Annals of International Occupational Therapy

Original Research freeRelationship Between Sensory Processing and Perceptions of and Participation in Play and Leisure Activities Among Typically Developing Children: An Exploratory Study Esther E. Matthews, BOccTher(Hons), ; , BOccTher(Hons) Ted Brown, PhD, MSc, MPA, BScOT(Hons), GCHPE, OT(C), OTR, MRCOT, FOTARA, FAOTA, ; and , PhD, MSc, MPA, BScOT(Hons), GCHPE, OT(C), OTR, MRCOT, FOTARA, FAOTA Karen Stagnitti, PhD, BoccThy, GCHE, , PhD, BoccThy, GCHE Esther E. Matthews, BOccTher(Hons) , Ted Brown, PhD, MSc, MPA, BScOT(Hons), GCHPE, OT(C), OTR, MRCOT, FOTARA, FAOTA , and Karen Stagnitti, PhD, BoccThy, GCHE Published Online:May 10, 2020https://doi.org/10.3928/24761222-20200501-03PDFAbstract ToolsAdd to favoritesDownload CitationsTrack CitationsCopy LTI LinkHTMLAbstractPDF ShareShare onFacebookTwitterLinkedInRedditEmail SectionsMoreAbstractBackground:Occupational therapists often assess children's play, leisure interests, and sensory processing skills. This study explored relationships between parent-reported sensory processing factors in children 8 to 12 years old, children's self-reported perceptions of play, and children's participation in and choice of play and leisure activities.Methods:This cross-sectional quantitative study included children (n = 21) from regional Victoria, Australia, who were typically developing. The children indicated their participation in and choice of play and leisure activities using the Children's Leisure Assessment, and they indicated their views on their recreation using the Children's Perception of Their Play scale. The parents (n = 15) reported the children's sensory processing abilities measured by the Sensory Processing Measure. Data were analyzed with Spearman's rho correlations and linear regressions, and bootstrapping was applied to assess the accuracy of statistical results via resampling.Results:Significant positive correlations were found between vestibular skills and the variety, frequency, and preference of participation in games and sports. Sensory processing factors of social participation, vision, hearing, touch, proprioception, and praxis correlated negatively with children's value of choice within their play. Reduced proprioception was predictive of increased value of choice within play.Conclusion:The sensory processing factors of balance and proprioception were predictive of participation in play and perception. Further research is recommended. [Annals of International Occupational Therapy. 2021;4(2):85–92.]IntroductionPlay is an important occupation of childhood that promotes development, health, and well-being. Play is characterized by joyfulness, spontaneity, imagination, free choice, active engagement, intrinsic motivation, and inherent pleasure (Hurd & Anderson, 2011). A child's engagement in play and leisure activities is influenced by developmental stage, physical and psychological self, and cognitive abilities (Morrison et al., 1991). Leisure is viewed from three perspectives: leisure as an activity, leisure as a state of mind, and leisure as time (Hurd & Anderson, 2011). Leisure is defined as an activity that people engage in during uncommitted or free time. Leisure also relates to perceived freedom, perceived competence, and positive affect (Primeau, 2009). Finally, leisure refers to uncommitted or free time away from paid employment (Hurd & Anderson, 2011).According to Primeau (2009), "Play and leisure participation is defined as the client's engagement in play and leisure occupations that are typically expected of and available to a person of the same age and culture in home, school, work, and community settings" (p. 635). Participation in play and leisure is affected by environmental (including the physical, social, and political space) and personal factors (including age, gender, education level, cultural beliefs, and socioeconomic status) (Brown et al., 2011).Children and parents both recognize that play is different from physical activity (Curtis et al., 2012). Play is a highly social occupation and can support the child's social engagement and identity (Everley & Macfadyen, 2017). Play must be fun and include elements of choice (Curtis et al., 2012; Miller & Kuhaneck, 2008). Among this broad understanding of play, one quantitative measure was found that gave children the opportunity to voice their perceptions and views of their own play. Barnett's (2013) Children's Perception of Their Play (CPTP) scale provides researchers with an instrument to quantify the child's perspective and learn more about children's play. Sturgess et al. (2002) reviewed self-report questionnaires and concluded that there was growing awareness of the importance of including the child's voice. Rosenblum et al. (2010) developed the Children's Leisure Assessment Scale (CLASS) as a self-report measure of children's leisure participation. This instrument covers four dimensions of leisure participation: (a) frequency, (b) sociability, (c) preference, and (d) time spent.Sensory processing allows a person to use their body effectively by organizing sensations from the environment and their own body (Dunn, 2014). When children experience multiple sources of sensory input, sensory processing issues may affect their participation in play and leisure activities and their view of and priorities within their own play. Different types of play have been linked to sensory processing (Ismael et al., 2015; Watts et al., 2014), but surprisingly, no empirical studies have correlated the child's perceptions of play with sensory processing. Miller Kuhaneck and Britner (2013) established connections between social play and praxis in combination with sensory processing factors in children diagnosed with autism. When pretend play was studied among children 5 to 7 years, links were found between proprioception and object substitution and between social skills and the use of symbols (Roberts et al., 2018). Cosbey et al. (2012) focused on play behaviors and sensory processing skills and found a positive correlation between increased sensory processing skills and sociability of play. All children played socially; however, typically developing children did so more often and in larger groups.Although small to moderate relationships have been found between play and engagement in leisure activities and sensory processing, this emergent field of research requires further study to understand which aspects of sensory processing affect preferences and choices in the use of discretionary time. This area of study is relevant to occupational therapy because clinicians often assess children's play and leisure participation and sensory processing. The goal of this study was to investigate the way in which typically developing children, 8 to 12 years old, view, choose, and participate in their play and leisure activities as well as to identify associations between these observations and sensory processing factors, as reported by the parents. The study questions were: (1) What associations exist between a child's perception of their play and leisure activities and their sensory processing? and (2) What associations exist between a child's choice of play and leisure activities and their sensory processing?MethodsDesignA cross-sectional quantitative design was used to collect the quantitative data.ParticipantsChildren (n = 21) who were 8 to 12 years old and their parents (n = 15), from a regional city in Australia, were recruited through a flyer that was circulated on a social media platform and in person, with convenience and snowball sampling. The first author (E.E.M.) contacted interested parents and explained the project verbally. The parents were given an information packet containing plain language statements and consent forms for the parent and child as well as a demographic form that collected information to determine eligibility for inclusion. The inclusion and exclusion criteria ensured that all participants spoke and read English and that all children were typically developing. Children were excluded if a parent reported that they had a physical, developmental, or learning disability. Table 1 shows the demographic features of the children, including gender, age, school year, and number of siblings. Most of the participants were female (57.1% of children and 66.7% of parents).Table 1 Sample DemographicsCharacteristicn%M (SD)Gender Female1257.1 Male942.9Age, months 96–10729.6 108–119733.3 120–131314.2124.86 (13.89) 132–143733.3 144–15529.6Grade 214.8 3628.6 4419.0 5523.8 6523.8Siblings 029.5 1523.8 21152.4 314.8 429.5Note. n = 21; bootstrapped sample = 500.Power calculations were used to identify the number of child participants as 26, assuming .05 for the level of significance and 0.08 for statistical power (Taylor, 2017). However, because of resource and time limitations, only 21 children were recruited.MeasuresDemographic InformationA demographic form requested information on the child's gender, age, school grade, written and spoken language(s), number of siblings, and developmental history and on the parent's gender and written and spoken language(s).Sensory Processing MeasureThe Sensory Processing Measure (SPM) home form (Parham & Ecker, 2007) and manual (Parham et al., 2007) use a norm-referenced standardized system of eight scales (social participation, vision, hearing, touch, body awareness, balance and motion, planning and ideas, and total sensory systems) that assess responses to sensory stimuli. Suitable for children 5 to 12 years, the 75-item form is completed by the parent, who responds according to a 4-point Likert scale, where 1 = never, 2 = occasionally, 3 = frequently, and 4 = always. Raw scores are converted to standardized scores and interpreted as typical, some sensory problems, or definite dysfunction. The SPM was standardized with a sample of 1,051 children from the United States that found internal consistency (α) ranging from 0.77 to 0.95 and estimates of test-retest reliability ranging from 0.94 to 0.98 (Parham et al., 2007). Construct validity is shown within each scale, and the scales can be scored and interpreted separately. The SPM was developed from two scales, and the items for both scales had been subjected to multiple rounds of expert review.Children's Leisure Assessment ScaleThe CLASS (Rosenblum et al., 2010) is a 40-item self-report scale that is used to measure children's participation in play and leisure activities. Activities are rated across dimensions of participation; variety (range, 0–1), frequency (range, 0–4), sociability (range, 0–4), and preference (range, 0–10). The content and face validity of the scale were confirmed by an experienced panel. The internal consistency of the frequency domain of the instrument was rated as acceptable using the results of 249 participants from Israel who had a median age of 13.9 years, where α = 0.71 (Rosenblum et al., 2010).The internal reliability of the instrument was acceptable, with α ranging from 0.57 to 0.83 (Rosenblum et al., 2010). Factor analysis showed four subscales: (a) instrumental indoor activities, (b) outdoor activities, (c) self-enrichment activities, and (d) games and sports activities. Construct validity of the CLASS was investigated by comparing two age-matched groups of 114 girls and 114 boys selected from the original sample. Mean scores for all domains were calculated, and significant differences were found for variety, frequency, and preference, but not for sociability (Rosenblum et al., 2010). These findings differ from previous reports (Rosenblum et al., 2010). The CLASS can be used with children 8 to 18 years. With adult guidance, the instrument takes 15 to 30 minutes to complete.Children's Perception of Their PlayThe CPTP scale devised by Barnett (2013) is a quantitative method used to capture the child's voice in how they view and define play. The scale contains 25 items, and the child indicates their agreement with each item with a 4-point Likert scale, where 4 = definitely yes, 3 = probably yes, 2 = probably no, and 1 = no. Reverse scoring is used for eight of the items, which are phrased negatively. Factor analysis indicates six subscales: (a) child's choice (range, 6–24); (b) social play (range, 4–16); (c) planned activities (range, 5–20); (d) engagement (range, 4–16); (e) active play (range, 3–12); and (f) free time (range, 3–12). The internal reliability of the scale and the factor subscales was computed with coefficient ω from a sample of 689 U.S. participants who were 8 to 10 years. The results were ω = 0.88 for the whole scale and ranged from 0.79 to 0.91 for the subscales, indicating high internal consistency (Barnett, 2013). The form takes 10 minutes to complete.ProcedureEthical approval was obtained from the Human Ethics Advisory Group, Faculty of Health, Deakin University. All ethical requirements and guidelines were followed. Parents who responded to flyers that were distributed on a social media platform and in person were given both written and verbal explanations of the project and procedures. Parents provided written consent for their and their child's participation, and the child provided verbal assent if 9 years or younger and written assent if 10 years or older. After establishing consent, we arranged meeting times with the participants to complete the questionnaires. Some participants preferred to take the questionnaires home to complete. Appointments were held in a public space, such as a café or local library, were scheduled after school or on weekends, and took 1 to 2 hours. Participants could ask questions in person, by telephone, or by e-mail.We verbally explained the study and confirmed with each child their willingness to participate. Children who were 10 years or older also completed an assent form to indicate their willingness to participate. Children were asked to complete the CLASS and the CPTP on their own time, with either the parent or the researcher available for support. Parents were asked to complete the SPM for their children. Completed forms were returned in person, by e-mail, or by prepaid mail.Data AnalysisAll data underwent descriptive data analysis to yield frequency, percentage, mean, standard deviation, quartile, and range. The raw SPM data for each scale were reversed, so that a high score became indicative of greater skill, not greater challenge, and so that the scale ran in the same direction as the CLASS and the CPTP. These data and the reversed scores from the SPM were analyzed with the Statistical Package for Social Sciences, version 25, in accordance with the scoring protocol. All data were treated as ordinal.Correlations between the subscales of the SPM and the CLASS were calculated with Spearman's ρ. Where correlations were found, a general linear model was calculated to show predictive factors of the SPM on the factors of the CLASS. Correlations between the subscales of the SPM and the CPTP were calculated with Spearman's ρ. Where correlations were found, stepwise linear regression was calculated to show predictive factors of the SPM on the factors of the CPTP.Bootstrapping, in which a sample is used to estimate a population (Walker & Smith, 2017), was used to increase the rigor of all correlation calculations and regression models. Bootstrapping was performed according to the model indicated by the sample (n = 21) to infer results for a larger sample, by creating new iterations within the model by sampling from the data of the existing model. This process minimized the effect of the small sample size and improved the confidence interval (Preacher & Hayes, 2004). The specifications for the bootstrap procedure were a simple sampling method, a sample of 500, and a confidence interval of 95%. The confidence interval type was corrected for bias and accelerated.ResultsDescriptive statistics for the SPM, CLASS, and CPTP are shown in Table A, available in the online version of this article. Mean SPM subscale scores ranged from 22.48 to 30.14. The CLASS games and sports activities subscale had the highest mean scores for variety (M = 0.92), frequency (M = 2.63), sociability (M = 2.6), and preference (M = 7.35). On the CPTP, the child's choice subscale had the highest mean score (M = 16.43), whereas the active choice subscale had the lowest mean score (M = 8.67).Table A. Descriptive statistics for Sensory Processing Measure subscales and total score, Children's Leisure Assessment, and Children's Perceptions of Their Play (n = 21; bootstrapped sample = 500).MeasureStandardInterquartile rangesMeanDeviationMedianRangeQ1Q2Q3Sensory Processing Measure SOC25.436.092812 – 3021.52830 VIS30.144.853315 – 3329.53333 HEA22.482.482415 – 24212424 TOU29.715.313311 – 3326.53333 BOD28.292.393021 – 30273030 BAL31.712.353323 – 33313333 PLA24.524.032610 – 2723.52627 TOT155.917.3916494 – 168150164166Children's Leisure Assessment Total Variety.79.07.80.63 – .90.73.80.88 Frequency1.950.281.981.30 – 2.381.731.982.12 Sociability1.750.231.781.10 – 2.101.681.781.93 Preference6.060.976.083.83 – 7.905.616.086.64 Instrumental Indoor Activities Variety.86.15.88.38 – 1.00.75.881.00 Frequency2.440.492.501.38 – 3.502.192.502.75 Sociability1.610.401.500.75 – 2.381.381.501.94 Preference5.951.456.003.00 – 8.755.196.007.00 Outdoor Activities Variety.78.13.78.56 – 1.00.67.78.89 Frequency1.450.341.440.78 – 2.221.221.441.73 Sociability1.870.471.891.11 – 2.671.451.892.22 Preference6.131.356.114.11 – 8.225.066.117.39 Self-Enrichment Activities Variety.78.15.75.50 – 1.00.75.75.88 Frequency1.950.612.000.38 – 3.001.692.002.38 Sociability1.700.571.630.50 – 2.501.381.632.15 Preference5.661.875.631.00 – 9.004.515.636.82 Games and Sports Activities Variety.92.131.00.60 – 1.00.801.001.00 Frequency2.630.582.801.20 – 3.402.102.803.10 Sociability2.600.482.601.40 – 3.602.402.602.80 Preference7.341.447.603.60 – 9.406.607.608.20Children's Perceptions of Their Play Child's Choice16.432.251712 – 20151718 Social Play12.052.33128 – 16111213.5 Planned Activities11.712.17126 – 1510.51213 Engagement12.861.351311 – 15121314 Active Play8.672.0394 – 127.5910 Free Time9.951.77105 – 1291011Note: SOC = Social participation; VIS = Vision; HEA = Hearing; TOU = Touch; BOD = Body Awareness (proprioception); BAL = Balance and Motion (vestibular); PLA = Planning and Ideas (praxis); TOT = Total Sensory Systems.As seen in Table B, available in the online version of this article, significant positive correlations were found between the sensory processing factor of planning and ideas and the frequency of participation in self-enrichment activities (ρ = 0.435, p < .05) as well as between the subscale of balance and motion and the variety (ρ = 0.488, p < .05), frequency (ρ = 0.440, p < .05), and preference (ρ = 0.439, p < .05) domains of the subscale of games and sports activities.Table B. Correlation Coefficients of Sensory Processing Measure subscales and the Children's Leisure Assessment subscales (n = 21; bootstrapped sample = 500).CLASSSensory Processing Measure subscalesSOCVISHEATOUBODBALPLATOTTotal Score Variety.225.054−.037−.174.021.279−.067−.028 Frequency.067.191.151.131.057.053.178.122 Sociability.188.028−.11−.229−.149−.032.103−.135 Preference.27.15−.003.021.122.018.307.095Instrumental Indoor Activities Variety.01.073.007−.137−.293−.229.133−.249 Frequency−.045.028.002.018−.116−.374.251−.134 Sociability−.041.128.077.025−.192−.233.223−.08 Preference−.092−.023−.077−.191−.082−.341.276−.273Outdoor Activities Variety.116.053−.03−.211.073.165−.229.026 Frequency−.109.209.155−.111.225.34−.292.101 Sociability.152−.042−.139−.253.017.084−.23−.054 Preference.258.131−.076−.172.033.153.006.087Self-Enrichment Activities Variety.188−.025−.082−.033.106.221.239.039 Frequency.199.314.176.295.22.118.435*.393 Sociability.073−.025−.109−.282.023−.111.222−.134 Preference.126.281.179.265.422.009.305.315Games and Sports Activities Variety.174−.024.025−.125−.1.488*−.036.046 Frequency.145−.019.129.039−.072.440*.021.056 Sociability.301.083.02−.156−.195.131.323−.077 Preference.055−.255−.127−.107−.066.439*−.018−.086Note: CLASS=Children's Leisure Assessment. SOC = Social participation; VIS = Vision; HEA = Hearing; TOU = Touch; BOD = Body Awareness (proprioception); BAL = Balance and Motion (vestibular); PLA = Planning and Ideas (praxis); TOT = Total Sensory Systems.*=Correlation is significant at the 0.05 level (2-tailed).**=Correlation is significant at the 0.01 level (2-tailed).Bootstrap specifications: i) sampling method – simple; ii) number of samples – 500; iii) confidence interval (CI) level – 95%; and iv) CI type – bias-corrected and accelerated.A general linear model was calculated using these significant relationships, and a statistically significant effect was found where F(1, 5) = 3.198, p = .037, with the variety of games and sports activities accounting for 51.6% of the variance within the model. Therefore, the balance and motion factor was predictive of the variety of games and sports activities reported.Table C, available in the online version of this article, shows the results of correlation calculations between sensory processing factors and children's perceptions of play. Significant negative correlations were found between the social participation (ρ = −0.500, p < .05), vision (ρ = −0.583, p < .01), hearing (ρ = −0.515, p < .05), touch (ρ = −0.569, p < .01), planning and ideas (ρ = −0.539, p < .05), and total sensory systems (ρ = −0.558, p < .01) factors of the SPM and the CPTP child's choice factor. A significant negative correlation was found between the SPM body awareness factor (ρ = −0.691, p < .01) and the CPTP child's choice factor. No significant correlations were found between the SPM factors and any other CPTP factors.Table C. Correlation Coefficients between the Sensory Processing Measure subscales and the Children's Perceptions of Their Play subscales (n = 21; bootstrapped sample = 500).CPTP subscalesSensory Processing Measure subscalesSOCVISHEATOUBODBALPLATOTChild's Choice−.500*−.583**−.515*−.569**−.691**.073−.539*−.558**Social Play−.007.111.016.209.143−.088.033.158Planned Activities.063.105−.015−.031−.166−.173.128.012Engagement.016−.226−.214−.328−.136−.243.162−.372Active Play.233−.038−.203.064−.095−.210.275−.170Free Time.007.200.302.162.233.257.141.244Note: CPTP = Children's Perception of Their Play. SOC = Social participation; VIS = Vision; HEA = Hearing; TOU = Touch; BOD = Body Awareness (proprioception); BAL = Balance and Motion (vestibular); PLA = Planning and Ideas (praxis); TOT = Total Sensory Systems.*=Correlation is significant at the 0.05 level (2-tailed).**=Correlation is significant at the 0.01 level (2-tailed).Bootstrap specifications: i) sampling method – simple; ii) number of samples – 500; iii) confidence interval (CI) level – 95%; and iv) CI type – bias-corrected and accelerated.Significant results were entered into a multiple regression calculation. Because of the exploratory nature of this area of study, a stepwise method was used to enter the independent sensory processing variables. The regression results showed the following model: adjusted R2 = 0.293, F(1, 19) = 9.272, p = .007, 95% confidence interval [−0.168, −0.909]; β = −0.573. The SPM body awareness factor was the only independent variable that made a unique contribution to the model, accounting for 32.8% (p < .01) of variance. This finding indicated that the SPM body awareness factor was a significant predictor of the CPTP child's choice factor.DiscussionThe study findings show that sensory processing factors, as measured by the SPM, are partially related and predictive of participation in play and leisure activities, as measured by the CLASS, and of children's perceptions of their own play, as measured by the CPTP. Mean SPM subscale score values for the sample fell within the range that is seen in typically developing children.Emerging evidence indicates that there are relationships between children's sensory processing factors and their participation in and preference for play and leisure activities (Chien et al., 2016; Engel-Yeger, 2008; Roberts et al., 2018). This study is the first to examine relationships between sensory processing factors and play and leisure preferences and participation in typically developing Australian children who are 8 to 12 years old.Engel-Yeger (2008) explored sensory processing patterns and activity preferences among 6- to 11-year-old Israeli children with (n = 25) and without (n = 109) sensory processing challenges. Children with sensory processing challenges preferred activities that were more physically active. Those results differ from the current findings, which showed positive correlations between balance and motion and the variety, frequency, and preference domains of the games and sports factor. This finding suggests that children with robust sensory processing skills are more likely to participate in more types of games and sports activities, participate more often, and enjoy them more compared with children who have fewer sensory processing skills.Examining the relationship between play and children's sensory processing skills in typically developing children provides insights into different diagnostic groups. For example, children diagnosed with autism spectrum disorder are more likely than typically developing children to experience sensory processing challenges (Tomchek & Dunn, 2007), and they participate in play and leisure activities differently than their typically developing peers (Potvin et al., 2013). In a study by Potvin et al. (2013), all children who were diagnosed with autism (100%, n = 30) participated in playing computer or video games compared with 87.1% of their typically developing peers (n = 31). The CLASS games and sports factor includes playing computer games and playing board/card games (Rosenblum et al., 2010) as two of five items. Although there are correlations between the balance and motion factor of the SPM and three domains of the CLASS games and sports factor (variety, frequency, and preference), these items have less association with vestibular skills than the other constituent items of bicycle riding, playing outdoors, and team ball games (Rosenblum et al., 2010).Chien et al. (2016) investigated relationships between participation and sensory processing among a group of children (n = 36) with and without (n = 28) potential sensory impairments. Similar to the findings of the current study, they found that when children had lower energy ratings, they were less likely to participate in play and leisure activities. Children who were confident in their balance skills were more likely to adapt to and participate in a wider variety of physical activities. Although Chien et al. (2016) did not address the variety of sports participation, in a 3-year longitudinal study with children who were 6 to 9 years (n = 638) at intake, Vandorpe et al. (2012) found that motor coordination was predictive of continued participation in sports, which is congruent with the current findings. Motor coordination tests all involve elements of balance and movement, such as walking backward on a balance beam and jumping sideways over a set height for 15 seconds.Significant moderate to high negative correlations were found between the CPTP child's choice factor and the SPM vision, touch, hearing, body awareness, social participation, planning and ideas, and total sensory systems factors. The items within the CPTP child's choice subscale indicated a desire for control over the choice of activity, the time and content of play, and when to cease the activity (Barnett, 2013). Children who have fewer sensory processing skills showed a greater value for control over play activities. Free choice of activity has been theorized to be less important for children 9 to 11 years (Vitiello et al., 2012) and more important for children 5 to 6 years (Wing, 1995).Roberts et al. (2018) investigated sensory processing links with pretend play. The sensory processing factors of body awareness, balance and motion, and touch were related to children's ability to self-initiate elaborate play behaviors, choose how to play with the play materials, and direct the play. Body awareness also was related to initiating and choosing to play (Roberts et al., 2018), supporting the findings of this study. The current findings, which include vestibular and proprioceptive senses, were predictive of aspects of children's participation in play and their perspectives and values related to how they play. In the context of the findings of Miller Kuhaneck and Britner (2013) and Roberts et al. (2018), a growing body of evidence supports the connections between sensory processing factors and play and leisure.Implications for PracticeTypically developing children have a range of sensory processing skills that influence their participation and perceptions of play and leisure pursuits. Occupational therapists should consider that children with decreased sensory processing skills will be more likely to value the choice of activity as well as how and when the activity is performed and will be able to adapt interventions to support opportunities for increased choice. Occupational therapists can use the knowledge that children whose intrinsic physiology includes reduced vestibular capacity are less likely than their peers to participate in a wide variety of games and sports activities.Strengths and LimitationsThis study focused on the major occupations of childhood of play and leisure pursuits. Use of the CLASS and the CPTP to capture the voice of the child lends a client-centered focus to the research. Both the CLASS and the CPTP have established reliability and validity. This study was limited by the sample size and the specific geographical location, which may limit generalizability. However, bootstrapping was used to ensure that statistical analyses were robust. Respondent bias may be a limitation because participants may have adjusted their responses to be more socially desirable in cases where children asked for parental assistance in filling out the questionnaires. In addition, having the participants complete the questionnaires at home on their own or in the presence of the researcher may have led to bias.Correlating data derived from two different sets of informants (e.g., parents completing the SPM and children completing the CLASS and the CPTP) may have influenced the correlational analysis outcomes. However, no valid and reliable child report scale to measure sensory processing factors was available at the time. Because of the emergent nature of the topic, this research should be considered a pilot study.Future ResearchFuture research designs would include a mixed methods or qualitative approach to understand not only the values held by children about their own play but also the reasoning behind these perceptions, specifically at the intersection with sensory processing. This study could also be replicated to incorporate other factors, such as children with known clinical diagnoses, those from differing cultural backgrounds, or those living in different areas (e.g., rural vs. urban environment). Specific self-reported play activity preferences of children also could be correlated with sensory processing factors.ConclusionThis study examined the association between sensory processing and the child's voice in relation to participation in and preferences for play and leisure activities. Children's perception of play was significantly associated with body awareness and sensory processing. This finding informs occupational therapy practice, in particular, by emphasizing the importance of offering children a choice of play and leisure activities, particularly when their sense of body awareness and movement may not be well developed. Further research in this area is recommended.Barnett L. A. (2013). Children's perceptions of their play: Scale development and validation. Child Development Research, 2013, 1–18. 10.1155/2013/284741 CrossrefGoogle ScholarBrown T., O'Keefe S., & Stagnitti K. (2011). 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Early Childhood Research Quarterly, 10(2), 223–247. 10.1016/0885-2006(95)90005-5 CrossrefGoogle Scholar Previous article Next article FiguresReferencesRelatedDetails Request Permissions InformationCopyright 2021, SLACK IncorporatedPDF downloadAddress correspondence to Ted Brown, PhD, MSc, MPA, BScOT(Hons), GCHPE, OT(C), OTR, MRCOT, FOTARA, FAOTA, Associate Professor, Department of Occupational Therapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University–Peninsula Campus, Building G, 4th Floor, 47-49 Moorooduc Highway, Frankston, Victoria 3199, Australia; e-mail: ted.[email protected]edu.Ms. Matthews is an honours student, Occupational Science and Therapy Program, School of Health and Social Development, Faculty of Health, Deakin University–Waterfront Campus, Geelong, Victoria, Australia. Dr. Brown is Associate Professor and Undergraduate Course Coordinator, Department of Occupational Therapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University–Peninsula Campus, Frankston, Victoria, Australia. Dr. Stagnitti is Professor, Occupational Science and Therapy Program, School of Health and Social Development, Faculty of Health, Deakin University–Waterfront Campus, Geelong, Victoria, Australia.The authors have no relevant financial relationships to disclose. Received10/06/19Accepted3/09/20