JOURNAL OF APPLIED BEHAVIOR ANALYSIS
2015, 48, 690–695
NUMBER
3 (FALL)
INCREASING PHYSICAL ACTIVITY OF CHILDREN DURING SCHOOL RECESS LYNDA B. HAYES
AND
CAROLE M. VAN CAMP
UNIVERSITY OF NORTH CAROLINA WILMINGTON
Physical activity is crucial for children’s health. Fitbit accelerometers were used to measure steps of 6 elementary students during recess. The intervention included reinforcement, self-monitoring, goal setting, and feedback. Steps taken during the intervention phase (M ¼ 1,956 steps) were 47% higher than in baseline (M ¼ 1,326 steps), and the percentage of recess spent in moderate-tovigorous physical activity was higher during intervention (M ¼ 25%) than in baseline (M ¼ 4%). These methods successfully increased steps during recess and could be used to increase steps in other settings. Key words: Fitbit, physical activity, reinforcement, school recess, self-management
Given the benefits of physical activity such as aiding in the prevention of chronic diseases, public health recommendations suggest that children engage in a minimum 60 min per day of moderate-to-vigorous physical activity (MVPA), which typically includes walking, jogging, or playing sports like soccer (Centers for Disease Control and Prevention [CDC], 2012). Unfortunately, many children do not engage in adequate daily MVPA (CDC, 2012). Most children attend public schools, which presumably have at least two opportunities to encourage physical activity: physical education classes and recess. However, few schools implement sufficient regulations to increase physical activity in either setting. Given the opportunity to influence numerous children and the health- and academic-related benefits of physical activity (e.g., classroom attentiveness; CDC, 2012), school-based interventions to increase physical activity may be warranted. Behavioral assessments and interventions that have measured physical activity have used a variety of methods to quantify MVPA, including direct observation and mechanical devices (Sirard & Pate, 2001). Mechanical devices objectively This study is based on a thesis submitted by the first author in partial fulfillment of the requirements for the MA degree at the University of North Carolina Wilmington. Correspondence should be addressed to Carole Van Camp (e-mail: [email protected]). doi: 10.1002/jaba.222
measure physical activity, such as steps taken via pedometers and accelerometers. Step counts have been shown to be associated with different MVPA levels. Specifically, 100 steps per minute is a minimum standard of moderate physical activity in many studies that have compared step counts to heart rates (Tudor-Locke et al., 2011). One relatively new accelerometer, the Fitbit, has been validated (Gusmer, Bosch, Watkins, Ostrem, & Dengel, 2014) and is reliable when worn on multiple locations on the body (e.g., waist, pocket; Takacs et al., 2014). Another benefit of the Fitbit is the measurement of steps in 1-min intervals (Gusmer et al., 2014). Such data may be useful when evaluating not only whether individuals meet day-long or session-long step goals but also whether they engage in MVPA in any given minute. Two behavioral interventions that encourage and maintain physical activity are contingency management and self-management. Contingency management typically involves an outside agent who monitors behavior and delivers reinforcers when goals are met (e.g., Washington, Banna, & Gibson, 2014). Self-management typically involves observing and recording one’s own behavior and can be used in conjunction with contingency management. Research has shown that both of these methods are successful at increasing steps of adults (Normand, 2008; VanWormer, 2004), but they have only recently been expanded to increase physical activity in
690
INCREASING PHYSICAL ACTIVITY children. Hustyi, Normand, and Larson (2011) evaluated an intervention package that consisted of goal setting, feedback, and reinforcement to increase physical activity of two children during preschool recess. They reported modest increases with one child and no discernible effects of treatment with the other. However, one limitation cited by the authors was that no formal preference assessment was conducted to identify potential reinforcers; thus, the stimuli delivered contingent on meeting goals may not have functioned as reinforcers for at least one subject. The purpose of the current study was to evaluate the effects of an intervention package aimed at increasing the physical activity of school-aged children during unstructured recess. A preference assessment was conducted to identify potential reinforcers, and the intervention included both reinforcement and selfmanagement components. METHOD Subjects and Setting Subjects were recruited from one 3rd grade classroom. Ten students returned signed parental consent forms and gave their assent. Six typically developing 8-year-old girls who were part of an intact social group participated in the study. Based on calculation of body mass index, four subjects were considered healthy, one was underweight, and one was overweight. A total of 22 sessions (20 min in duration) were conducted. Sessions occurred 1 to 4 days per week on an elementary-school playground during regularly scheduled unstructured recess. Subjects were allowed to play freely in different areas: a walking track, basketball court, open field, and fixed-equipment space (e.g., swings). Loose playground equipment (e.g., soccer ball) was also available. Measurement and Reliability The dependent variable was number of steps taken as recorded by Fitbit accelerometers.
691
Before each session, Fitbit step totals were reset to zero. Each subject wore the same primary Fitbit each session, typically in the pants or short pocket or, when necessary, on the waistband, dress strap, or shirt collar. An experimenter recorded step data via the Fitbit display at the end of each session. Step data for sessions were later extracted and downloaded in 1-min bins from the Fitbit website. Reliability data were collected by having subjects wear a secondary Fitbit. At least one subject wore a secondary Fitbit each session, and the subjects were rotated such that reliability was assessed for each subject multiple times. Secondary Fitbits were secured to the same location on the opposite side of the body as the primary Fitbit. Observers recorded step totals for both Fitbits at the same time. Agreement was then calculated by dividing the smallest step total by the largest step total and converting the result to a percentage. Reliability was assessed for 100% of total sessions (14% to 29% of sessions per subject). Agreement averaged 95% (range, 66% to 100%; only one session had agreement below 89%). Procedure After subjects were equipped with Fitbits and intervention-related instructions were given, 20 min of unstructured recess began. Observers used whistles to signal the end of each session, at which time Fitbits were returned and placed on a table; observers then recorded step data. The intervention was evaluated in a reversal design and consisted of the following components: reinforcement, self-monitoring, goal setting, and feedback. Baseline. The output display of each Fitbit was masked with tape to prevent subjects from observing their step counts. At the end of each session, subjects did not receive feedback and were not allowed to view the number of steps taken. The criterion for moving out of baseline was stability or the absence of an upward trend as determined through visual analysis.
692
LYNDA B. HAYES and CAROLE M. VAN CAMP
Intervention. The Fitbits were unmasked and, in the self-monitoring component, subjects were encouraged daily to look at the Fitbit display of steps taken throughout the session. Subjects received their goals and their four preceding sessions’ step totals on a slip of paper, were encouraged to refer to their goals throughout the sessions, and were prompted to report meeting or not meeting goals at the end of recess. Step goals were set at a percentage increase above the average of each subject’s last four stable sessions. The initial goal was a 10% increase, which increased to 20% after two consecutive session goals were met. For the final intervention phase, three goals were given (20%, 30%, and 40% increases). During reinforcement, subjects were given one tangible reward immediately after the session if they met their goal, and during the final intervention phase, they could earn up to three reinforcers if they met all three goals. Available rewards were based on the preferred items identified in a prestudy multiple-stimulus-without-replacement assessment (DeLeon & Iwata, 1996) that consisted of three trials per subject. Stimuli were age-appropriate tangible items (e.g., crafts and small toys worth $3 or less). Subjects received feedback at the end of each session in the form of praise (for goal met) or encouragement to try again next session (for goal not met). RESULTS AND DISCUSSION Total step data for all subjects are displayed in Figure 1. Similar patterns were observed for all subjects. Steps counts were relatively higher during the first two baseline sessions, possibly due to reactivity. Thereafter, data stabilized below 1,200 steps per session for all subjects. During the initial intervention phase, all subjects increased their step totals and exceeded their goals. All but two subjects increased their steps from initial baseline to intervention by at least 40%; Sara and Kate increased their steps by 39% and 29%, respectively. All subjects decreased steps in the reversal to baseline, with
the final reversal session reaching initial baseline levels for most subjects. Finally, when the intervention was reimplemented, all subjects (except Sara, who was absent) increased their step total, four of five subjects met their tertiary goals (40% increase), and Fallon met her secondary goal (30% increase). Overall, subjects took 47% more steps during intervention phases (M ¼ 1,956 steps) than in baseline phases (M ¼ 1,326 steps). In addition, four subjects took over 100 steps per minute, which is indicative of MVPA (Tudor-Locke et al., 2011). The extent to which subjects met MVPA criteria (MVPAc) on a minute-by-minute basis was also evaluated. These data were available for 80% of baseline and 82% of intervention sessions. The percentages of minutes that met MVPAc during available baseline and treatment sessions are summarized for all subjects in Figure 2. The percentage of minutes that met MVPAc increased from baseline (M ¼ 4%; range, 2% to 6%) to intervention (M ¼ 25%; range, 10% to 41%) and decreased in the second baseline (M ¼ 13%; range, 4% to 20%) for all subjects. These data suggest that the intervention was successful at increasing total steps taken, at times to such an extent that an increase in MVPA levels of activity was evident. This study extends the previous literature in that effective reinforcers were identified and a relatively new device was used to measure total and minute-by-minute steps as an index of MVPA. There are several limitations to the current study. First, some data for MVPA calculations were lost due to syncing failures, including the last intervention session. Future research should take steps to ensure minimal data loss (e.g., syncing immediately after the session). In addition, the inclusion of a component analysis could identify which components are necessary and sufficient. Future research could also use momentary time sampling to collect data on activity type and social interactions, because previous research has found that peer presence
INCREASING PHYSICAL ACTIVITY
693
Figure 1. Steps per session across recess sessions for all subjects. The horizontal lines indicate the step goals during the intervention sessions.
694
LYNDA B. HAYES and CAROLE M. VAN CAMP
Figure 2. Average percentage of 20-min sessions that met MVPAc (>99 steps per minute) during available 20-min baseline (A) and intervention (B) sessions.
may influence physical activity (Larson, Normand, Morley, & Hustyi, 2014; Salvy et al., 2009). Further, although the reinforcers were relatively inexpensive, the total cost may exceed what is feasible for repeated classwide or day-long implementation. Finally, although we did show an effect of the intervention, overall activity levels did not consistently go above MVPAc. Time constraints prevented us from gradually increasing steps to above MVPAc and also required us to implement the baseline reversal before we saw further increases in steps during the initial intervention. However, there is some evidence that the subjects were capable of and motivated to meet higher goals (see Session 22). Although the clinical significance of increasing MVPA during brief 20-min sessions may be limited, any increase in MVPA is likely beneficial, and lessons learned from this intervention could be incorporated into other schoolbased or day-long interventions. REFERENCES Centers for Disease Control and Prevention. (2012). Nutrition, physical activity, and obesity. Retrived from http://www.cdc.gov/healthyyouth/npao/
DeLeon, I. G., & Iwata, B. A. (1996). Evaluation of a multiple-stimulus presentation format for assessing reinforcer preferences. Journal of Applied Behavior Analysis, 29, 519–533. doi: 10.1901/jaba.1996.29-519 Gusmer, R. J., Bosch, T. A., Watkins, A. N., Ostrem, J. D., & Dengel, D. R. (2014). Comparison of Fitbit Ultra to Actigraph GT1M for assessment of physical activity in young adults during treadmill walking. The Open Sports Medicine Journal, 8, 11–15. doi: 10.2174/ 1874387001408010011 Hustyi, K. M., Normand, M. P., & Larson, T. A. (2011). Behavioral assessment of physical activity in obese preschool cihldren. Journal of Applied Behavior Analysis, 44, 635–639. doi: 10.1901/jaba.2011.44-635 Larson, T. A., Normand, M. P., Morley, A. J., & Hustyi, K. M. (2014). The role of the physical environment in promoting physical activity in children across different group compositions. Behavior Modification, 38, 837– 851. doi: 10.1177/0145445514543466 Normand, M. P. (2008). Increasing physical activity through self-monitoring, goal setting, and feedback. Behavioral Interventions, 23, 227–236. doi: 10.1002/ bin.267 Salvy, S., Roemmich, J. N., Bowker, J. C., Romero, N. D., Stadler, P. J., & Epstein, L. H. (2009). Effect of peers and friends on youth physical activity and motivation to be physically active. Journal of Pediatric Psychology, 34, 217–225. doi: 10.1093/jpepsy/jsn071 Sirard, J. R., & Pate, R. R. (2001). Physical activity assessment in children and adolescents. Sports Medicine, 31, 439– 454. doi: 10.2165/00007256-200131060-00004 Takacs, J., Pollock, C. L., Guenther, J. R., Bahar, M., Napier, C., & Hunt, M. A. (2014). Validation of the Fitbit One activity monitor device during treadmill
INCREASING PHYSICAL ACTIVITY walking. Journal of Science and Medicine in Sport, 17, 496–500. doi: 10.1016/j.jsams.2013.10.241 Tudor-Locke, C., Craig, C. L., Brown, W. J., Clemes, S. A., De Cocker, K., Giles-Corti, B., . . . Blair, S. N. (2011). How many steps/day are enough? For adults. International Journal of Behavioral Nutrition and Physical Activity, 8, 79. doi: 10.1186/1479-5868-8-79 VanWormer, J. J. (2004). Pedometers and brief ecounseling: Increasing physical activity for overweight adults. Journal of Applied Behavior Analysis, 37, 421– 425. doi: 10.1901/jaba.2004.37-421
695
Washington, W. D., Banna, K. M., & Gibson, A. L. (2014). Preliminary efficacy of prize-based contingency management to increase activity levels in healthy adults. Journal of Applied Behavior Analysis, 47, 231–245. doi: 10.1002/jaba.119
Received October 2, 2014 Final acceptance March 4, 2015 Action Editor, Bethany Raiff
2015, 48, 690–695
NUMBER
3 (FALL)
INCREASING PHYSICAL ACTIVITY OF CHILDREN DURING SCHOOL RECESS LYNDA B. HAYES
AND
CAROLE M. VAN CAMP
UNIVERSITY OF NORTH CAROLINA WILMINGTON
Physical activity is crucial for children’s health. Fitbit accelerometers were used to measure steps of 6 elementary students during recess. The intervention included reinforcement, self-monitoring, goal setting, and feedback. Steps taken during the intervention phase (M ¼ 1,956 steps) were 47% higher than in baseline (M ¼ 1,326 steps), and the percentage of recess spent in moderate-tovigorous physical activity was higher during intervention (M ¼ 25%) than in baseline (M ¼ 4%). These methods successfully increased steps during recess and could be used to increase steps in other settings. Key words: Fitbit, physical activity, reinforcement, school recess, self-management
Given the benefits of physical activity such as aiding in the prevention of chronic diseases, public health recommendations suggest that children engage in a minimum 60 min per day of moderate-to-vigorous physical activity (MVPA), which typically includes walking, jogging, or playing sports like soccer (Centers for Disease Control and Prevention [CDC], 2012). Unfortunately, many children do not engage in adequate daily MVPA (CDC, 2012). Most children attend public schools, which presumably have at least two opportunities to encourage physical activity: physical education classes and recess. However, few schools implement sufficient regulations to increase physical activity in either setting. Given the opportunity to influence numerous children and the health- and academic-related benefits of physical activity (e.g., classroom attentiveness; CDC, 2012), school-based interventions to increase physical activity may be warranted. Behavioral assessments and interventions that have measured physical activity have used a variety of methods to quantify MVPA, including direct observation and mechanical devices (Sirard & Pate, 2001). Mechanical devices objectively This study is based on a thesis submitted by the first author in partial fulfillment of the requirements for the MA degree at the University of North Carolina Wilmington. Correspondence should be addressed to Carole Van Camp (e-mail: [email protected]). doi: 10.1002/jaba.222
measure physical activity, such as steps taken via pedometers and accelerometers. Step counts have been shown to be associated with different MVPA levels. Specifically, 100 steps per minute is a minimum standard of moderate physical activity in many studies that have compared step counts to heart rates (Tudor-Locke et al., 2011). One relatively new accelerometer, the Fitbit, has been validated (Gusmer, Bosch, Watkins, Ostrem, & Dengel, 2014) and is reliable when worn on multiple locations on the body (e.g., waist, pocket; Takacs et al., 2014). Another benefit of the Fitbit is the measurement of steps in 1-min intervals (Gusmer et al., 2014). Such data may be useful when evaluating not only whether individuals meet day-long or session-long step goals but also whether they engage in MVPA in any given minute. Two behavioral interventions that encourage and maintain physical activity are contingency management and self-management. Contingency management typically involves an outside agent who monitors behavior and delivers reinforcers when goals are met (e.g., Washington, Banna, & Gibson, 2014). Self-management typically involves observing and recording one’s own behavior and can be used in conjunction with contingency management. Research has shown that both of these methods are successful at increasing steps of adults (Normand, 2008; VanWormer, 2004), but they have only recently been expanded to increase physical activity in
690
INCREASING PHYSICAL ACTIVITY children. Hustyi, Normand, and Larson (2011) evaluated an intervention package that consisted of goal setting, feedback, and reinforcement to increase physical activity of two children during preschool recess. They reported modest increases with one child and no discernible effects of treatment with the other. However, one limitation cited by the authors was that no formal preference assessment was conducted to identify potential reinforcers; thus, the stimuli delivered contingent on meeting goals may not have functioned as reinforcers for at least one subject. The purpose of the current study was to evaluate the effects of an intervention package aimed at increasing the physical activity of school-aged children during unstructured recess. A preference assessment was conducted to identify potential reinforcers, and the intervention included both reinforcement and selfmanagement components. METHOD Subjects and Setting Subjects were recruited from one 3rd grade classroom. Ten students returned signed parental consent forms and gave their assent. Six typically developing 8-year-old girls who were part of an intact social group participated in the study. Based on calculation of body mass index, four subjects were considered healthy, one was underweight, and one was overweight. A total of 22 sessions (20 min in duration) were conducted. Sessions occurred 1 to 4 days per week on an elementary-school playground during regularly scheduled unstructured recess. Subjects were allowed to play freely in different areas: a walking track, basketball court, open field, and fixed-equipment space (e.g., swings). Loose playground equipment (e.g., soccer ball) was also available. Measurement and Reliability The dependent variable was number of steps taken as recorded by Fitbit accelerometers.
691
Before each session, Fitbit step totals were reset to zero. Each subject wore the same primary Fitbit each session, typically in the pants or short pocket or, when necessary, on the waistband, dress strap, or shirt collar. An experimenter recorded step data via the Fitbit display at the end of each session. Step data for sessions were later extracted and downloaded in 1-min bins from the Fitbit website. Reliability data were collected by having subjects wear a secondary Fitbit. At least one subject wore a secondary Fitbit each session, and the subjects were rotated such that reliability was assessed for each subject multiple times. Secondary Fitbits were secured to the same location on the opposite side of the body as the primary Fitbit. Observers recorded step totals for both Fitbits at the same time. Agreement was then calculated by dividing the smallest step total by the largest step total and converting the result to a percentage. Reliability was assessed for 100% of total sessions (14% to 29% of sessions per subject). Agreement averaged 95% (range, 66% to 100%; only one session had agreement below 89%). Procedure After subjects were equipped with Fitbits and intervention-related instructions were given, 20 min of unstructured recess began. Observers used whistles to signal the end of each session, at which time Fitbits were returned and placed on a table; observers then recorded step data. The intervention was evaluated in a reversal design and consisted of the following components: reinforcement, self-monitoring, goal setting, and feedback. Baseline. The output display of each Fitbit was masked with tape to prevent subjects from observing their step counts. At the end of each session, subjects did not receive feedback and were not allowed to view the number of steps taken. The criterion for moving out of baseline was stability or the absence of an upward trend as determined through visual analysis.
692
LYNDA B. HAYES and CAROLE M. VAN CAMP
Intervention. The Fitbits were unmasked and, in the self-monitoring component, subjects were encouraged daily to look at the Fitbit display of steps taken throughout the session. Subjects received their goals and their four preceding sessions’ step totals on a slip of paper, were encouraged to refer to their goals throughout the sessions, and were prompted to report meeting or not meeting goals at the end of recess. Step goals were set at a percentage increase above the average of each subject’s last four stable sessions. The initial goal was a 10% increase, which increased to 20% after two consecutive session goals were met. For the final intervention phase, three goals were given (20%, 30%, and 40% increases). During reinforcement, subjects were given one tangible reward immediately after the session if they met their goal, and during the final intervention phase, they could earn up to three reinforcers if they met all three goals. Available rewards were based on the preferred items identified in a prestudy multiple-stimulus-without-replacement assessment (DeLeon & Iwata, 1996) that consisted of three trials per subject. Stimuli were age-appropriate tangible items (e.g., crafts and small toys worth $3 or less). Subjects received feedback at the end of each session in the form of praise (for goal met) or encouragement to try again next session (for goal not met). RESULTS AND DISCUSSION Total step data for all subjects are displayed in Figure 1. Similar patterns were observed for all subjects. Steps counts were relatively higher during the first two baseline sessions, possibly due to reactivity. Thereafter, data stabilized below 1,200 steps per session for all subjects. During the initial intervention phase, all subjects increased their step totals and exceeded their goals. All but two subjects increased their steps from initial baseline to intervention by at least 40%; Sara and Kate increased their steps by 39% and 29%, respectively. All subjects decreased steps in the reversal to baseline, with
the final reversal session reaching initial baseline levels for most subjects. Finally, when the intervention was reimplemented, all subjects (except Sara, who was absent) increased their step total, four of five subjects met their tertiary goals (40% increase), and Fallon met her secondary goal (30% increase). Overall, subjects took 47% more steps during intervention phases (M ¼ 1,956 steps) than in baseline phases (M ¼ 1,326 steps). In addition, four subjects took over 100 steps per minute, which is indicative of MVPA (Tudor-Locke et al., 2011). The extent to which subjects met MVPA criteria (MVPAc) on a minute-by-minute basis was also evaluated. These data were available for 80% of baseline and 82% of intervention sessions. The percentages of minutes that met MVPAc during available baseline and treatment sessions are summarized for all subjects in Figure 2. The percentage of minutes that met MVPAc increased from baseline (M ¼ 4%; range, 2% to 6%) to intervention (M ¼ 25%; range, 10% to 41%) and decreased in the second baseline (M ¼ 13%; range, 4% to 20%) for all subjects. These data suggest that the intervention was successful at increasing total steps taken, at times to such an extent that an increase in MVPA levels of activity was evident. This study extends the previous literature in that effective reinforcers were identified and a relatively new device was used to measure total and minute-by-minute steps as an index of MVPA. There are several limitations to the current study. First, some data for MVPA calculations were lost due to syncing failures, including the last intervention session. Future research should take steps to ensure minimal data loss (e.g., syncing immediately after the session). In addition, the inclusion of a component analysis could identify which components are necessary and sufficient. Future research could also use momentary time sampling to collect data on activity type and social interactions, because previous research has found that peer presence
INCREASING PHYSICAL ACTIVITY
693
Figure 1. Steps per session across recess sessions for all subjects. The horizontal lines indicate the step goals during the intervention sessions.
694
LYNDA B. HAYES and CAROLE M. VAN CAMP
Figure 2. Average percentage of 20-min sessions that met MVPAc (>99 steps per minute) during available 20-min baseline (A) and intervention (B) sessions.
may influence physical activity (Larson, Normand, Morley, & Hustyi, 2014; Salvy et al., 2009). Further, although the reinforcers were relatively inexpensive, the total cost may exceed what is feasible for repeated classwide or day-long implementation. Finally, although we did show an effect of the intervention, overall activity levels did not consistently go above MVPAc. Time constraints prevented us from gradually increasing steps to above MVPAc and also required us to implement the baseline reversal before we saw further increases in steps during the initial intervention. However, there is some evidence that the subjects were capable of and motivated to meet higher goals (see Session 22). Although the clinical significance of increasing MVPA during brief 20-min sessions may be limited, any increase in MVPA is likely beneficial, and lessons learned from this intervention could be incorporated into other schoolbased or day-long interventions. REFERENCES Centers for Disease Control and Prevention. (2012). Nutrition, physical activity, and obesity. Retrived from http://www.cdc.gov/healthyyouth/npao/
DeLeon, I. G., & Iwata, B. A. (1996). Evaluation of a multiple-stimulus presentation format for assessing reinforcer preferences. Journal of Applied Behavior Analysis, 29, 519–533. doi: 10.1901/jaba.1996.29-519 Gusmer, R. J., Bosch, T. A., Watkins, A. N., Ostrem, J. D., & Dengel, D. R. (2014). Comparison of Fitbit Ultra to Actigraph GT1M for assessment of physical activity in young adults during treadmill walking. The Open Sports Medicine Journal, 8, 11–15. doi: 10.2174/ 1874387001408010011 Hustyi, K. M., Normand, M. P., & Larson, T. A. (2011). Behavioral assessment of physical activity in obese preschool cihldren. Journal of Applied Behavior Analysis, 44, 635–639. doi: 10.1901/jaba.2011.44-635 Larson, T. A., Normand, M. P., Morley, A. J., & Hustyi, K. M. (2014). The role of the physical environment in promoting physical activity in children across different group compositions. Behavior Modification, 38, 837– 851. doi: 10.1177/0145445514543466 Normand, M. P. (2008). Increasing physical activity through self-monitoring, goal setting, and feedback. Behavioral Interventions, 23, 227–236. doi: 10.1002/ bin.267 Salvy, S., Roemmich, J. N., Bowker, J. C., Romero, N. D., Stadler, P. J., & Epstein, L. H. (2009). Effect of peers and friends on youth physical activity and motivation to be physically active. Journal of Pediatric Psychology, 34, 217–225. doi: 10.1093/jpepsy/jsn071 Sirard, J. R., & Pate, R. R. (2001). Physical activity assessment in children and adolescents. Sports Medicine, 31, 439– 454. doi: 10.2165/00007256-200131060-00004 Takacs, J., Pollock, C. L., Guenther, J. R., Bahar, M., Napier, C., & Hunt, M. A. (2014). Validation of the Fitbit One activity monitor device during treadmill
INCREASING PHYSICAL ACTIVITY walking. Journal of Science and Medicine in Sport, 17, 496–500. doi: 10.1016/j.jsams.2013.10.241 Tudor-Locke, C., Craig, C. L., Brown, W. J., Clemes, S. A., De Cocker, K., Giles-Corti, B., . . . Blair, S. N. (2011). How many steps/day are enough? For adults. International Journal of Behavioral Nutrition and Physical Activity, 8, 79. doi: 10.1186/1479-5868-8-79 VanWormer, J. J. (2004). Pedometers and brief ecounseling: Increasing physical activity for overweight adults. Journal of Applied Behavior Analysis, 37, 421– 425. doi: 10.1901/jaba.2004.37-421
695
Washington, W. D., Banna, K. M., & Gibson, A. L. (2014). Preliminary efficacy of prize-based contingency management to increase activity levels in healthy adults. Journal of Applied Behavior Analysis, 47, 231–245. doi: 10.1002/jaba.119
Received October 2, 2014 Final acceptance March 4, 2015 Action Editor, Bethany Raiff
