Benefiting from social capital in Online support groups: An empirical study of cancer patients

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Volume 10, Number 4, 2007 © Mary Ann Liebert, Inc. DOI: 10.1089/cpb.2007.9986

Benefiting from Social Capital in Online Support

Groups: An Empirical Study of Cancer Patients



With measures specific to the online cancer environment and data from an online survey of cancer patients, the current study finds support for the following model: asynchronous on-line communication  social interaction  social support  positive health outcomes in terms of stress, depression, and coping. The findings suggest that the Internet can be a pos-itive cyber venue for cancer patients as they confront illness, undergo treatment, and seek out support.



FAMERICANSwho use the Internet, 28%

partic-ipate in online support groups related to med-ical conditions and personal problems.1About 58%

of patients with cancer use the Internet as a source of cancer information and support.2 This Internet

use leads to increases in social support, community, and coping and decreases in loneliness, depression, and anxiety.3,4

Central to understanding the impact of the Inter-net on cancer patients are the concepts of social cap-ital and social support. Social capcap-ital is the actual or potential resources that result from social connec-tions and senses of reciprocity and trust, which, when mobilized, can bring about outcomes at the individual and collective levels.5 Social support,

which involves advice and emotional reinforce-ment, is a behavioral outcome of social capital.6

Re-search has demonstrated two strong linkages: (1) tween mass media use and social capital; and (2) be-tween social capital and public health. Per the first linkage, social capital is predicted by news use,5,7

media campaign exposure,8–10and Internet use.7,11

Per the second linkage, social capital predicts health outcomes,12,13including those of cancer patients.4,14

These two linkages can be merged to form a three-step model: mass media use social capital public health outcomes.9 This three-step

model has been supported in terms of youth health13 and the stress and depression of

support-ers of cancer patients.6 The current study tests a

more complex model on cancer patients: Internet use social capital  social support  health out-comes in terms of coping, stress, and depression. Implicit to this model is that decreases in stress and depression15and an increase in coping16are

poten-tial bridges to cancer recovery.

1Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana. 2Department of Communication and Technology, National Chiao Tung University, Hsinchu, Taiwan.



An online survey was conducted from March 21, 2005, to April 30, 2005. Respondents were recruited from Yahoo! cancer-related discussion groups. Sur-vey responses were collected from 372 current can-cer patients.

Household income was measured on a seven-point scale (M 3.47, SD  1.78). The mean age was 42.47 years (SD 12.61). Education was from “no formal education” (0) to “graduate degree” (9) (M 7.47, SD  1.42). About 96% of participants were Caucasian, with 31 percent male.

Internet use for cancer purposes involved online-related communication and online information-seeking. Factor analysis (principal components, with orthogonal rotation) indicated three dimen-sions to the nine items.3,17 Asynchronous online

communication, including e-mail and discussion groups, had five items6(eigenvalue 3.89, variance

explained  31.13%; a  0.82; M  15.25 times during last month, SD 9.58). Offline communica-tion stimulated by online communicacommunica-tion had two items6 (eigenvalue 1.24, variance explained  19.36%; r 0.496, p  0.01; M  1.29 times during last month, SD 2.63). Synchronous online com-munication, including instant messages and chat-rooms, had two items6 (eigenvalue 1.02, variance explained 17.21%; r  0.464, p  .01; M  1.96 times during last month, SD 3.56).

Online information-seeking for cancer purposes involved Internet use by medium3and by

informa-tion type.18Factor analysis (principal components,

with orthogonal rotation) identified two dimen-sions. Information-seeking by information type, such as prevention, diagnosis, and treatment, had six items6(eigenvalue 5.52, variance explained 

40.03%; a 0.89). The mean was 3.27 on a five-point scale from “never” to “very often” (SD 0.94). In-formation-seeking by medium type, including search engines and weblogs, had four items6

(eigen-value 1.03, variance explained  25.46%; a  0.80). The mean was 3.38 days per week (SD 1.77). Social capital and social support were specific to the online cancer environment. Social interaction had two items6(r 0.496, p  .01), with a mean of

16.02 people during last month (SD 12.47). Inter-personal trust had four items6 (a 0.88), with a

mean of 3.53 on a four-point scale from “strongly disagree” to “strongly agree” (SD 0.64). Social support had three items from the Interpersonal Sup-port Evaluation List (ISEL) scale19(a 0.88), with a

mean of 3.46 on a four-point scale from “strongly disagree” to “strongly agree” (SD 0.84).

Coping had 10 items from the Brief COPE scale16

(a 0.66), with a mean of 3.49 on a four-point scale from “strongly disagree” to “strongly agree” (SD 0.48). Stress had eight items from the perceived stress scale20(a 0.85). The mean was 3.37 on a

five-point scale from “never” to “very often” (SD 0.62). Depression was measured with eight items from the Center for Epidemiologic Studies Depres-sion (CES-D) scale21 (a 0.90). The mean was 3.60

on a five-point scale from “never” to “very often” (SD 0.72). Some items were reversed, with higher levels indicating more positive health status.



Structural equation modeling (SEM) was imple-mented, with maximum likelihood method of esti-mation. Excellent model fit is indicated by a com-parative fit index (CFI) of 0.95 or higher, a nonsignificant 2, and a root-mean-squared error of

approximation (RMSEA) of close to 0.06 or less.22

Paths were drawn from Internet use measures to so-cial capital, from soso-cial capital to soso-cial support, and from social support to health outcomes. Demo-graphics were used as control variables. Nonsignif-icant paths were pruned, with paths added per modification indices. Where SEM indicated media-tion, another round of SEM was conducted to as-sess whether the addition of a direct path, bypass-ing the mediatbypass-ing variable, led to a significant model improvement.23


The theorized model was first run (2[27, 372]

36.05, p 0.114; CFI  0.99; RMSEA  0.03). After pruning and adding paths, a second model was run (2 [37, 372] 37.54, p  0.444; CFI  0.99;

RM-SEA 0.01). Endogenous variables without ties to one another were pruned, rendering a parsimonious final model (2 [19, 372] 23.11, p  0.232; CFI 

0.99; RMSEA 0.02). This final model (see Figure 1) accounted for the following variance: social sup-port, 28%; social interaction, 43%; interpersonal trust, 1%; stress, 15%; depression, 12%; and coping, 7%.

The posited four-step model receives support. Asynchronous online communication predicts so-cial interaction (  0.62). Social support is pre-dicted by social interaction (  0.32) and interper-sonal trust (  0.50). Social support predicts stress (  0.13), coping (  0.23), and depression (  0.13), with these positive coefficients signifying pos-itive health outcomes.

Figure 1 offers initial support for social inter-action’s mediation of the effect of asynchronous online communication on social support. In an ad-ditional step, a path was drawn directly from asyn-chronous online communication to social support.23

Because this new model did not represent a signif-icant improvement over the previous model (2[18,

372] 21.26, p  0.267; CFI  0.99; RMSEA  0.02), there is support for mediation. There is also support for the mediation role of social support. In the ad-ditional step, direct paths were drawn from social capital to the three health outcomes. The new model did not represent a significant improvement over the previous model (2[13, 372] 16.17, p  0.240;

CFI 0.99; RMSEA  0.03).


SEM offered support for the following four-step model: asynchronous online communication so-cial interaction social support  positive health outcomes in terms of depression, coping, and stress. Synchronous online communication falls out of the model, perhaps because of the lack of a consistent mass and continuity in terms of chatroom and in-stant-messaging participants.

The model has two blemishes that should be noted. First, the negative effect from information-seeking by medium type to depression relates to previous research indicating that, while the Internet can empower and inform cancer patients, it can also be intimidating, confusing, and frightening.24

Sec-ond, the lack of significant paths from Internet use to interpersonal trust suggests a complexity to me-dia effects on social trust. Previous research has demonstrated that Internet use does not predict in-terpersonal trust11or that such effects can be

indi-rect and vary by content type.5

Three limitations should be noted. First, general-ization of the findings is limited by this study’s fo-cus on only users of Yahoo! cancer groups. Second, to avoid overburdening cancer patients, certain de-cisions were made to limit the number of items used in measurement scales. Third, although SEM posits a direction of influence, it does not demonstrate causality to the degree that experimental research can do.

This study has implications for health practition-ers and researchpractition-ers. We hope health practitionpractition-ers will understand the benefits that online communi-cation can have in regards to the development of social capital, social support, and subsequent posi-tive health outcomes. In addition, we hope that re-searchers will continue to model the manner by which the mass media can influence public health outcomes via the mediation of social capital and so-cial support.


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Address reprint requests to:

Dr. Christopher E. Beaudoin Tulane University School of Public Health and Tropical Medicine 1440 Canal St., Suite 2315, TW-19 New Orleans, LA 70112 E-mail: BEAUDOIN AND TAO 590


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FIG. 1. Final structural equation model.
FIG. 1. Final structural equation model. p.2


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