Sheffield Hallam University

NIHR159040

The impact of interactive electronic devices: understanding the mechanisms of benefits and harms on young children’s development, behaviour and health outcomes using a multimethod approach.

PROSPERO - CRD42024543727

Evidence synthesis, Meta-analysis, Methods - Qualitative methods, Methods - Quantitative methods, Observational study, Prospective study

Professor Liane B. Azevedo (https://orcid.org/0000-0001-9966-9414)

Dr Peter Smith, Library Research Support, p.r.smith@shu.ac.uk or library-research-support@shu.ac.uk

Sheffield Hallam University, data management purpose and policy requirements are provided in this webpage (https://www.shu.ac.uk/research/excellence/ethics-and-integrity/data-management) and sharing data is provided on this webpage (https://libguides.shu.ac.uk/researchsupport/sharing-data).

Early years settings (i.e., childminders, day nurseries and school nurseries)

In this section and the following, we present the Data Management procedures for Work Package 2 - Prospective Study. Work Package 3 - Qualitative Study information will be provided later once we finalise the protocol and receive ethical approval. Work Package 2 This is a one-year prospective cohort study. We will use a cluster sampling approach to recruit school nurseries, day nurseries or childminders (educational unit clusters) located in the Council areas of Kirklees, Wakefield, Calderdale and Bradford (West Yorkshire). We aim to recruit approximately 38 educational units from each Council area (Kirklees, Wakefield, Calderdale and Bradford), with the number of units sampled from each Council area being distributed approximately evenly across index of multiple deprivation (IMD) tertiles in each Council. The sampling frame comprises all children enrolled at these educational units who meet the eligibility criteria. Children will be eligible to participate if they are between 36 and 48 months old at enrolment, have received parent/carer consent for participation, and have provided verbal assent. Children will be ineligible if parents or child do not speak and/or understand English or if the child is clinically diagnosed with a developmental disorder by a medical professional before either baseline or follow-up assessments.

We estimated a sample size of 695 (adjusted for clustering) before attrition loss to achieve 80% power to detect an effect on the emerging abilities (i.e., cognitive, self-regulatory, language, numeracy and social development) score at an alpha level of 0.05 for the given effect size of 0.01 (Kuzik et al., 2022, doi:10.1186/s44167-022-00002-4). We are assuming 40% attrition pre-baseline and 15% attrition between baseline and follow-up, bringing to an estimated sample of 1,377. Data will be collected at baseline and one year later (i.e., follow-up). We will measure children's exposure to interactive electronic devices (i.e. time and content) and child emerging abilities (primary outcome). We will also include other secondary health, behaviour and educational outcomes (e.g., BMI, physical activity, motor skills, parent-child interaction and school readiness). Measurement of the exposure: Parents will be asked to download an app from Google Play or App Store on the mobile phone and/or tablet that the child uses. This app will gather data every 20 min from the application programming interface (API), including the app name and its category (such as education, entertainment, communications) and duration, frequency and time of the day when it was accessed. The research team will export the data to a web application for processing. Participants will be asked to delete the app once data collection is completed. Measurement of the primary outcome: emerging abilities (EA) measured by the Early Years Toolbox (EYT), which has been previously validated for this age group. Measures include cognitive, self-regulatory, language, numeracy and social development. Data will be recorded on an iPad at the educational unit which the child attends and automatically saved to a database created by the research team. Measurement of the secondary outcomes: 1)BMI: height and weight will be measured using a portable stadiometer and a calibrated scale; 2) 24-hour movement behaviour - 24-hour movement behaviour will be assessed by Actigraph GT3X-BT accelerometers. Children will be advised to continuously wear the accelerometer on their right hip for one week to obtain a minimum of three days of at least 16 hours (Fairclough et al., 2023. doi:10.1186/s44167-023-00021-9) ; 3) Total motor development score will be assessed by the NIH Toolbox (Reuben et al., 2013) and will include: a) ‘Standing long jump’ t, b) ‘Supine-timed up and go’, c) ‘One-legged standing balance’, d) ‘handgrip dynamometer’, e) 9-hole pegboard test; 4) Parent-child interaction will be measured using the StimQ preschool questionnaire (Dreyer et al., 2018); 5) School readiness will be measured by the early years foundation stage profile (EYFSP) which assesses seven areas of learning (communication and language, physical development, personal, social and emotional development, literacy and mathematics). The data will be provided by the educational unit in an anonymised format at the end of the follow-up period. Controlling variables: We are including several control variables. These are: 1) Demographics: sex, age, ethnicity and maternal education; 2) Parenting style (Robinson et al., 2001); 3) Hours of childcare attendance; 4) Presence of screen viewing policy at the educational unit; 5) Parent smartphone addiction (SAS-SV, Kwon et al. 2013, doi: 10.1371/journal.pone.0083558. This data will be collected via Qualtrics and exported as a .csv file. Data will be managed and stored in accordance with the Sheffield Hallam University Research Data Management Policy. Pseudonymised data will be stored on the University's secure networked drives. All data collected will be numeric, stored in spreadsheets or databases and stored as .csv files as this is a standardised format used by many applications, which facilitate data sharing and long-term re-use of the data. Storage space required for this study is estimated to be less than 50 GB. Software used to collect new data in WP2 include Actilife (Child movement behaviour), Qualtrics (demographics, parenting style, child self-regulation and behaviour, childcare setting screen viewing policies, school readiness), Redcap (weight, height, motor skills), Excel (child interactive electronic device use) and the Early Years Tooblox app and Azure (child emerging abilities).

Researchers undertaking the measurements are highly experienced in the procedures and analysis. We are utilising only validated tools that are well-established in the literature. The exposure and use of interactive electronic devices (e.g., tablets and smartphones) will be measured by an app that will access information directly from the participant's devices (Android or iOS) using mobile sensing software to capture screen time and app usage. The information about the app has been reported in Lind et al. 2023 (doi: 10.2196/38920). The app has been successfully used to measure smartphone use in children (Bagot et al., 2022 - https://doi.org/10.1016/j.dcn.2022.101150; Wade et al., 2021 - https://doi.org/10.2196/29426). The measurement of emerging abilities, our primary outcome measure, has been validated and widely used in the literature ( (https://www.eytoolbox.com.au/publications/search.html). We will use an administration fidelity checklist to assess practice across researchers before we start data collection. We will be recording a range of secondary outcomes (i.e. BMI (calibrated scales and repeated measures), movement behaviour (accelerometry Cliff et al. 2024, https://doi.org/10.1186/s44167-024-00054-8), motor development (NIH toolbox, Reuben et al., 2013, doi:10.1212/WNL.0b013e3182872e01), parent and child interaction (StimQ2, Cates et al. 2023, doi: 10.1371/journal.pone.0286708). These measurements have been used in the SUNRISE study, which is collecting data on children at this age in 63 countries around the world (https://sunrise-study.com/) and published in a protocol (Okely et al., 2021, https://doi.org/10.1136/bmjopen-2021-049267). Training videos and support have been provided by the SUNRISE team and have been used to inform our study protocol. Since child development is influenced by a number of covariates, we have selected the most commonly reported in the literature and suggested by our PPI group (i.e. sex, age, ethnicity, maternal education, parenting style (PSDQ- SV. Robinson et al. 2001), attendance to childcare, parental addiction to smartphones (SAS-SV, Kwon et al., 2013, https://doi.org/10.1371/journal.pone.0083558) and screen viewing policy). Data capture forms have been created for applicable measures to standardise data input and aid with data entry. In terms of data processing and analysis. We will conduct preliminary data cleaning, exploring whether values of continuous variables are within range, plausibility of means and standard deviations, and validity of coded categories. We will assess data distributions and identify any univariate outliers from graphical methods and from cases with very large, standardised scores disconnected from other scores and multivariate outliers by graphical methods and inspection of leverage/Mahalanobis distances, discrepancy and influence statistics. Any possible errors will be investigated on an individual basis. We will also investigate the extent, pattern and nature of missing data. If the proportion of missing data is small (below 5%), we will consider complete case analyses. If the amount or pattern of missing data precludes complete case analysis, we will consider data imputation. We will use multiple imputations due to their robustness to the type of data missingness. If imputation is conducted, we will conduct sensitivity studies by comparing results derived from data with and without imputation.

Data will be managed and stored in accordance with the Sheffield Hallam University Research Data Management Policy. Pseudonymised data will be stored on the University's secure networked drives. Data will be backed up automatically on a daily basis and be securely kept in two remote locations for a period of 90 days. At project close down, relevant data relating to this project will be securely archived in the Sheffield Hallam University Research Data Archive for 10 years from the last time any third party requested access to the data, and all data will be deleted from the Research Store.

Data will be backed up automatically and can be fully recovered in the case of accidents. Access to data folders is restricted to Sheffield Hallam University researchers working on the project using secure passwords. Folder and file names will be used to clearly identify a file's content, status, type, and version. Types of data will be defined and included in the documentation. A document control and version history system will be used for priority data files and documentation files. We will pseudo-anonymise the data from the very start of the data collection process. The principal investigator and research fellows from Sheffield Hallam will be the only researchers holding the code key to decode the data. The data will only be fully anonymised by the end of the data collection process in July 2027. We will give participants the right to withdraw from the study until this point. After this period, the data will be fully anonymised, and it will not be possible for participants to withdraw from the study.

If data is requested during the project period, a data-sharing agreement will be issued between Sheffield Hallam University and the data requester. The data-sharing agreement will adhere to the legal and institutional requirements of Sheffield Hallam University and ethical requirements. Any data sharing will be restricted to ensure it does not impact the research team's publication plan. At the end of the project, all data (raw and analysed) will be deposited in the Sheffield Hallam University Research Data Archive (SHURDA) with a six-month embargo period to allow for publications. Fully anonymised data will be deposited in a community-recognised repository under a CC-BY license. This approach to open access will ensure the project's legacy by enabling follow-up and/or longitudinal studies to be compared with these initial raw data sets.

At the outset of analysing the follow-up data in July 2027, the linkage key that connects identifiers to personally identifiable information will be destroyed, ensuring the data is fully anonymised. No additional modifications to the data formatting will be made. The fully anonymised data will be stored in the Sheffield Hallam Research Data Archive (SHURDA). This data will be retained for ten years from the last time a third party requested access.

All data will be stored in CSV format (comma-separated values). CSV files are compatible with many software applications and will enable potential users to easily store and (re-)use the data. Data will be shared via a repository, and requests can be made directly to the Sheffield Hallam Research Data Archive.

Professor Liane Azevedo, the Chief Investigator for the project, will oversee data management, which includes data archiving and sharing. The research team from Sheffield Hallam University comprises Dr Colette Marr (Trial Manager), Dr Amy Hughes (Research Fellow), and Dr Joao Greca (Research Fellow). This team will be responsible for data capture and the creation of metadata. Professor Azevedo will take overall responsibility for implementing the Data Management Plan (DAMP) and reviewing and revising it as necessary. She will consult with the research team—Dr. Marr, Dr. Hughes, and Dr. Greca—during this process. Throughout and after the project period, we will collaborate with Sheffield Hallam’s Research Repository (SHURDA) and the Library Research Support teams to ensure effective data management and governance. They will provide storage and archiving services, assist with metadata records, issue a Digital Object Identifier (DOI), and facilitate access to the data when appropriate.

Time has been allocated in the project budget to cover the researchers' costs, including Prof. Liane Azevedo (FTE 0.2), Dr. Colette Marr (FTE 1.0), Dr. Amy Hughes (FTE 0.8), and Dr. Joao Greca (FTE 0.8). They will be responsible for planning data collection and preparing the data for analysis. Additionally, we will receive support for data storage and sharing from Sheffield Hallam's Research Repository (SHURDA) and the Library Research Support teams. No further changes to data formatting will be necessary after depositing the data in the university repository, as all required actions will have been completed throughout the research process.