechangeinfruitandvegetableintakebycomparingmeantreatmenteffects
witha24‐hourrecallatfollow‐up(Peterson,2008).
Thestudyofinterestconcentratedonfiveofthefifteenlocationsthatwere
partofTheBehaviorChangeConsortium(Peterson,2008).Atotalof315
participantsparticipatedandtooka24‐hourrecallandfruitandvegetablescreener
atbothbaselineandfollow‐up(Peterson,2008).Thefruitandvegetablescreener
consistedof19‐itemsthataskingaboutthefrequencyoftheusualconsumptionof
10categoriesoffruitsandvegetablesoverthepastmonth(Peterson,2008).The24‐
hourrecallwasconductedovertelephonebytrainedinterviewers(Peterson,2008).
Theresultsofthisstudyindicatedthatthefruitandvegetablescreener
overestimatedtheintakeoffruitsandvegetablescomparedtothe24‐hourrecall
(Peterson,2008).Theresultsaftertheinterventionshowedthatthecorrelationof
thefruitandvegetablescreenertothe24‐hourrecallimprovesovertimeas
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participantslearnhowtobetterestimatetheirintakeoffruitsandvegetables
(Peterson,2008).Thisissignificantinprovingthevalidityofafruitandvegetable
screener.Thestudybeingreviewedhadseverallimitations.Becausetherewasonly
3dayscountedinthe24‐hourrecall,thisresultsinpossibleerror.Anotherissueis
thevariationissamplepopulationsinwhichthedifferentinstrumentswereusedon.
Overallthestudyindicatedthatlargersamplesizeisneededtoexamthedifferences
infruitandvegetableconsumptionviatheuseofscreenersacrossvarioussample
populations.
TheNCIwantedtoknowthetest‐retestreliabilityofthreedifferentdietary
assessment‐screeningtools.Yaroch,andcolleagueswantedtoevaluatethree
differentshortfruitandvegetablescreeners,a2‐itemserving,a2‐itemcup,and
a16‐itemfruitandvegetablescreenerinadultsbyusingmultiple24‐hourrecallsas
thereferencetool(Yaroch,2012).Anothergoalofthestudywastodeterminethe
test‐retestreliabilityofthescreeningtoolsovera2‐3weekperiod(Yaroch,2012).
Thesamplingpopulationforthestudycamefromadults18yearsandolderwho
wereapartoftheSynovate’sConsumerOpinionPanelinthefallof2006(Yaroch,
2012).Astratifiedrandomsamplingwasdoneofhouseholdsonthepanel,with
oversamplingtakenfromAfrican‐Americanhouseholds(Yaroch,2012).
Forthevaliditystudyofthevariousscreeners,1,263participantswere
initiallycontacted,with254participantsreturningatleast2ofthe3screeners
(Yaroch,2012).Aseparategroupwasaskedtoparticipateinthetest‐retestto
determinethereliabilityofthescreeners.Ofthe663participantsinvitedto
participate,only401returnedthefirstsurvey,and335returnedthesecond,forthe
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2‐3weektimeperiodofthestudy(Yaroch,2012).Thestudyfoundthatshort
dietaryscreenerscouldbeusefultodeterminetheestimatesandrankindividuals
ontheirintakeoffruitsandvegetables.Thecurrentstudyfoundthatoverall;the16‐
itemscreenerprovidedtheleastamountofbiaswhendeterminingfruitand
vegetableintake,bothwithandwithoutfriedpotatoes(Yaroch,2012).Themedian
fruitandvegetableintakewasnotsignificantlydifferentforthe24‐hourrecall
comparedtothe16‐itemscreener(Yaroch,2012).
Limitationsfromthisstudyshouldbenoted.Becausethestudywascross‐
sectional,theresearcherswerenotabletotestthescreeners’sensitivitiestochange
infruitandvegetableintakeovertime,andaconveniencesamplewasused(Yaroch,
2012).Thestudyconcludedthateventhoughfruitandvegetablescreenersarecost‐
effectiveandeasytouse,theyshouldnotbeusedwhenwantingtoobtainaprecise
measurementoffruitandvegetableintake(Yaroch,2012).
USEOFTECHNOLOGYANDTRACKINGINTAKE
Smartphoneshavedramaticallychangedthewaywecommunicatewitheach
other,andkeepupwithourindividualbehaviorsofdailyliving.Researchhasbeen
donetoshowhowtheuseofsmartphonescanaffecthealthpromotionandtracking
ofdietaryintake(Bert,2013;Rusin,2013;Carter,2013;Forster,2015).Thefirst
studyofinterestwasconductedbyBertandcolleaguestodeterminetheavailability
ofsmartphoneapplicationsusedtopromotehealth,andtheadvantagesand
disadvantagesofthem.Abibliographicsearchwasconductedthroughmultiple
sciencebasedresearchdatabases,witharticlesthatwereselectedbytitle,abstracts,
17
andthenfull‐text(Bert,2013).Thedatabasesearchingresultedin4,669results
wereinitiallyfound,withonly32meetingtheinclusioncriteriaandspecifically
focusingonhealthpromotion(Bert,2013).Thearticleswereclassifiedintofour
categories:nutrition,fitnessandphysicalactivity,lifestyles,andhealthinelderly
(Bert,2013).
Limitationstothisstudywerementioned.Theresearchersdidnotconduct
anexhaustivesearchbecauseofthelanguagerestrictionofonlyusingEnglishand
Germanarticles,andthecriteriaofonlyincludingfull‐textavailabilityofthearticles
(Bert,2013).Anotherlimitationmentionedisthatthedatabasesusedwereonly
scientificliteraturefocused,andthecontinuousupdatingoftechnologyandphone
applicationsleadstogapsinthecurrentresearch(Bert,2013).Overall,the
researchersconcludedthattheuseofsmartphonesinthepromotionofhealthhas
manypositiveaspectsthatcouldpotentiallybefurtherresearched.Therelative
cheapness,smallsize,andwideavailabilityofsmartphonesmakethemapotentially
strongtoolinimprovinghealthpromotion(Bert,2013).
Keepingtrackonone’sdietiskeytoidentifyingchangesthatnutritionally
needtobemadetoimprovenutritionalhealth.Rusinandcolleaguesconducteda
studytosummarizetheexistingapproachestoself‐managefoodintakerecording
andtoanalyzethefunctionalityofinputmethods(Rusin,2013).Theresearchers
usedelectronicdatabasestolocatepublicationswithsystemsforrecordingfood
intake.Theyalsowantedtoknowmoreaboutthedifferentmobileapplications
availableforfoodloggingthatwereavailablefreeofcharge,withafreetrial,orona
18
“freemium”basis(Rusin,2013).Theresearchersfoundthattheelectronicsystems
usedforrecordingfoodintakemostcommonlyusetextdatainput.
Afewdrawbackswerefoundtoenteringfoodintakeintotheelectronic
sources.Someofthedatabasesrequiredtheuserstocalculatethecaloriesandor
carbohydratesofthefoodconsumed.Anotherdrawbackisobtaininginformationon
thenutritionalqualitiesoffoods,particularlymealswithdifferingpreparationstyles
(Rusin,2013).Also,theusersmustbeabletoestimatetheportionsizetheyare
consumingtoaccuratelyrecordtheirintake.
Rusinandcolleaguesalsolookedattheuseofautomaticmodeofinputting
foodintake.Thisistheprocesswherethemobileapplicationallowstheusertoscan
thebarcodeofthefooditem,allowingforminimaluserinput(Rusin,2013).The
mainlimitationofthestudywasthelackofevidenceabouttheusefulnessof
functionalitiesformstheselectionofapplications(Rusin,2013).Overallthestudy,
whichlookedatresearchfrom1998to2012suggestingthathavingthedatabase
keeptrackoffoodsmostcommonlyenteredbytheuser,wassomethingthatcould
beimproved(Rusin,2013).Someoftheseimprovementshavebeenmadetomobile
applicationssincethepublicationofthisstudy.
AstudyconductedbyCarterandcolleagues,andfocusedonthedifferencein
adherencetoasmartphoneapplicationcomparedtowebsiteandpaperdiaryfor
weightlossinapilotrandomizedcontroltrialstudyintheUnitedKingdom.The
researchersdevelopedamobileapplicationforthestudycalledMyMealMate,
containingalargeUnitedKingdom‐brandedfooddatabase,andwasscaledagainst
thecommerciallyknownmobileapplicationMyFitnessPal(Carter,2013).Theaim
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ofthisstudywastotesttheacceptabilityandfeasibilitybeforetestinginalarger
trial.Participantswererecruitedviaadvertising,andtobeeligiblehadtohavea
BMIof≥27kg/m2,betweentheagesof18to65yearsold,willingtocommitment
necessaryneededtimeandeffortforthestudy,employedbyalargeemployerin
Leed,notpregnant,breastfeeding,orplanningtobecomepregnant,nottakinganti‐
obesitymedicationsormedications/insulinfordiabetes,neverhadsurgeryfor
weightloss,nottakingtheantidepressantsertraline,andabletoreadandwritein
English,abletoaccesstheinternet,andwillingtoberandomizedintooneofthree
groups(Carter,2013).
Theinterventionusedinthestudyusedamobileapplicationthatallowedthe
userstosetaweightlossgoalandself‐monitordailycalorieintaketoreachtheir
goal(Carter,2013).Theinterventiongroupwasassignedtothemobileapplication,
thesecondgroupwasassignedtoaself‐monitoringwebsiteandthethirdwas
assignedafooddiaryaccompaniedbyacalorie‐countingbook(Carter,2013).The
studyinitiallyhad128participateswhometalleligibilityrequirement(Carter,
2013)andcompletedinitialassessments.Duringthe6‐weekfollow‐up
measurements,only94participantsreturned,andonly79returnedattheendofthe
6‐monthtrialperiod(Carter,2013).Theresearchersfoundthatsignificantlyfewer,
(p=0.001)peopledroppedoutofthemobileapplicationgroupcomparedtothe
diaryandwebsitegroups.Theyalsofoundthatweightlossinthegroupassignedthe
mobileapplicationwascomparablytoreportedweightlossinlargemulticenter
randomizedcontroltrialstudiesofpopularcommercialdietprograms(Carter,
2013).
20
Limitationstothestudywerethatmostoftheparticipantswerewhite,
female,andemployedinmanagerial/professionaloccupations(Carter,2013).Other
limitationswerethemobileapplicationsuggestedforusewasaprototype(Carter,
2013).Lastly,7participantsfromthesmartphonegroup,and7participantsfrom
thediarygroupreportedusingawebsite(Carter,2013).Ofthediarygroup,there
was4participantsusingthesmartphoneapp,and2fromthewebsitegroup
reportingtheyhaveusedasmartphoneapp(Carter,2013).Lastly,therewasone
participantoriginallyrandomizedtothediarygroupwhousedasmartphoneapp
forthedurationofthetrial(Carter,2013),potentiallyaffectingtheoveralloutcome
oftheresults.Theresearch,asawhole,showedtheuseofamobileapplication
couldbebothacceptableandfeasibleinaweightlossintervention.
Forsterandcolleaguesconductedaresearchstudytodeterminetheroleof
newtechnologiesindietaryassessment.Theresearchersfocusedonthegrowing
researchthatimplieshowprovidingindividualswithspecificpersonalizedfeedback
canbemoreeffectivethangenericinformationinimprovingoveralldietaryquality
(Forster,2015).Theobjectivesoftheresearchweretoexaminethecurrentrangeof
newtechnologiesdevelopedforassessingdietaryintake,andtoassessthe
utilizationandefficacyofthesenewtechnologies(Forster,2015).Because
technologyissowidespread,thereareseveralmethodsatwhichtheylookedat.
Theseincludedonlinewebsite‐baseddietaryassessments,mobileapplication
dietaryassessments,andwearablesensortechnologies(Forster,2015).Oneofthe
limitationstothisstudyisthemainauthorwasfinanciallysupportedforher
researchbyoneoftheonlinefoodfrequencyquestionnaires(Forster,2015).Overall
21
theresearchersfoundthatmoreresearchisneededontheuseofpersonalized
feedbackanditsuseinmotivatinguserstoreachtheirweightlossgoals(Forster,
2015).
Itiswellknownthattechnologyiswidelyusedamongthecollege‐age
population.AstudyconductedbyAnderson‐Billandcolleaguesaimedtoexamine
thesocialcognitivedeterminantsofnutritionandphysicalactivityamongweb‐
healthuserswhowereenrolledinanonlinesocialcognitivebasednutrition,
physicalactivity,andweightgainintervention(Anderson‐Bill,2011).Participants
wererecruitedviaweb‐basedemaillistservs,onlineandprintadvertisements,and
directmailings(Anderson‐Bill,2011).Theparticipants(n=1307)wereincludedin
thestudyiftheywerebetweentheages18to63yearsold,hadahighnormalto
obeseBMI,currentlynotactive,andotherwisenotedashealthy.Allcomponentsof
thebaselineassessmentwerecompletedby731oftheparticipants(Anderson‐Bill,
2011).Backgroundinformationwastakenontheparticipantsviathewebsite,and
theywerethenredirectedtoNutritionQuesttocompletetheBlock2005Food
FrequencyQuestionnaire(Anderson‐Bill,2011).Oncethequestionnairesand
surveyswerecompleted,theresearchersmailedtheparticipantsadigitalbathroom
scaleandapedometerfortrackingdailystepstakenforoneweek(Anderson‐Bill,
2011).Theresultsfoundthatthesewebusers’dietaryconsumptionwashigherin
fatandlowerinfruits,vegetables,anddietaryfibercomparedtotherecommended
intakevalues(Anderson‐Bill,2011).Theresearchersfoundthattheweb‐health
usersshowedhighlevelsofself‐efficacyformakingchangesandofexpectationsthat
changewouldhavehealthbenefits(Anderson‐Bill,2011).Nolimitationswere
22
identified,butattritionisevidentbasedonthenumberofparticipantswhodropped
out.Thisstudyconcludesthattheself‐efficacycontributestohigherlevelsof
physicalactivityandlowerdietaryfatintake,butnotincreasedlevelsoffiber,fruits,
andvegetablesamonginternethealthusers(Anderson‐Bill,2011).
DETERMINATIONOFBODYCOMPOSITION
WAISTCIRCUMFERENCE(WC)
Itiswellunderstoodthatwaistcircumferenceisastrongindicatorof
morbidityandmortalityrelatedtoobesity.AccordingtotheCenterforDisease
ControlandPrevention(CDC),waistcircumferencegreaterthan40inchesfor
males,andgreaterthan35inchesfornon‐pregnantfemales,areindicatorsforhigh
risksofdevelopingobesityrelatedhealthconditions(CDC,2015).TheCDC
recommendedmethodofmeasuringwaistcircumferencewhiletheparticipantis
standing:“placethetapemeasurearoundthemiddlejustabovethehipbones,
measurehorizontallyaroundthewaist,keeptapemeasuresnugbutnot
compressing,andtakethemeasurementonceparticipantbreathesout”(CDC,
2015).
Multiplestudieshavebeenconductedtocomparerecommendedmethodsof
measuringwaistcircumference.Alargecohortstudywith1,898participantswas
conductedtocomparewaistcircumference‐iliaccrest(WC‐IC)andwaist
circumference‐midline(WC‐mid)indeterminingcentralobesity(Ma,2012).The
WC‐ICwasmeasuredbyusingthehorizontalplaneatthesuperiorborderofthe
rightiliaccrest(Ma,2012).WC‐midwasmeasuredbyusingthehorizontalplane
23
midwaybetweenthelowestribandtheiliaccrest(Ma,2012).Todetermine
reproducibilityofthemeasurementmethods,3trainednursesmeasured10males
and10femalesover3consecutivedays(Ma,2012).ThemeasurementsofWC‐IC
valuesweresignificantlyhigherforbothmalesandfemalesthentheWC‐mid,with
thedifferencebeinggreaterinfemalestheninmales(p<0.001)(Ma,2012).The
studyfoundthatWC‐midisoverallabettermeasurementofcentralobesity.
Limitationstothisstudyarethatonlytwotypesofwaistcircumference
measurementswerecompared.Perthestudiesliteraturereview,thereareatleast
eightdifferentwaystomeasurewaistcircumference,butthetwomethodsusedare
thoserecommendedbytheWorldHealthOrganization,NCEP,ATPIII,andIDF(Ma,
2012).Basedonthisstudy,thelocationofwaistcircumferencemeasurementhasa
moresignificantimpactinfemalescomparedtomalesindefiningcentralobesity
(Ma,2012). Asecondstudyreviewedfocusedondeterminingthebestmeasurementfor
waistcircumferenceasitrelatestoobesity‐relatedmorbidityandmortality.Apanel
ofexpertsinthefieldofobesityidentificationandobesity‐relatedepidemiology
weregatheredtoreviewpublishedscientificliteratureandtherelationshipsof
waistcircumferencewithmorbidityfromcardiovasculardisease(CVD)and
diabetes,andwithmortalityfromallcauses,andfromCVD(Ross,2008).Overa
sixthmonthperiodarticlesweresearchedforonPubMed,andaftermanuallysifting
through,120articleswithatotalof236samples(Ross,2008)mettheinclusion
criteria.Theassociationofsamplesize,sex,age,raceandethnicityonthe
relationshipofwaistcircumferenceinmultivariatemodelswasalsoexaminedfor
24
thesignificantornon‐significantdifferences,andthepositiveornegative
associations(Ross,2008).
Themeasurementprotocolsinallofthestudieswereeitherbasedonbony
landmarksorexternallandmarks(Ross,2008).Thedataforthestudiesincluded:
midpoint(35studies),minimalwaist(32studies),umbilicus(34studies),iliaccrest
(5studies),1inchaboveumbilicus(2studies),lastrib(1study),andlargest
abdominalcircumference(3studies)(Ross,2008).Itshouldbenotedthat12ofthe
studieshadparticipantsmeasuringtheirownwaistcircumference,andthatexcept
fortheminimalwaisttheprotocolforeachoftheotherwaistcircumference
measurementswasconsistentacrossallthestudiesincluded(Ross,2008).
Ofallthestudiesexamined,29focusedonwaistcircumferenceandall‐cause
mortality(Ross,2008).Overhalf(54%)ofthesamplesinthesestudiesshoweda
statisticallysignificantassociationwhentheWCprotocolsrequired:measurement
atminimalwaist,umbilicus,midpoint,or1inchabovetheumbilicus(Ross,2008).
Therewasanon‐significantassociationfor44%ofthesampleswhentheprotocol
forWCwas:umbilicus,midpoint,orminimalwaist(Ross,2008).
Onelimitationofthestudyisthat101ofthe120studiesincludedinthe
review,reportedwaistcircumferenceasbeingmeasuredattheumbilicus,midpoint,
orminimalwaist(Ross,2008),possiblyskewingtheresults.Theotherlimitations
tothestudyinvolvednotbeingabletoconductameta‐analysisandthemethodused
tocalculatethestatisticalsignificantassociationofthestudies(Ross,2008).The
studyconcludedthatamongtheeightprotocolsthepanelidentified,theWC
25
measurementprotocolusedhasnosubstantialinfluenceontheassociationbetween
WCandmorbidityormortality(Ross,2008).
EventhoughWCmeasurementhasbeenvalidatedasastrongpredictorfor
obesityrelateddiseases,littlehasbeendonetolookintoerrorsofmeasurement.
MultiplestudieshavefoundthatlessvariabilityoccursinWCmeasurementswhen
theyareperformedbyatrainedhealthprofessionalratherthanaphysician
(Verweij,2012).Becauseofthevariabilityinmeasurements,afewpotentialissues
ariseforthevalidityofwaistcircumferencestudies.Theseissuesinclude:onlyafew
ofthestudiesareactuallyreliable,manystudiesreportreliabilitybutnotan
absolutemeasurementoferror,anditisnotclearwhatisknownasclinically
significantchangeinWC(Verweij,2012).
Verweijandcolleaguesreviewedcurrentliteraturetoexplaindifferencesin
reliabilityandmeasurementerrorofWC.Theyalsowantedtodocumentwhyitis
importanttodetermineerrorsinWC.Theaimsoftheirliteraturereviewwereto:
discusswhatisalreadyknownaboutmeasurementerrorsandthecontributing
factors;discusswhatisknownaboutclinicalrelevantchangesinWC;discusshow
knowledgeaboutclinicallyrelevantchangesmayhelpexplaintheimportanceand
impactofmeasurementerror;andproviderecommendationsforfutureresearch
(Veweij,2012).Theresearchersreportedtheimportanceofmeasurementerror,is
thatitprovidesanadvantageoverreliabilityforclinicalinterpretationasitis
displayedontheactualmeasurementscaleandnotasadimensionlessvalue
between0and1(Veweij,2012).Theliteraturereviewwasconductedusing
PubMed,searchingforstudiesbetweentheyearsof1975andFebruary2011,with
26
thesearchresultingin559studies,andonly9ofthemreportingintra‐orinter‐
observermeasurementerror(Veweij,2012).Thestudyoverallreportedintra‐
observermeasurementerrorwas0.7to9.2cm,and1.4to15cmforinter‐observer
error(Verweij,2012).Theresearchersreportedthatareductionofwaist
circumferenceof>5%couldbeconsideredaclinicallyrelevantchangeinsome
patientsfortheshortterm,andamaintainedwaistcircumferencereductionof>3%
forthelongtermforsomepatients(Verweij,2012).Theresearchersalsofoundthat
smallerintra‐andinter‐observererrorwasfoundinstudieswithlargersample
sizes.
Theresearchersfoundthatbecauseofthesmallnumberofstudies,a
conclusioncannotbemadeontheeffectsofmeasurementerrors,resultingina
significantlimitation.Basedonthefindingsofwhatwasnotablefromtheliterature
review,theresearchersconcludedthattrainingresearchassistants,repeatedWC
measurementswithaveragetaken,andadoptingastandardprotocol(Verwij,2012),
areallwaystoreducemeasurementerrors.
BODPOD®
Bodycompositionisavaluablemeasureindeterminingtheoverallhealthof
apatientorclient.TheBodPod®isaneggshapedchamberthatderivesbody
compositioninformation,particularlypoundsofleanbodymassandpoundoffat
massbyair‐displacementplethysmography(Tseh,2010).
Knowingthevalidityoflabmeasurementsisimportantinachievingproper
measurementsofbodycompositioninresearch.Wagner,D.,andHeyward,V.,tested
27
thevalidityandreliabilityofvariouslabandfieldtechniquesformeasuringbody
composition.Thelaboratorymethodsexaminedinthestudywere
hydrodensitometry,airdisplacementplethysmography(BodPod®),isotope
dilution,anddual‐energyx‐rayabsorptiometry(Wagner,1999).Thefield
measurementsassessedwerebioelectricalimpedanceanalysis,near‐infrared
interactance,skinfoldmeasurements,andanthropometry(Wagner,1999).Eachof
thelaboratoryandfieldmethodswasassessedforitsstrengthsandlimitations.A
briefdescriptionofthemethods,andbackgroundinformationonhowthevalidation
ofeachmethodwasdevelopedwasprovidedindetail.
Thestudyofthevariousmethodswasreviewedandcompared.Bodydensity
wascomparedfromthemeasurementstakenbytheBodPod®andhydrostatic
weighing.Theresearcherfoundthatbodydensitymeasuredbythetwomethods
washighlycorrelatedwithameandifferenceinpercentbodyfatof0.3%(Wagner,
1999).Theresearchersfoundthelaboratorymethodstobemoreprecisethanthe
fieldmethods(Wagner,1999).Nolimitationsforthisstudywereprovided.The
studyprovidesevidencethattheBodPod®isamoreprecisemeasureofbody
compositionwhencomparedtofieldmethods.
Inastudyconductedby,M.Malavolti,andcolleagues,theytestedthe
accuracyandvalidityofawearableenergyexpendituremonitorcomparedtoa
metaboliccartSensorMedicsVmaxwithventilatedcanopy(SM‐29N),andassessed
bodycomposition,percentagefatmass,andpercentagefatfreemassusingskinfold
thicknessmeasurements,bio‐electricalanalysis,andairdisplacement
plethysmography(BodPod®),andcomparedtheresultsinnormalweight,healthy
28
adults(Malavolti,2007).Thestudywasalmostevenlysplitbetweenmaleand
femaleparticipantswhowere30+/‐14years,andhadaBMI23+/‐3kg/m2
(Malavolti,2007).TomoreaccuratelyassesstheREEoftheparticipants,astudy
screeningandprotocolprocedurewasputinplace.Theparticipantswereaskedto
beexaminedbyaphysiciantoruleoutanymedicalconditionsorillnessesthatmay
affecttheresults,andtofollowtheirnormaldiettheweekbeforetheywereaskedto
comeinforthestudy.Themeasurementsforeachoftheparticipantsweretakenin
themorningafteranovernightfast,andaftertheparticipanthadvoided(Malavolti,
2007).
Restingenergyexpenditurewasmeasuredusingthewearableenergy
expendituremonitorandSM‐29N,byfollowingrecommendedfactorystandardsfor
bothmeasurementdevices.Thesameresearcherusingproceduresasstatedinthe
AnthropometricStandardizationReferenceManualmeasuredtheanthropometrics
ofallparticipants(Malavolti,2007).Bioimpedanceanalysiswasconductedusingan
eight‐polar‐electrodeimpedance‐meter(Malavolti,2007).Lastly,bodydensitywas
testedbyairdisplacementplethysmographyusingtheBodPod®.TheBodPod®
measurementsweretakenpermachineaccuracyrecommendations,with
participantsbeinginstructedtowearaswimcaptominimizeeffectofhairvolume,
removealljewelry,andwearatight‐fittingswimsuit(Malavolti,2007).
Becausetherewerenostatisticaldifferencesinageorgender,theresults
wereclusteredintoonesample.Theresultsfoundthatthemeasurementsoffatfree
masstakenbytheBodPod®weremorecloselycorrelatedwithrestingenergy
expendituremeasuredbywearableenergyexpendituremonitorthenrestingenergy
29
expenditurebySM‐29N.Thefindingsofthestudyconcludedthatmeasurements
takenbytheBodPod®werereliableandavalidtechniquethatcanquicklyand
safelybedoneonadiversepopulationofparticipants.
SUMMARY
Collegestudents,particularlycollegeathletes,havepoordietaryintakes.
Dietaryintakeiscriticalforperformanceandforoptimalbodycomposition.Thereis
aneedforinterventionstoimprovedietaryintakeandbodycompositioninthis
population.Theuseoftechnologytoimprovedietaryintakeispromising.Oneofthe
mostaccuratewaystoassessbodycompositionisbyusingtheBodPod®.Oneofthe
quickestandeasiestwaystoassessdietaryintakeistheNCIFruitandVegetable
Screener.Thisstudyseekstodeterminetheeffectivenessofatechnologyenhanced
11weeknutritioncurriculuminchangingfruitandvegetableintake,assessedbythe
NCIFruitandVegetableScreener,andbodycomposition,assessedbytheBodPod®,
incollegiatefemaleathletes.
CHAPTER3:METHODS
STUDYDESIGN
Thisstudywaspre‐experimental,usingaonegroupbeforeandafterdesign.
Dataforthisstudywascollectedaspartofalargerresearchprojectconductedin
collaborationwithresearchersfromtheDepartmentofDieteticsandHuman
NutritionattheUniversityofKentucky(UK).Thestudywasapprovedbythe
30
UniversityofKentuckyNon‐medicalInstitutionalReviewBoard(IRB).Thelarger
datacollectionconsistedofdeterminingbodycompositionusingtheBodPod®,
collectionofanthropometricsincludingweight,height,andwaistmeasurement,and
onestandardizedsurvey:theNationalCancerInstitute(NCI)FruitandVegetable
ScreenerofseveralNCAAsportsteams.
PARTICIPANTS
Thepopulationofinterestforthisparticularstudyconsistedofa
conveniencesampleoffemalecollegiateathletesonthesoftballteamwho
participatedinan11‐weeknutritioncurriculumthatwasindependentfromthis
researchandhadconsentedtoparticipateinthelargerresearchprojectwith
measurementstakenpre/posttothenutritioncurriculum.Thetotalsamplesize
consistedof25participantsages18‐21.Allparticipantsatbaselinewereincludedin
thedataanalysis,butifaparticipantwasmissingpre‐orpost‐data,theywere
excludedfromtheparticularanalysistest.Forthepre‐interventionassessment
therewere21participantsand18participantsofthemwereincludedinthepost‐
interventionassessment.Forthecorrelationtesttherewere14participantsfor
changeinfruitandvegetableconsumptionvs.changeinbodyfatpercentage,and13
participantsincludedinchangesinfruitandvegetableconsumptionvs.changesin
waistcircumference.Participantswereexcludediftheydidnothavebothpre‐and
post‐interventiondata.Participantswerealsoexcludediftheywereanoutlierbased
onthefruitandvegetablescreener.Athletesundertheageof18werenot
permittedtoparticipateinthisstudy.
31
MEASUREMENTS
Allmeasurementsandsurveysusedforthisstudywerecompletedinthe
NutritionAssessmentLaboratoryattheUniversityofKentuckyDepartmentof
DieteticsandHumanNutrition.Themeasurementsincludeddataonbody
compositioncollectedfromtheBodPod®,includingweight;andheightandwaist
measurementsthatwerecollectedmanually.
Themaindata‐collectinginstrumentusedwastheBodPod®.TheBodPod®
ismanufacturedbyCOSMED,Inc.,andisonlyusedforresearchpurposesby
researchersfromtheDepartmentofDieteticsandHumanNutrition.Themachineis
servicedannually,followingmanufacturerprotocol,andisproperlycalibratedper
manufacturerinstructionsbeforeeachtestingsession.
Measurementofheightandweightiscompletedthroughtheuseofthe
calibratedscalewhichispartoftheBodPod®systemandproperlymountedwall‐
measuringstadiometer.Allparticipantsworeswimwearortightathleticclothing
andnoshoesforallmeasurements.Themeasurementofwaistwascompleted
manuallywiththeaverageofthreemeasurementstaken.
TheNCIFruitandVegetableScreenerisastandardizedandvalidatedsurvey.
Providingthistypeofquestionnaireisstandardprocedureusedbydietitiansto
assessthenutritionalstatusofparticipants.Thescreenerwasevaluatedfor
performanceintheEatingatAmerica’sTableStudy,whichwasdesignedtovalidate
theDietHistoryQuestionnaire(DHQ)(NIH,).ToscoretheNCIFruitandVegetable
Screener,ascoringguideisprovidedwithvaluesforeachofthequestions.The
32
frrequencyoffdailyintak
keismeasurredonanavverage,andisgivenasccoreof0‐5.0
0,
dependingontheresponseofneverto5ormooretimespeerdayoffru
uitsand
vegetablesco
onsumedin
nthepastmonth.Particcipantswerreaskedtorreportbothraw
andcookedffruitsandveegetables.
Insco
oringthescrreener,thefruitsandvvegetablesaaredividedu
upinto
caategories,andgivenscoresbasedonthecateggoryclassiffication,and
dthenumbeerof
portionsizessbasedonccupequivaleents,seeFiggure1.Thecategoriesffromthe
sccreenerthatwereanalyzedforthiisstudyare:100%fruitjuice,fruitt,lettucesallad,
othervegetaables,French
hfries,otheerwhitepottatoes,andttomatosaucce.Thecateegory
notanalyzed
dincludecoo
okeddriedbeans.Thescoringoftthescreenerrwascompleted
byusingStattisticalAnallysisSoftwaare(SAS)Prrogram9.4.TheNCIhasspreviouslyy
developedprrogrammingcodesfordataanalyssistoscoretthescreeneer.
Fiigure3.1.FruiitandVegetab
bleScreeningttoolscoringfoorcupequivallentforeachccategory.
33
PROCEDURES
Priortotestingofbodycomposition,theathleteswereinstructedtofollow
guidelinesoutlinedbytheBodPod®manufacturertoallowforaccuratedata
collection.Participantswereaskedtorefrainfromexercise,eatinganddrinkingfor
twohoursprior,andtousetotherestroomimmediatelybeforetestingtoallowfor
moreaccuratedatacollection.Theparticipantswererequiredtowearatightly
fittingswimsuitorexerciseshortsandsportsbratop,andaswimcap.Alljewelry
andeyeglasseswereremovedimmediatelypriortodatacollection.
TheproceduresforoperatingtheBodPod®,werefollowedasoutlinedinthe
manufacturer’smanual.Atthebeginningofeachtest,IDoftheparticipantalong
withheightwasmanuallyenteredintotheBodPod®bytheresearcher.Lung
volumewaspredictedforeachparticipant.Priortoeachgroupbeingtested,theBod
Pod®requiredatwo‐pointcalibrationtest.Thefirstcalibrationwascompletedwith
thechamberempty.Thesecondcalibrationtestwascompletedsotheinstrument
couldmeasureanobjectofaknownvalue.Theobjectofknownvaluewasa
manufacturerprovidedhollowcylinderofapproximately49.974liters.Immediately
followingthetwo‐pointcalibrationthetotalbodymassofeachparticipantwas
measuredandautomaticallyrecorded.Thetotalbodymassmeasurementofthe
participantwastakentwiceduringtwo50‐secondintervals,withthemachinebeing
openedbetweeneachmeasurement.Theparticipantwasaskedtorelax,breathe
normally,remainquiet,andtositstillduringthemeasurementperiods.
34
Thewaistwasmeasuredthreetimesattheparticipant’snaturalwaist.The
naturalwaistwasdeterminedbyfindingwheretheparticipantbends,andtaking
themeasurementfromtherewithatapemeasure.Theaverageofthethree
measurementswastaken.
UponenteringtheNutritionAssessmentLaboratory,theparticipantwas
askedtocompletethe10‐questionNCIFruitandVegetableScreener.
Priortothefinaltestingofbodycomposition,an11‐weeknutrition
curriculumwasprovidedtothesoftballteambythesportsteamdietitian.Oncea
weektheteamwouldmeetasagroupwiththedietitianforabout20minuteseach
session.Adiscussionoverthepreviousweekwouldtakeplacefollowedbyashort
lessonandanassignmentforthecomingweek.Thecurriculumandassignmentsare
outlinedinTable1.Theathleteswereaskedtotracktheirfoodintakeduringthe11‐
weekperiodelectronicallybyeithertheuseoftheirphoneorcomputer,throughan
App,suchasMyFitnessPal®.Attheconclusionofthe11‐weeknutritionintervention,
bodycomposition,waist,andheightmeasurementswerereassessedusingthesame
methodsaspreviouslyidentifiedpre‐curriculum.TheNCIFruitandVegetable
screenerwasalsocompletedagainduringthefollow‐upmeasurements.
35
Table3.1.Outlineof11‐weeknutritioncurriculumprovidedtofemalecollegiate
softballteam.
Lesson
Assignment
Week1 WatchThatSugar
Eachathletewillbeassignedacalorieamount
Movie
totryandattainfortheweekbasedonbody
compositionmeasurements;10%ofcalories
fromaddedsugar;writedowncommonlyeaten
foods,andfindsubstitutestostaywithinsugar
allotment.
Week2 Whatfoodsare
Eachathleteassignedaratioofmacronutrients
carbohydratesand
basedonbodycompositiongoals.Willcontinue
whatisaservingsize? totryandmeetsugarrecommendation;no
athletewillbeallowedtofollowadietthatis
lessthan40%carbohydrate.
Week3 Whatisfiberandwhat Continuestayingwithincarbohydrate
aredifferenttypesof
requirementsassignedinweek2;trackdaily
fiber?
fiberintakewithgoalofreaching25g/day.
Week4 Cookingclass:cooking Cookadinnerusingwholegrains.
withwholegrains
Week5 Anti‐oxidantandphyto‐ Eachathlete’scarbohydrategoalsaredivided
nutrients:howtoget
intothreegroupstotryandstaywithin:
vitaminsandmineral
sugar/starch,fruits,vegetables.
throughfood
Week6 Fats:Howmuchfatwe Goalforfatintakeisbrokendowninto
need,thedifferent
MUFA/PUFAandSFA;athletewilltrytostay
types,andhowweget
withingoalamount.
theminourdiet.
Week7 FieldTrip:OliveOil
‐‐‐
Store;tastetesting
Week8 GroceryStoreTour:
‐‐‐‐
ScavengerHunton
LabelReading
Week9 CookingClass:Healthy Eatonemealadaywithnodistractions‐no
PartyFoods‐
phone,television,orotherpeople.Writeone
IntroductiontoMindful paragraphaboutexperienceandevaluate
Eating;videoon
hungerpreandpostmeal.
mindfulness
Week
‐‐‐
Continuemindfuleating.Eatonemealwith
10
non‐dominatehandandputforkdownbetween
bites;writeaboutexperienceandevaluate
hungerpreandpostmeal.
Week
‐‐‐
Makeonemealspecial;Makespecialin
11
whateverwayathletedeems.Postonepicture
perdayonTwitter,tweetedatRD,oremailed
toRDabouthowtheymadethedaybetter.
36
ANALYSIS
ThestatisticalanalysisprogramusedwasSAS9.4.Thedatawasanalyzedfor
differencesbetweenpre‐interventionandpost‐intervention.Descriptive
informationsuchasmean,standarddeviation,minimumvalue,andmaximumvalue
weredetermined.Apairedt‐testwascompletedtodeterminethesignificanceof
changeinbodycompositionofthefemaleathletesbetweenpre‐andpost‐
intervention.Correlationtestswereruntodeterminetherelationshipofthechange
inintakeoffruitsandvegetablesvs.changeinwaist,andchangeinintakeoffruits
andvegetablesvs.changeinpercentbodyfat.
CHAPTER4:RESULTS
DEMOGRAPHICS
Duringthepre‐interventiondatacollection,21participantswereincluded.
Forpostinterventiondata,participantswereexcludediftheydidnotcompletepre‐
interventionassessment.Therewere18participantsincludedinthepost‐
interventiondataassessment.Allparticipantsduringbothsetsofdatacollections
werefemale.Table4.1,4.2,4.3,and4.4displaythecharacteristicsofthe
participantsateachdatacollectionforbothbodycompositionandintakeoffruits
andvegetables.
37
Table4.1:CharacteristicsofBodyCompositionofSoftballplayersinAugust2015.
n
Minimum Maximum Mean
Standard
Deviation
Waist(cm)
21
62.33
97.07
74.13
8.23
Height(cm)
21
160.0
176.5
168.04
4.21
Age(years)
21
18.24
21.48
19.89
1.17
PercentBodyFat
21
16.5
38.0
25.59
5.78
(%)
BodyMass(kg)
21
59.33
102.66
74.06
11.01
Thesampleincluded21softballplayersatbaseline.Asshownintable4.1,
themeanwaistwas74.13±8.23cm,themeanheightwas168.04±4.21cm,meanage
was19.89±1.17years,themeanpercentbodyfatwas25.59±5.78%,andthemean
bodymasswas74.06±11.01kg.
Table4.2:CharacteristicsofFruitandVegetableintakeinAugust2015.
Standard
n
Minimum Maximum
Mean
Deviation
Fruitand
Vegetableintake
20
0.76
6.18
2.66
1.49
(cups)
SumofFruits
20
0.22
3.21
1.25
0.80
(cups)
JuiceIntake(cups)
20
0.0
1.63
0.35
0.37
FruitIntake(cups)
20
0.11
3.00
0.90
0.75
SumofVegetables
20
0.28
3.68
1.40
0.90
(cups)
LettuceSalad
20
0.03
1.00
0.31
0.26
Intake(cups)
OtherVegetable
19
0
2.25
0.54
0.59
Intake(cups)
TomatoSauce
20
0.02
1.00
0.17
0.22
Intake(cups)
VegetableSoup
20
0
0.5
0.13
0.17
Intake(cups)
FrenchFries
18
0
0.25
0.07
0.07
(cups)
WhitePotatoes
20
0.02
0.94
0.22
0.26
(cups)
38
Thesampleincluded20softballplayerswhocompletedthefruitand
vegetablescreener.Themeanforcombinedintakeoffruitsandvegetableswas
2.66±1.49cups.Foreachcomponentreportedonthemeanintakeforjuicewas
0.35±0.37cups,themeanintakeforfruitwas0.91±0.75cups,themeanintakefor
lettucesaladwas0.31±0.26cups,themeanintakeforothervegetableswas
0.54±0.59cups,themeanintakefortomatosaucewas0.17±0.22cups,themean
intakeforvegetablesoupwas0.13±0.17cups,themeanintakeforFrenchfrieswas
0.07±0.07cups,andthemeanintakeofwhitepotatoeswas0.22±0.26cups.The
individualquestionspertainingtofruitsweresummedforafruitintaketotal.The
participantsconsumedameanintakeof1.25±0.80cupsoffruits.Theindividual
questionspertainingtovegetableswerealsocombinedtodetermineasumof
vegetableintake.Themeanintakeofvegetableswas1.40±0.90cups
Table4.3:CharacteristicsofbodycompositionofsoftballplayersinNovember2015.
Standard
n
Minimum Maximum
Mean
Deviation
Waist(cm)
17
61.93
94.23
72.40
7.96
Height(cm)
18
161.3
175.3
168.13
3.55
Age(years)
18
18.47
21.71
20.13
1.26
PercentBodyFat
18
14.0
32.6
23.07
4.81
(%)
BodyMass(kg)
18
59.71
100.04
72.59
10.27
Thefinalsampleincludedatotalof18femalesoftballplayers,butonly17
participantsforwaistatfollowup.AsshowninTable4.3,meanwaistwas
72.40±7.96cm,themeanheightwas168.15±3.55cm,meanagewas20.13±1.25
39
years,themeanpercentbodyfatwas23.07±4.81%,andthemeanbodymasswas
72.59±10.27kg.
Table4.4:CharacteristicsofFruitandVegetableconsumptioninNovember2015.
Standard
n
Minimum Maximum
Mean
Deviation
Fruitand
VegetableIntake
16
0.77
5.45
3.11
1.30
(cups)
SumofFruit
16
0.17
3.35
1.61
0.89
(cups)
JuiceIntake
16
0
2.00
0.31
0.54
(cups)
FruitIntake
16
0.11
3.000
1.29
0.83
(cups)
Sumof
16
0.59
3.52
1.50
0.83
Vegetables(cups)
LettuceSalad
16
0
0.79
0.28
0.21
Intake(cups)
OtherVegetable
16
0.16
3.0
0.81
0.81
Intake(cups)
TomatoSauce
16
0
0.32
0.12
0.08
Intake(cups)
VegetableSoup
16
0
0.21
0.08
0.07
Intake(cups)
FrenchFries
16
0
0.16
0.05
0.04
(cups)
WhitePotatoes
15
0.05
0.38
0.15
0.11
(cups)
ThecharacteristicsoffruitandvegetableintakeinNovember2015are
summarizedinTable4.4.Therewere16participantswhocompletedthefruitand
vegetablescreener.Themeanintakeforfruitsandvegetableswas3.11±1.30cups.
Forjuicethemeanintakewas0.31±0.54cups,forfruitthemeanintakewas
1.29±0.83cups,forlettucesaladthemeanintakewas0.28±0.21cups,forother
40
vegetablesthemeanintakewas0.81±0.81cups,fortomatosaucethemeanintake
was0.12±0.08cups,forvegetablesoupthemeanintakewas0.13±0.17cup,for
Frenchfriesthemeanintakewas0.05±0.04cups,andforwhitepotatoesthemean
intakewas0.15±0.11cups.Thetotaloffruitswascombinedagain,andthe
participantsconsumedameanintakeof1.61±0.89cupsoffruit.Thetotalof
vegetableswascombinedaswell,andtheparticipantsconsumed1.50±0.83cupsof
vegetables.
BODYCOMPOSITION
Table4.5:Differenceinwaistcircumferenceandbodyfatpercentamongfemale
collegesoftballplayersfollowingan11‐weeknutritioneducationcurriculum.
Pairedt‐test
n
Mean
StandardDeviation Pr>|t|
Difference
WaistNovember2015
17
‐1.7318
2.18
0.0048
‐1.9056
2.3232
0.0029
WaistAugust2015
PercentBodyFatNovember2015
18
PercentBodyFatAugust2015
Thepairedt‐testforbothwaistcircumferenceandpercentbodyfatof
softballplayersbetweenAugust2015andNovember2015revealedstatistical
significance(p<0.05).Onaveragewaistcircumferencedecreasedby1.73cmand
percentbodyfatdecreasedby1.91%.Theseresultsindicatetherewasachangein
bodycompositionamongsoftballplayersafteran11‐weeknutritioncurriculum
coupledwithatechnologycomponent.
41
TOTALOFFRUITANDVEGETABLEINTAKE
Table4.6:Differenceinsumoffruitandvegetableintakeamongfemalecollege
softballplayersfollowingan11‐weeknutritioneducationcurriculum.
Pairedt‐test
n
Mean
Difference
Standard
Deviation
Pr>|t|
FruitandVegetableIntake
November2015
16
0.3708
1.5059
0.3403
FruitandVegetableIntakeAugust
2015
Therewasnosignificantdifferenceinfruitandvegetableintake(incups)for
softballplayersbetweenAugust2015andNovember2015afterapairedt‐testwas
performed.Onaveragealltheparticipatingsoftballplayersincreasedcombined
fruitandvegetableintakeby0.45cups.Theseresultsindicatethatalthoughthere
wasanincreaseinfruitandvegetableintake,theincreasewasnotsignificantafter
an11‐weeknutritioncurriculumcoupledwithatechnologycomponent.
FRUITINTAKE
Table4.7:Differenceoffruitjuiceintake,fruitintake(wholeorcutup),andsumof
classificationsoffruitgroupintakeamongfemalecollegesoftballplayersfollowing
an11‐weeknutritioneducationcurriculum.
Pairedt‐test
n
SumofFruitNovember2015
16
SumofFruitAugust2015
JuiceIntakeNovember2015
16
JuiceIntakeAugust2015
FruitIntakeNovember2015
16
FruitIntakeAugust2015
Mean
StandardDeviation Pr>|t|
Difference
0.2514
0.9354
0.2994
‐0.0478
0.3356
0.5776
0.2991
0.8707
0.1896
42
Therewasnosignificantdifferenceinfruitintakebasedonclassificationtype
orsumofbothjuicesandfruitamongsoftballplayersbetweenAugust2015and
November2015afterapairedt‐testwasperformed.Foralltheparticipants,the
meanintakeoffruitjuicedecreasedby0.05cups,meanintakeoffruitincreasedby
0.38cups,andthemeanintakeofthesumoffruitjuiceandfruitincreasedby0.45
cups.Theseresultsindicatetherewasnotasignificantincreaseinfruit
consumptionaftera11‐weeknutritioncurriculumcoupledwithatechnology
component.
VEGETABLEINTAKE
Table4.8:Statisticsofpairedt‐testanalysisofvegetableintakebasedonsum,and
classificationofvegetabletypesamongfemalecollegesoftballplayersfollowingan
11‐weeknutritioneducationcurriculum.
Pairedt‐test
SumVegetableIntakeNovember
2015
SumVegetableIntakeAugust2015
LettuceSaladIntakeNovember2015
LettuceSaladIntakeAugust2015
OtherVegetableIntakeNovember
2015
OtherVegetableIntakeAugust2015
TomatoSauceIntakeNovember
2015
TomatoSauceIntakeAugust2015
VegetableSoupIntakeNovember
2015
VegetableSoupIntakeAugust2015
FrenchFryIntakeNovember2015
FrenchFryIntakeAugust2015
WhitePotatoIntakeNovember2015
WhitePotatoIntakeAugust2015
43
n
Mean
Difference
Standard
Deviation
Pr>|t|
16
0.1195
1.0373
0.6517
16
‐0.0303
0.2975
0.6900
16
0.2603
0.8966
0.2637
16
‐0.0515
0.2111
0.3540
16
‐0.0324
0.1767
0.4747
14
‐0.0136
0.0654
0.4509
15
‐0.0198
0.1708
0.6600
Thereewasnosiggnificantchaangeinvegeetableintak
keofsoftballplayers
betweenAug
gust2015andNovemb
ber2015followingthe11‐weeknu
utrition
educationcu
urriculum.O
Onaverage,allthesoftb
ballplayers’’consumptiionof
vegetablesin
ncludedintthestudyinccreasedby0.12cups,intakeofletttucesalad
decreasedby
y0.03cups,intakeofotthervegetaablesincreassedby0.26cups,intak
keof
to
omatosauceedecreaseb
by0.05cups,intakeof vegetablesoupdecreasedby0.03
3
cu
ups,intakeofFrenchfrriesdecreassedby0.01cups,andin
ntakeofwhitepotatoess
decreasedby
y0.02cups.Theseresu
ultsindicateethatvegettableintakeamongthe
so
oftballplayeersdidnotsignificantly
yincreaseaafteran11‐w
weeknutrittioncurricu
ulum
co
oupledwith
hatechnolo
ogycomponent.
CORRELATIO
C
ONTESTBETWEENFRU
UITANDVE
EGETABLEIN
NTAKEAND
DBODYFAT
T
PERCENTAGE
P
E
Figure4.1:Scatterploto
ofchangeinfruitandveegetableinttakeversuschangeinb
body
faatpercentag
geamongfeemalecolleggesoftballp
playersfollo
owingan11‐weeknutriition
educationcu
urriculum.
44
Table4.9:Linearregressionanalysisofchangeinfruitandvegetableintakeversus
changeinbodyfatpercentageamongfemalecollegesoftballplayersfollowingan
11‐weeknutritioneducationcurriculum.
ChangeinFruit
R‐Square
n Correlation
andVegetable
Y‐Intercept Prob>|t|
Intake(slope)
0.23
14
0.484
0.26
0.81
0.0794
Participantswhodidnothavepre‐andpost‐dataforthefruitandvegetable
screenerandbodyfatpercentagewereexcludedfromthecorrelationtest.The
correlationofbodyfatpercentageandfruitandvegetableintakeofsoftballplayers’
presentedinascatterplot(Figure4.1)revealedapositivecorrelationbetweenthe
variables(0.484).
However,thecorrelationbetweenfruitandvegetableintakeandpercent
bodyfatwasnotsignificant(p=0.0794).
45
CORRELATIO
C
ONTESTBETWEENFRU
UITANDVE
EGETABLEIN
NTAKEAND
DWAIST
CIRCUMFERE
C
ENCE
Figure4.2:Scatterplotb
betweenfruitandvegettableintakeeandwaistcircumferen
nce
ch
hangeamon
ngfemaleco
ollegesoftballplayersffollowingan
n11‐weekn
nutrition
educationcu
urriculum.
Table4.10:L
T
Linearregreessionanaly
ysisofchanggeinwaistccircumferen
nceandchaange
in
nfruitandv
vegetablein
ntakeincupsforsoftballlplayersfrromAugust2015to
November2
N
015.
Ch
hangeinFru
uitand
R‐Square
R
VegetableIn
V
ntake
Y
Y‐Intercept Prob>|tt|
n Corrrelation
(slope))
0.35
13
0.59
0
0.42
1.02
0.0328
Amon
ngthesoftbaallplayers,therewasaapositiveco
orrelation(0
0.59279),
betweenthechangeinffruitandveggetableintaakeandchan
ngeinwaisttcircumfereence.
TheR‐Squar
T
evaluewassmoderatelystrong(0
0.351403),aandtheprob
babilitythattan
in
ncreaseinin
ntakeoffruitsandvegeetablesrelattedtoanin
ncreaseinw
waist
ciircumferenccewassignificant(p=0
0.0328).
46
Duetothisbeingaconveniencesample,accesswasunabletobeprovidedto
reviewthechangeindietaryintakethatwastrackedelectronically.Therefore,these
resultscannotbereported.
CHAPTER5:DISCUSSION
Thepurposeofthisstudywastodetermineifofferingan11‐weeknutrition
curriculumcoupledwithatechnologycomponentincreasedtheconsumptionof
fruitsandvegetableswhiledecreasingwaistcircumferenceandbodyfatpercentage
amongcollegiatesoftballplayers.Thisstudycomparedfruitandvegetableintakeof
softballplayerstowaistcircumference,andfruitandvegetableconsumptionto
bodyfatpercentagebothbeforeandaftertheofferingofan11‐weeknutrition
curriculumcoupledwithatechnologycomponent.Theobjectiveofthisstudywasto
determineiftherewasanimpactofanutritioncurriculumbasedinterventionon
fruitandvegetableintakeonpercentbodyfatandwaistcircumferenceincollege
athletes,specificallycollegiatefemalesoftballplayers.
Thehypothesesofthisresearchareofferingan11‐weeknutritioncurriculum
willdecreasebodyfatpercent,increasetheintakeoffruitsandvegetables,decrease
waist‐circumference,andresultinanegativecorrelationforchangesinbodyfat
percentageandwaist‐circumferencecomparedtofruitandvegetableintake.Based
onthepairedt‐test,bothwaistcircumferenceandbodyfatpercentagehada
statisticallysignificantdecrease.Mostresearchwilldemonstratethatanincreasein
fruitandvegetableconsumptionwillresultinalowerbodyfatpercentageand
waist‐circumferenceduetoreplacinghighcaloricfoodswithfruitsandvegetables
47
thatarelowercalorie.However,inthisresearch,statisticallytherewasno
significantchangeinfruitandvegetableintake.Inthefall,softballplayersarein
training.Thedecreaseinbothwaist‐circumferenceandpercentbodyfatmaybe
morereflectiveofthetraininglevelthanfruitandvegetableconsumption.
Overthe11‐weekperiod,theparticipantsonaverageincreasedtheirintake
ofwholefruitby0.38cups,anddecreasedtheirfruitjuiceintakeby0.05cups.This
reductionofjuiceandincreaseofwholefruitmayberelatedtothelessontaught
duringthethirdweekonincreasingtheintakeoffiber.
Overthe11‐weekperiod,thesumoffruitandvegetableconsumption
increasedby0.45cups.Althoughnotstatisticallysignificant,isstillencouraging
particularlywhencoupledwiththechangeinseasons.Thefirstsetofdatawas
collectedinAugust,whichisstillapeakfruitandvegetableseason.Thesecondset
ofdatawascollectedinNovemberwhenfreshfruitsandvegetablesarenotas
readilyavailable.Unfortunately,bothfruitandvegetableconsumptioncontinuedto
remainbelowUSDArecommendationsforthisagerangeoffemales.Fruitintake
was1.61cups,whiletheUSDArecommendationis2cups(USDA,2016).Vegetable
intakewas1.50cupswhiletheUSDArecommendationis2.5cupsforfemalesages
19‐30(USDA,2016).
Linearregressionmodelswerealsousedtodeterminethecorrelation
betweenachangeinbodyfatpercentageandfruitandvegetableintake,anda
changeinwaistcircumferenceandfruitandvegetableintakeinsoftballplayers.
Therewasapositivetrendforbothbodyfatpercentageandfruitandvegetable
intake,andwaistcircumferenceandfruitandvegetableintake.However,
48
statisticallyonlytherelationshipbetweenwaistcircumferenceandfruitand
vegetableintakewassignificant.Asfruitandvegetableintakeincreased,waist
circumferenceincreased.Oftheparticipantsincluded,43%werefreshman,andthe
durationofthestudywasfromAugusttoNovember.Studiesshowfreshmanyearof
collegeisassociatedwiththegreatestgainsinweightandbodyfatduetonewly
foundfoodindependence,socialcomparisonwithpeers,andtheinfluenceoffriends
andfamily(Smith‐Jackson,2012,Gropper,2012).Itcanbeassumedthatformanyof
theparticipants,thiswastheirfirsttimeawayfromhomeandacontrolledfood
environment.Thismayhaveimpactedtheresults.
LIMITATIONS
Thestudyhadseverallimitationsthatcouldhaveaffectedtheresultsfound.
Participantlimitationsincludethatthiswasaconveniencesample,only14
participantsmetinclusioncriteriaand43percentweretruefreshmen.Itcannotbe
assumedthatthesecollegiatesoftballplayersrepresentallcollegiatesoftball
players.
Otherlimitationsincludedthatdatawasnotavailableforparticipant
attendancenorthefoodrecordsduringthe11‐weekintervention.TheFruitand
VegetableScreenerwasadministeredintwodistinctfoodseasons.Anthropometric
measurementsweretakenintwodissimilarperformanceseasons.
Fortheselimitations,theresultsofthisstudycannotbegeneralizedtoa
largerpopulation.Forfutureresearch,havingaccesstohowmanynutritionlessons
eachparticipantattended,foodlogs,andexerciselogscouldaidinknowingif
49
attendancetonutritionlessonsiscorrelatedtochangesinbodyfatpercentage,
waistcircumference,andfruitandvegetableintake.
CHAPTER6:CONCLUSION
Thisstudysuggeststheremaybeapositivetrendbetweenfruitand
vegetableintakeandwaistcircumference,andfruitandvegetableintakeand
percentageofbodyfatamongthesefemalesoftballplayersafteran11‐week
nutritioncurriculumwithatechnologycomponent.Asfruitandvegetable
consumptionincreased,therewasasignificantincreaseinwaistcircumference.
Furtherresearchisneededtodeterminewhytherewasanincreaseinbodyfat
percentageandwaistcircumferenceasfruitandvegetableintakeincreased.Ideally,
adecreaseinwaistcircumference,bodyfatpercentage,andincreaseinfruitand
vegetableintakecanplayaroleinoptimizingathleticperformanceamongcollegiate
athletes.Sincethisstudydidnotcontrolforthenumberofsessionstheathletes
attended,oriftheyattendedanyatall,furtherresearchisneededtodeterminethe
effectsofattendingnutritionsessionswithatechnologycomponentonfruitand
vegetableintake,waistcircumferenceandbodyfatpercentageamongfemale
collegiatesoftballplayers.
50
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53
VITA
RachelThomasisfromOwensboro,Kentucky.ShewasawardedaBachelorof
ScienceinDieteticsfromtheUniversityofKentuckyin2015.Shehasheld
professionalpositionsofDieteticInternandTeachingAssistantattheUniversityof
Kentucky.
54