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GAMES FOR HEALTH JOURNAL: Research, Development, and Clinical Applications Volume 2, Number 3, 2013 ª Mary Ann Liebert, Inc. DOI: 10.1089/g4h.2013.0016
Effects of Behavioral Contingencies on Adolescent Active Videogame Play and Overall Activity: A Randomized Trial Gregory J. Norman, PhD,1 Marc A. Adams, PhD,2 Ernesto R. Ramirez, MA,1,3 Jordan A. Carlson, PhD,1,3 Jacqueline Kerr, PhD,1 Suneeta Godbole, MPH,1 Lindsay Dillon, MPH,1 and Simon J. Marshall, PhD1
Abstract
Objective: This study evaluated the effect of four active videogames (AVGs) varying in behavioral contingencies (behavior–consequence relations) on adolescent AVG play and overall activity levels over 4 weeks. Materials and Methods: Each AVG, manufactured by SSD/Xavix (Shiseido Co. of Japan, Tokyo, Japan), was coded and scored for the number of positive and aversive behavioral contingencies within the games. ‘‘Bowling’’ and ‘‘Tennis’’ were classified as having ‘‘higher contingency scores,’’ and ‘‘Boxing’’ and aerobic fitness training were classified as having ‘‘lower contingency scores.’’ Adolescents (n = 63; 11–15 years old; 62% male; 38% Hispanic; 44% overweight or obese) were randomized to play one of the four AVGs at home and recorded game play sessions in a paper log. Baseline and week 4 assessments were completed at home; week 1, 2, and 3 assessments were completed by telephone. Accelerometers were worn during baseline and weeks 1 and 4. Results: Accelerometer-measured sedentary and light activity hours/day were stable over time, whereas moderate–vigorous physical activity minutes/day increased in the higher contingency group and decreased in the lower contingency group (interaction effect, 6.43, P = 0.024). Reported game play minutes decreased in both groups from week 1 to week 4 (–29.42 minutes, P = 0.001). Discussion: There was some support for the hypothesis that AVGs with more behavioral contingencies, compared with AVGs with fewer behavioral contingencies, result in more physical activity. However, overall AVG play decreased substantially after the first week. Further study is needed to better understand how behavioral contingencies can be used in AVGs to enhance their potential to provide health benefits to game players. Introduction
S
edentary behavior is one of the most consistent determinants of childhood obesity and is distinct from physical activity as a determinant of health.1–8 Videogames are a popular form of sedentary entertainment for children. Cummings and Vandewater9 found that 36 percent of U.S. adolescents (80 percent of boys, 20 percent of girls) played videogames on average for 1 hour on weekdays and 1.5 hours on weekends. Television and videogames provide attractive entertainment choices to children compared with activities that require more energy expenditure, such as outdoor play and sports.10 For children, activity choice is largely a function of enjoyment and perception of ‘‘fun.’’11 Active videogames (AVGs) (e.g., those for the Nintendo of America [Redmond, WA] Wii, the Microsoft [Redmond]
Kinect, and the SSD’s XaviX Sports [Shiseido Co. of Japan, Tokyo, Japan]), are designed to increase movement during game play. Recent systematic reviews of AVGs for physical activity promotion indicate that AVGs increase activity levels above resting,12,13 indicating that they may be a means to displace some sedentary time with light to moderate intensity activity. However, a recent randomized controlled trial of children receiving a Nintendo Wii AVG in their homes for 12 weeks did not help children improve their daily physical activity levels.14 Other studies of AVGs in the home have had mixed results. A review by Daley15 identified three studies using AVGs as intervention programs for children ranging in age from 9 to 18 years. One study showed a linear decrease in use of ‘‘Dance Dance Revolution’’ (Konami Entertainment, El Segundo, CA) over a 6-month period.16 A second study did not
1
Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, California. School of Nutrition and Health Promotion, Arizona State University, Phoenix, Arizona. 3 Joint Doctoral Program in Public Health, University of California, San Diego and San Diego State University, La Jolla, California. This study is registered at ClinicalTrials.gov with trial registration number NCT01171261. 2
158
EFFECTS OF BEHAVIORAL CONTINGENCIES find a significant difference between groups in AVG play over 12 weeks when one group received an AVG upgrade package.17 In the third study, children were randomized to single-player or multiplayer dance simulation game groups for 12 weeks.18 The multiplayer group played more, but the difference was not statistically significant between the groups. In a study by Graves et al.,19 a peripheral device allowed players to control their characters’ movement on the screen of traditional videogames. In a 12-week study 58 children were randomized to the intervention or a control group. No differences were found between the groups on accelerometer-measured activity, but AVG time increased and sedentary videogame time decreased in the intervention group from baseline to 6 weeks relative to the control group. More studies are needed to assess the sustainability of AVGs over time as a way to influence children’s activity and sedentary time. The inconsistent effects of AVGs on sustained game play and physical activity may partially be a function of the game design and contingencies programmed into AVGs. The AVG ‘‘game experience’’ includes multiple levels that systematically present different types and escalating degrees of stimuli, rules, behavioral requirements, and feedback. These stimuli and the explicit and implicit rules and relations within the game are behavioral contingencies and are modifiable by the game programmers.20 Specifically, behavioral contingencies describe complex functional relations between behavior, the stimuli that precede it (antecedents), and the stimuli that follow (consequences). Mechner21 described behavioral contingencies as ‘‘if, then’’ situations that influence what people do and do not do. For example, in an active AVG version of tennis, if you serve the ball over the net and your opponent cannot return the ball, then you might earn a point, advance a level, receive praise (e.g., ‘‘Ace, nice shot!’’) or audio feedback (e.g., audience clapping), and/or gain access to a special area or item. These contingencies represent a theory-based principle that can be objectively measured and evaluated to understand the frequency and duration of game play over time. At the most basic level, the moment-to-moment relations between a player’s behavior and the game environment can be classified as two broad types of contingences: Reinforcement contingencies or aversive contingencies. Reinforcement contingences (positive and negative reinforcement) are designed to increase the frequency of a player’s actions in the game. Aversive contingencies are designed to decrease or suppress a player’s actions through punitive stimuli (e.g., annoying sounds) or penalties (e.g., loss of points/life). An individual’s behavioral choices over time may be postulated as primarily a function of the relative value of the aggregate contingencies of a game.22 Highly rewarding AVGs are hypothesized to have the greatest chance of being chosen to be used over alternative sedentary options such as television viewing and computer and videogames that require low levels of energy expenditure.23 Quantifying the reinforcing value, in addition to energy expenditure, could be how AVGs are identified to have the greatest public health impact on obesity. As a first step in understanding the potential reinforcing value of AVGs, the purpose of the present study was (1) to quantify the types and number of behavioral contingences in a sample of AVGs and (2) to assess the effect of AVGs classified by the quantity of behavioral contingencies (i.e., behavior–consequence relations) on adolescent game play and physical activity over a 1-month period in a randomized
159 trial. We hypothesized that AVGs with more behavioral contingencies would result in longer duration of game play and overall physical activity over time compared with AVGs with few contingencies. Materials and Methods Participants Adolescents between the ages of 11 and 15 years were recruited into the study. This age group was targeted because only 8 percent meet public health recommendations for physical activity.24 Eligible participants had a functioning telephone, had at least one television on which to play the game, planned to stay in San Diego, CA, for the 1-month study period, and did not have a medical condition preventing regular physical activity. Parents gave written consent for their child to participate, and each adolescent provided signed assent. Eligible participants with an existing AVG system in the home were excluded from participation. All study procedures were approved by the university institutional review board. Adolescents were recruited from community newspaper advertisements, Internet advertisements (e.g., Craig’s List), university staff listservs, and flyers posted in the community. Potential participants and their parents completed a phone screener to assess eligibility and to obtain demographic information. Description of the exergames Four AVGs manufactured by SSD/Xavix were used in this study. Each AVG cartridge consisted of multiple subgames, which provided variations on the basic game theme and games for developing player skills. ‘‘Xavix Tennis’’ simulates tennis using a tennis racket controller and an infrared sensor to detect the location, speed, intensity, and timing of the player’s swing of the racquet. The ‘‘Jackie Chan Studio Fitness’’ ( J-MAT) cartridge includes a four-panel floor mat made of flexible material that functions as the wireless interface game controller for tracking stepping, running, jumping, and touching movements; the celebrity actor Jackie Chan is the avatar character in the game that demonstrates and guides users in five subgames. ‘‘Xavix Bowling’’ uses a wireless bowling ball game controller to simulate bowling and includes five subgames. ‘‘Xavix Boxing’’ uses boxing gloves as the game controller and includes five subgames. Each AVG consisted of a game cartridge, the XavixPort platform, and game controllers (e.g., tennis racquets, boxing gloves, bowling ball, or floor mat). The XavixPort interfaces the AVG with the participant’s television and contains the infrared sensors that track the movement of the wireless game controllers. The AVG and needed game peripherals were provided at no cost to the participants. Procedure A home visit was scheduled for eligible and interested participants. Participants were randomized to one of four XaviX sports games: ‘‘Tennis,’’ ‘‘Bowling,’’ ‘‘Boxing,’’ or J-MAT. Participants were blinded to the contingency score of their game and were not aware of this aspect of the study. Two research assistants visited the participant’s home to complete written assent and consent forms, install the game on a television in the home, and give an overview on how to
160 play the game. Participants were instructed to record time spent playing the XaviX games on a paper log. Participants were asked to play the AVG for as much as they like for the next 4 weeks. The participant and parent completed a baseline survey during the home visit. During the 4 weeks of AVG play, participants in each study arm were contacted three time via telephone by study staff (approximately once a week, to complete short surveys about their game play and attitudes about the AVG and to address any questions from participants). After week 4, a second home visit was conducted to collect the game cartridge and system, administer final surveys to the participant and a parent, and distribute the incentive. Participants received $50 for completing all study measurements. Process of coding behavioral contingencies in AVGs Adams et al.20 published the conceptual framework for coding behavioral contingencies in AVG games. In brief, a research assistant played all games and levels for a given cartridge and explored all outcomes for each game. For example, the research assistant explored what stimuli occurred in the game for a correct player response, an incorrect response, and no response. The entire experience was recorded to a DVD. At least two other research assistants viewed the DVD and independently recorded descriptions of all observed implicit and explicit game stimuli using ‘‘if-then’’ rules. Each ‘‘if-then’’ contingency was defined with the ‘‘if’’ corresponding to a behavior and the ‘‘then’’ corresponding to a consequence.21 Disagreements for the ‘‘if-then’’ rules were resolved by consensus after reviewing the game multiple times. Three behavioral scientists adjudicated the master lists using standardized definitions of antecedents, behavioral dimensions, and consequences (available from the lead author on request). ‘‘If-then’’ descriptions were initially coded as (1) antecedent, (2) behavior, or (3) consequence. For the current analysis, consequences were further coded as (a) positive reinforcement (i.e., game presents a desired stimulus to increase behavior [e.g., giving a point] to increase the likelihood of a response), (b) negative reinforcement (i.e., game takes away an aversive stimulus [e.g., removing a count-down clock or challenging opponent] to increase response), (c) positive punishment (i.e., game presents an aversive stimulus [e.g., presents new obstacles or negative sounds] to decrease response), or (d) response cost/negative punishment (i.e., game takes away a desired stimulus [e.g., losing a life] to decrease response). Table 1 shows the behavior contingency scores resulting from the coding process for each subgame within an AVG cartridge. Scores were determined for reinforcing contingencies (i.e., consequences expected to increase the probability of a behavior) and aversive contingencies (i.e., consequences expected to decrease the probability of a behavior. Contingency scores were computed as the sum of reinforcing and aversive contingencies for each subgame (see total score column in Table 1). Subgame scores were then summed to create a cartridge level behavioral contingency score. As seen in Table 1, ‘‘Bowling’’ had the highest total behavioral contingency score of 170, whereas J-MAT had the lowest score of 113. For subsequent analyses ‘‘Bowling’’ and ‘‘Tennis’’ were grouped as the ‘‘higher behavioral contingency’’ games, whereas ‘‘Boxing’’ and J-MAT were grouped as the ‘‘lower behavioral contingency’’ games.
NORMAN ET AL. Table 1. Behavioral Contingency Scores Derived from Coding Active Videogame Consequences Behavioral contingencies XaviX cartridge, subgames ’’Bowling’’ ’’Against the Clock’’ ’’Moving Pins’’ ’’Panel Crusher’’ ’’Regular Game’’ ’’Tournament’’ Total ’’Tennis’’ ’’Rally Time’’ ’’Serve & Finish’’ ’’Serving Aces’’ ’’Target Challenge’’ ’’Tournament’’ Total ’’Boxing’’ ’’Punch Fast’’ ’’Panel Toucher’’ ’’Punch Red Ball’’ ’’Combination Training’’ ’’Championship’’ Total Aerobic J-MAT ’’Action Run’’ ’’Dash’’ ’’Reflex’’ ’’Step Lively’’ ’’Vigorous Step’’ Total
Reinforcement Aversive Total contingencies contingencies score 12 37 31 13 16 109
13 14 18 7 9 61
170
8 16 22 9 40 95
5 9 20 12 24 70
165
12 14 13 14 37 90
5 5 9 12 24 55
145
33 8 6 11 19 77
12 2 4 6 12 36
113
Each number is a count of coded descriptors. J-MAT, ‘‘Jackie Chan Studio Fitness.’’
Measures Game logs. Participants were given a paper game log on which to record the date and start and end times of game play. Participants also indicated the game mode (e.g., challenge games, tournament) and with whom, if anyone, they played the game. Participants were asked to keep the log next to the game console and record each game session as a new entry into the log. Game log information was collected by a research assistant during the weekly calls, and game logs were collected from the home at the end of week 4. Weekly minutes of game play and number of log entries were computed for each participant. Accelerometers. Daily physical activity and sedentary behavior were assessed with the GT1M device (Actigraph, Pensacola, FL) (www.theactigraph.com), a uni-axial accelerometer that is worn on a belt snugly around the waist. The GT1M records time-varying accelerations ranging in magnitude from approximately 0.05 to 2 Gs. The Actigraph technology has been shown to be valid for quantifying activity levels in laboratory and field settings.25 Sedentary, light, and moderate–vigorous (MVPA) physical activity intensities were determined in 30-second epochs using the age-adjusted cut points of Freedson and Miller26 and summed for minutes of valid days ( > 10 hours of wear time). Non–wear time was
EFFECTS OF BEHAVIORAL CONTINGENCIES
161
defined by 60 minutes of consecutive zero values. Accelerometers were sent to the participants’ homes to wear for 1 week prior to receiving the AVG. Participants continued to wear the accelerometer during the first week of AVG play and then returned the accelerometer by mail. At week 4, participants were mailed an accelerometer and instructed to wear it for 7 days.
Results Recruitment occurred between July 2009 and April 2010. Approximately 120 inquiries were made by telephone to the research office in response to the recruitment methods, and 107 of the inquiries were screened by a research assistant. In total, 35.5% (38 of 107) of those screened did not participate in the study. Twenty-five were ineligible because of an existing AVG in the home. Additionally, five declined to participate during screening, and eight could not be reached to complete the screening. In total, 69 (64.5 percent) adolescents were considered initially eligible to participate in the study and were randomized, using a random digit table, to an AVG condition; however, six adolescents did not complete the baseline home visit and did not begin the study. Thus 63 participants completed the baseline visit and began the study. Sample demographic characteristics are presented in Table 2. No statistically significant differences were found between higher and lower contingency score groups on demographic characteristics with the exception of overweight status, with more obese children in the lower compared with the higher contingency score group (P < 0.001).
Demographics. A parent of the participating adolescent completed a survey at baseline that included questions about the child’s gender, date of birth, race, Hispanic ethnicity, height, weight, typical grades in school (mostly As and Bs, mostly Cs, mostly Ds and Fs), and the approximate annual household income. The body mass index of adolescents was calculated as kilograms/meters squared. Body mass index-for-age percentile was determined from the Centers for Disease Control and Prevention’s national norms.27 The 85th percentile defined overweight, and the 95th percentile defined obese. Statistical analysis. Univariate statistics tested for differences between study conditions at baseline. Generalized Estimating Equations (GEE) regression models examined game play minutes/week and accelerometer outcomes as repeated measures over the 4 weeks, adjusting for autocorrelation, school week (not in session = 0, 1 = in session), and body mass index z-score. Outcomes were regressed on contingency score group (higher = 1, lower = 0), week, and contingency score group · week interaction. The GEE model was specified with identity link and exchangeable correlation matrix.
Game play logs Of the 63 participants, 97 percent returned game logs. Weekly game log entries ranged from 0 to 20 and showed that 98 percent (n = 60) played during week 1, 47.5 percent (n = 29) played at least once during week 2, 55.7 percent (n = 34) played during week 3, and 37.7 percent (n = 23) played during week 4. Only
Table 2. Sample Demographic Information by Active Videogame Randomization Xavix AVGs Higher contingency score ‘‘Bowling’’ Randomized (n) 17 Gender [n (%)] Male 9 (52.9) Female 8 (47.1) Age [mean (SD)] 13.11 (1.51) Ethnicity [n (%)] Hispanic 6 (38.1) White, non-Hispanic 5 (29.4) Black, non-Hispanic 2 (11.8) Asian 2 (11.8) Pacific Islander 1 (5.9) Other 1 (5.9) BMI percentile categories [n (%)] Normal 9 (56.3) Overweight 7 (43.8) Obese 0 Income [n (%)] < $30,000 3 (17.6) $30,000–$59,999 3 (17.6) $60,000–$89,999 4 (23.5) q$90,000 6 (35.3) Missing 1 (5.8) School grades [n (%)] Mostly As and Bs 12 (70.6) Mostly Cs 5 (29.4)
‘‘Tennis’’ 15
(40) (33.3) (6.7) (13.3) (6.7)
11 (73.3) 3 (20.0) 1 (6.7) 3 3 4 5 0
‘‘Boxing’’ 16
11 (73.3) 4 (26.7) 13.79 (1.24) 6 5 1 2 1 0
Lower contingency score
(20) (20) (26.7) (33.3)
12 (80.0) 3 (20.0)
11 (68.8) 5 (31.3) 12.7 (1.04) 8 5 2 0 1 0
(50) (31.3) (12.5)
J-MAT
Total
15
63
8 (53.3) 7 (46.7) 13.21 (1.28)
39 (61.9) 24 (38.1) 13.21 (1.30)
4 (26.7) 9 (60) 2 (13.3) 0 0 0
24 24 7 4 3 1
7 (43.8) 4 (25.0) 5 (31.3)
8 (53.3) 3 (20.0) 4 (26.7)
35 (56.5) 17 (27.4) 10 (16.1)
3 5 4 4 0
4 1 4 6 0
13 12 16 21 1
(6.3)
(18.8) (31.3) (25) (25)
13 (86.7) 2 (13.3)
(26.7) (6.7) (26.7) (40)
14 (93.3) 1 (9.1)
AVG, active videogame; BMI, body mass index; JMAT, ‘‘Jackie Chan Studio Fitness’’; SD, standard deviation.
(38.1) (38.1) (11.1) (6.3) (4.8) (1.6)
(20.6) (19.0) (25.4) (33.3) (1.6)
51 (82.3) 11 (17.7)
162
NORMAN ET AL. Table 3. Logged Game Play Minutes/Week and Actigraph Accelerometer Activity Estimates Week a
Game play minutes/week Higher contingency score Lower contingency score Sedentary hours/day Higher contingency score Lower contingency score Light activity hours/day Higher contingency score Lower contingency score MVPA minutes/day Higher contingency score Lower contingency score
0
1
2
3
4
— —
185.8 (146.8) 159.3 (146.8)
110.5 (202.1) 38.4 (72.9)
93.3 (152.7) 54.7 (92.7)
62.9 (127.4) 56.4 (115.6)
7.6 (1.49) 7.0 (1.23)
7.7 (1.31) 7.1 (1.22)
— —
— —
7.6 (1.74) 7.0 (1.52)
4.1 (1.05) 4.5 (0.97)
4.2 (0.79) 4.5 (1.02)
— —
— —
4.1 (1.32) 4.5 (0.88)
57.9 (39.42) 77.2 (45.29)
62.7 (56.43) 74.4 (42.19)
— —
— —
76.0 (63.37) 71.0 (33.29)
Data are mean (standard deviation) values. Accelerometer data were only collected at baseline and weeks 1 and 4. a 0 = baseline week before receiving the active videogame. MVPA, moderate–vigorous physical activity.
21.3 percent (n = 13) of participants played the AVG at least once all 4 weeks, 26.2 percent (n = 16) played the game for three of the 4 weeks, 23 percent (n = 14) played for two of the 4 weeks, and 29.5 percent (n = 18) played during one of the 4 weeks. Game play log and Actigraph outcomes over the 4 weeks Table 3 presents mean and standard deviation values for logged game play minutes/week and accelerometer-derived outcomes of sedentary hours/day, light activity hours/day,
and MVPA minutes/week. Game play minutes decreased in both groups from week 1 to week 4. Sedentary and light activity hours remained stable over time, whereas MVPA minutes increased in the higher contingency group and decreased in the lower contingency group. Table 4 presents the GEE model parameter estimates for game play minutes/week and accelerometer outcomes. Minutes of game play decreased significantly by week (–29.42, P = 0.001) for both groups. Game play minutes in the higher contingency score group decreased at a slower rate than the lower contingency score group, but this difference
Table 4. Generalized Estimating Equations Model Parameter Estimates for Game Log and Accelerometer Outcomes Parameter estimate Game play minutes/day Intercept Contingency score Week Contingency score · week Sedentary hours/day Intercept Contingency score Week Contingency score · week Light activity hours/day Intercept Contingency score Week Contingency score · week MVPA minutes/day Intercept Contingency score Week Contingency score · week
95% confidence interval
P value
164.72 61.16 - 12.78 32.09
< 0.001 0.799 0.001 0.157
6.57, - 0.17, - 0.17, - 0.34,
7.42 1.76 0.21 0.35
< 0.001 0.104 0.837 0.980
4.61 - 0.36 - 0.08 0.04
4.22, - 0.99, - 0.21, - 0.18,
4.99 0.27 0.05 0.25
< 0.001 0.264 0.241 0.743
80.46 - 19.81 - 3.50 6.43
64.71, - 45.86, - 8.19, 0.85,
96.20 6.23 1.19 12.00
< 0.001 0.136 0.143 0.024
123.42 - 9.13 - 29.42 5.39
82.13, - 79.43, - 46.07, - 21.30,
6.99 0.80 0.02 0.00
Behavioral contingency was coded as follows: 0 = lower contingency score, 1 = higher contingency score. Week for game play minutes/day was coded as follows: 0 = week 1, 1 = week 2, 2 = week 3, 3 = week 4. Week for accelerometer outcomes was coded as follows: 0 = baseline, 1 = week 1, 3 = week 4. Models were adjusted for school week and body mass index z-score. MVPA, moderate–vigorous physical activity.
EFFECTS OF BEHAVIORAL CONTINGENCIES did not reach statistical significance (5.39, P = 0.157). Accelerometer-measured sedentary and light activity hours/ day did not change over time. There was a statistically significant interaction for MVPA minutes/day, indicating a differential change over time between groups (6.43, P = 0.024). Although the lower contingency score group decreased MVPA over time, the higher contingency score group increased MVPA over time. Discussion In this study we sought to quantify the types and number of behavioral contingences in a sample of AVGs and to assess the effect of AVGs classified by the quantity of behavioral contingencies on adolescent game play and physical activity over a 1-month period. Adolescents were randomized to one of four AVGs coded and scored as either having a higher contingency score (‘‘Tennis’’ and ‘‘Bowling’’) or a lower contingency score ( JMAT and ‘‘Boxing’’). It was hypothesized that there would be more sustained game play and physical activity over time for the AVGs in the higher score group compared with the AVGs in the lower score group. Based on accelerometers, sedentary and light activity hours remained stable over time, whereas MVPA minutes increased in the higher contingency group and decreased in the lower contingency group. An increase in youth MVPA measured by accelerometers is consistent with some AVG intervention studies28,29 but contrary to other studies.14,17–19 These equivocal findings across studies suggest that under certain conditions and for certain durations of time AVGs may have an impact on physical activity levels. Differences in the game contingencies experienced during game play may contribute to these differences in MVPA levels either directly through continued AVG play or indirectly if positive experiences with an AVG lead to engaging in more MVPA from non-AVG sources (e.g., outdoor activities). Although speculative, this indirect influence may explain why the higher contingency group increased MVPA, whereas overall AVG play decreased over time. Such a phenomenon would also be consistent with the Social Cognitive Theory tenet of increasing self-efficacy through mastery experiences.30 A review by Foley and Maddison31 noted that a critical area of research was how to design AVGs to promote long-term physical activity in youth. Concepts of ‘‘gamification’’ or ‘‘game mechanics’’ are synonymous with behavioral contingencies and have been proposed as mechanisms for adoption and maintenance of game play.32 For example, in an experiment where contingencies influencing game difficulty (i.e., skill of opponents and prevalence of obstacles) were manipulated within a car-racing game, greater difficulty, which resulted in more failure experiences, reduced the participants’ reported propensity to continue playing.33 The current study showed that AVGs varied on the number and type of simple behavior– consequence contingencies, both within and between cartridges, ranging from 10 to 64 in subgames and from 113 to 170 when aggregated by game cartridge. No other studies to date have measured behavioral contingencies in AVGs or their relation to play time or physical activity outcomes. It should be noted that although our scoring system of AVGs was exploratory, our coding system of behavioral contingencies was theory driven and based on precise definitions from behavioral theory.21,22 Indeed, our simple scor-
163 ing system based on a sum of positive and aversive consequences (the most basic unit of a contingency is twoterm behavior–consequence relations) allowed us to examine the one indicator of the number and complexity of behavioral contingencies. Our scoring method did not account for competing functions of reinforcement and aversive contingencies within an AVG cartridge (i.e., increase versus decrease play time). However, a sum score of contingencies may be a crude indicator of the sophistication or immersion experience AVGs provide, with higher scores being better. Testing other scoring methods is needed to fully understand the usefulness of quantifying behavioral contingencies as a mechanism for AVG’s influence on physical activity. This is especially critical because game designers promote the purposeful and successful uses of these mechanisms for motivating play time.34 More intricate approaches to AVG scoring might incorporate antecedents into a scoring system or apply weights to the consequences to account for subtle but important differences in the qualities of consequences (e.g., reinforcement schedule effects or intensity of aversive consequences) or assess proper use of contingencies relative to behavior change theory (i.e., theoretical fidelity).35 A complementary approach to scoring might be to create a score that is a function of the game–player interaction, calculating a density of contingencies experienced over a duration of game play. This approach would require continuous measurement of the player’s experience over time, which is a possibility with newer game systems. Based on reported game play logs, adolescent AVG play was not sustained over the 4 weeks and decreased substantially after the first week. Only about half of the participants played their AVGs during the second and third weeks, and only about a third played during the fourth week. The decline in game play did not vary statistically (P = 0.157) by the two AVG contingency score groups but was in the correct direction with slower decline in game play over time favoring the higher contingency score group. The observed decline in AVG play was consistent with other studies that have examined AVG play over time.17,19 Process questions asked during the telephone calls and final assessment give some information about the user experience related to game play. During the week 1 phone call, 44 percent of adolescents reported there were other things preventing them from playing the AVG; school and homework were the most commonly reported reasons for not playing. About 26 percent of adolescents reported technical problems with the AVG, mostly related to the game not tracking their responses and movements. At the final assessment 60 percent of adolescents somewhat or strongly agreed that they enjoyed playing the AVG. However, only 47 percent somewhat or strongly agreed that they would recommend the AVG to friends. This feedback suggests that for many of the adolescents, the AVGs were initially novel and interesting to play, but they did not sustain interest over time. To be an effective component of an intervention program targeting increasing activity levels, decreasing sedentary time, or reducing overweight, AVGs may need to be coupled with other behavior change program components such as goal setting, prompts, and reward strategies to increase the frequency of AVG use and over an extended time period. Study limitations included the short follow-up period of 1 month. However, with the significant decrease in AVG play
164 after 1 week, there would likely be little gained by having a longer study duration. In addition, six of the randomized adolescents did not complete baseline and did not start the study. However, these six were evenly distributed between the higher and lower contingency score groups, which likely minimized any impact on the balance from randomization between the groups. The generalizability of the sample may be limited to adolescents living in Southern California and who had limited AVG experience prior to participating in the study. However, limiting enrollment to those without an AVG currently in the home was intended to control the effects of familiarity–novelty of the games and practice effects. One of the primary outcomes was game play time derived from self-report, which can be biased differentially from response burden and social desirability. The game play measure logs were not validated and resulted in somewhat different results compared with overall MVPA measured with the accelerometer. Ideally, using an objective measure of game play recorded by the AVG would provide an unbiased measure. In addition, the topography of game play behavior can change over time because some adolescents may adapt their behavior to play with more efficient movements (e.g., a flick of the wrist instead of a whole arm movement), which would result in less energy expenditure. The degree of this adaptation may differ by game type. Another limitation was that the behavioral contingency score could only be used as a composite for the AVG cartridge (e.g., ‘‘Bowling’’) that contained several subgames. The cartridge composite score decreased the range of possible contingency scores but was necessary because play time was not recorded in the game logs for the individual subgames. The AVG cartridges contained subgames with both higher and lower contingency scores, and certain subgames within a cartridge may have been more enjoyable than others to a participant. Although a strength of the study was randomizing participants to AVG cartridges, not knowing how much each subgame within the cartridge was played limited our ability to more precisely test the relationship between coded behavioral contingencies and game play time. Other study strengths included an ethnically diverse sample of adolescents and a high study completion rate. AVGs will continue to become more sophisticated in the kinds of activity experiences that can be offered. For example, more game platforms will track both upper body and lower limb movements to encourage more physical activity that will increase energy expenditure. From a public health perspective, we speculate that an ideal AVG would be one that produces significant energy expenditure and has sufficient behavioral contingencies to sustain individuals’ engagement in the game over an extended period of time. Further research is needed to better understand how behavioral contingencies in AVGs. Studies where behavioral contingencies of an AVG are systematically manipulated (e.g., adding or removing stimuli) and participants are randomized to different versions of the same AVG may provide a more definitive test of how game design principles can function to sustain play over time. Acknowledgments This study was supported by grant number 64439 from The Robert Wood Johnson Foundation.
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Address correspondence to: Gregory J. Norman, PhD Department of Family and Preventive Medicine University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0811 E-mail:
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