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date: 20 July 2017

Video Games and Gaming: Reaching Audiences With Health and Risk Messages

Summary and Keywords

Video games are a very popular form of entertainment media and have been the subject of much debate since their meteoric rise to popularity in the 1980s. Similar to the criticisms leveraged against other forms of media, video games have often been scrutinized for their potential to negatively influence those who play them. However, since the beginning of the 21st century, many new genres of video games have emerged and as a result, both public dialogue and research attention have shifted more toward understanding how certain games can be used for prosocial purposes. Exercise-based and active video games (AVGs) are a type of game which requires players to get up and move instead of simply sitting in front of the TV and pushing buttons. These types of games have received a lot of popular press and scholarly attention due to the fact that they encourage movement and may be used as a health intervention tool, especially to combat problems like obesity and overweight. Even though there has been significant research attention focused on the potential health benefits of playing these types of games, there is still much work to be done. While researchers have advanced a general understanding of why certain AVGs are effective or ineffective, there needs to be a greater emphasis on understanding the process by which these games can be motivating and influential. Shedding light on what makes AVGs potentially effective health management and intervention tools will not only be important for motivating people to become more active, but may also help inform research which focuses on how video games may be used in the health domain more generally.

Keywords: active video games, health, physical activity, exergaming, health and risk message design and processing

Introduction: The Rise of Research Focusing on Video Games for Health Purposes

Commercial video games first appeared in the late 1970s; since then, they have become as popular and ubiquitous as most other types of media. According to the Entertainment Software Association (2016), nearly 63% of all U.S. households have at least one person who plays video games regularly, and total consumer spending on video games topped out at $23.5 billion in 2015. Once considered a niche market characterized by significant volatility, the sustained success of the video game industry suggests that it will be part of our media usage patterns for many years to come. Almost simultaneously, the rise in video game popularity has been accompanied by an increasing interest in how video games influence those who interact with them.

Even though there are many different types of people who play video games, a diversity of game content, and a range of different playing experiences, the majority of the empirical research focusing on the effects of games has largely centered on negative consequences or outcomes resulting from the playing experience. For example, researchers have illustrated that there is a link between playing violent video games and aggression, addiction, and other antisocial behaviors (Anderson, Bushman, Donnerstein, Hummer, & Warburton, 2015; Gentile, Lynch, Linder, & Walsh, 2004). Another common criticism of video game play (and media use in general) is that it encourages inactivity and sedentary screen time (Stettler, Signer, & Suter, 2004; Vandewater, Shim, & Caplovitz, 2004), which can lead to obesity and other problems associated with inactivity. Although research focusing on the negative aspects of gaming is quite pervasive and will likely continue to receive attention in the future, there has been a shift in recent years toward understanding how video games can be used to encourage and promote healthy behaviors.

The literature on prosocial uses of video games is not as old or voluminous as that which has focused on negative effects of gaming. Studies in this area first began to appear in the late 1980s. Baranowski, Buday, Thompson, and Baranowski (2008) conducted a meta-analysis of 27 studies focusing on the effectiveness of health games and found that almost all of the studies (93%) provided evidence that playing video games can positively impact learning, behavior, attitude change, and even weight management. Even though there are still many important questions that remain unanswered, there is agreement that video games which are meant to facilitate health-related behavioral change are generally successful in doing so across a variety of different domains and populations. As a subset of the broader health games literature, there has been increased emphasis on understanding how active and exercise-based video games might be used to help combat obesity and encourage physical activity (PA), while acting as a catalyst for an overall healthier lifestyle. While recent research has illuminated the potential benefits (or lack thereof) of AVGs, scholars have stated that many important questions pertaining to the overall value and effectiveness of these games are still in need of research attention (Baranowski, 2015).

Research focusing on the effectiveness of AVGs and other health-driven video games has been heavily shaped by classic theories of psychology, persuasion, and the media. Often, theory is used to guide everything from the development of the main questions of interest, to the design of the study, and the interpretation of the results. Despite the fact that this approach has been very fruitful, and has led to a greater understanding of how video games might influence different health outcomes, there has been relatively little effort to organize and reflect upon the key variables which help us to understand why these games may or may not be effective. Therefore, the purpose of this article is to provide a general overview of the theories and variables that are important to consider when conducting research on AVGs, and by extension, health games in general. This synthesis of the literature will provide researchers with a common framework to consider when analyzing the design of active and health-related video games, and outcomes associated with playing. Before delving deeper into theory and key variables associated with this area of research, basic definitions and examples of different health-based video games and AVGs will be discussed.

Types of Active Video Games: Terminology and Parameters

“Serious games” is the broad umbrella term used to refer to any video games that transcend basic entertainment or enjoyment value and instead focus on something greater, such as learning, intervention, social change, or health outcomes. While there are many different ways to define serious games, especially because they vary in terms of their focus and scope, any game that blends aspects of simulation and learning with entertainment can be categorized as a serious game (Ritterfeld, Cody, & Vorderer, 2009). In an analysis of over 600 serious games, Ratan and Ritterfeld (2009) developed a classification and found that most of these games focus on skills practice, knowledge gain through play, and cognitive and social problem solving. Furthermore, these researchers found that a majority of serious games were designed and deployed on computer-based platforms rather than game consoles. To summarize, almost any game that is designed with a specific goal in mind, such as educating an at-risk population about a dangerous behavior or trying to alter behaviors and beliefs, can be regarded as a serious game. While AVGs that encourage PA can be considered as a subset of the serious game genre, the game catalogue itself is filled with a variety of unique games that vary in their characteristics, focus, scope, and goals.

Since the release of the Nintendo Wii in 2006, there has been a steady rise in research related to commercially available games that promote PA. This is primarily because these games have become rather commonplace to both casual and regular video game players. These games are sometimes referred to as “exergames” and more generally as “off-the-shelf” AVGs (Peng, Lin, & Crouse, 2011, p. 681). In contrast to video games with have a more serious emphasis, AVGs are often categorized as having a more “casual” orientation (Juul, 2009a). Casual, commercially available AVGs can encompass anything from dancing (e.g., Dance-Dance Revolution; Just Dance) to adventure/sports titles (e.g., Fifa Soccer; Kinect Adventures; Wii Sports), or games that focus specifically on exercise (e.g., Wii Fit; Biggest Loser; Shape Up). Regardless of the specific purpose of an AVG, they all share the commonality of encouraging movement or PA among those who interact with them.

For most of the studies that focus on using video games for “serious” outcomes and as intervention tools, the action of playing the game is often what researchers hope will exert influence on some real-world attitude, behavior, or thought process. Furthermore, researchers hope that this influence will be stable and lasting, even in the absence of the stimulus game. For example, researchers have used traditional and virtual reality exposure therapy treatments to determine their efficacy in helping to treat post-traumatic stress disorder (PTSD) and found no differences in traditional treatments versus the interactive virtual reality (VR) treatments (McLay, McBrien, Wiederhold, & Wiederhold, 2010). Though no differences were found between the two types of treatment, the researchers did not employ a design where participants were able to have continued access to the VR treatments. Consequently, there is a possibility that PTSD-affected participants would continue to use VR treatments if given the opportunity. Therefore, while it is important to understand how games can be used to change behavior, it is similarly important to understand “motivation,” or more simply, what keeps people playing.

Rosser, Lynch, Cuddihy, Gentile, Klonsky, and Merrell (2007) found that surgical residents who were more skilled at video games were actually better at suturing and performing laparoscopic procedures than those who were not as skilled at the games. As evidenced by this study, one behavioral outcome (surgical skill) is influenced by another behavior (continued use of video games). What separates commercially available AVGs from the broader serious game category is the fact that playing an AVG invites the user to perform and replicate the behavior (exercise/PA) that is ultimately desirable. This is a significant distinction because continuing to play an AVG is itself an important outcome. In comparison to video games that are simply used to spread knowledge or for intervention (where continued play may not be an important outcome), sustained use or motivation to continue playing an AVG can lead to outcomes such as weight loss, or physical, cognitive, or emotional well-being.

Given that playing an AVG is itself a healthy outcome, understanding what people find appealing about these games and what keeps them motived to play becomes of the utmost importance. Though there has been considerable research on AVGs to date, there has been relatively little effort to organize the variables that are meaningful in this context in a clear and systematic way. It is important for researchers to continue to investigate what properties of AVGs are appealing as well as how individual differences and gameplay experiences might moderate or mediate physiological, cognitive, affective, and behavioral outcomes.

Key Variables for Analyzing Video Games that Encourage Physical Activity

The literature on AVGs is quite vast and intersects with many different disciplines. Figure 1 provides a general framework of the key variables that have commonly been used in studies that investigate how AVGs encourage PA and other outcomes. The variables that appear in the figure are supported by a subset of studies that are discussed later in this article. In the figure, the variables are arranged in a way that reflects how they are often conceptualized in AVG research, even though practical reasoning or theory is what usually provides the structure of each research study and the questions that it is trying to address. The framework delineates between independent and dependent variables in AVG research and also discusses potential moderating and mediating influences. It is important to note that the general framework depicted in figure 1 is not meant to be a complete representation of all the variables that could be influential in understanding how people respond to AVGs, and their utility in promoting health outcomes. Also, it is important to note that the way the variables are grouped can change based on theory and the design of a particular study. For example, a researcher could study how variations in a particular AVG directly influences enjoyment of the playing experience, thus making enjoyment the dependent variable. Another researcher could propose a model where enjoyment is a mediating variable that explains the relationship between variations in a given AVG and some outcome such as energy expenditure or motivation. Similarly, a researcher could also explore moderating influences (such as personality traits or game-playing experience) to see if any of these moderating variables change the relationship between independent and dependent variables.

The figure describes independent variables (varying characteristics of the game, exposure, types of game play, and other factors) that influence the dependent variables, which are essentially composed of psychological and physiological responses. This framework is not meant to be a causal map or to imply causal influence among the variables, but instead is meant to provide a point of organization of variables that commonly appear in different research on AVGs. Before discussing the literature which supports the inclusion of these different variables in the framework, some of the theories which are most commonly applied to investigating AVGs will be highlighted.

Video Games and Gaming: Reaching Audiences With Health and Risk MessagesClick to view larger

Figure 1. A Framework of Commonly Explored Variables in AVG Research

Theories Commonly Applied in AVG Research

Most research focused on understanding the utility and appeal of active and exercise-based video games is grounded in theory (Papastergiou, 2009). Though there are many different theoretical perspectives that have been applied to help understand the effectiveness and appeal of AVGs, two of the more prominent are self-determination theory and social cognitive theory (Kooiman & Sheehan, 2015a). A brief synopsis of these two theories, as well their specific application in AVG research and other domains, follows.

Self-determination theory (SDT) is a meta-theoretical perspective which focuses on understanding how different social and environmental factors either facilitate or inhibit intrinsic or extrinsic motivation across a variety of domains (Deci & Ryan, 1985, 2008). Accordingly, intrinsically and extrinsically motivated behaviors are operationally differentiated by whether a reward is presented for performing a behavior. In a situation where behavior is performed simply to receive a reward (e.g., money) or to avoid some deleterious outcome (e.g., punishment), then the behavior is considered to be constrained by extrinsic factors, and is therefore extrinsically motivated (Deci, 1980). When there are no apparent extrinsic factors influencing one’s behavior or action, then that person’s behavior is thought to be a result of intrinsically motivated action (Deci & Ryan, 1985). Most SDT research has focused on understanding how certain behaviors are influenced by the fulfillment of basic needs. The basic-needs component of SDT articulates the interplay between the fulfillment of basic needs and its relationship to social, cognitive, physical, and psychological well-being. Research has shown that when the three basic needs of autonomy, competence, and relatedness are fulfilled, they bolster people’s intrinsic motivations and feelings of self-determined behavior (Deci & Ryan, 1985; Ryan & Deci, 2000).

SDT has been empirically tested across a variety of contexts, including education and learning, health care, political activity, environmental activism, exercise/sport, intimate relationships, and video game play (Deci & Ryan, 2000, 2008; Ryan, Rigby, & Przybylski, 2006). Research in the AVG domain has demonstrated that SDT is helpful when attempting to untangle exactly what people find fulfilling about their playing experiences. Studies have shown that playing an AVG can be an intrinsically fulfilling activity (Huang & Gao, 2013; Lyons, Tate, Ward, Ribisi, Bowling, & Kalyanaraman, 2014). Other research has illuminated that specific features of an AVG (e.g., choice, feedback, and difficulty) influence the fulfillment of basic needs differentially and, ultimately, how much people enjoy playing these types of games (Limperos, 2016; Lyons, 2015; Peng, Lin, Pfeiffer, & Winn, 2012). While SDT has been used to guide a lot of research focusing on the processes and outcomes associated with playing AVGs, social cognitive theory (SCT) has also been applied in this context.

In the most general sense, SCT suggests that people can learn specific behaviors by watching or mimicking others (Bandura, 2009). Though there are many different mechanisms which have been shown to influence the psychology of learning and behavioral enactment and change, the basic model of SCT suggests that socio-cognitive determinants (personal, behavioral, and environmental) influence one another by way of feedback loops, to concurrently shape how people learn and behave (Bandura, 1986, 2000). Because SCT places emphasis on human agency, people are seen as having the ability to learn through symbols by reflecting on information they encounter, leading to either the enactive or vicarious modeling of behavior (Bandura, 2000, 2009). This means that people have the ability to form cognitive plans based on observations of information, and then to translate those plans into behavior, even when symbolic environments may not mirror reality.

Self-efficacy is the main construct of interest in most SCT studies. According to Bandura (1982), self-efficacy is described as “judgments of how well one can execute courses of action required to deal with prospective situations” (p. 122). Sources of self-efficacy beliefs are numerous, and include performance accomplishments, social modeling or vicarious experiences, verbal or social persuasion, and emotional experiences, to name a few (McAuley & Elavsky, 2007). Similar to SDT, SCT has been utilized as the guiding theoretical perspective in hundreds of research studies which focus on education and learning, intervention, and promotion of health behavior (Bandura, 2004). It has also become one of the most popular theoretical frameworks to explore the benefits of video games that promote health outcomes (Lieberman, 1997, 2006).

In AVG studies, self-efficacy is often a primary variable of interest, regardless of whether or not the study follows all tenets of SCT. This is primarily because researchers are concerned with understanding how playing AVGs can bolster feelings of self-efficacy toward exercise or PA in general. Multiple research studies involving exercise-based interventions with AVGs have focused on explaining the conditions by which these video game can enhance self-efficacy and subsequent outcomes such as physical activity, exercise adherence, and weight management (Dos Santos, Bredehoft, Gonzalez, & Montgomery, 2016; Song, Peng, & Lee, 2011; Staiano, Abraham, & Calvert, 2013). Of course, each of these studies focuses on a different set of individual factors, social circumstances, and game characteristics that influence self-efficacy and the dependent outcomes stemming from AVG play.

Even though SDT and SCT are two of the theories that are used most regularly to investigate the response to and effectiveness of AVGs, there are many other theories (which appear less prominently) that are similarly well suited for guiding research in this domain. These include, but are not limited to: elaboration likelihood model; situated learning theory; dual-flow model; regulatory focus theory; loop theories; transtheoretical theory; information-motivation-behavioral skills model; and theories of reasoned action/planned behavior (Kooiman & Sheehan, 2015a; Straker, Fenner, Howie, Feltz, Gray, Lu, Mueller, Simons, & Barnett, 2015; Wiemeyer, Deutsch, Malone, Rowland, Swartz, Xiong, & Zhang, 2015). While the use of theory has become rather commonplace in the current research on AVGs, both Wiemeyer et al. (2015) and Kooiman and Sheehan (2015a) suggest that this has not often been the case historically. In fact, it appears that most studies that have sought to understand how and why AVGs are effective have not utilized theory, but instead have used a variable-centered approach based on practical questions of interest.

Differences that Make a Difference: Independent and Moderating Variables in AVG Research

Earlier in this article, very basic distinctions and examples of different types of AVGs were provided and a framework of key variables was introduced (see Figure 1). When trying to determine the means by which AVGs might be effective, it is important to consider all of the variables that could potentially influence this unique game-playing situation. There are many research articles that illuminate and describe the most desirable design principles of AVGs (Straker et al., 2015; Wiemeyer et al., 2015). Lyons and Hatkevich (2013) content-analyzed 18 different fitness video games and found that performance feedback, reinforcement, calories burned, and guided practice were some of the most commonly embedded features in these types of games. Even though research tells us what features are commonly found in AVGs, as well as what design principles should theoretically lead to good experiences with these types of games, understanding exactly how different features of games, gameplay situations, individual characteristics, and other technological/experiential factors work is crucial for explaining the experiences that people have with AVGs.

Characteristics and Features of Active Video Games That Influence the Playing Experience

All AVGS are not created equally. The overarching gameplay experience and goals of each AVG are likely to vary. Even if the main goal of an AVG is to get people to move, the way that the game is structured, its technological features, and the means by which people move are all likely to be different. Therefore, the variations in the characteristics, features, and scope of each game are likely to influence cognitive, affective, behavioral, and physiological outcomes related to game play. For example, Peng, Lin, and Crouse (2011) reported that AVGs that encourage full-body movement are superior at eliciting higher levels of energy expenditure than their counterparts. Consider the following three AVGs: Just Dance (dance video game), Your Shape Fitness Evolved (exergame), and Wii Sports (active sports game). When someone interacts with the dancing game, they may only be moving their feet. Similarly, when someone interacts with Wii Sports, they may only be required to use their upper body (e.g., swinging a tennis racquet or bowling). However, when one plays an exergame (typically requiring full-body movement), it should encourage more vigorous energy expenditure than the aforementioned games. Drilling down a bit further, the specific technological features that a game offers should be perceptually and cognitively significant to the game-playing experience.

Technological affordances or features are essentially any properties of technologies (e.g., modality and interactivity) which can shape how people respond to and use them (Sundar, 2008). Regardless of the type of AVG or the device/gaming system that is used to interact with it, the varying features can be organized and studied from an affordances perspective. Even though many studies on AVGs do not really delve into the theoretical construct of “affordances,” many have indeed tested how technological variations in games impact outcomes associated with the game-playing experience. Limperos (2014) compared identical exercise programs (one presented in the form of an exergame and the other as an exercise video) and found that the using an exergame was superior, primarily because the video game had the added modality of a motion controller, ultimately making the experience more interactive. Other studies have stressed the importance of feedback and have shown that the type of feedback or features embedded in the AVG (e.g., positive [supportive] or negative [non supportive]) can influence psychological responses to the game, either eliciting or suppressing desired outcomes (Kim & Timmerman, 2016; Peng et al., 2012). Some active and exercise-based games allow one to see oneself onscreen (in the form of an avatar) and others do not. Research has shown that seeing oneself on screen and variations in the avatar (actual/ideal/generic) can also have a significant impact on the gaming experience and desired outcomes (Jin, 2009, 2010; Song, Peng, & Lee, 2011). While it is nearly impossible to list all of the potential technological features in the AVG context which may be significant to the playing experience and outcomes, this small subset of studies and the variations within the patterns of findings suggest that both researchers and designers need to continue to study how these differences impact the playing experience.

Competitive, Cooperative, and Networked Play

Playing video games is a social activity, and thus researchers have always sought to understand how the mode of game play (single or multiplayer) influences the game-playing experience. With regard to AVGs, most research has often utilized a single-player mode and taken place in a laboratory setting. Referencing this deficiency, Peng and Crouse (2013) conducted a study that manipulated the playing mode between solo play, cooperative play, and competitive play, and found that people who were competitively playing against someone else reported greater motivation and intensity of the playing activity than those in the other conditions. At the very least, this finding suggests that playing against someone can actually enhance the AVG experience.

Staiano, Abraham, and Calvert (2013) conducted a similar study where they used an AVG as an intervention tool with high school students who were either in a control condition (no game play), a cooperative condition (supportive playing with others), or a competitive condition (playing to win against an opponent). These researchers compared biometric information and psychological responses before and after the gaming intervention study and found that cooperative game players lost more weight and felt that the exercise was more efficient than those in the control condition, but did not differ from the competitive condition. While the results of these studies seem to be at odds with one another, they both used different stimulus games, meaning that characteristics of the games, or simply the design of the study, could partially be responsible for why one study seems to suggest that competitive play is better, while the other suggests that cooperative play is more beneficial.

Competitive or cooperative gaming can take place over a network or when people are co-located. Even though there are many different types of video games that qualify as massive multiplayer online games (MMOGs), or utilize a network to facilitate competition and social interaction, AVGs are not typically played in in this manner (Kooiman & Sheehan, 2015b). However, there is a set of researchers who have developed and tested a dance-based massive multiplayer online exergame, and they have found that this type of AVG may be advantageous in terms of getting people more engaged with the game content and keeping them motivated to play (Johnston & Whitehead, 2011). To summarize, game play mode appears to have a profound impact on the way that people approach and respond to AVGs.

Difficulty Settings and Performance

In the broader video game-effects literature, many studies have sought to understand how variations in difficulty (easy vs. hard), and achievement while playing, influence responses to the playing experience (Juul, 2009b; Schmierbach, Chung, Wu, & Kim, 2014). Juul (2009b) explains that there is a delicate balance between difficulty settings and enjoyment, such that if a game is too hard, then players might not find it enjoyable. But, if a game is too easy, then game players might find it boring. Schmierbach et al. (2014) suggest that playing an easier game increases a sense of competence, and that heightened feelings of competence have a direct positive impact on enjoyment. Even though these studies do not focus specifically on AVGs, they do seem to suggest that these factors are important to consider when conducting research in this domain.

Researchers recently conducted a series of studies using a popular AVG called Dance-Dance Revolution and found that mastery of the skills needed to do well in the game was directly related to effort and enjoyment of the playing experience (Huang & Gao, 2013). This finding mirrors that of the broader video game literature and suggests that an AVG must facilitate mastery (and not be too difficult), or it could impede desired outcomes. Recently, Limperos and Schmierbach (2016) conducted a study in which participants were exposed to a challenge event in an exergame and their performance and subsequent responses to their experience were recorded. These researchers found that although performance in the challenge event was not directly related to the primary outcome (motivation to continue playing), it was indirectly related through the constructs of competence, presence, and enjoyment, which suggests that performance in an AVG does matter.

Overall, it is hard to separate how well one performs in an AVG from the level of difficulty that is imposed on the player. This is primarily because everyone that participates in a lab study or fills out a questionnaire brings different experiences into the game-playing situation. Furthermore, while experimental design and use of control groups can help researchers better isolate the influence of some independent factor on an outcome of interest, it is impossible to explore every potential variable that may have influence. Nonetheless, in addition to the independent variables discussed above, AVG research needs to continue to explore potential moderating factors (individual, situational, and environmental differences) that may help explain the relationship between playing AVGs and outcomes of interest.

Additional Considerations for Research and Design of AVGs

The bulk of research on the utility of AVGs is filled with examples of how certain types of games can be beneficial in a given situation or context. However, most of these studies fail to account for variables that may indeed be moderating or helping to explain the relative effective or ineffectiveness of a certain AVG. Basically, responses to any type of media are typically not received in a uniform manner. Therefore, it is important for researchers to account for as many individual, social, and environmental differences as possible in order to more fully explain how AVGs are selected and processed, as well as their relative influence on desired outcomes. Individual differences can include gender, race, age, academic standing, personality traits, and a host of other variables (Oliver & Krakowiak, 2009). Trying to account for every single difference in a particular research study would not be possible, because any list of variables to consider could, in theory, be endless. However, there are some variables that commonly appear in research on AVGs that researchers should continue to account for when possible (see Figure 1).

In a meta-analysis on AVGs and physical activity promotion, Peng et al. (2013) reports that childrens’ and adults’ responses to AVGs are usually different, underscoring the importance of accounting for a variable like age in both research and design of AVGs. Other research has illustrated that participant weight and body-image dissatisfaction can have a profound impact on enjoyment of AVGs, meaning that these traits alone have the ability to explain why people do or do not like playing a certain game (Lyons, Tate, Ward, Bowling, Ribisl, & Kalyararaman, 2011; Song et al., 2011). Previous experience with video games is another important variable, and research has shown that it differentially impacts effort when playing AVGs (Sell, Lillie, & Taylor, 2008). There have also been reported differences in terms of overall effort and enjoyment of AVGs between men and women, which suggests that differences in gender need to be considered (Howe, Barr, Winner, Kimble, & White, 2015). Every researcher cannot be expected to control for every possible influential variable in studies that focus on AVGs. However, researchers need to at least be cognizant of these “third” variables when conducting research, and explaining the parameters and generalizability of their research results. To summarize, there are many different variables, ranging from traits to experience, that are likely to influence any relationship between playing an AVG and dependent outcomes of interests.

Explaining the Effectiveness of AVGs: Dependent and Mediating Variables in AVG Research

The outcomes of interest in studies that focus on AVGs as intervention tools or catalysts for promoting a healthier lifestyle are of the utmost importance. Many researchers have issued a call for a greater focus on the psychology of playing exercise-based games and other AVGs, while others have summarized and tried to understand exactly how playing these games stacks up with other types of PA or whether they can actually impact serious outcomes like weight loss (Baranowski, 2015; Gao, Hannon, Newton, Huang, 2011; Limperos, Downs, Ivory, & Bowman, 2013; Lyons, 2015). What follows is a literature-based summary of some of the most commonly studied outcomes in AVG research. The outcomes are separated into two different groups: (1) biometic and physiological outcomes; and (2) cognitive, affective, and behavioral responses.

Biometric and Physiological Responses

Over the last six years, there have been many different efforts to summarize and compare all the physiological and biometric outcomes associated with playing different AVGs across different research studies by way of meta-analytic techniques. Therefore, many of the most common physiological outcomes associated with AVG research can be located in these studies. According to these studies, anthropometry (e.g., BMI, waist circumference), weight, energy expenditure, heart rate, VO2 (maximum volume of oxygen used), RER (respiratory exchange ratio), MET (metabolic rate), actigraphy (rest/activity cycles), cardiovascular fitness, blood pressure, heart function, motor skills, and type of PA (light, moderate, vigorous) are some of the most commonly studied biometric and physiological responses in AVG research (Gao, Chen, Pasco, & Pope, 2015; Lu, Kharrazi, Gharghabi, & Thompson, 2013; Papastergiou, 2009; Peng, Lin, & Crouse, 2011, 2013).

A key focus of many AVG studies is to understand how these games compare to other forms of PA as well as how playing them impacts human physiology and functioning. The list of variables provided above (Figure 1) is not exhaustive by any means, but it is representative of what is most typical in AVG research. These responses are often collected using equipment that is very basic in nature (e.g., accelerometers) to very advanced medical equipment (e.g., EEG, MRI).

Cognitive, Affective, and Behavioral Responses

In addition to biological and physiological outcomes, many studies also focus on cognitive, affective, and behavioral responses to AVGs. Measures of psychological and behavioral response are often utilized as either part of a larger survey on AVG use or immediately following some type of exposure to a certain type of game. Unlike physiological outcomes, these outcomes are harder to compare and analyze because they are not typically standardized across studies. For example, many researchers have measured concepts related to intrinsic motivation (competence, autonomy, and relatedness) in AVG research, but the actual scales used to account for these concepts often vary across studies (e.g., Huang & Gao, 2013; Li, Lwin, & Jung, 2014; Limperos & Schmierbach, 2016; Peng et al., 2012). The variation in these measures does not typically impact the patterns of associations found among the concepts. Across the broader landscape of quantitative research, it is not atypical for researchers to use different scales to measure the same underlying concept or construct. It is worth mentioning that in AVG research, a diversity of scales and measurement is more prevalent because the researchers who are studying the utility of these games often come from a variety of different fields (e.g., kinesiology, psychology, communications, and medical sciences), meaning that they approach problems and questions from different perspectives.

Figure 1 contains a list of psychological and behavioral responses that are often measured in AVG research. Flow (Robinson, Dixon, Macsween, van Schaik, & Martin, 2015; Thin, Hansen, & McEachen, 2011), rate of perceived exertion (Peng et al., 2013), presence (Jin & Park, 2009), immersion (Staiano, Abraham, & Calvert, 2012), engagement (Lyons, 2015), self-efficacy (Song et al., 2011; Peng et al., 2012), enjoyment (Lyons et al., 2014), motivation (Sun, 2012), perceptions of self (Jin, 2009), feedback (Kim & Timmerman, 2016), learning (Limperos, 2014), and behavioral intentions (Li & Lwin, 2016; Limperos & Schmierbach, 2016) have all either appeared as focal-dependent variables of interest, or been used as mediating variables to help explain the associated processes between AVG play and dependent outcomes of interest. Again, this list is not exhaustive, but it does contain some of the most common explanatory mechanisms and outcomes that are often associated with research on AVGs. These concepts are often measured using questionnaires (scales), with people indicating their agreement or disagreement with the statements offered to measure each variable. Despite the fact that most studies focus on psychological, behavioral, or physiological responses, gaining a complete and well-rounded understanding of how, why, and when AVGs are effective requires researchers to account for all of these variables.

Summary, Discussion of the Literature, and Future Research

The purpose of this article has been to provide an overview of the variables and theoretical perspectives often used to guide studies focusing on both the design of AVGs and research which assesses their utility in promoting various physiological, behavioral, and psychological outcomes. The framework presented in figure 1 is meant to illustrate how different characteristics of games and individual differences can influence various outcomes associated with playing. The framework illustrates the potential relationship between the independent and dependent variables, and the bidirectional arrow on the right side of the model, as well as the dotted arrows in the middle of the model, suggest how researchers can move past simply studying main effects to more process-orientated models that account for moderating and mediating influences. Future studies should make use of this framework, relevant theory, and practical reasoning in order to propose and test models of AVG use and influence. Such an approach should help contribute to a deeper understanding of what makes AVGs appealing and potentially effective as health improvement and maintenance tools.

In AVG research and the broader literature on games for health, there is a deficit of longitudinal research. Since it is critical to understand what keeps people motivated to play a particular game, future studies should focus on tracking a cohort over time or finding other creative ways to continually assess what is and is not working. Another area of improvement for AVG research lies with the parameters, focus, and scope of each study. Some researchers only focus on psychological/behavioral aspects of game play, while other focus solely on physiology. It is unlikely that communication and psychology researchers are going to suddenly learn about body composition and energy expenditure. Similarly, it is unlikely that medical personnel, kinesiology, and exercise physiologists are going to learn the ins and outs of psychometric measurement. Instead, the best way to approach studying AVGs and other health-driven games in a comprehensive manner is by collaborating with teams of multidisciplinary researchers. While it is true that there are already teams of researchers studying AVGs and other health games (some in a longitudinal manner), this nevertheless should increasingly become the norm.

In the 1980s, the idea of using video games to increase physical activity, cardiovascular fitness, or any other health outcome was almost non-existent. Things have changed quite significantly since then. In recent years, health practitioners and researchers have enthusiastically embraced the potential of video games for health purposes and are continually trying to figure out how to use games and game mechanics in general to help encourage, teach, and treat patients who face a variety of different diseases and ailments. Research has repeatedly shown that serious games and AVGs are effective as intervention tools for curbing unhealthy behavior and can be a catalyst for an overall healthier lifestyle. Despite the fact that there have been many studies and meta-analytic summaries on the utility of AVGs (and games for health in general) in recent years, there is still much opportunity for future research. Although video games are still primarily regarded as a form of entertainment, the research that shows their utility in the health domain is slowly starting to change that perception. This article and the organizing framework presented here provide a history and foundation that will not only be helpful for future research on the utility of AVGs, but also for shaping how we think about and approach studying the gamification of health behaviors via technology (e.g., mobile applications, other serious games, and wearables).

Further Reading

Baranowski, T., Anderson, C., & Carmack, C. (1998). Mediating variable framework in physical activity interventions. American Journal of Preventative Medicine, 15, 266–297.Find this resource:

Baranowski, T., Buday, R., Thompson, D., Lyons, E. J., Lu, A. S., & Baranowski, J. (2013). Developing games for health behavior change: Getting started. Games for Health Journal, 2, 183–190.Find this resource:

Biddiss, E. & Irwin, J. (2010). Active video games to promote physical activity in children and youth: A systematic review. Archives of Pediatric and Adolescent Medicine, 164, 664–672.Find this resource:

Gao, Z., Chen, S., Pasco, D., & Pope, Z. (2015). A meta-analysis of active video games on health outcomes among children and adolescents. Obesity Reviews, 16, 783–794.Find this resource:

Kato, P. M. (2010). Video games in health care: Closing the gap. Review of General Psychology, 14, 113–121.Find this resource:

Peng, W., & Liu, M. (2008). An overview of using electronic games for health purposes. In Ferdig, R. (Ed.), Handbook of research on effective electronic gaming in education (pp. 388–401). Hershey, PA: IGI Global.Find this resource:

Primack, B. A., Carroll, M. V., McNamara, M., Klem, M. L., King, B., Rich, M., Chan, C. W., & Nayak, S. (2012). Role of video games in improving health-related outcomes: A systematic review. American Journal of Preventative Medicine, 42, 630–638.Find this resource:

Ritterfeld, U., Cody, M. J., Vorderer, P. (2009). Serious games: Mechanisms and effects. New York: Routledge.Find this resource:

References

Anderson, C. A., Bushman, B. J., Donnerstein, E., Hummer, T. A., & Warburton, W. (2015). SPSSI research summary on media violence. Analyses of Social Issues and Public Policy, 15, 4–19.Find this resource:

Bandora, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147.Find this resource:

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.Find this resource:

Bandura, A. (2000). Exercise of human agency through collective efficacy. Current Directions in Psychological Science, 9, 75–78.Find this resource:

Bandura, A. (2004). Health promotion by social cognitive means. Health Education & Behavior, 31, 143–164.Find this resource:

Bandura, A. (2009). Social cognitive theory of mass communication. In Bryant, J., & Oliver, M. B. (Eds.), Media effects: Advances in theory and research (3d ed.). (pp. 94–124). Mahwah, NJ: Lawrence Erlbaum Associates.Find this resource:

Baranowski, T. (2015). Games for increasing physical activity: Mechanisms for change. Games for Health Journal, 4, 1–2.Find this resource:

Baranowski, T., Buday, R., Thompson, D. I., & Baranowski, J. (2008). Playing for real: Video games and stories for health-related behavior change. American Journal of Preventative Medicine, 34, 74–82.Find this resource:

Deci, E. L. (1980). The psychology of self-determination. Lexington, MA: Lexington Books.Find this resource:

Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.Find this resource:

Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227–268.Find this resource:

Deci, E. L., & Ryan, R. M. (2008). Facilitating optimal motivation and psychological well-being across life’s domains. Canadian Psychology, 49, 14–23.Find this resource:

Dos Santos, H., Bredehoft, M. D., Gonzalez, F. M., & Montgomery, S. (2016). Exercise video games and exercise self-efficacy in children. Global Pediatric Health, 3, 1–6.Find this resource:

Entertainment Software Association (2016). The 2016 essential facts about the computer and video game industry. Available at http://www.theesa.com/wp-content/uploads/2016/04/Essential-Facts-2016.pdf.

Gao, Z., Chen, S., Paseo, D., & Pope, Z. (2015). A meta-analysis of active video games on health outcomes among children and adolescents. Obesity Reviews, 16, 783–794.Find this resource:

Gao, Z., Hannon, J., Newton, M., & Huang, C. (2011) Effects of curricular activity on students’ situational motivation and physical activity levels. Research Quarterly for Exercise and Sport, 82, 536–544.Find this resource:

Gentile, D. A., Lynch, P. J., Linder, J. R., & Walsh, D. A. (2004). The effects of violent video game habits on adolescent hostility, aggressive behaviors, and school performance. Journal of Adolescence, 27, 5–22.Find this resource:

Howe, C. A., Barr, M. W., Winner, B. C., Kimble, J. R., & White, J. B. (2015). The physical activity energy cost of the latest active video games in young adults. Journal of Physical Activity and Health, 12, 171–177.Find this resource:

Huang, C. & Gao, Z. (2013). Associations between students’ situational interest, mastery experiences, and physical activity levels in an interactive dance game. Psychology, Health, & Medicine, 18, 233–241.Find this resource:

Jin, S. A. (2009). Avatars mirroring the actual self versus projecting the ideal self: The effects of self-priming on interactivity and immersion in an exergame, Wii Fit. Cyberpsychology & Behavior, 12, 761–765.Find this resource:

Jin, S. A. (2010). Does imposing a goal always improve exercise intentions in avatar- based exergames? The moderating role of interdependent self-construal on exercise intentions and self-presence. Cyberpsychology, Behavior, and Social-Networking, 13, 335–339.Find this resource:

Jin, S. A., & Park, N. (2009). Parasocial interaction with my avatar. Effects of interdependent self-construal and the mediating role of self-presence in an avatar-based console game, Wii. CyberPsychology & Behavior, 12, 723–727.Find this resource:

Johnston, H., & Whitehead, A. (2011). Pose presentation for a dance-based massively multiplayer online exergame. Entertainment Computing, 2, 89–96.Find this resource:

Juul, J. (2009a). A casual revolution: Reinventing video games and their players. Cambridge, MA: MIT Press.Find this resource:

Juul, J. (2009b). Fear of failing? The many meanings of difficulty in video games. In Wolf, M. J. P., & Perron, B. (Eds.), The Video Game Theory Reader 2 (pp. 237–252). New York: Routledge.Find this resource:

Kim, J., & Timmerman, C. E. (2016). Effects of supportive feedback messages on exergame experiences: A mediating role of social presence. Journal of Media Psychology. (Advance online publication).Find this resource:

Kooiman, B., & Sheehan, D. (2015a). Exergaming theories: A literature review. International Journal of Game-Based Learning, 5, 1–14.Find this resource:

Kooiman, B., & Sheehan, D. (2015b). Interacting with the past, present, and future of exergames: At the beginning of a new life cycle of video games?Society and Leisure, 38, 55–73.Find this resource:

Li, B. J., & Lwin, O. M. (2016). Player see, player do: Testing an exergame motivation model based on the influence of the self avatar. Computers in Human Behavior, 59, 350–357.Find this resource:

Li, B. J., Lwin, O. M., & Jung, Y. (2014). Wii, myself, and size: The influence of proteus effect and stereotype threat on overweight children’s exercise motivation and behavior in exergames. Games for Health Journal, 3, 40–48.Find this resource:

Lieberman, D. (1997). Interactive video games for health promotion: Effects on knowledge, self-efficacy, social support, and health. In Street, R. L., Gold, W. R., & Manning, T. R. (Eds.), Health promotion and interactive technology: Theoretical applications and future directions (pp. 103–120). Mahwah, NJ: Lawrence Erlbaum.Find this resource:

Lieberman, D. (2006). What can we learn from playing interactive games? In Vorderer, P., & Bryant, J. (Eds.), Playing video games: Motives, responses, and consequences (pp. 379–397). Mahwah, NJ: Lawrence Erlbaum.Find this resource:

Limperos, A. M. (2014). Are “Wii” exercising correctly? Understanding how exergames can be used to increase knowledge of exercise behavior. Games for Health Journal, 3, 25–30.Find this resource:

Limperos, A. M., Downs, E., Ivory, J. D., & Bowman, N. D. (2013). Leveling up: A review of emerging trends and suggestions for the next generation of communication research investigating video games’ effects. Communication Yearbook, 37, 349–377.Find this resource:

Limperos, A. M., & Schmierbach, M. (2016). Understanding the relationship between exergame play experiences, enjoyment, and intentions for continued play. Games for Health Journal, 5, 100–107.Find this resource:

Lu, A. S., Kharrazi, H., Gharghabi, F., & Thompson, D. (2013). A systematic review of health videogames on childhood obesity prevention and intervention. Games for Health Journal, 2, 131–141.Find this resource:

Lyons, E. J. (2015). Cultivating engagement and enjoyment in exergames using feedback, challenge, and rewards. Games for Health Journal, 4, 12–18.Find this resource:

Lyons, E. J., & Hatkevich, C. (2013). Prevalence of behavior changing strategies in fitness video games: theory-based content analysis. Journal of Medical Internet Research, 5, e81.Find this resource:

Lyons, E. J., Tate, D. F., Ward, D. S., Bowling, J. M., Ribisl, K. M., & Kalyararaman, S. (2011). Energy expenditure and enjoyment during video game play: Differences by game type. Medicine and Science in Sports and Exercise, 43, 1987–1993.Find this resource:

Lyons, E. J., Tate, D. F., Ward, D. S., Ribisi, K. M., Bowling, J. M., & Kalyanaraman, S. (2014). Engagement, enjoyment, and energy expenditure during active video game play. Health Psychology, 33, 174–181.Find this resource:

McAuley, E., & Elavsky, S. (2007). Self-efficacy, physical activity, and cognitive function. In Spirduso, W., Poon, L., & Chodzko-Zajko, W. (Eds.), Exercise and its mediating effects on cognition (pp. 69–84). Champagne, IL: Human Kinetics.Find this resource:

McLay, R., McBrien, C., Wiederhold, M., & Wiederhold, B. (2010). Exposure therapy with and without Virtual Reality treat PTSD while in the combat theater: A parallel case series. Cyberpsychology, Behavior, & Social Networking, 13, 37–42.Find this resource:

Oliver, M. B., & Krakowiak, K. M. (2009). Individual differences in media effects. In Bryant, J., & Oliver, M. B. (Eds.), Media effects: Advances in theory and research (3d ed., pp. 517–531). Mahwah, NJ: Erlbaum.Find this resource:

Papastergiou, M. (2009). Exploring the potential of computer and video games for health and physical education: A literature review. Computers & Education, 53, 603–622.Find this resource:

Peng, W., & Crouse, J. (2013). Playing in parallel: The effects of multiplayer modes in active video game on motivation and physical exertion. CyberPsychology, Behavior, & Social Networking, 16, 423–427.Find this resource:

Peng, W., Lin, J-H., & Crouse, J. (2011). Is playing exergames really exercising? A meta-analysis of energy expenditure in active video games. CyberPsychology, Behavior, and Social Networking, 14, 681–688.Find this resource:

Peng, W., Lin, J-H., & Crouse, J. (2013). Using active video games for physical activity promotion: A systematic review of the current state of research. Health Education & Behavior, 40, 171–192.Find this resource:

Peng, W., Lin, J-H, Pfeiffer K. A., & Winn, B. (2012) Need satisfaction supportive game features as motivational determinants: An experimental study of a self-determination theory guided exergame. Media Psychology, 15, 175–196.Find this resource:

Ratan, R., & Ritterfeld, U. (2009). Classifying serious games. In Ritterfeld, U., Cody, M., & Vorderer, P. (Eds.), Serious games: Mechanisms and effects (pp. 10–24). New York: Routledge.Find this resource:

Ritterfeld, U., Cody, M. J., & Vorderer, P. (2009). Serious games: Mechanisms and effects. New York: Routledge.Find this resource:

Robinson, J., Dixon, J., Macsween, A., van Schaik, P., & Martin, D. (2015). The effects of exergaming on balance, gait, technology acceptance and flow experience in people with multiple sclerosis: A randomized controlled trial. BMC Sports Science, Medicine, and Rehabilitation, 7(8).Find this resource:

Rosser, J., Lynch, P., Cuddihy, L., Gentile, D., Klonsky, J., & Merrell, R. (2007). The impact of video games on training surgeons in the 21st century. Archives of Surgery, 142, 181–186.Find this resource:

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.Find this resource:

Ryan, R. M., Rigby, C. S., & Przybylski, A. (2006). Motivation pull of video games: A self-determination theory approach. Motivation and Emotion, 30, 347–365.Find this resource:

Schmierbach, M., Chung, M-Y., Wu, M., & Kim, K. (2014). No one likes to lose: The effect of game difficulty on competency, flow, and enjoyment. Journal of Media Psychology: Theories, Methods, and Applications, 26, 105–110.Find this resource:

Sell, K., Lillie, T., & Taylor, J. (2008). Energy expenditure during physically interactive video game playing in male college students with different playing experience. Journal of American College Health, 56, 505–512.Find this resource:

Song, H, Peng, W., & Lee, K. M. (2011). Promoting exercise self-efficacy with an exergame. Journal of Health Communication, 16, 148–162.Find this resource:

Staiano, A. E., Abraham, A. A., & Calvert, S. L. (2012). Motivating effects of cooperative exergame play for overweight and obese adolescents. Journal of Diabetes Science and Technology, 6, 812–819.Find this resource:

Staiano, A. E., Abraham, A. A., & Calvert, S. L. (2013). Adolescent exergame play for weight loss and psychosocial improvement: A controlled physical activity intervention. Obesity, 21, 598–601.Find this resource:

Stettler, N., Signer, T. M., & Suter, P. M. (2004). Electronic games and environmental factors associated with childhood obesity in Switzerland. Obesity, 12, 896–903.Find this resource:

Straker, L. M., Fenner, A. A., Howie, E. K., Feltz, D. L., Gray, C. M., Lu, A. S., Mueller, F., Simons, M., & Barnett, L. M. (2015). Efficient and effective change principles in active videogames. Games for Health Journal, 4, 43–52.Find this resource:

Sun, H. (2012). Exergaming impact on physical activity and interest in elementary school children. Research Quarterly for Exercise and Sport, 83, 212–220.Find this resource:

Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In Metzger, M. J., & Flanigan, A. J. (Eds.), Digital media, youth, and credibility (pp. 73–100). Cambridge, MA: MIT Press.Find this resource:

Thin, A., Hansen, L., & McEachen, D. (2011). Flow experience and mood states while playing body movement-controlled video games. Games and Culture, 6, 414–428.Find this resource:

Vandewater, E. A., Shim, M-S., & Caplovitz, A. G. (2004). Linking obesity and activity level with children’s television and video game use. Journal of Adolescence, 27, 71–85.Find this resource:

Wiemeyer, J., Deutsch, J., Malone, L. A., Rowland, J. L., Swartz, M. C., Xiong, J., & Zhang, F. F. (2015). Recommendations for the optimal design of exergame interventions for persons with disabilities: Challenges, best practices, and future research. Games for Health Journal, 4, 58–62.Find this resource: