Journal of Health Communication, 19:710–720, 2014 Copyright © Taylor & Francis Group, LLC ISSN: 1081-0730 print/1087-0415 online DOI: 10.1080/10810730.2013.821558

Media Complementarity and Health Information Seeking in Puerto Rico YAN TIAN Department of Communication, University of Missouri-St. Louis, St. Louis, Missouri, USA

JAMES D. ROBINSON Department of Communication, University of Dayton, Dayton, Ohio, USA This investigation incorporates the Orientation1-Stimulus-Orientation2-Response model on the antecedents and outcomes of individual-level complementarity of media use in health information seeking. A secondary analysis of the Health Information National Trends Survey Puerto Rico data suggests that education and gender were positively associated with individual-level media complementarity of health information seeking, which, in turn, was positively associated with awareness of health concepts and organizations, and this awareness was positively associated with a specific health behavior: fruit and vegetable consumption. This study extends the research in media complementarity and health information use; it provides an integrative social psychological model empirically supported by the Health Information National Trends Survey Puerto Rico data.

Health communication scholars and researchers have become increasingly interested in public health information usage. Recognizing this need, the National Cancer Institute began gathering information on the public’s health information need and usage patterns and made that Health Information National Trends Survey (HINTS) data available to the public. Beginning in 2005, the National Cancer Institute has released three large-scale national survey datasets that have proven to be an invaluable resource for scholars who are interested in role communication plays in the treatment and prevention of cancer and has spawned numerous studies and programs of research, as evidenced by the number of publications produced from the datasets including special issues of the Journal of Health Communication in March 2006 and in September 2010. The national-level HINTS datasets, however, have some limitations. One limitation of the HINTS has been the lack of data to allow researchers the opportunity to perform local- or state-level analyses (Tortolero-Luna et al., 2010). Given the costs associated with the HINTS project, this limitation is understandable; however, the National Cancer Institute has made an effort to expand the HINTS program beyond national-level surveys. In 2009, the National Cancer Institute, the University of Puerto Rico Comprehensive Cancer Center, and the Puerto Rico Behavioral Risk Factors Address correspondence to Yan Tian, Department of Communication, University of Missouri-St. Louis, One University Boulevard, St. Louis, MO 63121-4400, USA. E-mail: [email protected]

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Surveillance System implemented HINTS in Puerto Rico, including more than 600 respondents from Puerto Rico, which allows some specific geographical area analyses to be performed (Tortolero-Luna et al., 2010). The inclusion of data from Puerto Rico is helpful to health communication researchers in several ways. First, national health studies often treat Latinos as if they were a homogenous group and this is clearly not the case (Elder, Ayala, Parra-Medina, & Talavera, 2009). Although Mexicans, Cubans, and Puerto Ricans “have similar needs and experience similar barriers in using health services,” “there is evidence that each group has specific characteristics that make it different and independent from one another” (Modiano, Villar-Werstler, Meister , & Figueroa-Valles , 1995, p. 35). Therefore, it is important to study Puerto Ricans as a unique cohort instead of combining them with other groups of Latinos in order to understand health information usage behavior. In addition, Puerto Ricans who live in Puerto Rico are different from Puerto Ricans who live in the United States. For example, the median income of a family in Puerto Rico is roughly 34% of the household income of the residents in the United States (Tortolero-Luna et al., 2010). Furthermore, more than 70% of people in the United States have access to the Internet, whereas only 45.3% do in Puerto Rico (International Telecommunication Union, 2010). Last, although outside the scope of this investigation, there is evidence that different race and ethnic groups suffer from different types of cancer at different rates (Chao et al., 1998). For these reasons, it is not unreasonable to expect residents of Puerto Rico to have some unique health information needs and usage patterns. Thus, the purpose of this investigation is to extend previous research in media complementarity in health information use to Puerto Rico. Specifically, this investigation examines the antecedents and outcomes of individual-level media complementarity of health information seeking with the Orientation1Stimulus-Orientation2-Response (O1-S-O2-R) approach (Markus & Zajonc, 1985). Toward that goal, we intend to provide an integrative social psychological model to studying the process of individual-level complementarity in health information seeking and test the model with empirical data. Last, we hope to help health communication and informatics researchers understand the dynamics of health information usage in Puerto Rico. As the HINTS Puerto Rico data covers the eight geographic regions of the Puerto Rico Department of Health (Tortolero-Luna et al., 2010), this dataset provides a starting point for studying health information needs and uses of Puerto Ricans as a unique ethnic group. Media Complementarity Research on the interrelations between different communication channels has been examined with two general perspectives: media displacement and medial complementarity. From the perspective of media displacement theory, media audiences have limited time and resources to spend on the media. With this perspective, media usage increases in one medium will lead to usage decreases in another medium (Dutta-Bergman, 2004a; Henke & Donohue, 1989; Tian & Robinson, 2008a). Proponents of media complementarity theory, however, argue that media usage is not a zero-sum game: Media users could use various media channels in a complementary fashion on the basis of their level of interest in a subject or content area (Dutta-Bergman, 2004a, 2004b; Tian & Robinson, 2008a). In short, when people are interested in a topic, they use all of the media channels available to them to gather the information that they

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need. This notion of media complementarity is rooted in the uses and gratifications theory and the selective exposure literature (Atkin, 1973; Chaffee & Kanihan, 1997). Recently, media complementarity theory has been applied to health information use among U.S. adults. Through a secondary analysis of the HINTS data, Tian and Robinson (2008a) confirmed the complementarity of health information use among U.S. cancer patients. Furthermore, they found that adults that had not been diagnosed with cancer also used the media for health information seeking in a complementary fashion. Their findings suggest complementarity between traditional channels of the mass media and the Internet, as well as the complementarity between the interpersonal channel and mass media channels, even after controlling for illness severity. A follow-up study (Tian & Robinson, 2008b) revealed the complementarity of active health information seeking and incidental health information use on the Internet among senior cancer patients, while complementarity of incidental health information use between traditional media channels and the Internet was partially confirmed with younger adults. Media complementarity research typically focuses on the relation between media channels, and in general, there is strong evidence for complementarity in media channel usage for health information (Tian & Robinson, 2008a, 2008b). To better understand media complementarity in health information use, we used the O1-S-O2-R model (Markus & Zajonc, 1985) to provide a theoretical framework for studying the antecedents and outcomes of media complementarity. To our knowledge, no study has examined the antecedents and outcomes of media complementarity in health communication or health information use. The O1-S-O2-R Model The O1-S-O2-R model expands the traditional Stimulus-Response (S-R) approach by taking into account pre- and postorientation variables (Markus & Zajonc, 1985). According to this model, orientations function as a selective control of use of stimuli (O1-S) and an antecedent outcome variable between the stimuli and the responses (S-O2-R; Markus & Zajonc, 1985). Researchers have used the O1–S–O2–R model in political communication (Holbert, 2005; Kwak, Williams, Wang, & Lee, 2005; McLeod, Kosicki, & McLeod, 2002), mass communication (Peter & Valkenburg, 2006), and health communication (Paek, 2008; Yoo & Tian, 2011). Specifically, Paek (2008) examined the relation between orientation variables and antismoking campaign effectiveness. She found that sensationseeking and antismoking education—the preorientation variables—were associated with adolescents’ awareness of antismoking campaigns and prosmoking messages, whereas exposures to smoking-related messages were related to smoking intention through negative attitudes toward tobacco companies and peer smoking norms. More recently, Yoo and Tian (2011) used the model to examine the relation between audience consumption of entertainment television programs and attitudes towards organ donation. This investigation revealed that organ donation knowledge (O1) predicts recall of entertainment television programs (S), which, in turn, predicts an audience’s perception of medical mistrust (O2), and this perception of medical mistrust negatively predicts audience attitudes toward organ donation (R). The present study extends the O1-S-O2-R model to studying individual-level media complementarity of health information use in Puerto Rico. It tests complementarity of health information use as the stimuli, related to sociodemographic variables

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and also cognitive and behavioral variables. The subsequent section explains each specific relation in the O1-S-O2-R model. O1-S Hypotheses Researchers have often identified education, gender (being female), and income as predictors of health information seeking (e.g., Aspden & Katz, 2001; Atkinson, Saperstein, & Pleis, 2009; Shim, Kelly, & Hornik, 2006). This was also found to be true in Puerto Rico (Tortolero-Luna et al., 2010). The present study extends previous research on health information seeking to individual-level complementarity of health information seeking. In previous research, health information seeking is defined as whether individuals seek health information and/or the frequency of individual’s health information seeking. To date media complementarity research has not focused on individual-level media complementarity. In this study, we conceptualized individual-level complementarity of health information use as the degree to which individuals use various media channels complementarily to gratify their health information needs. Meanwhile, we operationalized this individual-level construct as the number of media channels an individual uses in health information seeking. The first three hypotheses propose positive relations among education, gender, income and individual-level complementarity in health information seeking. Hypothesis 1: Education is positively associated with complementarity in health information seeking. Hypothesis 2: Gender (being female) is positively associated with complementarity in health information seeking. Hypothesis 3: Income is positively associated with complementarity in health information seeking. Previous analyses of the HINTS’ national-level data suggested that complementarity of health information use differed between older and younger cancer patients (Tian & Robinson, 2008b). Because older people probably have a stronger need for health information, and therefore are more likely to use multiple sources for health information seeking, we hypothesized a positive relation between age and complementarity of health information seeking: Hypothesis 4: Age is positively associated with complementarity in health information seeking. S-O2 Hypothesis Previous research identified a positive relation between health information use and health knowledge (Shim et al., 2006; Tian & Robinson, 2009). This relation has been observed in active health information seeking and passive information scanning/incidental health information use and health knowledge (Shim et al., 2006; Tian & Robinson, 2009). Thus, we hypothesized that the number of channels individual use for health information seeking will also be positively related to an individual’s awareness of health concepts and organizations. Hypothesis 5: Complementarity in health information seeking is positively associated with awareness of health concepts and organizations.

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O2-R Hypothesis Media use has been found to be related to health behavior (Redmond, Baer, Clark, Lipsitz, & Hicks, 2010; Shim et al., 2006). However, media use probably does not affect health behavioral in a direct way. Instead, it is possible that media use affects cognitive aspects of individuals first, which, in this study, is awareness of health concepts and organizations, and this awareness then affects health behavior. Previous research found that health knowledge could be related to health behavior (Beeker, Kraft, Southwell, & Jorgensen, 2000; Eaker, Adami, & Sparen, 2001). We, therefore, proposed a positive relation between individuals’ awareness of health concepts and organizations and health behavior. Hypothesis 6: Awareness of health concepts and organizations is positively associated with health behavior.

Method We performed a secondary analysis of the weighted sample HINTS Puerto Rico data. See Tortolero-Luna and colleagues (2010) for a detailed explanation of the data collection process and response rate. Measurement Complementarity Because the HINTS survey did not contain an individual-level media complementarity measure, an index was created using a series of HINTS questions on health information seeking. Specifically the survey asked respondents whether they had “ever looked for information about health or medical topics from any source?” For those who did seek health information, a follow-up question, “The most recent time where did you go first?” was asked, which, in turn, was followed by the question, “Did you look or go anywhere else?” and “Where else did you look or go?” The data were manually recoded as follows: If a respondent had never looked for health information, the complementarity variable was coded as 0, meaning the respondent used 0 channels for health information seeking; if a respondent reported using only one medium (e.g., the Internet) for health information seeking, the complementarity variable was coded as 1. If the respondent reported using two media (e.g., the Internet and books) for health information seeking, the complementarity variable was coded as 2, and so on. Sociodemographic Variables The variable education was measured by the question, “What is the highest grade or level of schooling you completed?” Income was measured by the question, “Thinking about members of your family living in this household, what is your {combined} annual income, meaning the total pre-tax income from all sources earned in the past year?” Gender and age were also measured. Awareness of Health Concepts and Organizations Measurement of awareness of health concepts and organizations was based on individuals’ answers to the questions whether they had heard about genetic tests, clinical

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trials, the National Cancer Institute, the Centers for Disease Control and Prevention, and the American Cancer Society. The answers were coded as 0 = no, 1 = yes. Respondents’ answers to these five questions were summed up to create the index for awareness of health concepts and organizations, with a big number representing more awareness. Health Behavior Heath behavior was operationalized as a specific aspect of behavior in healthy life style: fruit and vegetable consumption. We focused on the behavior of fruit and vegetable taking because “inadequate consumption of fruits and vegetables is regarded as an important behavioral risk factor for multiple types of cancer” (Cerully, Klein, & McCaul, 2006, p. 103). In our study, fruit and vegetable consumption was a latent variable, measured by the following two questions: “How many servings of fruits do you usually eat or drink each day?” and “How many servings of vegetables do you usually eat or drink each day?” Statistical Analysis We conducted a descriptive statistical analysis on each of the variables in the model. A maximum likelihood structural equation model was built to test the hypotheses. In the model, the O1variables (sociodemographic variables including education, gender, age, and income) were the antecedent variables, associated with the S variable (individual-level complementarity of health information seeking), which, in turn, was associated with the O2 variable (awareness of health concepts and organizations), and the O2 variable was associated with the final R, or the outcome variable (fruit and vegetable consumption). Thus, the sociodemographic variables were the exogenous variables, complementarity of health information seeking and awareness of health concepts and organizations were antecedent endogenous variables, while fruit and vegetable consumption was the outcome endogenous variable. To adjust for the nonnormality of the data and to test path strength, we used Monte Carlo bootstrap (bootstap number = 2000) for the structural equation model.

Results Descriptive Statistics Media Complementarity of Health Information Use With the weighted sample, 32.9% did look for health information. Among those health information seekers, 57.9% reported the Internet as the first information source they went to; followed by books (11.6%); health care providers (9.8%); and brochures, pamphlets, and so forth (9.7%). Also, 41% of information seekers have used more than one source for their most recent health information seeking. Values for the complementarity variable ranged between 0 and 7 (M = 0.54, SD = 0.94). Sociodemographic Variables Table 1 presents information on the sociodemographic variables including gender, age, income and education (see also Tortolero-Luna et al., 2010, for sociodemographic distributions of respondents).

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Y. Tian and J. D. Robinson Table 1. Sociodemographic information Variable Gender Female Male Age (years) 18–34 35–39 40–44 45 or older Income

Media complementarity and health information seeking in Puerto Rico.

This investigation incorporates the Orientation1-Stimulus-Orientation2-Response model on the antecedents and outcomes of individual-level complementar...
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