Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study

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Existing measures often focus on physical activity during play activities or prioritise the mapping of locations within which play occurs rather than evaluating play itself. Methods This paper introduces the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS) and provides the results of an initial study examining the utility and reliability of the toolkit. The M-TAPS includes observations of individual children and scan observations of predefined areas of the playground; children’s activities, adventure/risk level and affect are coded. In addition, the M-TAPS includes child self-report questionnaires about emotions during playtime and playtime activities. Results The reliability study provided evidence of good reliability between coders and for children’s self-report of their emotions during playtime. There was some indication of validity between child self-report and coder observation. Conclusions The paper suggests that the M-TAPS may be useful for research focused on improving children’s play in schools where a quantitative measure is sought. The M-TAPS provides a flexible tool for supporting researchers with the results highlighting how the M-TAPS can provide insights into schools playtimes and how observation can be combined with children’s self-report. There is room for further development and refinement of the toolkit. 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F1000Research 2025, 14 :476 ( https://doi.org/10.12688/f1000research.160920.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Method Article Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] Helen Dodd https://orcid.org/0000-0003-1446-5338 1,2 , Rachel Nesbit https://orcid.org/0000-0001-7540-3187 2 , Lily FitzGibbon 2,3 Helen Dodd https://orcid.org/0000-0003-1446-5338 1,2 , Rachel Nesbit https://orcid.org/0000-0001-7540-3187 2 , Lily FitzGibbon 2,3 PUBLISHED 06 May 2025 Author details Author details 1 Public Health and Sport Sciences, University of Exeter, Exeter, England, UK 2 School of Psychology and Clinical Language Sciences, University of Reading, Reading, England, UK 3 Division of Psychology, University of Stirling, Stirling, Scotland, UK Helen Dodd Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Rachel Nesbit Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Lily FitzGibbon Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Developmental Psychology and Cognition gateway. Abstract Background Despite increasing interest in changing and improving play opportunities in schools, there is lack of openly available methods for evaluating play quantitatively. Existing measures often focus on physical activity during play activities or prioritise the mapping of locations within which play occurs rather than evaluating play itself. Methods This paper introduces the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS) and provides the results of an initial study examining the utility and reliability of the toolkit. The M-TAPS includes observations of individual children and scan observations of predefined areas of the playground; children’s activities, adventure/risk level and affect are coded. In addition, the M-TAPS includes child self-report questionnaires about emotions during playtime and playtime activities. Results The reliability study provided evidence of good reliability between coders and for children’s self-report of their emotions during playtime. There was some indication of validity between child self-report and coder observation. Conclusions The paper suggests that the M-TAPS may be useful for research focused on improving children’s play in schools where a quantitative measure is sought. The M-TAPS provides a flexible tool for supporting researchers with the results highlighting how the M-TAPS can provide insights into schools playtimes and how observation can be combined with children’s self-report. There is room for further development and refinement of the toolkit. READ ALL READ LESS Keywords play; playtime; recess; measure; observation; child Corresponding Author(s) Helen Dodd ( [email protected] ) Close Corresponding author: Helen Dodd Competing interests: No competing interests were disclosed. Grant information: This research was funded by a UKRI Future Leaders Fellowship awarded to HD (MR/S017909/1). The funder had no role in influencing the study design or findings. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Dodd H et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Dodd H, Nesbit R and FitzGibbon L. Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.12688/f1000research.160920.1 ) First published: 06 May 2025, 14 :476 ( https://doi.org/10.12688/f1000research.160920.1 ) Latest published: 20 Oct 2025, 14 :476 ( https://doi.org/10.12688/f1000research.160920.2 )  There is a newer version of this article available. Suppress this message for one day. 1. Introduction Play is intrinsic to childhood and is protected as a fundamental right under the 1989 United Nations Convention on the Rights of the Child (Article 31; UNCRC, 1989 ). Play offers diverse opportunities for children to express themselves and supports healthy cognitive, social and physical development ( Andersen et al., 2023 ; Dodd et al., 2023 ; Herrington & Brussoni, 2015 ; Nijhof et al., 2018 ; Singer et al., 2006 ). There is mounting evidence that, at least in western societies, children’s opportunities for play are diminishing, particularly independent outdoor play ( Clements, 2004 ; Dodd et al., 2021 ; Tremblay et al., 2015 ) and time for play in schools ( Baines & Blatchford, 2019 ; Henley et al., 2010 ). Perhaps in response to declining opportunities for play, a burgeoning area of research focuses on the benefits and importance of play to children’s health, wellbeing, and holistic development ( Jackson et al., 2021 ; Zhao & Gibson, 2022 ). Aligned with this, school-based play programmes that aim to increase the diversity and quality of children’s play during school playtimes, are increasingly being delivered and evaluated ( Houser et al., 2019 ; Johnstone et al., 2018 ; Lee et al., 2020 ). School playtimes offer an important context for the promotion and study of play. They offer a play opportunity for all children who are attending school and can therefore reduce inequalities in access to play that may exist outside of school. They offer a unique context where children have dedicated time for play with a wide range of other children. In addition, school playtimes are positively associated with classroom behaviour as well as academic attainment ( Jarrett et al., 1998 ; Massey, Ku, et al., 2018a ; Pellegrini & Bjorklund, 1997 ; Pellegrini & Bohn, 2005 ). Given that schools are increasingly expected to support children’s physical activity and mental health, improved play during playtimes may help them to address this need, at least in part. An example of a play-based programme that has been developed for schools is the Lunchtime Enjoyment And Play (LEAP; Hyndman et al., 2014 ) intervention which introduces moveable and recycled materials (often referred to as loose parts) to school playgrounds with the aim of increasing physical activity. A similar approach was taken in The Sydney Playground Project ( Bundy et al., 2017 ), one of the first academic research programmes dedicated to improving play in schools. In the U.K., the Outdoor Play and Learning (OPAL) programme adopts a whole school approach to improving playtime, including the introduction of loose parts, opening up access to space and staff training around play planning and risk ( Lester et al., 2011 ). As research develops into the efficacy of school-based play programmes, it is increasingly important to have methods available to support the systematic measurement of play. Play research has a rich tradition of creative, child-centred qualitative, ethnographic and anthropological methods to capture children’s experiences of playtime ( FitzGibbon et al., 2024 ). These approaches provide unique and valuable insights that cannot, and should not, be replaced by quantitative methods. Nevertheless, for rigorous, empirical evaluation of programmes and comparisons across programmes, it is useful to complement these approaches with quantitative methods. The quantitative assessment of play is complex and different approaches all have strengths and weaknesses. For example, children’s perspectives on their own play are arguably the most important, and these can be captured in a quantitative way using self-report questionnaires. Nevertheless, children’s perspectives can be strongly influenced by external cues and their most recent experiences, affecting reliability over time. In contrast, observation of children’s play by independent observers can offer an objective perspective but observations can only capture what the observers can see, meaning that children’s internal experiences can be neglected or misinterpreted. Observations also take place over a limited period so what is observed may not always be representative of a typical playtime. A recent review examined existing measures of play in schools and found that the majority of studies use idiosyncratic measures, often designed for a specific study and only used once ( FitzGibbon et al., 2024 ). The review highlighted that these measures are rarely available for other researchers to use, and examination of psychometric properties is scarce. There are a few notable exceptions to this such as the Great Recess Framework-Observational Tool ( Massey, Ku, et al., 2018a ; Massey, Stellino, et al., 2018b ), System for Observing Play and Leisure Activities in Youth (SOPLAY; McKenzie et al., 2000 ) and the Observation of Playground Play ( Massey, Ku, et al., 2018a ). These are robust instruments that have been carefully developed but they are limited in that they either focus on physical activity levels/types of sport, features of the play environment rather than play per se or are designed to measure at the group level, rather than the individual level. This latter point is important for research that aims to map play onto individual difference variables such as age or mental health. It is also vital within an evaluation because it allows researchers to examine how the intervention affects specific subgroups of children, such as those with special education needs. While it has not yet been evaluated for use on school playgrounds, the Tool for Observing Play Outdoors (TOPO; Loebach & Cox, 2020 , 2022 ) has recently been developed as a systematic evaluation protocol for observing children’s play behaviours in outdoor spaces. This protocol has some advantages over those above, particularly the ability to use the tool to assess play at both the individual and group level. However, it is very labour-intensive and has been designed with early-years environments and relatively small groups of children in mind. In this paper we introduce the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS) which is inspired by and complements these existing methods. It includes two observation components and a child self-report component which capture types of play as well as level and type of risk, and children’s affect. The M-TAPS has been designed to be feasible for use in large-scale evaluation; whilst observation of play necessarily requires significant researcher time, some instruments are not practical for use within evaluations because they are extremely labour intensive or require video recordings to be made of school playgrounds, which raises ethical issues. The various components of the toolkit can be used flexibly according to research questions and study aims. In addition to introducing the measure, in this paper we also present an initial study evaluating the reliability of each component of the M-TAPS, which includes an assessment of whether the level of adventurousness in children’s play and their affect during their play can reliably be coded. Research into the importance of adventurous play (also termed risky play) has been increasing in recent years ( Dodd & Lester, 2021 ; Sandseter et al., 2023 ). Adventurous play refers to child-led play involving subjective feelings of excitement, thrill and fear ( Dodd & Lester, 2021 ; Sandseter, 2009 ). Despite this increased interest in adventurous play, measures of play in schools rarely include evaluation of this type of play and it remains unclear whether adventurous play can reliably be observed or whether it can only be assessed by asking children themselves. Similarly, it is unclear whether observers who are not familiar with the children they are observing can reliably code children’s emotions via observation. This is important because play and emotional experience are richly intertwined; for example, Sutton-Smith (2002) argued that one function of play is to help players achieve “emotional joy” ( Sutton-Smith, 2002 , p. 19). Play has been described as an ‘emotional toolbox—a safe space in which many emotions can be experienced without consequences or worrying about whether the emotion is acceptable’ ( PALS; 2020 ). Thus, children’s emotional experience of playtime is an important element of their play experience and ideally an assessment of play should capture this emotional experience. 2. Methods 2.1 Materials: Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS) The M-TAPS was created by the authors and is available at: https://osf.io/qjf8b/ . It aims to provide insight into children’s play by combining across child and observer perspectives. Given this, the M-TAPS includes: 1. Scan observations of playtimes, where the number of children engaging (or not) in certain types of play, including adventurous play, and the number of children displaying positive and negative affect are counted. The scan observations provide an overview of play across the playground. 2. Focal observations of specific children whose play, adventure level and affect are observed more closely and coded. Focal observations provide insight into individual children’s play and can be linked to individual difference factors or used to capture changes in children’s of play over time. 3. A child-report questionnaire pack that includes a questionnaire about their emotions during playtime and a questionnaire about their activities during playtime. These three components have been designed to be complementary; they can be combined but each could be used in isolation, depending on specific research questions. Each component will now be described in turn. 2.1.1 Scan observations For the scan observations, playgrounds are divided into defined areas which allow the researchers to be stationary and view the entire area without any significant obstructions. The number of areas required will vary by school but each area should be scanned at least twice. We recommend that observers visit the school to select the areas and pilot data collection with those areas ahead of starting the scan observations. A complete round of scan observations is then conducted for each area before moving to the next area. During each round of scan observations, observers count the number of children doing and not doing each of the following types of play (see extended data for full definitions for each code – available at: https://osf.io/qjf8b/ ): 1. Playing (yes/no) 2. Sport (yes/no) 3. Fixed equipment/markings (yes/no) 4. Active/chase (yes/no) 5. Nature/landscape (yes/no) 6. Rough and Tumble (yes/no) 7. Small world/Toys (yes/no) 8. Sport/play equipment (yes/no) 9. Loose parts/recycled materials (yes/no) 10. Antisocial (yes/no) These categories of play were initially developed based on those included in the OPP ( Massey, Ku, et al., 2018a ) and aligned with the activities on the Activities During Playtime measure (see 2.1.3). However, following discussion with adults familiar with UK playgrounds and piloting, some edits were made: we added ‘markings’ to the equipment category, added ‘landscape’ to the nature category and added two additional categories of ‘sport/play equipment’, ‘loose parts/recycled materials’. In addition, we removed ‘traditional playground games’ because during piloting it became clear that defining this was challenging given different childhood experiences of coders. Alongside these play categories we also explored the extent to which adventure level and affect could be coded during playground scans. This was motivated by the growing interest in adventurous play and a desire to capture children’s emotional experience if possible. Thus, the following were also counted during scan observations (see extended data for full definitions for each code): 11. High adventure (yes/no) 12. At least moderate adventure (yes/no) 13. Affect (positive/negative/unclear) Observers scan from left to right imagining a straight line moving across the space. Each child is counted as the imaginary line reaches them and, for each of the play categories above, is classified as doing (yes) or not doing (no), each activity. For example, an initial scan is completed from left to right with all children categorised as playing/not playing. Then a new scan begins left to right counting the number of children playing sport and so on until all categories have been coded. For all categories children are counted as doing the activity/adventure level (yes) or not doing it (no) with the exception of affect which is coded as positive, negative or unclear. Once scans have been completed for all categories, that round is complete and observers move to the next area and repeat. 2.1.2 Focal observations Specific children are selected for focal observations. These children can be selected at random or according to demographic or other characteristics depending on the purpose of the study. Observers first locate a focal child on the playground and then begin the observation. The observer observes the child for 10 seconds and then records: 1. Type of play (using the play categories listed for the scan observations above with ‘not playing’ instead of playing and with ‘social play’ and ‘other play’ added as options). Social play was added during piloting of the focal observations to capture children chatting or hanging out with friends. This was not included in the scans for this study because they were completed first but can be included in scan observations within future research if desired. 2. Level of adventure (High/Moderate/Low). If level of adventure is coded as moderate or high then the type of risk is also subsequently coded (Height, speed, impact, tools, rough and tumble, alone, vicarious, dangerous elements). These risk categories are based on previous research ( Kleppe et al., 2017 ; Sandseter, 2009 ). 3. Affect (positive/negative/unclear). The child’s affect is coded as positive or negative, only if it is clearly one or the other (e.g. a child giving a broad smile or a child crying), otherwise unclear was given. Observers are given 15 seconds to record this information and then the next 10 second observation period begins. For this study, observers completed 15 × 10 second observations per focal child before finding the next focal child and beginning their observation. 2.1.3 Child-report questionnaire pack The child report questionnaire pack includes two questionnaires. Emotions during playtime. An adapted version of the Positive and Negative Affect Scale for Children (PANAS-C; Laurent et al., 1999 ) was created to capture the emotions that children experience during school playtimes. The PANAS-C asks children to respond using a 5-point Likert scale (from ‘Not much or not at all’ to ‘A lot’) the extent to which they have experienced each of 30 emotions over the previous 2 weeks. To create the M-TAPS Emotions During Playtime measure we adapted the PANAS-C. The emotions and response scale are identical to the original measure but children are asked to respond to each item thinking about how they have felt during playtime over the past week. The PANAS-C was selected as the basis for the measure because of the broad range of positive and negative emotions included and because it is a well-established measure with strong psychometric properties ( Laurent et al., 1999 ). Note that although the PANAS-C originally included 30 items, following Laurent and colleagues, only 27 items contribute to the positive and negative affect scales with alert, fearless and daring excluded from the scale scoring. We included all 30 items to support future research which could explore emotional profiles of playtimes and also to consider whether individual items might give useful insights. The PANAS-C also exists as a 10-item measure ( Ebesutani et al., 2012 ) so a 10-item version of the Emotions During Playtime measure could be used if only an approximate measure of overall positive and negative affect during playtime is needed. Given that children’s report of their emotions may be affected by their state emotion, the Emotions During Playtime questionnaire is designed to be completed on two occasions at least 2 days apart, with responses averaged across the two completions. Activities during playtime. A second questionnaire was developed to capture what activities children say they do during playtimes. Children were asked to think about school break and lunchtimes over the past two weeks and for each of eight activities state how much they do the activity. Activities were: playing sports; playing on fixed play equipment or markings; playing with loose objects; playing with nature; playing chase games or running around; play fighting or wrestling; fighting, arguing or trying to break things; not playing (examples were provided). Children responded on a three-point scale: ‘Not at all’, ‘A little’, ‘A lot’. These categories of play were developed based on those included in the Observation of Playground Play (OPP; Massey, Ku, et al., 2018a ). At the end of the questionnaire, children were asked to write any other types of play and their favourite activity. 2.2. Methods: Reliability study 2.2.1 Participants To evaluate the reliability of each part of the M-TAPS, we recruited participants from a primary school located in Bristol, UK. The school was located in a relatively low-income urban area where residents are predominantly white; the school population and study participants reflect this demographic. Data were collected in March 2022. For child self-report, all children from one class in each of years 3, 4 and 5 respectively (aged 7-10 years) were invited to participate. We chose to include only children aged 7 years and above given the reading and cognitive capacity required to respond to the questionnaires. In total 62 children started the questionnaire pack. Scan observations were conducted across the entire playground meaning that all children from years 1 to 6 (aged 5 to 11 years) were included in these observations. In the UK, children attend primary school from age 4 to age 11 (or a combination of infant and junior school). Age 4-5 is termed Reception. These children follow a different curriculum to the older children and their playgrounds are often separate from the rest of the school. Given this, we chose to focus on children from Year 1 upwards but we have no reason to believe that the M-TAPS could not be used with younger children. Focal observations were conducted with 44 children (22 male, 22 female) selected at random. We did not collect any identifying information about these children but, because children at this school have playtimes staggered by age, we know that a range of ages were included. Of the focal children, 13 were in Year 1 (aged 5 or 6 years), 17 in Years 2, 3 or 4 (aged 6 to 9 years), and 14 in Years 5 or 6 (aged 9 to 11 years). For an inter-rater ICC of .8 (against a null of.5), a sample of 37 gives 99% power to detect a significant association between two raters and 95% power to detect a significant association across two points, therefore the sample size was adequate to address the primary aims related to assessing reliability ( Walter et al., 1998 ). Following consultation with the appropriate ethical review board, we chose to use an opt-out consent procedure. The school provided proxy informed consent for all participants and parents were given detailed information about the study with options to withdraw their child. No parent chose to remove their child from the study. The benefit of this approach is that a representative sample can be obtained and typical playtimes, with all children on the playground, could be observed. A limitation of this approach is that, to minimise any risks associated with opt-out consent, all data were collected anonymously. This means that no personal or demographic information was collected about individual participants. 2.2.2 Procedure The methods and study procedures were approved by the University of Reading Research Ethics Committee (Ref: 2021-163-RM) on 15th November 2021. The study adhered to the Declaration of Helsinki principles. Parents were provided with written information about the research and asked to inform the school or research team if they did not want their child to complete questionnaires and/or be on the playground during the observations. A range of response options were given including emailing, phoning or texting the school or research team. Children were given information about the study via a short, pre-recorded presentation and asked to provide assent before completing questionnaires. They were also given opportunity to ask their teacher and/or the researchers questions. Children were given paper copies of the questionnaire packs to complete during class time under the supervision of their class teacher and learning support assistant. The pre-recorded presentation told children that the questionnaires should be completed alone and that they should not discuss the answers with other children. Teachers were also given written information from the research team asking them to create test conditions for the completion of the questionnaires. Time 1 data collection consisted of two sessions (completed an average of 2.30 days apart (range 0-12 days)). During the first, all questionnaires were completed once. In the second session just the Emotions During Playtime measure was completed. At Time 2 (which began an average of 5.51 days after Time 1 (range 0-10 days), the same procedure was followed (the two sessions at Time 2 were completed an average of 5.16 days apart (range 2-12 days)). For the scan observations, the research team viewed the playground and divided it into five defined areas during a pre-visit. Data collection was then piloted to ensure that these areas were appropriate and to confirm standing positions for the coders. This process also gave children time to get used to the observers being on the playground and reduced the likelihood that the presence of the observers would affect children’s play. In total 15 area scans were completed by both observers with each area scanned at least twice (area 1 = 6 scans; area 2 = 3 scans; area 3 = 2 scans; area 4 = 2 scans; area 5 = 2 scans). Note that the Year 1 children were only allowed to use area 1 for their playtime so this area was scanned more frequently. Observers synced the start time of each scan to ensure they were observing the same play, for the purpose of assessing reliability. For the focal observations, the coders selected a child to observe at random and coordinated the start of the observation to ensure they were both observing the child at the same time. Where specific children need to be observed, we have piloted using coloured wrist-worn sweatbands to identify those children and this approach has worked well. Children wear the sweatbands on the outside of any sleeves or coats so that they are visible and each child is assigned a colour during the coding period. After coordinating the start of scan and focal observations, coders did not consult with one another about what codes to allocate to ensure that the reliability assessments were accurate and to avoid observers biasing one another. Scan and focal observations were supported by an app developed for the purpose of this research. The app was programmed in Microsoft PowerApps and can store data locally on a tablet/phone that is later transferred to secure databases within the Microsoft SharePoint environment. The materials required to create a usable instance of the app, including instruction videos and data templates, are available here: https://osf.io/qjf8b/ . 2.2.3 Missing data The questionnaire booklet was started by 62 children and the questionnaires were completed by the following numbers of children: Emotions During Playtime Positive Affect scale (T1 = 53; T2 = 53; T3 = 49; T4 =43); Emotions During Playtime Negative Affect scale (T1 = 54; T2 = 53; T3 = 48; T4 = 43); Activities During Playtime questionnaire (responses varied from 53 responses to 56 responses for each activity at T1 and from 47 responses to 50 responses at T2). For the focal observations, of 660 possible individual observation data points (15 per child x 44 children) a minority were missing, primarily due to technical errors or interruptions during coding: 11 for activity, 17 for adventure, and 20 for affect. Analyses are conducted with available data. 3. Results All analyses were conducted in R Studio version 2023.12.1 running R version 4.3.3 ( R Core Team, 2024 ). For the three components of the M-TAPS our primary aim was to examine reliability. For scan and focal observations, we focused on inter-rater reliability and used intra-class correlation coefficient (ICC) calculated the irr package ( Gamer et al., 2019 ) in R ( R Team, 2021 ) to examine consistency between raters on continuous variables and Cohen’s Kappa using the irr package in R for categorical data. For ICCs we examined absolute agreement using two-way random effects models with reliability estimated for a single rating. For questionnaires, we examined test-retest reliability using Concordance Correlation Coefficient (CCC) as measure of reliability for continuous data and Cohen’s kappa for categorical data. CCC is preferable to Pearson correlations because it captures both precision and accuracy. The CCCs were calculated using the DescTools ( Signorell, 2023 ) package in R. For questionnaires where items are summed to create scales we also examined internal consistency via Cronbach’s alpha using the ltm package ( Rizopoulos, 2006 ) in R. The analysis code, output and raw data are openly available via this link: https://osf.io/qjf8b/ . 3.1 Scan observations 3.1.1 Activity A total of 15 scans were conducted by each rater. During the scans each visible child in the area being coded was coded as engaging (or not) in each activity in turn. Table 1 shows each activity and the minimum, maximum and mean percentage of participants coded as engaging in each activity. The minimum and maximum values relate to single scans (i.e. 0% indicates that during at least one scan no child was observed engaging in that activity), with the mean values showing the mean percentage across all of the scans. The majority of children observed were coded as engaged in play (mean = 86.2%), with the most popular activities being playing with sports or play equipment followed by active play or chase. ICCs ranged from 0.73 to 1. Table 1. Minimum, mean, and maximum percentage of scan observations coded for each activity by rater, with ICC. Rater 1 Rater 2 ICC Min Mean Max Min Mean Max Activity Playing 57.1% 86.2% 100.0% 71.4% 89.3% 100.0% 0.73 Sports/play equipment 0.0% 29.6% 81.8% 0.0% 30.8% 81.8% 0.99 Active/chase 0.0% 17.1% 92.3% 0.0% 16.8% 91.7% 0.98 Sport 0.0% 12.4% 100.0% 0.0% 13.2% 100.0% 1.00 Small world/toys 0.0% 9.5% 28.6% 0.0% 11.0% 36.0% 0.96 Loose parts/recycled materials 0.0% 6.8% 50.0% 0.0% 7.9% 57.1% 0.98 Rough and tumble 0.0% 2.4% 12.9% 0.0% 2.2% 13.3% 0.94 Fixed equipment/markings 0.0% 2.3% 9.1% 0.0% 1.6% 6.2% 0.83 Nature/landscape 0.0% 1.2% 13.3% 0.0% 1.0% 15.4% 0.95 Antisocial 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Adventure level Moderate 0.0% 24.9% 83.3% 0.0% 23.1% 66.7% 0.80 High 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Affect Positive 0.0% 10.0% 26.7% 0.0% 9.7% 22.9% 0.76 Negative 0.0% 1.6% 11.8% 0.0% 0.6% 5.6% 0.73 Unclear 73.3% 88.5% 100.0% 77.1% 89.7% 100.0% 0.78 3.1.2 Adventure During the scan observations, each child’s adventure level was also coded. As shown in Table 1 only around one-quarter of children were observed to be playing with at least moderate adventure during scan observations. No children were coded by either observer to be playing with high adventure during scans. ICC for proportion of children playing with at least moderate adventure was 0.80 (see Table 1 ). 3.1.3 Affect During the scan observation, each child’s affect was also coded. For the majority of children affect was coded as unclear with only a very small minority of children expressing any clear negative affect. The ICC values ranged from 0.73 to 0.76 (see Table 1 ). 3.2 Focal observations Focal observations were made by each rater independently. Each rater coded the activity, adventure level and affect they observed during each observation period. For information, the proportions of total observations coded for each activity, adventure level and affect coded across all participants are shown in Table 2 , split by rater. The minimum and maximum values relate to single participants (i.e. 0% indicates that at least one child did not engage in that activity at all during their focal observation, and 100% indicates that at least one child engaged in that activity throughout their focal observation), with the mean values showing the mean percentage across all of participants. Table 3 also shows the proportion of adventurous play codes that were assigned to each type of risk. Note that risk categories were only available when raters coded adventure as moderate or high, so these proportions are from a total of 45 observations for Rater 1 and 48 observations for Rater 2. Table 2. Percentage of focal observations coded under each activity, adventure level and affect label, by rater with ICC. Rater 1 Rater 2 ICC Min Mean Max Min Mean Max Activity Sports/play equipment 0.0% 31.4% 100.0% 0.0% 32.3% 100.0% 1.00 Social play 0.0% 23.5% 100.0% 0.0% 22.8% 93.3% 0.97 Active/chase 0.0% 7.8% 46.7% 0.0% 7.3% 46.7% 0.98 Loose parts/recycled materials 0.0% 5.5% 100.0% 0.0% 5.3% 93.3% 1.00 Nature/landscape 0.0% 4.9% 66.7% 0.0% 4.4% 60.0% 0.98 Small world/toys 0.0% 4.4% 60.0% 0.0% 4.4% 60.0% 1.00 Fixed equipment/markings 0.0% 2.5% 26.7% 0.0% 2.7% 26.7% 0.98 Sport 0.0% 1.5% 66.7% 0.0% 1.5% 66.7% 1.00 Rough and tumble 0.0% 1.2% 20.0% 0.0% 1.4% 20.0% 0.90 Other play 0.0% 0.5% 7.1% 0.0% 0.5% 7.1% 1.00 Antisocial 0.0% 0.8% 26.7% 0.0% 0.8% 26.7% 1.00 Not playing 0.0% 16.1% 73.3% 0.0% 16.7% 73.3% 0.97 Adventure level Low 46.7% 92.6% 100.0% 40.0% 92.7% 100.0% 0.95 Moderate 0.0% 7.2% 53.3% 0.0% 7.1% 60.0% 0.96 High 0.0% 0.2% 10.0% 0.0% 0.2% 10.0% 1.00 Affect Negative 0.0% 1.7% 26.7% 0.0% 1.4% 26.7% 0.96 Positive 0.0% 14.8% 100.0% 0.0% 17.0% 100.0% 0.96 Unclear 0.0% 83.5% 100.0% 0.0% 81.7% 100.0% 0.96 Table 3. Percentage of focal observations coded under each risk category, by rater. Note that risk category was only coded when adventure level was moderate or high. Rater 1 Rater 2 Risk Speed 46.7% 45.8% Impact 26.7% 22.9% Height 24.4% 27.1% Rough and tumble 2.2% 2.1% Other 0.0% 2.1% Raters gave the same activity code on 96% of observations, Kappa = 0.95. For adventure level, raters agreed on 98.4% of observations, Kappa = 0.89. For affect, raters agreed on 96.4% of observations, Kappa = 0.88. For risk categories, only Height, Impact, Speed, Routh and Tumble and other were observed; raters agreed on 97.6% of the 41 observations where both coders categorised play as moderate or high adventure, Kappa = 0.96. The ICCs for all focal observation categories were excellent, ranging from 0.9-1 (see Table 2 ). 3.3 Questionnaires Section 2.2.3 shows the number of children who completed each questionnaire. 3.3.1. Emotions during playtime The Emotions During Playtime scales were completed twice at Time 1 and twice at Time 2 as detailed in 2.1.3 and 2.2.2. Table 4 displays the mean and standard deviation for the positive affect score and negative affect score for the Emotions During Playtime measure, at each completion point. Scores based on the 27-item version and the 10-item version (to align with the different versions of the PANAS-C) are included for comparison purposes. The values show that positive affect was slightly higher than negative affect across all completion points and that scores were slightly lower for both positive and negative affect when only 10 items were used. Table 4. Mean and standard deviation for positive and negative affect scores based on the Emotions During Playtime measure as calculated using 27-items and 10-items. Scale and completion point 27-item version 10-item version Mean (SD) Mean (SD) Positive Affect (T1) 2.98 (1.09) 2.97 (0.9) Positive Affect (T2) 3.20 (1.21) 3.11 (1.04) Positive Affect (T3) 3.22 (1.14) 2.95 (0.94) Positive Affect (T4) 3.18 (1.15) 2.95 (1.04) Negative Affect (T1) 2.17 (0.97) 2.09 (0.79) Negative Affect (T2) 2.23 (1.01) 2.15 (0.88) Negative Affect (T3) 2.14 (1.09) 2.06 (0.94) Negative Affect (T4) 2.01 (1.05) 1.88 (0.92) Our a priori intention was to average across the two completions at each timepoint to give an indication of emotions experienced during playtime. The test-retest reliability when this approach was used was good (CCC = 0.87 for positive affect and CCC = 0.81 for negative affect with 27 items; CCC = 0.82 for positive affect and CCC = 0.81 for negative affect with 10 items). To explore whether this approach was required or whether the consistency between a single completion of the Emotions During Playtime measure at Time 1 and Time 2 would be adequate the CCCs for the first Emotions During Playtime measure completed at T1 and the first completed at T2 were evaluated. These were weaker (CCC = 0.7 for positive affect and CCC = 0.55 for negative affect for 27 items; CCC = 0.72 and CCC = 0.56 for 10 items). This suggests that completing the Emotions During Playtime measure on two occasions gives a more reliable estimate of both positive and negative affect. Cronbach’s alpha was calculated for the positive affect and negative affect scales independently at each completion point, with complete data only and with all data. For positive affect, alpha values ranged from 0.80 – 0.90 and, for negative affect, alpha values ranged from 0.87-0.92 (for the 10-item scale, values ranged from 0.73 – 0.83 for positive affect and 0.73 – 0.85 for negative affect). As described previously, we included all 30 items to support future research which could explore emotional profiles of playtimes and also to consider whether individual items might give useful insights. We therefore also evaluated test-retest reliability for each item (see Table 6 ). The majority of the items showed reasonable consistency over time and sufficient variation that they could be used in isolation in future research if needed. Notable exceptions were ‘happy’, ‘disgusted’, ‘miserable’, ‘strong’ and ‘gloomy’ which had poor consistency. 3.3.2 Activities during playtime Table 5 shows the proportion of children who responded that they played each activity ‘Not at all’, ‘A little’ or ‘A lot’ during playtime. At both timepoints the most commonly reported activities were sport and chase/tag. Test-retest reliability regarding specific activities was relatively poor; Kappas ranged from 0.13 – 0.41 for the consistency of responses. We also examined whether reliability would be stronger if we collapsed ‘A little’ and ‘A lot’ responses to give a binary variable but reliability was only slightly improved with Kappas from 0.19 to 0.54 (see Table 5 ). Table 5. Proportion of children selecting each response on the Activities During Playtime questionnaire. Kappa (3) shows the Kappa value when all three categories were included. Kappa (2) shows the Kappa value when ‘A little’ and ‘A lot’ were combined. Time 1 Time 2 Kappa (3) Kappa (2) Sport Not at all 33.9% 16.0% 0.40 0.54 A little 30.4% 32.0% A lot 35.7% 52.0% Fixed Equipment Not at all 39.3% 42.9% 0.13 0.19 A little 44.6% 34.7% A lot 16.1% 22.4% Loose parts Not at all 47.3% 58.3% 0.35 0.37 A little 27.3% 29.2% A lot 25.5% 12.5% Nature Not at all 42.6% 46.9% 0.36 0.33 A little 42.6% 28.6% A lot 14.8% 24.5% Chase, tag Not at all 12.5% 16.0% 0.26 0.19 A little 41.1% 36.0% A lot 46.4% 48.0% Play fighting Not at all 54.5% 60.0% 0.26 0.43 A little 38.2% 30.0% A lot 7.3% 10.0% Fighting, arguing, breaking things Not at all 64.3% 61.7% 0.26 0.28 A little 21.4% 29.8% A lot 14.3% 8.5% Not playing Not at all 40.0% 47.9% 0.41 0.47 A little 41.8% 29.2% A lot 18.2% 22.9% Table 6. Concordance Correlation Coefficient (reliability) by item for the Emotions During Playtime questionnaire. Scores are averaged across two completions at T1 and two completions at T2, lower and upper 95% confidence intervals also shown. Item N Mean St. Dev. Min Max CCC CCC lwr.ci CCC upr.ci Interested 52 2.577 1.526 1 5 0.68 0.44 0.83 Sad 55 2.291 1.474 1 5 0.62 0.37 0.79 Frightened 56 1.714 1.057 1 4 0.70 0.48 0.83 Alert 52 2.327 1.167 1 5 0.59 0.30 0.77 Excited 53 3.019 1.538 1 5 0.71 0.51 0.84 Ashamed 52 1.769 1.198 1 5 0.74 0.56 0.86 Upset 53 2.245 1.329 1 5 0.66 0.41 0.81 Happy 54 3.481 1.563 1 5 -0.25 -0.43 -0.06 Strong 53 3.094 1.656 1 5 -0.06 -0.37 0.26 Nervous 54 2.185 1.361 1 5 0.63 0.37 0.80 Guilty 54 1.741 1.031 1 5 0.64 0.41 0.79 Energetic 53 3.358 1.618 1 5 0.85 0.72 0.93 Scared 54 2.148 1.485 1 5 0.65 0.39 0.81 Calm 53 2.811 1.481 1 5 0.66 0.41 0.81 Miserable 54 2.13 1.428 1 5 0.35 0.04 0.60 Jittery 51 2.098 1.33 1 5 0.55 0.26 0.75 Cheerful 54 2.889 1.562 1 5 0.80 0.63 0.89 Active 54 3.204 1.583 1 5 0.73 0.52 0.86 Proud 55 2.836 1.549 1 5 0.58 0.30 0.77 Afraid 55 1.927 1.274 1 5 0.72 0.50 0.85 Joyful 52 3 1.547 1 5 0.81 0.63 0.91 Lonely 53 2.17 1.411 1 5 0.69 0.42 0.84 Mad 55 2.4 1.382 1 5 0.75 0.53 0.87 Fearless 54 2.5 1.437 1 5 0.67 0.41 0.83 Disgusted 55 1.982 1.408 1 5 0.40 0.06 0.66 Delighted 53 2.623 1.457 1 5 0.64 0.38 0.81 Blue 53 2.226 1.436 1 5 0.82 0.65 0.91 Daring 52 2.327 1.581 1 5 0.65 0.39 0.82 Gloomy 52 2.327 1.618 1 5 0.32 -0.04 0.61 Lively 53 2.66 1.652 1 5 0.52 0.21 0.74 4. Discussion Our primary aim was to introduce the M-TAPS and describe it in such a way that it could be utilised in future research when combined the materials we have made openly available. In addition, we aimed to examine the reliability of each of the components of the M-TAPS. Specifically, we examined inter-rater reliability for the scan and focal observations and test-retest reliability as well as internal consistency, where appropriate, for the child self-report measures. We also explored whether adventure and affect could be coded effectively. Each component is now discussed in turn. For the scan and focal observations, reliability estimates fell in the good to excellent range for most of the activities as well as for adventure levels. The only exception was the reliability of affect categorisation during scan observations which was in the moderate range. Overall, very little adventure and negative affect were coded which raises the question of whether these codes are worthwhile retaining in future studies. The M-TAPS is designed to be flexible and users can decide whether they wish to code adventure and affect. Our own reflections are that the low levels of affect and adventure are due to different underlying issues. The coders reported that affect was very difficult to evaluate based only on appearance. This aligns with research showing that facial expressions do not always align with internal emotional state ( Barrett et al., 2019 ). The low rate of negative and positive affect coded was likely, therefore, due to uncertainty regarding emotion; it may therefore not be worthwhile attempting to code affect in future studies, particularly if children are completing the Emotions During Playtime measure, where they were able to provide relatively reliable self-report of emotions. In contrast, the low levels of moderate and high adventure during the observed playtimes seem to be an accurate representation of the adventure levels present. If a school made their playtimes more adventurous then we anticipate that the observation components of the M-TAPS would be able to capture these higher levels of adventure. Future research will need to explore this further to be certain though. Given the very low amount of high adventure coded, the current results do not allow us to conclude whether high adventure can reliably be coded but the reliability estimate for the ‘at least moderate adventure’ code allows us to be optimistic that adventure level can be captured objectively to some extent. The test-retest reliability and internal consistency for the Emotions During Playtime measure, which is based on the PANAS-C ( Ebesutani et al., 2012 ; Laurent et al., 1999 ) were good when the measure was completed twice at time 1 and twice at time 2. We planned to ask children to complete the measure twice and to average responses at both time points because we anticipated that children’s feelings about playtime on any particular day may affect responses. To examine whether this approach was necessary, we also evaluated the test-retest reliability of a single completion at Time 1 and Time 2. This fell in the moderate range and was notably lower than for the average of two completions at both timepoint. We therefore recommend that the measure is completed twice where practical. Given that the PANAS-C was originally designed with 30 items, which were subsequently reduced to 27 items and then to 10 items for the short-form, we examined the reliability and internal consistent for both the 27-item version and the 10-item version. The results showed that the 10-item version gave slightly lower scores on average and had slightly lower internal consistency, but the two versions were relatively consistent with one another overall. The 10-item version may therefore be preferred in future research. In contrast to the Emotions During Playtime measure, the Activities During Playtime Questionnaire showed poor test-retest reliability at the individual child level. There are a number of possible explanations for this. First, it is possible that children are heavily influenced by what they did during their most recent playtime and therefore aren’t able to give a reliable response about what they typically do over a two-week period. Second, children’s play activities may not be stable over time, so children may be providing valid responses but the low test-retest reliability may indicate that their play activities change over time. At the group level, the results from the Activities During Playtime questionnaire were somewhat consistent over time; chase/tag and sport were the most commonly played ‘a lot’ at both time points and the least frequently played ‘a lot’ at both time points were play fighting and fighting, arguing, breaking things. In contrast, the rankings of nature, loose parts and fixed equipment across the two timepoints were less consistent. This may therefore indicate that children are quite consistent in their playing of chase/tag or sport during playtimes but less consistent in their play with loose parts, fixed equipment and nature, perhaps moving between these activities over time. It is informative that the most common play activities observed during both scan and focal observations were also ‘using sport or play equipment’ and ‘active play or chase’ (with social play also being frequently observed during focal observations). Very little nature play, rough and tumble play or loose parts play was observed. This provides some initial validity across the components of the M-TAPS and supports the interpretation that children are consistently engaging in these types of play. Although our aim was to evaluate reliability of the M-TAPS components, the results demonstrate how the measure can provide insights into children’s play during school playtimes. In addition to highlighting the popularity of play with sports/play equipment and active play/chase, the results show that whilst a high proportion of children were engaged in play during the observations around 11-14% of children were not playing. From discussion with the coders there were various reasons for this. For example, a child may be walking between activities or may be looking around to decide what to do next. In addition, some children were observed to be disengaged or withdrawn from play. A further insight is that high levels of adventure were almost never observed and moderate levels of adventure were observed only between 10% and 25% of the time across the different types of observation. This is consistent with research showing that schools in the UK are hesitant about allowing adventurous play during playtime ( Nesbit et al., 2021 ). The study was designed as an initial evaluation of the reliability of the M-TAPS. There are therefore some limitations to consider. First, it may be useful to add in the perspective of school staff, especially those who supervise lunch and breaktimes; these members of school staff may be able to provide insight into trends in play that may not be captured during discreet periods of observation. Second, the reliability study was conducted within one school so should be considered preliminary; further evaluation across a range of schools would be ideal to ensure that the results can be generalised. This would also allow the factor structure of the Emotions During Playtime measure to be evaluated, which could not be conducted in this study due to power limitations. Furthermore, we have not evaluated test-retest reliability of the observation components. A final limitation to consider is that we focus on reliability rather than validity. Certainly, there is some evidence of validity due to the consistency across the M-TAPS components in terms of the most common and most rare activities, but the study was not designed as a rigorous evaluation of validity. 5. Conclusion The M-TAPS provides a flexible tool for supporting researchers to capture children’s play in schools in a quantitative way. The results highlight how the M-TAPS can provide insights into schools playtimes and the need for multiple perspectives; integrating observation with children’s self-report. An initial psychometric evaluation provides support for the various components, although child report of playtime activities may not be reliable at the individual child level. Suggestions of various options for researchers are included such as the decision of whether to evaluate affect during observations, and ideas and directions for further evaluation of the instrument are described. We anticipate that the M-TAPS will complement the rich methods available for qualitative approaches children’s play and existing measures of play in schools. Ethics and consent The methods and study procedures were approved by the University of Reading Research Ethics Committee (Ref: 2021-163-RM) on 15 th November 2021. The study adhered to the Declaration of Helsinki principles. Parents were provided with written study information sheets via their child’s school and were asked to inform the school or the study team if they did not want their child to participate. This written opt-out consent procedure was approved by the research ethics panel because all of the data collected for the study was anonymous and because the school gave permission for the data to be collected during school hours. No personal data was collected and it was not possible for the research team to link any data to a child’s name or other identifiable information. CRediT statement Conceptualization, H.D., R.N., L.F.; methodology, H.D., R.N., L.F.; software, n/a; validation, H.D., L.F.; formal analysis, H.D., L.F.; investigation, H.D., R.N., L.F.; resources, H.D., R.N., L.F.; data curation, H.D., L.F., writing—original draft preparation, H.D.; writing—review and editing, H.D., L.F., R.N.; visualization, L.F.; supervision, H.D.; project administration, H.D.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript. Data availability statement Underlying data OSF: Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS). https://osf.io/qjf8b/ ( Dodd et al., 2025 ). This project contains the following underlying data: - Scan data_raw.csv (raw data from scan observations) - focal obs data_reliability_wide.csv (raw data from focal observations) - Raw child data practice school_questionnaires.csv (raw data from child questionnaires) Data is available under the terms of the https://creativecommons.org/licenses/by/4.0/deed.en (CC-By Attribution 4.0 International) Code for replicating the analyses in R Studio available from: https://osf.io/qjf8b/ Extended data All materials used for the M-TAPS, including the app coding and the questionnaires, alongside the coding protocol, are available alongside the data at: https://osf.io/qjf8b/ . Acknowledgements Thank you to Brooke Oliver for her help with data collection and to Janet Loebach and Wiliam Massey for their helpful advice whilst we developed the measure. References Andersen MM, Kiverstein J, Miller M, et al. : Play in predictive minds: A cognitive theory of play. Psychol. Rev. 2023; 130 (2): 462–479. PubMed Abstract | Publisher Full Text Baines E, Blatchford P: School break and lunch times and young people’s social lives: A follow-up national study. Final Report to the Nuffield Foundation (Ref EDU/42402). 2019. Barrett LF, Adolphs R, Marsella S, et al. : Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements. Psychol. Sci. Public Interest. 2019; 20 (1): 1–68. PubMed Abstract | Publisher Full Text | Free Full Text Bundy AC, Engelen L, Wyver S, et al. : Sydney Playground Project: A Cluster-Randomized Trial to Increase Physical Activity, Play, and Social Skills. J. Sch. Health. 2017; 87 (10): 751–759. PubMed Abstract | Publisher Full Text Clements R: An investigation of the status of outdoor play. Contemp. Issues Early Child. 2004; 5 (1): 66–80. Publisher Full Text Dodd HF, FitzGibbon L, Watson BE, et al. : Children’s play and independent mobility in 2020: results from the British Children’s Play Survey. Int. J. Environ. Res. Public Health. 2021; 18 (8): 4334. PubMed Abstract | Publisher Full Text | Free Full Text Dodd HF, Lester KJ: Adventurous play as a mechanism for reducing risk for childhood anxiety: a conceptual model. Clin. Child. Fam. Psychol. Rev. 2021; 24 (1): 164–181. PubMed Abstract | Publisher Full Text | Free Full Text Dodd HF, Nesbit RJ, FitzGibbon L: Child’s Play: Examining the Association Between Time Spent Playing and Child Mental Health. Child Psychiatry Hum. Dev. 2023; 54 (6): 1678–1686. PubMed Abstract | Publisher Full Text | Free Full Text Dodd HF, Nesbit RJ, FitzGibbon L: Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS). [Data set]. OSF. 2025. Publisher Full Text Ebesutani C, Regan J, Smith A, et al. : The 10-Item Positive and Negative Affect Schedule for Children, Child and Parent Shortened Versions: Application of Item Response Theory for More Efficient Assessment. J. Psychopathol. Behav. Assess. 2012; 34 (2): 191–203. Publisher Full Text FitzGibbon L, Oliver BE, Nesbit RJ, et al. : A scoping review of methods and measures used to capture children’s play during school breaktimes. Educ. Rev. 2024; 1–26. Publisher Full Text Gamer M, Lemon J, Fellows I, et al. : irr: Various Coefficients of Interrater Reliability and Agreement. R package version 0.84.1.2019. Reference Source Henley R, McAlpine K, Mueller M, et al. : Does school attendance reduce the risk of youth homelessness in Tanzania? Int. J. Ment. Heal. Syst. 2010; 4 : 28. PubMed Abstract | Publisher Full Text | Free Full Text Herrington S, Brussoni M: Beyond Physical Activity: The Importance of Play and Nature-Based Play Spaces for Children’s Health and Development. Curr. Obes. Rep. 2015; 4 (4): 477–483. PubMed Abstract | Publisher Full Text Houser NE, Cawley J, Kolen AM, et al. : A Loose Parts Randomized Controlled Trial to Promote Active Outdoor Play in Preschool-aged Children: Physical Literacy in the Early Years (PLEY) Project. Methods Protoc. 2019; 2 (2): 27. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source Hyndman BP, Benson AC, Ullah S, et al. : Evaluating the effects of the Lunchtime Enjoyment Activity and Play (LEAP) school playground intervention on children’s quality of life, enjoyment and participation in physical activity. BMC Public Health. 2014; 14 (1): 164. PubMed Abstract | Publisher Full Text | Free Full Text Jackson SB, Stevenson KT, Larson LR, et al. : Outdoor Activity Participation Improves Adolescents’ Mental Health and Well-Being during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health. 2021; 18 (5): 2506. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source Jarrett OS, Maxwell DM, Dickerson C, et al. : Impact of Recess on Classroom Behavior: Group Effects and Individual Differences. J. Educ. Res. 1998; 92 (2): 121–126. Publisher Full Text Johnstone A, Hughes AR, Martin A, et al. : Utilising active play interventions to promote physical activity and improve fundamental movement skills in children: a systematic review and meta-analysis. BMC Public Health. 2018; 18 (1): 789. PubMed Abstract | Publisher Full Text | Free Full Text Kleppe R, Melhuish E, Sandseter EBH: Identifying and characterizing risky play in the age one-to-three years. Eur. Early Child. Educ. Res. J. 2017; 25 (3): 370–385. Publisher Full Text Laurent J, Catanzaro SJ, Joiner TE Jr, et al. : A measure of positive and negative affect for children: Scale development and preliminary validation. Psychol. Assess. 1999; 11 : 326–338. Publisher Full Text Lee RLT, Lane S, Brown G, et al. : Systematic review of the impact of unstructured play interventions to improve young children’s physical, social, and emotional wellbeing. Nurs. Health Sci. 2020; 22 (2): 184–196. PubMed Abstract | Publisher Full Text Lester S, Jones O, Russell W: Supporting school improvement through play: An evaluation of South Gloucestershire’s Outdoor Play and Learning Programme.2011. Reference Source Loebach J, Cox A: Tool for Observing Play Outdoors (TOPO): A New Typology for Capturing Children’s Play Behaviors in Outdoor Environments. Int. J. Environ. Res. Public Health. 2020; 17 (15). PubMed Abstract | Publisher Full Text | Free Full Text Loebach J, Cox A: Playing in ‘The Backyard’: Environmental Features and Conditions of a Natural Playspace Which Support Diverse Outdoor Play Activities among Younger Children. Int. J. Environ. Res. Public Health. 2022; 19 (19). PubMed Abstract | Publisher Full Text | Free Full Text Massey WV, Ku B, Stellino MB: Observations of playground play during elementary school recess. BMC. Res. Notes. 2018a; 11 (1): 755. PubMed Abstract | Publisher Full Text | Free Full Text Massey WV, Stellino MB, Mullen SP, et al. : Development of the great recess framework – observational tool to measure contextual and behavioral components of elementary school recess. BMC Public Health. 2018b; 18 (1): 394. PubMed Abstract | Publisher Full Text | Free Full Text McKenzie TL, Marshall SJ, Sallis JF, et al. : Leisure-time physical activity in school environments: an observational study using SOPLAY. Prev. Med. 2000; 30 (1): 70–77. PubMed Abstract | Publisher Full Text Nesbit RJ, Bagnall CL, Harvey K, et al. : Perceived Barriers and Facilitators of Adventurous Play in Schools: A qualitative systematic review. Children. 2021; 8 (8): 681. PubMed Abstract | Publisher Full Text | Free Full Text Nijhof SL, Vinkers CH, van Geelen SM , et al. : Healthy play, better coping: The importance of play for the development of children in health and disease. Neurosci. Biobehav. Rev. 2018; 95 : 421–429. PubMed Abstract | Publisher Full Text Pellegrini AD, Bjorklund DF: The role of recess in children’s cognitive performance. Educ. Psychol. 1997; 32 (1): 35–40. Publisher Full Text Pellegrini AD, Bohn CM: The Role of Recess in Children’s Cognitive Performance and School Adjustment. Educ. Res. 2005; 34 (1): 13–19. Publisher Full Text Play and Learn Scholars (PALS): Learning through play: more than laughter and smiles. Child and Family Blog. 2020. Reference Source R Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2021. Reference Source R Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2024. Reference Source Rizopoulos D: ltm: An R package for Latent Variable Modelling and Item Response Theory Analyses. J. Stat. Softw. 2006; 17 (5): 1–25. Publisher Full Text Sandseter EBH: Characteristics of risky play. J. Adventure Educ. Out. Learn. 2009; 9 (1): 3–21. Publisher Full Text Sandseter EBH, Kleppe R, Ottesen Kennair LE: Risky play in children’s emotion regulation, social functioning, and physical health: an evolutionary approach. Int. J. Play. 2023; 12 (1): 127–139. Publisher Full Text Signorell A: DescTools: Tools for Descriptive Statistics. R package version 0.99.49. 2023. Reference Source Singer DG, Golinkoff RM, Hirsh-Pasek K: Play = learning: How play motivates and enhances children’s cognitive and social-emotional growth. Oxford University Press; 2006. Publisher Full Text Sutton-Smith B: Recapitulation redressed.Roopnarine J, editor. Conceptual, social-cognitive, and contextual issues in the fields of play. Vol. 4 . ; Ablex; 2002; pp. 3–23. Tremblay MS, Gray C, Babcock S, et al. : Position Statement on Active Outdoor Play. Int. J. Environ. Res. Public Health. 2015, Jun 8; 12 (6): 6475–6505. PubMed Abstract | Publisher Full Text | Free Full Text United Nations: Convention on the rights of the child, Treaty no. 27531. United Nations Treaty Series. 1989. 1577, 3-178. (Accessed: 21 July 2023). Reference Source Walter SD, Eliasziw M, Donner A: Sample size and optimal designs for reliability studies. Stat. Med. 1998; 17 (1): 101–110. <a target="xrefwindow" id="d1790e4667" href="https://doi.org/10.1002/(sici)1097-0258(19980115)17:1 Publisher Full Text Zhao YV, Gibson JL: Evidence for Protective Effects of Peer Play in the Early Years: Better Peer Play Ability at Age 3 Years Predicts Lower Risks of Externalising and Internalising Problems at Age 7 Years in a Longitudinal Cohort Analysis. Child Psychiatry Hum. Dev. 2022; 54 : 1807–1822. PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 06 May 2025 ADD YOUR COMMENT Comment Author details Author details 1 Public Health and Sport Sciences, University of Exeter, Exeter, England, UK 2 School of Psychology and Clinical Language Sciences, University of Reading, Reading, England, UK 3 Division of Psychology, University of Stirling, Stirling, Scotland, UK Helen Dodd Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Rachel Nesbit Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Lily FitzGibbon Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This research was funded by a UKRI Future Leaders Fellowship awarded to HD (MR/S017909/1). The funder had no role in influencing the study design or findings. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 20 Oct 2025, 14:476 https://doi.org/10.12688/f1000research.160920.2 version 1 Published: 06 May 2025, 14:476 https://doi.org/10.12688/f1000research.160920.1 Copyright © 2025 Dodd H et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Dodd H, Nesbit R and FitzGibbon L. Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.12688/f1000research.160920.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 06 May 2025 Views 0 Cite How to cite this report: James M. Reviewer Report For: Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.5256/f1000research.176881.r384603 ) The direct URL for this report is: https://f1000research.com/articles/14-476/v1#referee-response-384603 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Jun 2025 Maeghan James , Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176881.r384603 Thank you for the opportunity to review this manuscript titled: “Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study”. In this paper, the authors introduce a novel tool for assessing play in schools ... Continue reading READ ALL Thank you for the opportunity to review this manuscript titled: “Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study”. In this paper, the authors introduce a novel tool for assessing play in schools and present reliability statistics of the tool. The authors concluded that some components of the M-TAPS tool were reliable and provide important considerations for future observational work using this tool. Overall, the paper was very well written – clear, concise and informative. The authors raise an important point regarding the need for more universal observational tools that can suit the needs of a variety of research projects. I believe the topic is of importance and will be a valuable contribution to the literature. Below I outline some questions/comments I had about the work that I believe, once addressed, would strengthen the paper. One of the activity categories is labeled as “playing,” but many of the listed activities would arguably fall under the general concept of play. It is unclear how “playing” is being specifically defined in contrast to other categories such as “active/chase.” Clarifying the definitions and providing examples would help distinguish between these overlapping categories. This is primarily why I indicated “partly” to whether sufficient details were provided to allow replication of the method development and its use by others. This information may have been somewhere on the OSF link but it wasn’t working for me so I could not determine if there was enough information on the codes themselves to allow for replication. I recommend including the full list of codes and their definitions either as a table within the manuscript or as a supplementary file. Given the subjective nature of observational data, I appreciate the inclusion of affect and adventurous play categories. However, it is essential that the tool and/or codebook explicitly challenge normative assumptions about what play looks like. For example, what “positive affect” or “adventurous play” looks like. This is particularly relevant for children and youth with diverse abilities and backgrounds. This could be addressed through specific training or guidance notes in the codebook. The results section suggests that most activities were coded under the general “playing” category. It remains unclear whether coders were allowed to select multiple categories (e.g., “playing” and “sports/play equipment”). If coders could only choose one, this may lead to a loss of context and detail regarding the type of play observed. The low ICC value for the “playing” category is notable. Could this be due to coders needing to choose between “playing” and a more specific type of activity, even when both might be appropriate? This suggests potential ambiguity in the coding decision process. A possible alternative approach could involve first determining whether a behavior constitutes “play” (yes/no), followed by a second level of coding to classify the type of play. This hierarchical strategy may improve reliability and clarity. While ICC values for affect (when coded as positive or negative) were strong, the majority of affective observations were coded as “unclear.” This suggests that identifying emotional expression via observation alone may be inherently limited. I appreciate the authors’ reflection on the challenges of coding affect. Rather than removing the affect category, it may be more productive to consider how visible indicators of emotion (e.g., facial expressions, body language) could be systematically coded to aid decision-making. The strong agreement among coders is encouraging, but it raises the question of whether these classifications are valid. Although validating affect codes was not the purpose of this study, it would be valuable to know if there are plans to evaluate this in future research. This may also warrant further discussion beyond the limitations section to acknowledge the inherent biases associated with observational research and to clarify that, in its current form, the tool cannot yet be considered validated which has important implications for practice. For the Activities During Playtime Questionnaire, it would be helpful to describe what measures were taken to ensure that children consistently understood each activity type (e.g., nature play vs. free play). It is possible that variability in children’s interpretations affected their responses? Were examples or visual aids provided to support consistent understanding at both time points? When discussing the reliability findings for the PANAS-C, it may strengthen the discussion to include a brief comparison with reliability estimates reported in previous literature, if available. Is the rationale for developing the new method (or application) clearly explained? Yes Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? Partly If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Observational research on children’s active and outdoor play, with a focus on child development, childhood disability, and the use of systematic observation protocols in playground settings. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT James M. Reviewer Report For: Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.5256/f1000research.176881.r384603 ) The direct URL for this report is: https://f1000research.com/articles/14-476/v1#referee-response-384603 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 20 Oct 2025 Helen Dodd , School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK 20 Oct 2025 Author Response Thank you for your thought-provoking review of our manuscript. In the revised version, we have clarified the reporting of the observational protocols and included a table to address several of ... Continue reading Thank you for your thought-provoking review of our manuscript. In the revised version, we have clarified the reporting of the observational protocols and included a table to address several of the issues you have raised. We also include now benchmark our reliability findings against relevant previous findings, as suggested. We respond to each of your comments in turn below. 1. One of the activity categories is labeled as “playing,” but many of the listed activities would arguably fall under the general concept of play. It is unclear how “playing” is being specifically defined in contrast to other categories such as “active/chase.” Clarifying the definitions and providing examples would help distinguish between these overlapping categories. This is primarily why I indicated “partly” to whether sufficient details were provided to allow replication of the method development and its use by others. This information may have been somewhere on the OSF link but it wasn’t working for me so I could not determine if there was enough information on the codes themselves to allow for replication. I recommend including the full list of codes and their definitions either as a table within the manuscript or as a supplementary file. Response: We appreciate having the lack of clarity around the categories for the scan observations drawn to our attention. Scans were done one category at a time, so were not designed to be mutually exclusive of one another. For example, the first scan “Playing” simply sought to determine the proportion of children on the playground who were considered to be playing, rather than engaged in some non-play activity such as eating, observing, or walking between areas of the playground. This first scan encompasses all kinds of play. Later scans identified whether specific types of play were occurring. For focal observations, the “playing” category was replaced with “not playing” and an “other play” category was also included so that play that did not fit into the other categories could be distinguished from non-play behaviour. We now make that clearer and, following your suggestion, we now include a table of definitions for each category (Table 1) that shows the response options for scan and focal observations side by side for easy comparison. 2. Given the subjective nature of observational data, I appreciate the inclusion of affect and adventurous play categories. However, it is essential that the tool and/or codebook explicitly challenge normative assumptions about what play looks like. For example, what “positive affect” or “adventurous play” looks like. This is particularly relevant for children and youth with diverse abilities and backgrounds. This could be addressed through specific training or guidance notes in the codebook. Response: This is a very important point that we agree has not yet been well addressed in our research, or indeed elsewhere. We now include this as a discussion point with a call for future research to evaluate the coding scheme with children with a diverse range of abilities and backgrounds. 3. The results section suggests that most activities were coded under the general “playing” category. It remains unclear whether coders were allowed to select multiple categories (e.g., “playing” and “sports/play equipment”). If coders could only choose one, this may lead to a loss of context and detail regarding the type of play observed. Response: Again, thank you for drawing our attention to the lack of clarity relating to the selection of the categories. For scan observations, each category was scanned separately, so categories were independent. In each scan, the coder simply thought about the category they were scanning for, say “playing” and considered whether each child was playing or not in that moment. One would expect that the proportion of children observed to be “playing” would be higher than for each of the sub-categories of play, and indeed for these sub-categories combined. This is not necessarily the case since the scans occur consecutively, and children may change activity between scans. Indeed, in our data, the proportion of children “playing” in the scan observations was similar to the proportion of children in the other play categories combined, suggesting that these categories did indeed describe most of the kinds of play children engaged in. For focal observations, there was no “playing” category, but if the child’s activity did not fit one of the play categories, they could choose whether they were engaged in “other play” and write some notes about that play afterwards, or “not playing”. We have clarified this in the manuscript and in Table 1. 4. The low ICC value for the “playing” category is notable. Could this be due to coders needing to choose between “playing” and a more specific type of activity, even when both might be appropriate? This suggests potential ambiguity in the coding decision process. A possible alternative approach could involve first determining whether a behavior constitutes “play” (yes/no), followed by a second level of coding to classify the type of play. This hierarchical strategy may improve reliability and clarity. Response: As outlined in the previous comment, the coders did not need to chose between “playing” and specific types of play/activity, so the lower ICC is not due to this. Instead, this lower ICC may be due to the definition of ‘play’ being more ambiguous than the specific types of play, and/or because the quick pass nature of scan observations does not give sufficient time for the presence/absence of play to be established. Consistent with this explanation, reliability of the category “not playing” in the focal observations was near perfect, suggesting that with more time observing a child, coders can more consistently evaluate whether a child is playing or not. We suggest that future researchers are cautious about using broad and subjective categories such as “playing” for scan observations where more time might be required to understand the context of a child’s behaviour (see also discussion of coding affect during scan observations). It is nonetheless useful to determine an approximate estimate of the number of children engaged in play activity because low estimates might be a key indicator of problems with a particular playground environment. 5. While ICC values for affect (when coded as positive or negative) were strong, the majority of affective observations were coded as “unclear.” This suggests that identifying emotional expression via observation alone may be inherently limited. I appreciate the authors’ reflection on the challenges of coding affect. Rather than removing the affect category, it may be more productive to consider how visible indicators of emotion (e.g., facial expressions, body language) could be systematically coded to aid decision-making. Response: It is true that there was a high incidence of the ‘unclear’ category being used by the coders. We now reflect upon this in more detail in the discussion, with reference to the prevenance of positive and negative emotions reported in the self-reported Emotions During Playtime measure. 6. The strong agreement among coders is encouraging, but it raises the question of whether these classifications are valid. Although validating affect codes was not the purpose of this study, it would be valuable to know if there are plans to evaluate this in future research. This may also warrant further discussion beyond the limitations section to acknowledge the inherent biases associated with observational research and to clarify that, in its current form, the tool cannot yet be considered validated which has important implications for practice. Response: Note that we had already stated “ there is some evidence of validity due to the consistency across the M-TAPS components in terms of the most common and most rare activities, but the study was not designed as a rigorous evaluation of validity”. In light of the reviewer's previous comment though, we have further considered validity in the final paragraph of the discussion. 7. For the Activities During Playtime Questionnaire, it would be helpful to describe what measures were taken to ensure that children consistently understood each activity type (e.g., nature play vs. free play). It is possible that variability in children’s interpretations affected their responses? Were examples or visual aids provided to support consistent understanding at both time points? Response: We have realised that the questionnaire packs were not available in the supplementary materials at the time of the original submission. The full questionnaire pack is now available in the supplementary materials. Given the limited explanation of the categories provided to children, we now suggest that poorer reliability of some categories may indicate a lack of understanding in the discussion and suggest that visual aids may improve the validity of children’s responses. We have added further detail about the protocol for the self-report questionnaire packs. 8. When discussing the reliability findings for the PANAS-C, it may strengthen the discussion to include a brief comparison with reliability estimates reported in previous literature, if available. Response: We now include comparison of the Emotions During Playtime internal consistency and test-retest reliability with previous PANAS-C findings. Thank you for your thought-provoking review of our manuscript. In the revised version, we have clarified the reporting of the observational protocols and included a table to address several of the issues you have raised. We also include now benchmark our reliability findings against relevant previous findings, as suggested. We respond to each of your comments in turn below. 1. One of the activity categories is labeled as “playing,” but many of the listed activities would arguably fall under the general concept of play. It is unclear how “playing” is being specifically defined in contrast to other categories such as “active/chase.” Clarifying the definitions and providing examples would help distinguish between these overlapping categories. This is primarily why I indicated “partly” to whether sufficient details were provided to allow replication of the method development and its use by others. This information may have been somewhere on the OSF link but it wasn’t working for me so I could not determine if there was enough information on the codes themselves to allow for replication. I recommend including the full list of codes and their definitions either as a table within the manuscript or as a supplementary file. Response: We appreciate having the lack of clarity around the categories for the scan observations drawn to our attention. Scans were done one category at a time, so were not designed to be mutually exclusive of one another. For example, the first scan “Playing” simply sought to determine the proportion of children on the playground who were considered to be playing, rather than engaged in some non-play activity such as eating, observing, or walking between areas of the playground. This first scan encompasses all kinds of play. Later scans identified whether specific types of play were occurring. For focal observations, the “playing” category was replaced with “not playing” and an “other play” category was also included so that play that did not fit into the other categories could be distinguished from non-play behaviour. We now make that clearer and, following your suggestion, we now include a table of definitions for each category (Table 1) that shows the response options for scan and focal observations side by side for easy comparison. 2. Given the subjective nature of observational data, I appreciate the inclusion of affect and adventurous play categories. However, it is essential that the tool and/or codebook explicitly challenge normative assumptions about what play looks like. For example, what “positive affect” or “adventurous play” looks like. This is particularly relevant for children and youth with diverse abilities and backgrounds. This could be addressed through specific training or guidance notes in the codebook. Response: This is a very important point that we agree has not yet been well addressed in our research, or indeed elsewhere. We now include this as a discussion point with a call for future research to evaluate the coding scheme with children with a diverse range of abilities and backgrounds. 3. The results section suggests that most activities were coded under the general “playing” category. It remains unclear whether coders were allowed to select multiple categories (e.g., “playing” and “sports/play equipment”). If coders could only choose one, this may lead to a loss of context and detail regarding the type of play observed. Response: Again, thank you for drawing our attention to the lack of clarity relating to the selection of the categories. For scan observations, each category was scanned separately, so categories were independent. In each scan, the coder simply thought about the category they were scanning for, say “playing” and considered whether each child was playing or not in that moment. One would expect that the proportion of children observed to be “playing” would be higher than for each of the sub-categories of play, and indeed for these sub-categories combined. This is not necessarily the case since the scans occur consecutively, and children may change activity between scans. Indeed, in our data, the proportion of children “playing” in the scan observations was similar to the proportion of children in the other play categories combined, suggesting that these categories did indeed describe most of the kinds of play children engaged in. For focal observations, there was no “playing” category, but if the child’s activity did not fit one of the play categories, they could choose whether they were engaged in “other play” and write some notes about that play afterwards, or “not playing”. We have clarified this in the manuscript and in Table 1. 4. The low ICC value for the “playing” category is notable. Could this be due to coders needing to choose between “playing” and a more specific type of activity, even when both might be appropriate? This suggests potential ambiguity in the coding decision process. A possible alternative approach could involve first determining whether a behavior constitutes “play” (yes/no), followed by a second level of coding to classify the type of play. This hierarchical strategy may improve reliability and clarity. Response: As outlined in the previous comment, the coders did not need to chose between “playing” and specific types of play/activity, so the lower ICC is not due to this. Instead, this lower ICC may be due to the definition of ‘play’ being more ambiguous than the specific types of play, and/or because the quick pass nature of scan observations does not give sufficient time for the presence/absence of play to be established. Consistent with this explanation, reliability of the category “not playing” in the focal observations was near perfect, suggesting that with more time observing a child, coders can more consistently evaluate whether a child is playing or not. We suggest that future researchers are cautious about using broad and subjective categories such as “playing” for scan observations where more time might be required to understand the context of a child’s behaviour (see also discussion of coding affect during scan observations). It is nonetheless useful to determine an approximate estimate of the number of children engaged in play activity because low estimates might be a key indicator of problems with a particular playground environment. 5. While ICC values for affect (when coded as positive or negative) were strong, the majority of affective observations were coded as “unclear.” This suggests that identifying emotional expression via observation alone may be inherently limited. I appreciate the authors’ reflection on the challenges of coding affect. Rather than removing the affect category, it may be more productive to consider how visible indicators of emotion (e.g., facial expressions, body language) could be systematically coded to aid decision-making. Response: It is true that there was a high incidence of the ‘unclear’ category being used by the coders. We now reflect upon this in more detail in the discussion, with reference to the prevenance of positive and negative emotions reported in the self-reported Emotions During Playtime measure. 6. The strong agreement among coders is encouraging, but it raises the question of whether these classifications are valid. Although validating affect codes was not the purpose of this study, it would be valuable to know if there are plans to evaluate this in future research. This may also warrant further discussion beyond the limitations section to acknowledge the inherent biases associated with observational research and to clarify that, in its current form, the tool cannot yet be considered validated which has important implications for practice. Response: Note that we had already stated “ there is some evidence of validity due to the consistency across the M-TAPS components in terms of the most common and most rare activities, but the study was not designed as a rigorous evaluation of validity”. In light of the reviewer's previous comment though, we have further considered validity in the final paragraph of the discussion. 7. For the Activities During Playtime Questionnaire, it would be helpful to describe what measures were taken to ensure that children consistently understood each activity type (e.g., nature play vs. free play). It is possible that variability in children’s interpretations affected their responses? Were examples or visual aids provided to support consistent understanding at both time points? Response: We have realised that the questionnaire packs were not available in the supplementary materials at the time of the original submission. The full questionnaire pack is now available in the supplementary materials. Given the limited explanation of the categories provided to children, we now suggest that poorer reliability of some categories may indicate a lack of understanding in the discussion and suggest that visual aids may improve the validity of children’s responses. We have added further detail about the protocol for the self-report questionnaire packs. 8. When discussing the reliability findings for the PANAS-C, it may strengthen the discussion to include a brief comparison with reliability estimates reported in previous literature, if available. Response: We now include comparison of the Emotions During Playtime internal consistency and test-retest reliability with previous PANAS-C findings. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 20 Oct 2025 Helen Dodd , School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK 20 Oct 2025 Author Response Thank you for your thought-provoking review of our manuscript. In the revised version, we have clarified the reporting of the observational protocols and included a table to address several of ... Continue reading Thank you for your thought-provoking review of our manuscript. In the revised version, we have clarified the reporting of the observational protocols and included a table to address several of the issues you have raised. We also include now benchmark our reliability findings against relevant previous findings, as suggested. We respond to each of your comments in turn below. 1. One of the activity categories is labeled as “playing,” but many of the listed activities would arguably fall under the general concept of play. It is unclear how “playing” is being specifically defined in contrast to other categories such as “active/chase.” Clarifying the definitions and providing examples would help distinguish between these overlapping categories. This is primarily why I indicated “partly” to whether sufficient details were provided to allow replication of the method development and its use by others. This information may have been somewhere on the OSF link but it wasn’t working for me so I could not determine if there was enough information on the codes themselves to allow for replication. I recommend including the full list of codes and their definitions either as a table within the manuscript or as a supplementary file. Response: We appreciate having the lack of clarity around the categories for the scan observations drawn to our attention. Scans were done one category at a time, so were not designed to be mutually exclusive of one another. For example, the first scan “Playing” simply sought to determine the proportion of children on the playground who were considered to be playing, rather than engaged in some non-play activity such as eating, observing, or walking between areas of the playground. This first scan encompasses all kinds of play. Later scans identified whether specific types of play were occurring. For focal observations, the “playing” category was replaced with “not playing” and an “other play” category was also included so that play that did not fit into the other categories could be distinguished from non-play behaviour. We now make that clearer and, following your suggestion, we now include a table of definitions for each category (Table 1) that shows the response options for scan and focal observations side by side for easy comparison. 2. Given the subjective nature of observational data, I appreciate the inclusion of affect and adventurous play categories. However, it is essential that the tool and/or codebook explicitly challenge normative assumptions about what play looks like. For example, what “positive affect” or “adventurous play” looks like. This is particularly relevant for children and youth with diverse abilities and backgrounds. This could be addressed through specific training or guidance notes in the codebook. Response: This is a very important point that we agree has not yet been well addressed in our research, or indeed elsewhere. We now include this as a discussion point with a call for future research to evaluate the coding scheme with children with a diverse range of abilities and backgrounds. 3. The results section suggests that most activities were coded under the general “playing” category. It remains unclear whether coders were allowed to select multiple categories (e.g., “playing” and “sports/play equipment”). If coders could only choose one, this may lead to a loss of context and detail regarding the type of play observed. Response: Again, thank you for drawing our attention to the lack of clarity relating to the selection of the categories. For scan observations, each category was scanned separately, so categories were independent. In each scan, the coder simply thought about the category they were scanning for, say “playing” and considered whether each child was playing or not in that moment. One would expect that the proportion of children observed to be “playing” would be higher than for each of the sub-categories of play, and indeed for these sub-categories combined. This is not necessarily the case since the scans occur consecutively, and children may change activity between scans. Indeed, in our data, the proportion of children “playing” in the scan observations was similar to the proportion of children in the other play categories combined, suggesting that these categories did indeed describe most of the kinds of play children engaged in. For focal observations, there was no “playing” category, but if the child’s activity did not fit one of the play categories, they could choose whether they were engaged in “other play” and write some notes about that play afterwards, or “not playing”. We have clarified this in the manuscript and in Table 1. 4. The low ICC value for the “playing” category is notable. Could this be due to coders needing to choose between “playing” and a more specific type of activity, even when both might be appropriate? This suggests potential ambiguity in the coding decision process. A possible alternative approach could involve first determining whether a behavior constitutes “play” (yes/no), followed by a second level of coding to classify the type of play. This hierarchical strategy may improve reliability and clarity. Response: As outlined in the previous comment, the coders did not need to chose between “playing” and specific types of play/activity, so the lower ICC is not due to this. Instead, this lower ICC may be due to the definition of ‘play’ being more ambiguous than the specific types of play, and/or because the quick pass nature of scan observations does not give sufficient time for the presence/absence of play to be established. Consistent with this explanation, reliability of the category “not playing” in the focal observations was near perfect, suggesting that with more time observing a child, coders can more consistently evaluate whether a child is playing or not. We suggest that future researchers are cautious about using broad and subjective categories such as “playing” for scan observations where more time might be required to understand the context of a child’s behaviour (see also discussion of coding affect during scan observations). It is nonetheless useful to determine an approximate estimate of the number of children engaged in play activity because low estimates might be a key indicator of problems with a particular playground environment. 5. While ICC values for affect (when coded as positive or negative) were strong, the majority of affective observations were coded as “unclear.” This suggests that identifying emotional expression via observation alone may be inherently limited. I appreciate the authors’ reflection on the challenges of coding affect. Rather than removing the affect category, it may be more productive to consider how visible indicators of emotion (e.g., facial expressions, body language) could be systematically coded to aid decision-making. Response: It is true that there was a high incidence of the ‘unclear’ category being used by the coders. We now reflect upon this in more detail in the discussion, with reference to the prevenance of positive and negative emotions reported in the self-reported Emotions During Playtime measure. 6. The strong agreement among coders is encouraging, but it raises the question of whether these classifications are valid. Although validating affect codes was not the purpose of this study, it would be valuable to know if there are plans to evaluate this in future research. This may also warrant further discussion beyond the limitations section to acknowledge the inherent biases associated with observational research and to clarify that, in its current form, the tool cannot yet be considered validated which has important implications for practice. Response: Note that we had already stated “ there is some evidence of validity due to the consistency across the M-TAPS components in terms of the most common and most rare activities, but the study was not designed as a rigorous evaluation of validity”. In light of the reviewer's previous comment though, we have further considered validity in the final paragraph of the discussion. 7. For the Activities During Playtime Questionnaire, it would be helpful to describe what measures were taken to ensure that children consistently understood each activity type (e.g., nature play vs. free play). It is possible that variability in children’s interpretations affected their responses? Were examples or visual aids provided to support consistent understanding at both time points? Response: We have realised that the questionnaire packs were not available in the supplementary materials at the time of the original submission. The full questionnaire pack is now available in the supplementary materials. Given the limited explanation of the categories provided to children, we now suggest that poorer reliability of some categories may indicate a lack of understanding in the discussion and suggest that visual aids may improve the validity of children’s responses. We have added further detail about the protocol for the self-report questionnaire packs. 8. When discussing the reliability findings for the PANAS-C, it may strengthen the discussion to include a brief comparison with reliability estimates reported in previous literature, if available. Response: We now include comparison of the Emotions During Playtime internal consistency and test-retest reliability with previous PANAS-C findings. Thank you for your thought-provoking review of our manuscript. In the revised version, we have clarified the reporting of the observational protocols and included a table to address several of the issues you have raised. We also include now benchmark our reliability findings against relevant previous findings, as suggested. We respond to each of your comments in turn below. 1. One of the activity categories is labeled as “playing,” but many of the listed activities would arguably fall under the general concept of play. It is unclear how “playing” is being specifically defined in contrast to other categories such as “active/chase.” Clarifying the definitions and providing examples would help distinguish between these overlapping categories. This is primarily why I indicated “partly” to whether sufficient details were provided to allow replication of the method development and its use by others. This information may have been somewhere on the OSF link but it wasn’t working for me so I could not determine if there was enough information on the codes themselves to allow for replication. I recommend including the full list of codes and their definitions either as a table within the manuscript or as a supplementary file. Response: We appreciate having the lack of clarity around the categories for the scan observations drawn to our attention. Scans were done one category at a time, so were not designed to be mutually exclusive of one another. For example, the first scan “Playing” simply sought to determine the proportion of children on the playground who were considered to be playing, rather than engaged in some non-play activity such as eating, observing, or walking between areas of the playground. This first scan encompasses all kinds of play. Later scans identified whether specific types of play were occurring. For focal observations, the “playing” category was replaced with “not playing” and an “other play” category was also included so that play that did not fit into the other categories could be distinguished from non-play behaviour. We now make that clearer and, following your suggestion, we now include a table of definitions for each category (Table 1) that shows the response options for scan and focal observations side by side for easy comparison. 2. Given the subjective nature of observational data, I appreciate the inclusion of affect and adventurous play categories. However, it is essential that the tool and/or codebook explicitly challenge normative assumptions about what play looks like. For example, what “positive affect” or “adventurous play” looks like. This is particularly relevant for children and youth with diverse abilities and backgrounds. This could be addressed through specific training or guidance notes in the codebook. Response: This is a very important point that we agree has not yet been well addressed in our research, or indeed elsewhere. We now include this as a discussion point with a call for future research to evaluate the coding scheme with children with a diverse range of abilities and backgrounds. 3. The results section suggests that most activities were coded under the general “playing” category. It remains unclear whether coders were allowed to select multiple categories (e.g., “playing” and “sports/play equipment”). If coders could only choose one, this may lead to a loss of context and detail regarding the type of play observed. Response: Again, thank you for drawing our attention to the lack of clarity relating to the selection of the categories. For scan observations, each category was scanned separately, so categories were independent. In each scan, the coder simply thought about the category they were scanning for, say “playing” and considered whether each child was playing or not in that moment. One would expect that the proportion of children observed to be “playing” would be higher than for each of the sub-categories of play, and indeed for these sub-categories combined. This is not necessarily the case since the scans occur consecutively, and children may change activity between scans. Indeed, in our data, the proportion of children “playing” in the scan observations was similar to the proportion of children in the other play categories combined, suggesting that these categories did indeed describe most of the kinds of play children engaged in. For focal observations, there was no “playing” category, but if the child’s activity did not fit one of the play categories, they could choose whether they were engaged in “other play” and write some notes about that play afterwards, or “not playing”. We have clarified this in the manuscript and in Table 1. 4. The low ICC value for the “playing” category is notable. Could this be due to coders needing to choose between “playing” and a more specific type of activity, even when both might be appropriate? This suggests potential ambiguity in the coding decision process. A possible alternative approach could involve first determining whether a behavior constitutes “play” (yes/no), followed by a second level of coding to classify the type of play. This hierarchical strategy may improve reliability and clarity. Response: As outlined in the previous comment, the coders did not need to chose between “playing” and specific types of play/activity, so the lower ICC is not due to this. Instead, this lower ICC may be due to the definition of ‘play’ being more ambiguous than the specific types of play, and/or because the quick pass nature of scan observations does not give sufficient time for the presence/absence of play to be established. Consistent with this explanation, reliability of the category “not playing” in the focal observations was near perfect, suggesting that with more time observing a child, coders can more consistently evaluate whether a child is playing or not. We suggest that future researchers are cautious about using broad and subjective categories such as “playing” for scan observations where more time might be required to understand the context of a child’s behaviour (see also discussion of coding affect during scan observations). It is nonetheless useful to determine an approximate estimate of the number of children engaged in play activity because low estimates might be a key indicator of problems with a particular playground environment. 5. While ICC values for affect (when coded as positive or negative) were strong, the majority of affective observations were coded as “unclear.” This suggests that identifying emotional expression via observation alone may be inherently limited. I appreciate the authors’ reflection on the challenges of coding affect. Rather than removing the affect category, it may be more productive to consider how visible indicators of emotion (e.g., facial expressions, body language) could be systematically coded to aid decision-making. Response: It is true that there was a high incidence of the ‘unclear’ category being used by the coders. We now reflect upon this in more detail in the discussion, with reference to the prevenance of positive and negative emotions reported in the self-reported Emotions During Playtime measure. 6. The strong agreement among coders is encouraging, but it raises the question of whether these classifications are valid. Although validating affect codes was not the purpose of this study, it would be valuable to know if there are plans to evaluate this in future research. This may also warrant further discussion beyond the limitations section to acknowledge the inherent biases associated with observational research and to clarify that, in its current form, the tool cannot yet be considered validated which has important implications for practice. Response: Note that we had already stated “ there is some evidence of validity due to the consistency across the M-TAPS components in terms of the most common and most rare activities, but the study was not designed as a rigorous evaluation of validity”. In light of the reviewer's previous comment though, we have further considered validity in the final paragraph of the discussion. 7. For the Activities During Playtime Questionnaire, it would be helpful to describe what measures were taken to ensure that children consistently understood each activity type (e.g., nature play vs. free play). It is possible that variability in children’s interpretations affected their responses? Were examples or visual aids provided to support consistent understanding at both time points? Response: We have realised that the questionnaire packs were not available in the supplementary materials at the time of the original submission. The full questionnaire pack is now available in the supplementary materials. Given the limited explanation of the categories provided to children, we now suggest that poorer reliability of some categories may indicate a lack of understanding in the discussion and suggest that visual aids may improve the validity of children’s responses. We have added further detail about the protocol for the self-report questionnaire packs. 8. When discussing the reliability findings for the PANAS-C, it may strengthen the discussion to include a brief comparison with reliability estimates reported in previous literature, if available. Response: We now include comparison of the Emotions During Playtime internal consistency and test-retest reliability with previous PANAS-C findings. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Barnett L. Reviewer Report For: Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.5256/f1000research.176881.r384599 ) The direct URL for this report is: https://f1000research.com/articles/14-476/v1#referee-response-384599 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 29 May 2025 Lisa Barnett , Deakin University, Melbourne, VIC, Australia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176881.r384599 This is a well written article, and it focuses on the need for new measurement in the field - which I agree with. The introduction provides a good background to the study and cites relevant literature. In terms of ... Continue reading READ ALL This is a well written article, and it focuses on the need for new measurement in the field - which I agree with. The introduction provides a good background to the study and cites relevant literature. In terms of methods, I am familiar with the SOFIT and SOPLAY tools and have undertaken modifications of these instruments myself for other purposes. One modification in the literature is the CAST instrument tested in the Australian environment and developed for children. CAST2 is a modified version still using momentary time sampling that was used to measure children’s physical activity levels in a school playground environment. CAST2 uses continuous 75 second scans of the break period, which they argued might be more reliable than intermittent scans. Can you comment on the duration used in your study for the scans and why this length of time was chosen? When I first read the initial part of the methods, I was dubious about being able to scan for affect and this was what was found in the results. The thing about these scanning tools is it must be very easy to see the behaviour for whole groups of children at once. That is why it works to detect physical activity behaviour such as standing or walking. But to assess affect requires a look at the children's face and a judgement and I don't think this is feasible when trying to document what large groups of children are doing in a quick observation. Antisocial also falls into this camp as how can this be quickly decided? The other categories of observation all make sense to me. I like the idea of focal observations of particular children to gain a more in-depth insight. I think affect would be more able/feasible to be coded for a focal child rather than for the broad scan. Can you please provide some psychometric information for the PANAS-C tool? Your adaptation rests on this tool but you have not provided evidence for it. For the activities during playtime tool you may have also wished to consult this paper as a starting point as the team used a pictorial format for the children: Parrish, A. M., Iverson, D., Russell, K., & Yeatman, H. (2010). The Development of a Unique Physical Activity Self-Report for Young Children: Challenges and Lessons Learned. Research in Sports Medicine, 18(1), 71–83. https://doi.org/10.1080/15438620903423874 Also, similar to above, in this section no psychometric information for the OPP tool is provided. For participants – can you give an idea how many children were on the playground at the same time? How many were in the school population? How exactly were the children selected at random for the focal point? Lucky you could use an opt out ethics! Can you comment in the discussion on whether a mean of five days apart for the measures is a good measure of reliability? How many observers/raters were there? Results The reliability scores are good The discussion is a good overview, and I agree on the findings with regard to the affect observations. To me it makes sense that emotions during playtime scale might be less reliable as emotions are less reliable than behaviours such as physical activity and we would assume that thee could change form playtime to playtime. Were images used in the Activities during playtime Questionnaire ? would be interesting to see if the use of images or not affect reliability with children. I agree with conclusion statement. Is the rationale for developing the new method (or application) clearly explained? Yes Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? Yes If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Motor skills, physical literacy, play, children, instrument development I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Barnett L. Reviewer Report For: Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.5256/f1000research.176881.r384599 ) The direct URL for this report is: https://f1000research.com/articles/14-476/v1#referee-response-384599 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 28 Oct 2025 Helen Dodd , School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK 28 Oct 2025 Author Response Thank you for your thorough and thoughtful review of our manuscript. We believe that the clarity of reporting is much improved thanks to your suggested revisions. Below we respond point-by-point ... Continue reading Thank you for your thorough and thoughtful review of our manuscript. We believe that the clarity of reporting is much improved thanks to your suggested revisions. Below we respond point-by-point to each of your comments and suggestions. 1. In terms of methods, I am familiar with the SOFIT and SOPLAY tools and have undertaken modifications of these instruments myself for other purposes. One modification in the literature is the CAST instrument tested in the Australian environment and developed for children. CAST2 is a modified version still using momentary time sampling that was used to measure children’s physical activity levels in a school playground environment. CAST2 uses continuous 75 second scans of the break period, which they argued might be more reliable than intermittent scans. Response: Thank you for bringing this to our attention. We now include the CAST2 in our list of previous measures of playground observation tools. 2. Can you comment on the duration used in your study for the scans and why this length of time was chosen? Response: For pragmatic reasons, the scans are not timed, but rather occur consecutively, beginning the next scan category after the last has finished. The playground is divided so that the scans were completed over an area that was visible to the researcher without them needing to move. The number of children scanned may vary between areas and between scans, so the length of time required for a scan to take place may also vary. Each area of the playground is scanned for each category consecutively. It is recommended that each area is scanned at least twice. We have revised the manuscript to clarify aims and practicalities of the scan observations. 3. When I first read the initial part of the methods, I was dubious about being able to scan for affect and this was what was found in the results. The thing about these scanning tools is it must be very easy to see the behaviour for whole groups of children at once. That is why it works to detect physical activity behaviour such as standing or walking. But to assess affect requires a look at the children's face and a judgement and I don't think this is feasible when trying to document what large groups of children are doing in a quick observation. Antisocial also falls into this camp as how can this be quickly decided? The other categories of observation all make sense to me. I like the idea of focal observations of particular children to gain a more in-depth insight. I think affect would be more able/feasible to be coded for a focal child rather than for the broad scan. Response: We agree that there are practical challenges assessing the affect of children quickly and from a distance (i.e., during scan observations), as reflected in the poorer inter-rater reliability. It is also the case that children’s facial expressions do not always align with their internal emotions, and that children did not themselves report particularly frequent experiences of emotions during playtimes in the self-report measure. This is now discussed in more detail. 4. Can you please provide some psychometric information for the PANAS-C tool? Your adaptation rests on this tool but you have not provided evidence for it. Response: We now include a more detailed description of the psychometric properties of the PANAS-C as well as the link to the original validation study. 5. For the activities during playtime tool you may have also wished to consult this paper as a starting point as the team used a pictorial format for the children: Parrish, A. M., Iverson, D., Russell, K., & Yeatman, H. (2010). The Development of a Unique Physical Activity Self-Report for Young Children: Challenges and Lessons Learned. Research in Sports Medicine, 18(1), 71–83. https://doi.org/10.1080/15438620903423874 Response: This is now included in the discussion where we consider improvements for future research. 6. Also, similar to above, in this section no psychometric information for the OPP tool is provided. Response: ICCs of the OPP observational tool reported in Massey et al. (2018) are now reported. No further psychometric information is available. 7. For participants – can you give an idea how many children were on the playground at the same time? How many were in the school population? Response: More details about the number of children in the school population and across the different parts of the study are now included in the Method. 8. How exactly were the children selected at random for the focal point? Response: The researchers were on the playground and opportunistically selected a child to observe immediately before starting each focal observation. This is not strictly ‘random’ so we have edited the wording. 9. Can you comment in the discussion on whether a mean of five days apart for the measures is a good measure of reliability? Response: The five-day timescale of the test-retest reliability assessment was fairly short in this study, we have added this to the limitations. 10. How many observers/raters were there? Response: There were two coders who completed all of the coding. They were trained by the first author. This is now stated explicitly. 11. Results The reliability scores are good Response: Thank you 12. The discussion is a good overview, and I agree on the findings with regard to the affect observations. Response: Thank you 13. To me it makes sense that emotions during playtime scale might be less reliable as emotions are less reliable than behaviours such as physical activity and we would assume that they could change form playtime to playtime. Response: Child report of emotions was actually more reliable than their reporting of activities. Note that we asked children to complete the emotions measure twice because we expected some fluctuation. Our reflections on this therefore remain largely unchanged in the revised manuscript and are found. 14. Were images used in the Activities during playtime Questionnaire ? would be interesting to see if the use of images or not affect reliability with children. Response: The suggestion to include pictorial cues for each activity is now included in the discussion. We also realised that the questionnaire packs were not made available in the Supplementary materials on OSF, these are now available. 15. I agree with conclusion statement. Response: Thank you, this remains unchanged after revision of the manuscript. Thank you for your thorough and thoughtful review of our manuscript. We believe that the clarity of reporting is much improved thanks to your suggested revisions. Below we respond point-by-point to each of your comments and suggestions. 1. In terms of methods, I am familiar with the SOFIT and SOPLAY tools and have undertaken modifications of these instruments myself for other purposes. One modification in the literature is the CAST instrument tested in the Australian environment and developed for children. CAST2 is a modified version still using momentary time sampling that was used to measure children’s physical activity levels in a school playground environment. CAST2 uses continuous 75 second scans of the break period, which they argued might be more reliable than intermittent scans. Response: Thank you for bringing this to our attention. We now include the CAST2 in our list of previous measures of playground observation tools. 2. Can you comment on the duration used in your study for the scans and why this length of time was chosen? Response: For pragmatic reasons, the scans are not timed, but rather occur consecutively, beginning the next scan category after the last has finished. The playground is divided so that the scans were completed over an area that was visible to the researcher without them needing to move. The number of children scanned may vary between areas and between scans, so the length of time required for a scan to take place may also vary. Each area of the playground is scanned for each category consecutively. It is recommended that each area is scanned at least twice. We have revised the manuscript to clarify aims and practicalities of the scan observations. 3. When I first read the initial part of the methods, I was dubious about being able to scan for affect and this was what was found in the results. The thing about these scanning tools is it must be very easy to see the behaviour for whole groups of children at once. That is why it works to detect physical activity behaviour such as standing or walking. But to assess affect requires a look at the children's face and a judgement and I don't think this is feasible when trying to document what large groups of children are doing in a quick observation. Antisocial also falls into this camp as how can this be quickly decided? The other categories of observation all make sense to me. I like the idea of focal observations of particular children to gain a more in-depth insight. I think affect would be more able/feasible to be coded for a focal child rather than for the broad scan. Response: We agree that there are practical challenges assessing the affect of children quickly and from a distance (i.e., during scan observations), as reflected in the poorer inter-rater reliability. It is also the case that children’s facial expressions do not always align with their internal emotions, and that children did not themselves report particularly frequent experiences of emotions during playtimes in the self-report measure. This is now discussed in more detail. 4. Can you please provide some psychometric information for the PANAS-C tool? Your adaptation rests on this tool but you have not provided evidence for it. Response: We now include a more detailed description of the psychometric properties of the PANAS-C as well as the link to the original validation study. 5. For the activities during playtime tool you may have also wished to consult this paper as a starting point as the team used a pictorial format for the children: Parrish, A. M., Iverson, D., Russell, K., & Yeatman, H. (2010). The Development of a Unique Physical Activity Self-Report for Young Children: Challenges and Lessons Learned. Research in Sports Medicine, 18(1), 71–83. https://doi.org/10.1080/15438620903423874 Response: This is now included in the discussion where we consider improvements for future research. 6. Also, similar to above, in this section no psychometric information for the OPP tool is provided. Response: ICCs of the OPP observational tool reported in Massey et al. (2018) are now reported. No further psychometric information is available. 7. For participants – can you give an idea how many children were on the playground at the same time? How many were in the school population? Response: More details about the number of children in the school population and across the different parts of the study are now included in the Method. 8. How exactly were the children selected at random for the focal point? Response: The researchers were on the playground and opportunistically selected a child to observe immediately before starting each focal observation. This is not strictly ‘random’ so we have edited the wording. 9. Can you comment in the discussion on whether a mean of five days apart for the measures is a good measure of reliability? Response: The five-day timescale of the test-retest reliability assessment was fairly short in this study, we have added this to the limitations. 10. How many observers/raters were there? Response: There were two coders who completed all of the coding. They were trained by the first author. This is now stated explicitly. 11. Results The reliability scores are good Response: Thank you 12. The discussion is a good overview, and I agree on the findings with regard to the affect observations. Response: Thank you 13. To me it makes sense that emotions during playtime scale might be less reliable as emotions are less reliable than behaviours such as physical activity and we would assume that they could change form playtime to playtime. Response: Child report of emotions was actually more reliable than their reporting of activities. Note that we asked children to complete the emotions measure twice because we expected some fluctuation. Our reflections on this therefore remain largely unchanged in the revised manuscript and are found. 14. Were images used in the Activities during playtime Questionnaire ? would be interesting to see if the use of images or not affect reliability with children. Response: The suggestion to include pictorial cues for each activity is now included in the discussion. We also realised that the questionnaire packs were not made available in the Supplementary materials on OSF, these are now available. 15. I agree with conclusion statement. Response: Thank you, this remains unchanged after revision of the manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 28 Oct 2025 Helen Dodd , School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK 28 Oct 2025 Author Response Thank you for your thorough and thoughtful review of our manuscript. We believe that the clarity of reporting is much improved thanks to your suggested revisions. Below we respond point-by-point ... Continue reading Thank you for your thorough and thoughtful review of our manuscript. We believe that the clarity of reporting is much improved thanks to your suggested revisions. Below we respond point-by-point to each of your comments and suggestions. 1. In terms of methods, I am familiar with the SOFIT and SOPLAY tools and have undertaken modifications of these instruments myself for other purposes. One modification in the literature is the CAST instrument tested in the Australian environment and developed for children. CAST2 is a modified version still using momentary time sampling that was used to measure children’s physical activity levels in a school playground environment. CAST2 uses continuous 75 second scans of the break period, which they argued might be more reliable than intermittent scans. Response: Thank you for bringing this to our attention. We now include the CAST2 in our list of previous measures of playground observation tools. 2. Can you comment on the duration used in your study for the scans and why this length of time was chosen? Response: For pragmatic reasons, the scans are not timed, but rather occur consecutively, beginning the next scan category after the last has finished. The playground is divided so that the scans were completed over an area that was visible to the researcher without them needing to move. The number of children scanned may vary between areas and between scans, so the length of time required for a scan to take place may also vary. Each area of the playground is scanned for each category consecutively. It is recommended that each area is scanned at least twice. We have revised the manuscript to clarify aims and practicalities of the scan observations. 3. When I first read the initial part of the methods, I was dubious about being able to scan for affect and this was what was found in the results. The thing about these scanning tools is it must be very easy to see the behaviour for whole groups of children at once. That is why it works to detect physical activity behaviour such as standing or walking. But to assess affect requires a look at the children's face and a judgement and I don't think this is feasible when trying to document what large groups of children are doing in a quick observation. Antisocial also falls into this camp as how can this be quickly decided? The other categories of observation all make sense to me. I like the idea of focal observations of particular children to gain a more in-depth insight. I think affect would be more able/feasible to be coded for a focal child rather than for the broad scan. Response: We agree that there are practical challenges assessing the affect of children quickly and from a distance (i.e., during scan observations), as reflected in the poorer inter-rater reliability. It is also the case that children’s facial expressions do not always align with their internal emotions, and that children did not themselves report particularly frequent experiences of emotions during playtimes in the self-report measure. This is now discussed in more detail. 4. Can you please provide some psychometric information for the PANAS-C tool? Your adaptation rests on this tool but you have not provided evidence for it. Response: We now include a more detailed description of the psychometric properties of the PANAS-C as well as the link to the original validation study. 5. For the activities during playtime tool you may have also wished to consult this paper as a starting point as the team used a pictorial format for the children: Parrish, A. M., Iverson, D., Russell, K., & Yeatman, H. (2010). The Development of a Unique Physical Activity Self-Report for Young Children: Challenges and Lessons Learned. Research in Sports Medicine, 18(1), 71–83. https://doi.org/10.1080/15438620903423874 Response: This is now included in the discussion where we consider improvements for future research. 6. Also, similar to above, in this section no psychometric information for the OPP tool is provided. Response: ICCs of the OPP observational tool reported in Massey et al. (2018) are now reported. No further psychometric information is available. 7. For participants – can you give an idea how many children were on the playground at the same time? How many were in the school population? Response: More details about the number of children in the school population and across the different parts of the study are now included in the Method. 8. How exactly were the children selected at random for the focal point? Response: The researchers were on the playground and opportunistically selected a child to observe immediately before starting each focal observation. This is not strictly ‘random’ so we have edited the wording. 9. Can you comment in the discussion on whether a mean of five days apart for the measures is a good measure of reliability? Response: The five-day timescale of the test-retest reliability assessment was fairly short in this study, we have added this to the limitations. 10. How many observers/raters were there? Response: There were two coders who completed all of the coding. They were trained by the first author. This is now stated explicitly. 11. Results The reliability scores are good Response: Thank you 12. The discussion is a good overview, and I agree on the findings with regard to the affect observations. Response: Thank you 13. To me it makes sense that emotions during playtime scale might be less reliable as emotions are less reliable than behaviours such as physical activity and we would assume that they could change form playtime to playtime. Response: Child report of emotions was actually more reliable than their reporting of activities. Note that we asked children to complete the emotions measure twice because we expected some fluctuation. Our reflections on this therefore remain largely unchanged in the revised manuscript and are found. 14. Were images used in the Activities during playtime Questionnaire ? would be interesting to see if the use of images or not affect reliability with children. Response: The suggestion to include pictorial cues for each activity is now included in the discussion. We also realised that the questionnaire packs were not made available in the Supplementary materials on OSF, these are now available. 15. I agree with conclusion statement. Response: Thank you, this remains unchanged after revision of the manuscript. Thank you for your thorough and thoughtful review of our manuscript. We believe that the clarity of reporting is much improved thanks to your suggested revisions. Below we respond point-by-point to each of your comments and suggestions. 1. In terms of methods, I am familiar with the SOFIT and SOPLAY tools and have undertaken modifications of these instruments myself for other purposes. One modification in the literature is the CAST instrument tested in the Australian environment and developed for children. CAST2 is a modified version still using momentary time sampling that was used to measure children’s physical activity levels in a school playground environment. CAST2 uses continuous 75 second scans of the break period, which they argued might be more reliable than intermittent scans. Response: Thank you for bringing this to our attention. We now include the CAST2 in our list of previous measures of playground observation tools. 2. Can you comment on the duration used in your study for the scans and why this length of time was chosen? Response: For pragmatic reasons, the scans are not timed, but rather occur consecutively, beginning the next scan category after the last has finished. The playground is divided so that the scans were completed over an area that was visible to the researcher without them needing to move. The number of children scanned may vary between areas and between scans, so the length of time required for a scan to take place may also vary. Each area of the playground is scanned for each category consecutively. It is recommended that each area is scanned at least twice. We have revised the manuscript to clarify aims and practicalities of the scan observations. 3. When I first read the initial part of the methods, I was dubious about being able to scan for affect and this was what was found in the results. The thing about these scanning tools is it must be very easy to see the behaviour for whole groups of children at once. That is why it works to detect physical activity behaviour such as standing or walking. But to assess affect requires a look at the children's face and a judgement and I don't think this is feasible when trying to document what large groups of children are doing in a quick observation. Antisocial also falls into this camp as how can this be quickly decided? The other categories of observation all make sense to me. I like the idea of focal observations of particular children to gain a more in-depth insight. I think affect would be more able/feasible to be coded for a focal child rather than for the broad scan. Response: We agree that there are practical challenges assessing the affect of children quickly and from a distance (i.e., during scan observations), as reflected in the poorer inter-rater reliability. It is also the case that children’s facial expressions do not always align with their internal emotions, and that children did not themselves report particularly frequent experiences of emotions during playtimes in the self-report measure. This is now discussed in more detail. 4. Can you please provide some psychometric information for the PANAS-C tool? Your adaptation rests on this tool but you have not provided evidence for it. Response: We now include a more detailed description of the psychometric properties of the PANAS-C as well as the link to the original validation study. 5. For the activities during playtime tool you may have also wished to consult this paper as a starting point as the team used a pictorial format for the children: Parrish, A. M., Iverson, D., Russell, K., & Yeatman, H. (2010). The Development of a Unique Physical Activity Self-Report for Young Children: Challenges and Lessons Learned. Research in Sports Medicine, 18(1), 71–83. https://doi.org/10.1080/15438620903423874 Response: This is now included in the discussion where we consider improvements for future research. 6. Also, similar to above, in this section no psychometric information for the OPP tool is provided. Response: ICCs of the OPP observational tool reported in Massey et al. (2018) are now reported. No further psychometric information is available. 7. For participants – can you give an idea how many children were on the playground at the same time? How many were in the school population? Response: More details about the number of children in the school population and across the different parts of the study are now included in the Method. 8. How exactly were the children selected at random for the focal point? Response: The researchers were on the playground and opportunistically selected a child to observe immediately before starting each focal observation. This is not strictly ‘random’ so we have edited the wording. 9. Can you comment in the discussion on whether a mean of five days apart for the measures is a good measure of reliability? Response: The five-day timescale of the test-retest reliability assessment was fairly short in this study, we have added this to the limitations. 10. How many observers/raters were there? Response: There were two coders who completed all of the coding. They were trained by the first author. This is now stated explicitly. 11. Results The reliability scores are good Response: Thank you 12. The discussion is a good overview, and I agree on the findings with regard to the affect observations. Response: Thank you 13. To me it makes sense that emotions during playtime scale might be less reliable as emotions are less reliable than behaviours such as physical activity and we would assume that they could change form playtime to playtime. Response: Child report of emotions was actually more reliable than their reporting of activities. Note that we asked children to complete the emotions measure twice because we expected some fluctuation. Our reflections on this therefore remain largely unchanged in the revised manuscript and are found. 14. Were images used in the Activities during playtime Questionnaire ? would be interesting to see if the use of images or not affect reliability with children. Response: The suggestion to include pictorial cues for each activity is now included in the discussion. We also realised that the questionnaire packs were not made available in the Supplementary materials on OSF, these are now available. 15. I agree with conclusion statement. Response: Thank you, this remains unchanged after revision of the manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 06 May 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 2 (revision) 20 Oct 25 read read Version 1 06 May 25 read read Lisa Barnett , Deakin University, Melbourne, Australia Maeghan James , Children's Hospital of Eastern Ontario, Ottawa, Canada Sylvia Fernandez Rao , ICMR National Institute of Nutrition, Hyderabad, India Oliver Traynor , University of Glasgow, Glasgow, UK Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Traynor O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 11 Dec 2025 | for Version 2 Oliver Traynor , University of Glasgow, Glasgow, Scotland, UK 0 Views copyright © 2025 Traynor O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This paper addresses an important gap in play research by developing a standardised scalable and openly available assessment toolkit. This paper is likely to be of interest to researchers wishing to quantitively evaluate play interventions and children’s play behaviours in school settings and can complement qualitative approaches. I have structured the following suggestions in line with the paper’s headings. Introduction The authors provide a good background to the study and cite relevant literature. Methods The authors acknowledge that the reliability testing of the tool is conducted within one school, in a demographically narrow setting. This could be emphasised more strongly in the discussion with reference to how context such as school-level policies, playground structure, and cultural norms may affect reliability and category relevance. Some categories overlap conceptually such as “sport”, “sport/play equipment”, “active/chase.” Clarification would help the reader understand how coders differentiate between these. Results Given that high adventurous play was not observed and negative affect was rarely observed, the authors could discuss plans for future validation in settings that offer more adventurous play. A sizeable proportion of children were coded as “not playing” with reasons including transition between activities and social withdrawal. I question whether transition between activities should be classified as “not playing.” I think this requires more nuance and recommend that future work should distinguish transitions from genuine disengagement – I think TOPO categorises these behaviours well. Conclusion The paper presents a valuable tool and strong initial reliability. The openness of the materials and methodological transparency are key strengths and will contribute to advancing the field of assessing children’s play behaviours. I hope the authors find my comments useful. Is the rationale for developing the new method (or application) clearly explained? Yes Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? Yes If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Children's play behaviours, the influence of the natural environment on health behaviours, developing and evaluating complex interventions. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Traynor O. Peer Review Report For: Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.5256/f1000research.189788.r425681) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-476/v2#referee-response-425681 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Fernandez Rao S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 08 Nov 2025 | for Version 2 Sylvia Fernandez Rao , ICMR National Institute of Nutrition, Hyderabad, India 0 Views copyright © 2025 Fernandez Rao S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript covers the development and initial reliability testing of the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS), a promising instrument combining scan observations, focal observations, and child self-report measures. The paper makes a valuable contribution to the growing literature on school-based play assessment. However, before indexing, some parts require clarification and strengthening regarding generalisability, validity evidence, statistical reporting, and treatment of missing data. The reliability testing was undertaken in a single UK primary school serving a somewhat homogeneous, low-income, primarily white community. While this is acknowledged, the constraint might be explained more plainly. Please comment on how the school context (staggered playtimes, playground size/layout, staff supervision, and risk policies) might have influenced both the observed prevalence of adventurous play and inter-rater reliability. It would be helpful to note that psychometrics may not generalise to schools with various sociodemographic circumstances, playground systems, or cultural attitudes toward risk. The focus on reliability is good for an initial review, but at times the manuscript indicates that M-TAPS is already validated. It would strengthen the manuscript to more clearly divide what has been established (reliability) from what has to be tested (validity). The manuscript says that studies were undertaken “with available data,” but details are lacking. Missing data may affect reliability estimations if not random also describe how missing data were handled. The Activities During Playtime questionnaire demonstrates low test–retest reliability at the individual level authors highlighted plausible causes (state effects, variability), but it would help to translate this into practical guidance for future researchers. Briefly explain coder training, calibration, and blinding techniques to help readers determine feasibility. Correct minor typos (“Routh and Tumble” for “Rough and Tumble”) and verify all references and table captions are complete. Overall, this is a good, well-implemented work that provides a valuable methodological contribution. With additional clarity on generalisability, validity, and missing data, the manuscript will be appropriate for indexing. I recommend a minor revision focused on clarity and refining assertions. Is the rationale for developing the new method (or application) clearly explained? Yes Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? Partly If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Early child development I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Fernandez Rao S. Peer Review Report For: Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.5256/f1000research.189788.r425684) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-476/v2#referee-response-425684 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 James M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Jun 2025 | for Version 1 Maeghan James , Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada 0 Views copyright © 2025 James M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Thank you for the opportunity to review this manuscript titled: “Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study”. In this paper, the authors introduce a novel tool for assessing play in schools and present reliability statistics of the tool. The authors concluded that some components of the M-TAPS tool were reliable and provide important considerations for future observational work using this tool. Overall, the paper was very well written – clear, concise and informative. The authors raise an important point regarding the need for more universal observational tools that can suit the needs of a variety of research projects. I believe the topic is of importance and will be a valuable contribution to the literature. Below I outline some questions/comments I had about the work that I believe, once addressed, would strengthen the paper. One of the activity categories is labeled as “playing,” but many of the listed activities would arguably fall under the general concept of play. It is unclear how “playing” is being specifically defined in contrast to other categories such as “active/chase.” Clarifying the definitions and providing examples would help distinguish between these overlapping categories. This is primarily why I indicated “partly” to whether sufficient details were provided to allow replication of the method development and its use by others. This information may have been somewhere on the OSF link but it wasn’t working for me so I could not determine if there was enough information on the codes themselves to allow for replication. I recommend including the full list of codes and their definitions either as a table within the manuscript or as a supplementary file. Given the subjective nature of observational data, I appreciate the inclusion of affect and adventurous play categories. However, it is essential that the tool and/or codebook explicitly challenge normative assumptions about what play looks like. For example, what “positive affect” or “adventurous play” looks like. This is particularly relevant for children and youth with diverse abilities and backgrounds. This could be addressed through specific training or guidance notes in the codebook. The results section suggests that most activities were coded under the general “playing” category. It remains unclear whether coders were allowed to select multiple categories (e.g., “playing” and “sports/play equipment”). If coders could only choose one, this may lead to a loss of context and detail regarding the type of play observed. The low ICC value for the “playing” category is notable. Could this be due to coders needing to choose between “playing” and a more specific type of activity, even when both might be appropriate? This suggests potential ambiguity in the coding decision process. A possible alternative approach could involve first determining whether a behavior constitutes “play” (yes/no), followed by a second level of coding to classify the type of play. This hierarchical strategy may improve reliability and clarity. While ICC values for affect (when coded as positive or negative) were strong, the majority of affective observations were coded as “unclear.” This suggests that identifying emotional expression via observation alone may be inherently limited. I appreciate the authors’ reflection on the challenges of coding affect. Rather than removing the affect category, it may be more productive to consider how visible indicators of emotion (e.g., facial expressions, body language) could be systematically coded to aid decision-making. The strong agreement among coders is encouraging, but it raises the question of whether these classifications are valid. Although validating affect codes was not the purpose of this study, it would be valuable to know if there are plans to evaluate this in future research. This may also warrant further discussion beyond the limitations section to acknowledge the inherent biases associated with observational research and to clarify that, in its current form, the tool cannot yet be considered validated which has important implications for practice. For the Activities During Playtime Questionnaire, it would be helpful to describe what measures were taken to ensure that children consistently understood each activity type (e.g., nature play vs. free play). It is possible that variability in children’s interpretations affected their responses? Were examples or visual aids provided to support consistent understanding at both time points? When discussing the reliability findings for the PANAS-C, it may strengthen the discussion to include a brief comparison with reliability estimates reported in previous literature, if available. Is the rationale for developing the new method (or application) clearly explained? Yes Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? Partly If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Observational research on children’s active and outdoor play, with a focus on child development, childhood disability, and the use of systematic observation protocols in playground settings. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 20 Oct 2025 Helen Dodd, School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK Thank you for your thought-provoking review of our manuscript. In the revised version, we have clarified the reporting of the observational protocols and included a table to address several of the issues you have raised. We also include now benchmark our reliability findings against relevant previous findings, as suggested. We respond to each of your comments in turn below. 1. One of the activity categories is labeled as “playing,” but many of the listed activities would arguably fall under the general concept of play. It is unclear how “playing” is being specifically defined in contrast to other categories such as “active/chase.” Clarifying the definitions and providing examples would help distinguish between these overlapping categories. This is primarily why I indicated “partly” to whether sufficient details were provided to allow replication of the method development and its use by others. This information may have been somewhere on the OSF link but it wasn’t working for me so I could not determine if there was enough information on the codes themselves to allow for replication. I recommend including the full list of codes and their definitions either as a table within the manuscript or as a supplementary file. Response: We appreciate having the lack of clarity around the categories for the scan observations drawn to our attention. Scans were done one category at a time, so were not designed to be mutually exclusive of one another. For example, the first scan “Playing” simply sought to determine the proportion of children on the playground who were considered to be playing, rather than engaged in some non-play activity such as eating, observing, or walking between areas of the playground. This first scan encompasses all kinds of play. Later scans identified whether specific types of play were occurring. For focal observations, the “playing” category was replaced with “not playing” and an “other play” category was also included so that play that did not fit into the other categories could be distinguished from non-play behaviour. We now make that clearer and, following your suggestion, we now include a table of definitions for each category (Table 1) that shows the response options for scan and focal observations side by side for easy comparison. 2. Given the subjective nature of observational data, I appreciate the inclusion of affect and adventurous play categories. However, it is essential that the tool and/or codebook explicitly challenge normative assumptions about what play looks like. For example, what “positive affect” or “adventurous play” looks like. This is particularly relevant for children and youth with diverse abilities and backgrounds. This could be addressed through specific training or guidance notes in the codebook. Response: This is a very important point that we agree has not yet been well addressed in our research, or indeed elsewhere. We now include this as a discussion point with a call for future research to evaluate the coding scheme with children with a diverse range of abilities and backgrounds. 3. The results section suggests that most activities were coded under the general “playing” category. It remains unclear whether coders were allowed to select multiple categories (e.g., “playing” and “sports/play equipment”). If coders could only choose one, this may lead to a loss of context and detail regarding the type of play observed. Response: Again, thank you for drawing our attention to the lack of clarity relating to the selection of the categories. For scan observations, each category was scanned separately, so categories were independent. In each scan, the coder simply thought about the category they were scanning for, say “playing” and considered whether each child was playing or not in that moment. One would expect that the proportion of children observed to be “playing” would be higher than for each of the sub-categories of play, and indeed for these sub-categories combined. This is not necessarily the case since the scans occur consecutively, and children may change activity between scans. Indeed, in our data, the proportion of children “playing” in the scan observations was similar to the proportion of children in the other play categories combined, suggesting that these categories did indeed describe most of the kinds of play children engaged in. For focal observations, there was no “playing” category, but if the child’s activity did not fit one of the play categories, they could choose whether they were engaged in “other play” and write some notes about that play afterwards, or “not playing”. We have clarified this in the manuscript and in Table 1. 4. The low ICC value for the “playing” category is notable. Could this be due to coders needing to choose between “playing” and a more specific type of activity, even when both might be appropriate? This suggests potential ambiguity in the coding decision process. A possible alternative approach could involve first determining whether a behavior constitutes “play” (yes/no), followed by a second level of coding to classify the type of play. This hierarchical strategy may improve reliability and clarity. Response: As outlined in the previous comment, the coders did not need to chose between “playing” and specific types of play/activity, so the lower ICC is not due to this. Instead, this lower ICC may be due to the definition of ‘play’ being more ambiguous than the specific types of play, and/or because the quick pass nature of scan observations does not give sufficient time for the presence/absence of play to be established. Consistent with this explanation, reliability of the category “not playing” in the focal observations was near perfect, suggesting that with more time observing a child, coders can more consistently evaluate whether a child is playing or not. We suggest that future researchers are cautious about using broad and subjective categories such as “playing” for scan observations where more time might be required to understand the context of a child’s behaviour (see also discussion of coding affect during scan observations). It is nonetheless useful to determine an approximate estimate of the number of children engaged in play activity because low estimates might be a key indicator of problems with a particular playground environment. 5. While ICC values for affect (when coded as positive or negative) were strong, the majority of affective observations were coded as “unclear.” This suggests that identifying emotional expression via observation alone may be inherently limited. I appreciate the authors’ reflection on the challenges of coding affect. Rather than removing the affect category, it may be more productive to consider how visible indicators of emotion (e.g., facial expressions, body language) could be systematically coded to aid decision-making. Response: It is true that there was a high incidence of the ‘unclear’ category being used by the coders. We now reflect upon this in more detail in the discussion, with reference to the prevenance of positive and negative emotions reported in the self-reported Emotions During Playtime measure. 6. The strong agreement among coders is encouraging, but it raises the question of whether these classifications are valid. Although validating affect codes was not the purpose of this study, it would be valuable to know if there are plans to evaluate this in future research. This may also warrant further discussion beyond the limitations section to acknowledge the inherent biases associated with observational research and to clarify that, in its current form, the tool cannot yet be considered validated which has important implications for practice. Response: Note that we had already stated “ there is some evidence of validity due to the consistency across the M-TAPS components in terms of the most common and most rare activities, but the study was not designed as a rigorous evaluation of validity”. In light of the reviewer's previous comment though, we have further considered validity in the final paragraph of the discussion. 7. For the Activities During Playtime Questionnaire, it would be helpful to describe what measures were taken to ensure that children consistently understood each activity type (e.g., nature play vs. free play). It is possible that variability in children’s interpretations affected their responses? Were examples or visual aids provided to support consistent understanding at both time points? Response: We have realised that the questionnaire packs were not available in the supplementary materials at the time of the original submission. The full questionnaire pack is now available in the supplementary materials. Given the limited explanation of the categories provided to children, we now suggest that poorer reliability of some categories may indicate a lack of understanding in the discussion and suggest that visual aids may improve the validity of children’s responses. We have added further detail about the protocol for the self-report questionnaire packs. 8. When discussing the reliability findings for the PANAS-C, it may strengthen the discussion to include a brief comparison with reliability estimates reported in previous literature, if available. Response: We now include comparison of the Emotions During Playtime internal consistency and test-retest reliability with previous PANAS-C findings. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern James M. Peer Review Report For: Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.5256/f1000research.176881.r384603) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-476/v1#referee-response-384603 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Barnett L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 May 2025 | for Version 1 Lisa Barnett , Deakin University, Melbourne, VIC, Australia 0 Views copyright © 2025 Barnett L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This is a well written article, and it focuses on the need for new measurement in the field - which I agree with. The introduction provides a good background to the study and cites relevant literature. In terms of methods, I am familiar with the SOFIT and SOPLAY tools and have undertaken modifications of these instruments myself for other purposes. One modification in the literature is the CAST instrument tested in the Australian environment and developed for children. CAST2 is a modified version still using momentary time sampling that was used to measure children’s physical activity levels in a school playground environment. CAST2 uses continuous 75 second scans of the break period, which they argued might be more reliable than intermittent scans. Can you comment on the duration used in your study for the scans and why this length of time was chosen? When I first read the initial part of the methods, I was dubious about being able to scan for affect and this was what was found in the results. The thing about these scanning tools is it must be very easy to see the behaviour for whole groups of children at once. That is why it works to detect physical activity behaviour such as standing or walking. But to assess affect requires a look at the children's face and a judgement and I don't think this is feasible when trying to document what large groups of children are doing in a quick observation. Antisocial also falls into this camp as how can this be quickly decided? The other categories of observation all make sense to me. I like the idea of focal observations of particular children to gain a more in-depth insight. I think affect would be more able/feasible to be coded for a focal child rather than for the broad scan. Can you please provide some psychometric information for the PANAS-C tool? Your adaptation rests on this tool but you have not provided evidence for it. For the activities during playtime tool you may have also wished to consult this paper as a starting point as the team used a pictorial format for the children: Parrish, A. M., Iverson, D., Russell, K., & Yeatman, H. (2010). The Development of a Unique Physical Activity Self-Report for Young Children: Challenges and Lessons Learned. Research in Sports Medicine, 18(1), 71–83. https://doi.org/10.1080/15438620903423874 Also, similar to above, in this section no psychometric information for the OPP tool is provided. For participants – can you give an idea how many children were on the playground at the same time? How many were in the school population? How exactly were the children selected at random for the focal point? Lucky you could use an opt out ethics! Can you comment in the discussion on whether a mean of five days apart for the measures is a good measure of reliability? How many observers/raters were there? Results The reliability scores are good The discussion is a good overview, and I agree on the findings with regard to the affect observations. To me it makes sense that emotions during playtime scale might be less reliable as emotions are less reliable than behaviours such as physical activity and we would assume that thee could change form playtime to playtime. Were images used in the Activities during playtime Questionnaire ? would be interesting to see if the use of images or not affect reliability with children. I agree with conclusion statement. Is the rationale for developing the new method (or application) clearly explained? Yes Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? Yes If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Motor skills, physical literacy, play, children, instrument development I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 28 Oct 2025 Helen Dodd, School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK Thank you for your thorough and thoughtful review of our manuscript. We believe that the clarity of reporting is much improved thanks to your suggested revisions. Below we respond point-by-point to each of your comments and suggestions. 1. In terms of methods, I am familiar with the SOFIT and SOPLAY tools and have undertaken modifications of these instruments myself for other purposes. One modification in the literature is the CAST instrument tested in the Australian environment and developed for children. CAST2 is a modified version still using momentary time sampling that was used to measure children’s physical activity levels in a school playground environment. CAST2 uses continuous 75 second scans of the break period, which they argued might be more reliable than intermittent scans. Response: Thank you for bringing this to our attention. We now include the CAST2 in our list of previous measures of playground observation tools. 2. Can you comment on the duration used in your study for the scans and why this length of time was chosen? Response: For pragmatic reasons, the scans are not timed, but rather occur consecutively, beginning the next scan category after the last has finished. The playground is divided so that the scans were completed over an area that was visible to the researcher without them needing to move. The number of children scanned may vary between areas and between scans, so the length of time required for a scan to take place may also vary. Each area of the playground is scanned for each category consecutively. It is recommended that each area is scanned at least twice. We have revised the manuscript to clarify aims and practicalities of the scan observations. 3. When I first read the initial part of the methods, I was dubious about being able to scan for affect and this was what was found in the results. The thing about these scanning tools is it must be very easy to see the behaviour for whole groups of children at once. That is why it works to detect physical activity behaviour such as standing or walking. But to assess affect requires a look at the children's face and a judgement and I don't think this is feasible when trying to document what large groups of children are doing in a quick observation. Antisocial also falls into this camp as how can this be quickly decided? The other categories of observation all make sense to me. I like the idea of focal observations of particular children to gain a more in-depth insight. I think affect would be more able/feasible to be coded for a focal child rather than for the broad scan. Response: We agree that there are practical challenges assessing the affect of children quickly and from a distance (i.e., during scan observations), as reflected in the poorer inter-rater reliability. It is also the case that children’s facial expressions do not always align with their internal emotions, and that children did not themselves report particularly frequent experiences of emotions during playtimes in the self-report measure. This is now discussed in more detail. 4. Can you please provide some psychometric information for the PANAS-C tool? Your adaptation rests on this tool but you have not provided evidence for it. Response: We now include a more detailed description of the psychometric properties of the PANAS-C as well as the link to the original validation study. 5. For the activities during playtime tool you may have also wished to consult this paper as a starting point as the team used a pictorial format for the children: Parrish, A. M., Iverson, D., Russell, K., & Yeatman, H. (2010). The Development of a Unique Physical Activity Self-Report for Young Children: Challenges and Lessons Learned. Research in Sports Medicine, 18(1), 71–83. https://doi.org/10.1080/15438620903423874 Response: This is now included in the discussion where we consider improvements for future research. 6. Also, similar to above, in this section no psychometric information for the OPP tool is provided. Response: ICCs of the OPP observational tool reported in Massey et al. (2018) are now reported. No further psychometric information is available. 7. For participants – can you give an idea how many children were on the playground at the same time? How many were in the school population? Response: More details about the number of children in the school population and across the different parts of the study are now included in the Method. 8. How exactly were the children selected at random for the focal point? Response: The researchers were on the playground and opportunistically selected a child to observe immediately before starting each focal observation. This is not strictly ‘random’ so we have edited the wording. 9. Can you comment in the discussion on whether a mean of five days apart for the measures is a good measure of reliability? Response: The five-day timescale of the test-retest reliability assessment was fairly short in this study, we have added this to the limitations. 10. How many observers/raters were there? Response: There were two coders who completed all of the coding. They were trained by the first author. This is now stated explicitly. 11. Results The reliability scores are good Response: Thank you 12. The discussion is a good overview, and I agree on the findings with regard to the affect observations. Response: Thank you 13. To me it makes sense that emotions during playtime scale might be less reliable as emotions are less reliable than behaviours such as physical activity and we would assume that they could change form playtime to playtime. Response: Child report of emotions was actually more reliable than their reporting of activities. Note that we asked children to complete the emotions measure twice because we expected some fluctuation. Our reflections on this therefore remain largely unchanged in the revised manuscript and are found. 14. Were images used in the Activities during playtime Questionnaire ? would be interesting to see if the use of images or not affect reliability with children. Response: The suggestion to include pictorial cues for each activity is now included in the discussion. We also realised that the questionnaire packs were not made available in the Supplementary materials on OSF, these are now available. 15. I agree with conclusion statement. Response: Thank you, this remains unchanged after revision of the manuscript. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Barnett L. Peer Review Report For: Introducing the Multidimensional Toolkit for the Assessment of Play in Schools (M-TAPS): a reliability study [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :476 ( https://doi.org/10.5256/f1000research.176881.r384599) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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