JOURNAL OF APPLIED BEHAVIOR ANALYSIS

2014, 47, 219–230

NUMBER

2 (SUMMER)

FURTHER EVALUATION OF A FUNCTIONAL ANALYSIS OF MODERATE-TO-VIGOROUS PHYSICAL ACTIVITY IN YOUNG CHILDREN TRACY A. LARSON, MATTHEW P. NORMAND, ALLISON J. MORLEY, AND BRYON G. MILLER UNIVERSITY OF THE PACIFIC

Inadequate physical activity increases the risks related to several health problems in children; however, increasing physical activity mitigates these risks. In this study, we examined the relations between moderate-to-vigorous physical activity (MVPA) and several environmental conditions (attention, interactive play, alone, escape) with 4 preschool children. We compared the experimental conditions to a control condition and a naturalistic baseline according to a combined multielement and reversal design. Results indicated that all participants were most active in the interactive play condition and that the percentage of MVPA varied across experimental and control conditions. In addition, the frequency and duration of bouts of MVPA were greatest in the interactive play condition. The current study presents a methodology for the identification of environmental contingencies that support increased levels of MVPA in young children, and it holds promise for improving our understanding of the variables related to physical activity. Key words: behavioral assessment, conjugate reinforcement, functional analysis, function-based treatment, health, physical activity

The World Health Organization (WHO) defines physical activity as “any bodily movement produced by skeletal muscles that requires energy expenditure,” with moderate-to-vigorous physical activity (MVPA) including activities such as cycling, walking, climbing and running (WHO, n.d.). To improve overall health (e.g., cardiovascular, metabolic, and bone), organizations such as the Centers for Disease Control and Prevention (CDC, 2013) and the WHO (n.d.) recommend that young children engage in at least 60 min of moderate-intensity activity every day of the week, with most of this activity being aerobic. Vigorous-intensity physical activity is recommended at least three times per week as part of activities such as game play, running, and 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 the Pacific. Allison Morley is now at Syracuse University. Bryon Miller is now at the University of South Florida. Please address correspondence to Matthew Normand, University of the Pacific, Department of Psychology, 3601 Pacific Ave., Stockton, California 95211 (e-mail: mnormand@ pacific.edu). doi: 10.1002/jaba.127

jumping (WHO, n.d.). Unfortunately, current estimates suggest that many children in the U.S. fail to meet these guidelines (Levi, Segal, St. Laurent, Lang, & Rayburn, 2012; Troiano et al., 2008). This finding is alarming given that physical inactivity is a factor that contributes to a number of health problems, including overweight and obesity (Dowda, Pate, Trost, Almeida, & Sirard, 2004), that make children more susceptible to preventable health conditions including cardiovascular disease and diabetes (Freedman, Mei, Srinivasan, Berenson, & Dietz, 2007; Reilly & Kelly, 2011). Conversely, increased physical activity can have several health benefits, even independent of weight loss (e.g., Ekelund et al., 2012; Janz et al., 2001; Sääkslahti et al., 2004), and might help to prevent the development of overweight and obesity (Kwon, Burns, Levy, & Janz, 2013). It is clear that increasing physical activity, especially MVPA, is an important target for programs that aim to improve the health and fitness of children. Understanding the environmental variables that are functionally related to physical activity is important if patterns of physical activity are to be

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understood and ultimately changed in a therapeutic way. To date, there have been relatively few studies that have examined physical activity patterns and associated variables in very young children. This lack of research is unfortunate because, practically speaking, intervention in early childhood to increase MVPA is a promising strategy for at least two reasons. First, because the environments of very young children typically are more controllable than those of older children and adults, the likelihood of successful behavior change is increased. Second, establishment of healthy patterns of activity at a young age might prevent the later development of less healthy behavior patterns; some data suggest that past levels of physical activity are a good predictor of physical activity in childhood and adolescence (Sallis, Prochaska, & Taylor, 2000). Investigation of the variables that are related to physical activity requires a means of measuring physical activity and recording potentially relevant environmental events. A variety of mechanical measures (e.g., pedometers, accelerometers) can estimate physical activity levels, but direct observation is considered the gold standard of physical activity measurement because it provides the most comprehensive account of physical activity and related environmental variables (Sirard & Pate, 2001). Several direct observation systems have been developed to record activity level and relevant contextual events (e.g., Brown et al., 2006; Klesges, 1984; McKenzie et al., 1991; Puhl, Greaves, Hoyt, & Baranowski, 1990). In these systems, physical activity is broken into discrete categories and reported as activity level, with activity ranked from lowest to highest (e.g., 1 to 5, with 1 ¼ sedentary activity and 5 ¼ vigorous activity). A discontinuous partial-interval recording system is often used to score physical activity level and potentially relevant environmental variables that occur within the same interval. For example, Brown et al. (2009) used a discontinuous partial-interval recording system in which observers noted the highest level of activity and potentially relevant contextual events observed during the first 5 s of a 30-s interval. For

the remaining 25 s, the observed activity level and contextual events (e.g., activity type, engagement, initiator of activity, group composition, prompts delivered) were scored. Although direct observation provides the most comprehensive account of physical activity, purely descriptive accounts of physical activity do not, by definition, involve experimental manipulations. Thus, these accounts are unable to identify functional relations between environmental variables and behavior. Still, descriptive accounts are valuable in that they help to identify environmental variables that are potentially related to increased levels of physical activity so that those variables can ultimately be manipulated experimentally. A wealth of literature suggests that determination of the environmental variables that are functionally related to a particular behavior is not only possible, but also that doing so leads to more nuanced interventions with more robust effects (Hanley, Iwata, & McCord, 2003). Because there are many variables that can be possibly correlated with physical activity (Hinkley, Crawford, Salmon, Okely, & Hesketh, 2008), experimental determination of the particular environmental variables that are related to physical activity in individual children is warranted. In a functional analysis of physical activity, experimenters arrange contextual variables that might affect physical activity level, such as activity context, group composition, and reinforcement contingencies. For example, Hustyi, Normand, Larson, and Morley (2012) experimentally manipulated the activity contexts reported by Brown et al. (2009) to be most associated with physical activity in children. The results of Hustyi et al. indicated that participants were usually sedentary, but the analysis identified differentiated levels of activity across conditions. For all participants, the fixed equipment condition produced the highest levels of MVPA. It is worth noting that the results of Hustyi et al. did not correspond to the results from the descriptive analyses reported by Brown et al. The reasons for this discrepancy are unclear and might well be

FUNCTIONAL ANALYSIS OF MVPA due to differences in the populations studied or the manner in which the observations were conducted. However, it also is possible that descriptive assessments of physical activity, much like descriptive assessments of problem behavior (see Lerman & Iwata, 1993; Pence, Roscoe, Bourret, & Ahearn, 2009; Thompson & Iwata, 2007), do not always identify valid functional relations. Thus, additional experimental analyses of putative functional relations with physical activity are warranted. Although the analysis reported by Hustyi et al. (2012) can be used to identify environmental contexts that evoke high levels of physical activity, the kinds of consequences that were related to physical activity were left undetermined. To address this limitation, Larson, Normand, Morley, and Miller (2013) used a functional analysis, similar to that reported by Iwata, Dorsey, Slifer, Bauman, and Richman (1982/1994) for the assessment of problem behavior, to assess the role of social and nonsocial consequences on levels of MVPA exhibited by two preschool children. Larson et al. observed the amount of MVPA exhibited by two preschool children in four experimental conditions: alone, attention contingent on MVPA, adult interaction contingent on MVPA, and escape from task demands contingent on MVPA. The amount of MVPA observed in these four conditions was compared to the amount of MVPA observed in a naturalistic baseline and a control condition. Their results indicated that contingent interactive play and adult attention resulted in the highest levels of MVPA for both participants. The results of Larson et al. (2013) are promising, and replications of this procedure are warranted. However, an important limitation of that study involved a potential confounding effect that was present in the interaction condition. Specifically, attention was provided contingent on MVPA in both the attention and the interactive play conditions. Therefore, the interactive play condition actually constituted an attention plus interactive play condition, in

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which the effects of interactive play alone could not be determined. To address these issues, the current study provided a partial replication of Larson et al. and extended that research in several ways. First, we included a brief treatment evaluation following the functional analysis, which consisted of repeated exposures to the condition that produced high levels of MVPA to determine the effects on activity levels. Second, a fixed-time (FT) schedule of attention was included in the interactive play condition to control for the attention that was necessarily delivered during the interactive play condition. Third, the tasks in the escape condition were modified to be more ecologically valid antecedent events. METHOD Participants and Setting Four typically developing 4-year-old preschool children who attended a local day care participated. Participants were recruited via flyers distributed to local preschool classrooms and by word of mouth. Caregivers who expressed an interest in the study were given an informed consent form that explained the purposes, procedures, and potential risks of the study. The participants were the children of the first four parents to return consent forms. The local institutional review board approved all aspects of this study. We conducted all experimental sessions on outdoor playgrounds at one of two local elementary schools. The playgrounds were each surrounded by a fence and contained a fixed structure with climbing fixtures and a slide, an open grassy area measuring approximately 15 m2, and a permanent outdoor table. A video camera was used to record all sessions. Materials Outdoor toys (e.g., assorted balls, a hula hoop, a jump rope) and indoor activities (e.g., coloring books, small blocks) were available on the

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Table 1 Activity Level Codes Level

Activity

1

Stationary or motionless

2 3

Stationary with limb or trunk movements Slow, easy movements

4

Moderate movements

5

Fast movements

Operational definition 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 limbs or trunk without translocation (e.g., standing up, holding a moderately heavy object, hanging on 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).

playground. The experimenter used a Flip digital video camera to record all experimental sessions. A stopwatch was used to time conditions and to time the delivery of consequences. Response Measurement and Reliability Because research suggests that the activity codes specified by the Observational System for Recording Physical Activity in Children (OSRAC; McIver, Brown, Pfeiffer, Dowda, & Pate, 2009) are valid measures of physical activity in young children (Larson, Normand, & Hustyi, 2011), we used these codes to score the activity level of participants during the baseline observations and all experimental conditions (see Table 1). However, the discontinuous measurement strategy (i.e., 5 s of observation followed by 25 s of recording) reported by McIver et al. (2009) was not used. Instead, using video records of the experimental sessions, observers recorded the highest activity level observed using a 1-s partial-interval recording strategy. The InstantData software suite for PC (Samaha, 2002) allowed observers to score each activity level continuously by depressing the keys that corresponded to each activity level in real time. With this program, the generated data were subsequently arranged into 1-s intervals. Although data were collected on the occurrence of all activity codes, Levels 4 and 5 were collapsed to represent

MVPA, with the data for each participant ultimately depicted as the percentage of 1-s intervals in which MVPA occurred. This analysis of physical activity also targets the CDC (2013) activity recommendations for young children. To assess reliability, a second independent observer simultaneously recorded data in the manner described above. Reliability was calculated by dividing the number of intervals containing scoring agreements (on the occurrence of one of the five activity codes) by the total number of intervals and converting the result to a percentage. Reliability data were collected for 28% of experimental sessions for Grace and Humphrey, 29% of sessions for Greta, and 27% of sessions for Vivien. Interobserver agreement was 88% (range, 84% to 99%) for Grace, 93% (range, 85% to 99%) for Greta, 91% (range, 87% to 95%) for Vivien, and 93% (range, 89% to 99%) for Humphrey. For approximately 25% of sessions for each participant, observers also collected data on the integrity with which the experimenter maintained the specified antecedent and consequent conditions during the experimental sessions. Observers collected data on the following antecedents: (a) withholding attention in the attention and interactive play conditions (scored as correct if no attention was delivered before MVPA occurred) and (b) the delivery of

FUNCTIONAL ANALYSIS OF MVPA instructions in the escape condition (scored as correct if delivered every 10 s  1 s). Observers also collected data on the following consequences: (a) providing brief, contingent attention in the attention condition (scored as correct if attention was delivered within 10 s of the initiation of MVPA), (b) providing contingent interaction in the interactive play condition (scored as correct if interaction was initiated within 10 s of MVPA), (c) providing FTattention in the interactive play condition (scored as correct if delivered approximately every 30 s throughout the condition), and (d) terminating demands contingent on MVPA in the escape condition (scored as correct if terminated within 5 s of the initiation of MVPA). Experimenter integrity remained above 95% across all experimental conditions. Observer Training Observers were trained using the five steps outlined in Cooper, Heron, and Heward (2007). They first reviewed the operational definitions and specific examples and nonexamples of the activity codes and then completed a multiplechoice quiz that required them to identify activities that correspond to the various activity codes. Next, during a question-and-answer session, observers had the opportunity to ask questions about the coding system. They then coded a series of staged video vignettes that depicted adults engaging in various activities on a playground so that observers could practice coding the range of possible activities. After this practice, observers coded videos of children engaging in physical activity in the natural environment. A range of videos was presented so that observers could practice coding a variety of activities in which children of this age group typically engage. Finally, they coded actual participant videos from a previous study (Larson et al., 2013) that employed a functional analysis to assess physical activity, so they could practice scoring child activity during the functional analysis and the integrity of the experimenter.

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For each training phase, observers were required to reach a criterion of 90% agreement with a master score sheet before they moved to the next phase. Agreement was determined by comparing observer scores with a previously coded master score sheet. Procedure A combined multielement and reversal design was used. Initial observations were conducted (three 5-min samples) during the normal recess time when there were no constraints placed on the participants in terms of activities or playground location. During this naturalistic baseline, observers collected data on activity level using the OSRAC category codes. Each experimental session lasted 5 min, and three to six sessions were conducted per day, 3 to 5 days per week. After the experimental conditions, there was a return to the naturalistic baseline arrangement, which was followed by a return to the experimental condition that produced the most MVPA for each participant. Conditions The experimental conditions were similar to those reported by Larson et al. (2013) and were conducted on a playground that contained fixed equipment and an open grassy area. These conditions were conducted when no peers or adults (other than the experimenters) were present on the playground. If the participant attempted to leave the session area, the experimenter guided him or her back to the area. To enhance the discriminability of the experimental conditions (see Conners et al., 2000), the experimenter began the sessions for each condition in a different location on the playground (e.g., interactive play conditions began near the side of the playground, whereas attention conditions began near the middle of the playground) and by making a statement (see below) related to the condition in place. At the outset of each condition, the child had access to the entire playground. At least two experimenters

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were present, except during the alone condition, when only one experimenter was present to operate the video camera. Naturalistic baseline. The naturalistic baseline was conducted on the participant’s typical recess playground and consisted of three 5-min sessions. Peers and staff were present on the playground, and activities occurred according to normal daily routines. The participant was allowed to play freely while the experimenter moved around the playground appearing busy. No attention or consequences were delivered contingent on MVPA. This condition was used to determine the level of MVPA the participants exhibited in the absence of any experimental manipulations. Interactive play. The experimenter guided the participant to the session area and said, “If you run, jump or climb, I’ll play with you, but if you don’t run, jump or climb, I have to do some work.” Brief attention (e.g., “I like the way you’re running”) was delivered according to an FT 30-s schedule. Contingent on MVPA, the experimenter engaged in the activity with the participant for as long as he or she continued to engage in MVPA. This arrangement constituted a conjugate schedule of reinforcement in which reinforcement duration was proportional to MVPA duration. For example, if the participant began to climb on the fixed equipment, the experimenter climbed with him or her until he or she stopped climbing. When the participant stopped engaging in MVPA, the experimenter walked back to the area in which the session started and became “too busy” (i.e., pretended to read or write notes) to interact with the participant. Although the experimenter appeared to be busy, she continued to observe the participant to determine when to play and when to deliver attention (according to the FT 30-s schedule). If the participant attempted to engage the experimenter when MVPA was not occurring, she briefly reminded the participant, “I’m busy right now, we can talk later” without making eye contact. This condition assessed whether MVPA was sensitive to positive rein-

forcement in the form of adult engagement in physical activity (play). Attention. The experimenter guided the participant to the session area and said, “If you run, jump, or climb, I’ll watch you and I’ll talk to you. If you don’t run, jump, or climb, I have to do some work.” Contingent on MVPA, the experimenter made eye contact and delivered brief attention in the form of specific praise. For example, if the participant began to run, the experimenter delivered attention by stating, “I love how you’re running!” If he or she continued to engage in MVPA, specific praise was delivered every 10 s. Eye contact and praise were only delivered contingent on MVPA. This arrangement constituted a conjugate schedule of reinforcement, in that reinforcement occurred every 10 s proportional to the duration of MVPA. However, the absolute duration of reinforcement was not matched to the duration of MVPA, as it was in the interactive play conditions. If MVPA was not occurring, the experimenter turned slightly away from the participant and became “too busy” (i.e., pretended to read a book or write notes) to interact with the participant. As in the previous condition, although the experimenter appeared not to be watching when the participant was not engaged in MVPA, the participant was continuously observed to determine when consequences were to be delivered. If the participant attempted to engage the experimenter when MVPA was not occurring, she briefly reminded the participant “I’m busy right now, we can talk later” without making eye contact. This condition was designed to assess the effects of social reinforcement in the form of adult attention on the percentage of observation intervals recorded with MVPA. Escape. The experimenter guided the participant to the session area and said, “It’s time to clean up the playground. If you don’t want to clean or you get tired of cleaning, you can go run, jump, or climb. If you stop running, jumping, or climbing, you have to come back and help me.” She then delivered instructions to clean up the

FUNCTIONAL ANALYSIS OF MVPA playground (pick up toys, put bark back into the bark box, or rearrange toys or equipment). This condition assessed whether MVPA was sensitive to negative reinforcement in the form of escape from nonplay activities. Verbal prompts to clean up were terminated contingent on MVPA, and the experimenter turned away from the participant and ceased all demands for 30 s. After 30 s elapsed, the experimenter again delivered instructions. If MVPA was occurring at the end of the 30 s, a changeover delay was implemented; the experimenter waited until the participant ceased to engage in MVPA. If MVPA did not occur, the experimenter continued to deliver instructions every 10 s throughout the condition. This condition was designed to assess the effects of social negative reinforcement in the form of escape from demands on the percentage of observation intervals recorded with MVPA. When necessary, three-step guided compliance was used to gain compliance with instructions. Following noncompliance to the initial instruction, a model prompt was delivered. If the participant did not comply with the model prompt, a physical prompt was delivered. The physical prompt involved gently guiding the child by putting a hand on his or her back and lightly pushing, or by taking his or her hand and lightly pulling. Alone. The experimenter guided the participant to the session area and said, “I have to go inside and do some work; play out here for a little bit” and then walked into the building. She remained out of sight but observed the participant from at least 10 m away from the play area. A second experimenter remained on the playground but out of sight whenever possible. No attention or consequences were delivered. If the participant attempted to leave, he or she was guided back to the session area with minimal verbal or physical attention. This condition assessed the sensitivity of MVPA to potential automatic reinforcement from engagement in MVPA. Control. The experimenter guided the participant to the session area and said, “Let’s color.” Sessions were conducted at a table in the play

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area. No prompts for activity were delivered, and attention in the form of praise (e.g., “I love what you are coloring”) was delivered approximately every 30 s. The experimenter stayed within 3 m of the participant at all times, and there were no programmed consequences for MVPA. This condition served as a control for the social variables in the other conditions, including experimenter presence, attention, and interaction, and task demands. RESULTS In general, participants were sedentary during the naturalistic baseline. Figure 1 depicts the percentage of MVPA across baseline and experimental conditions. For all participants, baseline levels of MVPA were low, with an average of 14%, 3.9%, 4.9%, and 9.3% of intervals with MVPA for Grace, Greta, Vivien, and Humphrey, respectively. Activity levels were highest in the interactive play condition, and activity remained low during the control and escape conditions for all participants. However, the level of MVPA varied across participants, as well as within participants, across experimental conditions. Grace exhibited the highest levels of MVPA in the interactive play condition during the initial experimental conditions. During the return to baseline, MVPA again dropped to low levels. When interactive play was reintroduced, MVPA returned to high levels. Greta’s results were similar, in that low levels of MVPA were evident during the initial baseline, MVPA was highest during interactive play, decreased during the return to baseline, and increased to high levels when interactive play was reintroduced. For Vivien, baseline levels of MVPA were low. MVPA was at high and stable levels during interactive play. During the return to baseline, MVPA dropped to low levels, although not to initial baseline levels. The return to interactive play resulted in higher levels of MVPA than those seen during the alternated experimental conditions. Humphrey’s level of MVPA was low during

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Figure 1.

Percentage of moderate-to-vigorous physical activity (MVPA) observed across conditions for all participants.

baseline and high during interactive play. MVPA returned to baseline levels, and increased again when interactive play was reintroduced. Like Greta, Humphrey’s MVPA during the final interactive play conditions overlapped with other conditions during the alternated experimental sessions. These data provide information on specific social and nonsocial environmental conditions that affect levels of MVPA. DISCUSSION The results of the current study indicate that patterns of MVPA varied within and, to a lesser extent, across participants, with MVPA varying as a function of the contingencies arranged. For all four participants, higher levels of MVPA were consistently associated with one or more conditions, whereas other experimental conditions

and the control condition produced lower levels of MVPA. Moreover, one or more experimental conditions produced higher levels of MVPA relative to baseline for all participants. MVPA was observed most often in the interactive play condition for all participants. Although MVPA occurred most often during interactive play for all participants, elevated levels of MVPA also were observed during the attention condition for two participants (Grace and Greta). The degree to which adult interactions produced elevated levels of MVPA for all participants, both in this study and in Larson et al. (2013), is notable, and these findings have several clinical implications. First, the results suggest that positive reinforcement in the form of both adult engagement and adult attention might play an important role in increasing activity levels for many children. Teachers or parents could be

FUNCTIONAL ANALYSIS OF MVPA taught to deliver attention or engage in activities (or some combination of both) contingent on elevated activity levels. In the attention condition, specific praise was delivered contingent on engagement in MVPA. For example, contingent on MVPA, the experimenter used descriptive praise such as, “I love how you’re running!” It is possible that the type of adult attention delivered during this condition could result in differences in activity level. Provision of a greater variety of descriptive praise or inclusion of prompts to maintain or increase activity level might reduce the likelihood of satiation when interventions to increase physical activity involve repeated deliveries of adult attention. Also, because the clinical utility of this functional analysis methodology is unknown without more extensive evaluations, future research might investigate the success of treatments based on the results of the functional analysis compared to interventions that might be contraindicated by the analysis or chosen arbitrarily. In addition to information about the variables that are functionally related to MVPA in young children, this study and Larson et al. (2013) also contribute to a relatively small literature that has used functional analyses to evaluate appropriate (i.e., nonproblem) behavior. Several researchers have reported functional analyses of verbal behavior (see Plavnick & Normand, 2013, for a review) and, more specifically, functional communication training (Schieltz et al., 2010), but these numbers are few in comparison to published research on functional analyses of problem behavior (cf. Beavers, Iwata, & Lerman, 2013; Hanley et al., 2003). Understanding the environmental variables that are functionally related to appropriate behavior seems as worthwhile as understanding functional relations that are related to problem behavior, especially if we hope to arrange environments that will support appropriate behavior. Essentially, functional analyses of appropriate behavior, like the kind reported here, constitute systematic intervention analyses that can identify one or

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more environmental (including social) arrangements that will increase or maintain a targeted behavior. The results of this study suggest that functional analysis is a promising approach for the identification of environmental conditions that produce elevated levels of activity, although several limitations should be noted. First, the magnitude of reinforcement across the interactive play and the attention conditions was not equivalent because the duration of reinforcement delivered in interactive play was matched to the duration of MVPA, whereas the duration of reinforcement in the attention conditions was proportional, but not matched, to the duration of MVPA. The extent to which this was a problem can be estimated by examining the activity-bout data from the interactive play conditions. All participants engaged in the greatest frequency and duration of MVPA bouts in interactive play, which resulted in a greater amount of programmed reinforcement than in the attention condition. During interactive play, the experimenter engaged in the activity with the participant for as long as he or she engaged in MVPA. In the attention condition, by contrast, specific praise was delivered approximately every 10 s and was interspersed with periods of silence as long as the participant continued to engage in MVPA. In the first three interaction conditions, reinforcement was delivered for approximately 667 s, 545 s, 443 s, and 419 s for Grace, Greta, Vivien, and Humphrey, respectively. In the attention condition, by contrast, reinforcement was delivered for approximately 159 s, 114 s, 138 s, and 120 s, for Grace, Greta, Vivien, and Humphrey, respectively. Therefore, although social-positive reinforcement was delivered contingently, the total duration of reinforcement was greater in the interactive play condition than in the attention condition. Second, it is unclear whether the tasks used in the escape condition actually were aversive for the participants. Although all four participants contacted the contingency during the analysis,

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they were generally compliant with the instructions and rarely engaged in MVPA as a form of escape from task demands. It is possible that the task (i.e., cleaning up the playground) did not establish escape as a reinforcer. An assessment of compliance across various activities would be valuable to determine a task that is least preferred by the participant for use in the escape condition. It is also possible that the participants’ history of reinforcement for compliance to adult-delivered instructions resulted in high levels of compliance. Participants were told they could stop cleaning to engage in MVPA at any time, but it is possible that the instruction used (e.g., “Put the wood chips back in the sandbox”) exerted control over their behavior. The escape condition was included to see if negative reinforcement contingencies would maintain MVPA even though we had no reason to suspect that such contingencies already were occurring. In this study and in Larson et al. (2013), these contingencies were ineffective. Future research might simply eschew the escape condition and focus on social-positive contingencies, or more specifically match assessment conditions to hypothesized functions based on indirect assessments. Third, time constraints limited the intervention analysis because a relatively small sample of behavior was obtained. Whether MVPA would persist under extended periods of adult interaction is unknown (e.g., a decreasing trend is suggested in Humphrey’s treatment analysis data). If MVPA does persist under such conditions, it might be useful to investigate ways to alter the intervention so that it is easier to implement, perhaps by thinning the schedule of reinforcement or incorporating the play of other adults or children as reinforcers. Making interventions more practical is especially important if the goal is to increase MVPA to meet the WHO (2011) and CDC (2013) recommendation of 60 min per day. Although each individual experimental condition reported here was a relatively brief 5 min, the cumulative effects of multiple conditions per day might well be

therapeutic, because the WHO suggests that the recommended 60 min of MVPA can be achieved by engaging in multiple bouts of activity each day. However, parents or teachers would likely have difficulty in implementing such a rich schedule of reinforcement consistently and for extended periods of time, which underscores the importance of further intervention evaluations such as those involving schedule thinning. Physical inactivity is a problem of great social significance that behavior analysis is well suited to address. Inadequate physical activity increases the risks related to several health problems in children, most notably obesity and its corresponding range of chronic diseases (Reilly & Kelly, 2011). The rates of childhood obesity have quadrupled over the last three decades (Levi et al., 2012), and projections suggest that the problem will only get worse (Wang, Beydoun, Liang, Caballero, & Kumanyika, 2008), with obese children and adolescents more likely to be obese adults (Guo, Wu, Chumlea, & Roche, 2002; Whitaker, Wright, Pepe, Seidel, & Dietz, 1997). Physical activity can help to prevent weight gain and promote weight loss, but it is also an important health-related behavior independent of weight (e.g., Ekelund et al., 2012; Janz et al., 2001; Sääkslahti et al., 2004). The current study provides support for a method to identify environmental contingencies that increase levels of MVPA in young children (see Hustyi et al., 2012; Larson et al., 2013). Research of this kind promises to improve our understanding of the variables that are related to physical activity so that effective interventions can be designed to improve children’s health and well-being. REFERENCES 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 Brown, W. H., Pfeiffer, K. A., McIver, K. L., Dowda, M., Addy, C. L., & Pate, R. R. (2009). Social and

FUNCTIONAL ANALYSIS OF MVPA environmental factors associated with preschoolers’ nonsedentary physical activity. Child Development, 80, 45–58. doi: 10.1111/j.1467-8624.2008.01245.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. (2013). Physical activity facts. Retrieved from http://www.cdc. gov/healthyyouth/physicalactivity/facts.htm Conners, J., Iwata, B. A., Kahng, S. W., Hanley, G. P., Worsdell, A. S., & Thompson, R. H. (2000). Differential responding in the presence and absence of discriminative stimuli during multielement functional analyses. Journal of Applied Behavior Analysis, 33, 299–308. doi: 10.1901/jaba.2000.33-299 Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River, NJ: Pearson. Dowda, M., Pate, R. R., Trost, S. G., Almeida, M. J., & Sirard, J. R. (2004). Influences of preschool policies and practices on children’s physical activity. Journal of Community Health, 29, 183–196. Ekelund, U., Luan, J., Sherar, L. B., Esliger, D. W., Griew, P., & Cooper, A. (2012). Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. Journal of the American Medical Association, 307, 704–712. doi: 10.1001/jama.2012.156 Freedman, D. S., Mei, Z., Srinivasan, S. R., Berenson, G. S., & Dietz, W. H. (2007). Cardiovascular risk factors and excess adiposity among overweight children and adolescents: The Bogalusa heart study. The Journal of Pediatrics, 150, 12–17. doi: 10.1016/j. jpeds.2006.08.042 Guo, S. S., Wu, W., Chumlea, W. C., & Roche, A. F. (2002). Predicting overweight and obesity in adulthood from body mass index values in childhood and adolescence. The American Journal of Clinical Nutrition, 76, 653– 658. 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 Hinkley, T., Crawford, D., Salmon, J., Okely, A. D., & Hesketh, K. (2008). Preschool children and physical activity: A review of correlates. American Journal of Preventative Medicine, 34, 435–441. doi: 10.1016/j. amepre.2008.02.001 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, 45, 401–405. doi: 10.1901/jaba.2012.45-401 Iwata, B. A., Dorsey, M. F., Slifer, K. J., Bauman, K. E., & Richman, G. S. (1994). Toward a functional analysis of

229

self-injury. Journal of Applied Behavior Analysis, 27, 197–209. doi: 10.1901/jaba.1994.27-197 (Reprinted from Analysis and Intervention in Developmental Disabilities, 2, 3–20, 1982) Janz, K. F., Burns, T. L., Torner, J. C., Levy, S. M., Paulos, R., Willing, M. C., & Warren, J. J. (2001). Physical activity and bone measures in young children: The Iowa bone development study. Pediatrics, 107, 1387–1393. doi: 10.1542/peds.107.6.1387 Klesges, R. C. (1984). The FATS: An observational system for assessing physical activity in children and associated parent behavior. Behavioral Assessment, 6, 333–345. Kwon, S., Burns, T. L., Levy, S. M., & Janz, K. F. (2013). Which contributes more to childhood adiposity—High levels of sedentarism or low levels of moderate-throughvigorous physical activity? The Iowa bone development study. The Journal of Pediatrics, 162, 1169–1174. doi: 10.1016/j.jpeds.2012.11.071 Larson, T. A., Normand, M. P., & Hustyi, K. M. (2011). Preliminary evaluation of an observation system for recording physical activity in children. Behavioral Interventions, 26, 193–203. doi: 10.1002/bin.332 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 Lerman, D. C., & Iwata, B. A. (1993). Descriptive and experimental analyses of variables maintaining selfinjurious behavior. Journal of Applied Behavior Analysis, 26, 293–319. doi: 10.1901/jaba.1993.26-293 Levi, J., Segal, L. M., St. Laurent, R., Lang, A., & Rayburn, J. (2012). F as in fat: How obesity threatens America’s future, 2012. The Robert Wood Johnson Foundation. Retrieved from http://healthyamericans.org/assets/ files/TFAH2012FasInFat18.pdf McIver, K. L., 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., Sallis, J. F., Nader, P. R., Patterson, T. L., Elder, J. P., Berry, C. C., … Nelson, J. A. (1991). BEACHES: An observational system for assessing children’s eating and physical activity behaviors and associated events. Journal of Applied Behavior Analysis, 24, 141–151. doi: 10.1901/jaba.1991.24-141 Pence, S. T., Roscoe, E. M., Bourret, J. C., & 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 Plavnick, J. B., & Normand, M. P. (2013). Functional analysis of verbal behavior: A brief review. Journal of Applied Behavior Analysis, 46, 349–353. doi: 10.1002/ jaba.1 Puhl, J., Greaves, K., Hoyt, M., & Baranowski, T. (1990). Children’s Activity Rating Scale (CARS): Description

230

TRACY A. LARSON et al.

and calibration. Research Quarterly for Exercise and Sport, 61, 26–36. Reilly, J. J., & Kelly, J. (2011). Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: Systematic review. International Journal of Obesity, 35, 891–898. doi: 10.1038/ijo.2010.222 Sääkslahti, A., Numminen, P., Varstala, V., Helenius, H., Tammi, A., Viikari, J., & Välimäki, I. (2004). Physical activity as a preventive measure for coronary heart disease risk factors in early childhood. Scandinavian Journal of Medicine and Science in Sports, 14, 143–149. Sallis, J. F., Prochaska, J. J., & Taylor, W. C. (2000). A review of correlates of physical activity of children and adolescents. Medicine and Science in Sports and Exercise, 32, 963–975. Samaha, A. L. (2002). Instant data for PC (version 1.1) [Computer software]. Gainesville, FL: University of Florida. Schieltz, K. M., Wacker, D. P., Harding, J. W., Berg, W. K., Lee, J. F., & Padilla Dalmau, Y. C. (2010). An evaluation of manding across functions prior to functional communication training. Journal of Developmental and Physical Disabilities, 22, 131–147. doi: 10.1007/s10882-009-9181-5 Sirard, J. R., & Pate, R. R. (2001). Physical activity assessment in children and adolescents. Sports Medicine, 31, 439–454. doi: 0112-1642/01/0006-0439 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 and Science in Sports and Exercise, 40, 181–188. Wang, Y., Beydoun, M. A., Liang, L., Caballero, B., & Kumanyika, S. K. (2008). Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity, 16, 2323– 2330. doi: 10.1038/oby.2008.351 Whitaker, R. C., Wright, J. A., Pepe, M. S., Seidel, K. D., & Dietz, W. H. (1997). Predicting obesity in young adulthood from childhood and parental obesity. New England Journal of Medicine, 337, 869–873. doi: 10.1056/NEJM199709253371301 World Health Organization. (2011). Global recommendations on physical activity for health (5–17 years old). Retrieved from http://www.who.int/dietphysicalactivity/physical-activity-recommendations-5-17years.pdf World Health Organization. (n.d.). Physical activity and young people. Retrieved from http://www.who.int/ dietphysicalactivity/factsheet_young_people/en/index. html

Received March 15, 2013 Final acceptance March 10, 2014 Action Editor, Joel Ringdahl