A content relevance model for social media health information.

CIN: Computers, Informatics, Nursing

& Vol. 32, No. 4, 189–200 & Copyright B 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins

F E A T U R E A R T I C L E

A Content Relevance Model for Social Media Health Information GAYLE LINDA PRYBUTOK, MBA, BSN CHANG KOH, PhD VICTOR R. PRYBUTOK, PhD

Health educators seek to improve public health by reaching targeted populations with timely, accurate, and relevant health information that facilitates healthy behavior change.1 Previously, health educators relied on traditional offline mechanisms like printed media, radio, and television to disseminate health messages.2 However, the health education delivery model moved beyond traditional media when Web 2.0, the interactive Internet, emerged. Web 2.0 technologies are appealing to younger users, who are technologically adept and socially active in these venues.3 In fact, the rapid acceptance and growth of social media on the Internet ‘‘represents one of the fastest uptakes of a communication technology since the Web was developed in the early 1990’s.’’4 Given the growing use of Internetbased health education and intervention programs, it is important to design health messages that are effective in reaching target audiences and in motivating them to change their behaviors.

LITERATURE REVIEW Consumer health informatics (CHI) is the development and implementation of Internet-based systems to deliver health risk management information and health intervention applications to the public.5 The application of CHI to a wide range of public health educational and interventional efforts has garnered attention from both consumers and health researchers in recent years. Kreuter and Wray6 reported that in health communication, targeted messages are those designed to reach a population subgroup based on characteristics that members of the subgroup share. Health communication researchers rec-

Consumer health informatics includes the development and implementation of Internet-based systems to deliver health risk management information and health intervention applications to the public. The application of consumer health informatics to educational and interventional efforts such as smoking reduction and cessation has garnered attention from both consumers and health researchers in recent years. Scientists believe that smoking avoidance or cessation before the age of 30 years can prevent more than 90% of smoking-related cancers and that individuals who stop smoking fare as well in preventing cancer as those who never start. The goal of this study was to determine factors that were most highly correlated with content relevance for health information provided on the Internet for a study group of 18- to 30-year-old college students. Data analysis showed that the opportunity for convenient entertainment, social interaction, health information-seeking behavior, time spent surfing on the Internet, the importance of available activities on the Internet (particularly e-mail), and perceived site relevance for Internet-based sources of health information were significantly correlated with content relevance for 18- to 30-year-old college students, an educated subset of this population segment. KEY WORDS Consumer health informatics & Health education & Information-seeking behavior & Social media

ognize that a thorough understanding of the target audience is a priority in designing relevant health information materials.7 While conventional health communication methods are still successful in reaching some segments of the population, Author Affiliations: Department of Library Information Science, College of Information, University of North Texas, Denton (Ms Prybutok); Department of Information Technology and Decision Sciences, College of Business, University of North Texas, Denton (Drs Koh and Prybutok). The authors have disclosed that they have no significant relationship with, or financial interest in, any commercial companies pertaining to this article. Corresponding Author: Gayle Linda Prybutok, MBA, BSN, Department of Library Information Science, College of Information, University of North Texas, 1155 Union Circle, Denton, TX 76203 ([email protected]). DOI:10.1097/CIN.0000000000000041

CIN: Computers, Informatics, Nursing & April 2014 Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

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they are less effective in communicating health risk management and disease prevention messages to a significant number of younger users. Many within this group have transitioned to heavy engagement in Internet-based socialization and communication. Reliance on the Internet and participation in social networks as a daily routine have made online social interaction a trusted and familiar form of engagement for young users.8 In this study, the role of the Social Interaction construct and the Convenient Entertainment construct in the user’s determination of content relevance for Internet health information was assessed based on work done by Haridakis and Hanson.9 These researchers suggested that social interaction is an activity that drives users to a technology-based social medium like YouTube and that the Internet offers a wide variety of sites that provide a convenient and readily available source of entertainment. Prybutok10 studied the ability of college students to learn about safe sex using two different YouTube videos with the same message content and determined that YouTube was an effective educational medium regardless of message delivery style. The opportunity to reach college students with important health messages delivered via social media venues is significant and worthy of further investigation. Health message delivery style is an important consideration in health communication, and researchers have proposed that message delivery style affects message efficacy and associated behavior change. The Persuasive Health Message Framework11 is based on three theories: the Theory of Reasoned Action,12 the Elaboration Likelihood Model,13 and the Extended Parallel Process Model (EPPM).14 The Theory of Reasoned Action12 suggests that to change a behavior, you must identify and change the underlying set of beliefs. The update of Petty and Wegener15 of the Elaboration Likelihood Model suggested that people who do not think deeply about a persuasive message may display temporary attitude change, but those who think more deeply about a message are likely to show more stable behavior change. Witte14 proposed the EPPM, a fear appeal theory. A fear appeal is a persuasive message designed to scare people by describing the terrible things that will happen to them if they do not follow the recommendations in the message. She believed that people exposed to a threat message respond in one of three ways: danger control, fear control, and low/no threat control. Danger control behaviors provoke recipients to learn more about the threat or to take action to reduce or eliminate it. Fear control behaviors result in message rejection and limited action. Low/no threat control responses often lead message recipients to take no action at all. It is desirable to move a message recipient to a state of danger control, which makes the recipient more likely to process the message and change his/her behavior. Kleinot and Rogers16 demonstrated that fear appeals that had both high levels of threat and high levels of efficacy (ie, proof of the recipient’s ability to avoid threat through a behavior change) resulted in message 190

acceptance. In fact, Witte et al17 reported that messages about health risk are most effective when they are delivered in combination with an action that the user can take to mitigate risk and avoid a negative health consequence. Fear appeal messages are a commonly used health communication message style, and their relevance to 18- to 30-year-old college students is worthy of investigation. The use of humor is also a prevalent topic in health communication research. Paek et al18 examined 934 YouTube smoking-related videos for their use of dramatic audio or visual components that would provoke a sensory or emotional response. They called this Message Sensation Value or MSV, and they ranked each video in their sample on this measure. They classified videos in three categories by the type of message appeal used in the video: threat appeal, humor appeal, or social appeal, and studied user viewing frequency and rating for each type. Threat appeal videos were frightening and emphasized the negative physical or social consequences of ignoring the recommendation not to smoke. Both social and humor appeal videos presented the antismoking message in a way that displayed the positive emotional and physical benefits of complying with antismoking recommendations. The researchers stated that users viewing the videos reported that the humorous videos were their favorite, that they recalled a feeling of warmth and happiness when they watched the videos, and that they were more likely to remember a video with this type of message appeal. Summerfelt et al19 conducted a series of experiments that confirmed that humor is an important factor in stimulating recall of information, which becomes especially important when information is acquired incidentally. Research has also shown that health information is more believable when the associated pictures, videos, music, or problem is familiar and representative of the target population.20 The potential for a humorous or social appeal YouTube video to convey an antismoking health message perceived relevant to 18- to 30-year-olds users is worthy of examination. Therefore, in this study, we presented three antismoking YouTube videos to college student participants in three different message presentation styles. Participants report their responses to each video based on four measures: the ability of the video to encourage a smoking behavior change, the ability of the video to provoke them to share it with friends or family, the ability of the video to entertain the viewer, and the likelihood that the user would remember information delivered via the video.

INFORMATION-SEEKING MODELS Studies of active information seeking abound and there are many theories about information-seeking behavior when users intentionally search for information. Belkin’s21 theory of the Anomalous State of Knowledge proposed that


the search for information is provoked by an individual’s recognition that his knowledge is anomalous (or inadequate) with regard to a goal. Dervin22 suggested that information seeking was a process of ‘‘sense making,’’ where an individual finds meaning in information that is compatible with what he/she already knows, after a series of choices. Kuhlthau23 said that users seek information to fill a cognitive ‘‘gap.’’ However, healthy college students, educated but not aware that they have a need for important health messages about risk management and disease prevention, are less likely to seek information about health issues. In fact, they may not even have a healthcare provider.24 Despite not seeking health information, 18- to 30-year-old college students will benefit from information that provokes healthy lifestyle behavior choices. Their engagement in social media may provide a unique opportunity to present important health education messaging within a venue that they already find enjoyable and memorable. A number of information science researchers have studied the discovery of information when not looking for it. Williamson25(p25) said that people often ‘‘picked up information through [media] sources—information they had not even known that they needed until they heard or read it.’’ Wilson26 suggested that people acquire information accidently while monitoring their environment. Erdelez27(p412) called this type of information acquisition ‘‘information encountering.’’ Smith et al28 found that memorable messages, remembered later, had the ability to influence user behavior. Kelly et al29 reported that users can incidentally acquire a significant amount of health information, and Tian and Robinson30 identified a link between the acquisition of health information on the Internet and incidental information seeking. There are also users of all ages that come across health information on the Internet incidentally, while they are participating in other activities within social media sites. These users represent a new group for whom access to health information can improve, and their activities are worthy of study.

SOCIAL MEDIA AS A HEALTH INFORMATION CHANNEL Social media takes many forms, ranging from well-known sites like Twitter, Facebook, MySpace, and YouTube, to health blogs and Internet-based medical support groups. Social media venues offer anonymity for young users who want to privately explore sensitive health topics.31 Social media also offers users a chance to share their opinions and ideas.32 Scale33 suggests that young users’ familiarity and sense of security in exchanging personal information on these sites can make these venues attractive to 18- to 30-year-olds in general and to the segmented study population of 18- to 30-year-old college students targeted in this investigation. In this study, the construct Impor-

tance of Internet Activities is assessed based on work by Weaver et al,34 who recognized that the Internet offered users a single source of a wide variety of possible activities, which could be instrumental in health communication. The opportunity to use social media venues to communicate health risk management and disease prevention messages to targeted segments of the population (18- to 30-year-old college students) is clear. The Centers for Disease Control and Prevention35 published the Social Media Toolkit in 2011 to guide health educators in the use of a variety of social media venues to present health information messages. As a result, health educators have identified social media as viable venues for health information delivery for young adults, aged 18 to 30 years. They have explored a number of social media venues to determine which venues are most able to reach consumers in different age groups with important health messages and which are most relevant for the topic area under discussion. These users are already using YouTube and other social media venues for entertainment, social engagement, relaxation, and information exchange,3,36 making it more likely that they will encounter important health information posted on these sites.37 For this study, the Surf Time construct, or the amount of time that college students spent surfing the Internet weekly, was assessed using a single question that was developed based on the work of Weaver et al.34 They noted that the amount of time users spent seeking information online surfing the Internet each week increased the users’ exposure to all kinds of information. These venues can deliver health information cost effectively and in real time and can reach a high volume of users simultaneously.38 Through social media, young users can access the most up-to-date health information available for making important health decisions, can communicate with others coping with the same issues, and can assume responsibility for managing their own health.39 Social media transcends differences in race, ethnicity, educational status, and healthcare access to reach users.24 Haridakis and Hanson9 investigated how and why users choose a particular venue within social media. They determined that people select social media venues that satisfy their needs at the time and that are compatible with their personalities and social interaction styles. To determine whether college students in this age group find health information content presented via social media relevant, it is important to investigate what college student user characteristics correlate with relevance determination among this segment of 18- to 30-year-olds. This article examined factors that increase the likelihood that 18- to 30-year-old college students will deem health information content presented on social media relevant. These include user demographics, the desire for convenient entertainment, the desire for social interaction, health information-seeking behaviors, time spent surfing the Internet, the importance of various Internet-based


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activities, and user perception of relevance for Internet sites that provide information. This study takes place within the context of the delivery of antismoking messages to college student Internet users in this at-risk age group, and this investigation has implications for the delivery of health information that 18–30 year old college students are not necessarily seeking but that can contribute to their longterm health and wellness. Content relevance has been an important topic in the educational and motivational literature for quite a while. Keller’s40,41 Attention, Relevance, Confidence, and Satisfaction (ARCS)Theory of Motivation suggests that students will be internally motivated to learn if their attention is captured by the educational material, if content is deemed personally relevant, and if students are confident in their ability to learn and use the information presented. Content relevance is essential in effective health message communication when behavior change is the goal. We know that for health information presented in social media venues to be relevant to 18- to 30-year-old college students, a positive user perception of site relevance and content relevance is essential. Marton42 said that the investigation of specific user groups would aid in the creation of context-specific and relevant health information Web sites. In this study, the construct Perception of Site Relevance is assessed based on Marton’s42 examination of user relevance perception for various sources of health information on the Internet. This is consistent with the User-Centered Theory of Relevance of Schamber and Eisenberg,43 which proposed that relevant information is information that the user finds satisfying because it is specific to the user’s current situation.

THE LINK BETWEEN SOCIAL MEDIA AND SMOKING-RELATED CANCER PREVENTION Tobacco is the primary cause of preventable disease and early death in the United States, causing more than 400 000 premature deaths each year, or almost one of every five annual deaths.44 Between 1999 and 2004, physicians diagnosed 2.4 million smoking-related cancers.45 Jha45 also reports that scientists believe that smoking avoidance or cessation before the age of 30 years can prevent more than 90% of smoking-related cancers and that individuals who stop smoking fare as well in preventing cancer as those who never start. Therefore, a significant way to successfully reduce the number of smoking-related cancer deaths is to encourage current young smokers to stop and to prevent young people from developing this dangerous habit.45 The effectiveness of using social media to prevent smoking or to convince smokers younger than 30 years to quit is related to the information-seeking behavior of targeted segments of this population of users.45 The information’s effectiveness is dependent partially on its ease of access 192

and availability to this group of users, who may not be intentionally seeking information related to smoking cessation/prevention. Based on Jha’s45 report, this research will explore the use of social media as an antitobacco health information channel for college students, aged 18 to 30 years. Internet users in this special population may or may not see a social media venue as a viable health information channel. Also, this at-risk population may not be seeking antitobacco health information, because as young, healthy people, they do not consider themselves at risk; because they do not use tobacco and have no interest in learning about risks associated with it; or because they currently use tobacco and do not want to stop. However, this preventive message is one that can greatly reduce their risk of developing smoking-related cancers. This study will guide health educators in framing important health messages in a video message presentation style identified by college student participants as most likely to encourage them not to smoke or to quit smoking, most likely to motivate them to share the video with others, and most likely to help them to remember the message.

THE IMPORTANCE OF USER CHARACTERISTICS An extensive review of the literature identified user characteristics that may correlate with the perception of content relevance among 18- to 30-year-old users for antismoking health information presented via social media (YouTube videos). Items from validated instruments in each of the studies below were selected for inclusion in the ‘‘Internet and Me’’ survey created for this study. The variables and their sources appear below, and the number of items contextualized for use in the survey from each of the identified studies is shown in Table 1. Chou et al,24 who studied social media use in the United States using the Health Information National Trends Survey, 2007, found that user demographics were an important factor. Social media sites appeal to the largest proportion of Internet users. In addition, they reported that 76.4% of 18- to 24-year-olds and 57.3% of 25- to 34-year-olds were active social media users. Social media use patterns varied by race, with white Americans using social media less frequently than nonwhites were. Also, these social media users were young and generally healthy and were thus less likely to have a regular medical provider. Consistent with these findings, age, gender, marital status, race, educational level, and family income were included as demographic measures in the survey. Escoffery et al46 studied the use of the Internet to acquire health information among college students. They found that college students spent significant amounts of time on the Internet each week and were willing to use the Internet as a source for health information. College


T a b l e 1 Summary of Variables and Sources Construct Demographics Content Relevance (dependent variable)

Definition

Source

Age, sex, race, marital status, household income, education level Perception that information content is meaningful and useful to the user

6 items

Adapted 4 items from Anonymous (2011), a conference paper submitted to the International Communication Association, to measure the construct Content Relevance Selected 13 items measuring the construct Convenient Entertainment These items explore the role that the Convenient Entertainment from Haridakis (independent variable) availability of convenient entertainment and Hanson (2009) on the Internet plays in the user’s determination of content relevance. Social Interaction (independent These items explore the role that social Selected 2 items measuring the construct variable) interaction plays in the user’s Social Interaction from Haridakis and determination of content relevance. Hanson (2009) Adapted 4 relevant items from a 6-item Health Information Seeking An assessment of the impact of time scale by Weaver et al (2009) to measure (independent variable) spent seeking various types of health the Health Information Seeking information on the Internet on Behavior construct the user’s determination of content relevance A single item developed in the manner of Surf Time (independent variable) Amount of time in a typical week that Weaver et al (2009) for this survey to the participant spends surfing the measure Time Spent Surfing the Internet Internet (apart from work and school responsibilities) and its role in the determination of content relevance Importance of Internet Activities The importance of 6 different activities Adapted 6 items from Weaver et al (2009) (independent variable) on the Internet to determination that appeared relevant to this study to of content relevance measure the construct Importance of Internet Activities Perceived Site Relevance The relevance of various sources Contextualized 13 items of Marton’s (2003) (independent variable) of health information to the user’s perceived relevance of health information determination of content relevance sources to fit this study and measure the construct Perceived Site Relevance

students in this age group relied on a variety of Internet sites that they considered credible for health information. In fact, ‘‘overall, 74% of these college students reported that at some time they have received health information online and more than 40% reported that they frequently searched the Internet for information’’ using search engines and a variety of different sites.46(p183) Weaver et al34 also evaluated health information–seeking behaviors and found differences among adult users based on the type of health information that they were seeking (wellness information vs information related to illness) and the users’ perception of their own health at the time that they looked for health information on the Internet. In this study of college students, the impact of the construct Health Information Seeking, which relies on an assessment of time spent seeking various types of health information on the Internet, is based on work done by Weaver et al.34 The research team believed that how often users accessed various types of Internet-based health information sources was an important factor in understanding the health informationseeking process.

Marton42 examined the role that contextual or situational relevance played in users’ perception of information trustworthiness and authoritativeness. She argued that user relevance perception of health information presented on Internet sites would be improved if health information is tailored to reflect the context in which health information seeking occurs. Context includes factors like ‘‘health status, age, gender, sexual orientation, income, nationality, ethno-racial identity, and cultural and religious beliefs concerning health and illness.’’42(p204) In this study, demographic information was collected from a study population of college students, and the construct Perceived Site Relevance was examined based on Marton’s work. The unknown authors47 of ‘‘Reflecting Students’ True Perceptions of Content Relevance: A Revised Content Relevance Scale’’ presented their work as a conference paper submitted to the 2011 International Communication Association. This work contributed a new view of content relevance that examined whether students’ perceptions of content relevance were based on the relevance of material presented or the belief that the information provider


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(instructor or, in this case, information source) made the content relevant. In this study, Content Relevance was assessed based on this work. This is a worthy consideration in health communication when determining where to place important health information on the Internet so that it most effectively reaches college student users. We propose the following model of the correlation between the dependent variable (content relevance) and the independent variables shown above (Figure 1).

RESEARCH HYPOTHESES The following hypotheses were tested using correlation analysis and multiple regression analysis to explore the multivariate relationship between the independent variables and the dependent variable of content relevance. Ha1: There is a positive correlation between Convenient Entertainment and content relevance. Ha2: There is a positive correlation between Health Information Seeking and content relevance. Ha3: There is a positive correlation between Social Interaction and content relevance. Ha4: There is a positive correlation between Site Relevance and content relevance. Ha5: There is a positive correlation between Internet Engagement as measured by Surf Time and Importance of E-mail and content relevance.

RESEARCH METHODOLOGY Approval was received from the University of North Texas (UNT) Institutional Review Board, ensuring compliance with ethical guidelines for the conduct of research. An online Qualtrics (Qualtrics Labs Inc, 2009; Provo, UT) survey

instrument was disseminated to college student participants via an Internet link. Qualtrics is an Internet-based survey vehicle available to academic departments conducting research at UNT. Qualtrics allows the creation of custom surveys that are available to participants electronically, via a link embedded in an e-mail posted in Blackboard (Blackboard, Washington, DC), the university’s learning management system. To create the survey, items were drawn from the studies identified in Table 1. The six demographics collected in the survey were gender, age, educational level, race, marital status, and average family income. These basic demographics were collected to allow comparisons of incomplete survey respondents with those that were complete. The comparisons showed no significant differences. Survey items that measured Internet Social Media Use Behavior were drawn from several studies. The construct Convenient Entertainment was measured in 13 dimensions adapted from Haridakis and Hanson.9 The construct Time Spent on the Internet was measured in four dimensions drawn from Weaver et al.34 The construct Time Spent Surfing the Internet was measured using a single question that was developed in the manner of Weaver et al.34 Site Relevance of health information sources was measured using 13 Perceived Relevance dimensions found in Marton.42 Content relevance for the three YouTube antismoking videos was measured in four dimensions drawn from Anonymous,47 a conference paper submitted to the International Communication Association. After viewing each video, college students were asked to answer four questions about each video. The questions were designed to determine (1) if the antismoking message presentation style (fear control message, humorous message, or social message) would encourage respondents to avoid smoking or consider quitting if they already smoked, (2) if the video would motivate them to share a link to the

FIGURE 1. Internet Health Information Content Relevance Model.

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video with family members or friends, (3) if they found the video entertaining, and (4) if they would remember the message presented in the video. Once the survey instrument was created based on the items discussed above and identified in Table 1, the investigator searched YouTube to identify three antismoking videos to include in the survey instrument. The videos were included to assess participants’ perception of content relevance based on the message delivery style of each video. Using the search term antismoking videos, the investigator reviewed available YouTube videos that delivered educational content about smoking prevention/cessation. The initial search yielded 145 000 results. The search filters were as follows: short, G 4 minutes, and video and were added to the base search term, and this resulted in a selection of more than 85 000 videos. The goal was to identify three tasteful and highly viewed antismoking videos with three different message delivery styles: one fear control message, one humorous message, and the third social, but effective. Additional search filters were added to isolate each type of video. First, the term scary was added to the search term antismoking videos to search for a video that contained a fear appeal message. When none of the search results were satisfying, a new search strategy in which sad was added to the search term antismoking videos was implemented to identify a video with a fear control message. The fear control video, entitled ‘‘Smoking Kills, the Bryan Curtis Story,’’ is found at https://www.youtube.com/watch?v=dVLtNgAhPRg. The video was uploaded to YouTube on May 17, 2009, and an article with the same story was published in the Saint Petersburg Times (Australia) by Sue Landry on June 15, 1999, and posted on www.WhyQuit.com (http://whyquit .com/whyquit/bryanleecurtis.html) on July 15, 1999. The available video statistics showed that on the selection date, the 3-minute video had 1 630 331 views. An additional search term, award winning, was added to the search term antismoking videos in an effort to locate a humorous or entertaining video that would engage viewers. The humorous video, entitled ‘‘Award Winning Antismoking Commercial,’’ is found at https://www.youtube .com/watch?v=JndtG8Y7yfw and on the selection date had 483 173 views. It was uploaded to YouTube on August 2, 2007, and is 2 minutes long. The additional search term funny was added to the search term antismoking videos to find a social appeal video that was engaging but that communicated the antismoking message effectively. The social appeal video, entitled ‘‘Funny Campaign against Smoking,’’ is found at https://www.youtube.com/ watch?v=mz0N-jVrRWU and was uploaded to YouTube on November 7, 2006. It is 1 minute 1 second long and on the selection date had 470 070 views. The investigator embedded these videos at the end of the survey, and college students were asked to respond to the same four questions about each video. The goal in this

portion of the study was to identify the message presentation style perceived by college students in the target age range as most content relevant. The survey instrument was administered to 250 undergraduate and graduate students at a large southwestern university who were enrolled in College of Business classes during the spring 2013 semester. Participants provided informed consent on the initial screen of the Qualtrics survey instrument, prior to accessing the Qualtrics survey questions. During the designated study period, students could access the survey via an Internet link at a time that was convenient, and their instructors offered them extra credit for participation. At the completion of the study period, 250 surveys were collected from a potential pool of 430. This represents a response rate of 58%. During data cleaning, 20 respondents were removed because they were out of the age range designated for the study. Eighteen respondents were removed because the survey instrument was incomplete, and 10 more were removed who completed the survey in too little time (G 10 minutes) or who had distinct patterns in their survey responses that indicated that they had not actually read the questions. After data cleaning, the results of 202 surveys were analyzed, which represents a usable response rate of 46.9%. This response rate limits the possibility of nonresponse bias.

DATA ANALYSIS Data were analyzed using SPSS predictive analytics software (v 20, 2011; IBM, Armonk, NY). The demographics of the college student participants were examined. That examination shows that 44.1% of the study participants were women and 55.9% were men. Of the 202 participants, 40.1% were between 18 and 20 years of age, 50.5% were between 21 and 25 years of age, and 9.4% were between 26 and 30 years of age. Ninety-eight percent of the participants were undergraduates, with only 2% of participants being either graduate students or other. The racial distribution of participants was as follows: 12.4% African American, 9.9% Asian/Pacific Islander, 56.9% white, 16.8% Hispanic, and 4% other. As anticipated, 92.6% of survey respondents were unmarried, while 6.4% were married and 1% was divorced. Data on annual family income also were collected, and annual income was less than $30 000 per year for 37.6% of the participants, $30 000 to $60 000 per year for 18.8% of respondents, $60 000 to $90 000 for 17.3% of respondents, $90 000 to $120 000 for 11.9% of respondents, and over $120 000 for 14.4% of respondents. Exploratory factor analysis was conducted on the items for the dependent variable (content relevance) as well as on the items for each video using principal component analysis with a Varimax rotation. Factor loadings were almost all above the recommended threshold of 0.7, which supports the discriminant validity of the measures.48 One item was


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Ta b l e 2 Cronbach’s ! and Factor Loadings for the Dependent and Independent Variables Dependent Variable and Video Rating Constructs Construct Name and Reliability Content Relevance, ! = .933

Fear control message video Fear control message, ! = .719

Humorous message video Humorous message, ! = .843

Social message video Social message, ! = .886

Questions (Item Name)

Factor Loadings

The information within the videos made the content relevant to me. (CONREL) The information in the videos made it explicitly clear how the material related to me or to my life in general. (CONCLR) The information in the videos allowed me to apply the content to my own interest. (CONAPLY) The information provided in the videos helped me to understand the importance of the content. (CONIMPT)

0.89

The video above would encourage me to avoid smoking or to consider quitting if I already smoked. (BCAVOID) The video above would motivate me to share this video link with a friend or family member. (BCSHRE) I found the video above entertaining. (BCENTTN) I would remember the message of this video. (BCRMBR) The video above would encourage me to avoid smoking or to consider quitting if I already smoked. (BCAVOID)

0.873

The video above would encourage me to avoid smoking or to consider quitting if I already smoked. (HUMAVOID) The video above would motivate me to share this video link with a friend or family member. (HUMSHRE) I found the video above entertaining. (HUMENT) I would remember the message of this video. (HUMRMBR)

0.845

The video above would encourage me to avoid smoking or to consider quitting if I already smoked. (INDAVOD) The video above would motivate me to share this video link with a friend or family member. (INDSHRE) I found the video above entertaining. (INDENT) I would remember the message of this video. (INDRMBR) The video above would encourage me to avoid smoking or to consider quitting if I already smoked. (INDAVOD)

0.88

0.929 0.924 0.908

0.85 0.39 0.844 0.873

0.89 0.689 0.87

0.914 0.783 0.872 0.88

Independent Variables Associated With Convenient Entertainment, Site Relevance, and Health Information Seeking Behavior Construct Name and Reliability Convenient Entertainment (CONNTAV2), ! = .935

Questions (Item Name) Because it is entertaining (CONENT1R) Because it amuses me (CONENT2R) Because it is enjoyable (CONENT3R) Because it is fun just to play around and check things out (CONENT4R) I just like to use it (CONENT5R) When I have nothing better to do, I use it (CONENT9R) Because I can use it anywhere, anytime (CONNT12R) I like to see what’s out there (CONNT13R)

Factor 1

Factor 2

Factor 3

0.834 0.874 0.866 0.896 0.871 0.684 0.779 0.749 (continues)

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T a b l e 2 Cronbach’s ! and Factor Loadings for the Dependent and Independent Variables, Continued Independent Variables Associated With Convenient Entertainment, Site Relevance, and Health Information Seeking Behavior Construct Name and Reliability Health Information Seeking (TimeAve), ! = .896

Perceived Site Relevance of health information sources (RVTMedAv), ! = .892

Questions (Item Name) Time spent on the Internet obtaining health information (TIMEHI) Time spent on the Internet obtaining diet information (TMEDIET) Time spent on the Internet obtaining exercise information (TIMEEX) Time spent on the Internet obtaining medication information (TMEMED) Websites (RVTWEBR) Pamphlets (RVTPAMPR) Newspapers or Magazines (RVTNWSR) Books (RVTBKSR) Television Programs (RVTTVSHR)

retained below 0.7 for theoretical considerations when making the comparison. In this manner, the measures for the three videos remained the same among the three videos. As a result, the item BCENTTN (measuring whether the fear control video was entertaining), with a factor loading of 0.390, which was below 0.7, was retained because that item was above 0.7 on the other two videos and the intent was to use the same measure for all three videos. The Cronbach’s ! values supported internal reliability because all were above the minimum value of 0.7 and most were above the preferred value of 0.8.49 Table 2 shows the Cronbach’s ! value and factor loadings associated with the dependent variable and video rating constructs. The exploratory factor analysis with the independent variables was conducted separately from the dependent variable. The results in Table 2 show the final items after item removal when cross-loadings exceeded 0.5 and loading on the main factor exceeded 0.5 for the CONENT (Convenient Entertainment), Time (Time Spent), and RVT (Site Relevance of Health Information Sources) constructs allowed developing three independent one-dimensional constructs.49 Table 2 shows the rotated factor matrix that resulted from using principal component analysis with a Varimax rotation. Variables with the letter R at the end were reverse coded so that 7 was a positive response on a 7-point Likert scale, using the coding scheme 8 minus the original response. The mean ratings for the videos were compared using analysis of variance (ANOVA). The ANOVA results show a statistically significant difference (P = .006) among the three means. Tukey multiple comparison procedure found that BC (the fear control video) was significantly different than HUM (the humorous video) but that IND (the social message video) was not different from either of the other two

Factor 1

Factor 2

Factor 3 0.834 0.904 0.885 0.839

0.837 0.826 0.82 0.824 0.809

videos. In addition, BC (the fear control video) was the highest mean rating and HUM (the humorous video) was the lowest. Before conducting the analysis, all items that were reverse scored were recoded so that each item was scored with 7 as a positive response (8 minus the original response). This was done so that the calculation of the means and interpretation of the regression results would be more intuitive. As a result, a positive regression coefficient shows a positive relationship between the construct and content relevance. A regression model using content relevance as the dependent variable and the three videos as the independent variables allowed determination of the relationship between the videos and perceived content relevance. The results obtained using SPSS v 20 showed that all three videos were significantly related to content relevance and explained 52.3% of the variance in content relevance. Examining the standardized betas shows that the IND (social message video) had the highest correlation with content relevance. This result is worthy of further investigation because IND (the social message video) did not have the highest mean among the three videos (Table 3). The theoretical model was tested using a regression model with content relevance as the dependent variable and Social Interaction (SocIntAv), Importance of Internet Activities (EMAILR), Surf Time on the Internet (SURFTM), Convenient Entertainment (CONNTAV2), Health Information Seeking (TimeAve), and Perception of Relevance (RVTMedAv) as the independent variables. This analysis allowed determination of the relationship between these variables and perceived content relevance. The results showed that the group of variables was significantly related to content relevance and explained 25.9% of the variance in content relevance. The regression results show


197

Ta b l e 3 Regression Model Using Content Relevance as the Dependent Variable and the Three Videos as the Independent Variables Model

R a

1 F test for the model

0.723 F calculated = 72.4

Unstandardized Coefficients

R2

Adjusted R 2

SE of the Estimate

0.523 P = .000

0.516

0.96422

Standardized Coefficients

B

SE

"

t

P

1.029 0.237 0.145 0.416

0.312 0.069 0.057 0.067

.204 .175 .453

3.297 3.437 2.535 6.212

.001 .001 .012 .000

Model 1 (Constant) BCAve HUMAve INDAve a

Dependent variable: CONRELAve.

that each of the independent variables was significant at the 5% level. Examining the standardized betas shows that EMAILR followed by RVTMedAv had the highest correlations with content relevance (Table 4).

STUDY RESULTS This study focused on a segmented target population of 18- to 30-year-old college students because college students in this age group are most likely to benefit from antismoking health messages presented via Internet social media. The goal of the study was to determine factors that were most highly correlated with content relevance for health information provided on the Internet for college students in

this age group. Data analysis showed that the opportunity for convenient entertainment, social interaction, health information-seeking behavior, time spent surfing on the Internet, the importance of available activities on the Internet (particularly e-mail), and perceived site relevance for Internet-based sources of health information were significantly correlated with content relevance for 18- to 30-year-old college students. This confirms previous studies that have shown that users in this age group spend a significant amount of time on the Internet engaged in a variety of activities. The view that the degree of social interaction available through social media is a strong draw to social media sites has been confirmed in this study. E-mail is used universally both by individuals and businesses, and its ease and efficiency contribute to the level of comfort

Ta b l e 4 Regression Using Content Relevance as the Dependent Variable and SocIntAv, EMAILR, SURFTM, CONNTAV2, TimeAve, and RVTMedAv as the Independent Variables Model

R a

1 F test for the model

0.509 F calculated = 11.4

Unstandardized Coefficients

R2

Adjusted R 2

SE of the Estimate

0.259 P = .000

0.236

1.21112

Standardized Coefficients

B

SE

"

t

P

0.746 0.121 0.207 j0.119 0.247 0.253 0.263

0.625 0.051 0.058 0.053 0.090 0.118 0.079

.159 .228 j.144 .195 .142 .225

1.194 2.371 3.564 j2.239 2.742 2.139 3.315

.234 .019 .000 .026 .007 .034 .001

Model 1 (Constant) SocIntAv EMAILR SURFTM CONNTAV2 TimeAve RVTMedAv a

Dependent variable: CONRELAve.

198


that college students in this age group have for Internetbased communication. E-mail was the Internet activity most highly correlated with content relevance, followed by Site Relevance Perception. The results confirm that social media venues are viable health communication channels with the ability to reach 18- to 30-year-old college students with important health messages. College student participants were asked to provide their reactions to three YouTube antismoking videos with different message presentation styles to assess, first, whether users in this age group would find YouTube videos with embedded health messages content relevant, and second, to assess if they had a preferred message presentation style. Data analysis showed that all three videos and message presentation styles were significantly correlated with content relevance, confirming again that YouTube can be an effective and accessible health information channel for college students in this age group. Interestingly, the social message video showed the highest correlation with content relevance, even though the fear control video had the highest mean. The exploration of message delivery style for health information messages continues to be a worthy area for future study.

STUDY SIGNIFICANCE This research can make a valuable contribution to the current body of knowledge in healthcare education and can extend the reach of current educational efforts to college students. As technology continues to drive clinical practice, so must improvements in information technology guide the delivery of important health education messages and help health educators to bring these messages to the public through the creative and effective use of evolving information channels. Armed with a greater understanding of the user characteristics that correlate with user content relevance perception, educators can design and present health messages that engage users and positively affect health behaviors for segmented population groups.

LIMITATIONS Several limitations to this work should be noted. As a quantitative study, this work is intended to produce results that inform health educators about the characteristics of at-risk college students in the target age group that correlate positively with user perception of relevance for health information presented on social media. The use of college students as the research sample may not allow the results to be generalized to the general population of 18- to 30-year-olds. While this issue is partially addressed by the diversity of the student population at UNT, an important area for future work will be to verify the results using a larger

and more general population sample of 18- to 30-year-old college students and to compare results from this segment of the population with results from a non–college student segment of 18- to 30-year-olds. This study does not offer health educators specific guidelines for which social media venue works most effectively for users of different ages and does not distinguish between the nature of the health information presented when a venue is selected. The venue chosen for the presentation of health information related to a specific disease process may differ greatly from the venue selected to present wellness information. The need for privacy related to a health concern is also not addressed in this study, and the current issues associated with health information accuracy when sites are not monitored by medical professionals is very real and should be addressed in future work.

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