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The park physical activity questionnaire (Park-PAQ): A reliable measurement tool for park-based and total physical activity
Nicole Edwards * , Paula Hooper
The Australian Urban Design Research Centre, School of Design, The University of Western Australia. Australia. Level 2, 1002 Hay Street, Perth, WA 6000, Australia
ARTICLE INFO
Keywords:
Moderate and vigorous physical activity
Reliability
Parks
Park-based physical activity
Physical activity measurement
Park features
ABSTRACT
Background: Few studies have explicitly quantified the proportion of park-based physical activity to park users’ overall physical activity levels. Population studies need new context-specific physical activity measurement tools to achieve this. The objective of this study was to develop a reliable measure of self-reported park use and physical activity undertaken within and outside of parks to determine the contribution that park-based physical activity makes to overall physical activity levels.
Methods: A test-retest reliability study (n = 104) was conducted using the Park Physical Activity Questionnaire (Park-PAQ), an instrument based on the Active Australia Survey. Park-PAQ items captured the frequency and duration of walking for recreation or exercise, walking for transport, moderate and vigorous physical activity and strength, conditioning and balance activities done in parks and elsewhere.
Results: Recall of doing any walking for recreation (kappa = 0.649, p < 0.001) and any vigorous physical activity (kappa = 0.772, p < 0.001) was ‘substantial’ , recall of doing any moderate physical activity (kappa = 0.553, p < 0.001) was ‘moderate/acceptable’ , and recall of any walking for transport (kappa = 0.840, p < 0.001) ‘near perfect’ Recall of the time spent walking for recreation in parks (ICC = 0.928, p < 0.001) was ‘near perfect’ , whilst recall of time spent doing moderate activity in parks (ICC = 0.925, p < 0.001) and vigorous activity in parks (ICC = 0.962, p < 0.001) was ‘near perfect’ . Time spent walking for transport in a park (ICC = 0.200, p = 0.056) showed ‘poor’ agreement. Repeatability of the usual level of park use was ‘substantial’ (kappa = 0.744). Conclusions: The Park-PAQ reliably measures six domains of physical activity and quantifies the proportion of physical activity done in parks as a proportion of total physical activity. The Park-PAQ, used alone or embedded into park or physical activity surveys, will reliably capture context-specific activities that will optimise population level physical activity interventions, park programming and park management and design.
1. Background
Parks are an integral part of healthy towns and cities. Freely accessible parks facilitate physical activity and social connections that, in turn, foster abundant physical and psychological health benefits at population levels (Twohig-Bennett and Jones, 2018; Wolf and Wohlfart, 2014). Additionally, at a time where cities face a wide range of challenges, sustainable development efforts place parks and public spaces at the forefront of revitalisations and transformations. In fact, transforming public spaces to promote physical activity is considered pivotal to achieve the United Nation’s Sustainable Development Goals (World Health Organization, 2018). However, the role that parks play in promoting physical activity depends on how they are used and, in particular, whether they are used for moderate and vigorous physical activity
* Corresponding author.
(Cohen and Leuschner, 2019).
The physical environment strongly determines population physical activity levels and many studies have reported the provision of, and access to, parks to be associated with increased physical activity (Sallis et al., 2016; Karmeniemi et al., 2018; Tcymbal et al., 2020; Bedimo-Rung et al., 2005) (Cohen et al., 2014) (Schipperijn et al., 2017) (Hooper et al., 2020). Additionally, several studies have found adults who used parks were more likely to achieve recommended physical activity levels than those who did not (Hooper et al., 2020; Akpinar and Cankurt, 2017; Deshpande et al., 2005; Giles-Corti et al., 2005; Villeneuve et al., 2018; Yuen et al., 2019; Zhang et al., 2018; Hughey et al., 2021) and greater physical activity time in parks is often associated with better well-being (Hansmann et al., 2007; Richardson et al., 2013). Yet, despite a multitude of studies finding positive associations between park use and
E-mail addresses: nicole.edwards@uwa.edu.au (N. Edwards), paula.hooper@uwa.edu.au (P. Hooper).
https://doi.org/10.1016/j.healthplace.2023.103085
Received 7 December 2022; Received in revised form 31 March 2023; Accepted 11 July 2023
Availableonline29July2023 1353-8292/©2023PublishedbyElsevierLtd.
physical activity, mixed findings persist (Zhang et al., 2018; Lachowycz and Jones, 2011). Differences in socio-demographic, geographical, cultural, and environmental associations with park-based physical activity are apparent in the literature (Kaczynski et al., 2011; Font´ an-Vela et al., 2021; Hartig et al., 2014; Uijtdewilligen et al., 2019; McCormack et al., 2014) and questions remain around which park attributes consistently facilitate park-based physical activity.
In a review of studies that found inconsistencies in associations between park environments and park-based physical activity, Zhang et al. concluded that the evidence is still limited and the extent to which environmental attributes of park-based physical activity are generalizable, remains unexplored. Further, Zhang et al. (2018) noted methodological issues as likely to increase the prevalence of inconsistent findings and reported, to date, research has mostly focussed on total physical activity as opposed to context-specific physical activity in parks (Zhang et al., 2018). As such, the review called for intervention studies to better understand how parks can promote physical activity (Zhang et al., 2018). However, there is a lack of cohesive evidence around the effectiveness of park-based interventions that promote physical activity (Xu et al., 2022). In their review of park-based interventions and health-related outcomes, Derose et al. (2021) suggested place-based intervention studies may provide useful references to researchers planning for future intervention studies and further noted that future research methods should combine place-based evaluations and cohort studies to best inform park policy, exercise programming and park design to promote health and well-being (Derose et al., 2021). Moreover, the literature is sparce on attempts to quantify the contribution of park-based physical activity (Cohen and Leuschner, 2019; Han et al., 2013; Evenson et al., 2013; Stewart et al., 2018).
Accordingly, the accurate measurement of physical activity within parks is essential to understanding the contribution parks make to the population prevalence of physical activity and the evaluation of parkbased interventions that aim to increase population-level physical activity. However, physical activity and park studies have not typically measured, in context, the intensity (i.e., frequency and duration) of actual park-based physical activity in adults (Bedimo-Rung et al., 2005). Consequently, this context-free physical activity measurement has limited applicability in guiding built environment interventions to improve population level physical activity (Kajosaari and Laatikainen, 2020). Further, few studies have attempted to quantify the proportion of park-based physical activity to overall physical activity levels (Cohen et al., 2014; Han et al., 2013; Evenson et al., 2013; Stewart et al., 2018) and to our knowledge, no studies have attempted to quantify this at the individual level.
In order to measure park use and park-based physical activity, various observational techniques have been used (Joseph and Maddock, 2016a, 2016b). One of the most popular tools has been the ‘Systematic Observation and Recreation in Communities’ (SOPARC) (McKenzie et al., 2006). In a study that used SOPARC to quantify the contribution of neighbourhood parks to time spent in moderate-vigorous physical activity, Han et al. provided an important examination of the role of neighbourhood parks in facilitating a local population’s physical activity (Han et al., 2013). However, the study was limited by its use of national physical activity estimates to represent local populations and a small number (n = 10) of parks studied (Han et al., 2013).
Other observational approaches for measuring park use include behaviour mapping (Cosco et al., 2010) and time-lapse video camera recording (Arnberger et al., 2005). However, video monitoring is more suited for small spaces and less conducive to large urban parks with multiple entrances (Park and Ewing, 2017). Recently, Park and Ewing employed unmanned aerial vehicles (i.e., drones) to survey park-based physical activity (Park and Ewing, 2017). Compared to on-ground observations (using SOPARC), the recorded drone observations were more suitable for analysing the number of users and patterns of spatial park use but less for collecting detailed user information such as age and activity levels (Park and Ewing, 2017). However, the labour
intensiveness of these observational protocols often limits the generalizability of this method of park-based research to a few specific parks. This makes it difficult to gauge overall park use and activity levels across broader populations (Engelhard et al., 2001; McKenzie and van der Mars, 2015; Walker et al., 2009). Additionally, observational methods cannot quantify the contribution that park-based physical activity makes to total physical activity levels.
Observational techniques, use of Global positioning system (GPS), and Geographic Information Systems (GIS) have been used to study physical activity in the context of destinations such as parks (Yi et al., 2019). GPS technology has the potential to provide location-specific information assessing where physical activity occurs, including in parks (Maddison and Mhurchu, 2009), and, as such, a small number of studies have tracked park-related physical activity. Whilst the use of GPS is becoming easier and cheaper with the increasing reach of smartphones, fitness trackers and improved technology, the costs and participant burden associated with using GPS for research purposes, plus privacy concerns around being “tracked” , may explain the relatively small numbers of participants in studies to date. As such, self-report measures of physical activity using questionnaires justifiably remain a popular method for large-scale population-based studies because they are low-burden and can capture contextualised data from many people at a low cost (Curtis et al., 2020; Sallis and Saelens, 2000; Sylvia et al., 2014; Lee et al., 2011). Recently, several studies have measured physical activity participation in park settings. For example, several studies from Australia (Hooper et al., 2020; Brown et al., 2014, 2018) and Singapore (Petrunoff et al., 2021) have identified the exact parks study participants used and measured activity types and time spent in these settings. In addition, the Physical Activity in Parks Setting (PA-PS) instrument was developed in the US to measure park use and asks participants to estimate (i) the length of time they were physically active in parks and (ii) the types of physical activities undertaken in the park (Walker et al., 2009). However, studies that have used this tool have not been able to quantify the contribution of this park-based physical activity to total physical activity volumes and intensity levels (Kaczynski et al., 2014).
Existing population instruments such as the Global Physical Activity Questionnaire (GPAQ) (Bull et al., 2009), the International Physical Activity Questionnaire (IPAQ) (Craig et al., 2003) and Active Australia (Australian Institute of Health and Welfare, 2003) were developed for surveillance and monitoring of population level, health-related physical activity and different domains of physical activities, i.e., walking, moderate and vigorous intensity activities. They were not designed to measure context and do not collect location-specific physical activity data. To our knowledge, just one population-level survey instrument has been developed to capture the location of physical activities - the Neighbourhood Physical Activity Questionnaire (NPAQ) (Giles-Corti et al., 2006). NPAQ was developed in response to a need to differentiate between recreational and transport-related walking within and outside neighbourhoods. No standardised surveys assess population-level physical activity specific to park settings, which limits our ability to track park-based physical activity trends and contributions to global physical activity levels (Parks, 2008).
There are a vast number of options available for researchers to assess population physical activity levels however, there is still a crucial need for new tools, with improved contextual specificity, to capture and measure the urban quality of life and the health impacts of planning decisions (Mouratidis, 2021). A survey instrument that assesses population level, park-based and total physical activity is needed to understand how much physical activity occurs in parks and to what extent park-based physical activity contributes to overall physical activity levels (Lachowycz and Jones, 2011; Walker et al., 2009; Petrunoff et al., 2021; Parks, 2008). As such, we have developed the ‘Park Physical Activity Questionnaire’ (Park-PAQ) which aims to fill the above-mentioned gaps in the literature. The survey tool captures contextualised physical activity undertaken both within, and outside of, parks and in doing so, is able to quantify the contribution of park-based physical activity to
overall physical activity levels.
The Park-PAQ is designed to stand alone, or be embedded into physical activity or park survey tools that aim to capture contextualised, park-related data. This paper describes the development and test-retest reliability of the new Park-PAQ that aims to capture context specific park use for physical and mental health outcomes.
2. Methods
The Park-PAQ was adapted from the Active Australia survey, which has been shown to have acceptable validity and reliability in measuring physical activity behaviours in adult populations across various socioeconomic settings (Craig et al., 2003; Australian Institute of Health and Welfare, 2003; Brown et al., 2008). The Australian Bureau of Statistics also uses Active Australia as part of the Australian National Health Survey (Craig et al., 2003; Australian Institute of Health and Welfare, 2003), so it was also deemed best practice to develop a measure compatible with the current national measure of physical activity surveillance.
Content validity of the new items (including the list of park features regularly used for physical activity) was assessed by a panel of experts (n = 6) with experience in physical activity measurement, including those with prior involvement in the development of the Active Australia survey. Experts were asked to review and comment on each iteration of the survey items until all experts were satisfied with the content.
The survey measures six domains of physical activity undertaken in the last seven days: (i) walking for recreation, exercise, or sport; (ii) walking for transport to get to or from places; (iii) vigorous intensity activities; (iv) moderate intensity activities; (v) strength and conditioning activities; and (vi) balance and flexibility activities. The original Active Australia survey included a single walking item (Australian Institute of Health and Welfare, 2003). However, other recent versions, including the Australian National Health Survey instrument (ABS. National Health Survey, 2019), have included separate items of time spent in recreational and transport-related walking (Brown et al., 2004). Therefore, to ensure consistency with the National Health Survey, the Park-PAQ asked separately for ‘recreation’ and ‘transport-related’ walking estimates.
Respondents self-reported whether, in the last seven days, they spent time doing any “walking continuously for 10 min or more for recreation, exercise or sport” If participants answered “yes” , they then recorded the number of times they walked for recreation, exercise or sport in the last seven days and the total amount of time spent walking in this way in the previous seven days. They were then asked to estimate the total amount of walking for recreation time that was undertaken in a park. The same set of questions were asked for walking for transport and moderate and vigorous-intensity physical activities.
To align with the Australian physical activity guidelines for older adults, that recommends including muscle strengthening, flexibility and balance to physical activity Australian Government Department of Health and Aged Care, 2021, and the National Health Survey, that includes items asking participants to report the number of days in the last week they participated in strength and conditioning and balance and flexibility activities, we included items of the same nature in the ParkPAQ and added additional items asking participants to indicate the number of days on which these activities were undertaken in a park. Participants were also asked to indicate their usual level of park use (response options: daily, weekly, monthly, rarely/a few times per year, I don’t use parks).
Finally, the survey included an item that asked participants to indicate (by ticking all that apply) which park features they regularly used for physical activity (response options: trails, footpaths, dog exercise areas, marked areas for organised sports, grassed areas for fitness classes, yoga, tai chi, social sports etc, shaded areas, outdoor gym/fitness equipment, skatepark/BMX/pump track, basketball or netball hoops and cricket nets).
Whilst the Active Australia survey had originally been administered via telephone interview, the Park-PAQ was developed to be completed online. This allowed for some critical changes to the question format. Firstly, icons were added to illustrate examples of activities under each physical activity domain. Secondly, to minimise data entry errors and misinterpretation in the cleaning phases, all time responses to the frequency and duration questions were presented as drop-down options. Options for the frequency of activity undertaken in the last week ranged from 1 to 35 (i.e., times in the last week). Duration of activity time options were presented in 5-min increments to 870 min (15 h) with a final option of “more than 15 h per week” The cut-off points aligned with Active Australia’s data cleaning and truncation requirements.
2.1. Test-retest reliability sample
A convenience sample was recruited from colleagues within the authors’ academic institution, staff networks and external collaborators. The Park-PAQ survey was distributed to study participants on two occasions, 48 h apart. On both occasions, participants were emailed a link to complete the Park-PAQ online. Data collection occurred during the fall/autumn of April 2022 and there were no extremes in weather conditions that may have impacted park use of park-based physical activity.
An important difference between this study and others that have previously explored the test-retest reliability of physical activity instruments, was that the retest survey was sent to participants 48 h following completion of the initial survey. In this way, it was intended that the recall period for both surveys included at least five days that were common to both. This is in contrast to the methods used in many previous studies which have conducted the second interview up to seven days after the first, allowing the two recall periods to contain large variations in physical activity behaviours. Under those circumstances it is impossible to know whether any differences in responses are due to the different recall period or to actual response differences. The average time taken to complete the Park-PAQ was determined using completion times captured by survey software.
The University of Western Australia’s Human Ethics Committee provided ethics approval (2022/ET000080).
2.2.
Park-PAQ physical activity measures
Respondents who only completed one of the two tests were removed from the study (n = 38) before analyses and Park-PAQ data were cleaned to conduct a test-retest analysis for all variables. Physical activity participation was quantified for each activity domain (i.e., walking for fitness, recreation, or sport; walking for transport to get to or from places; vigorous-intensity activities; moderate-intensity activities, strength and conditioning; and flexibility and balance) using the data from the Park-PAQ items: (i) the number of sessions (i.e., frequency): of physical activity during the last week; and (ii) the total time spent in the activity (i.e., duration) during the last week. The total time spent in each activity in a park during the last week was used to calculate the percentage of the total physical activity time for the physical activity domains. For the remaining two domains (i.e., ‘strength and conditioning’ and ‘balance and flexibility’), the percentage of days were calculated.
2.3. Statistical analyses
All analyses were performed using SPSS version 26.0. Kappa scores (k) were used to assess the reliability of the categorical Park-PAQ items (e.g., recreation walking (yes/no), transport walking (yes/no), moderate-intensity physical activity, and vigorous-intensity physical activity (yes/no). Intra-class correlations (ICC, two-way random-effects model with 95% confidence intervals) were used to assess the reliability of continuous Park-PAQ items (e.g., minutes of recreation walking, moderate and vigorous activity) and the derived physical activity
variables (e.g., total duration of physical activity inside, and outside of, parks). Interpretation of the coefficients relied on agreement level ratings as suggested by Landis and Koch Landis and Koch, 1977: 0–0.2 = poor agreement; 0.21–0.40 = fair agreement; 0.41–0.60 = moderate/acceptable agreement; 0.61–0.80 = substantial agreement; 0.81–1.0 = near perfect, to perfect agreement.
3. Results
3.1. Sample characteristics
The study sample comprised participants who completed the test and retest Park-PAQ surveys (n = 104). The demographic characteristics of the study sample are presented using data from Test 1, and are outlined in Table 1 There were no significant differences between age, gender and dog ownership among the study sample and those who were excluded for not completing both the test and retest. The study sample was mostly female (70%) with all age generations represented. Dog ownership and sports membership was evenly distributed. Seventy two percent of the study sample reported using park use daily or weekly, 86% reported walking for recreation, fitness, or sport in the past seven days, 62% reported doing vigorous-intensity physical activity in the past seven days and almost half (46.1%) reported doing moderate-intensity physical activity.
3.2. Test-retest reliability of the Park-PAQ
The study included existing Active Australia items (n = 17) and newly developed Park-PAQ items (n = 8) and newly developed computed variables (n = 6). The median time between Park-PAQ surveys was 3.5 days, with all second (retest) surveys completed within 1–13 days. On average, it took eight minutes for participants to complete
Table 1
Participant demographics of the Park-PAQ test-retest sample (n = 104).
Characteristic
Prefer not to say
Age/Generation Silent generation (born 1928–1945)
(2.0)
(1.9) Baby boomer (born 1946–1964)
Gen X (born 1965–1979)
Gen Y/Millennials (born 1980–1994)
Gen Z (born 1995–2015)
Children under 12a
Yes
Dog Ownera
Any walking for fitness, recreation or sport in past 7 days
Yes
Any vigorous-intensity physical activity in past 7 days
Yes
(16.3)
(27.9)
(15.4)
(38.5)
(21.4)
(78.6)
the Park-PAQ (Test 1) survey. Table 2 presents the Kappa and ICC results of the Park-PAQ test and retest. Of the seventeen existing Active Australia items, most showed ‘near perfect’ (n = 10) while the remaining showed ‘substantial’ (n = 5) or ‘moderate/acceptable’ (n = 2) agreement between the test and retest surveys (Table 2). Four of the eight new items that measured walking for recreation, vigorous and moderate physical activity, and balance and flexibility undertaken in parks had ‘near perfect’ agreement and number of strength training days in a park had substantial agreement.
Recall of doing any walking for recreation (kappa = 0.649, p < 0.001) and any vigorous physical activity (kappa = 0.772, p < 0.001) was ‘substantial’ while recall of doing any moderate physical activity (kappa = 0.553, p < 0.001) was ‘moderate/acceptable’ recall of any walking for transport (kappa = 0.840, p < 0.001) ‘near perfect’ (Table 2). Recall of doing any strength or toning activities (kappa = 0.734) and the number of days in the last week (ICC = 0.946) were ‘substantial.’ Recall of doing any balance or flexibility activities (kappa = 0.674) was also ‘substantial’ and the number of days in the last week these activities were undertaken (ICC = 0.946 and ICC = 0.902 respectively) was ‘near perfect’ . Recall of the number of days these activities were done in a park was ‘substantial’ for strength or toning activities (ICC = 0.639), and ‘near perfect’ for balance or flexibility activities (ICC = 0.979).
Assessment of the remaining new park-based items revealed recall of the time spent walking for recreation in parks (ICC = 0.928, p < 0.001), recall of time spent doing moderate activity in parks (ICC = 0.930, p < 0.001), and recall of vigorous activity in parks (ICC = 0.962, p < 0.001) was ‘near perfect’ However, the time spent walking for transport in a park (ICC = 0.200, p = 0.219) showed ‘poor’ agreement. Subsequently, this item was dropped from the Park-PAQ tool and does not appear in the current version of the tool (APPENDIX 1).
The computed percentage of total time (minutes) of walking for recreation undertaken in a park (ICC = 0.797, p < 0.001); doing moderate physical activity in a park (ICC = 0.732, p < 0.001); and vigorous activity in a park (ICC = 0.730, p < 0.001) was ‘substantial’ , whilst the total minutes of walking for transport in a park, as a percentage of total walking for transport, was only moderately acceptable (ICC = 0.568, p = 0.002). Reliability of the usual level of park use (kappa = 0.744) was ‘substantial’ whilst recall of typical activity during the last week showed only moderately acceptable recall (kappa = 0.570).
The assessment of park features regularly used for physical activity, revealed substantial agreement for trails, footpaths, play equipment, shaded areas and skatepark/BMX and pump tracks (p < 0.001). Near perfect scores were achieved for dog exercise (i.e., both fenced and offleash) areas (p < 0.001) however, only moderate agreement was reached for marked areas for organised sport, grassed areas for fitness classes, tai chi, yoga and social sports), outdoor gym equipment, basketball and netball hoops and cricket nets (p < 0.001). The most reported park features used for physical activity were; footpaths, trails, shaded areas, dog off-leash areas, and grassed areas.
In this study sample, on average, park-based recreational walking accounted for 54.25% and 49.22% of overall recreation walking (test, retest respectively), park-based moderate activity accounted for 25.52% and 25.49% of overall moderate physical activity (test, retest respectively), and park-based vigorous activity accounted for 23.68% and 39.27% of overall vigorous physical activity (test, retest respectively) in the last week. Notably, more than half the time spent doing ‘strength and conditioning’ and ‘balance and flexibility’ activities was done in parks.
(82.7)
(17.3)
4.
Discussion
(38.2)
(61.8) No
Any moderate-intensity physical activity in past 7 days
a Missing data not reported.
This study has responded to a call to ‘incorporate key park, recreation and leisure items into existing public health surveillance efforts’ (Kruger et al., 2007) through the design of a surveillance tool that can stand alone or be embedded into larger population level surveys.
This paper examined the test-retest reliability of the Park-PAQ, an
N. Edwards and P. Hooper
Table 2
Test-retest reliability results of the Park-PAQ items and computed physical activity variables.
Park-PAQ items
Walking for fitness, recreation, or sport
Any walking last week
Time spent walking last week
Time spent walking in a park last weeka
Percentage of total walking time in a park last weeka^
(SD)
P Agreement
Near
Table 2 (continued ) Park-PAQ items Test 1 Test 2 Kappa/ ICC P Agreement in a park last weeka^
Moderate
Frequency of doing moderate intensity activities last week
Near perfect
Walking for transport to get to and from places
Any walking for transport to get to and from places last week
Near perfect Frequency of walking for transport last week
<
Time spent walking for transport in a park last weeka
Near perfect
Time spent doing moderate activities last week
Time spent doing moderate activities in a park last week
Percentage of total moderate activity time a park last weeka^
Strength and conditioning activities Any strength and conditioning activities
of strength and conditioning days in a park last weeka^
Balance and flexibility activities Any balance and flexibility activities last week
Number of days doing balance or flexibility activities last week
<
Near perfect (continued on next page)
Table 2 (continued )
Park-PAQ items Test 1 Test 2 Kappa/ ICC P Agreement
flexibility days in a park last weeka^
Park features used for physical activitya
Trails 52 (50.00) 56 (53.80) 0.731 <0.001 Substantial
Footpaths 74 (71.20) 71 (68.30) 0.750 <0.001 Substantial
Play equipment 16 (15.40) 18 (17.30) 0.789 <0.001 Substantial
Dog exercise (Off Leash)
Dog exercise (Fenced Areas)
30 (28.80) 31 (29.80) 0.903 <0.001 Near perfect
11 (10.60) 10 (9.60) 0.947 <0.001 Near perfect
Organised sport (Marked Areas) 9 (8.70) 11 (10.60)
Grassed areas (Fitness Classes and other activities)
Shaded areas 32 (30.80) 36 (34.60) 0.651 <0.001 Substantial Outdoor Gym/ Fitness equipment 10 (9.60) 6 (5.80) 0.596 <0.001 Moderate/ acceptable
Skatepark/ BMX track 4 (3.80) 4 (3.80) 0.740 <0.001 Substantial
Basketball or netball hoops 5 (4.80) 2 (1.90) 0.559 <0.001 Moderate/ acceptable
Cricket nets 3 (2.90) 1 (1.00) 0.493 <0.001 Moderate/ acceptable
Was last week a typical week for physical activity? a Yes 63 (61.80) 64 (63.40) No, I did more activity than usual 8 (7.80) 7 (6.90)
No, I did less physical activity than usual 31 (30.40) 30 (29.70) 0.570 <0.001 Moderate/ acceptable
Usual level of park use
Daily 30 (29.4) 31 (30.70)
Weekly 44 (43.1) 38 (37.60) Monthly 12 (11.80) 19 (18.80) Rarely/a few times per year 14 (13.70) 10 (9.90)
I do not use parks 2 (2.00) 3 (3.00) 0.744 <0.001 Substantial
a Newly developed Park-PAQ item ^ computed item.
online self-administered, self-report instrument that quantifies the frequency, duration, and intensity of: (i) walking for recreation; (ii) walking for transport; (iii) moderate physical activity; (iv) vigorous activity; and the frequency of: (v) strength and conditioning; and (vi) balance and flexibility undertaken both within, and outside of parks. These data can provide a total measure of physical activity and can quantify the proportion of an individual’s total physical activity that is undertaken within parks. Additionally, the survey instrument reliably captures ‘usual’ park use, park features used for physical activity, and time spent doing strength and conditioning, and balance and flexibility, exercises in a park.
The reliability of the newly developed Park-PAQ items showed ‘substantial’ or ‘near perfect’ agreement, except for ‘time spent walking for transport in a park in last week’ Importantly, the addition of the park-based questions did not adversely affect the reliability of the original Active Australia questions. Reliability coefficients indicated high levels of agreement and were aligned with previous test-retest studies of the Active Australia instrument (Brown et al., 2008). Thus, this study demonstrates that the online Park-PAQ is reliable for capturing overall and park-based physical activity. Further, Park-PAQ
can collect physical activity data at a population scale suitable for physical activity surveillance and monitoring.
This study used a convenience sample, which has implications for its generalizability. It should also be noted that this study does not validate the new mode of online administration, although the modified parkbased questions are based on questions previously validated as items in the Active Australia interviewer-administered survey. Additionally, whilst survey content was examined by a panel of experts, further studies should increase the sample size and demographic diversity of study participants to improve generalizability and better understand the validity of the tool i.e., if it accurately captures park-based and total physical activity as well as park contextual factors.
The Park-PAQ test-retest reliability estimates are comparable with other self-report and commonly used international physical activity instruments that collect large-scale data (Bull et al., 2009; Craig et al., 2003; Sember et al., 2020) (Brown et al., 2008) and the NPAQ, that differentiated between walking undertaken within and outside of the neighbourhood (Giles-Corti et al., 2006). The Park-PAQ also compared favourably to the reliability results of the PA-PS instrument (Walker et al., 2009). However, the stronger reliability results for the Park-PAQ might relate to the specificity of the questions eliciting a response for different domains of physical activity compared to the more general question of the PA-PS instrument (i.e., “During your last visit how long were you physically active in the park.").
Similar to the ABS National Health Survey (ABS. National Health Survey, 2019) the Park-PAQ measured recreational and transport-related walking separately. We deemed collecting recreational walking and walking for transport separately important, given that recreation walking is often undertaken in parks. A previous test-retest study of Active Australia found poor repeatability of walking for transport (Brown et al., 2004). Conversely, this study found the test and re-test of walking for transportation to and from places in the last week, showed ‘near perfect’ agreement. However, the test-retest of time walking for transport to get to or from places in a park in the last week showed only ‘poor’ agreement. This suggests that people’s ability to report accurately on the portion of a transport-related trip that is within a park is more challenging, or the behaviour exhibits more variability (Brown et al., 2004). Indeed, the time spent walking for transport in a park may have been difficult to recall if participants walk for both purposes simultaneously. For this reason, we removed this item from the final version of Park-PAQ and recommend future studies that aim to quantify this type of walking should provide more clarification to make differentiation easier.
The challenges associated with self-report measures of physical activity are well documented. This includes accuracy limitations due to participants’ recall capabilities and differentiation between physical activity intensity (i.e., moderate versus vigorous), duration, and frequency. However, we found the frequency and duration recall of vigorous and moderate physical activity using Park-PAQ was mostly ‘near perfect’ These results suggest respondents may find it easier to recall more accurately when asked for detailed physical activity participation. Additionally, the recall of the moderate physical activity items in this study was higher than found in previous reliability studies of physical activity survey instruments (Brown et al., 2004; Washburn et al., 2000). This may be, in part, due to the inclusion of diagrams in Park-PAQ that depicted examples of walking, moderate and vigorous physical activities or a result of the inclusion of innovative drop-down lists to capture frequency and duration. These lists prevented outliers and unrealistic values from being reported. Additionally, it is possible that by prompting the recall of physical activity in a specific environmental context (i.e., parks), participants were able to reliably recall participation details. Even so, whilst the Park-PAQ tool was successful in creating a context-specific park physical activity measure, it may not fully explain which park attributes consistently facilitate park-based physical activity. Further, not all parks are created equal, and nor should they be, therefore, although the list of ‘parks features used for
physical activity’ in this iteration of Park-PAQ was reviewed by Australian experts in the field, the items in the list might not be applicable to all countries or communities. We suggest future studies further validate this list for wider generalizability.
This study reduced the typical time frame between test and re-test distribution to 48 h to ensure the recall periods for the two surveys included at least five days common to both. However, the median time between the Park-PAQ test and retest surveys was 3.5 days, reducing the number of common days between the recall periods of the two tests, which may have impacted the reliability results. Moreover, the results should be interpreted in the knowledge that physical activity, park use and park-based physical activity are variable behaviours that may exhibit considerable day-to-day variability (Park et al., 2022). As such, there are likely discrepancies in the agreement of the behaviours reported (i.e., type of physical activity undertaken, and time spent doing the physical activity) between the two test time points. It is plausible to assume 100% agreement would not be attainable between the two surveys (Berchtold, 2016), despite the participants understanding the construct of the question and thus being able to answer the questions reliably (i.e., different types of physical activity within and outside of parks). Indeed, the Park-PAQ results are comparable with test-retest studies that have conducted the second survey up to seven days after the first (Brown et al., 2008; Booth et al., 1996) and the Australian National Health Survey (ABS. National Health Survey, 2019).
The online survey can be distributed for low cost and can provide information on many persons in a relatively short time. The contextual information in the Park-PAQ is imperative to resource allocation for all levels of government, in particular local governments that are responsible for the maintenance and upkeep of parks, leisure programming and the fulfilling the needs of the population, for public health gains. For example, the results of this study found, more than half the total reported time spent doing ‘strength and conditioning’ and ‘balance and flexibility’ activities was done in parks. Similarly, half of total walking for fitness, recreation or sport was done in parks. Accordingly, footpaths, trails and grassed areas were reported as the most used park features for physical activity. This provides a valuable insight for local governments and physical activity promoters and should be further investigated with local, more diverse, and larger study samples. Finally, the Park-PAQ addresses the methodological challenges associated with park-based physical activity measurement. This includes the lack of ability to measure frequency, duration and intensity within parks and examine park-based activity with national physical activity recommendations (Kruger et al., 2007).
Park-PAQ can inform health practitioners, policy makers, intervention planners and park designers with contextual and reliable parkrelated physical activity data. These data can help track population trends of physical activity over time, assess demographic and geographic variations in park-related physical activity participation and inform design interventions to increase participation rates (Kruger et al., 2007).
5. Conclusion
It is prudent, and in the best interest of park planners and those responsible for managing these spaces, to differentiate between and measure the physical activity undertaken in parks. This study found Park-PAQ to be a reliable and valid method for the measurement of parkbased physical activity. The Park-PAQ reliably measures various dimensions (e.g., frequency, duration, and intensity) of physical activity undertaken within parks and at the same time, provides a measure of total physical activity that can be assessed against recommended physical activity guidelines. The Park-PAQ also provides a measure of behaviour that may be suitable to evaluate the success of park interventions aimed at promoting physical activity and health outcomes. Moreover, as an online survey, it is inexpensive to administer and has a low burden for participants to complete.
By adapting an existing physical activity surveillance tool (Active
Australia), the Park-PAQ is a valuable supplement to the current national surveillance survey of physical activity in Australia. Whilst the Park-PAQ provided a reliable measure of usual park use, it could also be integrated as a set of questions within a public participatory GIS mapping survey to identify the exact parks people use. This would enable a deeper exploration of the park types and design features that promote park-based physical activity. Lastly, the Park-PAQ could be easily integrated into local government park-related community surveys to provide benchmarks of community physical activity benefits from parks and evaluate any park upgrades or interventions.
Author’s contributions
PH and NE were responsible for the conceptualisation and design of the study and the development of the Park-PAQ measure. NE and PH conducted the data collection. NE conducted the data cleaning and analysis. PH and NE created the first draft of the manuscript and PH and NE produced subsequent revisions. Both authors have read, contributed to and approved the final manuscript.
Funding
The work was supported by funding from a WA Near Miss Funding Award, from the Western Australian Future Health Research and Innovation Fund (Government of Western Australia, Department of Health).
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Ethics approval and consent to participate
Ethical approval was granted for the study by The University of Western Australia Human Ethics Committee (RA/4/1/8734). All aspects of data collection and storage were in compliance with the standards specified by this body.
Consent for publication
Not applicable.
Declaration of competing interest
The authors declare that they have no competing interests.
Data availability
Data will be made available on request.
Acknowledgements
The Park-PAQ was developed as part of the wider Park Life project. We acknowledge the work of the Park Life project team who were involved in the development of the broader survey, including Dr Julian Bolleter, Dr Sarah Foster, Dr Bryan Boruff, Dr John Duncan, Professor Michael Burton, Dr Ram Pandit and Dr Maksym Polyakov. We also acknowledge the contribution of Professor Wendy Brown and Professor Billie Giles-Corti for their assistance with Park-PAQ development.
Abbreviations
Park-PAQ Park physical activity questionnaire ICC Intra-class correlation
N. Edwards and P. Hooper
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi. org/10.1016/j.healthplace.2023.103085
References
ABS. National Health Survey, 2019. In: Statistics ABo
Akpinar, A., Cankurt, M., 2017. How are characteristics of urban green space related to levels of physical activity: examining the links. Indoor Built Environ. 26 (8), 1091–1101
Arnberger, A., Haider, W., Brandenburg, C., 2005. Evaluating visitor-monitoring techniques: a comparison of counting and video observation data. Environ. Manag. 36 (2), 317–327
Australian Institute of Health and Welfare, 2003. The Active Australia Survey: a Guide and Manual for Implementation, Analysis and Reporting. Canberra
Bedimo-Rung, A.L., Mowen, A.J., Cohen, D.A., 2005. The significance of parks to physical activity and public health: a conceptual model. Am. J. Prev. Med. 28 (2), 159–168
Berchtold, A., 2016. Test–retest: agreement or reliability? Methodol. Innov. 9, 2059799116672875
Booth, M.L., Owen, N., Bauman, A.E., Gore, C.J., 1996. Retest reliability of recall measures of leisure-time physical activity in Australian adults. Int. J. Epidemiol. 25 (1), 153–159
Brown, W.J., Trost, S.G., Bauman, A., Mummery, K., Owen, N., 2004. Test-retest reliability of four physical activity measures used in population surveys. J. Sci. Med. Sport 7 (2), 205–215
Brown, W.J., Burton, N.W., Marshall, A.L., Miller, Y.D., 2008. Reliability and validity of a modified self-administered version of the Active Australia physical activity survey in a sample of mid-age women. Aust. N. Z. J. Publ. Health 32 (6), 535–541
Brown, G., Schebella, M.F., Weber, D., 2014. Using participatory GIS to measure physical activity and urban park benefits. Landsc. Urban Plann. 121, 34–44
Brown, G., Rhodes, J., Dade, M., 2018. An evaluation of participatory mapping methods to assess urban park benefits. Landsc. Urban Plann. 178, 18–31
Bull, F.C., Maslin, T.S., Armstrong, T., 2009. Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J. Phys. Activ. Health 6 (6), 790–804.
Cohen, D.A., Leuschner, K.J., 2019. How can neighborhood parks be used to increase physical activity? Rand health quarterly 8 (3)
Cohen, D.A., Sturm, R., Han, B., Marsh, T., 2014. Quantifying the Contribution of Public Parks to Physical Activty and Health: Introducing SOPARC
Cosco, N.G., Moore, R.C., Islam, M.Z., 2010. Behavior mapping: a method for linking preschool physical activity and outdoor design. Med. Sci. Sports Exerc. 42 (3), 513–519
Craig, C.L., Marshall, A.L., Sjostrom, M., Bauman, A.E., Booth, M.L., Ainsworth, B.E., et al., 2003. International physical activity questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 35 (8), 1381–1395
Curtis, R.G., Olds, T., Plotnikoff, R., Vandelanotte, C., Edney, S., Ryan, J., et al., 2020. Validity and bias on the online active Australia survey: activity level and participant factors associated with self-report bias. BMC Med. Res. Methodol. 20 (1), 6
Derose, K.P., Wallace, D.D., Han, B., Cohen, D.A., 2021. Effects of park-based interventions on health-related outcomes: a systematic review. Prev. Med. 147, 106528
Deshpande, A.D., Baker, E.A., Lovegreen, S.L., Brownson, R.C., 2005. Environmental correlates of physical activity among individuals with diabetes in the rural midwest. Diabetes Care 28 (5), 1012–1018
Engelhard, S., Stubbs, J., Weston, P., Fitzgerald, S., Giles-Corti, B., Milat, A.J., et al., 2001. Methodological considerations when conducting direct observation in an outdoor environment: our experience in local parks. Aust. N. Z. J. Publ. Health 25 (2), 149–151.
Evenson, K.R., Wen, F., Hillier, A., Cohen, D.A., 2013. Assessing the contribution of parks to physical activity using global positioning system and accelerometry. Med. Sci. Sports Exerc. 45 (10), 1981–1987
Fontan-Vela, M., Rivera-Navarro, J., Gullon, P., Díez, J., Anguelovski, I., Franco, M., 2021. Active use and perceptions of parks as urban assets for physical activity: a mixed-methods study. Health Place 71, 102660
Giles-Corti, B., Broomhall, M.H., Knuiman, M., Collins, C., Douglas, K., Ng, K., et al., 2005. Increasing walking: how important is distance to, attractiveness, and size of public open space? Am. J. Prev. Med. 28 (2), 169–176. Supplement 2
Giles-Corti, B., Timperio, A., Cutt, H., Pikora, T.J., Bull, F.C.L., Knuiman, M., et al., 2006. Development of a reliable measure of walking within and outside the local neighborhood: RESIDE s Neighborhood Physical Activity Questionnaire. Prev. Med. 42 (6), 455–459
Han, B., Cohen, D., McKenzie, T.L., 2013. Quantifying the contribution of neighborhood parks to physical activity. Prev. Med. 57 (5), 483–487
Hansmann, R., Hug, S.-M., Seeland, K., 2007. Restoration and stress relief through physical activities in forests and parks. Urban For. Urban Green. 6 (4), 213–225
Hartig, T., Mitchell, R., De Vries, S., Frumkin, H., 2014. Nature and health. Annu. Rev. Publ. Health 35, 207–228
Hooper, P., Foster, S., Edwards, N., Turrell, G., Burton, N., Giles-Corti, B., et al., 2020. Positive HABITATS for physical activity: examining use of parks and its contribution to physical activity levels in mid-to older-aged adults. Health Place 63, 102308
Hughey, S.M., Wende, M.E., Stowe, E.W., Kaczynski, A.T., Schipperijn, J., Hipp, J.A., 2021. Frequency of neighborhood park use is associated with physical activity among adults in four US cities. J. Phys. Activ. Health 18 (5), 603–609
Joseph, R.P., Maddock, J.E., 2016a. Comparative analysis of five observational audit tools to assess the physical environment of parks for physical activity, 2016 Prev. Chronic Dis. 13, E166
Joseph, R.P., Maddock, J.E., 2016b. Observational Park-based physical activity studies: a systematic review of the literature. Prev. Med. 89, 257–277
Kaczynski, A.T., Wilhelm Stanis, S.A., Hastmann, T.J., Besenyi, G.M., 2011. Variations in observed park physical activity intensity level by gender, race, and age: individual and joint effects. J. Phys. Activ. Health 8 (Suppl. 2), S151–S160
Kaczynski, A.T., Besenyi, G.M., Stanis, S.A.W., Koohsari, M.J., Oestman, K.B., Bergstrom, R., et al., 2014. Are park proximity and park features related to park use and park-based physical activity among adults? Variations by multiple sociodemographic characteristics. Int. J. Behav. Nutr. Phys. Activ. 11 (1), 146.
Kajosaari, A., Laatikainen, T.E., 2020. Adults’ leisure-time physical activity and the neighborhood built environment: a contextual perspective. Int. J. Health Geogr. 19 (1), 35
Karmeniemi, M., Lankila, T., Ikaheimo, T., Koivumaa-Honkanen, H., Korpelainen, R., 2018. The built environment as a determinant of physical activity: a systematic review of longitudinal studies and natural experiments. Ann. Behav. Med. 52 (3), 239–251
Kruger, J., Mowen, A.J., Librett, J., 2007. Recreation, parks, and the public health agenda: developing collaborative surveillance frameworks to measure leisure time activity and active park use. J. Phys. Activ. Health 4 (Suppl. 1), S14–S23
Lachowycz, K., Jones, A.P., 2011. Greenspace and obesity: a systematic review of the evidence. Obes. Rev. 12 (5), e183–e189
Landis, J.R., Koch, G.G., 1977. The measurement of observer agreement for categorical data. Biometrics 33 (1), 159–174
Lee, P.H., Macfarlane, D.J., Lam, T.H., Stewart, S.M., 2011. Validity of the international physical activity questionnaire short form (IPAQ-SF): a systematic review. Int. J. Behav. Nutr. Phys. Activ. 8 (1), 115
Maddison, R., Mhurchu, C.N., 2009. Global positioning system: a new opportunity in physical activity measurement. Int. J. Behav. Nutr. Phys. Activ. 6
McCormack, G.R., Rock, M., Swanson, K., Burton, L., Massolo, A., 2014. Physical activity patterns in urban neighbourhood parks: insights from a multiple case study. BMC Publ. Health 14, 962
McKenzie, T.L., van der Mars, H., 2015. Top 10 research questions related to assessing physical activity and its contexts using systematic observation. Res. Q. Exerc. Sport 86 (1), 13–29
McKenzie, T.L., Cohen, D.A., Sehgal, A., Williamson, S., Golinelli, D., 2006. System for observing play and recreation in Communities (SOPARC): reliability and feasibility measures. J. Phys. Activ. Health 3 (Suppl. 1), S208–S222
Mouratidis, K., 2021. Urban planning and quality of life: a review of pathways linking the built environment to subjective well-being. Cities 115, 103229
Park, K., Ewing, R., 2017. The usability of unmanned aerial vehicles (UAVs) for measuring park-based physical activity. Landsc. Urban Plann. 167, 157–164 Park, S.H., Petrunoff, N.A., Wang, N.X., van Dam, R.M., Sia, A., Tan, C.S., et al., 2022. Daily park use, physical activity, and psychological stress: a study using smartphonebased ecological momentary assessment amongst a multi-ethnic Asian cohort. Mental Health and Physical Activity 22, 100440
Parks, Kruger J., 2008. recreation, and public health collaborative. Environ. Health Insights 2, 123–125
Petrunoff, N.A., Yi, N.X., Dickens, B., Sia, A., Koo, J., Cook, A.R., et al., 2021. Associations of park access, park use and physical activity in parks with wellbeing in an Asian urban environment: a cross-sectional study. Int. J. Behav. Nutr. Phys. Activ. 18 (1), 87
Richardson, E.A., Pearce, J., Mitchell, R., Kingham, S., 2013. Role of physical activity in the relationship between urban green space and health. Publ. Health 127 (4), 318–324
Sallis, J.F., Saelens, B.E., 2000. Assessment of physical activity by self-report: status, limitations, and future directions. Res. Q. Exerc. Sport 71 (Suppl. 2), 1–14.
Sallis, J.F., Cerin, E., Conway, T.L., Adams, M.A., Frank, L.D., Pratt, M., et al., 2016. Physical activity in relation to urban environments in 14 cities worldwide: a crosssectional study. Lancet 387 (10034), 2207–2217
Schipperijn, J., Cerin, E., Adams, M.A., Reis, R., Smith, G., Cain, K., et al., 2017. Access to parks and physical activity: an eight country comparison. Urban For. Urban Green. 27, 253–263
Sember, V., Meh, K., Soric, M., Starc, G., Rocha, P., Jurak, G., 2020. Validity and reliability of international physical activity questionnaires for adults across EU countries: systematic review and meta analysis. Int. J. Environ. Res. Publ. Health 17 (19)
Stewart, O.T., Moudon, A.V., Littman, A.J., Seto, E., Saelens, B.E., 2018. Why neighborhood park proximity is not associated with total physical activity. Health Place 52, 163–169
Sylvia, L.G., Bernstein, E.E., Hubbard, J.L., Keating, L., Anderson, E.J., 2014. Practical guide to measuring physical activity. J. Acad. Nutr. Diet. 114 (2), 199–208
Tcymbal, A., Demetriou, Y., Kelso, A., Wolbring, L., Wunsch, K., Wasche, H., et al., 2020. Effects of the built environment on physical activity: a systematic review of longitudinal studies taking sex/gender into account. Environ. Health Prev. Med. 25 (1), 1–25
Twohig-Bennett, C., Jones, A., 2018. The health benefits of the great outdoors: a systematic review and meta-analysis of greenspace exposure and health outcomes. Environ. Res. 166, 628–637
N. Edwards and P. Hooper
Uijtdewilligen, L., Waters, C.N.-H., Aw, S., Wong, M.L., Sia, A., Ramiah, A., et al., 2019. The Park Prescription Study: development of a community-based physical activity intervention for a multi-ethnic Asian population. PLoS One 14 (6), e0218247
Villeneuve, P.J., Jerrett, M., Su, J.G., Weichenthal, S., Sandler, D.P., 2018. Association of residential greenness with obesity and physical activity in a US cohort of women. Environ. Res. 160, 372–384
Walker, J.T., Mowen, A.J., Hendricks, W.W., Kruger, J., Morrow Jr., J.R., Bricker, K., 2009. Physical activity in the park setting (PA-PS) questionnaire: reliability in a California statewide sample. J. Phys. Activ. Health 6 (Suppl. 1), S97–S104
Washburn, R.A., Heath, G.W., Jackson, A.W., 2000. Reliability and validity issues concerning large-scale surveillance of physical activity. Res. Q. Exerc. Sport 71 (Suppl. 2), 104–113
Wolf, I.D., Wohlfart, T., 2014. Walking, hiking and running in parks: a multidisciplinary assessment of health and well-being benefits. Landsc. Urban Plann. 130, 89–103. World Health Organization, 2018. Towards More Physical Activity in Cities: Transforming Public Spaces to Promote Physical Activity–A Key Contributor to
Achieving the Sustainable Development Goals in Europe. World Health Organization. Regional Office for Europe.
Xu, Y., Wheeler, S.A., Zuo, A., 2022. The effectiveness of interventions to increase participation and physical activities in parks: a systematic review of the literature. Int. J. Environ. Res. Publ. Health 19 (19), 12590
Yi, L., Wilson, J.P., Mason, T.B., Habre, R., Wang, S., Dunton, G.F., 2019. Methodologies for assessing contextual exposure to the built environment in physical activity studies: a systematic review. Health Place 60, 102226
Yuen, J.W.M., Chang, K.K.P., Wong, F.K.Y., Wong, F.Y., Siu, J.Y.M., Ho, H.C., et al., 2019. Influence of urban green space and facility accessibility on exercise and healthy diet in Hong Kong. Int. J. Environ. Res. Publ. Health 16 (9), 1514
Zhang, R., Wulff, H., Duan, Y., Wagner, P., 2018. Associations between the physical environment and park-based physical activity: a systematic review. J. Sport Health Sci. 8 (5).