543466 research-article2014
BMOXXX10.1177/0145445514543466Behavior ModificationLarson et al.
Article
The Role of the Physical Environment in Promoting Physical Activity in Children Across Different Group Compositions
Behavior Modification 2014, Vol. 38(6) 837–851 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0145445514543466 bmo.sagepub.com
Tracy A. Larson1, Matthew P. Normand1, Allison J. Morley1, and Kristin M. Hustyi1
Abstract Physical activity is an important health-related behavior, but the environmental variables that promote or abate it are not well understood. The purpose of this study was to conduct a functional analysis evaluating the effect of the physical environment on moderate-to-vigorous physical activity (MVPA) in preschool children, and to evaluate the utility of the methodology across different group compositions. The Observational System for Recording Physical Activity in Children was used to define the test conditions and the measures of physical activity for eight preschool children. The functional analysis was implemented according to a multi-element experimental design. The highest levels of MVPA were observed when fixed playground equipment was available and at least one peer was present. Moreover, differential responding was observed across group compositions. The implications of this methodology and these findings on the development of interventions to increase MVPA are discussed.
1University
of the Pacific, Stockton, CA, USA
Corresponding Author: Matthew P. Normand, Department of Psychology, University of the Pacific, 3601 Pacific Avenue, Stockton, CA 95211, USA. Email: [email protected]
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Keywords physical activity, functional assessment, functional analysis Physical activity is an important behavioral target for interventions designed to improve health and fitness. The increased prevalence of obesity in the United States has been well documented (e.g., Flegal, Carroll, Ogden, & Curtin, 2010; Flegal, Carroll, Ogden, & Johnson, 2002; Ogden, Carroll, Curtin, Lamb, & Flegal, 2010; Ogden et al., 2006; Ogden, Flegal, Carroll, & Johnson, 2002), and physical activity plays an important role in promoting and abating this trend (Daniels et al., 2005; Janssen & LeBlanc, 2010). However, the benefits of physical activity extend beyond weight management, with movement being important for musculoskeletal development and coordination, especially in young children (Janz et al., 2010). In addition, physical activity is correlated with a host of health benefits (Janssen & LeBlanc, 2010). Increasing physical activity to levels associated with health benefits is, therefore, an important clinical goal. The Centers for Disease Control and Prevention (CDC, 2011) recommend that children engage in 60 min of moderate-to-vigorous physical activity (MVPA) every day of the week; however, current estimates suggest that many children are not this active (Strong et al., 2005; Troiano et al., 2008). Given the importance of physical activity for health and fitness, understanding the environmental variables related to physical activity is important, as these variables must be changed if physical activity is to be increased. Research suggests that the social (e.g., McKenzie, Crespo, Baquero, & Elder, 2010; Morrissey, Wenthe, Letuchy, Levy, & Janz, 2012; cf. Hastmann, Foster, Rosenkranz, Rosenkranz, & Dzewaltowski, 2013) and physical (e.g., Brink et al., 2010; Brown et al., 2009; see Ding, Salis, Kerr, Lee, & Rosenberg, 2011 for a review) environments influence activity levels, with the physical environment being potentially more influential for young children, as their ability to move among different environments is limited (Ding et al., 2011). In addition, like with any behavior problem, the specific environmental influences that are most influential are likely to differ from child to child, suggesting a need for individualized assessment methods. To date, however, few studies have investigated the role of the physical environment on the physical activity of young children (Ding et al., 2011). A number of different strategies can be used to assess potential functional relations between environmental variables and behavior, including descriptive (correlational) and functional (experimental) analyses (see Beavers, Iwata, & Lerman, 2013; Hanley, Iwata, & McCord, 2003). Of these assessment strategies, descriptive methods are the most commonly reported in the
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physical activity literature. For example, Brown and colleagues (Brown et al., 2009; Brown et al., 2006) developed an observational system called the Observational System for Recording Physical Activity in Children–Preschool Version (OSRAC-P). The OSRAC-P uses five activity categories to code varying levels of physical intensity as well as a variety of environmental events (e.g., activity type, location, indoor activity context, outdoor activity context, activity initiator, group composition, prompts, engagement, and TV use). Subsequently, Brown et al. (2009) conducted a descriptive analysis using the OSRAC-P as a means of determining predictors of various levels of physical activity by analyzing changes in physical activity levels across observations when environmental stimuli were allowed to vary naturally. Their results indicated that, among other things, the availability of outdoor toys and open play space were correlated with increased activity for the children observed. Descriptive information about the predictors of physical activity might prove useful in guiding social and physical manipulations for increasing physical activity. However, there is reason to be concerned about the emphasis on descriptive analyses of physical activity because the resulting data are correlational and, as such, do not constitute an empirical demonstration of functional relations. Studies comparing descriptive and functional analyses of problem behavior have consistently reported disagreements in the outcomes of the two assessment methods (e.g., Lerman & Iwata, 1993; Pence, Roscoe, Bourret, & Ahearn, 2009; Thompson & Iwata, 2007). The degree to which such disagreements would be seen with physical activity assessments is not known, but it stands to reason that disagreements would be evident. Only recently have functional analyses of physical activity been reported (Hustyi, Normand, Larson, & Morley, 2012; Larson, Normand, Morley, & Miller, 2013, 2014); however, there is a voluminous literature reporting the success of pretreatment experimental analyses for problem behavior. Overall, the vast collection of literature on functional behavior analysis has made it a “gold standard” of practice in the field of behavioral assessment and intervention (Hanley et al., 2003). Considering the prevalence of overweight and obesity in children, the limitations of descriptive assessments, and the demonstrated utility of functional analysis methods across a variety of contexts, functional analyses of the environmental and social variables related to physical activity might provide useful information about physical activity and the contexts in which intervention is most relevant. For example, Hustyi et al. (2012) experimentally manipulated several environmental contexts that were previously reported (e.g., Brown et al., 2009) to correlate with MVPA in young children. Four children were provided controlled access to an open grassy play area, a
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play area containing outdoor toys, a play area containing fixed playground equipment (i.e., jungle gym), and a play area containing a table and indoor activities (i.e., coloring books and army guys). Participants were exposed to each activity context several times according to a multi-element experimental design. Results indicated that MVPA was a function of the activity contexts evaluated, with the most MVPA observed when the participants were in the fixed equipment condition, suggesting that increasing access to the fixed equipment would be a prudent strategy to induce more physical activity by those children. Although those results are suggestive, more research on the utility of the assessment methods is warranted. Determining the extent to which functional analysis methods can be used across differing group contexts (i.e., with one or more peers present) seems especially important, as the participants in Hustyi et al. (2012) were assessed only when they were playing alone on the playground. Because children are likely to play with other children and might even be expected to play more actively when playing together, it is important to establish the utility of functional analysis methods across different group contexts. The purpose of the present study, then, was to (a) replicate the assessment methodology reported by Hustyi et al. as a step toward establishing the generality of those methods and (b) extend that work by using the same methods with participants who were playing alone, playing with one peer, and playing with a group of peers.
Method Participants and Setting Eight typically developing children attending a local daycare participated. Ganon was a 4-year-old male with a body mass index (BMI) that placed him above the 95th percentile of the CDC BMI-for-age growth charts indicating that Ganon was obese. Katherine was a 3-year-old female with a BMI that placed her above the 85th percentile indicating that she was overweight. Rico was a 3-year-old male, Marcus was a 4-year-old male, and Jessica and Emma were both 4 year-old females with normal BMIs in the 50th percentile. Lisa was a 4-year-old female and Max was a 4-year-old male with BMIs below the 5th percentile indicating that both Lisa and Max were underweight. Participants were recruited from the daycare via flyers that were distributed by the daycare operators. All children for whom parental informed consent was obtained participated in the study. Sessions were conducted at the daycare center on a fenced-in outdoor playground, 3 to 5 days per week. During solitary arrangements, one experimenter and the target child were present during the test and control sessions.
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During one-peer-present and group arrangements, two experimenters and the target children were present during the test and control sessions. One experimenter video recorded the session and the other experimenter ensured that the children remained in the target context. The physical environment included open grassy areas, fixed equipment, and several outdoor toys. Data were collected following the experimental sessions using the video records.
Response Measurement and Reliability During the initial observation and all subsequent sessions, the OSRAC activity codes (McIver, Brown, Pfeiffer, Dowda, & Pate, 2009) were used to assess the level of physical activity of each of the participants (see Table 1). Trained observers scored video-recorded sessions using a continuous, 5-s partialinterval recording method in which the highest level of physical activity observed during each interval was recorded. No activities were suggested to the participants during the analysis and no prompts were given except to stay inside the session area. In addition, the experimenters provided minimal attention to the participants. To calculate interobserver agreement (IOA), we used a point-by-point method in which we divided the number of agreements on OSRAC activity codes by the number of agreements plus disagreements for each interval and multiplied by 100 (Kazdin, 1982). An agreement was defined as both observers scoring an interval as MVPA (i.e., scoring codes 4 or 5), or both observers scoring the interval as low or no activity (i.e., scoring codes 1, 2, or 3). IOA was calculated for over 30% of sessions for all participants and was 91% for Ganon (range = 83%-100%), 90% for Katherine (range = 72%-98%), 89% for Rico (range = 80%-98%), 95% for Marcus (range = 82%-100%), 92% for Jessica (range = 79%-100%), 89% for Emma (range = 73%-100%), 86% for Lisa (range = 72%-100%), and 87% for Max (range = 79%-95%). Typically, agreement levels are considered acceptable if they meet or exceed 80% (Cooper, Heron, & Heward, 2007), although this standard is more a product of historical precedent than empirical validation (Kennedy, 2005). Still, the IOA results for the present study consistently exceeded 80% and approached, and sometimes exceeded, 90%, indicating good agreement between observers on the occurrence and non-occurrence of MVPA across sessions.
Design and Procedure All participants were exposed to three outdoor activity-context conditions and a control condition according to an alternating treatments experimental design (Barlow & Hayes, 1979; Sidman, 1960). A brief 30-min naturalistic
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Table 1. Activity Level Codes Specified by McIver, Brown, Pfeiffer, Dowda, and Pate (2009). Level
Activity
Operational definition
1
Stationary or motionless
2
Stationary with limb or trunk movements
3
Slow, easy movements
4
Moderate movements
5
Fast movements
Stationary or motionless with no major limb movements or major joint movement (e.g., sleeping, standing, riding passively in a wagon) Stationary with easy movements of limb(s) or trunk without translocation (e.g., standing up, holding a moderately heavy object, hanging off of bars) Translocation at a slow and easy pace (e.g., walking with translocation of both feet, slow and easy cycling, swinging without assistance and without leg kicks) Translocation at a moderate pace (e.g., walking uphill, two repetitions of skipping or jumping, climbing on monkey bars, hanging from bar with legs swinging) Translocation at a fast or very fast pace (e.g., running)
Note. Adapted from McIver et al. (2009).
observation was conducted before implementing the experimental conditions, in which the experimenters collected data on activity level without controlling for any variables. We coded the first two (Jessica, Lisa) or three (the remainder of the participants) 5-min samples in which the participant’s entire body was in view of the camera, with these samples serving as the naturalistic baselines. The one exception was that, for Jessica and Lisa, only the first two 5-min samples were used from the solitary condition.1 Following the naturalistic baselines, experimental conditions were presented in a randomized order and replications of each condition were counterbalanced. During these functional analyses, participants spent brief periods of time exposed to three outdoor activity contexts that were previously reported to be associated with non-sedentary behavior (Brown et al., 2009). In addition, we conducted the functional analyses in solitary, one-peerpresent, and group arrangements. Each session lasted 5 min, with 3 to 9 sessions conducted per day. The session lengths and frequencies were chosen for two primary reasons. First, research on functional analyses of problem behavior indicates that 5-min sessions produce data comparable with longer
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sessions (Wallace & Iwata, 1999) and that 5-min sessions are common session lengths in the published literature (Hanley et al., 2003). Second, because MVPA was the target behavior, briefer sessions seemed appropriate to avoid fatigue. The same logic influenced the number of sessions conducted per day and our previous research suggested that up to ten 5-min sessions spaced across a day was a reasonable frequency for this age group. Initially, participants were exposed to the experimental conditions while playing alone. Next, participants were exposed to all conditions with one peer present and then with two or three peers present. No programmed consequences were provided for responses during any of the conditions. If participants attempted to leave the session area, they were prompted to play only within the designated area. Outdoor toys. The experimenter guided the participant(s) to the session area where objects used in gross motor activities were present (i.e., one Frisbee®, one soft ball, one large bouncy ball, one medium bouncy ball, a jump rope, a bucket and shovel, several throwing toys, several orange cones, and a hula hoop). The experimenter directed the participant(s) to “play with the toys” and then stepped away from the session area and activated the video recorder. Fixed equipment. A jungle gym served as the fixed playground equipment on the daycare playground and was available during this condition. It included two slides, monkey bars, stairs, and several climbing areas. The experimenter directed the participant(s) to “play on the jungle gym” and then stepped away from the session area and activated the video recorder. Open space. This condition was identical to the previous conditions except that a large grassy area was available and no activity materials were present (e.g., balls, objects, or fixed equipment). The experimenter directed the participant(s) to “play in the grass” and then stepped away from the session area and activated the video recorder. Control. Sessions took place at a table in the play area, and activities not anticipated to evoke high levels of physical activity were made available to the participant (e.g., coloring print out pages, markers). The experimenter directed the participant(s) to “play at the table” and then stepped away from the session area and activated the video recorder.
Results Six participants were most active during the fixed equipment condition, with two participants having similarly high activity levels in the open space
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Figure 1. Percentage of MVPA per session for Jessica, Lisa, Max, and Rico during the solitary, one-peer-present, and group conditions. Note. MVPA = moderate-to-vigorous physical activity.
conditions. In addition, six of the eight participants were most active when a group of peers was present, and two participants were most active when one peer was present. Data are reported as the percentage of intervals in which MVPA (activity levels 4 or 5; see Table 1) was observed, as this is the level of physical activity corresponding to CDC (2011) recommendations. Figures 1 and 2 depict the results of the functional analyses across all group compositions for all participants.
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KATHERINE
Fixed Equipment
40
Control
20
Open Space Outdoor Toys
80 60 40 20 0
0
5
10
15
100
1 Peer PERCENTAGE MVPA
60
0
100
Solitary
80
PERCENTAGE MVPA
PERCENTAGE MVPA
100
0
5
SESSIONS
10
60 40 20 0
15
Group
80
0
5
SESSIONS
10
15
SESSIONS
EMMA
60 40 20
80 60 40 20 0
0
5
10 SESSIONS
15
100
1 Peer PERCENTAGE MVPA
80
0
100
Solitary PERCENTAGE MVPA
PERCENTAGE MVPA
100
0
5
10
60 40 20 0
15
Group
80
0
5
SESSIONS
10
15
SESSIONS
MARCUS
60 40 20
80 60 40 20 0
0
5
10 SESSIONS
15
100
1 Peer PERCENTAGE MVPA
80
0
100
Solitary PERCENTAGE MVPA
PERCENTAGE MVPA
100
0
5
10
60 40 20 0
15
Group
80
0
5
SESSIONS
10
15
SESSIONS
GANON
60 40 20
80 60 40 20 0
0
5
10 SESSIONS
15
100
1 Peer PERCENTAGE MVPA
80
0
100
Solitary PERCENTAGE MVPA
PERCENTAGE MVPA
100
0
5
10
15
Group
80 60 40 20 0
0
SESSIONS
5
10
15
SESSIONS
Figure 2. Percentage of MVPA per session for Katherine, Emma, Marcus, and Ganon during the solitary, one-peer-present, and group conditions. Note. MVPA = moderate-to-vigorous physical activity.
Solitary For five of the eight participants, MVPA was highest during the fixed equipment conditions, with Rico, Marcus, and Max being the exceptions. The results for Rico were not clearly differentiated across conditions and the results for Marcus indicate both open space and fixed equipment produced comparable levels of MVPA, overall. For Max, the amount of MVPA exceeded baseline levels during both the fixed equipment and open space conditions. In addition, for Ganon, clearly differentiated responding was not observed until the final sessions of the analysis. On first glance, there seems to be comparable levels of responding depicted in the fixed equipment and
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open space conditions for Jessica, but this is due primarily to a single elevated data point in the open space data series.
One Peer Present For three participants (Jessica, Lisa, Marcus), MVPA was highest during the fixed equipment conditions. For two participants (Katherine, Emma), MVPA was highest during the open space conditions. For Max and Rico, all conditions except for the control condition produced comparable high levels of MVPA. The results for Ganon also were undifferentiated, with no condition producing much MVPA.
Group For seven of eight participants, MVPA was highest during the fixed equipment conditions. Marcus, the only exception, engaged in the most MVPA during the outdoor toys conditions. MVPA was relatively low during all conditions for Katherine, Emma, and Ganon, with all conditions producing similar levels of MVPA; however, fixed equipment did still produce the most MVPA for these participants.
Discussion The results of the current study demonstrate that MVPA was functionally related to activity context, in that differential responding was observed across activity contexts and across group compositions for all participants. In addition, the functional analysis conditions produced differential responding across experimental conditions irrespective of group context. Moreover, MVPA might have been influenced by group composition, as suggested by the differing patterns of MVPA across group compositions for every participant. Katherine, for example, was most active in the fixed equipment condition when she was solitary and when a group of peers was present, but was most active in the open space condition when one peer was present. However, the degree to which group composition actually influenced MVPA is unknown, because group composition was not experimentally manipulated. Still, these results demonstrate the importance of activity context and suggest the importance of group composition on MVPA levels. These results replicate the work of Hustyi et al. (2012) with new participants in similar settings, with fixed equipment most consistently evoking the most MVPA. These results extend the work of Hustyi et al. by demonstrating the utility of the methodology across differing group contexts. This is
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valuable because understanding the variables that will promote MVPA when children are playing together is important. However, like the results of Hustyi et al., the results of the current study are not consistent with other published research employing descriptive, rather than experimental, assessments of physical activity. For example, the findings of Brown et al. (2009) suggested that children were more active with outdoor toys than when they were in other environmental contexts. In the current study, fixed equipment and open space conditions produced the highest levels of MVPA for all participants. Brown and colleagues (2009) also found that children were more active when solitary, whereas the participants in the current study were most active when at least one peer was present. The discrepancies observed between the results of this study and other published research employing descriptive analyses suggest that future research should directly compare the outcomes of descriptive and experimental assessments of physical activity to determine the extent to which they agree, much like has been done with functional assessments of problem behavior (e.g., Lerman & Iwata, 1993; Pence et al., 2009; Thompson & Iwata, 2007). The comparisons of descriptive and functional analyses of problem behavior suggest that descriptive assessments often identify attention as the relevant maintaining variable (Thompson & Iwata, 2007). This might be due, at least in part, to attention being a very common social variable even independent of problem behavior (Thompson & Iwata, 2001, p. 337). As such, attention can obscure the relevant functional relations in a descriptive assessment because it is frequently present and thus highly correlated with problem behavior even if it is not functionally related. It is possible that something similar could explain the discrepancies observed with physical activity. The events most correlated with MVPA in descriptive assessments might be events that are often present irrespective of physical activity, thereby obscuring the relevant functional relations. This is speculative, however, and future research should address the matter more systematically. Although the functional analysis methodology herein reported seems a promising approach to the behavioral assessment of physical activity, several limitations of this study should be noted. First, we only manipulated aspects of the general physical environment across experimental conditions. To determine which aspects of the physical environment support high levels of physical activity, this kind of analysis is appropriate. However, a more comprehensive functional analysis of behavior would manipulate both antecedent and consequent variables (e.g., Larson et al., 2013, 2014). Such analyses would provide additional information about the social environment that could be used to inform interventions.
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Second, only three outdoor activity contexts were evaluated. The choice to evaluate only three activity contexts was made because previous descriptive research indicated that these activity contexts were the best predictors of MVPA in preschool children. A more comprehensive analysis would assess activity levels in multiple activity contexts (e.g., playground games, outside chores, video games). Third, the clinical utility of this functional analysis methodology remains to be seen. Future studies should conduct the functional analyses, design interventions based on the results of the analyses, and evaluate the effects of those interventions, especially in comparison with interventions that seem contraindicated by the functional analysis results. For example, Hanley, Tiger, Ingvarsson, and Cammilleri (2009) altered preschoolers’ preference for various classroom activities by adding an embedded reinforcement contingency to an instructional activity when many activities were available. Initially, the participants rarely chose the instructional activity. However, following multiple exposures to the instructional activity, the children began to choose that activity over play-oriented activities. Applying similar methods to physical activity might increase the likelihood that children engage with environmental contexts that produce the most MVPA. In summary, the current study presents a method for determining which aspects of the physical environment support elevated levels of physical activity in young children. Similar to the functional analysis methodologies developed to inform the treatment of severe problem behavior (Hanley et al., 2003), the functional analysis of physical activity has the potential to improve our understanding of the roles setting and context play in promoting or abating physical activity and thereby lead to the development of better interventions. Function-based interventions for increasing physical activity in young children might well improve the health and well-being of overweight and obese children and even improve the prospects for those children at risk of becoming overweight or obese. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
Note 1. The data from the solitary analysis for Lisa and Jessica were published previously in Hustyi, Normand, Larson, and Morley (2012).
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Barlow, D. H., & Hayes, S. C. (1979). Alternating treatments design: One strategy for comparing the effects of two treatments in a single subject. Journal of Applied Behavior Analysis, 12, 199-210. Beavers, G. A., Iwata, B. A., & Lerman, D. C. (2013). Thirty years of research on the functional analysis of problem behavior. Journal of Applied Behavior Analysis, 46, 1-21. doi:10.1002/jaba.30 Brink, L. A., Nigg, C. R., Lampe, S. M., Kingston, B. A., Mootz, A. L., & van Vliet, W. (2010). Influence of schoolyard renovations on children’s physical activity: The learning landscapes program. American Journal of Public Health, 100, 1672-1678. Brown, W. H., Pfeiffer, K. A., McIver, K. L., Dowda, M., Addy, C. L., & Pate, R. R. (2009). Social and environmental factors associated with preschoolers’ nonsedentary physical activity. Child Development, 80, 45-58. doi:10.1111/j.14678624.2008.01245.x Brown, W. H., Pfeiffer, K. A., McIver, K. L., Dowda, M., Almeida, M. J., & Pate, R. R. (2006). Assessing preschool children’s physical activity: The observational system for recording physical activity in children-preschool version. Research Quarterly for Exercise and Sport, 77, 167-176. Centers for Disease Control and Prevention. (2011). How much physical activity do children need? Retrieved from http://www.cdc.gov/physicalactivity/everyone/ guidelines/children.html Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River, NJ: Pearson. Daniels, S. R., Arnett, D. K., Eckel, R. H., Gidding, S. S., Hayman, L. L., Kumanyika, S., . . .Williams, C. L. (2005). Overweight in children and adolescents: Pathophysiology, consequences, prevention, and treatment. Circulation, 111, 1999-2012. doi:10.1161/01.CIR.0000161369.71722.10 Ding, D., Salis, J. F., Kerr, J., Lee, S., & Rosenberg, D. E. (2011). Neighborhood environment and physical activity among youth: A review. American Journal of Preventative Medicine, 41, 442-455. doi:10.1016/j.amepre.2011.06.036 Flegal, K. M., Carroll, M. D., Ogden, C. L., & Curtin, L. R. (2010). Prevalence and trends in obesity among US adults, 1999-2008. Journal of the American Medical Association, 303, 235-241. Flegal, K. M., Carroll, M. D., Ogden, C. L., & Johnson, C. L. (2002). Prevalence and trends in obesity among US adults, 1999-2000. Journal of the American Medical Association, 288, 1723-1727. Hanley, G. P., Iwata, B. A., & McCord, B. E. (2003). Functional analysis of problem behavior: A review. Journal of Applied Behavior Analysis, 36, 147-185. doi:10.1901/jaba.2003.36-147 Hanley, G. P., Tiger, J. H., Ingvarsson, E. T., & Cammilleri, A. P. (2009). Influencing preschoolers’ free-play activity preferences: An evaluation of satiation and embedded reinforcement. Journal of Applied Behavior Analysis, 42, 33-41. doi:10.1901/jaba.2009.42-33
Downloaded from bmo.sagepub.com at CARLETON UNIV on March 16, 2015
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Behavior Modification 38(6)
Hastmann, T. J., Foster, K. E., Rosenkranz, R. R., Rosenkranz, S. K., & Dzewaltowski, D. A. (2013). Effect of adult leader participation on physical activity in children. Open Journal of Preventive Medicine, 2, 429-435. Hustyi, K. M., Normand, M. P., Larson, T. A., & Morley, A. J. (2012). The effect of outdoor activity context on physical activity in preschool children. Journal of Applied Behavior Analysis, 41, 401-405. Janssen, I., & LeBlanc, A. G. (2010). Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 7, 1-16. Janz, K. F., Letuchy, E. M., Gilmore, J. M. E., Burns, T. L., Torner, J. C., Willing, M. C., & Levy, S. M. (2010). Early physical activity provides sustained bone health benefits later in childhood. Medicine & Science in Sports & Exercise, 42, 1072-1078. Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings. New York, NY: Oxford University Press. Kennedy, C. H. (2005). Single-case research designs for educational research. Boston, MA: Allyn & Bacon. Larson, T. A., Normand, M. P., Morley, A. J., & Miller, B. G. (2013). A functional analysis of moderate-to-vigorous physical activity in young children. Journal of Applied Behavior Analysis, 46, 199-207. doi:10.1002/jaba.8 Larson, T. A., Normand, M. P., Morley, A. J., & Miller, B. G. (2014). Further evaluation of a functional analysis of moderate-to-vigorous physical activity in young children. Journal of Applied Behavior Analysis, 47, 219-230. Lerman, D. C., & Iwata, B. A. (1993). Descriptive and experimental analysis of variables maintaining self-injurious behavior. Journal of Applied Behavior Analysis, 26, 293-319. doi:10.1901/jaba.1993.26-293 McIver, K., Brown, W. H., Pfeiffer, K. A., Dowda, M., & Pate, R. R. (2009). Assessing children’s physical activity in their homes: The observational system for recording physical activity in children-home. Journal of Applied Behavior Analysis, 42, 1-16. doi:10.1901/jaba.2009.42-1 McKenzie, T. L., Crespo, N. C., Baquero, B., & Elder, J. P. (2010). Leisure-time physical activity in elementary schools: Analysis of contextual conditions. Journal of School Health, 80, 470-477. Morrissey, J. L., Wenthe, P. J., Letuchy, E. M., Levy, S. M., & Janz, K. F. (2012). Specific types of family support and adolescent non-school physical activity levels. Pediatric Exercise Science, 24, 333-346. Ogden, C. L., Carroll, M. D., Curtin, L. R., Lamb, M. M., & Flegal, K. M. (2010). Prevalence of high body mass index in US children and adolescents, 2007-2008. Journal of the American Medical Association, 303, 242-249. Ogden, C. L., Carroll, M. D., Curtin, L. R., McDowell, M. A., Tabak, C. J., & Flegal, K. M. (2006). Prevalence of overweight and obesity in the United States, 1999-2004. Journal of the American Medical Association, 295, 1549-1555. doi:10.1001/jama.295.13.1549 Ogden, C. L., Flegal, K. M., Carroll, M. D., & Johnson, C. L. (2002). Prevalence and trends in overweight among US children and adolescents, 1999-2000. Journal of the American Medical Association, 288, 1728-1732.
Downloaded from bmo.sagepub.com at CARLETON UNIV on March 16, 2015
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Pence, S. T., Roscoe, E. M., Bourret, J. M., & Ahearn, W. H. (2009). Relative contributions of three descriptive methods: Implications for behavioral assessment. Journal of Applied Behavior Analysis, 42, 425-446. doi:10.1901/jaba.2009. 42-425 Sidman, M. (1960). Tactics of scientific research. New York, NY: Basic Books. Strong, W. B., Malina, R. M., Blimkie, C. J., Daniels, S. R., Dishman, R. K., Gutin, B., . . .Trudeau, F. (2005). Evidence based physical activity for school-age youth. The Journal of Pediatrics, 146, 732-737. Thompson, R. H., & Iwata, B. A. (2007). A comparison of outcomes from descriptive and functional analyses of problem behavior. Journal of Applied Behavior Analysis, 40, 333-338. doi:10.1901/jaba.2007.56-06 Troiano, R. P., Berrigan, D., Dodd, K. W., Mâsse, L. C., Tilert, T., & McDowell, M. (2008). Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise, 40, 181-188. doi:10.1249/ mss.0b013e31815a51b3 Wallace, M. D., & Iwata, B. A. (1999). Effects of session duration on functional analysis outcomes. Journal of Applied Behavior Analysis, 32, 175-183. doi:10.1901/ jaba.1999.32-175
Author Biographies Tracy A. Larson is a behavior analyst at ABA Solutions, Inc., Tampa, FL. Her research interests include environmental factors related to obesity and physical activity, and the assessment and measurement of these socially significant problems. Matthew P. Normand is an associate professor in the department of psychology at the University of the Pacific. His primary scientific interests, broadly defined, are the application of basic behavioral principles to problems of social significance (including obesity and community health issues), verbal behavior, and the philosophy of science. Allison J. Morley is a graduate student in the School Psychology Program at Syracuse University. Her primary research interests include assessment and interventions aimed at increasing levels of physical activity in preschool children. Kristin M. Hustyi is a research assistant in the Center for Interdisciplinary Brain Sciences Research at Stanford University. Her primary research interest is the pathogenesis of aberrant behavior common in genetic disorders such as fragile X syndrome and Prader–Willi syndrome.
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BMOXXX10.1177/0145445514543466Behavior ModificationLarson et al.
Article
The Role of the Physical Environment in Promoting Physical Activity in Children Across Different Group Compositions
Behavior Modification 2014, Vol. 38(6) 837–851 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0145445514543466 bmo.sagepub.com
Tracy A. Larson1, Matthew P. Normand1, Allison J. Morley1, and Kristin M. Hustyi1
Abstract Physical activity is an important health-related behavior, but the environmental variables that promote or abate it are not well understood. The purpose of this study was to conduct a functional analysis evaluating the effect of the physical environment on moderate-to-vigorous physical activity (MVPA) in preschool children, and to evaluate the utility of the methodology across different group compositions. The Observational System for Recording Physical Activity in Children was used to define the test conditions and the measures of physical activity for eight preschool children. The functional analysis was implemented according to a multi-element experimental design. The highest levels of MVPA were observed when fixed playground equipment was available and at least one peer was present. Moreover, differential responding was observed across group compositions. The implications of this methodology and these findings on the development of interventions to increase MVPA are discussed.
1University
of the Pacific, Stockton, CA, USA
Corresponding Author: Matthew P. Normand, Department of Psychology, University of the Pacific, 3601 Pacific Avenue, Stockton, CA 95211, USA. Email: [email protected]
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Keywords physical activity, functional assessment, functional analysis Physical activity is an important behavioral target for interventions designed to improve health and fitness. The increased prevalence of obesity in the United States has been well documented (e.g., Flegal, Carroll, Ogden, & Curtin, 2010; Flegal, Carroll, Ogden, & Johnson, 2002; Ogden, Carroll, Curtin, Lamb, & Flegal, 2010; Ogden et al., 2006; Ogden, Flegal, Carroll, & Johnson, 2002), and physical activity plays an important role in promoting and abating this trend (Daniels et al., 2005; Janssen & LeBlanc, 2010). However, the benefits of physical activity extend beyond weight management, with movement being important for musculoskeletal development and coordination, especially in young children (Janz et al., 2010). In addition, physical activity is correlated with a host of health benefits (Janssen & LeBlanc, 2010). Increasing physical activity to levels associated with health benefits is, therefore, an important clinical goal. The Centers for Disease Control and Prevention (CDC, 2011) recommend that children engage in 60 min of moderate-to-vigorous physical activity (MVPA) every day of the week; however, current estimates suggest that many children are not this active (Strong et al., 2005; Troiano et al., 2008). Given the importance of physical activity for health and fitness, understanding the environmental variables related to physical activity is important, as these variables must be changed if physical activity is to be increased. Research suggests that the social (e.g., McKenzie, Crespo, Baquero, & Elder, 2010; Morrissey, Wenthe, Letuchy, Levy, & Janz, 2012; cf. Hastmann, Foster, Rosenkranz, Rosenkranz, & Dzewaltowski, 2013) and physical (e.g., Brink et al., 2010; Brown et al., 2009; see Ding, Salis, Kerr, Lee, & Rosenberg, 2011 for a review) environments influence activity levels, with the physical environment being potentially more influential for young children, as their ability to move among different environments is limited (Ding et al., 2011). In addition, like with any behavior problem, the specific environmental influences that are most influential are likely to differ from child to child, suggesting a need for individualized assessment methods. To date, however, few studies have investigated the role of the physical environment on the physical activity of young children (Ding et al., 2011). A number of different strategies can be used to assess potential functional relations between environmental variables and behavior, including descriptive (correlational) and functional (experimental) analyses (see Beavers, Iwata, & Lerman, 2013; Hanley, Iwata, & McCord, 2003). Of these assessment strategies, descriptive methods are the most commonly reported in the
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physical activity literature. For example, Brown and colleagues (Brown et al., 2009; Brown et al., 2006) developed an observational system called the Observational System for Recording Physical Activity in Children–Preschool Version (OSRAC-P). The OSRAC-P uses five activity categories to code varying levels of physical intensity as well as a variety of environmental events (e.g., activity type, location, indoor activity context, outdoor activity context, activity initiator, group composition, prompts, engagement, and TV use). Subsequently, Brown et al. (2009) conducted a descriptive analysis using the OSRAC-P as a means of determining predictors of various levels of physical activity by analyzing changes in physical activity levels across observations when environmental stimuli were allowed to vary naturally. Their results indicated that, among other things, the availability of outdoor toys and open play space were correlated with increased activity for the children observed. Descriptive information about the predictors of physical activity might prove useful in guiding social and physical manipulations for increasing physical activity. However, there is reason to be concerned about the emphasis on descriptive analyses of physical activity because the resulting data are correlational and, as such, do not constitute an empirical demonstration of functional relations. Studies comparing descriptive and functional analyses of problem behavior have consistently reported disagreements in the outcomes of the two assessment methods (e.g., Lerman & Iwata, 1993; Pence, Roscoe, Bourret, & Ahearn, 2009; Thompson & Iwata, 2007). The degree to which such disagreements would be seen with physical activity assessments is not known, but it stands to reason that disagreements would be evident. Only recently have functional analyses of physical activity been reported (Hustyi, Normand, Larson, & Morley, 2012; Larson, Normand, Morley, & Miller, 2013, 2014); however, there is a voluminous literature reporting the success of pretreatment experimental analyses for problem behavior. Overall, the vast collection of literature on functional behavior analysis has made it a “gold standard” of practice in the field of behavioral assessment and intervention (Hanley et al., 2003). Considering the prevalence of overweight and obesity in children, the limitations of descriptive assessments, and the demonstrated utility of functional analysis methods across a variety of contexts, functional analyses of the environmental and social variables related to physical activity might provide useful information about physical activity and the contexts in which intervention is most relevant. For example, Hustyi et al. (2012) experimentally manipulated several environmental contexts that were previously reported (e.g., Brown et al., 2009) to correlate with MVPA in young children. Four children were provided controlled access to an open grassy play area, a
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play area containing outdoor toys, a play area containing fixed playground equipment (i.e., jungle gym), and a play area containing a table and indoor activities (i.e., coloring books and army guys). Participants were exposed to each activity context several times according to a multi-element experimental design. Results indicated that MVPA was a function of the activity contexts evaluated, with the most MVPA observed when the participants were in the fixed equipment condition, suggesting that increasing access to the fixed equipment would be a prudent strategy to induce more physical activity by those children. Although those results are suggestive, more research on the utility of the assessment methods is warranted. Determining the extent to which functional analysis methods can be used across differing group contexts (i.e., with one or more peers present) seems especially important, as the participants in Hustyi et al. (2012) were assessed only when they were playing alone on the playground. Because children are likely to play with other children and might even be expected to play more actively when playing together, it is important to establish the utility of functional analysis methods across different group contexts. The purpose of the present study, then, was to (a) replicate the assessment methodology reported by Hustyi et al. as a step toward establishing the generality of those methods and (b) extend that work by using the same methods with participants who were playing alone, playing with one peer, and playing with a group of peers.
Method Participants and Setting Eight typically developing children attending a local daycare participated. Ganon was a 4-year-old male with a body mass index (BMI) that placed him above the 95th percentile of the CDC BMI-for-age growth charts indicating that Ganon was obese. Katherine was a 3-year-old female with a BMI that placed her above the 85th percentile indicating that she was overweight. Rico was a 3-year-old male, Marcus was a 4-year-old male, and Jessica and Emma were both 4 year-old females with normal BMIs in the 50th percentile. Lisa was a 4-year-old female and Max was a 4-year-old male with BMIs below the 5th percentile indicating that both Lisa and Max were underweight. Participants were recruited from the daycare via flyers that were distributed by the daycare operators. All children for whom parental informed consent was obtained participated in the study. Sessions were conducted at the daycare center on a fenced-in outdoor playground, 3 to 5 days per week. During solitary arrangements, one experimenter and the target child were present during the test and control sessions.
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During one-peer-present and group arrangements, two experimenters and the target children were present during the test and control sessions. One experimenter video recorded the session and the other experimenter ensured that the children remained in the target context. The physical environment included open grassy areas, fixed equipment, and several outdoor toys. Data were collected following the experimental sessions using the video records.
Response Measurement and Reliability During the initial observation and all subsequent sessions, the OSRAC activity codes (McIver, Brown, Pfeiffer, Dowda, & Pate, 2009) were used to assess the level of physical activity of each of the participants (see Table 1). Trained observers scored video-recorded sessions using a continuous, 5-s partialinterval recording method in which the highest level of physical activity observed during each interval was recorded. No activities were suggested to the participants during the analysis and no prompts were given except to stay inside the session area. In addition, the experimenters provided minimal attention to the participants. To calculate interobserver agreement (IOA), we used a point-by-point method in which we divided the number of agreements on OSRAC activity codes by the number of agreements plus disagreements for each interval and multiplied by 100 (Kazdin, 1982). An agreement was defined as both observers scoring an interval as MVPA (i.e., scoring codes 4 or 5), or both observers scoring the interval as low or no activity (i.e., scoring codes 1, 2, or 3). IOA was calculated for over 30% of sessions for all participants and was 91% for Ganon (range = 83%-100%), 90% for Katherine (range = 72%-98%), 89% for Rico (range = 80%-98%), 95% for Marcus (range = 82%-100%), 92% for Jessica (range = 79%-100%), 89% for Emma (range = 73%-100%), 86% for Lisa (range = 72%-100%), and 87% for Max (range = 79%-95%). Typically, agreement levels are considered acceptable if they meet or exceed 80% (Cooper, Heron, & Heward, 2007), although this standard is more a product of historical precedent than empirical validation (Kennedy, 2005). Still, the IOA results for the present study consistently exceeded 80% and approached, and sometimes exceeded, 90%, indicating good agreement between observers on the occurrence and non-occurrence of MVPA across sessions.
Design and Procedure All participants were exposed to three outdoor activity-context conditions and a control condition according to an alternating treatments experimental design (Barlow & Hayes, 1979; Sidman, 1960). A brief 30-min naturalistic
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Table 1. Activity Level Codes Specified by McIver, Brown, Pfeiffer, Dowda, and Pate (2009). Level
Activity
Operational definition
1
Stationary or motionless
2
Stationary with limb or trunk movements
3
Slow, easy movements
4
Moderate movements
5
Fast movements
Stationary or motionless with no major limb movements or major joint movement (e.g., sleeping, standing, riding passively in a wagon) Stationary with easy movements of limb(s) or trunk without translocation (e.g., standing up, holding a moderately heavy object, hanging off of bars) Translocation at a slow and easy pace (e.g., walking with translocation of both feet, slow and easy cycling, swinging without assistance and without leg kicks) Translocation at a moderate pace (e.g., walking uphill, two repetitions of skipping or jumping, climbing on monkey bars, hanging from bar with legs swinging) Translocation at a fast or very fast pace (e.g., running)
Note. Adapted from McIver et al. (2009).
observation was conducted before implementing the experimental conditions, in which the experimenters collected data on activity level without controlling for any variables. We coded the first two (Jessica, Lisa) or three (the remainder of the participants) 5-min samples in which the participant’s entire body was in view of the camera, with these samples serving as the naturalistic baselines. The one exception was that, for Jessica and Lisa, only the first two 5-min samples were used from the solitary condition.1 Following the naturalistic baselines, experimental conditions were presented in a randomized order and replications of each condition were counterbalanced. During these functional analyses, participants spent brief periods of time exposed to three outdoor activity contexts that were previously reported to be associated with non-sedentary behavior (Brown et al., 2009). In addition, we conducted the functional analyses in solitary, one-peerpresent, and group arrangements. Each session lasted 5 min, with 3 to 9 sessions conducted per day. The session lengths and frequencies were chosen for two primary reasons. First, research on functional analyses of problem behavior indicates that 5-min sessions produce data comparable with longer
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sessions (Wallace & Iwata, 1999) and that 5-min sessions are common session lengths in the published literature (Hanley et al., 2003). Second, because MVPA was the target behavior, briefer sessions seemed appropriate to avoid fatigue. The same logic influenced the number of sessions conducted per day and our previous research suggested that up to ten 5-min sessions spaced across a day was a reasonable frequency for this age group. Initially, participants were exposed to the experimental conditions while playing alone. Next, participants were exposed to all conditions with one peer present and then with two or three peers present. No programmed consequences were provided for responses during any of the conditions. If participants attempted to leave the session area, they were prompted to play only within the designated area. Outdoor toys. The experimenter guided the participant(s) to the session area where objects used in gross motor activities were present (i.e., one Frisbee®, one soft ball, one large bouncy ball, one medium bouncy ball, a jump rope, a bucket and shovel, several throwing toys, several orange cones, and a hula hoop). The experimenter directed the participant(s) to “play with the toys” and then stepped away from the session area and activated the video recorder. Fixed equipment. A jungle gym served as the fixed playground equipment on the daycare playground and was available during this condition. It included two slides, monkey bars, stairs, and several climbing areas. The experimenter directed the participant(s) to “play on the jungle gym” and then stepped away from the session area and activated the video recorder. Open space. This condition was identical to the previous conditions except that a large grassy area was available and no activity materials were present (e.g., balls, objects, or fixed equipment). The experimenter directed the participant(s) to “play in the grass” and then stepped away from the session area and activated the video recorder. Control. Sessions took place at a table in the play area, and activities not anticipated to evoke high levels of physical activity were made available to the participant (e.g., coloring print out pages, markers). The experimenter directed the participant(s) to “play at the table” and then stepped away from the session area and activated the video recorder.
Results Six participants were most active during the fixed equipment condition, with two participants having similarly high activity levels in the open space
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Figure 1. Percentage of MVPA per session for Jessica, Lisa, Max, and Rico during the solitary, one-peer-present, and group conditions. Note. MVPA = moderate-to-vigorous physical activity.
conditions. In addition, six of the eight participants were most active when a group of peers was present, and two participants were most active when one peer was present. Data are reported as the percentage of intervals in which MVPA (activity levels 4 or 5; see Table 1) was observed, as this is the level of physical activity corresponding to CDC (2011) recommendations. Figures 1 and 2 depict the results of the functional analyses across all group compositions for all participants.
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KATHERINE
Fixed Equipment
40
Control
20
Open Space Outdoor Toys
80 60 40 20 0
0
5
10
15
100
1 Peer PERCENTAGE MVPA
60
0
100
Solitary
80
PERCENTAGE MVPA
PERCENTAGE MVPA
100
0
5
SESSIONS
10
60 40 20 0
15
Group
80
0
5
SESSIONS
10
15
SESSIONS
EMMA
60 40 20
80 60 40 20 0
0
5
10 SESSIONS
15
100
1 Peer PERCENTAGE MVPA
80
0
100
Solitary PERCENTAGE MVPA
PERCENTAGE MVPA
100
0
5
10
60 40 20 0
15
Group
80
0
5
SESSIONS
10
15
SESSIONS
MARCUS
60 40 20
80 60 40 20 0
0
5
10 SESSIONS
15
100
1 Peer PERCENTAGE MVPA
80
0
100
Solitary PERCENTAGE MVPA
PERCENTAGE MVPA
100
0
5
10
60 40 20 0
15
Group
80
0
5
SESSIONS
10
15
SESSIONS
GANON
60 40 20
80 60 40 20 0
0
5
10 SESSIONS
15
100
1 Peer PERCENTAGE MVPA
80
0
100
Solitary PERCENTAGE MVPA
PERCENTAGE MVPA
100
0
5
10
15
Group
80 60 40 20 0
0
SESSIONS
5
10
15
SESSIONS
Figure 2. Percentage of MVPA per session for Katherine, Emma, Marcus, and Ganon during the solitary, one-peer-present, and group conditions. Note. MVPA = moderate-to-vigorous physical activity.
Solitary For five of the eight participants, MVPA was highest during the fixed equipment conditions, with Rico, Marcus, and Max being the exceptions. The results for Rico were not clearly differentiated across conditions and the results for Marcus indicate both open space and fixed equipment produced comparable levels of MVPA, overall. For Max, the amount of MVPA exceeded baseline levels during both the fixed equipment and open space conditions. In addition, for Ganon, clearly differentiated responding was not observed until the final sessions of the analysis. On first glance, there seems to be comparable levels of responding depicted in the fixed equipment and
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open space conditions for Jessica, but this is due primarily to a single elevated data point in the open space data series.
One Peer Present For three participants (Jessica, Lisa, Marcus), MVPA was highest during the fixed equipment conditions. For two participants (Katherine, Emma), MVPA was highest during the open space conditions. For Max and Rico, all conditions except for the control condition produced comparable high levels of MVPA. The results for Ganon also were undifferentiated, with no condition producing much MVPA.
Group For seven of eight participants, MVPA was highest during the fixed equipment conditions. Marcus, the only exception, engaged in the most MVPA during the outdoor toys conditions. MVPA was relatively low during all conditions for Katherine, Emma, and Ganon, with all conditions producing similar levels of MVPA; however, fixed equipment did still produce the most MVPA for these participants.
Discussion The results of the current study demonstrate that MVPA was functionally related to activity context, in that differential responding was observed across activity contexts and across group compositions for all participants. In addition, the functional analysis conditions produced differential responding across experimental conditions irrespective of group context. Moreover, MVPA might have been influenced by group composition, as suggested by the differing patterns of MVPA across group compositions for every participant. Katherine, for example, was most active in the fixed equipment condition when she was solitary and when a group of peers was present, but was most active in the open space condition when one peer was present. However, the degree to which group composition actually influenced MVPA is unknown, because group composition was not experimentally manipulated. Still, these results demonstrate the importance of activity context and suggest the importance of group composition on MVPA levels. These results replicate the work of Hustyi et al. (2012) with new participants in similar settings, with fixed equipment most consistently evoking the most MVPA. These results extend the work of Hustyi et al. by demonstrating the utility of the methodology across differing group contexts. This is
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valuable because understanding the variables that will promote MVPA when children are playing together is important. However, like the results of Hustyi et al., the results of the current study are not consistent with other published research employing descriptive, rather than experimental, assessments of physical activity. For example, the findings of Brown et al. (2009) suggested that children were more active with outdoor toys than when they were in other environmental contexts. In the current study, fixed equipment and open space conditions produced the highest levels of MVPA for all participants. Brown and colleagues (2009) also found that children were more active when solitary, whereas the participants in the current study were most active when at least one peer was present. The discrepancies observed between the results of this study and other published research employing descriptive analyses suggest that future research should directly compare the outcomes of descriptive and experimental assessments of physical activity to determine the extent to which they agree, much like has been done with functional assessments of problem behavior (e.g., Lerman & Iwata, 1993; Pence et al., 2009; Thompson & Iwata, 2007). The comparisons of descriptive and functional analyses of problem behavior suggest that descriptive assessments often identify attention as the relevant maintaining variable (Thompson & Iwata, 2007). This might be due, at least in part, to attention being a very common social variable even independent of problem behavior (Thompson & Iwata, 2001, p. 337). As such, attention can obscure the relevant functional relations in a descriptive assessment because it is frequently present and thus highly correlated with problem behavior even if it is not functionally related. It is possible that something similar could explain the discrepancies observed with physical activity. The events most correlated with MVPA in descriptive assessments might be events that are often present irrespective of physical activity, thereby obscuring the relevant functional relations. This is speculative, however, and future research should address the matter more systematically. Although the functional analysis methodology herein reported seems a promising approach to the behavioral assessment of physical activity, several limitations of this study should be noted. First, we only manipulated aspects of the general physical environment across experimental conditions. To determine which aspects of the physical environment support high levels of physical activity, this kind of analysis is appropriate. However, a more comprehensive functional analysis of behavior would manipulate both antecedent and consequent variables (e.g., Larson et al., 2013, 2014). Such analyses would provide additional information about the social environment that could be used to inform interventions.
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Second, only three outdoor activity contexts were evaluated. The choice to evaluate only three activity contexts was made because previous descriptive research indicated that these activity contexts were the best predictors of MVPA in preschool children. A more comprehensive analysis would assess activity levels in multiple activity contexts (e.g., playground games, outside chores, video games). Third, the clinical utility of this functional analysis methodology remains to be seen. Future studies should conduct the functional analyses, design interventions based on the results of the analyses, and evaluate the effects of those interventions, especially in comparison with interventions that seem contraindicated by the functional analysis results. For example, Hanley, Tiger, Ingvarsson, and Cammilleri (2009) altered preschoolers’ preference for various classroom activities by adding an embedded reinforcement contingency to an instructional activity when many activities were available. Initially, the participants rarely chose the instructional activity. However, following multiple exposures to the instructional activity, the children began to choose that activity over play-oriented activities. Applying similar methods to physical activity might increase the likelihood that children engage with environmental contexts that produce the most MVPA. In summary, the current study presents a method for determining which aspects of the physical environment support elevated levels of physical activity in young children. Similar to the functional analysis methodologies developed to inform the treatment of severe problem behavior (Hanley et al., 2003), the functional analysis of physical activity has the potential to improve our understanding of the roles setting and context play in promoting or abating physical activity and thereby lead to the development of better interventions. Function-based interventions for increasing physical activity in young children might well improve the health and well-being of overweight and obese children and even improve the prospects for those children at risk of becoming overweight or obese. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
Note 1. The data from the solitary analysis for Lisa and Jessica were published previously in Hustyi, Normand, Larson, and Morley (2012).
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Barlow, D. H., & Hayes, S. C. (1979). Alternating treatments design: One strategy for comparing the effects of two treatments in a single subject. Journal of Applied Behavior Analysis, 12, 199-210. Beavers, G. A., Iwata, B. A., & Lerman, D. C. (2013). Thirty years of research on the functional analysis of problem behavior. Journal of Applied Behavior Analysis, 46, 1-21. doi:10.1002/jaba.30 Brink, L. A., Nigg, C. R., Lampe, S. M., Kingston, B. A., Mootz, A. L., & van Vliet, W. (2010). Influence of schoolyard renovations on children’s physical activity: The learning landscapes program. American Journal of Public Health, 100, 1672-1678. Brown, W. H., Pfeiffer, K. A., McIver, K. L., Dowda, M., Addy, C. L., & Pate, R. R. (2009). Social and environmental factors associated with preschoolers’ nonsedentary physical activity. Child Development, 80, 45-58. doi:10.1111/j.14678624.2008.01245.x Brown, W. H., Pfeiffer, K. A., McIver, K. L., Dowda, M., Almeida, M. J., & Pate, R. R. (2006). Assessing preschool children’s physical activity: The observational system for recording physical activity in children-preschool version. Research Quarterly for Exercise and Sport, 77, 167-176. Centers for Disease Control and Prevention. (2011). How much physical activity do children need? Retrieved from http://www.cdc.gov/physicalactivity/everyone/ guidelines/children.html Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River, NJ: Pearson. Daniels, S. R., Arnett, D. K., Eckel, R. H., Gidding, S. S., Hayman, L. L., Kumanyika, S., . . .Williams, C. L. (2005). Overweight in children and adolescents: Pathophysiology, consequences, prevention, and treatment. Circulation, 111, 1999-2012. doi:10.1161/01.CIR.0000161369.71722.10 Ding, D., Salis, J. F., Kerr, J., Lee, S., & Rosenberg, D. E. (2011). Neighborhood environment and physical activity among youth: A review. American Journal of Preventative Medicine, 41, 442-455. doi:10.1016/j.amepre.2011.06.036 Flegal, K. M., Carroll, M. D., Ogden, C. L., & Curtin, L. R. (2010). Prevalence and trends in obesity among US adults, 1999-2008. Journal of the American Medical Association, 303, 235-241. Flegal, K. M., Carroll, M. D., Ogden, C. L., & Johnson, C. L. (2002). Prevalence and trends in obesity among US adults, 1999-2000. Journal of the American Medical Association, 288, 1723-1727. Hanley, G. P., Iwata, B. A., & McCord, B. E. (2003). Functional analysis of problem behavior: A review. Journal of Applied Behavior Analysis, 36, 147-185. doi:10.1901/jaba.2003.36-147 Hanley, G. P., Tiger, J. H., Ingvarsson, E. T., & Cammilleri, A. P. (2009). Influencing preschoolers’ free-play activity preferences: An evaluation of satiation and embedded reinforcement. Journal of Applied Behavior Analysis, 42, 33-41. doi:10.1901/jaba.2009.42-33
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Hastmann, T. J., Foster, K. E., Rosenkranz, R. R., Rosenkranz, S. K., & Dzewaltowski, D. A. (2013). Effect of adult leader participation on physical activity in children. Open Journal of Preventive Medicine, 2, 429-435. Hustyi, K. M., Normand, M. P., Larson, T. A., & Morley, A. J. (2012). The effect of outdoor activity context on physical activity in preschool children. Journal of Applied Behavior Analysis, 41, 401-405. Janssen, I., & LeBlanc, A. G. (2010). Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 7, 1-16. Janz, K. F., Letuchy, E. M., Gilmore, J. M. E., Burns, T. L., Torner, J. C., Willing, M. C., & Levy, S. M. (2010). Early physical activity provides sustained bone health benefits later in childhood. Medicine & Science in Sports & Exercise, 42, 1072-1078. Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings. New York, NY: Oxford University Press. Kennedy, C. H. (2005). Single-case research designs for educational research. Boston, MA: Allyn & Bacon. Larson, T. A., Normand, M. P., Morley, A. J., & Miller, B. G. (2013). A functional analysis of moderate-to-vigorous physical activity in young children. Journal of Applied Behavior Analysis, 46, 199-207. doi:10.1002/jaba.8 Larson, T. A., Normand, M. P., Morley, A. J., & Miller, B. G. (2014). Further evaluation of a functional analysis of moderate-to-vigorous physical activity in young children. Journal of Applied Behavior Analysis, 47, 219-230. Lerman, D. C., & Iwata, B. A. (1993). Descriptive and experimental analysis of variables maintaining self-injurious behavior. Journal of Applied Behavior Analysis, 26, 293-319. doi:10.1901/jaba.1993.26-293 McIver, K., Brown, W. H., Pfeiffer, K. A., Dowda, M., & Pate, R. R. (2009). Assessing children’s physical activity in their homes: The observational system for recording physical activity in children-home. Journal of Applied Behavior Analysis, 42, 1-16. doi:10.1901/jaba.2009.42-1 McKenzie, T. L., Crespo, N. C., Baquero, B., & Elder, J. P. (2010). Leisure-time physical activity in elementary schools: Analysis of contextual conditions. Journal of School Health, 80, 470-477. Morrissey, J. L., Wenthe, P. J., Letuchy, E. M., Levy, S. M., & Janz, K. F. (2012). Specific types of family support and adolescent non-school physical activity levels. Pediatric Exercise Science, 24, 333-346. Ogden, C. L., Carroll, M. D., Curtin, L. R., Lamb, M. M., & Flegal, K. M. (2010). Prevalence of high body mass index in US children and adolescents, 2007-2008. Journal of the American Medical Association, 303, 242-249. Ogden, C. L., Carroll, M. D., Curtin, L. R., McDowell, M. A., Tabak, C. J., & Flegal, K. M. (2006). Prevalence of overweight and obesity in the United States, 1999-2004. Journal of the American Medical Association, 295, 1549-1555. doi:10.1001/jama.295.13.1549 Ogden, C. L., Flegal, K. M., Carroll, M. D., & Johnson, C. L. (2002). Prevalence and trends in overweight among US children and adolescents, 1999-2000. Journal of the American Medical Association, 288, 1728-1732.
Downloaded from bmo.sagepub.com at CARLETON UNIV on March 16, 2015
851
Larson et al.
Pence, S. T., Roscoe, E. M., Bourret, J. M., & Ahearn, W. H. (2009). Relative contributions of three descriptive methods: Implications for behavioral assessment. Journal of Applied Behavior Analysis, 42, 425-446. doi:10.1901/jaba.2009. 42-425 Sidman, M. (1960). Tactics of scientific research. New York, NY: Basic Books. Strong, W. B., Malina, R. M., Blimkie, C. J., Daniels, S. R., Dishman, R. K., Gutin, B., . . .Trudeau, F. (2005). Evidence based physical activity for school-age youth. The Journal of Pediatrics, 146, 732-737. Thompson, R. H., & Iwata, B. A. (2007). A comparison of outcomes from descriptive and functional analyses of problem behavior. Journal of Applied Behavior Analysis, 40, 333-338. doi:10.1901/jaba.2007.56-06 Troiano, R. P., Berrigan, D., Dodd, K. W., Mâsse, L. C., Tilert, T., & McDowell, M. (2008). Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise, 40, 181-188. doi:10.1249/ mss.0b013e31815a51b3 Wallace, M. D., & Iwata, B. A. (1999). Effects of session duration on functional analysis outcomes. Journal of Applied Behavior Analysis, 32, 175-183. doi:10.1901/ jaba.1999.32-175
Author Biographies Tracy A. Larson is a behavior analyst at ABA Solutions, Inc., Tampa, FL. Her research interests include environmental factors related to obesity and physical activity, and the assessment and measurement of these socially significant problems. Matthew P. Normand is an associate professor in the department of psychology at the University of the Pacific. His primary scientific interests, broadly defined, are the application of basic behavioral principles to problems of social significance (including obesity and community health issues), verbal behavior, and the philosophy of science. Allison J. Morley is a graduate student in the School Psychology Program at Syracuse University. Her primary research interests include assessment and interventions aimed at increasing levels of physical activity in preschool children. Kristin M. Hustyi is a research assistant in the Center for Interdisciplinary Brain Sciences Research at Stanford University. Her primary research interest is the pathogenesis of aberrant behavior common in genetic disorders such as fragile X syndrome and Prader–Willi syndrome.
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