One of the strengths characterizing Web-based health behavior change programs is the ability to reach participants based in disparate geographic locations. This is particularly important when a set of conditions obtains: (a) when individuals in rural settings are found to have serious modifiable health behaviors; (b) when rural individuals have more limited personal income to pay for personal care and/or transportation to care; and (c) when there are fewer per capita clinic-based health professionals 1. Although the rural use of the Internet still lags behind usage levels observed for urban areas, Internet usage nonetheless is increasing rapidly 23. In fact, recent data indicates that the adoption rate of broadband Internet services in rural settings is now on parity with urban areas 3. These trends are encouraging and support using Web-based delivered interventions as one viable approach to reach underserved rural Americans 4. When describing these interventions, it would be helpful to routinely describe participant rurality in addition to other demographic characteristics. This data can inform recruitment and social marketing efforts, and facilitate the targeting and tailoring of program content.

Fortunately, there is a convenient, empirically-based methodology called RUCAs (Rural-Urban Commuting Area Codes) that can help US researchers accomplish this goal. In this report, we examine the use of RUCAs in two exemplar randomized controlled trials of Web-based tobacco cessation programs: one for smokeless tobacco cessation and the other for smoking cessation to better understand the nature of the population of users for these two intervention trials. We also provide examples of how RUCAs can be used to better understand the reach of behavior change programs delivered via the Internet.

Illustrative example data on participant rurality in the Web-based smokeless tobacco cessation trial (ChewFree.com) and the Web-based smoking cessation trial (SHIP) are displayed in Table 3. Owing in large part to large sample sizes and successful randomization, there were no observed differences in rurality between conditions within the ChewFree.com trial (χ²(3, N = 2507) = 0.55, p = .91) and the SHIP trial (χ²(3, N = 2263) = 0.12, p = .99). When we collapsed data across conditions and aggregated RUCA categories to permit an examination of the rural:urban dichotomy, results in both trials indicated that participation differed significantly by rurality: the ChewFree.com trial (χ²(1, N = 2507) = 192.67, p < .001) and the SHIP trial (χ²(1, N = 2263) = 830.60, p < .001).

In Table 4 we consider participant rurality in each of the two trials using the broader context of the percentage of US population in the same RUCAs groupings 10. While the rurality of smokers in the SHIP trial closely mirrored the national population distribution, the rurality of smokeless tobacco users in the ChewFree.com trial differed significantly from the population distribution – both when examining all four RUCAs groups presented in Table 4 (χ²(3, N = 2507) = 511.80, p < .001) and also when the data were collapsed into a rural:urban dichotomy (χ²(1, N = 2507) = 478.50, p < .001) with almost twice as many rural participants than would be expected from the distribution of rurality in the US population (36.2% vs. 19.0%, respectively). These Chewfree.com results reflect both smokeless tobacco usage patterns and ChewFree.com's targeted marketing effort.

We also examined type of Internet access and rurality among participants in Enhanced condition in ChewFree.com program. Results (see Table 5) showed roughly equivalent dial-up vs. broadband access among urban participants. In contrast, among rural participants, there was a clear gradient such that dial-up access increased its relative percentage share as the home location became more rural, a result that underscores the importance of taking into consideration the bandwidth constraints of many rural Internet users.

Web-based health behavior change programs are well-suited to reach persons in many settings, including rural locations. Their potential public health impact is particularly important for rural locations wherein access to behavior change services (e.g., tobacco cessation services) is limited by relatively low income, lower rates of health insurance, and the poor availability of health care 2431. If the target population is known to be segmented according to rural:urban settings, then describing the study sample according to its RUCA characteristics can facilitate our understanding of the extent to which interventions reached their intended audience 3233. RUCAs can also provide valuable feedback to help refine marketing campaigns for Web-based interventions 33. For example, we found that recruitment to the ChewFree.com research project benefited from using a mixture of both Internet-based marketing and "earned-media" promotions through more traditional media channels (e.g., newspapers and radio) many of which were based in rural settings 21. In addition, RUCAs can be used to target as well as tailor program content and health messages 34.

Considering tobacco cessation, it is important to note that although Doescher et al. 24 recently reported the rural vs. urban distribution of smoking behavior by counties, to date we do not have finer-grained data that describes the number of smokers or smokeless tobacco users by ZIP codes. Thus, for tobacco cessation and very likely for many other important health behaviors, RUCAs do not yet provide us with the denominator to describe the percentage of eligible participants reached by Web-based tobacco cessation interventions 35. Nonetheless, ZIP code-based RUCAs datasets for age, race, sex, population, and income (planned), and travel distance time to cities with a population of 50,000 or more can also be used to fine-tune the research. Likewise, additional analyses could combine RUCAs with other geocoded datasets such as the ZIP code-based Consumer Health Profiles available through the Office of Cancer Communications of the National Cancer Institute 3637, proprietary market segmentation datasets from Claritas, Inc3839, and US Census Bureau data on defined region, division, and state 40. Finally, it is helpful to consider ways that RUCAs can mesh with other geography-based tools such as spatial analysis 41.

One noteworthy limitation of the current report is that it uses a taxonomy that applies only to US locations. Not only do US-based Web interventions attract participation from other countries [e.g., 27], but Web-based interventions obviously can be hosted in and marketed to participants in countries other than the US. In this regard, other countries have their own measures for categorizing rurality that could potentially be usefully employed to describe participation in their Web-based programs. Examples include the use of Rural and Small Town (RST) designations as a part of the Metropolitan Area and Census Agglomeration influenced Zones (MIZ) in Canada 4243, the Accessibility/Remoteness Index (ARIA+) in Australia 44, and the Carstairs area deprivation measures in the UK 4546.

Because RUCAs linked to ZIP code data can be downloaded at no cost, there appear to be few barriers to obtaining the considerable benefits associated with routinely reporting RUCA-measured rurality along with other key indices of participant demographics in reporting results of Web-based health behavior change programs targeted to US participants. Using data drawn from two Web-based RCTs we highlighted examples showing ways that RUCAs can be used as an integral evaluation component.

The author(s) declare that they have no competing interests.

BGD conceived of the study, conceptualized ideas, supervised its conduct, and designed and coordinated the data collection phase. LGH, HGM, and HHS all helped to interpret findings and contributed to the text. All authors reviewed drafts of the manuscript and approved the version to be published.