Movement behaviours, air pollution and health in school-aged children: a cross-sectional study to guide the co- creation of healthier environments - The MOVE-AIR Project

doi:10.21203/rs.3.rs-5312146/v1

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Study protocol

Movement behaviours, air pollution and health in school-aged children: a cross-sectional study to guide the co- creation of healthier environments - The MOVE-AIR Project

https://doi.org/10.21203/rs.3.rs-5312146/v1

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The MOVE-Air study was designed to explore the role of movement behaviours on the association between air pollutants and health outcomes in Portuguese children. Secondarily, it aims to characterize the settings (both indoor and outdoor) where children are exposed to air pollutants and to co-create solutions with participants to mitigate the exposure to air pollutants in children´s daily life. This study aims to detailed describe the MOVE-AIR study protocol. Data from at least 22 primary school children aged 9-to-11 years will be assessed for indoor and outdoor air pollutants (PM_2.5 and PM_10,and carbon dioxide), geo-tracked for distinct settings (i.e., home/school, indoor/ outdoor) along the day, through an optical monitoring sensor with global position system incorporated. Health-related biological outcomes, such as Interleukin 6 (IL6), Tumor Necrosis Factor Alpha (TNF- α), and oxidative parameters, including Total Antioxidant Status (TAS), and Total Oxidant Status (TOS) will be evaluated, and the Oxidative Stress Index (OSI) will be calculated. Children´s cardiopulmonary fitness will be assessed through the Shuttle-run Test, and movement behaviours will be evaluated through accelerometers (wGT3-x). Children´s sex, age, and parental socioeconomic status will be provided by parents through a questionnaire. The influence of movement behaviours in the link between pollution and health will be analyzed through compositional analysis and structural equation models using R software (4.2.0). A sub-sample of class teachers, school leaders, parents, and children will be invited to a co-creation process to create solutions to mitigate their daily exposure to air pollutants. The results will contribute to further understanding the influence of movement behaviours in the association between air pollution and health, adding a biological layer to the mechanistic links underlying these potential relationships that have not been explored in this target population. Finally, enhancing our comprehension of the living environments and contexts where children are more exposed to air pollution can help to cooperatively create solutions to mitigate their daily exposure to those harmful pollutants.

air quality

movement behaviours

biomarkers

urban health

Clean air is essential to human health whereas ambient air pollution is a critical environmental factor of mortality and morbidity worldwide [1], responsible for 4.2 million deaths in 2016 [2]. Exposure to air pollution is associated with a wide range of health harms, including respiratory and cardiovascular diseases [3], and increased risk of dementia in the elderly [4], but it is also a significant risk to children’s health. Their developing immune system and lungs, associated with their relatively high inhalation rate [5], put children at risk of detrimental clinical consequences, such as pulmonary and systemic inflammation, increased oxidative stress [6], premature death, and delayed cognitive development [7].

Alongside other major global health risks, physical inactivity was estimated to cause 3.2 million deaths [2]. In contrast, physical activity (PA) has been shown to enhance cardiopulmonary fitness, vascular and endothelial activity, and resistance to infection [8, 9]. Exercising in polluted environments may increase the risk of inhalation of pollution particles [e.g., airborne particulate matter < 2.5 µm (PM _2.5) and < 10 µm (PM₁₀)], increasing respiratory rate and reducing nasal resistance [10–12]. Studies even suggest that regular PA in polluted environments might impair cardiorespiratory fitness and pulmonary function, contributing to upper respiratory tract infections, pulmonary dysfunction, and upper airway hyperactivity [13]. In 2021, a study conducted by Varaden and colleagues [14] assessed English children´s exposure to air pollutants using backpacks with built-in air quality sensors. The authors identified that children who walked to and from school through busy main roads were exposed to 33% higher levels of air pollution than those who travelled through back streets.

Indeed, outdoor air pollution, PA, and other co-dependent movement behaviours, namely sleep and sedentary behaviour [15, 16], seem intrinsically related and might cooperatively impact children´s health. PA, sedentary behaviour, and sleep are known as movement behaviours, which coexist on a continuum that interacts within a 24-hour period. Optimal movement behaviours, including high PA, sufficient sleep, and minimal sedentary behavior, lead to better health outcomes among children and youth [17]. However, fewer than 10% of individuals meet these guidelines [18]. For instance, high concentrations of outdoor air pollutants have been positively linked to poor sleep quality, though findings are inconsistent for sleep duration and sedentary behaviour [15]. This is critical because poor sleep has also become an important public health concern, affecting as many as one-third of all children [19]. In 2021, Kim et al. [20] conducted a review on the impact of outdoor air pollution on movement behaviours, revealing two conflicting patterns: one depicting an association between reduced PA, increased sedentary behaviour, and poorer sleep; and other suggesting no association between air pollutants and movement behaviours. Thus, evidence on the topic is still lacking. Specifically, it remains uncertain whether children exposed to air pollution experience adverse health effects primarily due to their suboptimal movement behaviours or the direct impact of air pollution. Additionally, it is unclear if children with healthy movement behaviours are less susceptible to the detrimental effects of air pollution.

Air quality has traditionally been monitored using fixed-site continuous air quality stations, which are typically located at background sites or traffic hotspots. While these stations are effective for tracking overall trends and meso-level regional differences, their high installation and maintenance costs restrict data collection to a limited number of locations making them not useful for individual-level studies [21]. Additionally, fixed-site measurements may not accurately reflect the air pollution dynamically experienced by individuals, due to the spatial and temporal variability of air pollution [22]. Note that air pollution varies greatly over small distances, and the careful consideration of areal units to assign exposure is essential to avoid exposure misclassification [23].

In parallel, indoor air pollution has traditionally received less attention than outdoor pollution. Studies exploring the association between indoor air pollution, children´s movement behaviours, and health outcomes are scarce, even though indoor air pollution levels are typically twice as high [24], and school-aged children spend between 23% and 35% of the day at indoor environments at schools. This high-risk period includes morning hours when the highest peak in ambient air pollution is commonly noted [25]. This is important because the exposure to contaminants in such school indoor environments has a great influence on the performance and attendance of students at schools [26]. Moreover, exposure to distinct concentrations of air pollutants in specific settings (i.e., indoor/outdoor) could have differential associations with movement behaviours and, consequently, with health outcomes.

Understanding these relationships may help to uncover the potential mechanisms underpinning detrimental health outcomes, and may be of clinical importance in planning preventive and therapeutic strategies. Moreover, aligned with this scientific knowledge, proposals to mitigate children exposure to air pollutants should involve relevant stakeholders to align the scientific understanding with their needs and concerns, while reinforcing the applicability of research, and increasing the chances of successful uptake [27].

Based on the exposed, the MOVE-AIR project was designed to explore the role of movement behaviours on the association between air pollutants and health outcomes in Portuguese children. Secondarily, it aims to characterize the settings (both indoor and outdoor) where children are exposed to higher and lower levels of air pollutants and to co-create solutions with participants to mitigate the exposure to air pollutants in children´s daily life. The current study aims to provide a detailed description of the MOVE-AIR protocol.

This cross-sectional observational study will be designed to explore the influence of movement behaviours on the association between air pollution and health outcomes of primary school-children. A collaboration with the Northern Regional Educational Board and with Physical Education (PE) teachers from the selected primary schools will be established to collect the primary data in the school context. We will use the logistic and material resources from the CIAFEL (Centro de Investigação em Actividade Física, Saúde e Lazer) and the EPIUnit (Unidade de Investigação em Epidemiologia) research centres to optimize financial and human resources for the project.

The project will follow scientific procedures to describe observational studies (STrengthening the Reporting of OBservational studies in Epidemiology- STROBE) [28].

Ethical issues and data protection:

This project will follow all the ethical aspects related to research with human beings and vulnerable populations, and was approved by the Ethics Committee of the Faculty of Sport University of Porto (CEFADE 32-2023). The study follows the EU General Data Protection Regulation under close supervision of the Data Protection Office of the Institute of Public Health from the University of Porto (ISPUP).

Participants and their legal guardians will be invited for voluntary participation. Given the participants' age, in addition to their assent, written consent will be asked of their guardians, approving the involvement in data collection, storage, and use of the information for research purposes. Risks caused by the cardiorespiratory fitness assessment are of minor impact and similar to those in Physical Education classes and school breaks (e.g., falls, sprains). We will minimize it by close interaction with the educators, and including only small groups (one class per session) for assessment. Although the integration of GPS technology in tracking children may raise ethical concerns related to children´s privacy, we will develop a robust consent mechanism involving parents, children, and the school, to ensure transparent communication on the purposes and security of the assessed information, as well as the empowering of families and schools with the right to choose how and when to use the device. We will use devices purchased specifically for this purpose and not mobile applications, so the data collected will be maintained offline and available only to the research team. All project-specific documents (except signed consent) will be stored securely in confidential conditions, and the participant will be referred by an anonymous identification code, not the name or other trackable information. A protected cloud-based platform will be designed and open to the research team, to centralize data into a common repository. Data will be made available just to members of the MOVE-AIR project. Once data analysis is complete, data will be kept for three years after the project is complete, and then will be destroyed following data destruction standard guidelines [29]. All the participants and legal guardians will receive individual reports, and the schools will receive a general anonymous report.

Setting and participants

The project will be conducted in Porto Metropolitan Area. Three primary schools in the urban parishes of Matosinhos municipality, and three primary schools in the suburban parishes of Valongo municipality will be conveniently invited to be part of the study. Schools in urban and suburban areas will be selected to ensure enough variability in the air quality parameters to be assessed. For instance, in urban areas, there are schools in the city centre, close to the beach and in the Porto airport zone, while in the suburban area, the schools are in agricultural areas. Currently, there is no study that assessed the associations under investigation and our primary outcomes in the target population. The minimum sample size required for this project was estimated using typical values for the health outcomes to be measured, considering an alpha error of 0.05, and 15% increase/decrease due to exposure, and an effect size of 0.36. Based on these criteria, the minimum sample size of 22 children (9-to-11 years-old) was estimated to be recruited. To account for anticipated refusals and drop-outs during the assessment period, an additional 20% was added, bringing the total number of children to be recruited to 27. Typical neurodeveloped children who agree to participate and whose legal guardians give consent will be eligible for the study.

Since schools have at least one class for each of the 3rd and 4th level of schooling (approximately 20–25 children per school), 6 schools (3 in each living context) will be invited to participate in the project to recruit the number of children that we need to assess, since this number is expected to be lower in suburban areas due to the lower number of inhabitants of the selected suburban context.

We will obtain all pedagogical and institutional approvals in each school. The pre-screened schools will be contacted by a research team member to ensure they meet our inclusion/exclusion criteria (i.e. agree in participating; provide a room for biological outcomes´ assessments; provide a PE class for cardiorespiratory fitness assessment). If a pre-screened school does not attend the eligible criteria, other school in the same area will be selected.

Assessments

All participating children will undergo assessments involving various exposure variables, outcome variables, movement behaviours, and additional covariates. Regarding exposure variables, indoor and outdoor air pollutants, specifically PM_2.5, PM₁₀ and carbon dioxide (CO₂), will be measured. The settings (indoor vs. outdoor) and the time children spend in each environment throughout the day will be tracked using a global positioning system (GPS). Health outcomes will include the evaluation of salivary inflammatory biomarkers, such as Interleukin 6 (IL-6) and Tumor Necrosis Factor Alpha (TNF-α), as well as oxidative stress parameters like Total Antioxidant Status (TAS) and Total Oxidant Status (TOS). From these measurements, the Oxidative Stress Index (OSI) will be calculated. Additionally, cardiopulmonary fitness will be assessed through a cardiorespiratory field test. Movement behaviours, including PA, sedentary behaviour, and sleep duration, will be monitored using accelerometers. Information regarding children's sex, age, and parental socioeconomic status will be collected through parent-provided questionnaires. Below, we describe these assessments in more detail.

Biomarkers

Children will be directed individually to a private school´s room to assess biological health outcomes. They will receive Salivette® tubes (SARS51.1534, SARSTEDT, DE) to collect a saliva sample between 8.00–10.00 a.m., at least 60 minutes after liquid or solid food intake or tooth brushing. The collected material will be stored in ice-cold conditions and immediately transported to the Biochemistry laboratory of the Faculty of Sport. After swab centrifugation, saliva will be collected, aliquoted in microtubes, and stored at -80ºC for later assessment of pro-inflammatory markers and oxidative stress parameters. Salivary Total Antioxidant Status (TAS) is usually used to measure the overall antioxidant level, and Total Oxidant Status (TOS) is usually used to estimate the overall oxidant level. These parameters will be measured by commercially available kits (EBC-K801-M and E-BC-K802-M, respectively; Elabscience, USA). The ratio of TOS to TAS will be used to calculate the Oxidative Stress Index (OSI), an indicator of the degree of oxidative stress. Salivary pro-inflammatory biomarkers IL-6 and TNF-α will be quantified by commercially available ELISA kits, according to the company instructions (#43057 and #430207, respectively, BioLegendm, USA). Children with evident periodontal disease (e.g. periodontitis) or dental plaque will be excluded of the saliva collection process to avoid bias [30].

Cardiopulmonary fitness

Children´s cardiopulmonary fitness will be assessed using the reliable, valid, and feasible cardiorespiratory fitness test 20-mSRT [31], based on the prototype of Léger [32], to predict maximal aerobic power (i.e., the maximal oxygen uptake - \(\:\dot{V}{O}_{2max}\); ml·kg^-1·min^-1) from maximal running speed, defined as the maximal aerobic speed. This test will be performed by running continuously between two points at a distance of 20 m. The time of change from one point to another will be determined by audio feedback via a characteristic ‘beep’ sound. Children will run to an audible signal pace at an initial speed 8.5 km·h^-1 and speed will be increased by 0.25 km·h^-1 every minute. After each minute, a vibrating sound will indicate an increase in speed level (level change). This test terminates when the participant: i) is unable to continue the test because of fatigue or other symptoms (voluntary withdrawal), or ii) fails to reach the marker on time for 3 times.

Movement behaviours: Physical activity, sedentary behaviour, and sleep

Participants will wear tri-axial accelerometers ActiGraph wGT3X on their left hip for 24-hours per day, over 7 consecutive days. The devices will be initialized to record at 90 Hz and the subsequent data will be downloaded using ActiLife (versions 6.13.4). The raw data files (gt3x format) will be processed in R using package GGIR version 2.8-2. The raw triaxial accelerometer signals will be converted to one omnidirectional measure of body acceleration (Euclidean Norm Minus-One; ENMO) expressed in milligravitational units (mg) [33]. For this, the vector magnitude (VM) will be taken from the three axes and then subtracted by the value of gravity (g) as in (x² + y² + z²) ½– 1, after which, negative values will be rounded up to zero, referred to ENMO. Data will be further reduced by calculating the average values per 1-s epoch. Then, we will calculate the average of these 1-s epoch values over the 7 monitored days to represent the average acceleration to be included in the statistical analyses. Signal processing will be done offline in R (http://cran.r-project.org/). The resulting values will be expressed in gravity-based acceleration units (g), where g = 9.81 m·s^− 2. Children´s specific hip ENMO cut-points of 48 mg [34], 201 mg, and 707 mg [35] will define the estimated upper threshold of sedentary time and lower threshold of light, moderate, and vigorous physical activity, respectively. Sleep duration will be estimated using a polysomnography-validated accelerometer algorithm [36] valid for children´s use in the hip, based on the distribution of change in the z-angle (i.e., corresponding to the axis positioned perpendicular to the skin surface). All the children will receive the accelerometer diary with instructions, which will be completed by their parents. Parents will receive a daily message via mobile to remember about the use of the device and the completion of the diary.

Air pollutants and global position system

Children will use optical sensors to continuously assess PM₁₀ and PM_2.5. CO₂ will be measured by nondispersive infrared sensors, following national regulations for resolution and maximum admissible errors. The settings (indoor/outdoor) where children spend their daily time will be tracked using a global position system (GPS). Ambient temperature and relative humidity will also be measured. Due to battery life constraints, air pollutants will be measured for 48 hours, comprising two representative weekdays (between Tuesday and Thursday). During this period, the sensors will be used at all times, precisely tracking air quality and position values at every minute.

The selection of sensors for air quality pollutants was based on the following criteria: the existence of validation data published in peer-reviewed journals and meeting the specifications presented in Despacho 1618/22 of February 9th [37] (Table 1).

Table 1

– Requirements for indoor air quality measurement instruments, according to Portuguese Legislation – in Despacho 1618/22 of February the 9th .
Parameter	Reference method	Equivalent methods	Max admissible error	Resolution
CO₂	Nondispersive infrared (NDIR) sensors	Electrochemical sensors Fourier transform infrared spectroscopy (FTIR) Photoacoustic sensor (PAS)	50 ppm or 10%, whichever greater	1 ppm
PM₁₀ and PM_2.5	Gravimetry	Laser or UV-based optical dispersion Beta ray absorption sensors Tapered element oscillating microbalances (TEOM) Piezoelectric resonance	10 µg/m³ or 10%, whichever greater	1 µg/m³

The MH-Z19B nondispersive infrared CO₂ sensor was selected to integrate the system [38], while the NOVA SDS011 optical dispersion sensor was used to measure PM₁₀ and PM_2.5 [39]. The DHT22 sensor was used to measure ambient temperature and relative humidity [40]. The NEO6MV2 module was used to track geolocation as latitude and longitude. An HW-125 SPI reader with a 16GB microSD card was used for local data storage. A DS3231 real-time clock (RTC) equipped with a 3V emergency battery was used to track time. The inclusion of the RTC over GPS time was to avoid timestamp data losses due to eventual signal loss. The system was continuously supplied 5V DC through a commercially available external power bank (BI-B61). The system, which we named as MIDAS (Fig. 1), was controlled by an ESP32 microcontroller and the firmware was programmed in C++, using Arduino IDE. The ESP32 was chosen due to being low-cost, having integrated Bluetooth and showing high overall performance with low energy expenditure. MIDAS was programmed to take a measure of each parameter every minute, including date, time, temperature (ºC), relative humidity (%), CO₂ (ppm), PM₁₀ and PM_2.5 (ug/m³), and latitude and longitude (º). In order to improve precision, particulate matter measurements were based on the average count of particles performed in the last 30 seconds, calculated by the SDS011, rather than the instantaneous count [39]. The sensors were installed into a separate case positioned in the backpack strap to capture readings close to the children's breathing area.

Data collection protocol

The research team will be in each school for one month. This period was established to guarantee two weeks of assessments for each class, due to the number of available devices to assess at least 22 children in each school. The class will perform the Fitnessgram Test, and each child will receive an accelerometer to be used for 7 consecutive days, and a backpack containing the optical air quality monitoring system and GPS, to be used for 2 consecutive days (from Tuesday to Monday), and will be instructed on the correct way to use them. In that day, all the children will be provided with parent´s questionnaire to collect sociodemographic information (please see Supplementary file 2) and an accelerometer diary, along with clear instructions for completion. At the beginning of the second week, children will return the devices, the questionnaire, and the diary, and will carry out the salivette® assessment. The entire assessment protocol will last 7 days for each class. In the subsequent week, another class will be assessed.

Data processing and statistical analysis

We will synchronize location and accelerometer data. The GPS and accelerometer data will be matched by date and time using existing software to obtain a dataset where for each recorded GPS point there is a measure of activity.

The location-based categorization of the matched data points will be conducted in a geographical information system, ArcGIS Pro 3.1 (ESRI, Redlands, CA, USA). Each participant’s data points will be imported into ArcGIS Pro. Participant’s home and school will be geocoded and also imported into ArcGIS Pro. Participant’s data points will then be overlayed with home and school locations, as well as cartography depicting administrative/census boundaries, land uses, green space/vegetation levels, proximity to roads and industry, and other geographical features. This will allow us to characterize children’s activity spaces in terms of setting (home and school, outdoor and indoor) and in terms of built, biophysical and social characteristics and to identify hotspots of high and low air pollution exposure.

For the statistical analysis, descriptive statistics such as the mean and standard deviation, or median and interquartile range (IQR), will be used for continuous variables. Categorical variables will be reported as frequencies and percentages. To compare independent variables, either the Student’s t-test for independent samples or the Mann–Whitney test will be applied, depending on the distribution of the variables. The chi-squared test or Fisher’s exact test will be used to compare proportions.

Health-related outcomes will be operationalized according to their nature. Cardiopulmonary fitness will be computed as the number of completed laps and maximal oxygen consumption (VO₂max) will be estimated according to the following equation (Eq. 1):

VO ₂ max = 31.025 + 3.238(speed, km.hr ^− 1 ) − 3.248(age, yr) + 0.1536(age ⋅ speed) (Eq. 1)

Results of the shuttle run test will be qualitatively interpreted according to normative data provided in Tomkinson et al [41]. Markers of inflammation (IL6, TNF- α) and oxidative stress (TAS, TOS, and OSI) will be operationalized in isolation, and correlations will be explored with other relevant variables such as exposure to ambient pollutants and physical activity volume and intensity metrics. Movement behaviours will be operationalized in isolation and as a composition of 24-hour behaviours, using compositional data analysis (CoDA), as it has been established as a suitable statistical method to study 24-hour movement behaviour composition [42].

To assess the moderating effect of movement behaviours on the association between air pollution and health outcomes, we will test for both additive and multiplicative interactions by including interaction terms in the regression models. Logistic regression models will be used for dichotomous outcome variables, while linear regression models will be employed for continuous outcome variables. The indirect effects of movement behaviours on this association will be estimated using structural equation modelling (SEM) with the R package "lavaan". All models will be adjusted for potential sociodemographic confounders, including the child's age and sex, as well as the parent's socioeconomic status. All statistical analyses will be performed using R software (version 4.2.0, http://www.R-project.org, The R Foundation).

Co-creation process

Based on their availability and interest to participate, a sub-sample of children most exposed to air pollution, plus class teachers, school leaders and parents will be invited to a participatory 3-stage co-creation process, that will be facilitated by the research team. The goal of this process phase is to define solutions to mitigate the exposure of children to air pollutants in their daily lives. The stages will be aligned with the Double Diamond Design Approach (DDDA) by employing divergent and convergent thinking processes, which reflects how the specialists will discover, define, develop, and deliver solutions to the ‘problem’, and how best facilitate real-world implementation [43]. The DDDA is a well-regarded approach within design thinking and has been used in other co-creation research involving children [44]. The process will also observe the four principles of co-creation advanced by Leask and colleagues [45]: planning, conducting, evaluating and reporting.

Prospective participants will be invited to a session where members of the research team will present the co-creation process, including its goals, its different stages and methods, and the role that both participants and researchers will have. Formal informed consent will be requested from participants willing to accept (in the case of children, consent will be requested both to the children and their legal guardians). The first stage of the co-creation process will consist in another session where researchers will share scientific evidence about the health impacts of air pollutants on children’s health. This step will integrate the findings of the previous phase of the project in the co-creation process, and is consistent with the idea that participants have the right to receive information about the evidence base for the health issue being targeted, in order to inform their decisions. Stage two will consist of a participative workshop. Adult stakeholders (class teachers, school leaders and parents) will be divided in smaller groups, which will then engage in various activities in order to develop individual and place level solutions to mitigate the exposure to air pollutants in children´s daily lives. Each group will be engaged in a brainstorming session, generating ideas about the mitigation of exposure to air pollutants in children’s daily lives, corresponding to the “discover” phase of the DDDA. This will be followed be “define” phase: the generated ideas will then be discussed, refined and further developed into proposed solutions. By the end of the session, each group will present its solutions, and participants will vote on the most relevant ones to be included in the proposal. PA, epidemiology, health geography and sociology specialists who conduct the workshop will integrate existing research evidence using creative methods (e.g., post-it note tasks, worksheets, and drawings) to facilitate group´s discussion. To engage the children in the process, without them feeling inhibited in expressing their thoughts in the presence of adults, stage two consists of a similar workshop process with a children´s groups. Stage three, the “develop” phase, consists of a meeting with the whole group to present the solutions that were developed and selected on the two previous co-creation workshops, to hear feedback from all participants and structure a proposal to be published and discussed with decision makers and the schools involved (the final “deliver” phase of the DDDA). Finally, at the end of the session, participants will complete an evaluation questionnaire of the co-creation process. Data collection tools including audio records, observation notes, participant worksheets, and drawings will be used with the intention to illuminate discussion points and allow the participants voices to be represented. Data collected from the co-creation process will be subjected to an inductive thematic analysis, following the Braun and Clarke reflexive thematic analysis approach [46, 47], using NVIVO 14.0.

Major expected outcomes

We hypothesize that: a) children exposed to settings with greater air pollutants concentration will have increased inflammatory markers, oxidative stress and lower cardiopulmonary fitness; b) children less exposed to ambient air pollution will have healthier movement behaviours profile (high PA, sufficient sleep, and minimal sedentary behaviour) and thereby lower inflammatory markers, oxidative stress and better cardiopulmonary fitness; and c) the adverse impact of air pollutants on children´s health are attenuated or absent for those children that are moderately active, with greater sleep duration and quality, and less sedentary. Furthermore, the co-creation process will contribute to increase participants´ awareness and understanding on the topic and their health consequences.

Dissemination

The project´s plan for results dissemination includes both scientific dissemination and science outreach and translation activities to achieve distinct interested groups in a way that all may benefit with the results. Scientific dissemination will be based on publications and presentation of the results at conferences. Along the assessments, the research team will prepare individual reports, translating the results into an easily understandable language to participants, parents, and school staff. Furthermore, we will communicate research evidence in a way that is meaningful to decision makers and the general population. This will be accomplished through the creation of information factsheets and infographics summarizing the most practically impactful project results; TV and radio interviews and social media; press releases of major project results; and open-access publications.

The MOVE-AIR project addresses the intersections between air pollution, movement behaviours, and health outcomes in children. This research is particularly relevant given the important implications of air quality on public health and the unique vulnerabilities of the pediatric population.

Understanding the impact of air pollution on children’s health is critical because children are more susceptible to pollutants due to their higher respiration rates and ongoing development [5, 6]. The hypothesis that children with healthier movement behaviours (i.e., higher PA, adequate sleep, and lower sedentary time) may experience reduced adverse effects from air pollution is both innovative and plausible. Indeed, previous studies have suggested that PA can enhance cardiovascular and respiratory health, potentially mitigating some harmful effects of pollution [11, 12]. Conversely, high pollution levels could impair these benefits by aggravating respiratory and cardiovascular issues. Thus, a nuanced understanding of how movement behaviours interact with pollution exposure is of major public health relevance.

Considering that children are especially susceptible to air pollution, it is critical to understand how movement behaviours influence the association between air pollution and children´s health, especially because most children live in urban environments characterized by elevated levels of air pollution [48]. This project could inform public policies on health, urban planning, and transportation by reducing health risks for children, promoting green spaces, supporting active commuting, and implementing traffic regulations. The co-creation process and citizen science approach will generate feasible strategies to guide targeted public policies.

Moreover, the MOVE-AIR project is timely and aligns with global and national health goals, including the United Nations’ Sustainable Development Goals (SDGs) 3 (“Good health and well-being”), and 11 (“Sustainable cities and communities”), of the United Nations, with the Global Action Plan on Physical Activity 2018–2030, and with The Portuguese National Strategic Plan for the Promotion of Physical Activity, Health and Well-Being − 2016–2025.

The project also addresses the often-overlooked aspect of indoor air quality. Given that children spend significant time indoors at schools and homes, understanding how indoor air pollutants contribute to health outcomes, alongside outdoor pollution, is essential. Previous studies have indicated that indoor pollution can be significantly higher than outdoor levels [49], yet there is limited research on its impact on children’s health in conjunction with movement behaviours. By exploring both indoor and outdoor environments, the MOVE-AIR project aims to provide a comprehensive view of pollution exposure and its health implications.

An important strength of the MOVE-AIR project is its emphasis on co-creation and community involvement. Engaging children, parents, teachers, and school leaders in the research process not only enhances the study’s relevance and applicability but also ensures that the solutions developed are practical and grounded in real-world experiences. This participatory approach may lead to more effective and widely accepted interventions, thereby translating research findings into tangible improvements in public health.

Moreover, while the MOVE-AIR project collects sensitive spatial data, the research team will adhere to GDPR regulations and implement robust geoprivacy-preserving strategies to safeguard participants' privacy [50]. This includes utilizing non-commercial, project-specific wearable devices designed explicitly for the study. In fact, a significant innovation step of the project is the development of the MIDAS system, a low-cost system developed by the team that integrates air pollution, meteorological data, and location tracking. This novel system facilitates the simultaneous and comprehensive assessment of multiple parameters, enhancing the efficiency of the data collection process. This is particularly significant given that Portugal's fixed air quality monitoring network is sparse, which limits the ability to conduct localized and individual-level health research.

Furthermore, the project will employ compositional data analysis (CoDA) to investigate 24-hour movement behavior composition, providing a better understanding of how different movement behaviours interact and contribute to health outcomes throughout the day, which will offer information that conventional analyses might fail to capture.

Despite its strengths, the MOVE-AIR project will face several challenges. Ensuring consistent and accurate data collection across multiple settings and participants requires meticulous planning and execution. Similarly, measuring children’s exposure to air pollution presents logistical challenges, especially in terms of ensuring compliance with the monitoring protocol. Furthermore, the cross-sectional study design of the MOVE-AIR project will limit our ability to establish causal relationships. Future research should build on our findings and explore longitudinal impacts to better understand how exposure to air pollution and movement behaviors influence health over time. Additionally, our focus on a convenience sample of schools in specific geographical locations may affect the external validity of the study. Expanding research to include a broader range of geographical locations and diverse populations could enhance the generalizability of the findings and improve the applicability of the recommendations.

In conclusion, the MOVE-AIR project represents a significant step towards understanding and addressing the multifaceted issue of air pollution and its effects on children's health. Its outcomes have the potential to inform public health policies and practices, contributing to healthier environments and improved well-being for children. The emphasis on co-creation and real-world assessment using personal sensing underscores the project's commitment to addressing a critical public health issue in a meaningful and impactful way.

Funding information

CM; RZ; HF; and MPS were funded by Fundação para a Ciência e Tecnologia (FCT) under grants UIDB/00617/2020 (DOI:10.54499/UIDB/00617/2020); and UIDP/00617/2020 (DOI: 10.54499/UIDP/00617/2020).

JR is funded by the Fundação para a Ciência e Tecnologia (FCT) under Grant Stimulus for Scientific Employment Individual Support (2020.01350.CEECIND), with DOI reference 10.54499/2020.01350.CEECIND/CP1623/CT0001.

This work was supported by FCT - Fundação para a Ciência e Tecnologia, I.P. under Grants UIDB/04750/2020 (DOI:10.54499/UIDB/04750/2020) and LA/P/0064/2020 (DOI: 10.54499/LA/P/0064/2020).

AIP was supported by the Fundação Para a Ciência e Tecnologia (FCT; grants UIDB/00617/2020, DOI: 10.54499/UIDB/00617/2020 and LA/P/0064/2020, DOI: 10.54499/LA/P/0064/2020) and FCT grant (DL57/2016/CP1455/CT0001, DOI:10.54499/DL57/2016/CP1455/CT0001).

R.Z. was supported by LA/P/0064/2020

AIR was supported by National Funds through FCT, under the ‘Stimulus of Scientific Employment – Individual Support’ programme within the contract CEECIND/02386/2018 (https://doi.org/10.54499/CEECIND/02386/2018/CP1538/CT0001).

Author contributions

CM, JR, JPS, and AIR contributed to study conception, design, and proposal development. AP, HF, LB, MPS, and RZ reviewed the manuscript. All authors read and approved the final manuscript.

Data availability

Data are available from the authors upon reasonable request.

Competing interests

The authors declare no competing interests.

Author Contribution

CM, JR, JPS, and AIR contributed to study conception, design, and proposal development. AP, HF, LB, MPS, and RZ reviewed the manuscript. All authors read and approved the final manuscript.

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No competing interests reported.

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Movement behaviours, air pollution and health in school-aged children: a cross-sectional study to guide the co- creation of healthier environments - The MOVE-AIR Project

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