Journal of Health Communication, 20:147–156, 2015 Copyright # Taylor & Francis Group, LLC ISSN: 1081-0730 print/1087-0415 online DOI: 10.1080/10810730.2014.914606
Testing the Proclaimed Affordances of Online Support Groups in a Nationally Representative Sample of Adults Seeking Mental Health Assistance DAVID C. DEANDREA School of Communication, The Ohio State University, Columbus, Ohio, USA
In this study, explanations for why people turn to the Internet for social support are tested using a nationally representative sample of adults who sought mental health support through a traditional treatment outlet, an in-person support group, or an online support group. Results indicate that the more adults report having social stigma concerns, the more likely they are to seek support online instead of help from an in-person support group or traditional treatment. Likewise, as the reported number of logistical barriers to mental health treatment increases, a corresponding increase occurs in the odds of adults seeking online support instead of traditional treatment. These findings as well as estimates of demographic variation in the use of online support are discussed.
The use of health-related online support groups and discussion forums is becoming ubiquitous among those with access to the Internet. Recent estimates indicate that approximately 1 in 5 Internet users in the United States have gone online to find others with health concerns similar to their own. These numbers are not surprising given that a majority of adults favor family, friends, and others with similar health issues over medical professionals for receiving emotional support (Fox, 2011). Although the benefits of online support groups have been documented and examined extensively, it remains unclear how greatly they affect help-seeking behavior in general. This study uses a nationally representative sample of adults who received either traditional treatment, help from an in-person support group, or online support for mental health problems to (a) test theoretical explanations for why people go online for support and (b) estimate the extent to which the affordances of online support are reflected in the help-seeking behavior of adults in the United States. There are two important reasons (among others) why more studies are needed that examine the purported benefits of online support groups. First, previous research has primarily examined the advantages and disadvantages of online support groups exclusively from the perspective of active users (e.g., Tanis, 2008; Walther & Boyd, 2002). Although such research has made important contributions to our understanding of the perceived strengths and weaknesses
of online support groups, it often does not encompass the perspective of those seeking other forms of treatment and those not actively using online support groups (e.g., past users, never users). As such, it is difficult to discern the extent to which the same advantages espoused by some active users might detract others from using an online support forum. For example, if for every online support group user who champions the affordance of anonymity, there is an individual who does not use online support groups as a result of mistrusting information from anonymous individuals (as some noted in Coulson & Knibb, 2007), then this ‘‘advantage’’ would not be as robust as thought. Second, how the advantages of online support groups affect help-seeking behavior, in general, has not been investigated as thoroughly as possible. If one form of help seeking (e.g., online support) has certain advantages over other forms of help seeking (e.g., professional treatment), then empirical manifestations of this advantage should be identifiable in the help-seeking behavior of the population at large. This study seeks to improve upon the limitations of previous work by using a nationally representative sample to estimate the extent to which concerns about social stigma and logistical barriers to treatment (e.g., no transportation) are differentially associated with online help seeking and more traditional forms of help seeking among adults with unmet mental health needs. Health-Related Social Stigma
Address correspondence to David C. DeAndrea, School of Communication, The Ohio State University, 3016 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA. E-mail: [email protected]
Before detailing explanations why online support environments can be especially beneficial for people dealing with social stigma, a brief review of the harmful health-related effects of stigma is provided. Public social stigma refers to
148 a perception that an individual has some flaw or characteristic that is deemed undesirable by society at large (Vogel, Wade, & Haake, 2006). Corrigan (2004) argued that being viewed by others as having a mental health problem can lead to many detriments such as social rejection and the inability to find a job (for a review, see Vogel et al., 2006). The negative effects of public stigma can even be seen within the general health care system: ‘‘People labeled mentally ill are less likely to benefit from the depth and breadth of available physical health care services than people without these illnesses’’ (Corrigan, 2004, p. 616). Self-stigma, in contrast, refers to perceptions that oneself is socially unacceptable as a result of some flaw or characteristic (Vogel et al., 2006). Corrigan (2004) noted that self-perceptions of having a mental health ailment can lead to diminished levels of self-esteem and self-efficacy. These perceptions, in turn, affect help seeking: Feelings of shame and incompetence as a result of a mental health problem have been inversely associated with the likelihood to seek treatment (Corrigan, 2004). Research by Vogel, Wade, and Hackler (2007) indicates that greater levels of public stigma are associated with greater levels of self-stigma, which result in less favorable attitudes toward treatment and a lower willingness to seek help. Given the harmful effects stigma can have on psychological well-being and treatmentseeking behavior, considerable attention has been given to how online support environments can ameliorate perceptions of stigma and their noxious effects. The Affordances of Online Support Groups The deleterious concealment of stigmatized ailments (see Crisp, Gelder, Rix, Meltzer, & Rowlands, 2000) can be overcome online because people are able to express important aspects of themselves without fear of repercussion and with anonymity (Caplan & Turner, 2007; Wright & Bell, 2003). This affordance is likely to be especially appealing to individuals with mental health problems because poor social skills (Mueser, Bellack, Douglas, & Morrison, 1991) and physical appearance cues (Penn, Mueser, & Doonan, 1997) engender stigmatized attitudes. Furthermore, research suggests that computer-mediated communication is at least equal—if not superior—to face-to-face communication in reducing bogus stereotypes (Walther, DeAndrea, & Tong, 2010). The anonymity afforded by computer-mediated communication also helps promote self-disclosure (Barak, Boniel-Nissim, & Suler, 2008; Barney, Griffiths, & Banfield, 2011; Tanis, 2008; Tidwell & Walther, 2002; Walther & Boyd, 2002). McKenna and Bargh (1998) discussed how self-disclosure can be the impetus for improving feelings of self-worth and being more open about one’s condition with others outside of the virtual world. Research has supported this assertion by indicating the usefulness of online groups for individuals suffering from social stigma such as homosexuals (McKenna & Bargh, 1998), people with AIDS (Bar-Lev, 2008), and suicide survivors (Feigelman, Gorman, Chastain Beal, & Jordan, 2008). Beyond affording anonymity, online support environments offer people the ability to connect with similar others,
D. C. DeAndrea overcoming obstacles such as distance, time constraints, and a lack of diversity in one’s offline social network (White & Dorman, 2001). An estimated 25–30 million Americans are living with a rare disease, which is defined as an ailment affecting less than 200,000 people (National Organization for Rare Disorders, 2012). As noted by Fox (2011), people’s emotional support networks are expanding to include online peers, particularly among those with rare diseases. The asynchronous and mediated nature of online communication helps alleviate time and space barriers that exist for support settings that require the simultaneous presence of conversational partners—making it easier for people all over the world to connect with one another (Turner, Grube, & Meyers, 2001). Collectively, previous research suggests that online support groups offer advantages over other forms of support= treatment because of the ability to remain anonymous and overcome logistical obstacles (i.e., constraints resulting from distance or time). A major strength of the body of literature examining online support groups is its breadth and depth (see Barak et al., 2008). Unfortunately, the same limitations tend to permeate most studies in this abundant area of research. When studying online communities or support groups, establishing a sampling frame can be difficult (Wright, 2005). Often there is not a registry of users from which to sample. Even if a list of members can be obtained, individuals self-select into online groups in a way that limits the generalizability of any findings. Exacerbating self-selection biases, heightened participation in an online community= group may increase the likelihood or willingness to respond to survey invitations: Individuals with transient participation are more difficult to track down and less likely to participate. Randomized controlled trials have been conducted that indicate the efficacious potential of online support groups (see Eysenbach, Powell, Englesakis, Rizo, & Stern, 2004). However, it still remains unclear whether the advantages of online support groups are reflected in the help-seeking behavior of the public at large. In addition to garnering the perspective of online support group users and conducting randomized controlled trials, another way to evaluate the utility of online support groups is to test whether people who seek help online differ from people who seek help from more traditional outlets in meaningful ways. Specifically, if the anonymity afforded online is attractive to those facing social stigma, then adults seeking support online should be more likely to report having social stigma concerns relative to adults using an in-person support group or obtaining traditional treatment (i.e., inpatient treatment, outpatient treatment, or prescribed medicine). Likewise, adults seeking support online should be more likely to report facing logistical barriers to treatment (e.g., no transportation) relative to adults seeking in-person support or traditional treatment. This study uses data from a nationally representative sample of adults with unmet mental health treatment needs to empirically test these theoretical predictions and estimate the extent to which the affordances of online support seeking are reflected in the help-seeking behavior of adults in the United States.
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Online Mental Health Support Demographic Variation in Health-Related Internet Use Although the primary focus of this study is to test the extent to which the proclaimed affordances of online support groups are reflected in the help-seeking behavior of the public, subsidiary attention is also given to estimating demographic variation in help seeking. Overall, approximately 4% of adults use online support groups (Hesse et al., 2005). Recent research indicates that younger adults (relative to adults 65 years of age or older) and adults with chronic health conditions are more likely to seek out similar others for support online (Fox, 2011). However, it is largely unclear whether the use of online support groups varies among other demographic factors. It is exceedingly difficult to make a priori predictions about demographic differences in the types of mental health treatment sought (e.g., online support or traditional treatment) because seeking traditional mental health treatment and having Internet access share the same demographic correlates. That is, the same socioeconomic factors associated with a lack of access to more traditional forms of mental health treatment (see Wang et al., 2005) have also been linked to disparities in Internet access that constitute the digital divide (see Fox, 2005). Previous research has examined demographic variation in more traditional forms of mental health support (e.g., Wang et al., 2005); however, online support as an outlet for help seeking has not been given specific attention. As such, the estimates derived in this study can serve as an important foundation for future research. Specifically, the estimates may prove valuable for research that uses the integrative model of eHealth use to examine how social inequalities affect health care, Internet access, and health literacy (Bodie & Dutta, 2008). The integrative model of eHealth use posits that macro-level disparities (e.g., income, education) affect health and computer literacy, which, in turn, affect eHealth use and a variety of healthrelated outcomes (e.g., beliefs and behaviors). Rather than examining the effect of macro-level disparities on specific forms of help seeking in isolation, the estimates from this study explore how these disparities might differentially affect forms of help-seeking behavior. That is, do macro-level disparities affect traditional help seeking and online help seeking in an equivalent fashion? Or, are the effects more pronounced for certain types of help seeking?
Method Research Design and Population Overview In this study, the research design is an annual cross-sectional survey, originating from the National Survey on Drug Use and Health (Substance Abuse and Mental Health Services Administration, 2008, 2009a, 2010a). Data from the years 2008 to 2010 (i.e., the three most recently available years) were analyzed using standard survey analysis methods that take into account analysis weights. Although the nature of the research design is cross-sectional, some of the observed variables are time-invariant correlates that should qualify as predictors of the type of mental health treatment sought (e.g., sex, race=ethnicity).
The National Survey on Drug Use and Health study population is designated to include civilian, noninstitutionalized U.S. residents 12 years of age and older in a large nationally representative sample drawn through multistage area probability sampling. Assessment of many of the key mental health measures (including online help seeking) were not made for respondents younger than 18 years of age. Accordingly, the total effective sample size for the present investigation is 115,223 adults (pooled from 2008 to 2010). The analyses focus on two subpopulations of these adults: those who sought online support or traditional treatment (n ¼ 15,805) and those who sought online support or in-person support (n ¼ 1519). Table 1 provides a complete demographic breakdown for each selected subpopulation of adult respondents. The study protocols were approved by the cognizant institutional review boards for protection of human subjects in research. Measures In February 2010, the Substance Abuse and Mental Health Services Administration released a reliability study of key measures in the National Survey on Drug Use and Health. The response rate for time 1 of the reliability study was 82%, and the response rate for time 2 was 92% (for complete details, see Substance Abuse and Mental Health Services Administration, 2010b). Cohen’s kappa is reported for each variable used in this study that was included in the reliability report: when a kappa coefficient is not reported, it was unavailable. Outcome Measures: Type of Mental Health Treatment Sought Two outcome variables were constructed for this study from items in the National Survey on Drug Use and Health. The first is a variable that assesses whether respondents used traditional mental health treatment or online mental health support in the past 12 months. A variable constructed in the National Survey on Drug Use and Health indicates whether, in the past 12 months, respondents received inpatient treatment, outpatient treatment, or prescribed medicine for problems with their emotions, nerves, or mental health (i.e., traditional treatment). Another variable assesses whether respondents used an Internet support group or chat room in the past 12 months for problems with their emotions, nerves, or mental health (j ¼ .95). The first constructed outcome measure differentiated adults who received traditional treatment but not online support from adults who received online support but not traditional treatment (n ¼ 15,805). The second constructed outcome variable assesses whether respondents used an in-person support group or online support in the past 12 months. A variable in the National Survey on Drug Use and Health reports whether respondents used an in-person support group in the past 12 months for problems with their emotions, nerves, or mental health (j ¼ .80). The final measure differentiated adults who used an in-person support group but not online support from adults who used online support but not an in-person support group (n ¼ 1,519).
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Table 1. Demographic characteristics of each adult subpopulation examined Unweighted number of adults in sample seeking traditional treatment or online support
Total Sex Male Female Age (years) 18–25 26–34 35–49 50þ Race=ethnicity Non-Hispanic White Non-Hispanic Black Hispanic Education Less than high school High school graduate Some college College graduate Income Less than $20,000 $20,000–$49,999 $50,000–$74,999 $75,000 or more
Unweighted number of adults in sample seeking in-person support or online support
Traditional
Online
In-person
Online
14,894
191
1,065
454
4,649 10,245
48 143
335 730
116 338
6,366 2,411 3,850 2,267
130 25 28 8
426 157 314 168
249 71 108 26
11,462 1,106 1,329
137 17 21
748 109 132
344 33 38
2,290 4,463 4,592 3,549
13 48 68 62
153 291 314 307
39 106 168 141
4,128 4,748 2,376 3,642
45 71 35 40
348 339 135 243
109 158 88 99
Note. The only categories of the race=ethnicity variable that are reported are those that contain a sufficient number of observations to include in the analyses.
Failure to Receive Needed Treatment or Support All adult respondents were asked whether, in the past 12 months, there was any time when they needed mental health counseling or treatment but did not get it (j ¼ .65). Respondents that answered ‘‘yes’’ (n ¼ 7,777) were then presented a list of explanations as to why they did not receive needed support. From these responses, it is possible to discern whether specific reasons for not seeking treatment, such as fear of social stigma and=or logistical barriers, are differentially associated with the use of online support or other forms of treatment (e.g., traditional treatment or in-person support group). Explanations for Not Seeking Needed Mental Health Treatment Four statements reflected potential explanations for not receiving needed care as a result of social stigma concerns. They were as follows: ‘‘You were concerned that getting mental health treatment or counseling might cause your neighbors or community to have a negative opinion of you’’ (j ¼ .86), ‘‘You were concerned that the information you gave the counselor might not be kept confidential’’ (j ¼ .79), ‘‘You didn’t want others to find out that you needed treatment,’’ and ‘‘You were concerned that you might be committed to a psychiatric hospital or might have to take medicine’’ (j ¼ .91; Substance Abuse and Mental Health Services Administration, 2009b, p. 316). Respondents reported if any of these statements did or did not apply to
them. These explanations were examined individually, and then in summary form, using a constructed social stigma variable that ranged from 0 (none of the social stigma explanations selected) to 2 (two or more explanations selected). Two explanations were examined that reflected logistical barriers to receiving needed treatment. They were as follows: ‘‘You didn’t have time (because of job, childcare, or other commitments),’’ and ‘‘You had no transportation, or treatment was too far away, or the hours were not convenient’’ (Substance Abuse and Mental Health Services Administration, 2009b, p. 316). These explanations were examined individually, and then in summary form, using a constructed logistical barrier variable with a range from 0 (none of the logistical explanations selected) to 2 (both logistical barrier explanations selected). Health Coverage An item constructed in the National Survey on Drug Use and Health was used to control for differences in health coverage that might exist between people seeking various forms of mental health treatment. In the National Survey on Drug Use and Health, a respondent was classified as having health coverage if they reported having one of the following: private insurance (j ¼ .88), Medicare (j ¼ .91), Medicaid=Children’s Health Insurance Program (j ¼ .82), military insurance (j ¼ .88), or another form of medical insurance (j ¼ .73).
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Online Mental Health Support Serious Psychological Distress To control for variation in psychological problems that might exist between people seeking different forms of mental health treatment, a variable assessing serious psychological distress was included in the analyses. It was constructed by the National Survey on Drug Use and Health from six questions known as the K6 that assess symptoms of distress such as feeling nervous, hopeless, restless, depressed, lethargic, and worthless (j ¼ .64; Furukawa, Kessler, Slade, & Andrews, 2003).
Results Before detailing the results, an overview of the data analysis plan is provided. The first analytic step was to estimate the proportion of adults who use traditional treatment, in-person support groups, or online support for mental health problems by calculating weighted estimates of the U.S. adult population. The next step was to examine associations between the reasons for not getting needed mental health treatment and each outcome measure (i.e., traditional treatment vs. online support; in-person vs. online support). These associations were examined controlling for health coverage, presence of serious psychological distress, and the demographic factors that collectively create the digital divide (i.e., sex, race=ethnicity, income, education, age). Then associations between the covariates and each outcome variable were estimated. All weighted analyses were conducted using Stata svy commands for complex survey design data. Between 2008 and 2010, approximately three per every 1,000 community-dwelling adults in the United States sought online mental health support (95% CI [.0025, .0034]). Approximately 1 per every 100 adults sought help from an in-person support group (95% CI [.010, .012]) and 13 per every 100 adults sought traditional mental health treatment (95% CI [.131, .138]). Table 2 provides the weighted proportions for each year and the pooled summary estimates. During the same time period (2008–2010), approximately 1 in every 20 adults reported that at some point during the previous year they did not receive needed mental health treatment (95% CI [.048, .051]). Only adults reporting unmet treatment needs were asked to indicate the reasons why they failed to receive needed help. As such, perceptions of unmet mental health treatment need are held constant in the following analyses and the results are generalizable
to the approximately 11.7 million adults in the United States who annually experience some form of unmet mental health treatment need. The focus of the following analyses is to test the prediction that—compared to adults seeking traditional treatment or in-person support—adults using online mental health support are more likely to report concerns about social stigma and logistical barriers to treatment. All of the logistic regression analyses reported in Table 3 were weighted and control for sex, age, race=ethnicity, income, education, medical coverage, and past year serious psychological distress. The following results focus on the traditional treatment or online support outcome measure. Overall, there was a significant association (OR ¼ 2.4, p < .01) between the number of social stigma concerns reported and the type of treatment sought. That is, for every one-unit increase in the constructed social stigma variable, the odds of seeking online support were 2.4 times greater. The results for each social stigma item appear in Table 3 and help elucidate the aforementioned finding. For example, the odds of using online support instead of traditional treatment were 6 times greater for adults who indicated that they were concerned about others finding out about their mental health problem (OR ¼ 6.0, p < .01). Overall, a significant association was also found between the number of logistical barrier concerns reported and the type of treatment sought (OR ¼ 2.3, p < .01). As a final test, both the constructed social stigma variable and the logistical barriers variable were included with the covariates in a logistic regression model to examine if the reported associations were independent of one another. The associations remained significant and did not change appreciably for both the social stigma (OR ¼ 2.2, p < .01) and logistical barriers variable (OR ¼ 2.0, p < .01). The same analyses were replicated for the second outcome measure (in-person support or online support). Again, there was a significant association (OR ¼ 2.5, p < .01) between the number of social stigma concerns reported and the type of treatment sought. However, there was not a significant association between the logistical barrier explanations and the type of treatment sought. Table 3 provides odds ratios, 95% confidence intervals, and p values for all of the estimated associations. Although the main focus of the manuscript was to test theoretical explanations for why people are likely to turn to the Internet for support, estimates between the covariates
Table 2. Weighted proportion of adults using mental health treatment In-person support group
Traditional treatment Year 2008 2009 2010 2008–2010
Weighted proportion .131 .137 .135 .135
95% CI .124, .131, .130, .131,
.139 .144 .140 .138
Note. Treatment=support categories are not mutually exclusive.
Weighted proportion .010 .010 .012 .011
Online support group
95% CI .009, .009, .010, .010,
.012 .012 .014 .012
Weighted proportion .0031 .0029 .0028 .0029
95% CI .0022, .0022, .0021, .0025,
.0040 .0036 .0035 .0034
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Table 3. Suspected influence of mental health treatment obstacles on type of mental health treatment sought Traditional treatment or online support (n ¼ 3,435)
Afraid others would find out Confidentiality concern Worried about being committed Worried community will form negative opinion Constructed social stigma measure Didn’t have the time Too inconvenient Constructed logistical barrier measure
In-person support or online support (n ¼ 530)
OR
CI
p
OR
CI
p
6.0 2.6 2.5 2.4 2.4 2.4 3.3 2.3
2.1, 17.1 0.92, 7.2 0.89, 7.2 1.2, 5.1 1.4, 4.0 1.1, 5.6 0.71, 15.2 1.3, 4.0
Testing the Proclaimed Affordances of Online Support Groups in a Nationally Representative Sample of Adults Seeking Mental Health Assistance DAVID C. DEANDREA School of Communication, The Ohio State University, Columbus, Ohio, USA
In this study, explanations for why people turn to the Internet for social support are tested using a nationally representative sample of adults who sought mental health support through a traditional treatment outlet, an in-person support group, or an online support group. Results indicate that the more adults report having social stigma concerns, the more likely they are to seek support online instead of help from an in-person support group or traditional treatment. Likewise, as the reported number of logistical barriers to mental health treatment increases, a corresponding increase occurs in the odds of adults seeking online support instead of traditional treatment. These findings as well as estimates of demographic variation in the use of online support are discussed.
The use of health-related online support groups and discussion forums is becoming ubiquitous among those with access to the Internet. Recent estimates indicate that approximately 1 in 5 Internet users in the United States have gone online to find others with health concerns similar to their own. These numbers are not surprising given that a majority of adults favor family, friends, and others with similar health issues over medical professionals for receiving emotional support (Fox, 2011). Although the benefits of online support groups have been documented and examined extensively, it remains unclear how greatly they affect help-seeking behavior in general. This study uses a nationally representative sample of adults who received either traditional treatment, help from an in-person support group, or online support for mental health problems to (a) test theoretical explanations for why people go online for support and (b) estimate the extent to which the affordances of online support are reflected in the help-seeking behavior of adults in the United States. There are two important reasons (among others) why more studies are needed that examine the purported benefits of online support groups. First, previous research has primarily examined the advantages and disadvantages of online support groups exclusively from the perspective of active users (e.g., Tanis, 2008; Walther & Boyd, 2002). Although such research has made important contributions to our understanding of the perceived strengths and weaknesses
of online support groups, it often does not encompass the perspective of those seeking other forms of treatment and those not actively using online support groups (e.g., past users, never users). As such, it is difficult to discern the extent to which the same advantages espoused by some active users might detract others from using an online support forum. For example, if for every online support group user who champions the affordance of anonymity, there is an individual who does not use online support groups as a result of mistrusting information from anonymous individuals (as some noted in Coulson & Knibb, 2007), then this ‘‘advantage’’ would not be as robust as thought. Second, how the advantages of online support groups affect help-seeking behavior, in general, has not been investigated as thoroughly as possible. If one form of help seeking (e.g., online support) has certain advantages over other forms of help seeking (e.g., professional treatment), then empirical manifestations of this advantage should be identifiable in the help-seeking behavior of the population at large. This study seeks to improve upon the limitations of previous work by using a nationally representative sample to estimate the extent to which concerns about social stigma and logistical barriers to treatment (e.g., no transportation) are differentially associated with online help seeking and more traditional forms of help seeking among adults with unmet mental health needs. Health-Related Social Stigma
Address correspondence to David C. DeAndrea, School of Communication, The Ohio State University, 3016 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA. E-mail: [email protected]
Before detailing explanations why online support environments can be especially beneficial for people dealing with social stigma, a brief review of the harmful health-related effects of stigma is provided. Public social stigma refers to
148 a perception that an individual has some flaw or characteristic that is deemed undesirable by society at large (Vogel, Wade, & Haake, 2006). Corrigan (2004) argued that being viewed by others as having a mental health problem can lead to many detriments such as social rejection and the inability to find a job (for a review, see Vogel et al., 2006). The negative effects of public stigma can even be seen within the general health care system: ‘‘People labeled mentally ill are less likely to benefit from the depth and breadth of available physical health care services than people without these illnesses’’ (Corrigan, 2004, p. 616). Self-stigma, in contrast, refers to perceptions that oneself is socially unacceptable as a result of some flaw or characteristic (Vogel et al., 2006). Corrigan (2004) noted that self-perceptions of having a mental health ailment can lead to diminished levels of self-esteem and self-efficacy. These perceptions, in turn, affect help seeking: Feelings of shame and incompetence as a result of a mental health problem have been inversely associated with the likelihood to seek treatment (Corrigan, 2004). Research by Vogel, Wade, and Hackler (2007) indicates that greater levels of public stigma are associated with greater levels of self-stigma, which result in less favorable attitudes toward treatment and a lower willingness to seek help. Given the harmful effects stigma can have on psychological well-being and treatmentseeking behavior, considerable attention has been given to how online support environments can ameliorate perceptions of stigma and their noxious effects. The Affordances of Online Support Groups The deleterious concealment of stigmatized ailments (see Crisp, Gelder, Rix, Meltzer, & Rowlands, 2000) can be overcome online because people are able to express important aspects of themselves without fear of repercussion and with anonymity (Caplan & Turner, 2007; Wright & Bell, 2003). This affordance is likely to be especially appealing to individuals with mental health problems because poor social skills (Mueser, Bellack, Douglas, & Morrison, 1991) and physical appearance cues (Penn, Mueser, & Doonan, 1997) engender stigmatized attitudes. Furthermore, research suggests that computer-mediated communication is at least equal—if not superior—to face-to-face communication in reducing bogus stereotypes (Walther, DeAndrea, & Tong, 2010). The anonymity afforded by computer-mediated communication also helps promote self-disclosure (Barak, Boniel-Nissim, & Suler, 2008; Barney, Griffiths, & Banfield, 2011; Tanis, 2008; Tidwell & Walther, 2002; Walther & Boyd, 2002). McKenna and Bargh (1998) discussed how self-disclosure can be the impetus for improving feelings of self-worth and being more open about one’s condition with others outside of the virtual world. Research has supported this assertion by indicating the usefulness of online groups for individuals suffering from social stigma such as homosexuals (McKenna & Bargh, 1998), people with AIDS (Bar-Lev, 2008), and suicide survivors (Feigelman, Gorman, Chastain Beal, & Jordan, 2008). Beyond affording anonymity, online support environments offer people the ability to connect with similar others,
D. C. DeAndrea overcoming obstacles such as distance, time constraints, and a lack of diversity in one’s offline social network (White & Dorman, 2001). An estimated 25–30 million Americans are living with a rare disease, which is defined as an ailment affecting less than 200,000 people (National Organization for Rare Disorders, 2012). As noted by Fox (2011), people’s emotional support networks are expanding to include online peers, particularly among those with rare diseases. The asynchronous and mediated nature of online communication helps alleviate time and space barriers that exist for support settings that require the simultaneous presence of conversational partners—making it easier for people all over the world to connect with one another (Turner, Grube, & Meyers, 2001). Collectively, previous research suggests that online support groups offer advantages over other forms of support= treatment because of the ability to remain anonymous and overcome logistical obstacles (i.e., constraints resulting from distance or time). A major strength of the body of literature examining online support groups is its breadth and depth (see Barak et al., 2008). Unfortunately, the same limitations tend to permeate most studies in this abundant area of research. When studying online communities or support groups, establishing a sampling frame can be difficult (Wright, 2005). Often there is not a registry of users from which to sample. Even if a list of members can be obtained, individuals self-select into online groups in a way that limits the generalizability of any findings. Exacerbating self-selection biases, heightened participation in an online community= group may increase the likelihood or willingness to respond to survey invitations: Individuals with transient participation are more difficult to track down and less likely to participate. Randomized controlled trials have been conducted that indicate the efficacious potential of online support groups (see Eysenbach, Powell, Englesakis, Rizo, & Stern, 2004). However, it still remains unclear whether the advantages of online support groups are reflected in the help-seeking behavior of the public at large. In addition to garnering the perspective of online support group users and conducting randomized controlled trials, another way to evaluate the utility of online support groups is to test whether people who seek help online differ from people who seek help from more traditional outlets in meaningful ways. Specifically, if the anonymity afforded online is attractive to those facing social stigma, then adults seeking support online should be more likely to report having social stigma concerns relative to adults using an in-person support group or obtaining traditional treatment (i.e., inpatient treatment, outpatient treatment, or prescribed medicine). Likewise, adults seeking support online should be more likely to report facing logistical barriers to treatment (e.g., no transportation) relative to adults seeking in-person support or traditional treatment. This study uses data from a nationally representative sample of adults with unmet mental health treatment needs to empirically test these theoretical predictions and estimate the extent to which the affordances of online support seeking are reflected in the help-seeking behavior of adults in the United States.
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Online Mental Health Support Demographic Variation in Health-Related Internet Use Although the primary focus of this study is to test the extent to which the proclaimed affordances of online support groups are reflected in the help-seeking behavior of the public, subsidiary attention is also given to estimating demographic variation in help seeking. Overall, approximately 4% of adults use online support groups (Hesse et al., 2005). Recent research indicates that younger adults (relative to adults 65 years of age or older) and adults with chronic health conditions are more likely to seek out similar others for support online (Fox, 2011). However, it is largely unclear whether the use of online support groups varies among other demographic factors. It is exceedingly difficult to make a priori predictions about demographic differences in the types of mental health treatment sought (e.g., online support or traditional treatment) because seeking traditional mental health treatment and having Internet access share the same demographic correlates. That is, the same socioeconomic factors associated with a lack of access to more traditional forms of mental health treatment (see Wang et al., 2005) have also been linked to disparities in Internet access that constitute the digital divide (see Fox, 2005). Previous research has examined demographic variation in more traditional forms of mental health support (e.g., Wang et al., 2005); however, online support as an outlet for help seeking has not been given specific attention. As such, the estimates derived in this study can serve as an important foundation for future research. Specifically, the estimates may prove valuable for research that uses the integrative model of eHealth use to examine how social inequalities affect health care, Internet access, and health literacy (Bodie & Dutta, 2008). The integrative model of eHealth use posits that macro-level disparities (e.g., income, education) affect health and computer literacy, which, in turn, affect eHealth use and a variety of healthrelated outcomes (e.g., beliefs and behaviors). Rather than examining the effect of macro-level disparities on specific forms of help seeking in isolation, the estimates from this study explore how these disparities might differentially affect forms of help-seeking behavior. That is, do macro-level disparities affect traditional help seeking and online help seeking in an equivalent fashion? Or, are the effects more pronounced for certain types of help seeking?
Method Research Design and Population Overview In this study, the research design is an annual cross-sectional survey, originating from the National Survey on Drug Use and Health (Substance Abuse and Mental Health Services Administration, 2008, 2009a, 2010a). Data from the years 2008 to 2010 (i.e., the three most recently available years) were analyzed using standard survey analysis methods that take into account analysis weights. Although the nature of the research design is cross-sectional, some of the observed variables are time-invariant correlates that should qualify as predictors of the type of mental health treatment sought (e.g., sex, race=ethnicity).
The National Survey on Drug Use and Health study population is designated to include civilian, noninstitutionalized U.S. residents 12 years of age and older in a large nationally representative sample drawn through multistage area probability sampling. Assessment of many of the key mental health measures (including online help seeking) were not made for respondents younger than 18 years of age. Accordingly, the total effective sample size for the present investigation is 115,223 adults (pooled from 2008 to 2010). The analyses focus on two subpopulations of these adults: those who sought online support or traditional treatment (n ¼ 15,805) and those who sought online support or in-person support (n ¼ 1519). Table 1 provides a complete demographic breakdown for each selected subpopulation of adult respondents. The study protocols were approved by the cognizant institutional review boards for protection of human subjects in research. Measures In February 2010, the Substance Abuse and Mental Health Services Administration released a reliability study of key measures in the National Survey on Drug Use and Health. The response rate for time 1 of the reliability study was 82%, and the response rate for time 2 was 92% (for complete details, see Substance Abuse and Mental Health Services Administration, 2010b). Cohen’s kappa is reported for each variable used in this study that was included in the reliability report: when a kappa coefficient is not reported, it was unavailable. Outcome Measures: Type of Mental Health Treatment Sought Two outcome variables were constructed for this study from items in the National Survey on Drug Use and Health. The first is a variable that assesses whether respondents used traditional mental health treatment or online mental health support in the past 12 months. A variable constructed in the National Survey on Drug Use and Health indicates whether, in the past 12 months, respondents received inpatient treatment, outpatient treatment, or prescribed medicine for problems with their emotions, nerves, or mental health (i.e., traditional treatment). Another variable assesses whether respondents used an Internet support group or chat room in the past 12 months for problems with their emotions, nerves, or mental health (j ¼ .95). The first constructed outcome measure differentiated adults who received traditional treatment but not online support from adults who received online support but not traditional treatment (n ¼ 15,805). The second constructed outcome variable assesses whether respondents used an in-person support group or online support in the past 12 months. A variable in the National Survey on Drug Use and Health reports whether respondents used an in-person support group in the past 12 months for problems with their emotions, nerves, or mental health (j ¼ .80). The final measure differentiated adults who used an in-person support group but not online support from adults who used online support but not an in-person support group (n ¼ 1,519).
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Table 1. Demographic characteristics of each adult subpopulation examined Unweighted number of adults in sample seeking traditional treatment or online support
Total Sex Male Female Age (years) 18–25 26–34 35–49 50þ Race=ethnicity Non-Hispanic White Non-Hispanic Black Hispanic Education Less than high school High school graduate Some college College graduate Income Less than $20,000 $20,000–$49,999 $50,000–$74,999 $75,000 or more
Unweighted number of adults in sample seeking in-person support or online support
Traditional
Online
In-person
Online
14,894
191
1,065
454
4,649 10,245
48 143
335 730
116 338
6,366 2,411 3,850 2,267
130 25 28 8
426 157 314 168
249 71 108 26
11,462 1,106 1,329
137 17 21
748 109 132
344 33 38
2,290 4,463 4,592 3,549
13 48 68 62
153 291 314 307
39 106 168 141
4,128 4,748 2,376 3,642
45 71 35 40
348 339 135 243
109 158 88 99
Note. The only categories of the race=ethnicity variable that are reported are those that contain a sufficient number of observations to include in the analyses.
Failure to Receive Needed Treatment or Support All adult respondents were asked whether, in the past 12 months, there was any time when they needed mental health counseling or treatment but did not get it (j ¼ .65). Respondents that answered ‘‘yes’’ (n ¼ 7,777) were then presented a list of explanations as to why they did not receive needed support. From these responses, it is possible to discern whether specific reasons for not seeking treatment, such as fear of social stigma and=or logistical barriers, are differentially associated with the use of online support or other forms of treatment (e.g., traditional treatment or in-person support group). Explanations for Not Seeking Needed Mental Health Treatment Four statements reflected potential explanations for not receiving needed care as a result of social stigma concerns. They were as follows: ‘‘You were concerned that getting mental health treatment or counseling might cause your neighbors or community to have a negative opinion of you’’ (j ¼ .86), ‘‘You were concerned that the information you gave the counselor might not be kept confidential’’ (j ¼ .79), ‘‘You didn’t want others to find out that you needed treatment,’’ and ‘‘You were concerned that you might be committed to a psychiatric hospital or might have to take medicine’’ (j ¼ .91; Substance Abuse and Mental Health Services Administration, 2009b, p. 316). Respondents reported if any of these statements did or did not apply to
them. These explanations were examined individually, and then in summary form, using a constructed social stigma variable that ranged from 0 (none of the social stigma explanations selected) to 2 (two or more explanations selected). Two explanations were examined that reflected logistical barriers to receiving needed treatment. They were as follows: ‘‘You didn’t have time (because of job, childcare, or other commitments),’’ and ‘‘You had no transportation, or treatment was too far away, or the hours were not convenient’’ (Substance Abuse and Mental Health Services Administration, 2009b, p. 316). These explanations were examined individually, and then in summary form, using a constructed logistical barrier variable with a range from 0 (none of the logistical explanations selected) to 2 (both logistical barrier explanations selected). Health Coverage An item constructed in the National Survey on Drug Use and Health was used to control for differences in health coverage that might exist between people seeking various forms of mental health treatment. In the National Survey on Drug Use and Health, a respondent was classified as having health coverage if they reported having one of the following: private insurance (j ¼ .88), Medicare (j ¼ .91), Medicaid=Children’s Health Insurance Program (j ¼ .82), military insurance (j ¼ .88), or another form of medical insurance (j ¼ .73).
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Online Mental Health Support Serious Psychological Distress To control for variation in psychological problems that might exist between people seeking different forms of mental health treatment, a variable assessing serious psychological distress was included in the analyses. It was constructed by the National Survey on Drug Use and Health from six questions known as the K6 that assess symptoms of distress such as feeling nervous, hopeless, restless, depressed, lethargic, and worthless (j ¼ .64; Furukawa, Kessler, Slade, & Andrews, 2003).
Results Before detailing the results, an overview of the data analysis plan is provided. The first analytic step was to estimate the proportion of adults who use traditional treatment, in-person support groups, or online support for mental health problems by calculating weighted estimates of the U.S. adult population. The next step was to examine associations between the reasons for not getting needed mental health treatment and each outcome measure (i.e., traditional treatment vs. online support; in-person vs. online support). These associations were examined controlling for health coverage, presence of serious psychological distress, and the demographic factors that collectively create the digital divide (i.e., sex, race=ethnicity, income, education, age). Then associations between the covariates and each outcome variable were estimated. All weighted analyses were conducted using Stata svy commands for complex survey design data. Between 2008 and 2010, approximately three per every 1,000 community-dwelling adults in the United States sought online mental health support (95% CI [.0025, .0034]). Approximately 1 per every 100 adults sought help from an in-person support group (95% CI [.010, .012]) and 13 per every 100 adults sought traditional mental health treatment (95% CI [.131, .138]). Table 2 provides the weighted proportions for each year and the pooled summary estimates. During the same time period (2008–2010), approximately 1 in every 20 adults reported that at some point during the previous year they did not receive needed mental health treatment (95% CI [.048, .051]). Only adults reporting unmet treatment needs were asked to indicate the reasons why they failed to receive needed help. As such, perceptions of unmet mental health treatment need are held constant in the following analyses and the results are generalizable
to the approximately 11.7 million adults in the United States who annually experience some form of unmet mental health treatment need. The focus of the following analyses is to test the prediction that—compared to adults seeking traditional treatment or in-person support—adults using online mental health support are more likely to report concerns about social stigma and logistical barriers to treatment. All of the logistic regression analyses reported in Table 3 were weighted and control for sex, age, race=ethnicity, income, education, medical coverage, and past year serious psychological distress. The following results focus on the traditional treatment or online support outcome measure. Overall, there was a significant association (OR ¼ 2.4, p < .01) between the number of social stigma concerns reported and the type of treatment sought. That is, for every one-unit increase in the constructed social stigma variable, the odds of seeking online support were 2.4 times greater. The results for each social stigma item appear in Table 3 and help elucidate the aforementioned finding. For example, the odds of using online support instead of traditional treatment were 6 times greater for adults who indicated that they were concerned about others finding out about their mental health problem (OR ¼ 6.0, p < .01). Overall, a significant association was also found between the number of logistical barrier concerns reported and the type of treatment sought (OR ¼ 2.3, p < .01). As a final test, both the constructed social stigma variable and the logistical barriers variable were included with the covariates in a logistic regression model to examine if the reported associations were independent of one another. The associations remained significant and did not change appreciably for both the social stigma (OR ¼ 2.2, p < .01) and logistical barriers variable (OR ¼ 2.0, p < .01). The same analyses were replicated for the second outcome measure (in-person support or online support). Again, there was a significant association (OR ¼ 2.5, p < .01) between the number of social stigma concerns reported and the type of treatment sought. However, there was not a significant association between the logistical barrier explanations and the type of treatment sought. Table 3 provides odds ratios, 95% confidence intervals, and p values for all of the estimated associations. Although the main focus of the manuscript was to test theoretical explanations for why people are likely to turn to the Internet for support, estimates between the covariates
Table 2. Weighted proportion of adults using mental health treatment In-person support group
Traditional treatment Year 2008 2009 2010 2008–2010
Weighted proportion .131 .137 .135 .135
95% CI .124, .131, .130, .131,
.139 .144 .140 .138
Note. Treatment=support categories are not mutually exclusive.
Weighted proportion .010 .010 .012 .011
Online support group
95% CI .009, .009, .010, .010,
.012 .012 .014 .012
Weighted proportion .0031 .0029 .0028 .0029
95% CI .0022, .0022, .0021, .0025,
.0040 .0036 .0035 .0034
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Table 3. Suspected influence of mental health treatment obstacles on type of mental health treatment sought Traditional treatment or online support (n ¼ 3,435)
Afraid others would find out Confidentiality concern Worried about being committed Worried community will form negative opinion Constructed social stigma measure Didn’t have the time Too inconvenient Constructed logistical barrier measure
In-person support or online support (n ¼ 530)
OR
CI
p
OR
CI
p
6.0 2.6 2.5 2.4 2.4 2.4 3.3 2.3
2.1, 17.1 0.92, 7.2 0.89, 7.2 1.2, 5.1 1.4, 4.0 1.1, 5.6 0.71, 15.2 1.3, 4.0
