University of Southern California Dissertations and Theses

Benefits of social networking in online social support groups

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Benefits of social networking in online social support groups

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BENEFITS OF SOCIAL NETWORKING IN ONLINE SOCIAL SUPPORT GROUPS

by

Jae Eun Chung

__________________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirement for the Degree
DOCTOR OF PHILOSOPHY
(COMMUNICATION)

August 2010

Copyright 2010 Jae Eun Chung

ii
TABLE OF CONTENTS

List of Tables iv

List of Figures vi

Abstract vii

CHAPTER 1: INTRODUCTION 1
Purpose of the Study 1
Chapter Summaries 4

CHAPTER 2: LITERATURE REVIEW 6
Social Support and Support Groups 6
Online Support Groups 7
Communication Perspective on Social Support 9
Supportive Communication in Online Support Groups 9
Social Networking Sites 11
Health-related Social Networking Sites 13
Uses and Gratifications as a Theoretical Framework 15

CHAPTER 3: RESEARCH QUESTIONS AND HYPOTHESES 18
Research Question One 18
Research Question Two 20
Research Question Three 22
Hypotheses One and Two 24
Hypotheses Three and Four 26
Hypotheses Five and Six 28

CHAPTER 4: METHOD 33
Study Site 33
Participant Recruitment 35
Survey Administration 36
Measures 36
Motivation 36
Online Support Group (OSG) Feature Use 37
OSG Support Perception 38
OSG Use Intensity 39
OSG Friendship 41
Extraversion 41
Satisfaction with Offline Social Support 41
Control Variables 42
Analysis 42

iii

CHAPTER 5: RESULTS 44
Descriptive Statistics 44
Correlation Analysis 45
Research Question One 49
Research Question Two 53
Research Question Three 57
Hypothesis One 60
Hypothesis Two 61
Hypothesis Three 64
Hypothesis Four 66
Hypothesis Five 68
Hypothesis Six 71

CHAPTER 6: CONCLUSION 72
Discussion 72
Implications 80
Limitation and Directions for Future Research 84

BIBLIOGRAPHY 87

iv
LIST OF TABLES

Table 1: Study Site Characteristics 34

Table 2: Individual Items and Response Scales for OSG Use Intensity 40

Table 3: Demographic and Health Characteristics of Participants 44

Table 4: Means, Standard Deviations, and Zero-order Correlations 47

Table 5: Exploratory Factor Analysis of the Motivation Items 50

Table 6: Exploratory Factor Analysis of OSG Feature Use 54

Table 7: Regression Analysis Predicting Use of Different Features from
Motivation
56

Table 8: Multiple Regression Analyses Predicting OSG Informational Support
from OSG Feature Use Patterns
58

Table 9: Multiple Regression Analyses Predicting OSG Emotional Support
from OSG Feature Use Patterns
59

Table 10: Multiple Regression Analyses Predicting OSG Informational Support
from OSG Use Intensity
60

Table 11: Multiple Regression Analyses Predicting OSG Emotional Support
from OSG Use Intensity
61

Table 12: Multiple Regression Analyses Predicting OSG Friendship from OSG
Use Intensity
62

Table 13: Multiple Regression Analyses Predicting OSG Informational and
Emotional Support from OSG Use Intensity and OSG Friendship
63

Table 14: Multiple Regression Analyses Predicting OSG Friendship from
Extraversion and OSG Use Intensity x Extraversion
64

Table 15: Multiple Regression Analyses Predicting Informational and Emotional
Support from Extraversion and OSG Use Intensity x Extraversion
66

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Table 16: Multiple Regression Analyses Predicting OSG Friendship from Offline
Support Satisfaction and OSG Use Intensity x Offline Support
Satisfaction
69

Table 17: Multiple Regression Analyses Predicting Informational and Emotional
Support from Offline Support Satisfaction and OSG Use Intensity x
Offline Support Satisfaction
71

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LIST OF FIGURES

Figure 1: Research Questions 32

Figure 2: Hypotheses 32

Figure 3: Mean Comparisons of Motivations 52

Figure 4: Extraversion and OSG Use Intensity on OSG Friendship 65

Figure 5: Extraversion and OSG Use Intensity on OSG Informational Support 67

Figure 6: Extraversion and OSG Use Intensity on OSG Emotional Support 67

Figure 7: Offline Support Satisfaction and OSG Use Intensity on OSG Friendship 70

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ABSTRACT

An increasing number of health-related support groups online are embracing
social networking features. So far little is known about how patients use and adopt social
networking features integrated within online social support groups (OSGs) and whether
the use of networking features enhances experiences of OSG users. Although there are
abundant studies on the topic of general purpose social networking sites (e.g., Facebook),
no studies have been published of social networking sites as supportive care resources for
patients.
By taking the uses and gratifications perspective as a theoretical backbone, the
current study examined individual variations in motivations, OSG use patterns, and
appreciation of benefits from OSG use and explored the role individual users’ personal
social circumstances and psychological dispositions play in using and appreciating
resources available within OSGs. By conducting an online survey of the current users of
OSGs, the present study investigated the effectiveness of OSGs in providing emotional
and informational support to their users.
Findings from the survey suggest that OSG users are making use of selected
features depending on their needs and that certain features are more effective than others
in providing certain types of support. For example, discussion board was most frequently
used by information seekers and most effective in providing informational support. On
the other hand, information seekers are least likely to make one-to-one connection to
other users by friending, although the friending feature (social networking feature) was
found to be effective in providing emotional support.

viii
The current study makes important contributions toward a better understanding of
health-related social networking sites and provides practical lessons on how to build
OSGs that can better serve the needs of their users.

Keywords: computer-mediated support group, online support group, social support,
health communication, virtual communities, social networking sites

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CHAPTER 1: INTRODUCTION
Purpose of the Study
The use of the internet for health information has been rising. Studies and reports
have shown the popularity of online health information seeking among patients and
caregivers. A recent report by iCrossing (Elkin, 2008) shows that the internet is the most
widely used resource for health information. According to this report, about two-thirds of
internet users in the United States searched the internet to obtain health and wellness
information. Pew Internet (Fox, 2008) and Harris Interactive (2008) reported a slightly
higher number; about 80 percent of internet users and 66 percent of all adults in the
United States, have turned to the internet for health information and advice. More
recently, emerging social media, such as patient blogs, online forums, message boards,
and health-related social networking sites, have drawn the attention of health information
seekers. Approximately a third of online health information seekers have used such social
media resources (Elkin, 2008). Similarly among those diagnosed by a health professional
as having a serious health condition about thirty percent reported that they have found
online communities dedicated to their health issues (Annenberg National Health
Communication Survey, 2008). The number of people using health-related online social
media is expected to grow (Jupiter Research, 2007; Sarasohn-Kahn, 2008, 2009).
Health-related social media provide a new venue for patients to connect with
others. Exchange of information along with sharing of experiences and emotions have
been the core elements of online support groups (OSGs). With the rising popularity of
OSGs, substantial research efforts have been made to explore motivations behind
participation (e.g. Buchanan & Coulson, 2007; Maloney-Krichmar & Preece, 2005),

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types of support exchanged among OSG members (e.g. Barnett & Hwang, 2006; Coulson,
2005; Eysenbach, Powell, Englesakis, Rizo, & Stern, 2004; Malik & Coulson, 2008;
Meier, Lyons, Frydman, Forlenza, & Rimer, 2007), and outcomes associated with
participation in OSGs (e.g. Baum, 2004; Montazeri et al., 2001; Rodgers & Chen, 2005).
These papers reveal a good deal about why people use OSGs, what people discuss in
OSGs, and what types of health benefits OSGs afford.
Online health support groups have started incorporating features of social
networking sites (Cohen, 2008; Fenech, 2009; Holahan, 2008; Landro, 2006; C. C. Miller,
2008; Morphy, 2008; Morrison, 2009). The social networking component is the latest
application which is deliberately designed to facilitate social interaction. This component
can become a convenient tool of support networks, connecting people with similar health
conditions and interests and easing the formation and development of virtual social
relationships. Social networking sites typically provide users a variety of tools that
facilitate social interactions, such as creation of online profiles, status update, articulation
of “friends” lists, sharing of pictures and videos, and exchange of messages. The core
functionality of social networking sites is assumed to be social interaction (Lampe,
Ellison, & Steinfield, 2006). The use of social networking features in OSGs is expected
to grow, as the current MySpace and Facebook generation gets older and cares more
about health issues (Coliver, 2007).
So far little is known about how patients use and adopt social networking features
integrated within OSGs. Similarly, little is known about whether the use of networking
features enhances OSG use experiences. Though there are abundant studies on the topic
of general purpose social networking sites such as Facebook and Myspace (e.g.,

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Bumgarner, 2007; Ellison, Steinfield, & Lampe, 2007; Fogel & Nehmad, 2009; Raacke
& Bonds-Raacke, 2008; Ross et al., 2009; Sheldon, 2008), no studies have been
published of social networking sites as supportive care resources for patients (Bender,
O'Grady, & Jadad, 2008).
As Shaw and colleagues (Shaw, McTavish, Hawkins, Gustafson, & Pingree,
2000) note, computer-mediated support groups do not work in the same way for all
segments of patients. For example, benefits accrued from the use of OSGs vary by
patients with different support group use motives (Tanis, 2008; Wright, 2002), different
health conditions (Cummings, Sproull, & Kiesler, 2002; Davison, Pennebaker, &
Dickerson, 2000), and varying degrees of participation (Pleace, Burrows, Loader, Muncer,
& Nettleton, 2000; Schweizer, Leimeister, & Krcmar, 2006; Shaw, Hawkins, McTavish,
Pingree, & Gustafson, 2006). These studies have shown the importance of
acknowledging individual differences in use patterns, motivations, and gains (or losses)
associated with support group participation. Individuals can participate in OSGs in
varying ways, making use of selected features provided in support groups. As a growing
number of OSGs add new features on their sites, there is a need to study individual
differences in usage patterns and following outcomes associated with usage of new
features.
Taking the uses and gratifications perspective (Rosengren, 1974; Rubin, 2002) as
a theoretical backbone, the current study proposes to examine perceived benefits from
OSG participation as an outcome which depends on individuals’ usage patterns, and to
examine whether the features recently added from social networking sites, such as
networking, status updating, photo and video sharing, and messaging, realize their

4
potential to provide an environment where patients can more easily establish
relationships with other members and foster the perception that social support is widely
available within the group. Patients’ use of social support groups will also be examined in
the context of their offline social support network, such as families and friends.
This study seeks to answer the following array of questions: What are motivations
behind using OSGs? How is the salience of various needs related to the use of different
features available on OSG sites? How does the integration of social networking features
help develop patients’ perceptions of support from OSG communities? How do users of
social networking features develop the feelings of being cared for and supported? Can the
use of OSGs be particularly beneficial to any particular segment of the patient population
with certain personal and contextual characteristics? Finding answers to these questions
can ultimately advance our understanding of patients’ use of the internet as health care
resources and help design OSGs that can maximize patients’ satisfaction and optimize
their use experiences.
Chapter Summaries
To address the above questions, the next chapter, chapter 2, begins by reviewing
past literature and ongoing discussions on social support, support group intervention,
OSGs, supportive communication, and social networking sites. Responding to a call for
empirical evaluation of health support resources online, a set of hypotheses and research
questions are developed and presented in chapter 3. Chapter 4 describes data sources,
survey administration and procedures, participant recruitment, and measures used to
empirically test hypotheses and answer research questions. Chapter 5 presents descriptive
characteristics of the sample and reports results of statistical analyses. The last chapter,

5
chapter 6, discusses findings in connection to the results of previous studies, both
scholarly and practical implications, as well as suggestions for future research.

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CHAPTER 2: LITERATURE REVIEW
Social Support and Support Groups
Social support refers to the generalized perception of the availability of others
who can provide comfort, assistance, esteem, information, and advice (Cohen, 1988).
Numerous studies have shown that social support plays a vital role in the mental and
physical well-being of people (e.g. Albrecht & Goldsmith, 2003; Burleson, Albrecht, &
Sarason, 1994). Social support can increase well-being through two mechanisms. The
direct effect mechanism model suggests that social support decreases feelings of isolation,
encourages healthy behaviors, promotes positive psychological states, and provides
information. The indirect effect mechanism model suggests that social support
ameliorates negative impacts of stress on health by helping reframe threat appraisals and
self-conceptions and improve coping strategies (Cohen & Wills, 1985; Lazarus &
Folkman, 1984). The indirect, buffering mechanism is effective in the context of stressful
events whereas the direct mechanism is held to have a constant effect regardless of stress
level (Burleson & MacGeorge, 2002).
Social support can be provided by both informal networks (e.g., family, friends,
and neighbors) and formal networks (e.g., social workers, health professionals, and
support group members). Health professionals have tried implementing several types of
social support interventions in order to enhance existing social network linkages and
develop new social network linkages (Heaney & Israel, 2002). Examples of the first type
of social support intervention include training and counseling programs. The latter type
includes mentoring programs, buddy list systems, and social support group interventions.

7
Upon diagnosis of illness, many patients experience a range of psychological,
social, and physical difficulties and social support helps cope with such difficulties.
Social support has been shown to facilitate psychological adjustment to stress (Krause,
Liang, & Yatomi, 1989), improve mood (Dunn, Steginga, Occhipinti, & Wilson, 1999)
and coping processes (Penninx et al., 1998), and expedite recovery from disease (Burg et
al., 2005). Social support groups are especially beneficial to those with health problems
for the following three reasons. First, the sharing of experiences and fears gives
individuals a sense of universality and normalcy (Festinger, 1954; Yalom, 1970). Social
support group members can learn, from others’ stories that their experiences are normal
and that others have similar issues. Second, social support groups offer an opportunity to
learn coping behaviors (Posluszny, Hyman, & Baum, 2002), as support group
participants can model their behavior after those who have successfully recovered from
similar health issues. Third, social support groups can provide emotional support that a
patient’s close face-to-face network may not be able to give.
Online Support Groups
With the rapid growth of internet access and computer-mediated communication
over the last decade, cyberspace provides a venue for social support groups. Features of
traditional social support also exist online (e.g., Braithwaite, Waldron, & Finn, 1999;
Eastin & LaRose, 2005; Owen, Yarbrough, Vaga, & Tucker, 2003; Walther, Pingree,
Hawkins, & Buller, 2005; Wright & Bell, 2003) and motivations to join OSGs are not
different from motivations to join offline groups (Voerman et al., 2007). The number of
OSGs has dramatically increased, to become an alternative to offline social support

8
groups (Turner, Grube, & Meyers, 2001). In addition, several features of computer-
mediated communication give OSGs advantages over face-to-face support groups.
First, OSGs are not constrained by temporal and geographical limitations. They
are accessible at all times and enable individuals to access them at their convenience.
Geographical dispersion not only affords those with mobility constraints an opportunity
to participate (Braithwaite et al., 1999), but also enables those with a rare disease to
easily locate people at a distance who suffer from the same disease (Lasker, Sogolow, &
Sharim, 2006). Secondly, the limited social cues available in computer-mediated
communication provide a unique opportunity for self-presentation. People can reveal
their problems and discuss issues that are taboo in everyday conversation. People can
more freely discuss sensitive, socially embarrassing or stigmatizing health issues (Cooper,
2004; Davison et al., 2000). The availability of limited cues to individual characteristics
can lead support groups members to focus on the issues they have in common, as well
(Postmes, Spears, & Lea, 1998). Lastly, OSGs can offer an opportunity for silent
participation. Though some OSGs require registration for access to their contents, a
person can stay completely or partially invisible until posting a message. Such anonymity
in OSGs offers a chance to participate for those who want to be unseen but still want to
learn from the experiences of others. This relates to the concept of ‘silent support’ where
there is no emotional cost for the receipt of support (Bolger, Zuckerman, & Kessler,
2000). Support seekers online can be free from support providers’ expectations of
reciprocity (von dem Knesebeck & Siegrist, 2003).
Because of these advantages, some even argue for the superiority of OSGs over
face-to-face groups (e.g., Boyd & Walther, 2002). In addition to characteristics of online

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context, the communication perspective on social support helps us understand why online
communities can be particularly effective in exchanging supportive messages.
Communication Perspective on Social Support
There are three principal perspectives on social support (Burleson & MacGeorge,
2002). First, the sociological perspective emphasizes integration of individuals within a
social network and the individuals’ belonging to a social community (Berkman & Syme,
1979; House, 1981). Second, the psychological perspective focuses on individuals’
perception of available support and its influence on cognitive and emotional processes
(Cohen & Wills, 1985). Third, the recently emerging communication perspective
(Albrecht & Adelman, 1984; Albrecht, Burleson, & Goldsmith, 1994) sees social support
as being “conveyed through messages directed by one individual to another in the context
of relationship” (Burleson et al., 1994, p. xviii). Communication is “the principal process
through which individuals coordinate their actions in support-seeking and support-giving
encounters” (Albrecht et al., 1994, p. 421). Studies that take the communication
perspective examine elements of communication, such as communication competence of
support givers (Jones, 2004), familiarity of context (Egbert, 2003), and person-
centeredness of communicated messages (Burleson & Samter, 1985; Jones & Burleson,
1997; Kunkel & Burleson, 1999).
Supportive Communication in Online Support Groups
One major stream of research from the communication perspective is to identify
features of effective supportive communication (Burleson & MacGeorge, 2002). Support
messages are perceived as most helpful when they 1) take politeness strategies and do not
threaten the support recipient’s face (Goldsmith, 1992, 1994), 2) clearly exhibit

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supportive intentions (Dakof & Taylor, 1990; Lehman & Hemphill, 1990), and 3)
sensitively attend to support recipients’ situations and feelings (Burleson & Samter,
1985). Review of previous research shows unique characteristics of computer-mediated
communication make OSGs an ideal venue for supportive communication.
Communication in electronic support groups has an edge over offline supportive
communication in that it easily meets the above mentioned criteria for effective
supportive communication.
First, the anonymous nature and lack of face-to-face contact online help protect
the face of support seekers or recipients and lessen the risk of face-threatening acts
(Duthler, 2006). Receiving support may imply the vulnerability of support recipients and
a kind gesture of support provision can become uncomfortable. Therefore it is important
to redress the face threats inherent in supportive communication (Caplan & Samter, 1999;
MacGeorge, Feng, Butler, & Budarz, 2004). Visiting OSGs warrants the support seeking
intention of the stressed and frees support givers from concern over the correct timing of
support provision. When support is given when the stressed are not ready or willing to
take others’ support, the unwanted but provided support can be interpreted as
overinvolvement and intrusiveness (Cutrona & Suhr, 1992; Jacobson, 1986). Secondly,
due to the comparative lack of non-verbal cues online, supportive intention is very clear
online. For example, delivery of support messages relies on the use of explicit words,
such as “I really want to help you” or “I am here for you.” Such explicit use of language
makes the helper’s concern for the distressed clear and the recognition of helpers’
supportive intention can lead to the feeling of being cared for and supported (Cialdini,
2001). Finally, the asynchronous nature of computed-mediated conversation gives

11
individuals greater control over management of their interactions (Walther, 1996).
Selective message construction enhances the capacity to sensitively attune messages to
support recipients’ situations and tailor messages to sound more socially desirable.
Overall the aforementioned characteristics of computer-mediated communication
make supportive communication online more desirable and help develop more intimate,
hyperpersonal relationships with strangers met online (Robinson & Turner, 2003). They
explain why social interaction within internet support groups is sometimes discussed as a
viable or even superior alternative to offline interaction and they help us understand the
proliferation of support groups online.
Social Networking Sites
Over the past few years, social networking sites have mushroomed. For example,
a popular social networking site, Facebook, now has more than 150 million users
(Gaudin, 2009). A social networking site is defined as a site where users create a public
or semi-public profile, articulate a list of other users with whom they share a connection,
and view and browse their list of connections and others’ profiles and list of connections
(boyd & Ellison, 2007). A social networking site is an aggregation of ego-centered
networks. It is built on its members’ relationships with other members (Barsky, 2006)
and grows as members of the site add each other as friends, update their profiles, and
communicate with each other by commenting on each other’s profile pages. Personal
identity information presented in profile pages can provide cues for areas of commonality,
help develop common ground, lower the barriers to social interaction, and enable
connections between people which might not otherwise take place (Ellison, Lampe, &
Steinfield, 2009; Lampe, Ellison, & Steinfield, 2007).

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Unlike online communities in which conversations center around a specific theme
or interest, people on social networking sites are connected in a person-to-person manner
(Rau, Gao, & Ding, 2008). Contrasting online communities and social networking sites,
Mayfield (2005) listed top-down, topic-driven, centralized, and architected, as the
characteristics of online communities, whereas bottom-up, people-centric, user-controlled,
decentralized, and self-organizing, as the characteristics of social networking sites.
Social networking sites facilitate social interaction by “providing tools for self-
presentation, for managing connections, for keeping updated with acquaintances and for
initiating new connections” (Rau et al., 2008, p. 2759). They bring together people to talk,
exchange ideas, and share interests (Chan, 2008). Ellison, Steinfield, and Lampe (2007)
found that the use of a social networking site was helpful in maintaining and
strengthening college students’ relationships with weak-tie as well as strong-tie networks.
The majority of studies on social networking sites have demonstrated that people use
social networking sites to maintain pre-existing, offline social connections (Joinson,
2008; Lampe et al., 2006; Ross et al., 2009). Most users of social networking sites report
that they neither looked for new relationships nor added strangers to their friend network
on social networking sites (Subrahmanyam, Reich, Waechter, & Espinoza, 2008). Some
researchers are more critical about the role of social networking sites in maintenance and
development of social relationships. For example, Rosen (2007) is skeptical about
relationships on social networking sites because friends on social networking site are
more likely to be fluid and public, and only serve as a tool for some people seeking
higher status.

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Growth of more narrowly focused, niche social networking sites that cater to a
particular interest, theme, hobby, or vocation, is also accelerating, although few studies
on focused, niche social networking sites have been published. Examples of niche social
networking sites include 43things.com (connecting people with shared personal goals),
goodreads (connecting book lovers), imeem (connecting people based on their interests in
music), myfolia (connecting people with interests and hobbies in gardening), flixter
(connecting movie lovers), dogster (connecting dog lovers), catster (connecting cat
lovers), and so on. These focused social networking sites enable people to create a
tailored network based on their interests as well as offline associations. Questions remain
as to what extent site users build new relationships on these sites, whom they interact
with, and how their use of social networking sites relates to offline social networks.
Health-related Social Networking Sites
Health care fields have also adopted social networking applications (Bender et al.,
2008; Kamel Boulos & Wheeler, 2007). Social networking affords an opportunity to “talk,
act, and connect with diverse strangers, acquaintances, and friends” (Ellison et al., 2009).
This new application has been shown to help college students with their transition to
college life by helping them maintain past connections and initiate new relationships
(Ellison et al., 2007; Steinfield, Ellison, & Lampe, 2008). Likewise, in the area of health,
social networking applications are being adopted as a promising tool to ease adjustment
to illness experiences by supporting interpersonal connections.
Networking features on social networking sites and buddy programs in the health
care area share commonalities. Buddies with similar health conditions and goals can
provide support and encouragement to each other. Buddy programs have a proven record

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of success in health support intervention, such as smoking cessation programs (Palmer,
Baucom, & McBride, 2000). Establishment of new social ties is especially useful when
the existing network is unable to provide satisfactory quality or quantity of support
(Heaney & Israel, 2002). Social networking applications provide the best chance to
realize benefits of the buddy program idea online.
The first generation of online health support groups was based on one application,
such as an email list (e.g., listserv) or Usenet. Online health support groups later adopted
a number of different components. Now such sites in most cases feature a mix of various
applications, such as discussion boards, chat rooms, private message systems,
information resources, mailing systems, and sometimes personalized interactive feature
(Cummings et al., 2002; Feil, Noell, Lichtenstein, Boles, & McKay, 2003). The latest
application that enables interaction among site users on support group sites is social
networking.
Examples of health-related social networking sites include trusera, healthchapter,
Wellescent, hopecube, Inspire, DailyStrength, ToolsToLife, Health Care 2.0, LiveStrong,
Everydayhealth and revolutionhealth. There are also social networking sites that are
dedicated to a specific health issues, such as MyCancerPlace, Planet Cancer, No
Surrender, and my crazysexy life for cancer, Prostate Cancer Infolink for prostate cancer,
Psych Central for mental health, sobercircle for alcoholism, diabetic connect and
tudiabetes.com for diabetics and DailyPlate for dieting and eating. These representative
sites take a hybrid form of the traditional group-based support site and a person-centered
networking site. These sites spread in “a quasi-coherent networked fashion” (Baym,
2007), where both topic-centered groups and individual-centered networks coevolve.

15
Barak, Boniel-Nissim, and Suler (2008) noted five factors that are central in
producing positive effects on OSG users: writing, expression of and connecting to
emotions, acquisition of information and knowledge, development of virtual social
relationships, and enhancement of decision-making skills. By providing members their
own profile page for personal expression, connecting them through a friend list, and
enabling one-to-one conversation, health-related social networking applications afford
the possibility of realizing the full potential of the above mentioned five factors and
maximize benefits from support group participation.
Studies have shown that OSGs are effective in reducing psychological distress,
improving mood, and enhancing perceived social support and a sense of belonging (e.g.,
Barrera, Glasgow, McKay, Boles, & Feil, 2002; Freeman, Barker, & Pistrang, 2008;
Hoybye, Johansen, & Tjornhoj-Thomsen, 2005; Lieberman et al., 2003; S. M. Miller,
2008; Nguyen, Carrieri-Kohlman, Rankin, Slaughter, & Stulbarg, 2004; Rains & Young,
2007; Shaw et al., 2000; Ussher, Kirsten, Butow, & Sandoval, 2006; Winzelberg et al.,
2003; Zrebiec, 2005). These studies taken together make a strong case for the benefits of
OSGs. However, OSGs often comprise a set of multiple features. Why and how one uses
OSGs differs by individuals, but previous work has scarcely examined these sources of
variation. With the increasing complexity of support group sites’ offerings, there is a
need for studies that document how individuals benefit from online support and link
outcomes from OSG participation to specific features of online interaction.
Uses and Gratifications as a Theoretical Framework
Rather than focusing on what media do to people, the uses and gratifications
approach emphasizes why people use particular media and how people differently use

16
media to obtain satisfaction for their needs (Rosengren, 1974; Rubin, 2002). Individual
differences in media selection and media consumption patterns originate from differential
needs. Such differences in needs influence the expectations an individual holds toward
certain media, media usage patterns, and finally assessment toward the media (Blumler &
Katz, 1974). As McQuail (1994) noted:
“personal social circumstance and psychological dispositions together
influence both general habits of media use and also beliefs and
expectations about the benefits offered by media, which shape specific
acts of media choice and consumption, followed by assessments of the
value of the experience (with consequences for further media use) and
possibly, appreciation of benefits acquired in other areas of experience and
social activity” (p. 319).
The uses and gratifications perspective is not a specific theory but a general
framework suggesting an active role of the audience in media choice, consumption,
assessment, and appreciation. This active audience view is particularly applicable to the
study of online behaviors because the internet by its nature is an intentionally consumed
medium and internet users have choice and control over their online experiences
(Rayburn, 1996).
As Dutta and Feng (2007) suggest, the uses and gratifications approach can
become a useful framework for studying individual use of OSGs. Although studies
identified users’ motives for participating in health-related support groups (e.g. Buchanan
& Coulson, 2007; Coulson, 2005; Kral, 2006; Maloney-Krichmar & Preece, 2005; Meier
et al., 2007; Preece & Ghozati, 2001) and online communities in general (Ardichvili,

17
Page, & Wentling, 2003; Grace-Farfaglia, Dekkers, Sundararajan, Peters, & Park, 2006;
Ishii, 2008; Ridings & Gefen, 2004), researchers have rarely tapped into the next-level
research question of how the particular needs affect use patterns and how different use
patterns in turn affect appreciation of virtual support.
Barrera et al. (2002) argued that, in studying the benefits of computer mediated
support groups, a more focused evaluation of each support intervention component on
online support sites is necessary. We know little about the ability of each component to
change individual perceptions of support. The increasing complexity of health-related
support group sites and the addition of new features such as networking tools augment
the need to conduct more systematic research and test whether the added feature realizes
its potential as promoted in the media. As Walther et al. (2005) noted, guidance is needed
to teach how to use health support group sites to the best benefits of individual users.
With uses and gratifications as a theoretical framework, this paper proposes to
study relationships among three major sets of variables – motivation, use patterns, and
appreciation of benefits – and explores the role individual users’ personal social
circumstances and psychological dispositions play in using and appreciating resources
within OSG sites. The uses and gratifications framework helps to answer whom the
recent addition of social networking site features attracts, whether users of certain
features are driven by different or the same gratification motives compared to those of
people who do not make connections or communicate with others within the site, and
ultimately whether positive outcomes expected to result from the use of social
networking features actually materialize.

18
CHAPTER 3: RESEARCH QUESTIONS AND HYPOTHESES
Research Question One
A number of studies have examined motivations for participating in OSGs. The
most often discussed motivations are the exchange of information and advice (Buchanan
& Coulson, 2007; Coulson, 2005; Leimester & Krcmar, 2006; Meier et al., 2007;
Rodgers & Chen, 2005; Tanis, 2008) and the sharing of emotions (Buchanan & Coulson,
2007; Kral, 2006; Preece & Ghozati, 2001; Rodgers & Chen, 2005; Tanis, 2008). Often
participants visit OSGs to share information on symptom interpretation, illness
management, and advice on interaction with health professionals. Participants also visit
OSGs to share fears and anxieties, to find others who are experiencing similar medical
issues, and to reciprocate their emotions. Although early investigations assumed that the
role of OSGs would be solely or primarily the exchange of emotional social support
(Barnett & Hwang, 2006), OSGs have been found to be a place for the exchange of
informational support as well. This is particularly true for support groups for certain types
of diseases, such as rare diseases (Lasker et al., 2006).
In recent years, a number of support communities online have started embracing
features of social networking sites. Social networking features include: 1) construction of
a public or semi-public profile, 2) articulation of connection, or a list of other users, and
3) sharing and browsing of contents among connected users (boyd & Ellison, 2007). Yet,
very few studies have done about how these newly integrated social networking features
help connect their users with other users as well as reconnect their users with their loved
ones offline. Health-related social networking sites, or other kinds of niche-targeted

19
social networking sites, are unique in that they center on a specific topic and that their
discussion boards have a central role in their sites’ growth (Mayfield, 2005).
Mayfield (2005) suggests that there exists a difference in the way social
networking sites and other types of online communities operate. In online communities
that are based on discussion forums, group-level social identity is often most salient and
each users’ personal characteristics are hidden due to a limited number of cues about
individuals (Spears & Lea, 1992). On the other hand, social networking sites provide
individual-centric space (such as individual profile page) where individuals can keep
personal journals, update their status, and share their own photos and videos (Sheldon,
2010). Social networking sites leave much more room for personal expression (such as
profile page) and facilitate social interactions with one-on-one connection (such as
‘friends’ list and access to friends’ photos, videos, and writings) (Mayfield, 2005; Rau et
al., 2008). Rau et al. (2008) suggested that gratification sought from social networking
sites is different from that sought from other types of online communities because social
networking sites are specifically set up to meet the needs for social and emotional
connection.
Directionalities of relationship development (such as online-to-offline vs. offline-
to-online) are also different between social networking sites and other types of online
communities (Ellison et al., 2007; Parks & Floyd, 1996; Rheingold, 2000;
Subrahmanyam et al., 2008). Early research on online communities focused on the role of
online communities to help bring strangers together and develop intimate relationships
based on shared interests, as opposed to reinforce existing offline relational ties (Parks &
Floyd, 1996; Rheingold, 2000). Unlike early research on online communities, later

20
research on social networking sites found that offline-to-online directionality is much
more common among social networking site users than online-to-offline directionality of
relationship development (Ellison et al., 2007; Subrahmanyam et al., 2008). The main
motivation for using social networking sites was found to strengthen already existing
social relationships and reconnect with friends who one knows from offline, rather than
to make new ones.
Little is known about motivations behind participation in health-related social
networking sites. Although these sites recommend that their users invite offline friends
and families and keep connected through the use of their sites, evidence is weak as to
whether users of health-related social networking sites are actually using these sites to
maintain their existing offline relationships. It is unclear whether people are using health-
related social networking sites in a fashion that is more consistent with the use of OSGs
or the use of general social networking sites, like Facebook. Therefore this study seeks to
identify the primary needs of health-related social networking site users. The following
research question explores motives of using a health-related social networking site.
Research Question 1. What are the primary motivations for using health-related
social networking sites?
Research Question Two
Health-related social networking sites operate on a combination of diverse
resources and features – discussion boards, information resources, and social
networking features. Health-related support sites often run discussion forums where their
members can post and read messages surrounding specific health conditions. They also
typically have a space for informational resources where site users can learn about illness,

21
treatment, and medication options. Site users can also interact with other users in various
ways using social networking components by posting messages on each other’s profiles,
by checking and updating status, and by sharing photos and videos. While diverse
features are available in health-related social networking sites, no published research thus
far has examined how their members make use of diverse components. The addition of
social networking features on support sites is new and our knowledge of how OSG users
are using diverse features to satisfy their needs is very limited.
According to the uses and gratifications perspective, choice of media as well as
content consumption pattern is strategically and intentionally made by individual users.
Certain needs lead to the use of some types of media content and not others. Especially
use of the Internet and consumption of online content can hardly be passive (Rayburn,
1996). When diverse features are available within an online system, each individual
makes decisions as to what features to use. For example, in a study of HIV/AIDS
patients’ use of electronic resource, some patients spent more time on conversational and
communicative services, such as discussion boards, whereas other spent more time on
educational and informational resources (Smaglik et al., 1998). Following the assumption
that needs and media consumption patterns are closely related, this study explores the
link between motivation and use patterns in the context of OSG that includes social
networking features. The following research question addresses the issue of whether
there are variations in use pattern, if any, by motivation.
Research Question 2. How does the use of different features on health-related
support sites relate to different motivations?

22
Research Question Three
In addition to the motivation-use link, the uses and gratifications approach also
highlights the relationship between media use patterns and appreciation or evaluation of
consumed content. Several studies showed that specific outcomes result from specific use
patterns within support group sites (Barrera et al., 2002; Freeman et al., 2008; Shaw et al.,
2007; Weis et al., 2003). For example, OSG users were found to develop more positive
appraisal of their doctors when they used information features, but not when they used
communicative functions, such as discussion boards (Shaw et al., 2007). Similarly in the
study of patients’ use of general internet use, emotional and functional wellbeing of
patients was enhanced only when their use involved communicative functions (such as
email and messaging) but not when their use involved non-communicative, non-social
functions (Beaudoin & Tao, 2007; Walther et al., 2005).
More specifically, in the use of OSGs, studies have shown that the perception of
support depends on how one makes use of OSG sites. A few experiments were conducted
to see whether peer support forums are indeed beneficial to their users (Barrera et al.,
2002; Freeman et al., 2008). For example, Barrera et al. (2002) randomly assigned type II
diabetes patients into four conditions where they had access to an internet site with 1)
information about diabetes only, 2) information plus peer support forums, 3) information
plus professional help on diet, and 4) information, peer support forums, and professional
help. After three months, patients who participated in peer support forums had a
significantly greater increase in perception of social support compared to patients who
only received information.

23
In an attempt to learn variations in support perception among users of OSGs,
Weis et al. (2003) surveyed users of a Multiple Sclerosis online support community and
found that support perception was greatest among those who highly valued the site for
both informational (newsletter, health news, ask-an-expert) and support resources
(discussion groups, messaging, chat rooms). This study provides evidence that one’s
perceptions of social support are also influenced by one’s appreciation of OSG resources.
In sum, the above reviewed studies taken together suggest that perceptions of
support are largely dependent on how one uses the site. The current study also builds
upon the assumption that some content and features are more effective than others in
providing support (Han et al., 2009).
Social networking features are unique in that they let their users convene
conversations on individuals’ own profile page, share personal stories, and make one-to-
one connections with other site users. The use of social networking features is expected
to foster more intimate, one-to-one conversation, enhance the exchange of information,
and facilitate the development of virtual friendship and companionship. As one major
function of social networking sites is building of interpersonal relationships (Ellison et al.,
2007; Subrahmanyam et al., 2008) and as social support is an outcome of interpersonal
relationships (Berkman, Glass, Brissette, & Seeman, 2000; Leimeister, Schweizer,
Leimeister, & Krcmar, 2008; Lin & Anol, 2008; Walther et al., 2005), this study
hypothesizes a positive relationship between the use of social networking features and the
perceptions of support within the site. In other words, those who use social networking
features more actively are expected to perceive a greater degree of virtual social support
available within an online community.

24
Although there is good reason to expect that users of social networking features
will perceive the site as a more supportive venue than non-users, our knowledge of how
people use different features in OSGs that include social networking features is minimal.
Therefore the following research question is formulated, with the tentative expectation
that those who only make use of the site in a traditional way (solely make use of
discussion boards) will perceive the site to provide less support than those who make full
use of social networking features available in the site.
Research Question 3. How does the use of different features on the OSG site
relate to perceived social support from OSG?
Hypotheses One and Two
As OSG participation is voluntary, participants in OSGs have freedom to choose
their degree of use, such as how much time they spend, how frequently they visit, and
how actively they respond to and send friending requests.
In the use of online community, active participation compared to passive
participation often leads to a greater level of benefits (Barak & Dolev-Cohen, 2006;
Cummings et al., 2002; Han et al., 2009; Shaw, Hawkins, McTavish et al., 2006;
Welbourne, Blanchard, & Boughton, 2009). For example, active participation, as
measured by the greater disclosure of personal thoughts and feelings, is related to better
outcomes such as a decrease in negative emotions and an increase in emotional well-
being (Shaw, Hawkins, McTavish et al., 2006). Similarly in a study of support group use
by severely distressed adolescents, an increase in activity level was correlated with a
decrease in distress (Barak & Dolev-Cohen, 2006). Those who actively responded to and
were responded to by other members gained more emotional relief than those who

25
remained passive. In a study of an online community dealing with infertility issues, even
observing active exchange of emotional support was found to foster the feeling of
belonging (Welbourne et al., 2009). Also, in a study of an online community for hearing
impairment, members who more frequently visited the site, read messages, and sent
messages to other community members reported gaining greater informational and
emotional benefits (Cummings et al., 2002). In line with these findings, in a support
community among older adults, the degree of involvement, as measured by the number of
hours spent communicating in the support community, was also a significant predictor of
reduced level of life stress (Wright, 2000).
Although the degree of participation or use is operationalized in different ways
and the outcomes of interest also vary by study, these studies point in the same direction:
positive psychological outcomes are associated with active use of a support group.
Therefore, the current study hypothesizes that OSG support perception is dependent on
the degree of OSG use intensity such that active use of OSG will result in better support
perception. Those who use an OSG in a more intense manner perceive a greater level of
support from the OSG they are participating in.
Hypothesis 1. OSG use intensity will be positively associated with the perception
that social support is widely available within the OSG (OSG support perception).
As the feeling of social support is an outcome of social relationships, the current
study expects to find a mediating role of virtual friendship between OSG site use and
OSG support perception. In other words, the current study hypothesizes that the support
perception from OSG use develops through the process of social relationship building
within the support community: Those who use the site more actively are more likely to

26
make friends within the OSG and these friendships in turn will translate into OSG
support perception.
Hypothesis 2. The relationship between OSG use intensity and OSG support
perception will be mediated by friendships one builds within OSG (OSG
friendship).
Hypotheses Three and Four
As online spaces promise new venues for social relationships, researchers have
been paying attention to who benefits more or less from these interactions. One of
moderators of interest has been personality. In particular, Orchard and Fullwood
(forthcoming) and Weaver (2000) stressed the importance of studying personality-based
differences in internet use and argued that this line of research on personality can be
understood under the framework of uses and gratifications.
One particular personality dimension that has been a salient topic in computer-
mediated communication research is extraversion. Extraversion involves attributes like
being sociable, assertive, lively, and talkative, whereas introversion is associated with
attributes like being quiet and reserved (Eysenbach & Eysenbach, 1991; John, Naumann,
& Soto, 2008). Because of the link between extraversion-introversion and sociability,
investigations on personality and internet use have focused on comparing and contrasting
the use of social communication functions on the internet between extraverted and
introverted people (Amichai-Hamburger & Ben-Artzi, 2000; Anolli, Villani, & Riva,
2005; Goby, 2006; Schrock, 2009) and examined differences in psychological and social
impact (such as feelings of loneliness, isolation, and social connectedness) (Ellison et al.,
2009; Kraut et al., 2002; Williams, 2007).

27
Two competing hypotheses, in particular, were suggested to explain psychosocial
outcomes of internet use by personality trait. The first hypothesis, rich-get-richer
hypothesis (Kraut et al., 2002), sees that the impacts of internet use are more
advantageous to extraverted people than to introverted people. This hypothesis states that
internet use will be socially more beneficial to those who already have good social skills.
According to the rich-get-richer hypothesis, extraverted people will use the internet to
seek out additional opportunities to socialize. For example, in a study by Kraut et al.
(2002), internet use was associated with a decreased level of loneliness for extraverted
people but an increased level of loneliness for introverted people. Similar findings were
reported for a study on adolescents’ use of the internet: introverted adolescents were less
likely to disclose about themselves online and in turn less likely to develop online
friendship (Peter, Valkenburg, & Schouten, 2005). The rich-get-richer perspective is
based on the assumption that individuals’ online behaviors mimic their offline ones such
that extraverted people will make use of the internet in a more sociable way than
introverted people.
On the other hand, the second hypothesis, the social compensation hypothesis
(Kraut et al., 2002), proposes an alternative explanation. According to the social
compensation hypothesis, internet use is socially more beneficial to introverted people
than extraverted people because the internet provides a chance to overcome social
anxieties. The characteristics of online conversation such as anonymity and reduced
amount of social cues can help alleviate social anxieties, facilitate disclosure of inner self,
and develop genuine relationships (Bargh, McKenna, & Fitzsimons, 2002). Such unique
properties of online communication can be particularly beneficial for introverted people

28
who are more sensitive to others’ reactions in face-to-face conversation. Introverted
people were found to have a greater tendency to open themselves up and express their
genuine selves on the internet compared to extraverted people (Amichai-Hamburger,
Wainapel, & Fox, 2002; McKenna, 2007). Shy people also reported having a reduced
level of shyness and an increased level of social competence during online social
interaction compared to offline interaction (Stritzke, Nguyen, & Durkin, 2004).
As one fundamental function of social networking sites is to connect people, the
current study tests whether extraverted people take better advantage of such a function
following the rich-get-richer hypothesis, or whether introverted people make a more
beneficial use of the OSG site supporting the social compensation hypothesis. The two
tested outcomes in the current study are friendships one builds within OSG (OSG
friendship) and the perception of being supported and cared (OSG support perception).
Hypothesis 3. The relationship between online support group (OSG) use and OSG
friendship will be moderated by personality.
Hypothesis 4. The relationship between online support group (OSG) use and OSG
support perception will be moderated by personality.

Hypotheses Five and Six
As most relationships do not occur in a vacuum, it is important to understand the
role of OSGs in relation to offline support networks (Tanis, 2007; Wright & Bell, 2003).
There is a need to “understand what place OSGs take in the lives of the people who make
use of them and how they evaluate the support they encounter in these communities in
relation to the support found offline” (Tanis, 2007, p. 150). Carter (2005) in his

29
ethnographic work on virtual communities found that people come to the online arena to
meet new people and invest time and effort to maintain relationships as they do in offline
spaces. People embed these virtual relationships within everyday life, rather than keep
them separate from offline life. Online and offline worlds are psychologically connected
and these two worlds are felt and experienced as continuous spaces (Subrahmanyam &
Greenfield, 2008). How people integrate virtual support with support from offline support
sources is an important question that we need to answer if we want to better utilize this
new source of support. Relationships in offline social networks, such as friends and
family members, will also influence individuals’ perceptions of relationships built with
online social networks (Albrecht et al., 1994; Turner et al., 2001).
The experience of illness sometimes needs redefinition and reestablishment of
existing relationships as the illness experience itself can become a barrier between people
who are undergoing an illness and the ones surrounding them (Davison et al., 2000).
Even those who have a good number of close friends and families offline may not be
happy or satisfied with support they were given with regard to their medical condition, as
some friends and families may not know how to be helpful or may not be sensitive to the
needs of a support receiver (Sarason & Sarason, 2006). This partly explains why some
people go online to seek additional support. From this reasoning, offline social support
has been often studied as an antecedent of OSG participation. Those who are dissatisfied
with their offline support may come to OSGs to make new friends who can support and
understand them. A study suggests that people who are less satisfied with their face-to-
face social network use the internet in a way to satisfy their interpersonal needs
(Papacharissi & Rubin, 2000).

30
Although this logic suggests that those who find existing social support
inadequate are more likely to participate in OSGs (Cummings et al., 2002; Grande, Myers,
& Sutton, 2006; Pilisuk, Wentzel, Barry, & Tennant, 1997), findings of empirical studies
have been inconsistent. For example, unlike the expectation, studies have found that
those with a greater amount of offline support participated more actively in OSGs (Shaw
& Yun, 2000; Shaw, Hawkins, Arora et al., 2006).
The expectation that someone is more likely to use OSG resources when there is a
lack of offline social support is based on the reasoning that people would have a priori
knowledge about the existence of online social resources. Even when people do not have
enough offline support resources, they may not go online unless they already have some
expectation that OSGs can compensate for the deficiency. Therefore, this study examines
the role of offline social support not as a determinant of OSG participation but as a
moderator affecting use and appreciation of support group resources. In other words,
offline support satisfaction will be studied as a factor that affects how people interact
with others in OSG and appreciate support available in the site, rather than as a predictor
of support group participation. Therefore, a hypothesis is suggested that examines the
role of offline social support satisfaction moderating the relationship between OSG use
and OSG friendship. In other words, among those who are less satisfied with their offline
support network, OSG will be more used as a place to establish friendship as they
increase their use of the OSG.
Hypothesis 5. The relationship between OSG use and OSG friendship will be
moderated by offline social support satisfaction.

31
According to optimal matching theory (Cutrona & Russell, 1990), appreciation of
certain supportive behaviors is dependent on the needs of individuals such that, for a
certain supportive behavior to be welcomed by its recipients, it needs to match their
needs. Likewise, appreciation of experience in online support sites can depend on
satisfaction with offline support networks. It is expected that people with less social
support will find online social interaction more rewarding than people with a greater
amount of social support. Therefore, it is expected that those who have less satisfactory
relationships with their support network with regard to their illness experience are more
likely to value their experience online as they become more active users of social
networking sites. It is expected that those who already have satisfactory relationships
with their offline support network will not value the online experience as highly as those
who do not.
Hypothesis 6. The relationship between OSG use and OSG support perception
will be moderated by offline social support satisfaction.
In sum, research questions 1 through 3 explore the relationships among
motivations, OSG use patterns, and the perceptions of support from OSG; and hypothesis
1 through 6 test the mediating role of OSG friendship and the moderating role of
personality (extraversion) and contextual factor (offline support satisfaction). Figure 1
and Figure 2 depict the relationships among variables.

32

Figure 1
Research Questions

Figure 2
Hypotheses

33
CHAPTER 4: METHOD
Study Site
A list of possible study sites was compiled from various sources, including
magazines, newspaper articles, industry reports, and websites such as
100bestsocialnetworksites.com and findasocialnetwork.com. Sample sites were selected
based on the following three criteria: 1) the content focus of the site is on specific health
issues rather than general wellness or fitness issues, 2) the goal of the site is to provide
support to people who are themselves experiencing health issues, and 3) the site includes
components of social networking sites, as defined by boyd and Ellison (2007) (profile
pages and list of friends or connections). Social networking sites that focus on health
issues but not on support provision were excluded. For example, websites that are to
connect patients with pharmaceutical manufacturers or websites that are to facilitate
networking among doctors did not qualify as a study site. Social networking sites that are
set up to provide support to caregivers were excluded as well.
Screening was conducted in order to select only active sites. Sites with discussion
boards which had at least one posting during the most recent week at the time of
screening were considered to be active. Request for permission to recruit participants
from their site along with a letter detailing study plan, objectives, and brief explanation of
questionnaire was emailed to these selected sites’ moderators,. Among those who were
contacted, four site moderators replied and permitted recruitment of participants from
their site.
The characteristics of each site are presented in table 1.

Table 1.
Study Site Characteristics

Study
site
Area of health
interest and
target
population
When did
website
start?
a

Approximat
e number of
registered
users
b

Average
number of
blogs
posted per
month
c

Average
number of
postings in
discussion
board per
month
c

Sites
Linking In
d

Pageviews/
User
e

Time on
Site (min)
f

Site 1 Prostate cancer March 2008 1,500 7 17 23 2.4 2.27
Site 2 Cancer,
Young adults
September
1999
5,500 125 46 155 2.5 2.06
Site 3 Diabetes March 2007 150,000 220 320 84 3.3 4.7
Site 4 Diabetes March 2008 165,420 700 260 154 1.5 1.62
a
Based on the date of the first posting on discussion board
b
As of April 2010
c
Total postings divided by the number of months since the starting date of the site
d
The number of sites linking to this site. Updated quarterly. A measure of a website's reputation (Alexa.com)
e
Average daily pageviews per user (Alexa.com)
f
Average daily time on site per user (Alexa.com)

34

35
The initial plan was to collect data from patient population with a diverse set of
medical conditions. However, due to difficulty in obtaining approval from site operators,
responses could only be collected from participants undergoing two specific health
conditions: cancer (all types and prostate cancer) and diabetes.
Participant Recruitment
Participants were recruited through a recruitment message that was posted on the
discussion board. The recruitment message included brief information about the study
investigator, a description of the study objectives, a note on university approval to
conduct the survey, and a link to the online survey site. After initial posting of the
recruitment message on the discussion board, one site operator (site 1) also sent out an
email to its all registered users. Among the four site operators, three (site 1, 3, and 4)
wrote an endorsement comment below the recruitment message, such as “this survey has
been approved by the administration team.”
The recruitment message included a note on who could participate in the study.
The two eligibility criteria for survey participation were: 1) respondents needed to be 18
years and older; and 2) respondents needed to use the site for their own health concerns.
The first block of the survey included a question that asked whether one was using the
OSG site for one’s own health concerns and, if not, the survey ended with a thank-you
note. Although survey participation did not require respondents to be registered users of
OSG they were using, all respondents were found to be registered users who had a sign-in
account.
For each site, the survey was open for one month. The survey for each site was
conducted in February 2010 for site 1 and site 2, July 2009 for site 3, and September

36
2009 for site 4. From four sites, a total of 245 people participated in the survey. Among
245, responses from 50 participants were excluded due to their ineligibility according to
the above two mentioned criteria. Among the remaining 195, 172 people completed the
entire survey with no missing data. For missing data, pairwise deletion was used for each
analysis.
Survey Administration
Data were collected via an online survey administered on Qualtrics. The first
page of the survey described the objective of the study, the rights of study participants,
and confidentiality of the data. Study participants were able to indicate their agreement to
participate in the study once they read information on the first page. The survey took an
average of fifteen minutes to complete. It did not collect any personally identifiable
information and survey participation remained anonymous. Survey participants who
completed the survey were given a chance to enter a drawing for an online retailer gift
certificate. Those who completed the survey could choose to enter the drawing.
The online survey was set up to prevent anyone from taking the survey more than
one time. If there were more than one attempt to take the survey from a computer with
the same internet protocol address, the online survey system gave a message that the
survey had been already taken.
Measures
Motivation
A series of statements on motivations for participating in OSGs were gleaned and
developed from a number of studies on general internet use (Grace-Farfaglia et al., 2006;
Papacharissi & Rubin, 2000; Rodgers, Chen, Wang, Rettie, & Alpert, 2007), social

37
networking sites (Bumgarner, 2007; Ellison et al., 2007; Joinson, 2008; Nyland & Near,
2007; Raacke & Bonds-Raacke, 2008; Sheldon, 2008), online community (Ishii, 2008;
Ridings & Gefen, 2004), and health-related online forums (Tanis, 2008). Respondents
were given 28 statements with 5-point Likert scale ranging from “strongly disagree” to
“strongly agree” and asked to rate their degree of agreement to each statement. All
statements are presented in Table 5.
Online Support Group (OSG) Feature Use
Respondents were asked a series of questions about their frequency of using
specific features during their use of OSG. Respondents from four sites were asked to
indicate their frequency of using the following features: discussion board (posting,
commenting, reading), blog (posting, commenting, reading), photo sharing and browsing
(posting, commenting, viewing), video sharing and browsing (posting, commenting,
viewing), and messaging (sending, receiving). Response options varied from daily (=6),
two to three times a week (=5), once a week (=4), two to three times per month (=3),
once a month (=2), to never (=1). Some sites offered additional features and their
respondents were asked about the frequency of using these additional features, such as
chatting, recipe sharing and browsing (posting, commenting, and reading), and book
review sharing and browsing (posting, commenting, and reading).
In addition, participants were asked to provide the number of friends they have on
their friend list. In order to differentiate “friend” on friend list, “friend” used in this
context will be italicized and the act of adding friends to their friend list will be called
“friending.”

38
OSG Support Perception
Social support from OSG was measured using two subscales (emotional and
informational support) of the Social Support Behaviors Scale (Vaux, Riedel, & Stewart,
1987). The original Social Support Behaviors scale was shown to have a high alpha
reliability exceeding .85 and good construct validity with significant correlations with
social support network associations, support appraisals, and the inventory of Socially
Supportive Behaviors (Vaux et al., 1987).
Because of time limitations, five items with the highest factor loadings (when the
Social Support Behaviors Scale was previously used to measure friends’ support) were
selected, instead of all items (10 items for emotional support and 12 items for
informational support) listed in the original study.
For this study, response prompts were modified to specifically measure the
perception of social support from the OSG community. Respondents were asked to
indicate how likely members of the online community would be to provide a specific type
of support when they needed it. The response scale was 5-point scale ranging from “No
one would do this ” to “Most would certainly do this.”
Items for informational social support include: “would tell who to talk to for
help,” “would tell me what to do,” “would help me decide what to do,” “would give me
advice about what to do,” and “would suggest how I could find out more about a
situation.” Items for emotional support include: “would comfort me if I was upset,”
“would be sympathetic if I was upset,” “would listen if I needed to talk about my
feelings,” “would show affection for me,” and “would show me I was cared about.”

39
The reliability of the two scales was satisfactory. The Cronbach’s alphas for
informational and emotional support were .93 and .92 respectively.
OSG Use Intensity
OSG use intensity was created to reflect diverse aspects of use activity.
Borrowing a framework from Ellison et al. (2007)’s measure of Facebook use intensity,
the current study recoded answers to three questions (the frequency of site visits, the
amount of time spent on the site on a typical week, and the number of “friends” on the
friend list) and created a single summation score (ranging between 1 and 8). Table 2
shows the wordings and 8-point scale of each item. Cronbach’s alpha for this scale
was .72. Higher valuse indicated more active, intense use of OSG.

40

Table 2.
Individual Items and Response Scales for OSG Use Intensity

Frequency of site visit
How many days per week on average do you usually visit [site name]?
1 = 0 time/week (10.5%)
2 = 1 times/week (25.2%)
3 = 2 times/week (6.7%)
4 = 3 times/week (7.6%)

5= 4 times/week (8.4%)
6 = 5 times/week (10.1%)
7 = 6 times/week (6.7%)
8 = 7 times/week (24.8%)
Mean = 4.59, SD = 2.60

Time spent on site on a typical week
On average how many hours/minutes per week do you spend on [site name]?
1 = less than 30 minutes (25.2%)
2 = 30 minutes to less than 1 hour (11.3%)
3 = 1 hour to less than 2 hours (21.8%)
4 = 2 hours to less than 3 hours (13.9%)
5 = 3 hours to less than 4 hours (5.9%)
6 = 4 hours to less than 5 hours (3.8%)
7 = 5 hours to less than 10 hours (9.2%)

8 = 10 hours or longer (8.8%)
Mean = 3.56, SD = 2.29

Number of “friends” on friend list
How many people are on your friend list?
1 = None (20.1%)
2 = 1 to 4 friends (20.7%)
3 = 5 to 9 friends (16.2%)
4 = 10 to 19 friends (11.7%)
5 = 20 to 39 friends (11.2%)
6 = 40 to 99 friends (8.4%)
7 = 100 to 199 friends (5.6%)

8 = 200 and more friends (6.1%)
Mean = 3.51, SD = 2.13

41
OSG Friendship
Respondents were asked about how many friends they had formed from their use
of OSG. Respondents were asked to indicate the number of people 1) they feel at ease
with, 2) they write or talk to about what they have on their mind, 3) they enjoy
conversing with online, and 4) they consider to be friends. Among the four items that
were asked, the first two items were modified from a subscale of the Medical Outcomes
Study (MOS) social support survey (Sherbourne & Stewart, 1991). The original measure
was one-item scale, developed to gauge the structural dimension of support and read as
“About how many close friends and close relatives do you have whom you feel at ease
with and can talk to about what is in on your minds?” Cronbach’s alpha for the four-item
scale used in the present study was .97.
Extraversion
The degree of extraversion was measured using a subscale of the Big Five
inventory of personality (John et al., 2008). Respondents were asked to think of their
personal characteristics and indicate their agreement with eight statements using 5-poin
Likert scale ranging from “strongly disagree” to “strongly agree.” Examples of eight
statements include “is talkative,” “is outgoing, sociable,” and “tends to be quiet.” Among
eight statements, responses to three items were reverse coded. The Cronbach’s alpha for
this scale was .89 in the present study.
Satisfaction with Offline Social Support
Satisfaction with offline social support was measured using a 7-item subscale of a
short version of the Social Support Questionnaire (SSQ: Sarason, Sarason, Shearin, &
Pierce, 1987). Respondents were asked to think of people whom they can rely on for

42
seven circumstances described in each item and mark their degree of satisfaction with
support they had received using 7-point scale ranging from “Very satisfied” to “Very
unsatisfied” and “no one can provide me such support.”
The original scale had .83 for test-retest correlation and .88 for alpha reliability
(Sarason et al., 1987). The scale was also tested for correlations with personality,
adjustment, and life change measures and was shown to have good validity (Sarason et al.,
1987). The Crobach’s alpha for this scale was .97 in the present study.
Control Variables
Demographic variables (such as age, gender, education, residency (urban or not),
coresidency (live alone or together), ethnicity) and health status were included as control
variables. Respondents were also asked to indicate the number of months they have been
using OSG (OSG usage duration). These control variables were shown in the previous
research to be related to outcome variables included in the current study (e.g., Leimeister
et al., 2008; Nonnecke, Andrews, & Preece, 2006; Rains & Young, 2007; Rau et al.,
2008; Schweizer et al., 2006; Tanis, 2008).
Analysis
SPSS 17.0 was used for data analysis. To answer research question 1, the
motivation items were analyzed using factor analysis and their scores were compared to
each other. To answer research question 2, OSG use items were analyzed using factor
analysis and then the saved factor scores were used in multiple regression analyses as
dependent variables. To answer research question 3, multiple regression analyses were
used with a hierarchical method. Control variables were entered first and then the saved
OSG feature factor scores were entered. To test hypothesis 1 through hypothesis 5,

43
multiple regression analyses were conducted. For the testing of the mediation hypothesis
(hypothesis 2), three steps outlined by Baron and Kenny (1986) were followed. For each
regression models, multicolleniarity statistics were checked.

44
CHAPTER 5 RESULTS
Descriptive Statistics
Table 3 describes demographic and health characteristics of participants. The
mean age was 48 and about half of the participants were male (47.8%). The majority of
respondents were white (91.8%). About 4 in 10 (44.3%) participants had some college,
graduate, or professional degree. Most of participants (79.2%) were living with someone
else. About seventy percent of the respondents (71.7%) characterized themselves as
having good or better health.

Table 3.
Demographic and Health Characteristics of Participants (N = 195)
a

Variables Categories Percentage
Age 19-29 17.1
30-39 15.2
40-49 14.6
50-59 27.8
60-69 16.5
70- 8.9
Mean = 48, SD = 16.29
Gender Female 52.2
Male 47.8
Ethnicity White, non-Hispanic 91.8
Black 1.5
Asian 1.5
Other 3.0
Residency Urban 34.6
Suburban 47.2
Rural 18.2
Education No formal education or elementary school 22.8
Junior High School, some high school, or high
school graduate 32.9
Some college or college graduate 23.4
Graduate or professional degree 20.9

45

Table 3.
Demographic and Health Characteristics of Participants (N = 195)
a
(Continued)
Variables Categories Percentage
Marital Status Married 48.7
Living with a partner 5.0
Divorced 6.5
Separated 4.0
Never been married 13.6
Other 2.0
Live alone Yes 20.8
No 79.2
Health status Excellent/very good 33.3
Good 38.4
Fair 20.1
Poor/Very poor 8.2
Time since
diagnosis Less than 6 months 13.3
6 months to less than 1 year 10.1
1 year to less than 2 years 15.8
2 years to less than 3 years 13.3
3 years to less than 5 years 11.4
5 years to less than 10 years 12.0
10 years to less than 20 years 12.0
20 years or longer 12.1
a
Sample size slightly varies for each variable due to missing data

Correlation Analysis

Table 4 presents the correlation matrix. In general, the correlation coefficients
were below the recommended threshold of .70 (Campbell, 1998). Correlation coefficient
values greater than the threshold were checked for the case where two highly correlated
terms need to be entered into one regression model.
Two interaction terms had high correlation coefficients: The correlation
coefficient between OSG use intensity and OSG use intensity x extraversion was .860
while the correlation coefficient between OSG use intensity and OSG use intensity x

46
offline support satisfaction was .882. In order to solve problems of multicollienarity,
these variables were centered (Aiken & West, 1991) for further analyses. When the
variables were centered, the correlation coefficients between OSG use intensity and OSG
use intensity x extraversion and OSG use intensity and OSG use intensity x offline
support satisfaction dropped. Because of high correlations between two motivation terms,
motivation terms were entered separately in regression models (See Table 7)

Table 4.
Means, Standard Deviations, and Zero-order Correlations (N = 195)

1 2 3 4 5 6 7 8
1 Motivation to relax
2 Motivation to help others .324
**

3 Motivation to meet others .551
**
.590
**

4 Motivation to seek information .020 .188
*
.207
**

5 Motivation to maintain offline
relationship
.477
**
.459
**
.677
**
.160
*

6 Discussion board use -.253
**
-.433
**
-.573
**
-.274
**
-.407
**

7 Photo and video sharing and
browsing
-.179
*
-.126+ -.234
**
-.094 -.347
**
.000
8 Blog -.406
**
-.303
**
-.351
**
.052 -.300
**
.000 .000
9 Friending (Number of friends) .221
**
.494
**
.523
**
-.024 .373
**
.549
**
.134+ .206
**

10 Informational support .172
*
.296
**
.353
**
.204
**
.152+ .417
**
-.076 .091
11 Emotional support .257
**
.426
**
.549
**
.137+ .342
**
.527
**
.020 .204
**

12 OSG use intensity .345
**
.440
**
.588
**
.127+ .355
**
.685
**
.016 .308
**

13 OSG friendship (Number of OSG
friends)
.075 .204
**
.296
**
.137+ .151+ .318
**
.082 .087
14 Extraversion .039 .192
*
.041 .000 .033 .067 .043 .077
15 Satisfaction with offline support -.033 .082 .097 .101 .040 .047 .040 .061
16 OSG use intensity x Extraversion .287
**
.448
**
.513
**
.115 .330
**
.597
**
.081 .277
**

17 OSG use intensity x Satisfaction
with offline support
.245
**
.408
**
.542
**
.140+ .275
**
.620
**
.082 .273
**

Mean 2.82 4.04 3.50 4.28 2.51 0.00 0.00 0.00
SD 1.10 0.81 1.01 0.62 1.03 1.00 1.00 1.00
Note. Sample size slightly varies due to missing data. + p < .10, * p < .05, ** p < .01 (two-tailed)

47

Table 4.
Means, Standard Deviations, and Zero-order Correlations (N = 195) (Continued)

9 10 11 12 13 14 15 16 17
1 Motivation to relax
2 Motivation to help others
3 Motivation to meet others
4 Motivation to seek information
5 Motivation to maintain offline
relationship

6 Discussion board use
7 Photo and video sharing and
browsing

8 Blog
9 Friending (Number of friends)
10 Informational support .337
**

11 Emotional support .535
**
.839
**

12 OSG use intensity .756
**
.383
**
.529
**

13 OSG friendship (Number of OSG
friends)
.345
**
.207
**
.252
**
.352
**

14 Extraversion .177
*
.108 .178
*
.097 .237
**

15 Satisfaction with offline support .040 .081 .106 .028 .145+ .127
16 OSG use intensity x Extraversion .710
**
.360
**
.512
**
.860
**
.436
**
.554
**
.103
17 OSG use intensity x Satisfaction
with offline support
.686
**
.388
**
.521
**
.882
**
.403
**
.173
*
.442
**
.813
**

Mean 3.45 3.49 3.26 4.17 16.58 3.26 5.88 13.94 25.13
SD 2.01 1.14 1.26 1.85 49.57 0.78 1.37 7.49 12.58
Note. Sample size slightly varies due to missing data. + p < .10, * p < .05, ** p < .01 (two-tailed)
48

49
Research Question One
Research question 1 asked about primary motivations for visiting health-related
social networking sites. Twenty eight statements describing various motivations to use
health-related social networking sites were analyzed using principal component factor
analysis with Varimax rotation procedures. Using the rule of a minimum eigenvalue of
1.00 per factor, five factors were retained. Four statements which loaded low on any of
five factors were excluded for further analysis. Table 5 contains a summary of factor
loadings.
Factor 1 accounted for 17.2% of the total variance after rotation. It included five
statements describing the motivation to entertain and relax (Eigenvalue = 4.8). Factor 2
explained 15.6% of the total variance after rotation (Eigenvalue = 4.4). The four items
included in factor 2 were about helping others and providing support to others. Factor 3
accounted for 14.8% of the total variance (Eigenvalue = 4.1). Factor 3 tapped into the
motivation to meet people in a similar situation and develop new relationships. Factor 4,
motivation for information seeking, accounted for 12.8% of the total variance after
rotation (Eigenvalue = 3.6). Factor 5 explained 10.0% of the total variance and included
items for the motivation to keep in touch with already existing relationships (Eigenvalue
= 2.8).
For each factor, scores from each statement (1 = strongly disagree to 5= strongly
agree) were added and divided by the number of statements to get a composite score.

Table 5.
Exploratory Factor Analysis of the Motivation Items

Factor loadings
The reason why I visit [site name] is … 1 2 3 4 5
Factor 1 – Motivation to relax ( α = .93)
to pass time 0.92
to entertain myself 0.88
to occupy my time 0.88
to spend time when I am bored 0.78
to forget my worries 0.61
Factor 2 – Motivation to help others ( α = .90)
to help others 0.85
to provide support to others 0.82
to show others encouragement 0.80
to contribute to discussions 0.68
Factor 3 – Motivation to meet others ( α = .91)
to make new friends with similar interests 0.82
to meet new people with similar interests 0.69
to get to know other people 0.61
to keep in touch with people I have met through [site name] 0.61
to find people like me 0.56
to communicate with like-minded people 0.55
Note. Factor loadings below .50 are suppressed and not shown.

50

Table 5.
Exploratory Factor Analysis of the Motivation Items (Continued)

Factor loadings
The reason why I visit [site name] is … 1 2 3 4 5
Factor 4 – Motivation to seek information ( α = .85)
to gather information 0.82
to find out things that I need to know 0.75
to look for information I need 0.74
to talk to a knowledgeable individual about topics of my health issues 0.71
to get answers to specific questions 0.61
Factor 5 – Motivation to maintain offline relationship ( α = .86)
to keep connect with people who I otherwise would have lost contact with 0.74
to find out what old friends are doing now 0.72
to deepen relationships with people that I have met offline 0.67
to keep in touch with people who live far away 0.55
Excluded items
to learn what others think about something
to feel relaxed
to give my opinion on a topic of conversation
to respond to others on topics of interest to me
Note. Factor loadings below .50 are suppressed and not shown.

51

52
The salience of each motivation factor was inspected by mean comparison. Mean
comparison shows that motivation to seek information was strongest (M = 4.28, SD
= .62), followed by motivation to help others (M = 4.04, SD = .81), motivation to meet
others in similar conditions (M = 3.50, SD =1.01), and motivation to relax (M = 2.82, SD
= 1.10). The least scored motivation was to maintain offline relationships (M = 2.51, SD
= 1.03). Figure 3 shows the order of motivation and answers to research question 1. All
mean differences were statistically significant (p < .001).

Figure 3
Mean Comparisons of Motivations

53
Research Question Two
The second research question explores patterns of using diverse features on OSG
and examines to see whether there exists any relationship between OSG feature use
patterns and motivations. In order to answer this question, factor analysis was conducted
on 14 items that asked about the frequency of using different features available on the
OSG site and one additional item that asked about the number of friends on friend list.
Only items that are commonly available on all four study sites were included for factor
analysis. This factor analysis resulted in four factors. Each factor was extracted using
principal component method and rotated using Varimax procedures. On close inspection
of the factor loadings, two items that loaded high on both factor1 and factor 2 were
eliminated. The factor analysis was repeated and yielded four factors. Table 6 reports
factor loadings.
Factor 1 explained 29.6% of the total variance and was about posting, viewing,
and commenting on photos and videos uploaded by other users (Eigenvalue = 3.8). Factor
2, discussion board usage (posting, reading, and commenting on discussion board),
accounted for 19.6% of the total variance (Eigenvalue = 2.6). Factor 3 included three
items on blog usage (writing, reading, and commenting on blogs) and explained 17.6% of
the total variance (Eigenvalue = 2.3). Factor 4 which was one item on the number of
friends explained 9.2% of the total variance (Eigenvalue = 1.2). The rotated factor scores
were then saved for further analyses. Higher scores indicate more frequent use for factor
1 through factor 3 and greater number of friends for factor 4.

54
Table 6.
Exploratory Factor Analysis of OSG Feature Use
Factor loadings
1 2 3 4
Factor 1 - Photo and video sharing and browsing
How frequently do you…
post photos (excluding profile photos)? 0.66
view others' photos? 0.66
comment on others’ photos? 0.82
post videos? 0.63
view others' videos? 0.87
comment on others’ videos? 0.88
Factor 2 - Discussion board use
How frequently do you…
read postings on discussion forum? 0.82
reply to postings on discussion forum? 0.84
post messages on discussion forum, excluding
comments? 0.70
Facbor 3 - Blog
How frequently do you…
write blogs? 0.88
read others’ blogs? 0.72
comment on others’ blogs? 0.69
Facbor 4 - Freinding
How many people are on your friend list? 0.87
Excluded items
How frequently do you…
send private messages?
receive private messages?
Note. Factor loadings below .50 are suppressed and not shown.

55
Regression models were run to test the relationship between motivational factors
and site use pattern. Table 7 shows the results of regression analyses predicting
discussion board use, blog use, photo and video function use, and friending pattern
respectively. Holding control variables constant, results show that discussion board use
was significantly related to motivation to seek information ( β = .218, p < .01), motivation
to help other β = .352, p < .001), motivation to meet other ( β = .485, p < .001), and
motivation to maintain offline relationships ( β = .319, p < .001). Motivation to relax did
not predict the use of discussion board ( β = .128, p = .12). For the model predicting blog
use, motivation to relax ( β = .208, p < .01) and motivation to maintain offline
relationships ( β = .185, p < .01) were two statistically significant predictors. Use of photo
and video functions was related to motivations to meet others ( β = .189, p < .05) and to
maintain offline relationships ( β = .299, p < .001). Motivation to meet others ( β = .287, p
< .001), motivation to help others ( β = .278, p < .001), and motivation to maintain offline
relationships ( β = .220, p < .001) were the three significant predictors of friending pattern.

Table 7.
Regression Analysis Predicting Use of Different Features from Motivation
Model 1 Model 2 Model 3 Model 4
Discussion board use

Blog use

Photo and video
sharing and browsing
Friending
(Number of friends)
Standardized β Standardized β Standardized β Standardized β
Motivation to seek information
0.218** 0.091 0.100 -0.064
Motivation to help others
0.352*** 0.093 0.138 0.278***
Motivation to meet others
0.485*** 0.143 0.189* 0.287***
Motivation to relax
0.128 0.208* 0.034 -0.570
Motivation to maintain offline
relationship
0.319*** 0.185* 0.299*** 0.220***
+ p < .10, * p < .05, ** p < .01, *** p < .001
Note. Control variables include age, gender, coresidency (live together), education, urban residency, and ethnicity. In order
to avoid multicollinearity problems, five motivation factors were entered separately to the regression models following
control variables.

56

57
Research Question Three
The third research question examined how the use of specific features is related to
the perception of support availability. Table 8 (informational support) and 9 (emotional
support) show the results of multiple regression analyses in which two types of OSG
support perception were regressed against specific OSG feature use factors.
Results in Table 8 show that the only feature that is significantly related to
informational support perception was the use of discussion board ( β = .321, p < .01).
Results in Table 9 shows that the emotional support perception was dependent on the use
of discussion board ( β = .364, p < .001) and blog ( β = .128, p < .10). Use of video and
photo functions was related to neither of two types of social support ( β = -.047, p = .568
for informational support, β = .001, p = .993 for emotional support).

58

Table 8.
Multiple Regression Analyses Predicting OSG Informational Support from OSG
Feature Use Patterns
Standardized β Standardized β
Control variables
Age -.109-.081
Gender (Female = 1) -.079 .040
Live alone (Yes = 1) .028 .044
Education -.188* -.062
Residency (Urban =1) .016 -.003
Ethnicity (Non-Hispanic White=1) .209 ** .171 *
Health Status (Referent = poor)
Very good -.030 .005
Good .025 .031
Fair .016 .017
OSG usage duration .156 + .139 +
Site use pattern variables
Discussion board use .321 **
Photo and video sharing and browsing -.047
Blog use .050
Friending (Number of friends) .101
Incremental R
2
.162** .097 **
Total R
2
.162 .259
F 2.789** 3.499 *
+ p < .10, * p < .05, ** p < .01, *** p < .001

59

Table 9.
Multiple Regression Analyses Predicting OSG Emotional Support from OSG Feature
Use Patterns
Standardized β Standardized β
Control variables
Age -.161+ -.061
Gender (Female = 1) -.249 ** -.068
Live alone (Yes = 1) .060 .089
Education -.178* -.005
Residency (Urban =1) .031 -.004
Ethnicity (Non-Hispanic White=1) .163 * .132 +
Health Status (Referent = poor)
Very good .059 .126
Good .069 .104
Fair .071 .075
OSG usage duration .189 * .146 *
Site use pattern variables
Discussion board use .364 ***
Photo and video sharing and browsing -.001
Blog use .128 +
Friending (Number of friends) .237 *
Incremental R
2
.292*** .172 ***
Total R
2
.292 .464
F 5.888 *** 8.580 ***
+ p < .10, * p < .05, ** p < .01, *** p < .001

60
Hypothesis One
Hypothesis 1 predicted a positive relationship between OSG use intensity and the
perception of OSG support. Controlling for demographic and health-related variables,
OSG use intensity was shown to have a positive relationship with perceptions of both
informational ( β = .317, p < .001) and emotional support ( β = .413, p < .001) (Table 10
and Table 11). Therefore, hypothesis 1 was supported.

Table 10.
Multiple Regression Analyses Predicting OSG Informational Support from OSG Use
Intensity
Standardized β Standardized β
Control variables
Age -.109-.052
Gender (Female = 1) -.079 .022
Live alone (Yes = 1) .028 .042
Education -.188* -.085
Residency (Urban =1) .016 -.020
Ethnicity (Non-Hispanic White=1) .209 ** .200 *
Health Status (Referent = poor)
Very good -.030 .031
Good .025 .076
Fair .016 .057
OSG usage duration .156 + .140 +
OSG use intensity .317 **
Incremental R
2
.104*** .054 **
Total R
2
.104 .156
F 2.789** 3.853 ***
+ p < .10, * p < .05, ** p < .01, *** p < .001

61

Table 11.
Multiple Regression Analyses Predicting OSG Emotional Support from OSG Use
Intensity
Standardized β Standardized β
Control variables
Age -.161+ -.087
Gender (Female = 1) -.249 ** -.121
Live alone (Yes = 1) .060 .084
Education -.178* -.039
Residency (Urban =1) .031 -.020
Ethnicity (Non-Hispanic White=1) .163 * .153 *
Health Status (Referent = poor)
Very good .059 .142
Good .069 .136
Fair .071 .124
OSG usage duration .189 * .166 *
OSG use intensity .413 ***
Incremental R
2
.242***.091 ***
Total R
2
.242 .335
F 5.888 *** 7.998 ***
+ p < .10, * p < .05, ** p < .01, *** p < .001

Hypothesis Two
The second hypothesis stated that the relationship between OSG use intensity and
support perception would be mediated by friendship built within the site. In other words,
the current study expected that OSG support perception would be the outcome of
friendship one builds through OSG and thus the number of people one considers as
friends within the OSG would positively affect the support perception. In order to test the
mediation hypothesis, regression analyses were conducted following three steps outlined
by Baron and Kenny (1986). In the testing of hypothesis 1, the direct relationship
between OSG use intensity and support perception was confirmed (step 1). As the next
step (step 2), the mediator, OSG friendship, was regressed on OSG use intensity.

62
Regression model prediction OSG friendship (Table 12) shows that the coefficient
associated with this relation were also significant ( β = .255, p < .05). Lastly (step 3), the
relationship between the mediator, OSG friendship, and OSG support perception was
tested after controlling for OSG use intensity. Results (Table 13) show that the condition
for mediation was not met, meaning the coefficient for the final step regression was not
significant ( β = .058, p = .473 for informational support, β = .030, p = .681 for emotional
support). Thus, hypothesis 2 was not supported. OSG friendship did not mediate the
relationship between OSG use and OSG support perception.

Table 12.
Multiple Regression Analyses Predicting OSG Friendship from OSG Use Intensity

Standardized
β
Standardized β
Control variables
Age -.142-.095
Gender (Female = 1) -.152 -.071
Live alone (Yes = 1) -.013 -.003
Education -.114 -.031
Residency (Urban =1) -.015 -.045
Ethnicity (Non-Hispanic White=1) .024 .014
Health Status (Referent = poor)
Very good .062 .112
Good .136 .176
Fair .053 .087
OSG usage duration .061 .048
OSG use intensity .255 *
Incremental R
2
.119* .035 *
Total R
2
.119 .153
F 1.936* 2.349 *
+ p < .10, * p < .05, ** p < .01, *** p < .001

63
Table 13.
Multiple Regression Analyses Predicting OSG Informational and Emotional
Support from OSG Use Intensity and OSG Friendship
Model 1 Model 2
Informational
support
Emotional
support
Standardized β Standardized β
Control variables
Age -.046-.085
Gender (Female = 1) .028 -.116
Live alone (Yes = 1) .043 .086
Education -.084 -.040
Residency (Urban =1) -.016 -.016
Ethnicity (Non-Hispanic White=1) .199 * .153
Health Status (Referent = poor)
Very good .024 .138
Good .065 .130
Fair .047 .114
OSG usage duration .139 + .167 *
OSG use intensity .299 ** .402 ***
OSG Friendship .058 .030
Total R
2
.218 .381
F 3.270*** 7.190 ***
+ p < .10, * p < .05, ** p < .01, *** p < .001

64
Hypothesis Three
Hypothesis 3 tests the moderating role of personality such that the relationship
between OSG use intensity and OSG friendship varies by the degree of a person’s
extraversion. Regression results (Table 14) show a significant effect for the interaction
term, indicating the moderating role of extraversion ( β = .230, p < .01). Figure 4 shows
that those who are extraverted compared to those who are introverted were more likely to
make friends from their interactions within the OSG. Thus hypothesis 3 was supported.

Table 14.
Multiple Regression Analyses Predicting OSG Friendship from Extraversion and
OSG Use Intensity x Extraversion
Standardized β
Control variables
Age -0.096
Gender (Female = 1) -0.026
Live alone (Yes = 1) 0.031
Education -0.077
Residency (Urban =1) -0.039
Ethnicity (Non-Hispanic White=1) 0.013
Health Status (Referent = poor)
Very good 0.071
Good 0.142
Fair 0.065
OSG usage duration 0.018
OSG use intensity 0.216 *
Extraversion 0.178 *
OSG use intensity x Extraversion 0.23 **
Total R
2
0.246
F 3.543 ***
+ p < .10, * p < .05, ** p < .01, *** p < .001

65

Figure 4
Extraversion and OSG Use Intensity on OSG Friendship
a

a
Those with average plus one standard deviation score on the extraversion scale were
grouped as extraverted whereas those with average minus one standard deviation score on
extraversion scale were grouped as introverted. The same categorization applies to both
Figure 5 and Figure 6.

66
Hypothesis Four

Hypothesis 4 tests the moderating role of personality such that the relationship
between OSG use intensity and support perception changes with the degree of a person’s
extraversion. Regression results (Table 15) show that coefficients for interaction terms
were not significant ( β = -.032, p = .687 for informational support; β = -.027, p = .631 for
emotional support). Thus, hypothesis 4 was not supported. Figure 5 and Figure 6 show
that extraverted and introverted people were no different in their perceptions of social
support from OSG whether they are more or less intense users of OSG.

Table 15.
Multiple Regression Analyses Predicting Informational and Emotional Support from
Extraversion and OSG Use Intensity x Extraversion

Informational
support
Emotional
support
Standardized β Standardized β
ontrol variables
Age -0.049-0.084
Gender (Female = 1) 0.039 -0.099
Live alone (Yes = 1) 0.043 0.082
Education -0.085-0.045
Residency (Urban =1) -0.012 -0.007
Ethnicity (Non-Hispanic White=1) 0.206 ** 0.158 *
Health Status (Referent = poor)
Very good 0.028 0.134
Good 0.0780.136
Fair 0.0620.129
OSG usage duration 0.145 + 0.172 *
OSG use intensity 0.318 ** 0.41 ***
Extraversion 0.0760.093
OSG use intensity x Extraversion -0.032 -0.027
Total R
2
0.222 0.391
F 3.086 *** 6.9 ***
+ p < .10, * p < .05, ** p < .01, *** p < .001

67

Figure 5
Extraversion and OSG Use Intensity on OSG Informational Support

Figure 6
Extraversion and OSG Use Intensity on OSG Emotional Support

68
Hypothesis Five
Hypothesis 5 proposed that satisfaction with offline support networks will
moderate the relationship between OSG use intensity and OSG friendship such that those
who are less satisfied with their existing offline support network are more likely to
develop friends within OSG as they increase their use of the site. Regression results
(Table 16) indicate a significant effect for the interaction term, indicating the moderating
role of offline support satisfaction ( β = .161, p < .10). However this moderating effect
was contrary to the direction that was hypothesized. With an increasing amount of OSG
use those who are more satisfied with their offline social network made a greater number
of friends within OSG (Figure 7). Thus, hypothesis 5 was not supported.

69
Table 16.
Multiple Regression Analyses Predicting OSG Friendship from Offline Support
Satisfaction and OSG Use Intensity x Offline Support Satisfaction

Standardized β
Control variables
Age -0.086
Gender (Female = 1) -0.054
Live alone (Yes = 1) 0.048
Education -0.041
Residency (Urban =1) -0.064
Ethnicity (Non-Hispanic White=1) 0.012
Health Status (Referent = poor)
Very good 0.028
Good 0.098
Fair -0.005
OSG usage duration 0.052
OSG use intensity 0.249 *
Offline support satisfaction 0.176 *
OSG use intensity x Offline support
satisfaction
0.161 +
Total R
2
0.134
F 2.609 **
+ p < .10, * p < .05, ** p < .01, *** p < .001

70

Figure 7
Offline Support Satisfaction and OSG Use Intensity on OSG Friendship
a

a
Those with an average plus one standard deviation score on offline support satisfaction
scale were categorized into high group whereas those with an average minus one standard
deviation score on offline support satisfaction scale were categorized into low group.

71
Hypothesis Six
Hypothesis 6 tests the moderating role of offline support satisfaction such that
those who are less satisfied with their existing offline support network will appreciate
support from the site to a greater extent. Regression results (Table 17) show that
coefficients for interaction terms were not significant ( β = .053, p = .516 for
informational support; β = -.012, p = .872 for emotional support). Thus hypothesis 6 was
not supported.

Table 17.
Multiple Regression Analyses Predicting Informational and Emotional Support from
Offline Support Satisfaction and OSG Use Intensity x Offline Support Satisfaction

Informational
support
Emotional
support
Standardized β Standardized β
Control variables
Age -0.051 -0.097
Gender (Female = 1) 0.028 -0.12
Live alone (Yes = 1) 0.068 0.112
Education -0.093 -0.055
Residency (Urban =1) -0.03 -0.027
Ethnicity (Non-Hispanic White=1) 0.199 * 0.15 *
Health Status (Referent = poor)
Very good -0.009 0.107
Good 0.039 0.107
Fair 0.023 0.115
OSG usage duration 0.14 + 0.163 *
OSG use intensity 0.309 ** 0.393 ***
Offline support satisfaction 0.098 0.115
OSG use intensity x Offline support
satisfaction
0.053

-0.012

Total R
2
0.225 0.334
F 3.155*** 7.015 ***
+ p < .10, * p < .05, ** p < .01, *** p < .001

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CHAPTER 6 CONCLUSION
Discussion
Use of social networking sites has become common among teenagers and young
adults and an increasing number of adults are also joining the sites (Lenhart, Purcell,
Smith, & Zickuhr, 2010). Health insurance companies are also joining the wave and
creating social networking sites to disseminate information about health services and
health improvement tips ("UCLA," 2010). A number of social networking sites have been
created to connect people to share their goals to stay fit, track their diet, and keep up with
their workout plan (de Avila, 2007). Online social networking features are also being
increasingly integrated into support communities that are dedicated to specific health
issues. Yet little is known about their members’ experiences with regard to these newly
integrated social networking features. Also unknown is their effectiveness in providing
support to their users.
The current study examined online support communities that have integrated
social networking features and sought to understand their users’ needs and use patterns
and answer the questions of how to better provide support to their users. To do so, the
current study drew on the framework of uses and gratifications and conducted an online
survey of the current users of already existing health-related social networking sites. A
number of interesting findings were observed and these findings are discussed in the
following paragraphs.
First, the first research question explored motivations behind visiting health-
related social networking sites. Consistent with findings from research on OSGs
(Buchanan & Coulson, 2007; Coulson, 2005; Leimester & Krcmar, 2006; Meier et al.,

73
2007; Rodgers & Chen, 2005; Tanis, 2008), the strongest motivation to join health-
related social networking sites was found to be information seeking: to learn about one’s
health condition, find information about medication and health services, obtain answers
for health-specific questions, and obtain advice from people undergoing or having
undergone the same health experiences. The motivation to “network” and socialize with
others did not appear to be the primary reason to join the community. Health-related
social networking sites appeal to their users primarily as information resources.
The second most important motivation was provision of support to other
community members. It was interesting to learn that people not only come to seek help
but also to share what they have with others. OSG participants in a previous study (van
Uden-Kraan, Drossaert, Taal, Shaw et al., 2008) reported that they like to share their
experiences with other group members because as a result of sharing they get a feeling of
becoming a better and helpful person. Also from interviews with people who were
infected with HIV, Reeves (2000) found that helping was one important coping strategy.
The two strongest motivations, motivation to seek informational support and
motivation to provide help, are in one sense complementary to each other. It is reasonable
to expect that a good balance of these two motivations would help keep an online
community vibrant.
The third most important motivation was the need to develop new relationships.
This need was mentioned as more important than the motivation to maintain existing
relationships. Unlike general purpose social networking sites, such as Facebook, where
the main goal of use is to locate old friends and keep connected with existing offline
contacts (Ellison et al., 2007; Raacke & Bonds-Raacke, 2008), health-related social

74
networking sites were found to be valued to a greater extent as a venue to make new
friends with similar interests, reach out and get to know new people, and develop new
relationships. The current paper shows that the offline-to-online directionality does not
much apply to OSG that have integrated social networking features (Orchard & Fullwood,
forthcoming).
The following research question was then to investigate OSG feature use patterns
and any variations in OSG feature use patterns by motivation; in short, whether and how
users of health-related social networking sites display different patterns of OSG site
usage when their motivations to join the community are different from those of others.
According to the uses and gratifications perspective, individuals use a medium in a way
that best fulfills their needs (Blumler & Katz, 1974). Following the assumption that
specific needs lead to certain patterns of use, the current study found that certain features
and functions of health-related social networking sites are used more by people with
certain needs than others.
For example, the blog function (writing and reading) was most actively used by
people who use the site as a means of relaxation and entertainment. Sharing and browsing
of photos and videos were most commonly done by those who use the site as a tool for
socializing, be it to build new relationships or to maintain existing relationships. Not
surprisingly those who join health-related social networking sites to seek information
made the least use of various features available in the OSG site. They limited their use
mostly to discussion board. A close look at one distinct feature of social networking sites,
friending, reveals that those who have friended many people tend to have strong

75
motivations to meet new people, maintain their offline relationships, and help others.
Friending did not seem to appeal to those who mainly visit OSG for information search.
Information seekers tend to use social networking features to a minimum and do
not seem to make personal connection with other users. Such a rather passive style of use
can be partly explained in the literature on lurking (Mo & Coulson, forthcoming;
Nonnecke et al., 2006; Nonnecke, Nonnecke, Preece, & Andrews, 2004; Preece,
Nonnecke, & Andrews, 2004). It has been found that lurkers compared to posters are
little interested in social interaction and companionship within online communities,
whereas they are keen on obtaining and learning new information as much as posters are
(Nonnecke et al., 2006; Nonnecke et al., 2004). It clearly appears that information seekers
in health-related social networking sites are trying to meet their needs without making
use of features that facilitate one-on-one social interactions or self-expression. These
information seekers in OSGs appear to take advantage of the limited amount of social
cues online (Cooper, 2004; Davison et al., 2000).
It was socializers who actively friended others and browsed through photo and
video updates from their friends. As findings from Rau et al. (2008) show, people in
social networking sites show a high level of engagement when they believe that their
social and emotional needs would be met by the site. These OGS members with strong
socializing and emotional needs seem to try to overcome the reduced amount of social
cues by using various tools for self-expression and making personal connections to other
OSG members.
In sum, as the uses and gratifications framework suggests, users of health-related
social networking sites use features available within the site to the extent that these

76
features serve their needs better than the other features. This selective use of site features
can then lessen the likelihood of receiving unwanted and unwelcomed support as the
users of OSGs can make decisions as to the degree of involvement and explicitness in
support request and exchange (Duthler, 2006). The next research question (research
question 3) is to see how and whether people would develop different perceptions toward
availability and types of support in the health support community depending on their
patterns of OSG use.
Results show that use of discussion boards was positively related to the perception
that the community offers both information and emotional support. Blog features and
friending, on the other hand, were only positively associated with the perception that
many people in the community would provide emotional support. Sharing and browsing
of photos and videos did not seem to enhance the perceptions of either informational or
emotional support. In sum, results show that the social networking features (such as
friending and sharing of personal stories, photos, and videos) that are added to the
traditional format of online community (discussion board format), are indeed beneficial
to people in satisfying their needs for emotional support. The older format of online
community, the discussion board, can be sufficient for people who just want information.
Integration of social networking feature was found to be most helpful for people who are
in need of emotional comfort.
The three research questions centered on the linkages among three major
constructs from the uses and gratifications framework and focused on individual
variations in motivations, use patterns, and accruing benefits from OSG use. If the three
research questions concerned variations by specific features and motivations, the

77
following six hypotheses scrutinized perceptions of OSG support as a function of general
OSG use intensity. In doing so, the current study also looked into personal and situational
factors, such as personality and satisfaction with offline support network.
OSG general use intensity, as measured by how often one visits, how much time
one spends in, and how many friends they have on an OSG site, was found to have
positive relationships with two types of OSG support perception. In other words, those
who more actively engage in an OSG site were more likely to perceive the site to provide
both emotional and informational support. Active use of the site also led to a wider
network of friendship within the site as well. In other words, those who more actively
engage in a health-related social networking site developed a greater number of
relationships whom they would consider as friends.
One interesting finding is that the perception of support was not mediated by
friendship one has built within the site. Put differently, the perception of support was not
influenced by the number of friends one had from OSG. One possible reason is that OSG
members are not seeing support coming from specific individuals but from a community
as a whole (Turner et al., 2001). OSG can present unique opportunities to provide ‘silent
support’ (Bolger et al., 2000) because the receipt of support does not build upon close
one-to-one relationships and thus does not accompany expectations of direct reciprocity
(Nowak & Sigmund, 2005; von dem Knesebeck & Siegrist, 2003). Another possibility is
with the quantitative measurement of friendship in the current study. Support from one
friend can be as meaningful as that from ten friends. The current study’s quantitative
measure of friends may have been too coarse to test the mediating role of OSG social
relationships.

78
Further examined was the question of whether use of health-related social
networking sites can help overcome certain deficiencies one may have in one’s offline
setting. Two particular variables that were examined included personality (extraversion
vs. introversion) and satisfaction with one’s offline support network.
With regard to personality, this study re-visited two competing hypotheses on the
social outcomes of internet use. These two hypotheses – rich-get-richer and social
compensation hypotheses – are particularly relevant and applicable to the study of social
networking sites because establishment of social relationships is one of social networking
sites’ core functions (Rau et al., 2008). Results from the present study supported the rich-
get-richer hypothesis (Kraut et al., 2002): Those with more extraverted characteristics
reported having made a greater number of friends within virtual support communities.
Introverted people stayed shy in support communities as they do offline. With an
increasing use of OSG, extraverted people developed more social relationships than
introverted people did. But, luckily, this does not translate into differences in perceptions
of OSG support. Despite the advantage that extraverted people enjoyed in terms of
making friends in health support communities, the support perception was not influenced
by a person’s extraversion. This again shows that the perception of support does depend
on how actively one engages in one-to-one friend-making behaviors. The perception of
support may also just develop as a result of observing others exchanging support
(Welbourne et al., 2009).
Online and offline are psychologically connected (Subrahmanyam & Greenfield,
2008); and people’s evaluation of others’ supportive behaviors are not made in a vacuum
(Turner et al., 2001). Therefore the current study also examined contextual factors, such

79
as satisfaction with offline contacts. It was hypothesized that those with less satisfactory
relationship offline are trying to gratify their interpersonal needs through online
alternatives. Online communities may become particularly valuable for people who have
little face-to-face support (Cummings et al., 2002). However, the finding of the current
study was counterintuitive: dissatisfaction did not lead to more positive appraisal of OSG
support. Neither did dissatisfaction with offline support network lead to more efforts to
build friendship within the OSG. Results showed that those who have more satisfactory
offline relationships have made friends in OSG.
Using a different data set, Shaw and his colleagues (Shaw, Hawkins, Arora et al.,
2006) also found more active use of computer-mediated support systems among those
with more offline support and speculated that those who have more offline support may
feel they have more to lose and thus work harder (by participating in OSG) to keep
having sufficient social networks. Another possible explanation is that those who are not
satisfied with support they receive from offline contacts may have low expectations for
any kind of support from social relationships. They may see little benefit in building new
social relationships as they do not think new relationships can make up for their
dissatisfaction with their offline contacts. The hypothesis that those who have
dissatisfactory relationships offline are more likely to form friendship within a health
support community is based on the assumption that virtual relationships can help fulfill
the needs unmet by offline contacts. However, findings suggest that people may not use
OSG as an outlet to satisfy their unmet needs but rather to complement their existing
network of support.

80
The current study shows that neither extraversion nor satisfaction with one’s
offline support affects how much one appreciates support from OSG. Results show that
one’s perception of support within OSG is dependent on how one uses OSG but is little
influenced by one’s contextual or psychological factors. Although not directly tested in
the present study, how long one has been on an OSG (OSG usage duration) was found to
be another critical factor affecting OSG support perception. A few other longitudinal
studies reported similar findings in OSG participation such that the longer one
participates in OSG the greater support one perceives to be available within OSG (Rains
& Young, 2009). In a theoretical sense, this is in line with social information processing
theory (Walther & Burgoon, 1992): internet users over time develop positive perceptions
of others. Those who have been on the group for a long period perceive the OSG to
provide a greater level of both emotional and informational types of support. In sum, it
appears there are empirical reasons to suggest more active, enduring, and frequent use of
OSG sites to their users.
Implications
In a theoretical sense, the current study highlighted the importance of examining
individual variation in motivations, use patterns, and accruing benefits in the context of
health-related social networking site use. Findings demonstrate that the uses and
gratifications perspective can be a useful framework in conducting focused assessment of
each feature and systematic analysis of OSG user experience (Barrera et al., 2002; Dutta
& Feng, 2007). The uses and gratifications framework as well as optimal matching theory
in the supportive communication literature suggest that any differences in media choice
and appreciation need to be viewed in conjunction with individual differences, such as

81
individual needs, psychosocial characteristics, and situational factors. The current study
found that patterns of health-related social networking sites’ use fluctuate with
motivations and that some of outcomes are dependent on use patterns.
Methodologically, one of strengths of this study is in its design. By surveying
people who have been using OSG in natural settings, this study has overcome some of
limitations found in experimental design (Barrera et al., 2002; Freeman et al., 2008). In
spite of their own merits, experimental designs often cannot explain the processes
through which OSGs become efficacious on their users (Han et al., 2009). Sense of
community and perceptions of social support often grow over time, thus measuring them
in an experiment where study duration is short can be problematic. Also in experiments,
participation in OSG interventions is not necessarily preceded by motivations. By asking
people who have been using support groups from their voluntary needs, this study was
able to capture the link between needs, use of different OSG features, and their outcomes
in support perception.
In addition, there are three practical lessons that can be drawn from findings of
the current study. First, understanding of why people join health-related social
networking sites can be beneficial to organizations and moderators that host these sites.
Understanding reasons behind using health-related social networking sites helps develop
online communities that suit the needs of their users and develop content that their users
can easily tap into. Some health-related social networking sites make it an explicit aim to
connect patients with their loved ones via their sites and facilitate communication of
progress of their illness with their offline friends and families, as Facebook is used as a
way for people to share their personal updates from everyday life. For example, one

82
health-related social networking site states one of its aims to “simply and easily
communicate their progress with friends, family, supporters, and have those people
respond with encouragement and help” (DailyStrength, n.d.). However, findings show
that those who have chosen to become a user of health-related social networking sites are
little interested in using the site to keep connected with their existing friends and families.
Health-related social networking site users are rather interested in finding
information from, giving support to, and making new friends with people in a similar
situation from their use of OSG. As social comparison theory suggests, patients in an
OSG can obtain a sense of normalcy by being with people who are facing a similar issue
(Festinger, 1954). When diagnosed with a disease, patients sometimes have a desire to
have someone around whom they can easily relate to as they want to maintain a sense of
normalcy. They may also want to maintain distance from their close friends and families
as people who are very close may hold unrealistic anticipation for speedy recovery or
become excessively protective (Tanis, 2007).
Secondly, this paper assessed the values of health-related social networking sites
among patients. The results show that health-related social networking sites are a place to
gain both information and consolation regarding one’s illness experience. In particular,
one of the core features of social networking sites, sharing of personal stories, photos,
and videos, was found to be beneficial for people who seek emotional support. The
present study empirically tested and confirmed the claim that the use of these features
would bring extra benefits to their users and foster the perception that one is being cared
about (Fenech, 2009; Holahan, 2008; Landro, 2006; C. C. Miller, 2008; Morphy, 2008).

83
This finding suggests that social networking features can have distinct values for
people who need emotional support and comfort because of their health and medical
concerns. Specifically, social networking features can be helpful in connecting people
who suffer from marginal identity issues. Those with conspicuous marginal identity
issues (such as people who experience physical disabilities, skin problems, and obesity)
and those with concealable marginal identity issues (such as those with stigmatized
sexual preference) can have a better chance to get connected using social networking
features. Social networking features help find people whom one can count on for
empathy, understanding, compassion, and companionship (Barak et al., 2008; McKenna
& Bargh, 1998; Tanis, 2007). Recently a number of websites have been launched for
people with marginal identities, including social networking websites for people with
disabilities (www.disaboom.com) and people living with stigmatized sexual orientation
(www.glee.com). In addition, these online platforms and applications can be of particular
use to the chronically ill who, in many cases, need to make conscious efforts to monitor
and manage their health conditions on a regular or everyday basis (Sarasohn-Kahn, 2009).
Social networking tools can connect chronically ill patients who would then help each
other gain competence and adhere to wellness or medication regimens.
Lastly, understanding of health-related social networking sites from the
perspective of uses and gratifications also has practical implications for the design of
health-related social networking sites. The current study has shown that people, when
they have different types of needs, display different use patterns. Those who visit to seek
information, those who come to socialize, and those who visit to get away from their
worries, are interested in using some features more than some others. Therefore, one way

84
to design a site that can maximize positive outcomes associated with OSG use is to first
learn the use patterns by people with different needs, screen the needs of their users, and
then deliver content in a way that frequently used or sought features are highlighted more
than others. This way, the quality of OSG user experience may become enhanced.
Limitation and Directions for Future Research
There are limitations in this study that warrant attention and these limitations give
directions for future research. First, the current study was only able to reach and collect
responses from those who are currently using OSGs. Evidence is meager as to the
experience of those who have already left or discontinued using OSGs. The difficulty in
reaching those who have already left could have led to a positive bias in reporting the
outcomes of using OSGs. Also unexamined in the present study were potential negative
consequences. For example, Demiris (2006) raised concerns over the decreasing number
of face-to-face interactions among OSG participants and dubbed the trend as “progressive
dehumanization” (p. 183). Future studies, thus, need to study cases of those who already
left the OSG community, any excessive reliance on online networks, and their
detrimental impact on patients. It will be problematic if patients’ use of OSGs displace
higher-quality support from their offline support network (Adams, 2007; Caplan, 2003).
Second, it should be noted that the current study collected responses from a
convenience sample. Although attempts were made to collect data from a greater number
and a wider range of sites, the current study was only able to reach two particular groups
of patients: cancer and diabetes patients. It is thus unclear to what extent findings of the
current study can be generalized to other disease conditions. Thus it will be important for

85
future research to expand the study scope to investigate the use of health-related social
networking sites among patients across various health conditions.
In addition, the current study’s sample was slightly older and more educated than
general population. A majority of respondents were also Caucasian. As the current study
demonstrates the use of health-related social networking sites generate benefits to their
users, those who are underserved or those with little amount of social resources can
particularly benefit from their use. Future research, therefore, should also examine the
use of health-related social networking sites among patients with diverse socio-
demographic characteristics.
The third limitation of the current study is its reliance on self-reported data.
Because it was difficult for respondents to recall in detail how much time they spent on
different types of features, the survey had to ask about their frequency of use, instead of
time using certain features. When coupled with survey data, system-recorded data (such
as log of each participant’s connecting time, frequency of visit, and continuity of use) can
teach us a great deal about effective ways of achieving the goal of OSGs. For example, a
research team based at the University of Wisconsin developed a system which they had a
full access to log files and learned that the most effective use of computerized health
system is continuous, committed, and active use of the system over time, rather than
longer use of the system (Han et al., 2009). For that reason, research support from OSG
developers in accessing raw log files, can be especially desirable to further conduct
studies about OSGs and their users.
Another limitation of the current study is its sole focus on individual variations.
Although there may also exist site-level differences, those site-based differences were not

86
covered in the current study. It is reasonable to assume that individual’s perception of
support and their willingness to establish relationships would vary by sites. For example,
a site will be perceived to be more supportive if there are many members who read and
respond to others’ postings. The size of online community can also affect the perception
of support one would find from the online community (Rains & Young, 2009). A weak-
tie theory suggests that a large network can be beneficial in providing information in need
(Granovetter, 1983; Granovetter, 1973). Although the number of registered users in the
four studied sites varied from 1,500 to over 150,000, the current study was not able to run
analyses on site-based differences as the sample size for each community was too small
to make statistically valid claims. Therefore, future research should also examine site-
level differences and help design a community that can optimize the experience of its
users.
Lastly, future research needs to employ longitudinal designs and learn how
support perceptions influence strategies for coping with disease. Research suggests that
support received from online communities has the potential to foster feelings of
empowerment and a sense of control and independence (Barak et al., 2008; van Uden-
Kraan, Drossaert, Taal, Seydel, & van de Laar, 2008). OSG participation may not directly
change a user’s health status but can indirectly influence it through changes in general
psychosocial status (Barak et al., 2008; Seckin, 2009). These long-term changes can only
be effectively captured in longitudinal studies.

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Asset Metadata

Creator Chung, Jae Eun (author)

Core Title Benefits of social networking in online social support groups

School Annenberg School for Communication

Degree Doctor of Philosophy

Degree Program Communication

Publication Date 06/30/2012

Defense Date 05/19/2010

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag computer-mediated support group,health communication,OAI-PMH Harvest,online support group,social networking sites,social support,virtual communities

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor McLaughlin, Margaret L. (committee chair), Cody, Michael J. (committee member), Jordan-Marsh, Maryalice (committee member)

Creator Email chung_jae_eun@hotmail.com,jaeechun@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3161

Unique identifier UC1176417

Identifier etd-Chung-3805 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-349775 (legacy record id),usctheses-m3161 (legacy record id)

Legacy Identifier etd-Chung-3805.pdf

Dmrecord 349775

Document Type Dissertation

Rights Chung, Jae Eun

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu