Shadeequa “Dee” Miller, Project HealthDesign Ph.D. in Engineering Student, University of Wisconsin-Madison
Online communities are increasingly being used for health information purposes and social support — especially among adults who have been diagnosed with chronic diseases. In fact, online communities have been found to have a positive effect on participants’ well-being and health care behaviors (Nimrod, 2009). However, there is little research on the use of web-based communication technologies such as e-mails, mailing lists, news groups, forums/message boards, chat groups, interactive sites and blogs as sources of information for the development of tools to capture observations of daily living (ODL) data.
Last semester I observed discussion boards for several online communities that connect people who are living with different chronic diseases. I found discussions that covered all aspects of chronic diseases, from treatments and symptoms to basic health matters and interpersonal relationships. I paid particular attention to discussion boards that address adherence to medication or treatment regimens. As I reviewed the discussion boards, I tried to identify ODLs, keeping in mind that participants perceive online communities to be safe, informative, useful and fun places to meet other people with similar health circumstances, to share experiences and to find information that helps them manage and/or cope with a chronic disease. The ODLs I identified were more detailed than I had expected. The additional contextual information users provided helped me to identify communication patterns in the expression and prioritization of ODLs. This led me to question whether ODLs are the same in online settings as in offline settings. If they are not the same, then why and how are they different?
Also, can online communities serve as resources for quantifying ODLs by allowing researchers to convert qualitative, patient-reported ODLs into numeric/quantitative ODLs? I believe the answer to be yes. A content analysis of discussion board messages that address treatment regimen and adherence could be collected and analyzed to identify ODLs, which may be one way to quantify ODLs. Another useful technique that may be used is Latent Semantic Analysis (LSA), also known as Latent Semantic Indexing (LSI), which would allow researchers to analyze discussion boards to find the underlying meaning or concepts — ODLs in this case — of the boards.
My observations lead me to believe that some ODLs are the same in both online and offline settings. People may, however, provide more contextual information online. This trend may be related to the fact that online communities are places where users value trust and compassion. Patients find comfort in sharing health-related information, as well as emotions, online with peers who may have had similar experiences. These online peers can often provide the understanding and practical advice that clinicians are not always able to offer.
As I examined the online communities, I found that the manner in which ODLs were described changed as the intended audience changed (e.g., whether the intended audience used an avatar/was a regular participant in the forum) and as the community changed (e.g., comments varied between informal and formal communities). These are aspects that should be taken into consideration if online communities are to become resource through which to examine ODLs.
Do you think online communities could help researchers to quantify qualitative ODL data? Would this information be useful to clinicians? Is the integrity of online data compromised? What characteristics of online communities must be taken into consideration before using the information? Could device designers use these types of online information to help develop tools that capture ODLs?
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