FITNESS BENEFITS OF THE NINTENDO WII FIT
______
A Thesis
Presented
to the Faculty of
California State University, Chico
______
In Partial Fulfillment
of the Requirements for the Degree
Master of Arts
in
Kinesiology
______
by
© Victoria E. Sator 2010
Spring 2010 FITNESS BENEFITS OF THE NINTENDO WII FIT
A Thesis
by
Victoria E. Sator
Spring 2010
APPROVED BY THE INTERIM DEAN OF THE SCHOOL OF GRADUATE, INTERNATIONAL, AND INTERDISCIPLINARY STUDIES:
______Mark J. Morlock, Ph.D.
APPROVED BY THE GRADUATE ADVISORY COMMITTEE:
______George David Swanson, Ph.D. George David Swanson, Ph.D., Chair Graduate Coordinator
______Thomas D. Fahey Ed.D.
______Joshua M. Trout Ph.D. PUBLICATION RIGHTS
No portion of this thesis may be reprinted or reproduced in any manner unacceptable to the usual copyright restrictions without the written permission of the author.
iii DEDICATION
I would like to dedicate this thesis to my parents Kenneth and Rebecca, without whom I
would not have had the opportunity to receive a college education.
To my Dad, for his brave service to our country, bestowing me with his awesome sense of
humor and for giving me his strength.
To my Mom, for her corny jokes, enriching me with compassion for the environment, and
all the creatures in it, and her unfailing faith in me.
Thank you so much
for the massive amounts of love and support you both have given me.
Without you two, I would be nothing.
iv ACKNOWLEDGMENTS
My Uncle Robert and Aunt Vickie Sobey–You both are some of the hardest working people I know, thank you for leading me with your example, loving me, and for supporting me throughout my college career.
Earl and Betty Miller - for the many many books it took to educate me!
Dr. Swanson – Thank you so much for guiding me through the Masters program and giving me the freedom to learn and grow, I will always have so much gratitude for that!
Dr. Fahey – I couldn’t have gotten through this thesis without your help!
Thank You!
Dr. Trout – Thank you for giving me pep talks and for being honest with me.
It has meant so much!
Laurie Hartt – For being my editor-in-chief! You rock! Thank you!
Brett Hartt- For keeping me going when I would have melt downs, believing in me, and loving me.
My Best Gal Pals – Jessica Munger, Jessica Miller and Amelia Mattingly, My world is a much better place with the three of you in it!
v TABLE OF CONTENTS
PAGE
Publication Rights ...... iii
Dedication...... iv
Acknowledgments ...... v
List of Tables...... viii
List of Figures...... x
Abstract...... xi
CHAPTER
I. Introduction...... 1
Statement of the Problem ...... 3 Purpose of the Study...... 4 Rationale...... 4 Hypothesis ...... 6 Null Hypothesis...... 6 Delimitations of the Study...... 6 Limitations...... 6 Definition of Terms ...... 7
II. Review of Literature Review...... 9
History of Exergaming ...... 9 The Case for Physical Activity...... 11 Physical activity and Resting Heart Rate ...... 14 Video Gaming, Exergaming and Health ...... 15
vi CHAPTER PAGE
III. Methodology...... 19
Introduction ...... 19 Design of the Investigation...... 19 Subjects...... 21 Treatment...... 21 Instrument...... 21 Data Analysis Procedures...... 25
IV. Results...... 26
Subjects...... 26 Weight ...... 27 Body Mass Index...... 29 Resting Heart Rate...... 30 Duke Activity Status Index ...... 32
V. Discussion...... 49
Conclusion...... 51 Recommendations for Future Research...... 52
References ...... 53
vii LIST OF TABLES
TABLE PAGE
1. Treatment Group Subject Data ...... 22
2. Control Group Subject Data ...... 23
3. Paired t-Test Statistical Results Two Sample for Means (Weight Treatment) ...... 27
4. Paired t-Test Statistical Results Two Sample for Means (Control Group)...... 28
5. Between Group Weight Comparisons, Statistical Results from Vassar Stats ...... 30
6. t-Test Comparing Mean Weight Between Groups Two-Sample Assuming Unequal Variances ...... 31
7. Paired t-Test Statistical Results Paired Two Sample for Means (Treatment Group)...... 32
8. Paired t-Test Statistical Results Paired Two Sample for Means (Control Group)...... 33
9. Between Group BMI Comparisons, Statistical Results from Vassar Stats ...... 35
10. t-Test Comparing Mean BMI Between Groups Two-Sample Assuming Unequal Variances ...... 36
11. Paired t-Test Statistical Results Two Sample for Means (Treatment Group)...... 37
12. Paired t-Test Statistical Results Two Sample for Means (Control Group)...... 38
viii TABLE PAGE
13. t-Test Comparing Mean RHR Between Groups Two-Sample Assuming Unequal Variances ...... 40
14. Between Group RHR Comparisons, Statistical Results from Vassar Stats ...... 41
15. Paired t-Test Statistical Results Two Sample for Means (Treatment Group)...... 42
16. Paired t-Test Statistical Results Two Sample for Means (Control Group)...... 43
17. t-Test Comparing Mean Weight Between Groups Two-Sample Assuming Unequal Variances ...... 45
18. Between Group DASI Score Comparisons, Statistical Results from Vassar Stats...... 46
19. Treatment Group Differences ...... 47
20. Control Group Differences ...... 48
ix LIST OF FIGURES
FIGURE PAGE
1. Activities the Wii Fit Offers by Category...... 24
2. Treatment and Control Group Pre/Post Mean Weights ...... 29
3. Treatment and Control Group Pre/Post Mean BMI Values...... 34
4. Treatment and Control Group Pre/Post Mean RHR Beats Per Minute .... 39
5. Treatment and Control Group Pre/Post Mean DASI Scores ...... 44
x ABSTRACT
FITNESS BENEFITS OF THE NINTENDO WII FIT
by
© Victoria E. Sator 2010
Master of Arts in Kinesiology
California State University, Chico
Spring 2010
The purpose of this study was to determine if playing the Nintendo Wii Fit for 150 minutes per week for 12-weeks could have an impact on cardiovascular fitness and weight loss. Thirty one male and female subjects were recruited from the United
States. Twenty two subjects were a part of the control group, and 15 subjects were part of the treatment group. The control group aged 19 to 72 years and the treatment group aged 24 to 80 years was observed over a 3-month period where a pre and post test of resting heart rate (RHR), weight, Body Mass Index (BMI), and estimated peak oxygen uptake questionnaire (DASI) were taken. There was no attrition from this study.
Data were analyzed using paired t-Test, t-Test between groups, and
ANOVA. Comparisons between groups showed a significant (p<.0001) difference in pre and post data. The treatment group test subjects experienced a significant (p<.0001) decrease in weight, BMI, RHR, and DASI scores. We can infer from these data that
xi playing the Nintendo Wii Fit can increase physical fitness. The Wii Fit system is a pos- sible gateway to other forms of physical activity outside the home. Other research in this area has been conducted to measure energy expenditure during game play which can only be used to hypothesize the prolonged effects of exergaming. This study has expanded upon those previously conducted by measuring for physiological change over time. Although this study indicates significant results, more research is needed to pro- vide replication of these results, under other circumstances.
xii
CHAPTER I
INTRODUCTION
Physical inactivity leads to poor health. Physically inactive people are at an increased risk of being overweight or obese (World Health Organization, 2009). Playing moderate amounts of electronic games has been linked to obesity (Vandewater, Shim, &
Caplovitz, 2004). People use electronic technology today more than ever before. The incidence in obesity has been exacerbated by a decrease in physically active forms of entertainment with physically inactive pursuits, such as playing videogames or watching television (Carvalhal, Padez, Moreira, & Rosado, 2007). In 1996, gaming industry statistics reported computer and video game sales reached a net $2.7 billion world wide.
Sales jumped to over $7.0 billion in 2009 and people in more than 65% of the households in the United States play computer or video games on regular basis (Entertainment
Software Association (ESA), 2009).
Video games provide a virtual world, where a digital character can do almost any physical movement, directed by a handheld controller. A traditional handheld controller necessitates prolonged sitting and promotes physical inactivity. This handheld interface prompted the gaming industry to develop a different type of gaming experience, resulting in an alternative to the stereotypical sedentary activity.
The first popular video game that substituted the handheld controller was
Dance Dance Revolution (DDR), where a “dance platform,” a large floor mat controller,
1 2 to communicate with the video gaming console. While standing on the dance platform, the users coordinate his or her steps to visual and musical cues. If the dance movements were performed correctly, the player received a passing score that allowed them to unlock more challenging dance levels. Introducing the concept of exercise gaming or
“exergaming,” DDR offered an interface in which players were required to stand and physically involve their lower body. This provided gamers a way to enjoy fun videogames while simultaneously engaging in physical activity.
DDR quickly became popular with consumers and in 1996 was introduced for home use. The demand for more of this type of interface grew, giving birth to a new generation of exergaming from Nintendo (Stewart, 2009). The Nintendo Wii has since become the leader in exergaming and is number one in console sales worldwide, surpassing the Xbox 360 and the PlayStation 3 as of January 2010 (Nintendo, 2009b). In
December 2009, the Wii broke the one month best-selling record for a video gaming console in the United States (Purchese, 2010). Popularity for the Wii can be attributed to distinguishing features that no other video gaming console possesses. One of these features is a wireless handheld controller, the Wii Remote, which contains accelerometers that detect changes in direction and speed. The Wii Remote also works as a pointer that behaves the same way a “mouse” on a computer screen would. This hardware is used for all the games the Wii offers, Wii Sports being the most popular game.
Despite the uniqueness and versatility of the Wii remote, a new piece of hardware was needed to include more whole body movement. To fill this need, Nintendo created the Wii Fit, a game based on various forms of exercise involving the whole body.
Wii Fit utilizes a pressure-sensitive wireless platform peripheral called the Wii Balance
3
Board. This hardware is used to measure the user's weight, center of balance, and changes in pressure exerted on the board during game play. The user inputs his or her height into the Wii, and then the software calculates body mass index (BMI). Wii Fit exercise activities include aerobics, strength training, yoga and balance games. The focus of these activities is to provide a workout using methods that emphasize controlled motions for core strength and endurance as well as quick motions that bring up the heart rate.
When introduced to the gaming market, Wii Fit became popular and by
October 30, 2009, sold 22.5 million copies of Wii Fit worldwide. It is now considered the second best selling video game in history after Super Mario Brothers (Guinness World
Records, 2006; Nintendo, 2009a). Continued sales of the Wii console and its games are attributed to advertising that promotes physical activity as fun for any age group.
Statement of the Problem
Risk factors for disease morbidity and mortality increase as physical activity decreases and obesity increases (U.S. Department of Health and Human Services, 2008).
Obesity is a risk factor for various diseases such as diabetes, cancer, cardiovascular disease, hypertension, and hyperlipidemia (Dishman, Washburn, & Heath, 2004). The prevalence of these diseases continues to increase every year as more people become less active. The American Heart Association (AHA) stated that 32.5 percent of American adults are engaging in regular physical activity, thereby creating an expensive public health crisis (AHA, 2009). The financial burdens associated with physical inactivity are passed on to the individual taxpayers, employers, and taxes have now been raised to
4 subsidize public medical insurance programs. Individual health insurance premiums have also increased (U.S. Department of Health and Human Services, 2010)
Before the 1980s, approximately 16% of the American population was obese.
Today, 35% is obese (BMI ≥ 30) with an additional 31% who are overweight (BMI ≥25)
(CDC, 2009). This alarming trend translates to 144 million American adults who are overweight or obese. Of these, more than 71.6 million are obese (AHA, 2009). World wide, greater than 1 billion adults are overweight, and 300 million of them are diagnosed as clinically obese (WHO, 2009). Reaching epidemic proportions, obesity is now the foremost contributor to the social and economic burden of chronic disease and disability
(Dishman et al., 2004).
In 2004, deaths associated with obesity totaled approximately 260,000 people, which makes it second only to deaths related to tobacco use in the U.S. (Flegal,
Graubard, Williamson, & Gail, 2007, Table 3). Obesity has the potential to become the main cause of preventable death in the United States if these statistics continue (Sibbald,
2002).
Purpose of the Study
The purpose of this study is to investigate the effectiveness of the Wii Fit’s ability to impact cardiovascular fitness and weight loss.
Rationale
Lower resting heart rate is an indicator of good cardiac health and physical fitness (American College of Sports Medicine (ACSM), 2008). A measurement of resting heart rate, weight, and BMI was taken at the beginning and end of a three-month test
5 period. The Duke Activity Status Index facilitated baseline fitness levels or approximate volume of maximum oxygen consumption (VO2max) of each subject before the study began. The exposure group spent 150 minutes per week playing the Nintendo Wii Fit.
This process may determine if the Wii Fit can facilitate a change in fitness levels for persons in the exposure group.
It is important to understand the Wii Fits’ capabilities to create physiological change within the body. If consumers are to use it for that purpose, they should know how well it works. Technology is viewed as a hindrance to physical activity. If video gaming companies are on the right track in reversing this phenomenon, then they need to know how beneficial their products actually are. This way, gaming companies can continue creating fun and challenging games that are beneficial to human health. If these current products are not creating positive physiological differences, then gaming companies need to make changes to the software and come up with different ideas for more effective exergaming.
Current studies examining the fitness benefits of exergaming are sparse, and very few studies have been conducted on the current technology. DDR was the forerunner for exergaming; however, it has dipped in popularity since its release, and now the Wii Fit has taken over the exergaming industry. The Wii Fit has been available in the United States since May of 2008; this short amount of time has influenced the lack in available research. Research in this area is desperately needed if exergaming is to evolve. This study will contribute to the present knowledge regarding exergamings usefulness in health promotion. By measuring resting heart rate, we will be able to determine if a change in cardiovascular fitness has occurred from game play.
6
Hypothesis
Wii Fit players will experience a change in resting heart rate and weight after
3 months of 150 minutes per week of game play.
Null Hypothesis
Wii Fit players will not experience a change in resting heart rate or weight after 3 months of 150 minutes per week of game play.
Delimitations of the Study
1. This study was delimited to subjects who were 18 years old or older.
2. This study was delimited to subjects who were not athletes and did not engage in greater than or equal to150 minutes per week of exercise.
3. Maximum oxygen consumption was estimated indirectly with a questionnaire rather than in a laboratory.
4. Video game play was conducted in the homes of the subjects rather than in a laboratory.
5. This study was delimited by the assignment of subjects into the treatment group and control group based on access to a Wii Fit rather than random allocation.
Limitations
1. The results may be influenced by the subjects diet.
2. The results may be influenced by the treatment group subjects not fully exerting themselves while playing the Wii Fit.
7
3. Subject drop outs may occur.
4. The study may be limited by non-compliant subjects in the control group regarding restrictions of extra physical activity.
5. The study may be limited by the subjects non-compliance to caffeine and other substance restriction before resting heart rate measurements.
6. Resting heart rate may be influenced by stress.
Definition of Terms
Accelerometers
A device used to measure acceleration, specifically in the Wii Remote.
BMI
Body mass index, calculated by the formula BMI= Weight (kg)/ [Height (m)]
It is widely used as an indirect measure of body fatness.
DDR
Dance Dance Revolution, one of the first popular exergames.
EE
Energy expenditure is a measure of the metabolic cost of certain physical activities.
Exergaming
Exergaming describes videogames that use whole body input devices to communicate with the console.
8
RHR
Resting heart rate.
Video Gaming Console
A interactive entertainment computer that requires an input device such as a hand held controller or Wii Fit Balance Board and a output display such as a television screen.
VO2max
Volume of maximum oxygen consumption is the maximum amount of oxygen used by an individual during incremental exercise. This measurement reflects an individuals fitness levels.
Whole Input Device
A device used to communicate with video gaming consoles that requires whole body movement such as the Wii Balance Board.
Wii
A video gaming console created by Nintendo that uses the Wii Remote which requires bodily movement during play.
Wii Fit
Video game software that is used in the Wii. This game uses the Wii Fit Board which requires whole body movement in order to play.
CHAPTER II
REVIEW OF LITERATURE REVIEW
History of Exergaming
Exergaming describes videogames that use whole body input devices to communicate with the video gaming console. Traditional videogames have been associated with physical inactivity as a traditional handheld input device allows for marginal physical movement. Exergaming solves this problem through active whole body game play (Bogost, 2005).
Autodesk, a corporation that creates 2D and 3D design software for use in media entertainment, designed the games HighCycle and Virtual Racquetball in the early
1980s. These games were “virtual reality” games, in which users could wear a type of head-mounted display that gave the perspective of being inside the game (Rheingold,
1991). HighCycle and Virtual Racquetball used input devices that interacted with a virtual environment. HighCycle used an exercise bike where the player could peddle through a virtual environment and Virtual Racquetball used a hand held racquet that would “hit” a virtual ball. These virtual reality games lent new ideas to video games and how they were to be played.
The first true exergames were introduced in the late eighties and included Foot
Craz for the Atari 2600 and Power Pad for the Nintendo Entertainment System (Bogost,
2005). While these games were innovative, neither was successful in sales (Bogost,
9 10
2005). The first successful exergame was Dance Dance Revolution (DDR), introduced in the late 1990s. Originally designed for the arcade, DDR became a popular in home exergame and sold more than three million copies (Star, 2005). Due to the popularity of
DDR, several gaming companies attempted to follow suit. New devices and games forcing players to use their whole body began to surface in the early 2000s (Armstrong,
2007). The concepts of these various devices were inventive, however, unsuccessful in comparison to the popularity of DDR.
Finally, in 2006, Nintendo released the Wii console, using a new technology called the Wii Remote. Using accelerometers, the Wii remote provided a unique distinction among all other gaming systems. Accelerometers required movement from the player in order to interact with the game as they detect changes in direction and speed.
However, whole body movement wasn’t necessary for all of the games the Wii had to offer. Mario Cart, for example, only required the use of the arms and players could stay seated for the game. Nintendo then created a new peripheral called the Wii balance board, which was used with the softwear called Wii Fit.
The Wii Balance board is a pressure-sensitive wireless platform peripheral.
This hardware is used to measure the user's weight, center of balance and changes in pressure exerted on the board during game play. When introduced to the gaming market,
Wii Fit became popular and had sold 22.5 million copies world wide, as of October 30,
2009. It is now considered the second best selling video game in history (Nintendo,
2009a). After the extreme rise in popularity of exergaming and the Wii in particular, in
2007, the term “exergaming” became a part of the Collins English Dictionary.
11
The Case for Physical Activity
Evidence has accumulated over the past 30 years that implicates physical inactivity as a major risk factor for all-cause mortality in the United States and globally
(Dishman et al., 2004). Lowering the mortality rate involves reducing the risk of cardiovascular disease (CVD) and cancer, which are the two leading causes of death in industrialized nations (Dishman et al., 2009) Approximately 43% of deaths worldwide can be attributed to CVD and cancer (Strong, Mathers, Leeder, & Beaglehole 2005). Both share the same modifiable risk factors such as hypertension, obesity, smoking, hyperlipidemia, diabetes mellitus, and stress (Dishman et al., 2004). Physical activity has a marked effect in reducing these risk factors (Dishman et al., 2004; U.S. Department of
Health and Human Services, 2008). This review will examine the relationship between physical activity and all-cause mortality.
Twenty-three studies were included for this review, many of which investigated CVD and cancer risk in relation to physical activity. All studies were conducted from 2001 to 2010 with the exception of one study conducted in 1998 and took place in the United States, Europe, and Asia. More than 420,000 subjects aged 30-81 years, were examined and follow up ranged from 2 – 25 years in all of the prospective studies.
Sedentary lifestyles lead to persons becoming overweight or obese (Dishman et al., 2004). Being overweight is defined as having a BMI of 25 or more, and obesity is described as having a BMI greater that 29.9 (ACSM, 2008). Obese people are at an increased risk of all-cause mortality (Dishman et al., 2004). Some studies suggest risks associated with all-cause mortality are significantly increased with higher BMI values
12
(Crespo et al., 2002; Heidemann et al. 2004). One hundred sixteen thousand five hundred sixty four American women aged 30-55 were followed for 25 years and 10,282 of them died. The majority died of cancer or CVD and 216 died of other causes (Heidemann et al.
2004). Those that died had higher BMI values (Heidemann et al., 2004), suggesting higher BMI as a major risk factor for disease. However, there are two conflicting theories. Some studies suggest that BMI is the determining factor while others suggest physical activity is key because subjects can be overweight and still be physically active
(Crespo et al., 2002).
According to three studies, all-cause mortality increases with decreased physical activity, regardless of weight (Crespo et al., 2002; Janssen & Jolliffe, 2006:
Richardson, Kriska, Lantz, & Hayward, 2004). The applicability of the Hu et al. (2004) study may be limited because they did not collect data on the physical activity levels of the women. They may have found that the women who had higher BMI values were also physically inactive. Two studies suggested that physical inactivity was independent of not just weight but also smoking, sex, overall health, and age (Janssen & Jolliffe, 2006; Lam,
Ho, Hedley, Mak, & Leung, 2004). One thousand forty five elderly men and women with coronary artery disease were followed for 9 years. Despite their age, sex, adiposity, self- perceived health status or smoking habits, those with greater energy expenditure had a lower death rate (Janssen & Jolliffe, 2006). These findings are interesting, but it raises the question as to how much physical activity is necessary to incur such a reduction in all- cause mortality.
Any amount or type of physical activity—be it social, recreational, just walking or low intensity—is better than physical inactivity (Batty, Shipley, Marmot, &
13
Smith, 2002; Crespo et al. 2002; Gregg, Gerzoff, Caspersen, Williamson, & Narayan,
2003; Hakim et al., 1998; Lam et al., 2004; Manini, Everhart, & Patel, 2006; Matthews et al., 2007; Rockhill et al. 2001; Trolle-Lagerros et al. 2005). We can infer from these studies that sedentary people experience the greatest amount of gain in health benefits from physical activity (Batty et al., 2002; Crespo et al., 2002; U.S. Department of Health and Human Services, 2008). Two thousand, eight hundred ninety six adults were followed for 8 years and those that walked at least 2 hours a week had a significant reduction in all-cause mortality. Those physically inactive people have an increased risk of premature death compared mildly physically active people. Also, people who are mildly physically active were at an increased risk of premature death when compared to those who engaged in moderate amounts of physical activity (Gregg et al., 2003). Those who walked 3 to 4 hours a week had the lowest mortality rates (Gregg et al., 2003).
Although any activity is viewed as beneficial, moderate intensity physical activity is reported by several studies and organizations as optimal for health benefits
(ACSM, 2008; AHA, 2009; Gregg et al., 2003; Lam et al., 2004; Manini et al., 2006).
Ninety nine thousand, ninety nine women aged 30-49 were followed for 11 years in
Norway to track physical activity levels. One thousand, three hundred thirteen women died and Trolle-Lagerros et al. (1995) concluded a reduction in risk of death with the increase of physical activity. If the 11-year follow-up had been longer, perhaps the link between physical activity and all-cause mortality might have been stronger, since only
1,313 women had died. If they had sampled an older population, the number of deaths would have been greater. This would have made a larger comparison of deaths to physical activity and the conclusion more reliable. The frequency of physical activity has
14 a lot to do with health benefits, as discussed previously. Thus, regular moderate intensity physical activity is optimal for maintaing health. (Bucksch & Bucksch, 2005; Fujita et al.,
2004; Hamer, Stamatakis, & Saxton, 2009; Lan, Chang, & Tai 2006; Lee & Skerrett,
2001; Löllgen, Böckenhoff, & Knapp 2009; Park et al., 2009; Trolle-Lagerros et al.,
2005).
Physical Activity and Resting Heart Rate
Heart rate is a well known and common clinical measure that reflects cardiovascular health. Healthy adults have a resting heart rate (RHR) of 60–80 beats per minute (bpm) (AHA). RHR typically reflects physical fitness and cardiovascular health.
(Brooks, Fahey, & Baldwin, 2005). RHR in conditioned athletes, for example, is usually below 60 bpm (AHA). This review will discuss the effects physical activity on RHR and other measures of cardiovascular health.
Physically fit people have lower resting heart rates (Hoglund, 1986; Park et al., 2007). The lowering effects of exercise on heart rate have been known for decades
(Park et al., 2007). This physiological response is attributed to a rise in high density lipoproteins (good cholesterol), a decrease in low density lipoproteins (bad cholesterol) during and after exercise. It is also attributed to a decrease in oxygen demand of muscle tissue and an increase in the stroke volume of the heart (Brooks et al., 2005). However, the association of resting heart rate and disease is still uncertain.
Lower resting heart rates have been associated with a decrease in all-cause mortality. Nauman, Nilsen, Wisløff, and Vatten (2010) suggested that mortality risk increased by 18% for every10 beats per minute increase of in RHR in women, and
15 increased by 11% in men. Resting heart rate is also linked to blood platelet aggregation
(Brooks et al., 2005). Just as certain drugs such as aspirin decrease coagulation and fibrinolysis and reduce the risk of sudden cardiac death, exercise may create the same benefits decreasing platelet “stickiness.” These are all factors associated with the health benefits of reduced resting heart rate.
Video Gaming, Exergaming and Health
Exergaming has been credited to improve health through exercise, but few studies have been conducted to measure this (Sinclair, Hingston, & Masek, 2007). This review will report on 10 studies that have been conducted between the years of 2002 and
2010.
Videogames are often considered a contributing factor to the obesity epidemic. A study conducted in Portugal concluded that time spent playing electronic video games is associated with obesity (Carvalhal et al., 2007). The study suggested that a reduction in sedentary behaviors such as electronic game play could contribute to a reduction in obesity in industrialized nations (Carvalhal et al., 2007), but this study did not examine the effects of exergaming as a source of electronic game play.
Measuring energy expenditure (EE) can indicate exergaming’s ability to promote good health. Several studies have been conducted to measure the amount of energy expenditure (EE) exerted during game play (Graf, Pratt, Hester, & Short, 2009;
Graves, Stratton, Ridgers, & Cable 2007; Lanningham-Foster et al., 2008; Miyachi,
Yamamoto, Ohkawara, & Tanaka, 2010; Sell, Lillie, & Taylor, 2008; Siegel, Haddock,
Dubois, & Wilkin, 2009; Trout & Zamora, 2008). Although these studies have reported
16 significantly greater EE compared to traditional sedentary videogames, three of the eight concluded that playing these exergames does not require as much energy as actual exercise such as running, weight lifting or playing the equivalent sport (Graves, Ridgers,
& Stratton, 2008; Graves, Stratton, et al., 2007; Lanningham-Foster et al., 2009). Three studies concluded that EE while playing Wii Sports was not sufficient enough to reach the American College of Sports Medicines’ (ACSM) recommended daily amount of moderate intensity exercise (ACSM, 2008; Graves, Ridgers, et al., 2008; Graves,
Stratton, et al. 2007; Lanningham-Foster et al., 2009).
One study measured EE using an Intelligent Device for Energy Expenditure and Activity (IDEEA) (Graves, Stratton, et al. 2007). While this device has been shown to provide a suitable method for estimating EE of walking, running, sitting, and lying down (Zhang, Pi-Sunyer, Boozer, 2004), it does not accurately detect the principal arm and trunk movements used in Wii Sports (Miyachi et al., 2010). The other two studies measured EE with indirect calorimetry where the subjects had to wear a face mask connected to an analyzer which severely restricted the subjects natural movements during game play (Graves, Ridgers, et al., 2008; Lanningham-Foster et al., 2009; Miyachi et al.,
2010). The investigators concluded that EE during exergaming cannot reach sufficient moderate intensity levels. These conclusions may be inaccurate due to the instrumentation used to measure these results.
Several other studies conducted using different kinds of instrumentation have found that a significant amount of EE was reached during game play (Graf et al., 2009;
Miyachi et al., 2010; Sell et al., 2008; Siegel et al., 2009; Stoppani, 2009; Trout &
Zamora, 2008). One such study used a portable metabolic cart and HR monitor with
17 thirteen subjects between the ages of 20 and 30 (Siegel et al., 2009). These subjects were able to reach 60% or better of their heart rate reserve and met the ACSMs recommendations for EE during 30 minutes of exergaming (Siegel et al., 2009).
Three other studies found similar results, where EE met the ACSM recommendations for daily physical activity using different kinds of instrumentation
(Miyachi et al., 2010; Stoppani, 2009). Miyachi et al. (2010) found positive results by using an open-circuit indirect metabolic air tight chamber that collected expired gas while subjects freely played the Wii Fit inside. They concluded that exergames can be used as an effective in-home cardiovascular training activity and can count toward the ACSM’s daily exercise recommendations (Miyachi et al., 2010; Siegel et al., 2009; Stoppani,
2009).
Another study compared the EE of watching television to the EE of exergaming and to the EE of walking on a treadmill. The study concluded that EE increased 2 to 3 times the amount during exergaming or treadmill walking in comparison to watching television (Graf et al., 2009), indicating that EE during exergaming is similar to moderate-intensity treadmill walking (Graf et al., 2009). The Stoppani (2009) study also reached moderate intensity levels when playing DDR. They reported 10 calories burned for every minute of game play.
It has also been suggested that an increased frequency of exergaming is linked to improved health (Sell et al., 2008). A study conducted using 12 male college students found that the more experience people have with the game, exercise more intensely when playing them. They found that higher energy expenditure occurred in the subjects who had the most experience with the game (Sell et al., 2008).
18
Further research is still needed in order to replicate the amounts of EE rates measured in these studies. Although measuring EE can indicate exergamings ability to promote health, it cannot directly indicate the potential health benefits. Exergaming has been credited with improving health through exercise, but few studies have been conducted to measure this (Sinclair et al., 2007). Research in this area is needed if exergaming is to continue to evolve for the better.
Many of the studies conducted on exergaming have to do with energy expenditure for a single bout of play with the games and are conducted in a laboratory setting. The majority of them have found a positive correlation between exergaming and increased EE. However, research on the long term cardiac health effects of exergaming is non existent. Can exergaming aid in the reversal of the growing obesity epidemic? Can it promote cardiovascular fitness? These questions make this investigation unique to this field of study. Theoretically, if exergaming causes long term increases in EE, then the activity might have cardiovascular benefits. However, physiological response is yet to be tested over the course of three months.
CHAPTER III
METHODOLOGY
Introduction
Advanced technology and entertainment such as videogames promote physical inactivity and might contribute to the obesity epidemic in the United States.
(Carvalhal et al., 2007) Exergames such as the Nintendo Wii increase energy expenditure and have become increasingly popular. More than half of American adults (53%) currently play videogames, and about one in five adults (21%) play every day or almost every day (Lenhart, Jones, & Macgill, 2008). Only 25% of players are under 18 years old and the average game player is age 35. Forty nine percent are between the ages of 18 to
49 years, and 26% of players are 50 years or older (ESA, 2009). This investigation is seeking a better understanding of the effects of exergaming on the body. If exergaming can improve the fitness of the players, then we may have a popular activity that promotes health and weight control. This study was designed to test the Wii Fits ability to impact physical fitness.
Design of the Investigation
This study was conducted as a quasi-experimental design (non-randomized controlled trial). After participants were selected for the study, they were self selected to a control group and an experimental group. Each participant received a consent form
19 20 to sign, a Duke Activity Status Index (DASI) questionnaire to assess approximate
VO2max; and all were measured for RHR, weight, and BMI. Inclusion criteria for participants were as follows:
1. Subjects must be over the age of 18 years.
2. Subjects must not be an athlete or already engaging in 150 minutes a week of moderate intensity physical activity.
3. Subjects must be able to engage in 150 minutes a week of moderate intensity physical activity.
The experimental group played the Wii Fit for 150 minutes a week for the duration of 3 months. The control group did not play the Wii Fit and did not change their daily lifestyle or habits from the way they were living for the past 6 months. The experimental subjects input their height; the Wii Fit Board then weighed them, and the
Wii console calculated body mass index (BMI). The Wii Fit recorded time spent during play and reported it to the subjects after each game. Wii Fit exercise activities include aerobics, yoga, strength training, and balance games. This design allowed for subjects to play the Wii Fit as it was intended: at any time, in their homes, and with a choice of any preferred category of activities.
The pretest of RHR, DASI questionnaire, weight, and BMI was taken for both groups; and after 3 months time the post test was given and data were analyzed. The post test was the same as the pre test. This design investigated the effectiveness of the Wii
Fit’s ability to impact fitness levels as assessed with changes in RHR, weight, BMI, and items in the DASI questionnaire.
21
Subjects
Thirty-one male and female subjects were recruited from the United States.
All participants were recruited through Facebook, advertisement on the CSU Chico campus, and by word of mouth. Twenty-two subjects were a part of the control group, and 15 subjects were part of the treatment group (see Table 1). The control group consisted of 9 females aged 19 to 56 years and 13 males aged 19 to 72 years (see Table
2). The treatment group consisted of 8 females aged 24 to 73 years and 7 males aged 20 to
80 years. All subjects were at least 18 years old and met the inclusion criteria.
Treatment
A pretest measured weight, BMI and RHR. The subjects took the DASI questionnaire which estimated peak oxygen uptake (VO2max) Three measurements of
RHR were taken for a full minute while the participant was seated. RHR was measured in the morning and before any coffee or medication had been consumed. The same day of the pre-test, the participants in the treatment group began using the Wii fit. A post-test of the same measurements and questionnaire were administered to both groups.
Instrument
Nintendo Wii Fit is an exergame used with the Nintendo Wii (Ashby, 2008). It uses the Wii Balance Board peripheral as a whole body movement input device (Ashby,
2008). The Wii Fit games are divided into aerobics, strength training, yoga, and balance games. The focus of the activities is to provide a workout using methods that emphasize controlled motions for core strength and endurance as well as quick motions that bring up the heart rate. Figure 1 outlines the 50 activities Wii Fit offers.
22
Table 1
Treatment Group Subject Data
Subjects Age Weighta BMIb Sex
1 75 158 24.7 f
2 80 192 25.82 m
3 56 176 27.54 f
4 59 221 30.25 m
5 51 187 27.95 f
6 50 302 39.8 m
7 35 184 28.8 f
8 32 303 34.91 m
9 50 182 28.5 f
10 20 196 26.73 m
11 55 217 32.61 f
12 60 220 31.09 m
13 46 330 43.5 m
14 50 202 30.7 f
15 24 119 22.5 f
AVERAGE 49.53 212.6 30.36
SD 16.70 57.56 5.57 a Weight is Reported in Pounds b BMI in kg/m2
23
Table 2
Control Group Subject Data
Subjects Age Weighta BMIb Sex
16 56 142 21.6 f
17 20 185 27.3 m
18 20 190 27.3 m
19 21 150 20.9 f
20 20 176 23.9 m
21 20 131 19.9 f
22 20 146 22.9 m
23 19 128 22 f
24 20 135 19.9 f
25 21 176 22.6 m
26 19 137 23.5 f
27 21 135 20.5 f
28 72 286 35.7 m
29 21 195 26.4 m
30 47 150 23.5 m
31 25 135 23.2 f
32 24 185 27.3 m
33 65 192 26 m
34 60 189 23.6 m
35 23 158 25.5 f
36 26 220 33.4 m
AVERAGE 30.47 168.61 24.61
SD 17.52 37.85 4.07 a Weight is Reported in Pounds b BMI in kg/m2
24
Strength training Yoga
1. Single Leg Extension 1. Deep Breathing 2. Sideways Leg Lift 2. Half-Moon 3. Arm and Leg Lift 3. Dance 4. Single-Arm Stand 4. Cobra 5. Torso Twists 5. Bridge 6. Rowing Squat 6. Spinal Twist 7. Single Leg Twist 7. Shoulder Stand 8. Lunge 8. Warrior 9. Push-Up and Side Plank 9. Sun Salutation 10. Jackknife 10. Tree 11. Plank 11. Downward Facing Dog 12. Triceps Extension 12. Standing Knee 13. Push-Up Challenge 13. Palm Tree 14. Plank Challenge 14. Chair 15. Jackknife Challenge 15. Triangle Aerobics Balance games 1. Hula Hoop 1. Soccer Heading 2. Super Hula Hoop 2. Ski Jump 3. Rhythm Boxing 3. Ski Slalom 4. Basic Step 4. Snowboard Slalom 5. Advanced Step 5. Table Tilt 6. Free Step 6. Tightrope Walk 7. Basic Run 7. Balance Bubble 8. 2-P Run 8. Penguin Slide 9. Free Run 9. Lotus Focus
Figure 1. Activities the Wii Fit offers by category.
An animated piggy bank kept track of the minutes spent in each activity. This accumulation of time was called Fit Credits. When players accumulated enough Fit credits, the game unlocked new activities, such as new yoga poses or strength workouts.
Accumulation of Fit credits also unlocked more challenging settings for an activity that is already available such as increasing the number of repetitions or duration. Fit Credits are equal to the number of minutes spent engaging in an activity, rather than the number of minutes spent during overall game play including idle processes
25
Data Analysis Procedures
The data were summarized using descriptive statistics (means and standard deviations). Analysis-of-variance (ANOVA) was used to assess pre test and post test differences and efficacy of the Wii fit intervention. Statistical significance was set at α =
0.05. Microsoft Excel 2003 spreadsheet was used to create tables and conduct the paired t-test calculations. Vassarstats.com was used to conduct the ANOVA.
CHAPTER IV
RESULTS
Subjects
Fifteen subjects ranging in age from 20 to 80 years (49.5 ± 16.7 yr; mean ±
S.D.) were included in the treatment group (8 female and 7 male). Age, weight, height, gender, and BMI appear in Table 1. Estimated peak oxygen uptake by the DASI ranged from 26.5 to 34.6 mL/kg/min with a mean score of 29.4 ± 2.8 mL/kg/min. All subjects in the treatment group completed the study.
Twenty-one subjects, ranging in age from 19 to 65 yr (30.4 ± 17.5), were included in the control group (9 female and 12 male). Age, weight, BMI, and gender appear in Table 2. Estimated peak oxygen uptake by the DASI ranged from 20.0 to 34.6 mL/kg/min with a mean score of 31.7 ± 3.2 mL/min. All subjects in the treatment group completed the study.
The difference in initial mean ages between the control group and treatment group was 19.06 yr. The difference in initial mean weight between groups was 43 pounds and BMI was 5.9. The difference in initial DASI scores between groups was 2.28 and
RHR was 4.75 bpm.
26 27
Weight
The treatment groups weight dropped by a mean of 7.9 lbs (3.6kg), this was found significant in a paired t-test (see Table 3). Refer to Table 19 at the end of this chapter for the treatment groups’ weight differences. Weight in the control group was
1.09 lbs (.49kg), which was insignificant (p > 0.05). (See Table 4.) Weight increased in 8
Table 3
Paired t-Test Statistical Results Two Sample for Means (Weight Treatment)
Weight Treatment Group
Pre Weight Post Weight
Mean 212.6 204.6
Variance 3313.9 3176.2
Observations 15 15
Pearson Correlation 0.99
Hypothesized Mean Difference 0
df 14
t Stat 5.08
P(T<=t) one-tail 8.33E-05
t Critical one-tail 1.76
P(T<=t) two-tail 0.00016
t Critical two-tail 2.14
28
Table 4
Paired t-Test Statistical Results Two Sample for Means (Control Group)
Weight Control Group
Pre Weight Post Weight
Mean 168.6 169.7
Variance 1433.0 1487.4
Observations 21 21
Pearson Correlation 0.99
Hypothesized Mean Difference 0
df 20
t Stat -1.47
P(T<=t) one-tail 0.07
t Critical one-tail 1.72
P(T<=t) two-tail 0.15
t Critical two-tail 2.08
control subjects, did not change in 12 subjects, and decreased 1 to 2 pounds in 2 subjects
(see Table 20 at the end of this chapter). Figure 2 shows weight changes during the experiment and illustrates the differences between groups.
29
Mean Weight Pre Post Data
220
210
200 P < .0001 190 Treatment Group 180 Control Group Pounds 170
160
150
140 Pre Post
Figure 2. Treatment and Control group pre/post mean weights. A significant drop in weight was found in the treatment group.
Between group comparisons were made with ANOVA (see Table 5) and an independent t-test (see Table 6). Both tests indicated a statistical significance for a decrease in weight for the treatment group.
Body Mass Index
BMI decreased by 1.29 in the treatment group (p < 0.0001). (See Table 7.) but did not change in the control group (p > 0.05). BMI did not change in the control group
(see Table 8). BMI increased in 8 subjects, showed no change in 12 subjects, and decreased slightly in 2 subjects (see Table 20).
Figure 3 illustrates the differences between groups and the mean differences in BMI before and after the 3-month test period. Between group comparisons were made
30
Table 5
Between Group Weight Comparisons, Statistical Results from Vassar Stats
ANOVA Summary
A = groups: the between-subjects variable
B = the repeated-measures variable
Source SS df MS F P
Between Subjects 176109 35
A 27212 1 27212 6.21 0.01
Subjects within A 148897 34 4379.3
Within Subjects 868.5 36
B 130.68 1 130.68 11.82 0.001
A x B 361.91 1 361.91 32.72 <.0001
B x Subjects within A 375.91 34 11.06
Total 176977 71
SS = sum of squares, df = degrees of freedom, MS = mean square, F = Ratio of mean squares and P = probability
with ANOVA (see Table 9) and an independent t-test (see Table 10). Both tests showed a statistical significant decreases in BMI for the control group.
Resting Heart Rate
RHR decreased by 5 bpm in the treatment groups (p<0.0001, see Table 11).
RHR was unchanged in the control group (see Table 12). Seven subjects had a rise in
31
Table 6 t-Test Comparing Mean Weight Between Groups Two-Sample Assuming Unequal
Variances
Weight
Treatment Control
Mean -7.93 1.09
Variance 37.06 11.59
Observations 15 21
Hypothesized Mean Difference 0
df 20
t Stat -5.19
P(T<=t) one-tail 2.20 E-05
t Critical one-tail 1.72
P(T<=t) two-tail 4.41 E-05
t Critical two-tail 2.08
RHR, 9 subjects remained the same and 5 subjects experienced a drop of 1 to 3 beats per minute (see Table 19). See Table 20 for the control groups’ RHR differences.
Figure 4 illustrates changes in RHR before and after the three month experimental period and the results of the ANOVA and t-test are shown in Table 13 and
Table 14. Both tests showed a statistically significant decrease in RHR (p <0.0001).
32
Table 7
Paired t-Test Statistical Results Paired Two Sample for Means (Treatment Group)
BMI Treatment group
Pre BMI Post BMI
Mean 30.54 29.36
Variance 31.53 27.97
Observations 15 15
Pearson Correlation 0.98
Hypothesized Mean Difference 0
df 14
t Stat 4.98
P(T<=t) one-tail 9.94 E-05
t Critical one-tail 1.76
P(T<=t) two-tail 0.0001
t Critical two-tail 2.14
Duke Activity Status Index
DASI increased by 1.53 in the experimental group, which was statistically significant in a paired t-test (see Table 15). Table 19 shows the DASI scores before and after the experimental period. There were no significant differences in DASI scores for the control group (see Table 16 and Table 20)
33
Table 8
Paired t-Test Statistical Results Paired Two Sample for Means (Control Group)
BMI Control Group
Pre BMI Post BMI
Mean 24.61 24.81
Variance 16.58 17.82
Observations 21 21
Pearson Correlation 0.99
Hypothesized Mean Difference 0
df 20
t Stat -1.59
P(T<=t) one-tail 0.06
t Critical one-tail 1.72
P(T<=t) two-tail 0.12
t Critical two-tail 2.08
Figure 5 shows the differences between groups and the mean differences in
DASI scores before and after the 3-month test period. Statistical tests appear in Table 17 and Table 18. Both tests showed a statistically significant increase in DASI scores.
34
Mean BMI Pre Post Data
32
30
28 P < .0001 Treatment Group Control Group 26 BMI
24
22
20 Pre Post
Figure 3. Treatment and Control group pre/post mean BMI values. A significant drop in BMI was found in the treatment group.
35
Table 9
Between Group BMI Comparisons, Statistical Results from Vassar Stats
ANOVA Summary
A = groups: the between-subjects variable
B = the repeated-measures variable
Source SS df MS F P
Between Subjects 1993.4 35
A 481.13 1 481.13 10.82 0.002
Subjects within A 1512.3 34 44.48
Within Subjects 19.96 36
B 2.51 1 2.51 9.3 0.004
A x B 8.29 1 8.29 30.7 <.0001
B x Subjects within A 9.16 34 0.27
Total 2013.4 71
Note. SS = sum of squares, df = degrees of freedom, MS = mean square, F = Ratio of mean squares and P = probability
36
Table 10 t-Test Comparing Mean BMI Between Groups Two-Sample Assuming Unequal Variances
BMI
Treatment Control
Mean -1.29 0.2
Variance 0.51 0.33
Observations 15 21
Hypothesized Mean Difference 0
df 26
t Stat -6.70
P(T<=t) one-tail 2.06 E-05
t Critical one-tail 1.70
P(T<=t) two-tail 4.12 E-07
t Critical two-tail 2.05
37
Table 11
Paired t-Test Statistical Results Two Sample for Means (Treatment Group)
RHR Treatment Group
Pre RHR Post RHR
Mean 74.8 69.8
Variance 101.6 98.45
Observations 15 15
Pearson Correlation 0.94
Hypothesized Mean Difference 0
df 14
t Stat 6.08
P(T<=t) one-tail 1.42 E-05
t Critical one-tail 1.76
P(T<=t) two-tail 2.84
t Critical two-tail 2.84 E-05
38
Table 12
Paired t-Test Statistical Results Two Sample for Means (Control Group)
RHR Control Group
Pre RHR Post RHR
Mean 70.04 70.28
Variance 62.04 61.91
Observations 21 21
Pearson Correlation 0.98
Hypothesized Mean Difference 0
df 20
t Stat -0.79
P(T<=t) one-tail 0.21
t Critical one-tail 1.72
P(T<=t) two-tail 0.43
t Critical two-tail 2.08
39
Mean RHR Pre Post Data
76
75
74
73 P < .0001
72 Treatment Group
BPM 71 Control Group
70
69
68
67 Pre Post
Figure 4. Treatment and Control group pre/post mean RHR beats per minute. A significant drop in weight was found in the treatment group.
40
Table 13 t-Test Comparing Mean RHR Between Groups Two-Sample Assuming Unequal
Variances
RHR
Treatment Control
Mean -5 0.23
Variance 9.46 1.89
Observations 16 21
Hypothesized Mean Difference 0
df 20
t Stat -6.34
P(T<=t) one-tail 1.71 E-06
t Critical one-tail 1.72
P(T<=t) two-tail 3.42 E-06
t Critical two-tail 2.08
41
Table 14
Between Group RHR Comparisons, Statistical Results from Vassar Stats
ANOVA Summary
A = groups: the between-subjects variable
B = the repeated-measures variable
Source SS df MS F P
Between Subjects 5269.8 35
A 79.64 1 79.64 0.52 0.47
Subjects within A 5190.1 34 152.65
Within Subjects 278 36
B 68.06 1 68.06 25.78 <.0001
A x B 120.04 1 120.04 45.47 <.0001
B x Subjects within A 89.9 34 2.64
Total 5547.8 71
Note. SS = sum of squares, df = degrees of freedom, MS = mean square, F = Ratio of mean squares and P = probability
42
Table 15
Paired t-Test Statistical Results Two Sample for Means (Treatment Group)
DASI Treatment Group
Pre DASI Post DASI
Mean 29.46 31
Variance 7.85 5.58
Observations 15 15
Pearson Correlation 0.81
Hypothesized Mean Difference 0
df 14
t Stat -3.67
P(T<=t) one-tail 0.001
t Critical one-tail 1.76
P(T<=t) two-tail 0.002
t Critical two-tail 2.14
43
Table 16
Paired t-Test Statistical Results Two Sample for Means (Control Group)
DASI Control Group
Pre DASI Post DASI
Mean 31.75 31.75
Variance 10.58 10.57
Observations 21 21
Pearson Correlation 1
Hypothesized Mean Difference 0
df 20
t Stat -1
P(T<=t) one-tail 0.16
t Critical one-tail 1.72
P(T<=t) two-tail 0.32
t Critical two-tail 2.08
44
Mean DASI Pre Post Scores
32
31.5
31
30.5
Treatment Group 30 P < .0001 Control Group Scores 29.5
29
28.5
28 Pre Post
Figure 5. Treatment and Control group pre/post mean DASI scores. A significant rise in DASI scores was found in the treatment group.
45
Table 17 t-Test Comparing Mean Weight Between Groups Two-Sample Assuming Unequal
Variances
DASI Difference Difference
Mean 1.53 0
Variance 2.60 0
Observations 15 21
Hypothesized Mean Difference 0
df 14
t Stat 3.67
P(T<=t) one-tail 0.001
t Critical one-tail 1.76
P(T<=t) two-tail 0.002
t Critical two-tail 2.14
46
Table 18
Between Group DASI Score Comparisons, Statistical Results from Vassar Stats
ANOVA Summary
A = groups: the between-subjects variable
B = the repeated-measures variable
Source SS df MS F P
Between Subjects 633.53 35
A 40.34 1 40.34 2.31 0.13
Subjects within A 593.19 34 17.45
Within Subjects 35.82 36
B 7.34 1 7.34 13.59 0.0007
A x B 10.26 1 10.26 19 0.0001
B x Subjects within A 18.22 34 0.54
Total 669.35 71
Note. SS = sum of squares, df = degrees of freedom, MS = mean square, F = Ratio of mean squares and P = probability
TABLE 19 Treatment Group Differences
Pre Post WT Pre Post BMI Pre Post RHR Pre Post DASI Subjects Weight Weight Difference BMI BMI Difference RHR RHR Difference DASI DASI Difference
1 158 160 2 24.7 25.51 -0.81 68 68 0 26.5 29.18 2.68 2 192 191 -1 25.82 25.92 -0.1 77 74 -3 29.18 31.44 2.26 3 176 162 -14 27.54 25.26 -2.28 70 64 -6 29.18 31.44 2.26 4 221 200 -21 30.25 28.27 -1.98 76 66 -10 31.44 31.44 0 5 187 183 -4 27.95 27.04 -0.91 91 86 -5 26.5 29.18 2.68 6 302 295 -7 39.8 38.9 -0.9 82 78 -4 26.5 31.44 4.94 7 184 172 -12 30.7 28.7 -2 72 67 -5 31.44 31.44 0 8 303 294 -9 35.8 34.6 -1.2 64 60 -4 31.44 34.66 3.22 9 182 175 -7 28.5 27.4 -1.1 88 84 -4 29.18 31.44 2.26 10 196 187 -8 26.73 26.11 -0.62 64 60 -4 34.66 34.66 0 11 217 213 -4 32.61 31.27 -1.34 90 85 -5 29.18 29.18 0 12 220 219 -1 31.09 30.65 -0.44 79 74 -5 26.5 29.18 2.68 13 330 317 -13 43.5 41.08 -2.42 78 66 -12 26.5 26.5 0 14 202 188 -14 30.7 28.53 -2.17 63 55 -8 29.18 29.18 0 15 119 113 -6 22.5 21.3 -1.2 60 60 0 34.66 34.66 0
AVERAGE 212.6 204.6 -7.93 30.54 29.36 -1.29 74.8 69.8 -5 29.46 31 1.53 SD 57.56 56.36 6.08 5.61 5.28 0.71 10.08 9.92 3.1848 2.80 2.36 1.61 Note. Weight is reported in pounds, BMI in kg/m2, and DASI Scores in mL/min. 47
Table 20 Control Group Differences. Weight is reported in pounds, BMI in kg/m2, and DASI Scores in mL/min
Pre Post WT Pre Post BMI Pre Post RHR Pre Post DASI Subjects weight Weight Difference BMI BMI Difference RHR RHR Difference DASI DASI Difference 16 142 142 0 21.6 21.6 0 61 62 1 31.44 31.44 0 17 185 200 15 27.3 29.5 2.2 72 73 1 34.66 34.66 0 18 190 190 0 27.3 27.3 0 69 66 -3 34.66 34.66 0 19 150 150 0 20.9 20.9 0 68 68 0 31.44 31.44 0 20 176 176 0 23.9 25.2 1.3 76 76 0 31.44 31.44 0 21 131 133 2 19.9 20.2 0.3 88 87 -1 31.44 31.44 0 22 146 146 0 22.9 22.9 0 65 64 -1 34.66 34.66 0 23 128 129 1 22 22.1 0.1 59 59 0 34.66 34.66 0 24 135 135 0 19.9 19.9 0 78 78 0 31.44 31.44 0 25 176 176 0 22.6 22.6 0 67 67 0 34.66 34.66 0 26 137 138 1 23.5 23.7 0.2 74 74 0 29.18 29.18 0 27 135 135 0 20.5 20 -0.5 72 71 -1 31.44 31.44 0 28 286 288 2 35.7 36 0.3 55 58 3 20 20 0 29 195 193 -2 26.4 26.2 -0.2 68 67 -1 34.66 34.66 0 30 150 150 0 23.5 23.5 0 65 66 1 31.44 31.44 0 31 135 134 -1 23.2 23 -0.2 63 64 1 29.18 29.18 0 32 185 185 0 27.3 27.3 0 74 74 0 34.66 34.66 0 33 192 196 4 26 26.6 0.6 79 81 2 31.44 31.44 0 34 189 190 1 23.6 23.7 0.1 68 68 0 31.44 31.44 0 35 158 158 0 25.5 25.5 0 82 85 3 31.44 31.44 0 36 220 220 0 33.4 33.4 0 68 68 0 31.44 31.44 0 AVERAGE 168.62 169.7 1.09 24.61 24.81 0.2 70.04 70.28 0.23 31.75 31.75 0 SD 37.85 38.57 3.40 4.07 4.22 0.57 7.87 7.86 1.37 3.25 3.25 0 Note. Weight is reported in pounds, BMI in kg/m2, and DASI Scores in mL/min. 48
CHAPTER V
DISCUSSION
The purpose of this study was to determine if playing the Nintendo Wii Fit for
150 minutes per week for 12 weeks could have an impact on cardiovascular fitness and weight loss. The experimental group experienced a significant decrease in resting heart rate, weight and BMI (p<.0001). Subject number 4 showed a notable decrease of 21 lbs during the 12-week period. There were no significant changes in any variables in the control group during the 12-week study.
The control and experimental groups were notably different at the beginning of the experiment. The treatment group average age was 49 years old and the control group was 30 years old, creating a difference of approximately 19 years. The control group pre weight was an average of 44 lbs less than the treatment group’s pre weight.
Although the control group was younger and lighter, significant results were still reported in the paired t-Test two sample for means in all categories for the treatment group. No significant changes were reported for the control group. However, it is notable that the control groups’ pre and post DASI scores were unchanged. This was probably due to the younger mean age and by the higher fitness levels of the control group.
The results for between group comparisons could have been affected by the initial differences in weight, BMI and RHR. This difference was illustrated by the higher weight, age and RHR of the treatment group at the beginning of the experiment. DASI
49 50 scores were also initially lower in the experimental than the control group, which highlighted the treatment group’s lower rates of regular physical activity before the study began. This lower initial rate of regular physical activity could have increased the experimental group’s chances of experiencing changes in weight and RHR. However, subject numbers 4, 7, 10, 11, 13, 14, 15 scored higher on the DASI and also experienced weight loss, and lowered RHR. We can infer from these data that playing the Wii Fit elicits a physiological response that is consistent with recommendations for weekly physical activity.
Subjects were not randomly allocated into the groups. Those that owned a Wii
Fit or who could purchase a Wii Fit at the start of the study were included in the treatment group. Older adults were able to afford the equipment, which accounts for the older mean age of the subjects in the treatment group.
There was no attrition from this study. The high participation rate was attributed to the nature of the Wii Fit. Several subjects mentioned that it was enjoyable and fun to play, suggesting the Wii Fit might help improve exercise compliance in marginally fit people. This finding is supported by another study conducted on exergaming (Trout & Zamora, 2008) that reported participants experiencing a consistent high level of enjoyment while playing DDR. This dynamic could be very useful to future generations of exergaming and the populations that engage in them as the prevalence of hypokinetic disease continues to increase every year (AHA, 2009). The Wii Fit could be a primer or gateway to more forms of exercise. If sedentary players find this form of game play as a fun way to lose weight, then they may feel more confident or have more motivation to seek out other forms of physical activity. Subject number 14 participating
51 in the experiment made her less self conscious about exercise and encouraged her to walk in the morning around her neighborhood when the study was over.
The research design provided a new format for testing exergaming. All other research in this area has been conducted to measure energy expenditure during game play which can only be used to hypothesize the prolonged effects of exergaming and had been conducted in a laboratory setting. This study provided a setting where the subjects could play at their own leisure and in the comfort of their own home as the game was intended.
Several other studies have found that a significant amount of EE can be reached during game play (Miyachi et al., 2010; Sell et al., 2008Siegel et al., 2009;
Stoppani, 2009) and can meet the ACSMs recommendations for EE during 30 minutes of exergaming (Siegel et al., 2009). These findings support this studies conclusion, as results indicate EE for those playing Wii Fit may have been reached. This study does not support the findings in three other studies that concluded EE was not sufficient enough to meet
ACSM’s recommended daily amount of moderate intensity exercise (Graves, Ridgers, et al., 2008; Graves, Stratton, et al. 2007; Lanningham-Foster et al., 2009)
Although this study indicates significant results, more research is needed to provide replication of these results, under other circumstances.
Conclusion
After the three-month experimental period, the treatment group test subjects experienced a significant decrease in weight, BMI, RHR and DASI scores. We can infer from these data that playing the Nintendo Wii Fit can increase physical fitness. The Wii
Fit system is a possible gateway to other forms of physical activity outside the home.
52
Recommendations for Future Research
After completing this study, the following recommendations are suggested for future investigations:
1. Recruit a larger sample size of test subjects.
2. Allow for random allocation of subjects.
3. Test subjects over a longer period of time.
4. Use VO2 Max testing for pre and post fitness level changes.
5. Limit subjects to specific physical activity levels or age groups.
REFERENCES
REFERENCES
American College of Sports Medicine. (2008). ACSM's guidelines for exercise testing
and prescription (6th ed.). Hagerstown, MD: Lippincott Williams & Wilkins.
American Heart Association. (2009). Statistics you need to know: Statistics on lifestyle
compliance. Retrieved from
http://www.americanheart.org/presenter.jhtml?identifier=107
Armstrong, R. (2007). Couch athletes: How to get fit from the comfort of your sofa.
Retrieved from http://www.independent.co.uk/life-style/health-and-
families/health-news/couch-athletes-how-to-get-fit-from-the-comfort-of--
your-sofa-457626.html
Ashby, A. (2008, April). Wii fit: Exergaming the world. GamePro, 235, 19.
Batty, G., Shipley, M., Marmot, M., & Smith, G. (2002). Physical activity and cause-
specific mortality in men with type 2 diabetes/impaired glucose tolerance:
Evidence from the Whitehall study. Diabetes Medicine, 19(7), 580-588.
Bogost, I. (2005). The rhetoric of exergaming. Retrieved from
http://www.exergamefitness.com/pdf/The%20Rhetoric%20of%20Exergaming
Brooks, G., Fahey, T., & Baldwin, K. (2005). Exercise physiology: Human bioenergetics
and its applications (4th ed.). New York, NY: McGraw-Hill.
54 55
Bucksch, J., & Bucksch, J. (2005). Physical activity of moderate intensity in leisure time
and the risk of all cause mortality. British Journal of Sports Medicine, 39(9),
632-638.
Carvalhal, M., Padez, M., Moreira, P., & Rosado, V. (2007). Overweight and obesity
related to activities in Portuguese children, 7-9 Years. European Journal of
Public Health, 17(1), 42-46
Crespo, C., Palmieri, M., Perdomo, R., Mcgee, D., Smit, E., . . . Sempos, C. (2002). The
relationship of physical activity and body weight with all-cause mortality:
Results from the Puerto Rico heart health program. Annals of Epidemiology,
12(8), 543-552.
Dishman, R. K., Washburn, R. A., & Heath, G. W. (2004). Physical activity
epidemiology. Champaign, IL: Human Kinetics.
Entertainment Software Association (ESA). (2009). Ages of computer and video game
players. Retrieved from http://www.grabstats.com/statmain.asp?StatID=768
Flegal, K. M., Graubard, B. I., Williamson, D. F., & Gail, M. H. (2007). Cause-specific
excess deaths associated with underweight, overweight, and obesity. JAMA,
298(15), 2028 – 2037.
Fujita, K., Takahashi, H., Miura, C., Ohkubo, T., Sato, Y., Ugajjin, T., . . . Hisamichi, S.
(2004). Walking and mortality in Japan: The Miyagi cohort study. Journal of
Epidemiology, 14 (Suppl. 1), S26-S32.
Graf, D., Pratt, L., Hester, C., & Short, K. (2009). Playing active video games increases
energy expenditure in children. Pediatrics, 124(2), 534-540.
56
Graves L., Stratton G., Ridgers, N. D., & Cable, N. T. (2007, December). Comparison of
energy expenditure in adolescents when playing new generation and sedentary
computer games: Cross sectional study. BMJ, 335, 1282-1284.
Graves, L. E., Ridgers, N. D., & Stratton, G. (2008). The contribution of upper limb and
total body movement to adolescents' energy expenditure whilst playing
Nintendo Wii. European Journal of Applied Physiology, 104(4), 617-623.
Gregg, E., Gerzoff, R., Caspersen, C., Williamson, D., & Narayan, K. (2003).
Relationship of walking to mortality among us adults with diabetes. Archives
of Internal Medicine, 163(12), 1440-1447.
Guinness World Records. (2006). Best-selling video games. Retrieved from
http://web.archive.org/web/20060317005503/http://www.guinnessworldrecor
ds.com/content_pages/record.asp?recordid=52404
Hakim, A., Petrovitch, H., Burchfiel, C., Ross, G., Rodriguez, B., White, L. R., . . .
Abbott, R. D. (1998). Effects of walking on mortality among nonsmoking
retired men. New England Journal of Medicine, 338(2), 94-99.
Hamer, M., Stamatakis, E., & Saxton, J. (2009). The impact of physical activity on all-
cause mortality in men and women after a cancer diagnosis. Cancer Causes &
Control, 20(2), 225-231.
Heidemann, C., Schulze M. B., Franco O. H., van Dam R. M., Mantzoros C. S., & Hu F.
B. (2008). Dietary patterns and risk of mortality from cardiovascular disease,
cancer, and all causes in a prospective cohort of women. Circulation, 118(3),
230-237.
57
Hoglund, J. (1986). The effects of a 12-week cardiovascular exercise program on the
resting blood pressure, body weight and resting heart rate levels of
hypertensive individuals. (Unpublished master’s thesis). University of Oregon,
Eugene, Oregon.
Howard, R. (1991). Virtual reality. New York NY, Simon & Schuster.
Hu, F., Willett, W., Li, T., Stampfer, M., Colditz, G., & Manson, J. E. (2004). Adiposity
as compared with physical activity in predicting mortality among women. The
New England Journal of Medicine, 351(26), 2694-2703.
Janssen, I., & Jolliffe, C. (2006). Influence of physical activity on mortality in elderly
with coronary artery disease. Medicine & Science in Sports & Exercise, 38(3),
418-423.
Karvonen, M., Kentala, E., & Mustala, O. (2007). The effects of training on heart rate: A
‘longitudinal’ study. Journal of Physical Activity & Health, 4(3), 307-315.
Lam, T., Ho, S., Hedley, A., Mak, K, & Leung, G. (2004). Leisure time physical activity
and mortality in Hong Kong: Case-control study of all adult deaths in 1998.
Annals of Epidemiology, 14(6), 391-398.
Lan, T., Chang, H., & Tai, T. (2006). Relationship between components of leisure
physical activity and mortality in Taiwanese older adults. Preventive
Medicine: An International Journal Devoted to Practice and Theory, 43(1),
36-41.
Lanningham-Foster, L., Foster R. C., McCrady, S. K., Jensen, T. B., Mitre, N., & Levine,
J. A. (2009). Activity-promoting video games and increased energy
expenditure. Journal of Pediatrics, 154(6), 819-823.
58
Lee, I., & Skerrett, P. (2001). Physical activity and all-cause mortality: What is the dose-
response relation? Medicine & Science in Sports & Exercise, 33(6), S459.
Lenhart A., Jones S., & Macgill, A. (2008). Adults and video games. Retrieved from
http://www.pewinternet.org/Reports/ 2008/Adults-and-Video-Games.aspx.
Löllgen, H., Böckenhoff, A., & Knapp, G. (2009). Physical activity and all-cause
mortality: An updated meta-analysis with different intensity categories.
International Journal of Sports Medicine, 30(3), 213-224.
Manini, T., Everhart, J., & Patel, K. (2006). Energy expenditure and mortality in older
adults. Nutrition Research Newsletter, 25(8), 9-10.
Matthews, C., Jurj, A., Shu, X., Li, H., Yang, G., Li, G., . . . Zheng, W. (2007). Influence
of exercise, walking, cycling, and overall nonexercise physical activity on
mortality in Chinese women. American Journal of Epidemiology, 165(12),
1343.
Motohiko, M., Kenta, Y., Kazunori, O., & Shigeho, T. (2010). METs in adults while
playing active video games: A metabolic chamber study. Retrieved from
http://journals.lww.com/acsm-
msse/Abstract/publishahead/METs_In_Adults_While_Playing_Active_Video
_Games__A.99352.aspx
Nauman, J., Nilsen, T., Wisløff, U., & Vatten, L. (2010). Combined effect of resting heart
rate and physical activity on ischaemic heart disease: Mortality follow-up in a
population study (the hunt study, Norway). Journal of Epidemiology &
Community Health, 64(2), 175-181.
59
Nintendo. (2009a). Financial results briefing for the six-month period ended September
2009. Retrieved from
http://www.nintendo.co.jp/ir/pdf/2009/091030e.pdf#page=6
Nintendo. (2009b). Consolidated financial highlights. Retrieved from
http://www.nintendo.co.jp/ir/pdf/2009/091029e.pdf#page=9
Park, M., Chung, S., Chang, Y., Kim, M., Chung, S., Chang, Y., & Kim, K. (2009).
Physical activity and physical fitness as predictors of all-cause mortality in
Korean men. Journal of Korean Medical Science, 24(1), 13-19.
Purchese, R. (2010). Wii and DS thrash competition in US. Retrieved from
http://www.eurogamer.net/articles/wii-and-ds-thrash-competition-in-us
Richardson, C., Kriska, A., Lantz, P., & Hayward, R. (2004). Physical activity and
mortality across cardiovascular disease risk groups. Medicine & Science in
Sports & Exercise, 36(11), 1923-1929.
Rockhill, B., Willett, W., Manson, J., Leitzmann, M., Stampfer, M., Hunter, D. J., &
Colditz, G. A. (2001). Physical activity and mortality: A prospective study
among women. American Journal of Public Health, 91(4), 578-583.
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(5), 505-511.
Sibbald, B. (2002). Obesity may soon be leading cause of preventable death in us.
Canadian Medical Association Journal, 166(5), 642.
60
Siegel, S., Haddock, B., Dubois, A., & Wilkin, L. (2009). Active video/arcade games
(exergaming) and energy expenditure in college students. International
Journal of Exercise Science, 2(3), 165-174.
Sinclair, J., Hingston, P., & Masek, M. (2007, December). Considerations for the design
of exergames. Proceedings of the 5th International Conference on Computer
Graphics and Interactive Techniques in Australia and Southeast Asia (Perth,
Australia). New York, NY: ACM. doi: 10.1145/1321261.1321313
Star, L. (2005). Exercise, lose weight with 'exergaming'. Retrieved from
http://www.webmd.com/fitness-exercise/features/exercise-lose-weight-with-
exergaming
Stewart, E. (2009). Fitness meets technology. Fitness Informer, 6(Winter), 1.
Stoppani, J. (2009). Cardio corner: Feel like dancing? Flex, 27(3), 132.
Strong, K., Mathers, C., Leeder, S., & Beaglehole, R. (2005). Preventing chronic
diseases: How many lives can we save? Lancet, 366(9496), 1578-82
Trolle-Lagerros, Y., Mucci, L., Kumle, M., Braaten, T., Weiderpass, E., Hsieh, C. C., . . .
Adami, H. O. (2005). Physical activity as a determinant of mortality in
women. Epidemiology, 16(6), 780-785
Trout, J., & Zamora, K. (2008). Dance dance revolution: A physiological look at an
interactive arcade game. International Council for Health, Physical
Education, Recreation, Sport, and Dance Journal of Research, 3(1), 67-72
U.S. Department of Health and Human Services. (2008). Physical activity guidelines
advisory committee report, 2008. Washington, DC: Author.
61
U.S. Department of Health and Human Services. (2010). Insurance company leaders
discuss health insurance premiums. Retrieved from
http://www.hhs.gov/news/press/2010pres/02/20100224c.html
Vandewater, E., Shim, M., & Caplovitz, A. (2004). Linking obesity and activity level
with children’s television and video game use. Journal of Adolescence, 27(1),
71-85.
World Health Organization (WHO). (2009). Information sheet on obesity and
overweight. Retrieved from
http://www.who.int/dietphysicalactivity/publications/facts/obesity/en/
Zhang K., Pi-Sunyer F. X., & Boozer, C. N. (2004). Improving energy expenditure
estimation for physical activity. Medicine & Science in Sports & Exercise,
36(5), 883-889.