October 2007
Volume 45 Number 5

Features

Using Diffusion of Innovation Concepts for Improved Program Evaluation

William G. Hubbard
Senior Public Service Associate
College of Agriculture and Environmental Sciences
whubbard@uga.edu

Lorilee R. Sandmann
Associate Professor
Department of Lifelong Education, Administration, and Policy
sandmann@uga.edu

The University of Georgia
Athens, Georgia

Introduction

The diffusion of innovations theories, developed over a half century ago, have provided a popular framework to explain how new ideas and technologies are spread and adopted in a community (Rogers, 2003). The framework has been used for program planning, it has been empirically tested, and it has undergone critique from various perspectives since its inception in the 1950s (Yates, 2001). Throughout the years, it has remained instrumental to Extension professionals, scholars, and students alike and continues to be useful in countless other fields, including medicine, telecommunications, information technology, and social marketing (Rogers, 2003).

Although the framework has provided practitioners in Cooperative Extension and other fields with an overview of how information is diffused and adopted, its potential as a tool in the area of program evaluation has not been fully examined. The field of program evaluation is complex and comprehensive, but one of its basic goals is to determine whether program participants adopt a particular practice promoted by the educational program.

Adoption rates are often expressed as a simple percentage: for example "34% of participants adopted practice 'X' based on a follow-up survey conducted 'Y' months after the program." A number reflecting ultimate adoption of practice, however, does not indicate what role education may or may not have had in the decision to adopt or reject a particular practice.

For example, what do we know about those participants who did not adopt a particular practice? Was the proposed practice too expensive to implement? Was it too complex to comprehend and implement? Was it too risky? Did the program presenter provide inadequate information? Answers to these and other questions can provide educators with a better understanding of their role in influencing the adoption of practice. Future programs could then be designed to accommodate these factors and yield higher rates of adoption.

Extension educators and evaluation specialists have in fact included questions of this nature in post workshop surveys (Rollins, 1993) but not in a way that isolates the relative and absolute effects of the educational program. This article provides a theoretical argument for using classical diffusion of innovation concepts and theories along with modern multivariate statistical procedures such as regression analysis to gain a more robust understanding of the factors that influence the adoption of practice. In addition, a planned empirical test of this concept is discussed. Gaining this more detailed insight is essential for today's Extension professional who is interested in affecting change.

Diffusion of Innovations Background

"Diffusion is the process by which an innovation is communicated through certain channels over time among members of a social system" (Rogers, 1963). The diffusion framework is a fairly involved framework that includes several "sub-theories" or concepts. These concepts together provide insight into human and social nature, including how new information is accepted (or not accepted) by potential users. Because of this, the diffusions framework draws heavily from the fields of psychology and rural sociology (Beal & Bohlen, 1957).

Components of the classic diffusion framework include the innovation-decision theory, the individual innovativeness theory, the theory of rate of adoption, and the theory of perceived attributes (Rogers, 2003). In addition, the diffusion framework includes theories relating to communication aspects and channels. Only the individual innovativeness theory, the theory of perceived attributes, and concepts relating to communication channels are covered here because these are the most relevant to adoption of practice by individuals. Finally, some factors that are external to the diffusion framework but which affect adoption of practice are discussed.

The theory of individual innovativeness suggests that in most social systems there are innovators, early adopters, early majority adopters, late majority adopters and "laggards." These five categories are often visually represented as S- and bell-shaped curves (Rogers, 2003). The S-shaped curve indicates the cumulative number of adopters from innovators to laggards; the bell-shaped curve represents the resulting normal distribution.

The S-shaped curve illustrates the fact that there are relatively few adopters at first but that, as the technology, concept, or practice is picked up by innovators and early adopters, their influence will have an impact on the later adopters that make up a majority of potential adopters. The resultant bell-shaped curve graphically represents the different types of adopters and roughly reflects categories corresponding to standard deviations. That is, early and late majority adopters are often time statistically shown to be one standard deviation "above" the mean (average adopter), and the innovators, and early adopters, and laggards are two to three standard deviations "below" the mean (Rogers, 2003). Figure 1 depicts this classic graph.

Figure 1.
Bell-Shaped Graph Depicting Levels of Adopters (from Rogers, 2003)

Another diffusion theory, the theory of perceived attributes, focuses on how the program participant views characteristics of the practice under investigation. These have been typically categorized as those that relate to the complexity, compatibility, trialability, relative advantage, and observability of a practice or technology (Rogers, 2003). A brief description of each of these attributes follows.

• Complexity involves the degree of difficulty of understanding and implementing the practice from the perspective of the potential adopter.


• Compatibility concerns itself with a host of factors relating to the degree to which the practice is compatible to current objectives and philosophies of the program participant. Compatibility factors can be something as simple as not having the right resources (land or otherwise) to implement the practice or could involve more philosophical concepts such as concern over applying pesticides or fertilizers to an organic crop.


• Trialability deals with the potential to experiment with the practice on a smaller, less intensive scale. The expectation is that if an owner can implement the new practice on a trial basis he or she can possibly even modify the potential practice further to meet their specific needs.


• Relative advantage speaks to the possibility of increased income, reduced cost, or other factors that may make adopting this practice advantageous over other alternatives, including doing nothing.


• Observability relates to the degree to which the potential adopter has had the opportunity to see the practice implemented or see the results of the implemented practice. Some practices are obviously more observable than others (planting trees vs. preparing an estate plan, for example) and therefore might be adopted by individuals more quickly.

In summary, all things being equal, the more profitable, understandable, personally compatible, observable, and testable the participant considers the innovation, the higher the potential for adoption.

Other concepts relevant to the diffusion of innovations framework and having influence on adoption/rejection decisions include those relating to the communication channel, social networks, and external factors (Rogers, 2003). Communication channels include the change agent or agency and attributes of the communication program (for example, educational program type or means of disseminating information). Social networks and systems include support systems such as a local farmer/forest owner organization or association and the type and amount of interaction with professionals following the educational intervention (such as county agents or foresters). Finally, external factors such as markets, weather, natural disasters, policy, and unanticipated events all affect adoption of practices.

Diffusion of Innovation Framework for Evaluation

The diffusion of innovations approach to program evaluation recognizes that a variety of factors influence the adoption of a practice. This approach provides insight into why educational program participants adopt or reject a practice on which they have received information or training.

Diffusion of innovations research can provide information, for example, on barriers and motivations external to the educational program that may have strong influences on the decision to adopt or reject a practice. These factors may include the individuals' personality, socio-demographic characteristics, networks, and prior knowledge of the topic. Other influences may stem from the five perceived attributes associated with the practice or innovation under question (complexity, compatibility, trialability, relative advantage, and observability) or from the participant's social network and the availability of information and assistance from other sources.

Empirical examples that use this framework for evaluation purposes within the U.S. Extension System are not common. Research by King and Rollins (1995) indicated that change agent's attitude, participant's economic concerns, and technical information influenced the adoption of an agricultural innovation by participants who received information from a training program. Another study investigated factors that influence the adoption of practice and the participation in educational outreach of integrated pest management (IPM) in Utah. Characteristics that influenced farmers and producers to adopt practices or participate in educational programs included major source of income (on-farm or off-farm), farm size, market destination (in-state or out-of-state), diversity of crop produced, past intensity of IPM outreach efforts, and development of commodity organizations (Alston & Reding, 1998 ).

Research specifically linking the diffusion of innovations theories with program evaluation has been undertaken in the health field. Cervero and Rottet (1984) created an instrument designed to empirically test a diffusion-innovation-evaluation model hypothesized previously by Cervero Figure 2). The study sought to analyze the impact of the training program (Continuing Professional Education or CPE Program) on behavior change and performance (client outcomes) and to determine the extent of non-education program factors (proposed change, individual behavior, social system) on employee adoption of practice (measured as a change in performance).

A 51-item survey instrument was designed, tested for validity, and implemented. Data collection included the review of charts, interviews with participants and supervisors, and personal observation by the researchers themselves. Results indicate that a model based on Rogers's diffusion of innovations explains between 39 and 81% of the variance of the dependent variables (Rogers, 2003). This finding suggests that this design and a modified framework could be extremely useful as an evaluation tool in Extension and other educational fields.

Figure 2.
The Cervero Program Evaluation Model

From Theory to Practice in Extension

The Cervero model informs a way to test an evaluation model based on diffusion of innovations concepts. Such an empirical test of this model within the Extension System is underway for a regional forestry short course (The Master Tree Farmer Series). Past program participants will be surveyed to estimate the influence of various factors on the adoption of practice. Table 1 illustrates a sample of the proposed variables that will be used to study the influence of educational and other variables on the adoption of practice. These are strictly a sampling of the types of questions that will be asked.

A group of professional foresters and educators will provide more input into the reliability and validity of these questions and others before the actual survey is designed and delivered. As with the Cervero model, the study covers four categories of independent variable. These include those that relate to the educational program of interest, the individual participating in the program, the proposed practice (trialability, complexity, observability, relative advantage and compatibility), and the social system or network surrounding the participant. A sampling of subvariables and questions are also listed.

Other factors that may explain the variation in the dependent variable include location (are participants in some states more likely to apply a practice than those in others?) and time elapsed since attending the course (landowners may have had time to implement practices that were problematic immediately following their participation). While some of these variables may be correlated with others, the collection of this data at this time will be useful for descriptive purposes (for example, key stakeholders may wish to know if adoption of practice occurs in their state to a greater extent than in others).

To More Robust, Defensible Extension Evaluations

While the diffusion of innovations concepts were developed during the years of rapid agricultural innovation and dissemination, the theories have evolved over the years to incorporate user/client-based needs (Rogers, 2003). User/client needs-based models incorporate end-users into research design, implementation, and technology transfer activities. The diffusion of innovations framework for program evaluation should lead to a better understanding of any barriers or issues surrounding adoption of a practice by incorporating end user needs and obstacles.

As with the diffusion of innovations framework, the area of program evaluation is vast, diverse, and still developing. In Extension, evaluation has historically been one-dimensional. Measurement has focused solely on the impact of program participation in terms of change in behavior or adoption of practice. Characteristics of the educational program and influences of external factors have not normally been accounted for in an inclusive framework such as this. Using diffusion of innovations techniques to guide an Extension program evaluation can provide an opportunity to investigate what is going on inside "the black box" of program impact determination (Bush, Mullis, & Mullis, 1995). In addition, it can also move Extension evaluation toward more causal modeling.

Today's Extensionist is living in a complex world where social, economic, and environmental factors all influence adoption of practice (Clements, 1999). Applying statistical inferences to study these factors in a systematic intentional manner can yield a better understanding of the relative impact of education and information. This may be a particularly important investment in major, multi-year, or multi-state Extension programs.

Such research may also uncover valuable information for providing leadership among public service providers who also may influence adoption of practice. Examples in the forestry community, for instance, include state forestry agencies and associations, private forestry consultants, and others who provide support and assistance to private owners. Finally, this type of program evaluation model can be adopted for many disciplines within Extension and can be used with stakeholders and potential funding agencies to provide for more holistic, credible evaluations. The results of applying these concepts in an actual program evaluation are currently underway and will provide insight into the practical nature of such a model.

References

Abrussese, R. S. (1987). The Cervero model. The Journal of Continuing Education in Nursing, 18(1), 22-23.

Alston, D. G., & Reding, M. E. (1998). Factors influencing adoption and educational outreach of integrated pest management. Journal of Extension [On-line], 36(3). Available at: http://www.joe.org/joe/1998june/a3.html

Andrews, M. (1983). Evaluation: An essential process. Journal of Extension [On-line], 21(5). Available at: http://www.joe.org/joe/1983september/index.html

Beal, G. M., & Bohlen, J. M. (1957). The diffusion process. Ames: Iowa State University of Science and Technology.

Bennett, C. (1977). Analyzing impacts of extension programs (Slightly rev. July l977. ed.). Washington: Deptartment of Agriculture Extension Service.

Broderick, S. H., Snyder, L. B., & Tyson, C. B. (1996). Selling stewardship within the community: A social marketing approach. In M. J. Baughman & N. Boodman (Eds.), Proceedings: Symposium on nonindustrial private forests: Learning from the past, prospects for the future (pp. 255-263). St. Paul: University of Minnesota, Minnesota Extension Service Special Programs.

Bush, C., Mullis, R., & Mullis, A. (1995). Evaluation: An afterthought or an integral part of program development. Journal of Extension [On-line], 33(2). Available at: http://www.joe.org/joe/1995april/a4.html

Cervero, R. M., & Rottet, S. (1984). Analyzing the effectiveness of continuing professional educational: An exploratory study. Adult Education Quarterly, 34(3), 135-146.

Clements, J. (1999). Results? Behavior change! Journal of Extension [On-line], 37(2). Available at: http://www.joe.org/joe/1999april/comm1.html

King, R. N., & Rollins, T. (1995). Factors influencing the adoption decision: An analysis of adopters and nonadopters. Journal of Agricultural Education, 36(4), 39.

Lamble, W., & Seaman, D. (1994). Diffusion and adoption: Basic processes for social change. In D. J. Blackburn (Ed.), Extension handbook: Processes and practices. San Francisco: Thompson Educational Publishing.

Rogers, E. M. (1963). The adoption process: Part I. Journal of Extension [On-line], 1(1). Available at: http://www.joe.org/joe/1963spring/index.html

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.

Rollins, T. (1993). Profile of farm technology adopters. Journal of Extension [On-line], 31(3). Available at: http://www.joe.org/joe/1993fall/rb1.html

Yates, B. L. (2001, May 24-28, 2001). Applying diffusion theory: Adoption of media literacy programs in schools. International Communication Association Conference. Retrieved February 8, 2005, from: http://www.westga.edu/~byates/applying.htm

Use of Computer Technologies by Educators in Urban Community Science Education Programs

Alexey Kudryavtsev
Graduate Student
Cornell University
Ithaca, New York
ak383@cornell.edu

Marianne Krasny
Professor and Director of Graduate Studies
Cornell University
Ithaca, New York
mek2@cornell.edu

Gretchen Ferenz
Sr. Extension Associate and Program Leader, Urban Environment
Cornell University Cooperative Extension - NYC
New York, New York
gsf4@cornell.edu

Lisa Babcock
Extension Associate, Urban Environment
Cornell University Cooperative Extension - NYC
New York, New York
llb28@cornell.edu

Introduction

Outreach and Extension programs increasingly are using computer technologies to deliver services and resources to the public. Examples include videoconferences (Pankow, Porter, & Schuchardt, 2006), Webcasts (LGEAN 2004), electronic newsletters (Westa, Broderick, & Tyson, 2005), online communities (Kallioranta, Vlosky, & Leavengood, 2006; Schlager & Fusco, 2004), youth education programs (Mutchler, Anderson, Taylor, Hamilton, & Mangle, 2006), and curriculum and training materials on Web sites and CDs (Dunn, Thomas, Green, & Mick, 2006; Mayfield, Wingenbach, & Chalmers, 2006; Penuel, Bienkowski, Korbak, 2005; Zimmer, Shriner, & Scheer, 2006).

When working in low-income urban and other under-resourced communities, Extension staff need to ensure that audiences are able to access the various digital tools. The ability to use computer technologies is often viewed through the lens of the "digital divide," or the gap between those people and communities that can effectively use information and communication technologies and those that cannot (Norris & Conceicao, 2004; Shelley & Thrane, 2004; Warschauer, 2003).

Originally, the digital divide referred to the lack of access to computer technologies and Internet connectivity (Mitchell, 2003; Mossberger & Caroline, 2003). Even recently, studies that address the digital divide in communities served by Extension are based on the premise that the digital divide refers to access (Elbert & Alston, 2005). However, Cullen (2001) argued that this is a more complex issue, and identified four factors that may influence the digital divide:

• Physical access to computer technologies. Organizations may not have computers, other digital devices, software, and Internet connectivity.


• Computer skills and support. Individuals who have computers and other digital tools may not use them because of lack of skills.


• Attitudes and awareness. People may have and use computers but not deploy them to their full potential because they fail to see how sophisticated computer technologies can contribute to their professional development or provide other services.


• Content. People may not use the Internet and other computer technologies because the content of digital materials is not interesting or relevant.

Although access to computer technologies has been described in the literature (Elbert & Alston, 2005), little is known about other factors that are influencing the use of computers in community settings served by Extension. In this article, we explore the four digital divide factors in urban low-income communities and investigate how community educators are using digital materials in their after-school and other non-formal youth education programs.

Research Questions

We examined components of the digital divide and related computer use as follows:

• How do access to computers, educator skills and attitudes, and content of digital materials limit the use of digital technologies in education programs at community-based organizations (CBOs) in urban, low-income communities?


• How are urban educators using digital materials in after-school and other youth programs for their own professional development and in the implementation of youth education programs?

Methods

We conducted two separate studies. The first was a qualitative study conducted within the context of the Garden Mosaics program in NYC, and the second was a written survey of CBOs in six US cities.

Study 1. Computer Use in Garden Mosaics in NYC

Garden Mosaics Program

Garden Mosaics is a youth and community science education program, through which youth learn about environmental science in urban community gardens within an intergenerational, multicultural, and action context. Originally developed by Cornell University in collaboration with community education programs in cities across the U.S., it has recently moved to a permanent home with the American Community Gardening Association. Funders have included the USDA, NSF, and the Weed Science Society of America. The program's mission is "connecting youth and elders to investigate the mosaics of plants, people, and cultures in gardens; to learn about science; and to act together to enhance their community." From 2001-05, Garden Mosaics reached about 700 educators and 12,000 youth across the U.S. and in Canada <http://www.gardenmosaics.org>.

Garden Mosaics employs computer technologies for educator training, curriculum resources, and program implementation as follows:

• Curriculum on Web site, including illustrated fact and activity sheets, protocols for investigations and community action projects, and program overview;


• Online databases, where youth report findings from their Garden Mosaics investigations and action projects in community gardens and learn about Garden Mosaics inquiry activities in other cities;


• Interactive training DVD for educators, which includes footage of educators conducting activities with youth;


• Web forum, which allows educators from different cities to share their ideas about implementation of Garden Mosaics and about science education.

Sample

Participants in study 1 were eight educators from four after-school programs and four CBOs in low-income communities in NYC. In spring 2005, Cornell University Cooperative Extension-NYC created a list of about 100 CBOs in the South Bronx that work with 10-18 year-old youth, have community gardens in their neighborhoods, and have computers connected to the Internet. Educators from these CBOs were invited to participate in a Garden Mosaics training workshop, after which they were expected to implement the program with youth.

Fifteen educators took part in the 2-day training workshop in May 2005, during which they learned how to implement the Garden Mosaics curriculum with youth through demonstrations and hands-on activities and were briefly introduced to the Garden Mosaics online resources and interactive DVD. In June 2006, they were invited to participate in this research project.

Six educators accepted this invitation; nine others did not implement Garden Mosaics during summer 2005 and did not participate in this study. An additional two educators from community gardening organizations in Brooklyn who helped organize a Garden Mosaics workshop for garden activists in February 2005, also took part in this research. Among the eight educators, seven are women, three are immigrants, and six represent minority groups (African-American, Hispanic, and South-Asian). The size and number of staff in these organizations vary significantly, from organizations that do not have an office but have more than 10 volunteer educators, to after-school programs that are part of larger community development corporations and have several full-time educators.

Interviews and Observations

In June-July 2005, we conducted semi-structured interviews (Denzin & Lincoln, 2000; Mason, 2002) with eight educators to determine the availability of computer technologies in their organizations and their computer skills and attitudes. We also visited their organizations and observed their facilities (computer labs, available digital technologies) and conducted observations of the use of Garden Mosaics digital materials by educators in two organizations. In addition, three educators from the NYC CBOs voluntarily participated in a Garden Mosaics online forum during June-August 2005, along with 27 other educators from 10 states. We interviewed these three educators about the benefits that they received from participation in the forum relative to learning about the Garden Mosaics curriculum, sharing ideas about environmental and science education and networking with peers.

Study 2. CBO Survey of Computer Use

We conducted a written survey of educators from 21 urban CBOs serving minority and immigrant youth, which were identified as being likely to participate in future science enrichment programs by five larger educational organizations (Fairchild Tropical Botanic Garden in Miami FL, Lawrence Hall of Science in Berkeley CA, Saint Louis Science Center, University of the District of Columbia, and Cornell University). Rather than a random sample, the respondents could be considered as representative of CBOs likely to form collaborations with urban Extension programs. Only one CBO in NYC participated in both study 1 and study 2. The number of employees in the CBOs ranges from 1 to 42, and the majority of the youth and staff are from underrepresented minority groups (predominantly African-American and Hispanic). The survey included questions about Internet access and use of computers with youth.

Results

Study 1. Computer Use in Garden Mosaics in NYC

Access to Computer Technologies

Because educators were chosen for this research from the population that has computers and Internet access, questions about access focused on types of computer technologies they were using. All the educators use computers and the Internet at work and/or at home at least a few times a week. Their computers are adequate to run software for education programs; only one educator had obsolete computers in her organization and thus could not download some program resources (e.g., PDF files). Four CBOs have computer classrooms with more than six computers connected to the Internet available for use by youth. All CBOs have basic computer peripherals (printers, DVD players, speakers, etc.) and other digital devices (e.g., digital cameras).

Computer Skills

All interviewed educators were able to use generic computer applications, such as Microsoft Office and Internet browsers. Two participants of this study had basic Web design skills and used graphic design programs. Only one educator used computers just for checking emails and creating simple word-processing documents and was unwilling to learn other simple computer programs. All educators developed their computer skills themselves through "trial and error" and with help from colleagues; only one educator had participated in computer workshops.

Attitudes

The eight community educators were split in their attitudes toward computer technologies. Four educators enthusiastically described integration of computer technologies into their education programs; they also claimed that computers help them learn about education curricula and assist their professional development. These educators reported that they already use the Internet to download lesson plans, "find new ways to teach the same things," look for illustrations for teaching youth in their programs, and check for grant related information. One of these educators finds that the Internet is useful for locating community gardens and accessing neighborhood maps for outdoor activities with youth. Another participant posts her newsletter for members of a gardening CBO on the Web, which helps "to reach more people and reduce using paper."

In contrast, four educators were not aware of any benefits from the Internet and computer technologies for their education programs other than sending emails and text editing. For example, one educator wanted to become involved in some kind of networking with peers, but did not think that computer-mediated communication could help her to do that: "What I do not like about computers is the feeling that we are so removed from each other. I feel you get so much more when you talk face-to-face with somebody, and you get a real feel of what's going on and the overall enthusiasm."

Content: Use of Garden Mosaics Materials

Curriculum materials on the Web site. Garden Mosaics curriculum materials for educators are available on the Web site in PDF format; educators also received hard copies during the training workshops. Four of eight educators had not visited the Garden Mosaics Web site following the workshop because they thought the printed handouts had everything they needed. Only one of eight community educators visited the Garden Mosaics Web site repeatedly.

DVD. Of the six educators who participated in the training workshop and received the Garden Mosaics training DVD, three did not use the DVD in the 2 months following the workshop, and one stated that she learned all the important information she would need for program implementation at the workshop and that she did not learn much from the DVD afterwards. Another two educators watched the DVD soon after the workshop and found it very helpful for broadening their knowledge about Garden Mosaics. Interestingly, one educator used the DVD for educators to introduce the Garden Mosaics program to youth. Her group of 15 11-13 year-olds watched the DVD on the computer, saw other youth doing Garden Mosaics activities, and became excited about participation in this program.

Web forum. Of the three educators who participated in the Web forum, one had limited experience using computers and was nervous about the idea of joining the forum. However, after a trial experience she enjoyed communication with other educators throughout the country, and, in fact, was one of the most active Web forum participants. In the beginning of the forum, educators experienced some technical problems, which nearly discouraged them from participation. However, once we assisted them in overcoming technical problems, participants stated that the forum was helpful for learning about Garden Mosaics. Their messages on the Web forum discussion boards indicate that they benefited from networking with other educators across the U.S. (Kudryavtsev, 2006).

Online databases. Educators were informed at the workshops that youth can submit results of their investigations and action projects to the databases on the Garden Mosaics Web site. Two of the eight educators understood the educational value of the databases for youth. For example, one of them said, "I like the data-sharing and stories. It gives students the chance to write about and express their experience with the gardens; it allows them to read other experiences, so they do not feel that they are alone." However, six educators were not aware of benefits from using these databases with youth and did not understand how to submit the information to the databases.

Study 2. CBO Survey of Computer Use

Of the 21 CBOs surveyed in May-June 2006, 19 had fast Internet connections, and 18 used computers in youth programs. When asked to rate the importance of: "Using the latest 'hip' digital technologies" as a "means to get youth engaged in learning science," the educators' mean response was 2.1 on a Likert scale of 1-10, with 1 being most important. Using digital technologies ranked slightly lower than "hands-on activities" (1.4), "identifying a problem in your community and using science to help solve it" (1.8), and "communicating with scientists, including scientists who are young and culturally diverse" (2.0).

For the three CBOs that do not use computers with youth, reasons given included lack of access, prohibitive costs, logistics (youth group too large for computer lab), and an emphasis on nature-based rather than classroom type programs (this CBO used hand-held GPS devices with youth in their outdoor activities).

The remaining 17 CBOs cited the following uses of computers in their youth programs: Internet research (8 responses), online learning activities (4), GIS/GPS (2), presentations (2), and design projects (2). Several other uses received one response each, including for homework, as rewards, to demonstrate concepts using CDs, PDAs in field work, and for making budgets.

Discussion

The results of this study suggest that if Extension is to play a role in bridging the digital divide in low-income, urban minority communities, access to computers and the Internet may no longer be a major concern. Similarly, whereas lack of computer skills limited the use of computers among some NYC educators, this problem was readily overcome with minimal support.

Although the sample sizes in this study were small, the finding that access to computers is no longer a major barrier to urban Extension programs is supported by the fact that Cornell Cooperative Extension-NYC was able to identify approximately 100 CBOs in low-income communities with Internet access, and by the observations of our colleagues in other cities that CBOs generally have access to Internet technologies (personal communication, N Stein, Lawrence Hall of Science; H Hughes, Saint Louis Science Center). Thus, whereas we cannot definitively say that most CBOs have Internet access, it appears that finding CBOs working in urban minority communities that have Internet access is not a problem.

Attitude toward computer technologies and awareness of content appeared to be important factors limiting the use of digital resources in Garden Mosaics, where only half the educators used computers in their youth programs. However, the written survey conducted 9 months later suggests that computer use in CBO youth programs is much more widespread, with nearly all educators incorporating computers into their youth programs. It is possible that the difference in timing between the two studies accounts for some of this discrepancy because it may be that the use of computers in urban CBOs is relatively recent. Another possibility is that the CBOs in the second study had a longer history of collaboration with science museums and other larger organizations that supported their use of computers.

Furthermore, the ways in which Garden Mosaics uses technology in its youth programs, i.e., for reporting results to the Internet, may not be aligned with CBO youth practices. The survey indicated that youth in CBO programs use computers for Internet research and learning activities, rather than for data reporting and other uses. That reporting results is not a preferred use of computers by youth programs is supported by the results of a 2005 Garden Mosaics evaluation survey of 591 CBO and Extension educators from across the U.S., which revealed that even though 83% of educators had computer access at work, only 9% of Garden Mosaics youth programs submitted reports to the online databases. Of those groups that did submit reports, the educators felt that "youth seeing their work on the Web site" was more important than "computer use being a good experience for the youth" or "youth feeling as if they were contributing to the work of scientists" (Kudryavtsev, unpublished data).

Conclusion

Extension educators working with urban CBOs should no longer be guided by the notion of the digital divide as limited access to computer technologies. Rather our results suggest that Extension programs that target low-income urban communities need to consider demonstrations of new and innovative computer uses in educational programs and discussions of the values of these technologies as professional development and educational tools. Finally, knowing that youth are motivated by using "hip" technologies, Extension should make a concerted attempt to engage youth and adults working closely with youth in designing the technology component of their programs.

Acknowledgement

This work was funded by the National Science Foundation (ESI 0125582), the Cornell Urban Scholars Program, Edmund S. Muskie/FREEDOM Support Act Graduate Fellowship Program, and USDA. Thanks to Garden Mosaics Program Leader Keith Tidball and to Ken Reardon and Ruth Sinton of the Cornell Urban Scholars Program for their contributions to this research.

References

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Kudryavtsev, A. (2006). Use of computer technologies in dissemination and implementation of environmental education programs. MS Thesis. Cornell University. Available at: http://www.gardenmosaics.cornell.edu/pgs/aboutus/aboutus4.htm

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Preferred Information Delivery Methods of North Carolina Forest Landowners

Robert E. Bardon
Associate Professor and Extension Specialist
Department of Forestry and Environmental Resources
Raleigh, North Carolina
robert_bardon@ncsu.edu

Dennis Hazel
Assistant Professor and Extension Specialist
Department of Forestry and Environmental Resources
Raleigh, North Carolina
dennis_hazel@ncsu.edu

Kevin Miller
Extension Agent, Natural Resources
Catawba County
Newton, North Carolina
kevin_miller@ncsu.edu

North Carolina State University

Introduction

Dynamic forestland ownership patterns and increased demands for forest products together emphasize the need to deliver relevant forestry information to a growing and changing non-industrial private forestland (NIPF) owner population. North Carolina's NIPF population is estimated at 479,000 (Brown, New, Oswalt, Johnson, & Rudis, 2006). Giving one-on-one attention to each forestland owner would best satisfy their diverse needs, but would be impossible to accomplish. Since one-on-one attention is impractical, Extension educators will need to deploy other methods of information delivery in order to reach NIPF clientele.

Researchers suggest using a diversity of information delivery methods to reach clientele, but particular information delivery methods must be matched with target audiences to insure their efficacy (Egan, Welch, Page, & Sebastian, 1992; Rodewald, 2001; Londo & Gaddis, 2003; Radhakrishna, Nelson, Franklin, & Kessler, 2003; Cartmell II, Orr, & Kelemen, 2006). The choice of information delivery method used by an Extension educator may have serious consequences for program effectiveness. Some fear that using one information delivery method may alienate those who prefer another and particularly that "high-tech approaches may intimidate certain groups of clientele (e.g., older clientele)" (Rodewald, 2001). Because so many delivery methods are available today, the preference of the clientele for a particular method may be difficult to predict.

The purpose of the study reported here was to identify preferences for information delivery methods among groups of North Carolina's non-industrial private forest landowners and to investigate these groups for descriptive socio-demographic, land, or management experience characteristics. If information delivery method preferences can be linked with socio-demographic, land ownership, or management characteristics, educational efforts can be directed at specific groups of landowners using the methods they prefer.

Methods

Data for this analysis came from a 2005 mail survey of 2600 NIPF landowners from 13 counties. The 13 counties, selected using a stratified random sample, were chosen from a population of 100 counties distributed between seven Cooperative Extension districts. A stratified random sampling of the counties was done to ensure that all regions of the state were represented (Figure 1).

Figure 1.
Distribution of 13 North Carolina Counties (Shaded Counties) Selected Through Stratified Random Sampling Across Seven Cooperative Extension Districts

Within each county, 200 landowners were randomly selected from the 2004 present use-value tax records. Surveys were mailed to all 2,600 landowners, with a reminder postcard sent to the recipients 3 weeks after the original mailing. Late respondents were given the option to send in the original survey, request an additional survey by mail or telephone, or use a Web address on the postcard to access an identical copy of the survey that could be completed online.

The survey instrument was designed based on previous studies of NIPF owners (Birch, 1996) and using Surveying the Social World: Principles and Practice in Survey Research (Aldridge & Levine, 2001). Prior to mailing the survey it was reviewed by 10 people of various backgrounds to include local landowners, graduate students, natural resource professionals who work with the public, and North Carolina State University faculty members.

The survey asked participants about their preferences for information delivery methods, their socio-demographics, their land, and their forest management experience. The six information delivery methods included mail-based material, Web-based material, short programs, long programs, landowner association participation, and distance education. Mail-based material was defined as newsletters, brochures, compact discs, Extension publications, and magazine articles. Web-based material was defined as Web-site reading, downloadable publications, or streaming video. Short programs were defined as evening or less than half-day seminars or workshops at county facilities. Long programs were defined as full day/multiple day field site visits or demonstrations. Participation in a landowner association was considered self-explanatory. Distance education was defined as Web-based landowner courses, video-based landowner courses, or textbook-based correspondence courses. The options were not mutually exclusive.

Respondents were asked to rank each information delivery method on a 4-point continuum somewhere between would never use and would often use. The 4-point continuum interval is by one. Socio-demographics factors included gender, age, marital status, occupation, number of children below the age of 18, income, and education. Land ownership factors included acreage owned, land ownership tenure, resident or absentee landowner, and primary residence location. Forest management factors included past forest management experience, future plans for forest management, sources from which forestry information is obtained, and income needs from their forestland.

The definition of "past experience" refers to forest management practices previously undertaken, and the definition of "future plans" refers to the likelihood that a landowner will practice forest management on their land in the future. Both "past experience" and "future plans" were ranked on 10-point continuums, with "past experience" ranked somewhere between "not at all experienced" and "very experienced" and "future plans" ranked some where between "not at all likely" and "very likely". Each 10-point continuum's interval is by one. Nearly all survey questions inherently had categorical responses; the few that did not, age, land ownership tenure, and acreage owned, were categorized using Birch's (1996) classifications.

A K-means cluster analysis (SAS, 1999) was performed using only respondents' preference for information delivery methods. To investigate differences among clusters with regard to socio-demographics, land characteristics, and management experience, contingency tables analysis (SAS, 1999) was used. To determine whether or not clusters were statistically significantly different with respect to a given question, Pearson's Chi-Square was used.

Results

Results of the study are based on 460 returned questionnaires, those in which respondents answered all questions about information delivery methods and claimed at least one acre of forestland. The response rate was 17.7%.

K-means cluster analysis identified five groups of landowners that were cohesive with respect to preference for information delivery methods. Figure 2 shows the likelihood that a member of a given cluster will use a particular information delivery method. Each cluster has been given a memorable name that helps describe the preferred method of information delivery. The "Don't Bother Me" cluster is unlikely to use any information delivery method. The "Snail-Mailers" prefer only mail-based information delivery. The "Short-Mailers" prefer mail-based materials and short programs. The "Web-Mailers" are most likely to use mail-based information and the Internet. Finally, the "Fan Club" cluster will likely use any information delivery method.

Figure 2.
Preferred Method of Information Delivery of North Carolina Forest Landowners by Clusters

Contingency table analysis resulted in the identification of several socio-demographic characteristics that differed significantly across clusters (Table 1). These characteristics were retirement status, marital status, number of children under 18, age, occupation, income, and education. Gender was not significantly different among clusters at the 0.05 level (p = 0.12), indicating that each landowner cluster contained relatively the same ratio of males to females. Landowners in the "Don't Bother Me" cluster, "Snail-Mailers" cluster, and "Short-Mailers" cluster were more likely to be retired than the landowners in the "Web-Mailers" or "Fan Club" clusters. "Web-Mailers" landowners and "Fan Club" landowners were more likely to be married and have children under the age of 18. A similar pattern is seen with age class, occupation, income, and education, where "Web-Mailers" and "Fan Club" landowners dominated the lower age classes, have higher ratio of landowners in white-collar occupations, in upper income classes, and higher education levels.

Contingency table analysis revealed that clusters differed by acreage class and land ownership tenure, but not by location of primary residence (p = 0.13) or residence on their forestland (p = 0.13) (Table 2). The "Don't Bother Me" cluster and "Snail-Mailers" cluster consisted of landowners predominantly in the smaller acreage classes and dominated the longer tenure classes. Landowners in the "Short-Mailers," "Web-Mailers," and "Fan Club" clusters also owned land in the smaller acreage classes but had a greater ratio of landowners with in the larger acreage classes, 100 acres and larger. "Short-Mailers," "Web-Mailers," and "Fan Club" clusters had fewer landowners in the older tenure classes, tenure classes prior to 1960.

Analysis of the clusters' forest management experience characteristics revealed four key areas of differences: past management experience, future plans for management, the percent of the cluster's respondents who require income from their forestland, and sources from which forestry information has been obtained (Table 3). Three information sources, Forest Industry (p = 0.06), Logger/Timber Buyer (p = 0.74), and Neighbors (p = 0.13), were not significantly different among clusters at the 0.05 level.

Landowners in the "Don't Bother Me" cluster and "Snail-Mailers" cluster were less likely to have past management experience, less likely to have future plans for forest management, and less likely to required income from their land. Their top four sources for information received were State Forest Service, consulting foresters, Cooperative Extension, and logger/timber buyer. The "Short-Mailers," "Web-Mailers," and "Fan Club" clusters consisted of landowners much more involved with their land. These clusters had more landowners who have past management experience, future plans for forest management, and were more likely to require income from their land. The "Short-Mailers," "Web-Mailers," and "Fan Club" top four sources for forestry information were State Forest Service, consulting foresters, Cooperative Extension and logger/timber buyer.

Discussion

Krejcie and Morgan (1970) indicate that a sample size equal to 384 is statistically representative of a population of 1 million individuals. In North Carolina, the NIPF landowner population is estimated to be 479,000 owners (Brown, New, Oswalt, Johnson, & Rudis, 2006). Based on Krejcie and Morgan (1970), the response rate of the study reported here is statistically representative of North Carolina's NIPF owners.

Cluster analysis determined that there are five groups of landowners with respect to information delivery method preferences in North Carolina. One of the clusters, the "Don't Bother Me" cluster, expressed very little interest in any information delivery method or in managing their forestland. Because of this, the "Don't Bother Me" cluster is likely to be very difficult to reach. They only constitute 7% of the respondents, so expending effort to direct educational efforts at this group of people will be costly for the amount of impact that could be expected.

A second cluster, the "Fan Club" cluster, expressed interest in all information delivery methods. This group represents 23% of respondents and consists of landowners in all acreage classes, with a majority in the 100-500 acre class. A majority of respondents in this cluster have received forestry information from Cooperative Extension, State Forest Service, consulting foresters, and loggers/timber buyers. These landowners will not require Extension educators to target them with a specific information delivery method in order to be reached; information delivery methods targeted at other groups will reach this group.

The three remaining clusters, "Snail-Mailers," "Short-Mailers," and "Web-Mailers," which represent 21%, 24%, and 25% of the respondents, respectively, have particular preferences for methods of information delivery, and each has characteristics that allow for the identification of these target audiences. By being able to identify specific audiences among these three clusters and targeting them with their preferred delivery method, Extension educators will be most effective in delivering forestry education.

"Snail-Mailers"

Approximately 21% of respondents were classified as "Snail-Mailers." "Snail-Mailers" prefer mail-based information to all other delivery methods. Nearly two-thirds of this cluster is over 66 years old. More than two-thirds of this cluster is retired. Compared with "Short-Mailers" and "Web-Mailers", the "Snail-Mailers" cluster has a higher percentage of respondents (29.5%) who earned less than $40,000 in 2004, likely because many members of this cluster are retired (68.4%).

To reach this cluster most effectively, Extension educators should specifically target retirees. Educational information should be developed that can be direct mailed, such as newsletters and information pamphlets. Other possibilities for information delivery include newspapers, magazines, or journals.

Two-thirds of this cluster claims not to have received forestry information from the Cooperative Extension Service in the past, so Forestry Educators from Cooperative Extension can team with Family and Consumer Science Educators from Cooperative Extension, local community colleges, or other local community organizations that focus on retirees and lifelong-learners to develop, market, and deliver forestry information. Other opportunities exist for forestry educators to develop, market, and deliver educational programs by teaming with State Forest Service personnel, who have provided forestry information to more than 50% of this landowner group. Using mail as the preferred delivery method and teaming with organizations that target retirees may result in the greatest impact on delivery of forestry information to this audience.

"Short-Mailers"

"Short-Mailers" are most likely to use mail-based information and short programs such as half-day seminars or workshops. They constitute approximately 24% of respondents. They are somewhat similar socio-demographically to the "Snail-Mailers," but slightly younger and with a lower percentage of retirees. Aside from their willingness to attend short programs, "Short-Mailers" have experience with managing their forestland, have future plans for forest management, and many of them require income from their forestland. Nearly half own more than 100 acres of forestland and have owned that land for more than 25 years.

Over 70% of "Short-Mailers" have received information from the State Forest Service. The fact that so many of the "short-Mailers" have received information from the State Forest Service provides an opportunity for Cooperative Extension to collaborate with the state agency in developing and marketing forestry educational programs. Contact information for landowners who have sought technical and financial assistance from the State Forest Service can be compiled and used for marketing short programs. Because almost half of this group requires income from its forestland, they are likely interested in educational programs related to the monetary aspect of forestland ownership. Programs focused on timber marketing, selling timber, taxes, recreational income opportunities, and non-timber forest products may be of interest to this group.

"Web-Mailers"

The final focus cluster is the "Web-Mailers." This cluster constitutes 25% of respondents and prefers mail-based and Web-based information delivery. They are significantly younger, more likely to be married, more likely to have children, and less likely to be retired than members of the other focus clusters. Sixty percent of this cluster makes more than $70,000 per year, and two-thirds of "Web-Mailers" have at least a four-year college degree. Ninety percent of this group expressed that they are likely to manage their forestland in the future.

Many "Web-Mailers" have job or family responsibilities that can limit their ability to attend programs. However, cost-effective, non-traditional methods such as Internet-based information delivery may be effective in increasing their knowledge about forestry and forest management. For Extension educators, these landowners may be the hardest to connect with because there is no existing agency or organization with which this group of landowners may be associated. To reach this cluster, areas of high income and high levels of education should be targeted; this would include urban centers, universities, and community colleges. Advertising of Internet-based resources through newspapers and professional journals may also increase the chance of reaching this audience.

Conclusions

Results of thee study reported here revealed five distinct groups of landowners with particular preferences for information delivery methods. These groups include landowners who have little desire to receive forestry information, those who prefer to receive their forestry information through the mail only, those who prefer to receive forestry information through short educational programs lasting less than half a day, those who prefer obtaining their forestry information through Internet-based resources, and those who are likely to use all forms of information delivery.

The study identified associations between delivery method preferences and other characteristics of landowners, including socio-demographics, land ownership, and management experience. Connecting easily identifiable landowner characteristics with landowner preferences for information delivery methods allows Extension educators to identify delivery methods that are most likely to be effective in reaching their target audience.

By delivering information to the audience based on the audience's preferred method, Extension educators can have a greater impact in reaching their audience. By relying upon associations between landowner characteristics and delivery method preferences, educators can meet the changing needs of a dynamic NIPF population by matching their audiences with delivery methods most likely to be effective. They can save money and time by targeting specific groups of people with specific information delivery methods.

References

Aldridge, A., & Levine, K. (2001). Surveying the social world, Principles and practice in survey research. Buckingham: Open University Press. 196 pp.

Birch, T. W. (1996). Private forest-land owners of the United States, 1994. USDA Forest Service Resource Bulletin NE-134. Radnor, PA 183 pp.

Brown, M. J., New, B. D., Oswalt, S. N., Johnson, T. G., & Rudis, V. A. (2006). North Carolina's Forests, 2002. USDA Forest Service Resource Bulletin SRS-113. Asheville, NC 63 pp.

Cartmell II, D. D., Orr, C. L., & Kelemen, D. B. (2006). Effectively disseminating information to limited-scale landowners in the urban/rural interface. Journal of Extension [On Line], 44(1) Article 1FEA5. Available at: http://www.joe.org/joe/2006february/a5.shtml

Egan, M. W., Welch, M., Page, B., & Sebastian, J. (1992). Learners' perceptions of instructional delivery systems: Conventional and television. The American Journal of Distance Education, 6(2), 47-55.

Krejcie, R. V., & Morgan, D.W. (1970). Determining sample size for research activities. Educati onal and Psychological Measurement, 30, 607-610.

Londo, A. J., & Gaddis, D. A. (2003). Evaluating Mississippi non-industrial private forest landowners acceptance of an interactive video short course. Journal of Extension [On Line], 41(5). Available at: http://www.joe.org/joe/2003october/rb4.shtml

Radhakrishna, R. B., Nelson, L., Franklin, R., & Kessler, G. (2003). Information sources and Extension delivery methods used by private longleaf pine landowners. Journal of Extension [On Line], 41(4). Available at: http://www.joe.org/joe/2003august/rb3.shtml

Rodewald, A. D. (2001). Delivery systems - Is the "latest" technology the greatest? Journal of Extension [On Line], 39(4). Available at: http://joe.org/joe/2001august/tt2.html

SAS. (1999). SAS onlinedoc. Version eight. SAS institute, Inc. Cary, NC. Available at: http://v8doc.sas.com/sashtml/

A Preliminary Study of the Meanings Children Attach to Healthy and Unhealthy Lifestyles

Jennifer Paff Ogle
Associate Professor
Colorado State University
Fort Collins, Colorado
ogle@cahs.colostate.edu

Susan S. Baker
Assistant Professor
Colorado State University
Fort Collins, Colorado
sbaker@cahs.colostate.edu

Jan B. Carroll
Cooperative Extension 4-H Youth Development Specialist
Colorado State University
Fort Collins, Colorado
Jan.Carroll@ColoState.EDU

Brian D. Butki
Director, Activity Program and Youth Sport Camps
Colorado State University
Fort Collins, Colorado
Brian.Butki@ColoState.EDU

Mary Lynn Damhorst
Associate Professor
Iowa State University
Ames, Iowa
mldmhrst@iastate.edu

Over the past 30 years, the incidence of childhood obesity in the United States has tripled. Current estimates suggest that 15% of American children aged 6 to 19 are overweight, which places them at a higher risk for becoming overweight adults and thus for developing health problems in later years (e.g., cardiovascular disease and diabetes) (Ogden, Flegal, Carroll, & Johnson, 2002). This upward trend in childhood overweight and obesity has roused concern among health professionals, moving them to make calls for government, industry, and families to work toward the prevention of childhood obesity and the promotion of healthy lifestyles among children (International Food Information Council [IFIC], 2004; National Academies' Institute of Medicine [IOM], 2006).

As researchers in health and family and consumer sciences, we appreciate the importance of preventing childhood obesity and of teaching children to make healthful choices about diet and physical activity. At the same time, we are concerned about consumer culture messages that constitute an interpretive context for obesity prevention campaigns targeting children. In particular, we are troubled by consumer culture's idealization of extreme thinness and its equation of thinness and healthfulness (Shilling, 2003). In this context, appearance and healthfulness may become obscured, and bodies that do not meet demanding cultural norms of thinness may be assumed to be unhealthful (Edgley & Brissett, 1990).

With the present work, we build a foundation for responding to calls to promote healthful lifestyles among children and to prevent childhood obesity. This article describes results from four focus groups exploring the meanings that children attach to concepts such as health, healthy lifestyles, and healthy bodies. The focus groups were conducted as a preliminary study in support of a larger research agenda proposing to develop and test educational curricula aimed at concurrently promoting healthful eating, regular exercising, and an emphasis upon the physical functioning of the body rather than its size or appearance, a focus that is sometimes associated with the "health at any size" paradigm (Campos, Saguy, Ernsberger, Oliver, & Gaesser, 2006).

The need for the preliminary study arose as our interdisciplinary research team recognized that any efforts undertaken to develop educational programming and instruments to measure children's attitudes and knowledge about health must be informed by an "insider understanding" of the meanings that children attach to the ideas of "health" and "healthfulness."

However, a literature search revealed that much of the work exploring children's health-related beliefs has incorporated (a) forced-choice response formats, which do not allow participant perspectives to emerge, (b) questions worded such that the "right" answers were obvious, and (c) "yes/no" questions, which are problematic, given the tendency of children to acquiesce to adults (see Macaux, 2001; Proponnett, 1997; Young, 2003). The study reported here, which used an open-ended response format, was designed to overcome these methodological flaws in prior work.

Method

Four focus groups were conducted with children aged 5 to 12 years (n = 64). Focus groups are an effective way to access the meanings systems of young children, who may find one-on-one interviews intimidating (Madriz, 2000). Participants were divided into four groups by age and gender. Table 1 includes mean ages and body mass indices for each group.

Participants were children who had voluntarily enrolled in a "Fun LIFE" summer camp offered at a large U.S. university. Fun LIFE camp is designed for elementary school-aged children and focuses upon Learning to Improve Fitness and Eating (thus, the acronym "LIFE"). Campers participate in physical and creative classroom activities examining nutrition, fitness, and healthy lifestyles. Fun LIFE participants register for the camp on a first-come, first-served basis and represent diverse ethnic and socioeconomic backgrounds. Because the camp is administered by a university, permission to include the children in relevant research studies is sought from the campers and their parents.

Focus groups were conducted at the beginning of the camp session, before participants were exposed to the camp curriculum. Focus group questions explored definitions of a healthy body and what it means to lead a healthy lifestyle. Following are some sample focus group questions:

• When you hear the words, "healthy lifestyle," what does it mean to you?


• What things make you healthy?


• What is a healthy body?


• What can a healthy body do?


• Is it important to have a healthy body? Why/why not?

Data were audio-recorded and transcribed. All authors participated in the analysis process, which focused upon the identification of key themes. The authors' backgrounds represented diverse areas--including Cooperative Extension, family and consumer sciences education, nutrition education, youth development, health and exercise science, and body image--and thus afforded multiple perspectives on the data.

Emergent Themes: A Model of Children's Logic About Healthfulness and Unhealthfulness

Findings are visually represented in Figure 1, A Model of Children's Logic About Healthfulness and Unhealthfulness. The model includes two separate components: one that reflects the participants' logic about healthfulness and one that addresses participants' understanding of unhealthfulness. These two components were included because participants frequently defined healthfulness in terms of what it was not, contrasting what they perceived to be healthy and unhealthy behaviors. Additionally, the model illustrates the way in which participants conceptualized healthfulness and unhealthfulness in terms of "inputs" and "outputs." Here, behavioral antecedents related to food intake, exercise participation, and hygiene habits were seen as directly linked to specific well-being and appearance outcomes.

Figure 1.
A Model of Children's Logic About Healthfulness and Unhealthfulness

In this section, we discuss the components and relationships that are included in Figure 1. Where appropriate, we make observations about the possible influence of age and gender upon participant responses. The following abbreviations are used to identify remarks made by participants in the younger girls', younger boys', older girls', and older boys' groups: YG, YB, OG, and OB, respectively.

Antecedents of Healthfulness and Unhealthfulness

Good Versus Bad Foods

Participants viewed eating behavior as a key component of a healthy or unhealthy lifestyle, readily identifying foods that would contribute to or undermine healthfulness. Two ideologies about the healthfulness of various foods emerged. The first ideology was marked by a dualistic logic in which foods were categorically classified as either "good" or "bad." Foods frequently identified as good or healthy included fruits, fish, low-fat items, meats, salads, vegetables, milk, orange juice, and water. Junk foods, hamburgers, hotdogs, and foods high in salt and sugar were identified as potentially unhealthy. Younger children were more inclined to invoke this type of simplistic, binary logic in conceptualizing the relative healthfulness of foods: "[My] brother isn't healthy because he eats lots of sugar, doesn't eat carrots, and eats lots of junk food like hot dogs" (YG).

The second ideology used to conceptualize the healthfulness of various foods involved a more complex understanding of nutritional value in the context of one's overall diet. Here, participants acknowledged the importance of a balanced diet and conceded that a limited amount of fat and sugar could be part of a healthy lifestyle. Although such observations were more common among older children, even younger participants occasionally made comments to this effect: "We need a little fat in our diet, and we need carrots and vegetables. If we don't have fat, we won't be healthy. We need a little fat" (YG).

Additionally, some participants viewed the building of a healthy diet as a tricky task that required careful monitoring of foods consumed. Here, slight deviations from the "right proportions" of foods eaten were seen as threatening to health: "If you eat too much meat, you'll get too fat. If you only eat vegetables, you could get too skinny, and you might starve. And, you have to eat the right amount of things, you have to get the well-balanced diet" (OB).

Finally, participants--particularly girls--occasionally conceptualized healthful eating in terms of unpleasantness and denial. Older girls' remarks were sometimes reflective of behaviors associated with weight loss diets. These girls defined healthful eating as "not eating too much," and in some cases, they identified low-fat or low calorie foods (e.g., baked chips) as healthy.

Active versus Sedentary Lifestyles

Participants identified physical activity as part of a healthful lifestyle. Although both girls and boys identified exercise as essential to healthfulness, boys were much more likely than were girls to identify specific physical activities that they participated in. Boys named a wide variety of sport and game activities as contributing to a healthy way of living, including baseball, basketball, biking, cricket, golf, gymnastics, fencing, hockey, jogging, river rafting, rugby, taekwondo, and walking.

Participants identified a sedentary lifestyle as unhealthy, here again using a dualistic or categorical logic system to structure their thinking about healthy and unhealthy lifestyles (active = healthy, inactive = unhealthy). The older boys frequently mentioned prolonged or frequent participation in video or computer games as an unhealthy: "It's not activity to play video games! If you only play it once in awhile is okay. I only play them on vacation" (OB).

Similarly, older girls conceptualized "indoor activities" as more likely to be inactive and as less health promoting than outdoor activities.

Finally, although they acknowledged the value of physical activity, a few of the older boys indicated that their desire to exercise or play outside was impeded by safety concerns (e.g., living in a dangerous neighborhood) and difficulty in identifying nearby playmates.

Good Versus Bad Hygiene Habits

Participants in all groups except for that comprising younger boys identified good and poor hygiene habits as contributing to healthfulness and unhealthfulness, respectively. The following behaviors were viewed as central to good hygiene and thus good health: hand and face washing, showering, teeth brushing, getting an appropriate amount of sleep, and avoiding sunburns. These behaviors were conceptualized as part of "taking care of yourself" (OG). Conversely, not engaging in these behaviors on a regular basis was perceived as a threat to good hygiene, and consequently, to being healthy. Additionally, being too stressed was perceived as unhealthy.

Outcomes of Healthfulness and Unhealthfulness

Growth and Physical Effectiveness

Participants of all ages and genders indicated that eating a healthy diet and exercising frequently could bolster one's strength and the efficiency of one's bodily systems: "[with exercise and a healthy diet] your body will be able to build stronger immune systems, stronger blood systems, stronger muscles, tendons…and you can keep going" (OB).

Among younger participants, consuming a healthy diet was viewed as an essential antecedent to growing or to getting bigger. At this age, growing bigger and gaining weight were viewed in a positive light: "You should eat lots of fruits and vegetables because they give you weight and strong bones" (YG).

Additionally, being healthy was viewed as enhancing one's physical effectiveness, or his/her capacity for doing things and for enjoying life. This view was shared among participants of all ages and genders: "[Eating healthy] makes you grow bigger…if you are like that big, you can do anything" (YB). "If you're healthy you can go to camps, play sports, have fun" (OG).

Sickness/Disease

Leading a healthy life--including eating the right foods or a balanced diet, exercising regularly, and maintaining good hygiene--was viewed by participants as imperative to warding off sickness and disease: "My doctor [taught me] that eating healthy keeps you from getting sick" (YG).

Two primary themes emerged in relation to disease prevention. First, with one exception, a well body was conceptualized as an unequivocally thin and fit one: [A healthy body]: "looks like a pencil" (YB); "has muscles and not too much fat" (YG); "is skinny" (YG); and "is fit, strong, and lightweight" (OB). In some instances, participants even attributed an increased life expectancy to weight reduction: "[My dad] lost twenty-five pounds, and he added ten years to his life" (OB).

Second, participants envisioned specific hygiene practices--such as hand washing, teeth brushing, and getting an adequate amount of sleep--as integral to both physical and emotional wellness.

Conversely, unhealthy eating habits and an inactive lifestyle were seen as promoting sickness and disease. Across gender and age groups, poor eating and exercise habits were presumed to increase one's risk for diseases such as cancer and diabetes as well as for premature death: "[Living healthy] increases the time you live, because if you're healthier your body can last longer and work. It's stronger"(OB). "Don't get overweight and get diabetes. You have to be in the hospital for a long time" (OG).

As the second quote above illustrates, being overweight was framed as a grave threat to health. Although one younger girl participant indicated that she thought it was most healthy to be "middle size," none of the participants conceded that an overweight body also could be a healthy one. Thus, it is perhaps not surprising that participants frequently mentioned weight loss as a conduit to improved health. Implicit here were the assumptions that body size (a) can readily be altered through changes in diet and exercise and (b) is a matter of personal responsibility that warrants monitoring and attention. Here, individuals who did not keep their weight in check were perceived as needing to attend to their bodies by changing their lifestyles: "If you have too many carbs in your body, you have to burn those calories off" (OB).

Finally, a few participants also acknowledged that being too thin could pose a risk to one's health. Although none of the participants used the phrase "eating disorder" in their conversations, it was clear that they were aware of the potentially negative consequences of pervasive cultural pressures to be thin.

Attractiveness

Like the popular culture discourses surrounding them, participants frequently conflated issues of appearance and health. Thus, an attractive appearance was interpreted as a signal of good health and an unattractive one was thought to indicate unhealthiness. Among girls, especially, being attractive--or having good teeth, skin, and hair--was viewed as the product of fastidious hygiene habits. Conversely, having discolored teeth or unclear and oily skin was associated with being unhealthy or failing to take proper care of oneself.

Implicit here was the assumption that one can control such characteristics as the oiliness of one's skin through adherence to certain behaviors (e.g., face washing). Participants also suggested that thin and toned bodies were not only the healthiest, but also the most attractive. Both the social value accorded to the appearance of a healthy or thin body as well as the stigma attached to a body that does not appear to be healthy are reflected in the following comments, which suggest a concern among participants about appearance-related teasing: "And um the other thing about having a healthy lifestyle is um that people won't pick on you, and say, 'You're a freak and stuff.'" (OB). [Why is it important to have a healthy or thin body?] "So you won't be teased" (OG).

Discussion and Implications

Findings point to a number of concerns relevant to health-related Extension programming for elementary school-aged children. First, that the majority of participants readily identified foods widely understood among nutrition professionals to be healthy or unhealthy as "good" and "bad" foods, respectively, may reflect the effectiveness of on-going Extension efforts to prevent childhood obesity by communicating to children a better understanding of the relative healthfulness of various foods and the link between diet and well-being. At the same time, however, the participants' identification of "good" and "bad" foods may suggest a relatively simplistic and rigid understanding of what it means to be healthy.

The danger in this conceptualization is that it fuels an "all or nothing" approach to healthfulness in which some foods are treated as wholly "bad" and "off-limits." Such an ideology may lead to unhealthy eating behaviors and conflicts with the position of the American Dietetic Association (ADA), which suggests that all foods can fit into a healthful eating style, so long as they are consumed in moderation and combined with regular exercise (Freeland-Graves & Nitzke, 2002).

Although this view was expressed by a minority of participants in the present study, it was not widely held within this sample. As such, we believe that educational materials addressing health and obesity prevention should be designed to help educators move children--in developmentally appropriate ways--beyond the dualistic "good food/bad food" model. Such programming should aim to engender within children an understanding of healthfulness that emphasizes the overall diet or pattern of food consumed rather than focusing upon the relative healthfulness a few specific foods in isolation.

Second, that participants viewed being overweight as a potential health risk may suggest the efficacy of existing obesity prevention programming. However, this finding also suggests that participants did not recognize that healthy bodies can come in diverse shapes and sizes, a position that is gaining some support among health professionals (Campos et al., 2006; Miller & Jacob, 2001). Further, participants' comments suggest a stigmatization of people whose bodies do not meet cultural demands of thinness and an assumption that, with the "right" lifestyle, any body can be thin.

This observation, along with some female participants' equation of healthful eating with denial, points to a need for programming promoting healthful eating and exercise among children (as described by the ADA) while at the same time encouraging them (a) to question strong cultural messages promoting the value of a singular body ideal (i.e., thinness), (b) to consider factors beyond appearance (e.g., physical functioning) in assessing healthfulness, and (c) to adopt sensitive attitudes toward persons of diverse body shapes and sizes. Because research suggests that regular exercise improves health for people of any size (Blair & Church, 2004), this curriculum should focus on the health and social benefits of exercise and underscore the potentially negative outcomes of weight-related teasing (e.g., low self-esteem) (Klaczynski, Goold, & Mudry, 2004).

Third, that participants conceptualized hygiene as part of a healthful lifestyle was an unexpected finding and may point to the value of including a hygiene component in health curricula. To respect the ways in which appearance management practices may vary across individual families and cultural groups, we recommend that educational content related to hygiene emphasize health rather than appearance outcomes, underscoring the efficacy of given hygiene practices--such as regular hand washing and teeth brushing--in preventing disease (Carvalho, Van Nieuwenhuysen, & D'Hoore, 2001; Uhari & Mottonen, 1999).

Fourth, the finding that all participants viewed physical activity as part of a healthy lifestyle is encouraging. That girls did not discuss their participation in physical activities to the same extent as did boys, however, may highlight a potential need for programming designed to promote physical activity among girls. Before pursuing the development of such programming, further research should be undertaken to assess need; participants in this study were not directly asked to report the frequency of participated in physical activity.

Finally, that participants identified safety and social concerns as obstacles to participating in regular exercise points to the need for organized, adult-supervised physical activity events designed for youth residing in high risk neighborhoods. To afford all children safe and convenient access, these activities should be held on public school grounds or in close proximity to children's residences.

Conclusions

This preliminary study yields insights about the ways in which elementary school-aged children think about and understand healthy and unhealthy lifestyles. Figure 1 provides a valuable representation of the logic children may use in conceptualizing what behaviors are healthy or unhealthy and how those behaviors may contribute to health- and appearance-related outcomes. Findings provide valuable insights about the need to develop a health curriculum that encourages sensible food choices within the context of one's overall diet, regular exercise, and more flexible ideas about what a healthy body looks like. Beyond informing the development of curricula, the present findings also underscore the value of:

• Invoking qualitative approaches to identify meaning systems relevant to a research sample or the audience of an educational program;


• Being open to unexpected findings, which may shape the direction of subsequent research or curriculum development efforts; and


• Working with a multidisciplinary team whose diverse perspectives afford a multifaceted interpretation of the data.

Last, findings suggest fruitful directions for future research. Results provide a foundation for the development of quantitative instruments that tap children's health beliefs in a holistic manner and that incorporate ideas and language salient to young respondents. Additionally, because the present sample was both small and potentially biased (e.g., by virtue of their enrollment in a healthy lifestyle camp, it is likely that participants were growing up in families who were predisposed to value healthfulness), it will be important for future researchers to explore the health-related beliefs of children using larger, generalizable samples free of this bias.

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Preventing Diabetes: You Have the Power to Take Action

Marilyn Corbin
Associate Director and State Program Leader
Children, Youth, and Families
Penn State Cooperative Extension
The Pennsylvania State University
University Park, Pennsylvania
mcorbin@psu.edu

Nancy Ellen Kiernan
Program Evaluator
Penn State Cooperative Extension
The Pennsylvania State University
University Park, Pennsylvania
nekiernan@psu.edu

Mary Alice Gettings
Nutrition and Health Extension Educator
Penn State Cooperative Extension
Monaca, Pennsylvania
magettings@psu.edu

Introduction

The prevalence of diabetes continues to increase in epidemic proportions in all races, age groups, and states across the country. Nationally, 20.6 million people (9.6% of the population) have diabetes, which reflects an increase of 2.6 million people (0.7% of the population) in the past 2 years. Of these, 10.9 million (10.5%) men and 9.7 million (8.8%) women over age 20 years and older have diabetes. In the United States, 3.2 million non-Hispanic Blacks (13.3% of this population) and 2.5 million Hispanic/Latino Americans (9.5% of this population) aged 20 years of age or older have diabetes. On average, Mexican Americans, the largest Hispanic/Latino subgroup, are 1.7 times more likely to develop diabetes as compared to those non-Hispanic Whites of similar age (National Institute of Diabetes and Digestive and Kidney Diseases, 2005).

In Pennsylvania, the Department of Health's 2004 Behavioral Risk Factor Surveillance System (PA DOH BRFSS) reveals equally dramatic trends. In Pennsylvania, 6,033 (8%) have been told they have diabetes, a full 1% increase in the past 2 years. In the African-American and Hispanic populations, 483 (9%) have been told they have diabetes during the same time period (PA DOH, 2004).

The risk of diabetes increases with age. Of the people in Pennsylvania with diabetes, 1% were told when they were between the ages of 18 and 29, 3% between the ages of 30 and 44, 10% between the ages of 45 and 64, and 17% were age 65 or older. There is no difference in the incidence of diabetes between men and women aged 18 years or older in Pennsylvania (PA DOH, 2004).

The incidence of diabetes continues to increase. In 1980, 5.8% of the American population was diagnosed with diabetes. This increased to 11.1% in 1999 and to 14.7% in 2005 (a 3.6% increase in 6 years). In the U. S., it is estimated that 54 million people have pre-diabetes, with a fasting blood glucose between 110-125 mg/dl (American Diabetes Association, 2007).

A contributor to the increase in diabetes is the increase in the number of Americans who are overweight and/or obese, and this is reflected in the following statistics. The prevalence of overweight characteristics among youth and obesity among adults, particularly men, increased significantly from 1999-2004. Those between the ages of 2 and 19 saw an increase of 3.2% (13.9% to 17.1%), and those 20 years of age and older saw a 1.7% increase (30.5% to 32.2%) (Ogden, Carroll, Curtin, McDowell, Tabak, & Flegal, 2006). The increased body fat resulting in overweight and obesity is the primary contributing factor to the incidence of diabetes. According to the 2004 PA DOH BRFSS, 37% of Pennsylvanians are overweight and another 24% are obese.

The large number of Americans with diabetes affects quality of life and is an economic burden on those with the disease. Risk of death among people with diabetes is about twice that of people without diabetes of similar age. Adults with diabetes have stroke and heart disease death rates that are two to four times higher than adults without diabetes. Seventy-three percent of adults with diabetes have high blood pressure or use prescription medications for high blood pressure. Fifty percent of all leg and feet amputations are due to diabetes, and 60% of tho