Research ArticleOnline Course Increases Nutrition Professionals’ Knowledge,Skills, and Self-Efficacy in Using an Ecological Approachto Prevent Childhood ObesityChristina M. Stark, MS, RD, CDN; Meredith L. Graham-Kiefer, BS; Carol M. Devine, PhD, RD;Jamie S. Dollahite, PhD, RD; Christine M. Olson, PhD

Division oAddress foSciences, 3Fax: (607)�2011 SOdoi:10.1016

316

ABSTRACT

Objective: To assess the impact of an online continuing education course on the knowledge, skills, andself-efficacy of nutrition professionals to use an ecological approach to prevent childhood obesity.Design: Quasi-experimental design using intervention and delayed intervention comparison groups withpre/post-course assessments.Setting: Online continuing education course.Participants: Nutrition and health professionals in an online course (n¼ 105) and a delayed interventioncomparison group (n ¼ 37).Intervention: A 6-week, facilitated online course titled, Preventing Childhood Obesity: An EcologicalApproach.Main Outcome Measures: Changes in knowledge, skills, and self-efficacy in using an ecologicalapproach to address childhood obesity.Analysis: Paired and independent sample t tests, factor analysis, regression analysis.Results: In contrast to a comparison group, nutrition and health professionals who participated ina 6-week online course had statistically significant increases (P < .01) in their knowledge, skills, andself-efficacy related to using an ecological approach to prevent childhood obesity.Conclusions and Implications: A facilitated online course can be effective at increasing the knowledge,skills, and self-efficacy of community-based nutrition and health professionals in using an ecologicalapproach to prevent childhood obesity in their communities.KeyWords: obesity, child, primary prevention, distance education, online continuing education (J NutrEduc Behav. 2011;43:316-322.)

INTRODUCTION

In the past 3 decades, the proportionof overweight and obese children inthe United States has increased atalarming rates and remains high.1

Obesity prevention efforts have tradi-tionally focused on individual behav-ior change, which is necessary, butnot sufficient, to address this epi-demic. Although intensive clinical in-terventions may be useful to treatchildren who are already overweight,2

there is also a need for interventionapproaches to prevent childhood obe-sity. Several researchers and reportshave called for the use of ecological

f Nutritional Sciences, Cornell Univr correspondence: Christina M. Stark51 MVR Hall, Cornell University,255-0027; E-mail: cms11@cornell.edCIETY FOR NUTRITION EDUC/j.jneb.2011.01.010

approaches to obesity prevention asa needed public health strategy.3-5

There is an urgent need to increasethe capacity of community-based nu-trition and health professionals touse an ecological approach.

An ecological approach, which in-cludes, but is not limited to, address-ing factors in the multipleenvironments that influence behav-ior, may be effective in supportingand enhancing individual approachesto the prevention of overweight andobesity.3-5 A socio-ecological modelprovides the conceptual frameworkfor applying such an approach.At the center of the model is the

ersity, Ithaca, NY, MS, RD, CDN, Division of NutritionalIthaca, NY 14853; Phone: (607) 255-2141;uATION

Journal of Nutrition Education and Beh

individual, where the focus is on theinternal determinants of behavior,such as knowledge, attitudes, beliefs,and skills. This is the foundationlevel, but an ecological approach rec-ognizes the need to go beyond anindividual-based focus and also ad-dress environmental factors. Creatingenvironments that facilitate healthfulfood and activity choices has thepotential to prevent overweight inmany members of a community, notjust a few individuals.

For many community-based nutri-tion professionals, using an ecologicalapproach requires a new way of think-ing and working. Nutrition profes-sionals may need new knowledge,skills, and confidence to use an ap-proach that goes beyond providing in-formation and education targeted tochanging individual behavior. Oncetrained, these local professionals canfacilitate community-based collabora-tions to implement environmental

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Journal of Nutrition Education and Behavior � Volume 43, Number 5, 2011 Stark et al 317

interventions that support healthfuleating and active living. One chal-lenge is how to provide that training.

In a study of the professional devel-opment needs of community nutri-tion practitioners, Devine andcolleagues found that practitionersfelt professionally isolated andwanted more interaction with expertsand their peers.6 Many practitioners,particularly those in rural communi-ties, had limited time and resourcesfor travel to workshops and other‘‘in-person’’ professional develop-ment opportunities. Because of lim-ited time and funds, they wantedflexible, affordable, and convenientoptions for continuing professionaleducation. Online training, by provid-ing the flexibility and conveniencethey need, is a cost-saving and effi-cient way to reach these professionals.These advantages have led to anincrease in the availability ofWeb-based continuing professionaleducation over the past 15 years. Forexample, the Commission on DieteticRegistration lists over 700 Web-basedself-study activities in their databaseof continuing professional educationofferings.7

Although online continuing edu-cation can be a convenient way toreach community nutrition practi-tioners, its effectiveness needs to beinvestigated. Studies of the effective-ness of Internet-based continuing ed-ucation for health professionals havefocused primarily on programs forphysicians, nurses, pharmacists, anddentists.8-11 There have also beenstudies that have examined theeffectiveness of delivering food- andnutrition-related content online toteachers and librarians.12-14

Relatively few studies, however, haveexamined online continuingeducation programs that includenutrition professionals in the targetaudience.15-17

This study evaluated the effective-ness of a facilitated online course toincrease the knowledge, skills, andself-efficacy of community-based nu-trition and health professionals to ad-dress childhood obesity using anecological approach. The authors hy-pothesized that participants in an on-line course (intervention group)would report greater pre- to post-course increases in knowledge, skills,and self-efficacy in using an ecological

approach to prevent childhood obe-sity than participants in a delayed in-tervention comparison group.

METHODSStudy Design

A quasi-experimental design with in-tervention and delayed interventioncomparison groups was used. Pre- topost-course changes in course out-comes were assessed among nutritionand health professionals who enrolledin any 1 of 6 identical course sessions(intervention group) and comparedwith the responses of nutrition andhealth professionals who expressedinterest in enrolling in a future sessionof the course (comparison group).Both the intervention and compari-son groups completed online pre-and post-course surveys, delivered 6weeks apart. The project was deemedexempt by the Cornell University In-stitutional Review Board.

Intervention

The intervention was built on thefoundation of Cornell Nutrition-Works, an online professional devel-opment program for nutrition andhealth practitioners. The authorscreated an in-depth online course,Preventing Childhood Obesity: AnEcological Approach, working in col-laboration with experts in onlinecourse delivery systems. The coursecontent and evaluation tools weredeveloped with input from CornellCooperative Extension nutrition edu-cators and pilot-tested with a conve-nience group of 23 communitypractitioners, including extension ed-ucators, public health nutritionists,health educators, a dietetics instruc-tor, and a recreation program director.

The course content was organizedaround the PRECEDE-PROCEEDhealth program planning frame-work,18 which involves assessing theunderlying factors that contribute toa health problem in order to strategi-cally develop an intervention plan. Pri-orities and objectives are identified,which then become the targets andgoals of an action plan. Participantswere guided through this planningprocess in the course primarily viacourse presentations that were rein-forcedby interactionswitha facilitator.

The course lasted 6 weeks and hadset start and finish dates. It was deliv-ered using the learning platform ofeCornell, Cornell University's onlineprofessional and executive develop-ment program. Participants enrolledin 1 of six 6-week long courses. Con-tent was delivered in a variety of on-line audiovisual formats, includingslide shows. The course format al-lowed participants to work on theirown time and at their own pace, butwithin structured assignment dead-lines. The course required participantsto submit local obesity prevalencedata, participate in 2 asynchronousonline discussion forums, and com-plete a 4-part course project. Thecourse was facilitated by an instructor(CMS), who interacted with partici-pants throughout the entire course.She provided individual feedbackand approved each successfully com-pleted assignment, led the online dis-cussions, and was available to answerquestions. She also guided the paceof the course by sending remindersabout deadlines to keep participantson track.

The course objectives included theability to assess and prioritize individ-ual behaviors; environmental factors;and predisposing, enabling, and rein-forcing factors related to excessiveweight gain in children, and thenidentify appropriate interventionmethods and strategies for addressingthe priority factors. The specific objec-tives are available on the course Website.19 In the process of meeting thecourse objectives by completing the4-part course project, participants de-veloped an action plan describinga collaborative, ecological approachto addressing childhood obesity tai-lored to their own communities. Par-ticipants who successfully completedthe course earned 15 continuing pro-fessional education units.

Participants

Course participants were recruited byusing listservs that serve the target au-dience: Cornell NutritionWorksmem-bers, Cooperative Extension food andnutrition specialists, Society for Nutri-tion Education members, and publichealth nutritionists. Course informa-tion was also available on the CornellNutritionWorks, Division of Nutri-tional Sciences, and eCornell Web

Intervention n = 168

Comparisonn = 198

Pre-Survey Responses

136

Post-SurveyResponses

123

Pre-Survey Responses

74

Post-SurveyResponses

56

MatchedPre- to Post -

SurveyResponses

105

MatchedPre- to Post-

SurveyResponses

37

Figure 1. Number of intervention and comparison group participants at baseline andwith completed survey responses at each data collection point.

318 Stark et al Journal of Nutrition Education and Behavior � Volume 43, Number 5, 2011

sites, as well as in handouts distrib-uted at professional meetings. Thecourse was limited to 25-30 partici-pants or teams per session to allowsufficient time for facilitator feedbackand to enhance interaction amongparticipants. The maximum numberenrolled each time, and a waiting listwas maintained. Participants who en-rolled in any 1 of the 6 sessions wereput in the intervention group (n ¼168), and those who had expressed in-terest in the course but who had notyet enrolled were put in the compari-son group (n ¼ 198; Figure 1).

Instruments

Survey items used in the evaluationwere developed by the researchers.Face validity was established by asking7 community nutrition professionalswho represented the target audienceto review and comment on their un-derstanding of each item. Items wererevised as needed to improve clarity.The pre- and post-course surveyswere then field-tested as part of the pi-lot test of the course. The survey itemswere validated through triangulationin post-pilot focus group interviewswith a subset of the 23 pilot testers(n ¼ 16) to confirm that the itemsperformed as expected (data notpresented).

The pre-course survey included de-mographics (employment, education,credentials, years of experience, age)and then 16 questions using a 5-pointscale (‘‘not at all’’ to ‘‘extremely/extremely well’’) that measured self-assessed knowledge, skills, and self-efficacy related to using an ecologicalapproach to prevent childhood obe-sity. Knowledge and skills (KS) weremeasured within the same question,for example, ‘‘I know how to identifyenvironmental factors that contributeto excessive weight gain in children.’’An example of an item that measuredself-efficacy (SE) was ‘‘I feel confidentin my ability to implement an actionplan for addressing childhood obe-sity.’’ The pre-course survey also askedabout previous experience in takingan online course.

The post-course survey asked aboutthe degree of course completion (in-tervention group only) followed bythe same 16 KS and SE items. Thepost-course survey for the interven-tion group included items related to

course expectations and experienceand participants' intentions to applywhat they learned, whereas the post-survey for the comparison groupasked respondents if they had doneanything special to learn content sim-ilar to that of the course during the 6weeks between the pre- and post-course surveys. The Theory of PlannedBehavior suggests that intentions in-fluence behavior,20 so the partici-pants' intentions were measured toassess the likelihood they would applywhat they learned.

The underlying constructs of the16 questions were KS based, SE based,or combined KS and SE related to 4specific subject areas: environmentalfactors; predisposing, enabling, andreinforcing (PER) factors; collabora-tions; and the action plan. Cronbacha for each construct was calculated us-ing the comparison and interventiongroups as 1 sample. For each con-struct, the Cronbach a was above0.925, and the number of items perconstruct ranged from 2-9, whichdemonstrates an excellent item corre-lation within the constructs.

Procedures

The course was delivered 6 timesbetween 2006 and 2008, and eachsession was guided by the samefacilitator. Participants could enroll

as individuals or as part of a team. Par-ticipants who enrolled as individualsdid all 7 assignments by themselves.Participants who enrolled as part ofa team did 3 of the assignments bythemselves, but submitted the 4-partcourse project as a team. Even if some-one enrolled as part of a team, courseoutcomes were assessed on the levelof the individual enrollee who com-pleted pre- and post-course surveys.The pre-course survey was completedby 136 participants, and the post-course survey was completed by 123.Of those who completed the post-course survey, only 4 people had notcompleted the entire course.

A pre-course survey containing thesame demographic questions plus the16 survey items measuring KS and SEwas also delivered to a comparisongroup of nutrition and health profes-sionals. A group of 200 professionalswas randomly selected from a list of269 people who had expressed inter-est in taking the course, but had notyet done so. Half the comparisongroup was sent the pre-course surveyto coincide with the start of session5, the other half with session 6. Twopeople's e-mail addresses were no lon-ger valid, so a total of 198 received thesurvey. Six weeks after receiving thepre-course survey, individuals in thecomparison group received a post-course survey. The pre-course survey

Table 1. Characteristics of Intervention and Comparison Groups

Intervention Comparison Significance(n ¼ 105) (n ¼ 37) P

Employment .52Cooperative Extension 32 (31%) 7 (19%)Public health/WIC 27 (26%) 10 (27%)College/university 10 (10%) 5 (14%)Other 34 (33%) 15 (41%)

Education .94Less than master’s degree 42 (40%) 14 (39%)Master’s degree or more 64 (60%) 22 (61%)Dietetic credential 46 (44%) 18 (49%) .61

Years of experience .290–2 18 (17%) 3 (8%)3–10 37 (36%) 12 (32%)11 or more 49 (47%) 22 (60%)

Age, y .17< 29 20 (19%) 3 (8%)30–49 49 (47%) 16 (43%)$ 50 36 (34%) 18 (49%)

Online course experience .07Previous online experience 43 (41%) 21 (58%)

WIC indicates Special Supplemental Nutrition Program for Women, Infants, andChildren.

Journal of Nutrition Education and Behavior � Volume 43, Number 5, 2011 Stark et al 319

was completed by 74 people in thecomparison group and the post-course survey was completed by 56.Data analysis used only the matchedpre- and post-course survey responses(Figure 1). A power analysis estimatedthat in total, 128 people were neededto detect a difference in pre- to post-course change scores of 0.5 standarddeviation units between interventionand comparison groups, with power¼ .8 and a ¼ .05.

Data Analysis

Survey results from participants whoenrolled in any of the 6 sessions ofthe course were combined into 1group (intervention group) based onlack of evidence to suggest that therewere any significant demographic dif-ferences between the sessions (datanot shown). Survey results from thoseinterested but not enrolled in thecourse were also combined into 1group (comparison group).

The focus of the data analysis wasto determine whether there was a sig-nificant improvement in course par-ticipants' KS, SE, or subject area-basedKS and SE. Confirmatory factor analy-ses using the principal componentsextraction method were used to assesswhether the items loaded as antici-pated in the 6 constructs. The factoranalysis for KS used 8 items, and thefactor preserved 78.1% of the variabil-ity. For the other factors, the valueswere as follows: SE (6 items, 80.3%),environment (4 items, 85.2%), PER(3 items, 90.6%), collaboration(2 items, 93.1%), and action plan(5 items, 86.4%). Communalities forall items were > .50.

Survey data were determined to benormally distributed, and pairedt tests were first used to compare dif-ferences in pre- to post-course surveyscores. The authors compared differ-ences in pre- to post-course surveyscores for the 16 items among partici-pants in the intervention group andin the comparison group. Subse-quently, the authors used an indepen-dent sample t test to comparedifferences in pre- to post-course sur-vey change scores for the 16 itemsand the 6 constructs between theintervention and comparison groups.Finally, multiple linear regressionmodels were used to assess the effectof course participation while control-

ling for other variables. The depen-dent variables were differences in thechange from pre- to post-course scoresin the 6 constructs. The independentvariable was participation in the on-line course. Covariates included inthe model were demographics, previ-ous online course experience, pre-course survey scores, and their 2-wayinteractions with course participa-tion. Nonsignificant interaction termswere not included in the final regres-sion models. The statistical analysiswas done using SPSS (version 17.0,SPSS, Inc., Chicago, IL, 2008). Thelevel for determining statistical signif-icance was P< .05 for all comparisons.

RESULTS

The intervention group reported apos-itive experience with the onlinecourse. A large proportion agreed orstrongly agreed that the course waswell organized (99%) and met theirexpectations in terms of content(88%), cost (88%), credits (80%), andtime (73%). At post-course, 91% re-ported they intend to apply whatthey learned to their work, and 70%reported they intend to implementat least part of their action plan. Just

over half (52%) of the comparisongroup reported that they had partici-pated in some type of professional de-velopment activities during the 6weeks between surveys related to us-ing an ecological approach to addresschildhood obesity. These activities in-cluded reading professional journalarticles, attending workshops andconferences, and viewing Webcasts.

Table 1 shows the pre-interventioncharacteristics of those in the inter-vention and comparison groups forwhich the investigators had matchedpre- and post-course survey responses.There were no significant differencesbetween the intervention and com-parison groups on any of the charac-teristics of interest.

Intervention group participantshad significant positive changes (P <.01) from pre- to post-course surveyscores for all 16 items measuring KSand SE in using an ecological ap-proach. There were no significantchanges from pre- to post-course sur-vey scores for the comparison groupfor any of the 16 items. The interven-tion group differences from pre- topost-course were significantly greater(P< .01) than those in the comparisongroup for each of the 16 items. The dif-ferences between pre- and post-course

320 Stark et al Journal of Nutrition Education and Behavior � Volume 43, Number 5, 2011

survey scores for the interventiongroup compared with the comparisongroup were also significant (P < .01)for all 6 constructs (Figure 2). The t testswere corrected for multiple compari-sons using the Bonferroni correction.These results stayed significant even af-ter controlling for demographics andother variables.

Participation in the online coursewas positively and significantly asso-ciated with the amount of change ineach construct from pre to post. Inall multiple regression analyses, previ-ous online course experience and ed-ucational level were not associatedwith course outcomes. Pre-coursesurvey scores and being in the inter-vention group were significantly asso-ciated with outcomes for all 6 factors(Table 2). The negative b values forpre-course survey scores indicatedthat people with higher pre-coursesurvey scores had less change thanpeople with lower pre-course surveyscores. Even when controlling forpre-course survey scores, there wasstill a significant intervention effect,which indicates the interventiongroup had a larger positive changethan the comparison group.

Figure 2. Changes in pre- to post-course sdard error. All changes were significant at P

DISCUSSION

This study showed that this facilitatedonline course was effective in increas-ing community-based nutritionprofessionals' knowledge, skills, andself-efficacy in using an ecological ap-proach in childhood obesity preven-tion. Confidence in this conclusionis strengthened by the finding thata similar group of professionals whodid not take the course, but who re-ported doing other professional devel-opment activities, showed nosignificant changes in knowledge,skills, and self-efficacy in using an eco-logical approach over the same timeperiod. These findings confirm the au-thors' hypothesis. The present study isthe first of which the authors areaware that showed an online courseis effective at increasing the capacityof nutrition professionals to addresschildhood obesity using an ecologicalapproach.

Further research is needed to evalu-ate whether these changes in knowl-edge, skills, and self-efficacy lead tochanges in practice. The Theory ofPlanned Behavior proposes that in-tentions increase the likelihood of an

urvey mean scores for intervention group vers< .01.

action.20 Results from the presentstudy indicated the course did havean impact on participants' behavioralintentions, so it is reasonable to thinkchanges in practice will follow. Thisissue merits additional longitudinalresearch.

Consistent with prior researchwith other health professionals, thisstudy found that the online continu-ing education course was successfulin terms of knowledge gains and par-ticipant satisfaction.8-11 A few studieshave demonstrated the effectivenessof online continuing education inchanging practice patterns of healthprofessionals.11 When comparedwith other types of interventions,Internet-based learning in the healthprofessions has been associated withconsistent positive effects comparedwith no intervention, and the effec-tiveness is similar to that of tradi-tional methods.9,10 In futureresearch, it will be important todocument the effect of onlineeducation for nutrition professionalson their practice. Considering thelarge number of Web-based continu-ing education opportunities availablein the Commission on Dietetic

us comparison group; means and stan-

Table 2. Changes in Pre- to Post-Course Survey Mean Scores Using MultipleLinear Regression Models

b ± SE PChange in knowledge and skills (n¼ 134)

Intercept �.192 � .152 .21Educationa .026 � .114 .82Online course experienceb �.051 � .111 .65Pre: knowledge and skills �.649 � .065 < .001Interventionc 1.164 � .129 < .001Adjusted R2 ¼ 0.550

Change in self-efficacy (n ¼ 134)Intercept �.212 � .146 .15Educationa .029 � .112 .80Online course experienceb .019 � .111 .87Pre: self-efficacy �.614 � .064 < .001Interventionc 1.176 � .126 < .001Adjusted R2 ¼ 0.563

Change in environmental (n ¼ 136)Intercept �.230 � .155 .14Educationa .015 � .119 .90Online course experienceb �.030 � .117 .80Pre: environmental �.692 � .067 < .001Interventionc 1.156 � .133 < .001Adjusted R2 ¼ 0.561

Change in PER (n ¼ 137)Intercept �.271 � .150 .07Educationa .002 � .116 .99Online course experienceb .007 � .114 .96Pre: PER �.746 � .062 < .001Interventionc 1.138 � .130 < .001Adjusted R2 ¼ 0.606

Change in collaboration (n ¼ 137)Intercept �.169 � .172 .33Educationa �.086 � .132 .52Online course experienceb .167 � .130 .20Pre: collaboration �.626 � .074 < .001Interventionc 1.015 � .149 < .001Adjusted R2 ¼ 0.464

Change in action plan (n¼137)Intercept �.239 � .144 .10Educationa .055 � .111 .62Online course experienceb �.047 � .109 .67Pre: action plan �.583 � .062 < .001Interventionc 1.187 � .124 < .001Adjusted R2 ¼ 0.557

b indicates unstandardized regression coefficient; PER, predisposing, enabling,and reinforcing factors; SE, standard error of the parameter estimate.a1 ¼ master’s degree, doctoral degree and 0 ¼ high school or less, associate’sdegree, bachelor’s degree, some post-graduate education; b1 ¼ has some toextensive experience with taking an online course and 0 ¼ has no previous ex-perience taking an online course; c1 ¼ intervention group and 0 ¼ comparisongroup.

Journal of Nutrition Education and Behavior � Volume 43, Number 5, 2011 Stark et al 321

Registration database targeting die-tetic and nutrition professionals,7

studies evaluating their effectivenessare very limited.

Limitations

These findings are limited in that theevaluation is based on a self-selected

sample. The significant positivechanges in knowledge, skills, andself-efficacy in the intervention groupfound over a delayed interventioncomparison group, however, mini-mize the concern of this limitation.These results were also based on thoseindividuals for whom the investiga-tors had matched pre- and post-course survey data. The investigatorsare unable to tell whether those whocompleted both surveys were differentfrom those who did not completeboth, since survey responses wereanonymous. The authors also didnot randomly assign studyparticipants to the intervention andcomparison groups, although thecharacteristics of the groups did notseem to differ. Another limitationwas that the response rate of the com-parison group was lower than that ofthe intervention group. A final limita-tion is that the study design did notallow the investigators to discernwhether the impact was a result ofthe method of instruction (online) orto instruction itself in using an eco-logical approach, independent of themethod. The authors' objective, how-ever, was to determine whethera cost-saving and efficient method ofinstruction (online) was effective intraining professionals in a new ap-proach to addressing childhoodobesity compared with a delayed in-tervention comparison group, ratherthan to compare the effectiveness ofdifferent methods or amounts ofinstruction.

IMPLICATIONS FORRESEARCH ANDPRACTICE

The results of this project can serve asa model for how online continuingeducation programs can be used totrain community-based nutrition pro-fessionals in using an ecological ap-proach to prevent childhood obesity.The significant increases in knowl-edge, skills, and self-efficacy in usingan ecological approach help meetthe need to increase the capacity ofprofessionals to address childhoodobesity with more effective interven-tions. These results have implicationsfor how other types of community-based professionals, such as youth de-velopment professionals, recreation

322 Stark et al Journal of Nutrition Education and Behavior � Volume 43, Number 5, 2011

directors, or school nurses, could betrained in using an ecological ap-proach. More research is needed toevaluate how this training is appliedat the community level.

ACKNOWLEDGMENTS

The authors would like to acknowl-edge the contributions of WendyWolfe, PhD, Research Associate, Cor-nell University, to the course contentand for her review of the survey in-struments; Christine M. Porter, PhD,Assistant Professor of Public Health,University of Wyoming, for her in-structional design of the course andhelp with pilot testing of survey in-struments; and the technical produc-tion staff at eCornell for theirexpertise in creating the format forthe online course. Funding for thisproject came from United States De-partment of Agriculture (Smith Leverand Hatch funds) and from the Col-lege of Human Ecology at CornellUniversity.

REFERENCES

1. Ogden CL, Carroll MD, Curtin LR,Lamb MM, Flegal KM. Prevalence ofhigh body mass index in US childrenand adolescents, 2007-2008. JAMA.2010;303:242-249.

2. Epstein LH, Paluch RA,Roemmich JN, Beecher MD. Family-based obesity treatment, then andnow: twenty-five years of pediatricobesity treatment. Health Psychol.2007;26:381-391.

3. Sallis JF, Cervero RB, Ascher W,Henderson KA, Kraft MK, Kerr J. Anecological approach to creating active

living communities. Annu Rev PublicHealth. 2006;27:297-322.

4. Story M, Kaphingst KM, Robinson-O’Brien R, Glanz K. Creating healthyfood and eating environments: policyand environmental approaches. AnnuRev Public Health. 2008;29:253-272.

5. Institute of Medicine and National Re-search Council. Local Government Ac-tions to Prevent Childhood Obesity.Washington, DC: TheNational Acade-mies Press; 2009.

6. Devine CM, Jastran M, Bisogni CA.On the front line: practice satisfactionsand challenges experienced by dieteticsand nutrition professionals working incommunity settings in New YorkState. J Am Diet Assoc. 2004;104:787-792.

7. Commission on Dietetic Registration.Online Continuing Professional Educa-tion (CPE) Database. http://www.cdrnet.org/applications/cpe/index.cfm.Accessed June 9, 2011.

8. Cobb SC. Internet continuing educa-tion for health care professionals: an in-tegrative review. J Contin Educ HealthProf. 2004;24:171-180.

9. Wutoh R, Boren SA, Balas EA. eLe-arning: a review of Internet-based con-tinuing medical education. J ContinEduc Health Prof. 2004;24:20-30.

10. Cook DA, Levinson AJ, Garside S,Dupras DM, Erwin PJ, Montori VM.Internet-based learning in the healthprofessions: a meta-analysis. JAMA.2008;300:1181-1196.

11. Lam-Antoniades M, Ratnapalan S,Tait G. Electronic continuing educationin the health professions: an update onevidence from RCTs. J Contin EducHealth Prof. 2009;29:44-51.

12. Hague AL, White AA. Web-based in-tervention for changing attitudes of obe-sity among current and future teachers. JNutr Educ Behav. 2005;37:58-66.

13. Beffa-Negrini PA, CohenNL, LausMJ,McLandsborough LA. Developmentand evaluation of an online, inquiry-based food safety education programfor secondary teachers and their stu-dents. J Food Sci Educ. 2007;6:66-71.

14. Turner-McGrievy GM,CampbellMK.Nutrition information to the desktop:a pilot online nutrition course onsaturated fat for public librariansincreases knowledge, expectancies, andself-efficacy. J Nutr Educ Behav.2009;41:188-193.

15. Santerre CR. X-train: teaching profes-sionals remotely. J Nutr. 2005;135:1248-1252.

16. Kemper KJ, Gardiner P, Gobble J,Mitra A, Woods C. Randomized con-trolled trial comparing four strategiesfor delivering e-curriculum to healthcare professionals. BMC Medical Educa-tion. 2006;6:2. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1382227/pdf/1472-6920-6-2.pdf. Accessed June 9,2011.

17. Wallner S, Kendall P, Hillers V,Bradshaw E,Medeiros LC. Online con-tinuing education course enhances nu-trition and health professionals’knowledge of food safety issues ofhigh-risk populations. J Am Diet Assoc.2007;107:1333-1338.

18. Green LW, Kreuter MW. Health Pro-gram Planning: An Educational and Eco-logical Approach. 4th ed. New York,NY: McGraw-Hill; 2005.

19. Division of Nutritional Sciences, Cor-nell University. Preventing ChildhoodObesity: An Ecological Approach.Cornell NutritionWorks Web site.http://www.nutritionworks.cornell.edu/features/index.cfm?Action¼Course&CourseID¼75. Accessed June 9, 2011.

20. Ajzen I. The theory of planned behav-ior. Organ Behav Hum Decis Process.1991;50:179-211.