The making of expert patients: The role of online health communities in arthritis self-management

Abstract

Chronic disease is an epidemic, one that requires patients to play an active role in managing symptoms and disease affect. This study used ethnomethodology (N = 8231) to understand how patients with arthritis use online health communities to exchange disease-related information to better manage their chronic disease. The findings show that online health communities facilitate self-management behaviors through the exchange of health information and disease experience. These online health communities act as self-management programs led by peers with the same chronic disease through the exchange of health information based on experience, working to improve members’ health literacy related to arthritis.

Keywords

communication health behavior self-management

Introduction

Research has demonstrated the benefits of chronic disease self-management, including reducing hospitalizations (Lorig et al., 2001) and lessening the affect of disease symptoms (Dixon et al., 2007), yet self-management is the most challenging area of chronic disease management and the least implemented (Brady et al., 2013). However, there is a lack of attention being paid to online health communication exchanged by patients for patients in efforts to manage chronic disease.

People with chronic disease are among the most frequent users of the Internet, most often searching for health information (Pew Internet and American Life Project, 2011). Digital media offers a solution to disease management through personalized health information, social support, and psychosocial benefits (Elwell et al., 2011). However, health information that is exchanged online requires people to have both ample health knowledge and adequate literacy skills—or, health literacy. Simply, health literacy is the ability of a person to access and understand health information and use it to better his or her own health (Ratzan, 2001).

There are many barriers to improving health literacy, including the ability to read. Illiteracy is a pandemic affecting more than one billion people worldwide (World Health Organization, 2011). As many as half of US adults have limited literacy skills, and even a greater proportion have limited health literacy skills (Kutner et al., 2006). A person may be extremely literate and still have difficulty interpreting and acting on health information. The Web poses additional barriers to people, including access to and experience with the Internet, complexity of information, and searching and navigational difficulties (Kontos et al., 2007). Yet, health information is being generated by patients for other patients and is being exchanged within online communities.

The information that is exchanged among peers is publically available within these online communities, allowing researchers to take a “bird’s eye view” of how patients manage their chronic disease. Research has examined the online content found within health communities (see Hwang et al., 2011; Neuhauser and Kreps, 2002; Phoenix and Coulson, 2010; Rodgers and Chen, 2005; Sullivan, 2003; White and Dorman, 2001; Wright and Bell, 2003); however, no known research examines online health communities for the patient’s perspective of chronic disease self-management. This article applies ethnomethodology to understand the development of health literacy regarding chronic disease self-management by means of online health communities and the communication exchanged therein. More specifically, online health communities related to arthritis are examined. Arthritis is a chronic disease that causes pain, stiffness, and inflammation in the joints; it is the leading cause of disability in the United States and affects one in five adults (Centers for Disease Control and Prevention (CDC), 2011).

Literature review

Patients with the same disease diagnosis exchange health information within online communities, and all have very different experiences regarding living with and managing chronic disease (Frost and Massagli, 2008). People tend to trust others similar to themselves more than authority figures from industry sectors like business, government, and the news media (Edelman Trust Barometer, 2008); thus, information exchanged within online health communities may have the potential to influence behaviors.

Social learning theory provides a theoretical framework for understanding the impact of information exchanged within online health communities. This theory suggests that people can learn by observing and modeling others’ behaviors (Bandura, 1977). Rather than trying and failing, observing other people’s behavior allows for a safer and more efficient way of learning behaviors (Bandura, 1977). Online health communities offer an environment for people to engage in learning through observation and interaction with like-others. Jones (2003) defines expert patients as “people who understand that the quality of their lives is primarily up to them …” (p. 23) and actively participate in self-management of their disease. Self-management behaviors refer to the methods applied by patients to control disease symptoms (Lorig and Holman, 2003). The general health discourse is about promoting an empowered, self-aware patient as “self-managing and self-monitoring” (Lewis, 2006: 521), meaning the patient willingly claims responsibility for maintaining his or her own health.

Online health communities may act as an extension of the health-care system, offering information and social support deemed necessary by community members in living with and managing chronic disease. Because communication within these health communities tends to be task-oriented (Preece and Maloney-Krichmar, 2005) and self-management is so crucial to living with arthritis (Lorig and Holman, 2003), it stands to reason that community members are exchanging health information regarding self-management behaviors, such as personal experience with prescription medications, joint replacements, and weight-management advice.

There are many definitions of health literacy, most of which are concerned with the patient’s ability to comprehend health information (see American Medical Association Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs, 1999; Ratzan and Parker, 2006; Speros, 2005). However, more recent definitions conceptualize health literacy as a dynamic understanding of health, influenced by social determinants like sociopolitical systems, peer groups, mass media, and culture (Zarcadoolas et al., 2005). The current research argues that online content found in health communities has the potential to influence patients’ health literacy regarding self-management behaviors. Patients “see” how others manage their disease, learning from their experiences and thus work to improve their own health status.

Zarcadoolas et al. (2005) say that “a health literate person is able to use health concepts and information generatively—applying information to novel situations … participate in the ongoing public and private dialogues about health, medicine, scientific knowledge, and cultural beliefs” (p. 197). Furthermore, Smith and Hudson (2012) argue that “factors such as social support, societal stigmas and attitudes, natural and built environments” should be considered when defining health literacy. Patients’ health literacy is complex and influenced by both intrinsic and extrinsic factors, including the health tasks that are required (e.g. self-management behaviors), and is fluid in that patients adapt in order to survive (Smith and Hudson, 2012). Shared experience is the foundation of online health communities—the platform from which disease is discussed. Patients are able to problem solve based on shared experiences, learning to become “expert patients,” and properly self-manage their disease symptoms. This research looks specifically at online communities related to arthritis. Arthritis affects 175 million adults worldwide (World Health Organization, 2012) and requires constant patient self-management (Holman and Lorig, 2004). Patients must cope with psychological distress, pain, and loss of function (Zautra et al., 1995), and online communities allow for like-others to share experience in managing arthritis disease symptoms.

Method

Ethnomethodology was used to obtain a holistic understanding of self-management behaviors “in action” within the online health communities. As Rheingold (1993) noted, online communication overlaps with members’ “off-line” worlds and their daily, lived experiences. The primary aim of ethnomethodology is to understand how people cultivate a sense of shared order (Taylor and Cameron, 1987). Thus, ethnomethodology was used to gather “thick description” of four online health communities to understand the cultural norms and codes regarding managing arthritis (Geertz, 1973: 5). Ethnomethodology allows for exploration from the patient’s perspective (see Bowers, 1992a, 1992b; Dowling, 2007; Frank, 2013; Have, 1995; Morse and Field, 1995).

Ethnomethodology is essentially a “decoding operation” (Apgar, 1983: 70); the group’s language must be “learned” (Taylor and Cameron, 1987). Previous findings from ethnomethodology have added to our broader understanding of organizations, disease diagnoses and assessments, and the social construction of “facts” (Atkinson, 1988). Ethnomethodology seeks to make observations about behavior as expressed through online communication (Herring, 2004). Here, ethnomethodology is used to understand how people with arthritis facilitate self-management behaviors through computer-mediated communication within online health communities.

Sampling of online health communities

Four online arthritis-related communities were identified through popular search engines such as Google and Yahoo. These searches were meant to simulate patients’ online searches for arthritis-related communities, using search terms like “arthritis community,” “arthritis support,” or “arthritis group,” for example. The four online communities were chosen based on their popularity and frequency with which members engaged in the community (Herring, 2004). The communities that were identified are all dedicated to arthritis and have available history of previous discussion by community members. These communities use similar methods of organization like bulletin boards to exchange information, organizing topics into threads, including dates and usernames. The four online communities included in this sample are sponsored or hosted by organizations with special interest in arthritis but do not include health-care professionals or medical interventions. The four communities are open to the public and do not require membership; content is within the public domain. In order to protect the anonymity of the online communities, quotations and content presented below have been slightly altered. This is in compliance with the university’s institutional review board regulations for research of online communities.

The discourse of five members from each of the four arthritis-related communities was analyzed, for a total of 20 members. The five members were chosen based on the frequency of contribution to the online health community (Herring, 2004). Most methods of research require a sample to be selected from the totality of available data, but ethnomethodology is interested in the context, which is crucial to interpreting shared meaning (Zimmerman, 1978). Thus, the discourse from each member was analyzed from the original start date of contribution to and/or participation with the online health community through 31 December 2010 (N = 6578). For example, one of the community members selected joined the online health community on 5 November 2005. All of this member’s posts from 5 November 2005 through 31 December 2010 were read and analyzed (N = 734). In addition, posts from other community members outside of the selected 20 were included in the sample if self-management behaviors were discussed in the thread (N = 1653). For example, a thread entitled “Move It” contained five posts on physical exercise from one of the selected community members for study, but had a total of 11 posts from other members in the entire tread. Thus, the six additional posts in “Move It” were included in the sample because of the discussion on physical exercise. Including these extra posts provided the context that is necessary in ethnomethodology. In total, 8231 posts were analyzed, which was decided to be sufficient for understanding based on Strauss and Corbin’s (1997) theoretical saturation.

Data management and analysis

Two graduate students initially categorized the posts into arthritis self-management topic areas, including those outlined by Lorig et al. (1985): (1) disease information, (2) drug management, (3) symptom management, (4) management of psychological consequences, (5) lifestyle, (6) social support, and (7) communication. The researcher and two graduate students read through each post and grouped the data into reoccurring themes within each self-management behavior topic area. The themes were then discussed with a panel of patients recruited from a local rheumatology clinic (male and female; ages 37 to 85 years; disease experience between 2 and 38 years) to validate the findings of the researcher. The university’s institutional review board approved the current research.

Discussion of findings

The following are themes and contextual details observed within the online health communities. The “thick description” that follows seeks to unveil the interactions that people with arthritis engage in with like-others in order to understand self-management and disease symptoms. Because there were more similarities across the four online communities than differences, findings are presented here as shared patterns across the four communities. To start, the online communities’ membership is described.

In all, 15 of the community members observed were female, and 5 were male. In several instances, female community members would start “women only” threads, seeking information on personal issues (e.g. menopause, pregnancy, and motherhood). The members’ ages ranged from 21 to 83 years across the four communities. In two of the observed communities, threads were dedicated to specific age ranges. For instance, Community 2 had a thread entitled “Arthritis Ninjas”—which called for “anyone under 40 battling this arthritis enigma,” and Community 3’s “Old Farts” called for “anyone older than dirt” who wanted to talk about “the good old days, before my crooked fingers.” Within the four online health communities, the majority of members had rheumatoid arthritis (if specific type of arthritis was mentioned at all), while two had osteoarthritis.

This research focused specifically on the exchange of health information related to arthritis self-management behaviors in the context of online health communities. Three themes were identified across the four online health communities: (1) members’ construction of personal disease management programs, based on shared personal experience and negotiation of disability; (2) engagement is related to disease activity, including disease diagnosis, symptom “flares,” and feeling “down and out,” as means of problem solving; and (3) disease experience brings expertise in managing symptoms and disease affect, improving arthritis-related health literacy. Examples follow, including practical application to health education and promotion.

The general exchange of specific information

People with arthritis use online health communities to seek information and share experience with others. Seventy percent of the sampled posts (70%, N = 5762) contained content about self-management behaviors. In a few instances, caretakers (e.g. mothers of children with juvenile arthritis) posted within the online health communities in search of answers and recommendations. The majority of community members, however, had a diagnosis of arthritis themselves. Self-management dominated online discussions among members, providing evidence that online health communities related to arthritis might act as informal self-management education programs. Community members are actively seeking and exchanging content related to better management of their arthritis, suggesting further that health literacy might be improved through the peer-to-peer communication of disease experience.

Interesting here, however, is the treatment of arthritis diagnoses by the community members. According to the CDC (2011), arthritis is a general term for conditions that affect the joints, most commonly osteoarthritis and rheumatoid arthritis, but also includes fibromyalgia, lupus, and other rheumatic conditions. While osteoarthritis and rheumatoid arthritis are very different (i.e. symptoms, treatment, and prognosis), community members in the four communities rarely discussed type of arthritis with regard to self-management behaviors but treated the disease affect homogenously. Because the four online health communities were all related to arthritis, members assumed others had a similar understanding of “arthritis” (generally) and based on that general understanding, a sense of community was cultivated by discussing self-management strategies. In the general context of “arthritis,” community members could relate and share personal experiences of managing the disease.

Self-management is necessary for all community members and seemed to be synonymous among the four online health communities. For example, when discussing physical activity, one participant with rheumatoid arthritis wrote (Community 2), “It’s so hard for me to get out there and get moving. My knees give me fits the next day!” Another member with osteoarthritis responded, “I know what you mean. My knees grind every time I cycle, but I still snap, crack, and pop every Tuesday and Thursday in my spinning class!” Although these two members have different disease diagnosis, they both associated physical exercise with symptoms of pain. Both members engaged in some kind of physical activity, but the self-management behaviors were negotiated differently based on the disease consequences (e.g. grinding of the joints, pain, and stiffness). In this example, both community members expressed the importance of exercise, but were also familiar with its cost. The community members expressed benefits to physical exercise like “flexibility” and “improved range-of-motion,” but perceived consequences to engaging in such, including “grinding knees,” “swollen ankles,” or “stiff back.” It was not important that one community member reported “an awful disease flare” and another said she was “totally fatigued” after exercising. It was important, however, that other community members had experienced some kind of consequence to “getting my move on.”

As another example, it was common for members to talk about morning stiffness across the four online communities. None of the posts described the parameters of morning stiffness, neither the feeling of morning stiffness nor duration of morning stiffness. Instead, community members from all four communities discussed prevention strategies and ways to reduce the amount of time lost in the morning due to disease stiffness. The posts that mentioned morning stiffness all shared common strategies for overcoming this period, including the following:

I set my alarm extra early for work. I have to give myself a few hours to loosen up, for the hot shower to start working. (Community 3)

I go to water aerobics in the morning. I probably look pretty silly hobbling into the pool, but I feel great when I get out. The warm water really helps my morning stiffness. (Community 2)

Most of the time, I know when I’m going to wake up feeling stiff as a board. I just go to bed with this feeling … I take a Naproxen before I go to bed, and one again if I wake up in the middle of the night. I don’t know if that’s what my doctor ordered, but it seems to be doing the trick for these old bones. (Community 2)

This shared understanding provided a foundation for which specific communication was exchanged about arthritis self-management behaviors. The self-management behaviors were understood among the members without explanation or rationale because all were practicing these behaviors in some fashion. Community members had to engage in problem-solving skills, deciding what information and experience was relevant to their current health situation. Members learned to negotiate behaviors based on disability (e.g. disease status) and level of health literacy.

It was important for community members to provide feedback in response to practicing self-management behaviors. For example, one community member was prescribed a biologic medicine that must be injected subcutaneously by the patient. She posted (Community 2), “Help me, Ninjas. I don’t know what I’m doing. How do you inject Humira?” After several other community members responded with instructions on “how to” and examples of their experiences injecting Humira, the member wrote, “Thank you all for helping me! I stuck myself last night. It wasn’t too bad … I couldn’t have done it without you [the community members].” This member’s feedback validated the other members’ disease experience and learned self-management strategies. For instance, one member responded with, “I had to practice on an orange a few times before I had the courage to actually stick myself, but really—[injecting Humira] isn’t that bad. Glad I could help.” This example also lends itself to the social learning theory, demonstrating that the information and experience exchanged within online health communities has the potential to change behavior.

The negotiation of the self-management behavior(s) was unique to individual community members. For example, when discussing drug management, one community member said, “I split up my methotrexate … taking it different days.” Another member responded, “My doctor said not to split the dosage, that it wasn’t as effective. I take mine Friday nights in case [side effects].” Opposing opinions, differing advice from health-care professionals, and lived disease experience were exchanged among members in order to discuss the best approach to the self-management behavior. Community members must decide what information is valid and most appropriate, choosing from multiple perspectives that employed different self-management strategies and subsequently produced different health outcomes. Arthritis affects everyone differently, including the form of disease, mild or moderate symptoms, prescription medication(s) used to treat the disease, and health outcomes, for example (CDC, 2011). Thus, the information shared within these online health communities cannot be applied to every health situation. Community members must engage in problem solving in order to use the shared information. This is in accordance with previous research (see Cline and Haynes, 2001; Eysenbach et al., 2004; Frost and Massagli, 2008; Kreps and Neuhauser, 2010; Neuhauser and Kreps, 2003; Sillence et al., 2007).

Topics were discussed in detail before community members made health decisions. For example, joint replacements were debated in terms of “pros and cons” in Community 1, including phrases such as “best decision I’ve made” or “my knees still don’t feel like they used to.” Community members are able to make personal health decisions based on the shared disease experiences of others, combined with “off-line” information from doctors, family members, and friends. The disease experiences of community members were valued, however, providing examples of how to cope with arthritis and providing (in some cases) examples of long-term prognoses—information unique to online health communities in comparison with health-care professionals. Additionally, the communities and its members were available around the clock, providing information and support when needed. This is in contrast to members’ physicians and health-care providers, as most have limited patient office hours.

Community members need to discern from the shared information, decide what strategy is best given the current situation, and how to go about applying that strategy to control disease symptoms. For example, a community member posted the following in Community 2: “I’m having a flare. Help me, please?” Other members posted strategies to control flaring symptoms, including “use a heating pad,” “apply Icy Hot,” and “try to sleep it off.” After receiving 14 posts in response, the ailing member posted the following:

Thank you all for your suggestions. I ended up soaking my feet and ankles in Epsom salt. The pain and swelling went away eventually. Just had to wait it out. I’m hanging tough now. (Community 4)

Online health communities facilitate problem-solving skills, requiring members to be critical thinkers and to have an advanced level of healthy literacy pertaining to chronic disease management. This might also be considered a danger to participating in online health communities.

The exchange of information acts as education that is meant to contribute to community members’ knowledge of the disease and its consequences as means to improve members’ problem-solving skills (i.e. health literacy related to arthritis self-management). Living with a chronic disease requires a certain level of health literacy if patients are to self-manage the disease and its symptoms. For example, many people with arthritis “over do” daily activity, causing a “flare” in disease symptoms, like stiffness and pain. Understanding the body and how it reacts to physical activity and/or environmental stressors is important to controlling disease symptoms (Holman and Lorig, 2004). Additionally, making smart health decisions like knowing the body’s limitations helps control disease symptoms, including “flares.” This knowledge can come from experience with disease, or from “seeing” others’ experiences in the online health community with “over doing it.” People learn from watching others engage in specific behaviors, learning from the outcome, and then “modeling” improved behavior (Bandura, 1977). Archived content provides opportunity for community members to learn from others’ shared experiences and to build other members’ confidence in their ability to control their own arthritis symptoms. Community members are able to read and apply the content that is most useful to them given their environment and culture, and in accordance with their (current) understanding of the disease.

The making of an “expert” patient

Members seek and exchange health information through disease experience, working to improve health literacy to better manage their disease symptoms. Members become “experts” of arthritis self-management, over time and with disease progression. An “off-line” experience (e.g. new diagnosis, more disability) causes the member to seek information online. Community members ask new questions of the other members and engage in the topics already under discussion. The day-to-day experience of living with arthritis is enough to influence a member’s engagement with the online health community. The 20 selected members posted on an average of every 4 days to the respective online health community, demonstrating the importance of both informational and social support for people with arthritis. A wide body of literature explores online support within health communities (see Coulson et al., 2007; Ridings and Gefen, 2004; White and Dorman, 2001).

Unlike diabetes or cancer, arthritis is not usually life-threatening (CDC, 2011). It requires patients to be responsible for their health every day, which means managing daily symptoms (e.g. pain, disability, and emotion). Other community members’ shared experience and knowledge within the online community combined with the member’s own lived experience with the disease and other “off-line” factors (e.g. social support, physician, and culture) work together to inform the member’s health literacy, or understanding of the disease. The health communities were used to dissect self-management behaviors and propose new, more efficient strategies to improve health status; and then, community members “tested” the strategies proposed and reported back with the success and/or failure of the behavior. For example, one community member said (Community 4), “I tried stretching after my morning shower … and it really is the way to go.” Online health communities might be thought of as the stage on which health behaviors are performed. Community members are able to “watch the show” and decide how to best use the observed performance(s) based on their understanding of arthritis and their own assessment of their body’s needs.

Informational support in the context of the online community with like-others allowed for information to be tailored to members’ health needs. Members became “experts” on their disease not only by managing symptoms but also by “modeling” other behaviors that were discussed in the online health community. For example, a new member to Community 4 posted the following:

I finally did it. I joined the gym … I went swimming today—first time in 25 years. It’s like riding a bike! [Member’s name], you were so right. Swimming is perfect for me. I just needed a push, I guess.

Additionally, the psychosocial support not only helped members build confidence in their abilities but also allowed members to compare health status with others. For example, a community member posted the following after being diagnosed with lupus (Community 2): “You guys help me keep it together. I couldn’t handle this [diagnosis] without your support.” Another community member posted this (Community 3): “I keep gaining weight because of these steroids … How much is normal?” Online communities provided an opportunity for members to compare their disease symptoms, medication treatments, and also their level of disability. Community members wanted assurance from others that “this is what I’m supposed to look like” or that “you are feeling my pain.”

Over time, community members become familiar with arthritis and its symptoms and disease affect on their lives. Members use the community to ask questions and share information related to disease experience. For example, one member said (Community 2), “My doctor wants me to lose some weight and has given me a diet to follow. I’m not interested [in the diet]. Does anyone have a no-fail diet plan?” Peers are facilitating self-management behaviors within these online communities, suggesting that online communities related to arthritis could improve health outcomes by means of improved health literacy.

Self-management behaviors should be proactive, as to avoid disease symptoms (Holman and Lorig, 2004). In some instances, members were reacting to current health situations: “My medicine is making me so sick. Nothing I do seems to help. Any suggestions?” Arthritis symptoms must be constantly managed, and online health communities offer continuous informational and psychosocial support with regard to self-management behaviors. Members of these communities have the same goal, to “feel normal again,” and to “be able to make it all day at work,” because “this disease is not going to stop me.” The peer environment allows for lived experience to be communicated safely to like-others who most likely have suggestions on how to “live like I used to” and be able to “out-smart this stupid disease.” By participating in online health communities, members are able to “see” others’ disease experiences, including the problem, treatment options tried, and the results achieved. Online health communities provide a group education environment where peers openly discuss self-management behaviors.

Self-management behaviors are able to be refined to meet individual community members’ needs, being negotiated to adequately accommodate the current health situation. The communities rallied in support of other members’ practice of self-management, often encouraging others to “be your own advocate” because “no one else will be.” The online health communities promoted patient advocacy, encouraging members to participate in their own health-care decision-making. For example, a member wrote the following:

I don’t understand why this [arthritis] is happening to me. I just don’t see a lot of hope. I’m always in pain … or I’m tired … or I just don’t care anymore. I hate my life, and I really hate that no one understands. I mean, isn’t it bad enough to have arthritis, much less not have anyone—not even my own doctor—understand the disease? I’m sorry for whining. I’m just having one of those days … I hate what’s happening to my body. (Community 1)

Online health communities are “toolboxes” for “beating this shitty disease,” full of strategies and “ways to make me feel a little bit less disabled.” Community members use the information and experience to maintain and improve their current health status “off-line.” As seen here, online arthritis-related communities have the potential to improve health literacy and perhaps even health outcomes.

Conclusion

A chronic disease like arthritis is challenging for patients to self-manage because of the unpredictability of its symptoms. Not knowing what to expect, including flaring symptoms and disease progression, interferes with patients’ psychological acceptance and/or adjustment to arthritis (Parker et al., 2003). Evidence-based self-management programs address how to manage physical symptoms, psychological symptoms, and also facilitate health literacy regarding chronic disease (Lorig et al., 1985). For patients with arthritis, the disease process must be understood in order to manage its symptoms. For example, patients must know what triggers their symptoms, what effects medications have on their symptoms, or how to cope emotionally with the unpredictability of the disease. Online health communities provide a needed source of informational and psychosocial support for people living with arthritis.

Evidence here suggests these online communities act as informal self-management “programs” led by peers with the same chronic disease through the exchange of health information. Members’ sharing personal experience within the online communities offers a demonstration of self-management behaviors and might be thought of as “modeling.” Observing others’ successes and/or failures with certain self-management behaviors allows members to safely “see” what approach might be best. Patient-centered care invites the patient to take responsibility in his or her own health care (Sullivan, 2003), but often neglects the patient’s perspective. As demonstrated by this research, the patient’s perspective is important to self-management behaviors and must be considered in patient education. Online health communities provide opportunities for health behavior change messages to educate and persuade regarding arthritis self-management, including improving disease-related health literacy.

Health literacy is complex and is thought to evolve based on a number of factors, including health status, personal experience (Zarcadoolas et al., 2005), both intrinsic and extrinsic factors, and the health tasks required of the patient (Smith and Hudson, 2012). The communication exchanged within these communities provides opportunity for members to interact with information and also to tailor such information to their personal needs or current situation, applying problem-solving and critical-thinking skills. “Expert patients” are familiar with their arthritis and its symptoms, including the disease affect on their lives and the self-management behaviors necessary to reduce such disease affect. As members experience situations related to their arthritis, they employ problem-solving skills in order to make decisions to enhance individual quality of life. This process is commonly shared within online health communities, making self-management a community effort.

Peer-to-peer online health communities have been largely ignored as to their contribution to the health-care system. These online health communities already exist and are increasing in popularity among people with chronic health conditions (Fox, 2011). The public health narrative in the United States is moving toward prevention (CDC, 2010), and online health communities may offer a cost-effective way to implement chronic disease self-management interventions. Patients are responsible for managing their chronic disease every day, and online health communities provide extra informational and psychosocial support that facilitates better disease self-management and improves health literacy. Online health communities may be a logical extension of the health-care system if the power of peer-to-peer communication could be harnessed to meet public health goals with regard to chronic disease self-management.

Practical implications

There are several practical implications for health-care communicators and professionals who want to address the issue of self-management among people with arthritis. First, health-care communicators can use the findings here to create strategic health messages that promote health literacy of self-management behaviors from a patient’s perspective. Second, since online communities are acting as “informal” self-management programs, participation by health-care professionals and patient educators may improve understanding of the disease—as experienced by the patient, and also improve patients’ levels of health literacy. Additionally, online health communities may give insight into how to “speak” to people with arthritis regarding the disease and self-management behaviors. Similarly, online communities provide opportunity for health-care professionals to develop empathy and understanding of the psychosocial consequences of arthritis. This could help improve physician–patient communication, including patient compliance to medical treatment programs. It might be evaluated as to the cost-effectiveness of online health communities with regard to patient care and improved patient health outcomes.

Limitations and directions for future research

This study is not without limitations. The findings of this study are specific to arthritis and are not generalizable. More research should examine online health communities dedicated to other chronic diseases to determine the extent to which self-management behaviors are being discussed and how such behaviors are being used. Future research might also explore these online health communities as informal patient self-management programs and how they might be used to complement the health-care system. Research should examine why members are participating in the community and what effect participation has on their “off-line” worlds, including the practice of self-management behaviors. The proliferation of these online health communities provides opportunity for health communicators to tailor messages and utilize these communities for education regarding self-management. Continued research is needed to further examine the role of online communities in increasing members’ compliance with self-management behaviors and health literacy regarding chronic disease.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

American Medical Association Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs (1999) Health literacy: Report on the council on scientific affairs. JAMA 281: 552–557.

Apgar

(1983) Ethnography and cognition. In: Emerson

(ed.) Contemporary Field Research: A Collection of Readings. Prospect Heights, IL: Waveland, pp. 68–77.

Atkinson

(1988) Ethnomethodology: A critical review. Annual Review of Sociology 14: 441–465.

Bandura

(1977) Social Learning Theory. NJ: Prentice Hall. Englewood Cliffs, NJ, USA

Bowers

(1992a) Ethnomethodology I: An approach to nursing research. International Journal of Nursing Studies 29(1): 59–67.

Bowers

(1992b) Ethnomethodology II: A study of the community psychiatric nurse in the patient’s home. International Journal of Nursing Studies 29(1): 69–79.

Brady

Murphy

O’Colmain

BJ, et al

. (2013) A meta-analysis of health status, health behaviors, and health care utilization outcomes of the chronic disease self-management program. Preventing Chronic Disease 10. Available at: http://www.cdc.gov/pcd/issues/2013/pdf/12_0112.pdf

Centers for Disease Control and Prevention (CDC) (2010) Healthy people 2010. Available at: http://www.healthypeople.gov/

Centers for Disease Control and Prevention (CDC) (2011) Arthritis: Intervention programs. Available at: http://www.cdc.gov/arthritis/interventions.htm

10.

Cline

RJW

Haynes

(2001) Consumer health information seeking on the Internet: The state of the art. Health Education Research 16(6): 671–692.

11.

Coulson

Buchanan

Aubeeluck

(2007) Social support in cyberspace: A content analysis of communication within a Huntington’s Disease online support group. Patient Education and Counseling 68(2): 173–178.

12.

Dixon

Keefe

Scipio

. (2007) Psychological interventions for arthritis pain management in adults: A meta-analysis. Health Psychology 26(3): 241–250.

13.

Dowling

(2007) Ethnomethodology: Time for a revisit? A discussion paper. International Journal of Nursing Studies 44(5): 826–833.

14.

Edelman Trust Barometer (2008) http://www.isealalliance.org/sites/default/files/EDELMAN%20Trust%20Barometer%2020081.pdf

15.

Elwell

Grogan

Coulson

(2011) Adolescents living with cancer: The role of computer-mediated support groups. Journal of Health Psychology 16(2): 236–248.

16.

Eysenbach

Powell

Englesakis

. (2004) Health related virtual communities and electronic support groups: Systematic review of the effects of online peer to peer interactions. British Medical Journal 328: 1166.

17.

Fox

(2011) The social life of health information, 2011. Available at: http://www.pewinternet.org/Reports/2011/Social-Life-of-Health-Info.aspx

18.

Frank

(2013) From sick role to practices of health and illness. Medical Education 47(1): 18–25.

19.

Frost

Massagli

(2008) Social uses of personal health information within PatientsLikeMe, an online patient community: What can happen when patients have access to one another’s data. Journal of Medical Internet Research 10(3): e15.

20.

Geertz

(1973)Thick description: Toward an interpretive theory of culture. In: Geertz

(ed.) The Interpretation of Cultures: Selected Essays. New York: Basic Books, pp. 3–30.

21.

Have

(1995) Medical ethnomethodology: An overview. Human Studies 18: 245–261.

22.

Herring

(2004) Computer-mediated discourse analysis: An approach to researching online behavior. In: Barab

Kling

(eds) Designing for Virtual Communities in the Service of Learning. Cambridge: Cambridge University Press, pp. 338–376.

23.

Holman

Lorig

(2004) Patient self-management: A key to effectiveness and efficiency in care of chronic disease. Public Health Reports 119: 239–244.

24.

Hwang

Ottenbacher

Lucke

. (2011) Measuring social support for weight loss in an Internet weight loss community. Journal of Health Communication 16(2): 198–211.

25.

Jones

(2003) Can expert patients be created? In: Perspectives on the expert patients. Presentations from a seminar held at the Royal Pharmaceutical Society of Great Britian. May

26.

Kontos

Bennett

Viswanath

(2007) Barriers and facilitators of home computer and Internet use among urban novice computer users of low socioeconomic position. Journal of Medical Internet Research 9(4): e31.

27.

Kreps

Neuhauser

(2010) New directions in eHealth communication: Opportunities and challenges. Patient Education and Counseling 78(3): 329–336.

28.

Kutner

Greenberg

Jin

. (2006) The Health Literacy of America’s Adults: Results from the 2003 National Assessment of Adult Literacy. Washington, DC: National Center for Education Statistics, US Department of Education.

29.

Lewis

(2006) Seeking health information on the Internet: Lifestyle choice or bad attack of cyberchondria? Media, Culture, & Society 28(4): 521–539.

30.

Lorig

Holman

(2003) Self-management education: History, definition, outcome, and mechanisms. Annals of Behavioral Medicine 26(1): 1–7.

31.

Lorig

Lubeck

Kraines

. (1985) Outcomes of self-help education for patients with arthritis. Arthritis and Rheumatism 28(6): 680–685.

32.

Lorig

Ritter

Stewart

. (2001) Chronic disease self-management program: 2-year health status and health care utilization outcomes. Medical Care 39(11): 1217–1223.

33.

Morse

Field

(1995) Research Methods for Health Professionals. 2nd ed. Thousand Oaks, CA: SAGE.

34.

Neuhauser

Kreps

(2003) Rethinking communication in the e-health era. Journal of Health Psychology 8(1): 7–23.

35.

Neuhauser

Kreps

(2003) Rethinking communication in the e-health era. Journal of Health Psychology 8(7): 7–23.

36.

Parker

Smarr

Slaughter

. (2003) Management of depression in rheumatoid arthritis: A combined pharmacologic and cognitive-behavioral approach. Arthritis and Rheumatism 49: 766–777.

37.

Pew Internet and American Life Project (2011) The social life of health information. Available at: http://pewinternet.org/~/media//Files/Reports/2011/PIP_Social_Life_of_Health_Info.pdf

38.

Phoenix

KHM

Coulson

(2010) Living with HIV/AIDS and use of online support groups. Journal of Health Psychology 15(3): 339–350.

39.

Preece

Maloney-Krichmar

(2005) Online communities: Design, theory, and practice. Journal of Computer-Mediated Communication 10(4), article 1.

40.

Ratzan

Parker

(2006) Health literacy—Identification and response. Journal of Health Communication 11(8): 713–715.

41.

Ratzan

(2001) Health literacy: Communication for the public good. Health Promotion International 16(2): 207–214.

42.

Rheingold

(1993) The Virtual Community: Homesteading on the Electronic Frontier. Reading, MA: Addison-Wesley.

43.

Ridings

Gefen

(2004) Virtual community attraction: Why people hang out online. Journal of Computer-Mediated Communication 10(1).

44.

Rodgers

Chen

(2005) Internet community group participation: Psychosocial benefits for women with breast cancer. Journal of Computer-Mediated Communication 10(4): article 5. Available at: http://jcmc.indiana.edu/vol10/issue4/rodgers.html

45.

Sillence

Briggs

Harris

. (2007) How do patients evaluate and make use of online health information. Social Science & Medicine 64(9): 1853–1862.

46.

Smith

Hudson

(2012) Using the person-environment-occupational performance conceptual model as an analyzing framework for health literacy. Journal of Communication in Healthcare 5(1): 3–13.

47.

Speros

(2005) Health literacy: Concept analysis. Journal of Advanced Nursing 50(6): 633–640.

48.

Strauss

Corbin

(1997) Grounded Theory in Practice. Thousand Oaks, CA: SAGE.

49.

Sullivan

(2003) Gendered cybersupport: A thematic analysis of two online cancer support groups. Journal of Health Psychology 8(1): 83–104.

50.

Taylor

Cameron

(1987) Analyzing Conversation. Oxford: Pergamon.

51.

White

Dorman

(2001) Receiving social support online: Implications for health education. Health Education Research 16(6): 693–707.

52.

World Health Organization (2011) World health statistics. Available at: http://www.who.int/whosis/whostat/EN_WHS2011_Full.pdf

53.

World Health Organization (2012) Chronic rheumatic conditions. Available at: http://www.who.int/chp/topics/rheumatic/en/

54.

Wright

Bell

(2003) Health-related support groups on the Internet: Linking empirical findings to social support and computer-mediated communication theory. Journal of Health Psychology 8(1): 39–54.

55.

Zarcadoolas

Pleasant

Greer

(2005) Understanding health literacy: An expanded model. Health Promotion International 20(2): 195–203.

56.

Zautra

Burleson

Smith

. (1995) Arthritis and perceptions of quality of life: An examination of positive and negative affect in rheumatoid arthritis patients. Health Psychology 14(5): 399–408.

57.

Zimmerman

(1978) Ethnomethodology. The American Sociologist 13(1): 6–15.