The Effect of Mobile Devices on the Development and Health of Young Children (iKids)

Understanding the Impact of Interactive Electronic Devices on Young Children's Development and Health: the iKids Study

Interactive electronic devices (IEDs) have become a common part of young children's lives, yet research on this topic remains limited. Most studies utilise cross-sectional designs and present inconsistent evidence regarding the benefits and harms of IED use. Some findings suggest that IEDs may negatively impact sleep quality, be linked to visual impairment, and lead to reduced and more negative interactions between parents and children. However, it might also have a positive effect in helping language learning in young children when IEDs are co-viewed with parents and improving literacy, mathematics and science skills.

Due to this conflicting evidence, health guidelines for young children do not provide specific recommendations on using these devices, leading policymakers to request more information in this area. In conversation with parents and nursery practitioners, they told us they were confused about the benefits and hams of using these devices and wanted further guidance.

The primary aim of this study is to investigate the long-term association between IED use (duration and mode) and development outcomes in 3-to-5-year-old children. The researchers will also explore the longitudinal association between IED use (duration) and other outcomes, including BMI z-score, movement behaviour, motor skills, parent-child interaction and school readiness.

Children and their parents or caregivers from both low, mid and high-income areas in England will be invited to take part. Children can participate if they are between 3 and 4 years old when they join the study, have received consent from their parent or caregiver, and have provided verbal agreement to participate. However, children will not be eligible if their parents or caregivers do not speak or understand English or if the child has been diagnosed with a developmental disorder by a medical professional before the baseline or follow-up measurements.

Data collection will occur at the start of the study and one year later when children are 4 to 5 years old. Parents will be asked to download an app called EARS on the smartphone and/or tablet that the child uses. The app will measure how long they use the device (IED duration) and the specific apps accessed during device usage (IED mode).

Child development will be assessed through the following measures: 1) working memory, including visual-spatial and phonological aspects; 2) ability to control, referred to as inhibition; 3) the ability to control and redirect attention, defined as shifting; 4) self-regulation; 5) social development; 6) numeracy skills; and 7) expressive vocabulary. Child development will be measured using the Early Years Toolbox app and recorded on an iPad.

The researchers will also measure a set of secondary outcomes, including 1) BMI z-score; 2) 24-hour movement behaviour (i.e. physical activity, sedentary behaviour and sleep); 3) motor development (i.e., gross motor skills and fine motor skills); 4) parent-child interaction; 5) school readiness.

The researchers will also measure other things that might influence IED use or emerging abilities, such as participants' demographics (i.e., sex, age, ethnicity and caregiver education), parenting styles, parents' smartphone addiction, the presence of screen viewing policy at the early year's settings.

To thank the early years settings for participating, each will receive £100 for every data collection session. Parents will receive a £30 high street e-voucher for each data collection session in which they participate. There are no risks of physical injury or harm involved in this study. All researchers entering the nursery will have been subject to an enhanced Disclosure and Barring Service (DBS) check and are permitted to engage in controlled activity. If the research team observes a significant developmental delay in the child while conducting the health and development measures, they will notify the nursery staff, who will then communicate this information to the parents. Parents may feel uncomfortable downloading the app (EARS) onto their electronic devices to track how long the device is being used and the type of apps in use. The app has been designed for research purposes and approved by Sheffield Hallam University Digital Technology Services. Participants will download the app through the official Apple or Google Stores, which offers additional security and convenience. Participants will be advised to delete the app after each data collection point.

The investigators will have regular group meetings throughout the project with parents, carers, nursery teachers and policymakers to gather ideas and opinions and share our findings. These discussions will help researchers improve the project.

The findings will help inform public health guidance on children's device usage. The researchers will share the knowledge gained from this study with all participants, write policy briefs and scientific papers, and present the findings at conferences.

Study Overview

• Status: Recruiting
• Conditions: Parent-Child Relations; Education; Cognition; Behavior and Behavior Mechanisms; Development and Health; Physical Health; Technology Use

Detailed Description

Quality assurance Researchers undertaking the measurements are highly experienced in the procedures and analysis. The investigators 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 (Lind et al. 2023). The app has been successfully used to measure smartphone use in children (Bagot et al., 2022; Wade et al., 2021).

The measurement of emerging abilities, our primary outcome measure, has been validated and widely used in the literature (Howard et al. 2017). The investigators will use an administration fidelity checklist to assess practice across researchers before the start of data collection. A range of secondary outcomes will be recorded (i.e. BMI (calibrated scales and repeated measures), movement behaviour (accelerometry Cliff et al. 2024), motor development (NIH toolbox, Reuben et al., 2013), parent and child interaction (StimQ2, Cates et al. 2023). 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).

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, the investigators 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) and screen viewing policy.

Data processing 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. The investigators 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. The investigators 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. The investigators will also investigate the extent, pattern and nature of missing data. If the proportion of missing data is small (below 5%), they will consider complete case analyses. If the amount or pattern of missing data precludes complete case analysis, the investigators will consider data imputation. Multiple imputations will be used due to their robustness to the type of data missingness. If imputation is conducted, sensitivity studies will be conducted by comparing results derived from data with and without imputation.

Data analysis Descriptive analysis The sample will be summarised descriptively. The investigators will report the number of children in each educational unit and primary and secondary outcomes by time point (baseline and follow-up). For continuous outcomes, summary information will be presented as means (standard deviations (SD); ranges). For categorical outcomes, summary information will be presented as frequencies (percentages). The need for variable transformations to stabilise variance or achieve Normality will be assessed.

Inferential analysis The following test variables will be considered for the analysis: 1) IED duration; 2a) IED mode educational; 2b) IED mode age-appropriate.

1. Multilevel regression modelling of primary outcome The investigators conceptualised a 2-level random intercepts multilevel model, with children clustered within educational units. This model will consider EA at the follow-up to be the outcome measure designed to answer the primary research questions: How is IED duration (hours per day) and mode (educational vs. non-educational; age-appropriate vs. non-age-appropriate) at baseline, controlling for child-level covariates (including EA baseline scores) and educational unit-level covariates, associated with EA at follow-up in a multilevel model in which children are clustered within educational units? The variance partition coefficient (VPC; the proportion of residual variance associated with each level of the model) will be assessed via a null model before proceeding to a covariate model. The investigators will consider merging the levels in the model if VPC statistics reveal negligible clustering effects (negligible residual variance at the level of the educational unit). A non-linear multiple regression modelling will be conducted if there is evidence for a non-linear relationship between the level of IED usage at baseline and EA at follow-up, allowing for a single maximum (corresponding to optimum levels of baseline IED usage) or plateau; otherwise, linear regression models will be conducted. If data indicates an optimum IED level of usage associated with a specific maximum value of EA score at follow-up, alternative non-linear functional forms will be considered with maxima or plateauing features, including polynomial (e.g. quadratic) and logarithmic forms, and piecewise functions. The investigators will compare the fit of multiple distributions in the vicinity of any turning point and select the best-fitting model in this region to maximise the accuracy with which the maximum value of EA may be obtained. Confidence intervals will be fitted around the function to derive a range of values for the maximum value.

   The investigators will conduct both non-fully adjusted and adjusted models, with adjusted models adjusted for all covariates at each level of the model. Non-adjusted models will include (i) the single determinant of the level of IED usage (duration) at baseline, (ii) the determinants of IED predominant mode at baseline (as defined above), and (iii) determinants of IED duration and mode. These determinants will all be added in at the child level of the model. First-order interactions will be included within unadjusted models to capture any differential effects in assessing levels of IED usage with differing predominant modes of use. Any interaction revealed to be of substantive importance will be retained in a re-cast model alongside all main effects. Adjusted models will be based on the included variables of both IED duration and IED predominant mode, any interactions of substantive importance and all controlling covariates at the appropriate level of the model. The investigators will not use automated modelling strategies for variable selection and will retain all main effects in the adjusted model. However, collinearity will be assessed in adjusted models and consider deletion of controlling covariates if excessive collinearity is apparent (variance inflation factor >=5 for any variable).

2. Multilevel regression modelling of secondary outcomes The investigators will conduct multiple linear regression modelling on all numerical secondary outcomes and multiple logistic regression modelling for the binary secondary outcome measure, using the same model structure as for the main analysis of the primary outcome. If any evidence is revealed for non-linearity between the level of IED duration and the secondary outcomes, The investigators will consider non-linear modelling for the analysis of the primary outcome; else, they will consider linear modelling. In both cases, the same set of covariates and interactions defined for the primary outcome will be adjusted. No adjustment for multiple analyses will be made; hover, all analyses will be planned a priori and reported in full.

3. Multilevel regression modelling: subsidiary analysis To explore the socio-ecological correlates of IED duration at the individual (gender, age and ethnicity), interpersonal (maternal education, parenting style and smartphone addiction), and organisational (childcare policy and attendance) levels, will be conducted as a subsidiary analysis, considering the above set of variables as determinants of IED using the variables that are reported as controlling variables.

   Random intercepts and multiple linear regression modelling on the outcome of IED duration will be conducted at baseline. A 2-level hierarchical structure will be used, with variables designated as Individual or Interpersonal attached at the lower (child) level and variables designated as Organisational attached at the upper (educational unit) level.

   Any relationships revealed during this process will be used in future modelling to generate hypotheses within a wider structural equation modelling framework.

4. Sensitivity analysis and data reporting For the primary outcome, the plan is to conduct sensitivity analyses to assess the sensitivity of the model to certain assumptions, as mentioned above. The investigators will compare parameter estimates of tested variables in unadjusted and adjusted models. For the multilevel modelling of the relationship between IED duration and EA, both random slopes and random intercepts models will be used to assess variation in slopes between higher-level units. If data imputation is viable, models will be run with and without imputed data (see 'Data cleaning and assessment of missing data').

For linear and non-linear regression models of continuous numerical outcomes, in the main and subsidiary analyses, the investigators will report all unstandardised parameter estimates with associated 95% confidence intervals and p-values. For logistic regression models, all odds ratios with associated 95% confidence intervals and p-values will be reported. If evidence is revealed for a non-linear trend, the investigators will report the functional form of the best-fitting curve and identify the location of any maximum or commencement of plateauing effects. All regression modelling assumptions will be checked, including homogeneity of variance and normality of outcome variables for each value of an independent variable, using residual analysis. Statistical analyses will be conducted using MLwiN version 3.06 (Charlton et al., 2022) and Stata version 17 (Stata Corp 2021).

• Study Type: Observational
• Enrollment (Estimated): 874

Contacts and Locations

• Study Contact: Name: Liane Beretta de Azevedo, Professor; Phone Number: +44 (0) 114 225 5571; Email: l.azevedo@shu.ac.uk

Study Locations

• United Kingdom
  • Sheffield, United Kingdom, S1 1WB
    • Recruiting
    • Sheffield Hallam University
  • Sheffield, United Kingdom
    • Recruiting
    • Early Years settings
    • Contact: Liane Beretta de Azevedo, Professor; Phone Number: +44 (0) 114 225 5571; Email: l.azevedo@shu.ac.uk

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Participants will be recruited from diverse areas in the Midlands and the North of England. We will utilise two recruitment approaches: 1) through early years settings and 2) directly, using social media, flyers, and engagement with general practitioners (GPs).

Description

Inclusion Criteria:

• Children between 36 and 48 months old at the time of enrolment, who have received parent/carer consent for participation and provided verbal assent

Exclusion Criteria:

• Parents or child do not speak and/or understand English.
• Child who is clinically diagnosed with a developmental disorder by a medical professional prior to either baseline or follow-up assessments.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Emerging abilities - composite score (primary outcome)	A z-score will be calculated for each of the developmental outcome variables below, including visual-spatial working memory, phonological working memory, inhibition, shifting, self-regulation and social development, numeracy and mathematical concepts, and expressive vocabulary. The mean z-score will be used to create a composite score called emerging abilities, which is the primary outcome of this study.	Baseline and repeated 12 months later
Visual-spatial working memory	Visual-spatial working memory, which is the ability to retain and process visual information in memory, will be measured by the 'Mr Ant' task from the Early Years toolbox (Howard et al., 2017).	Baseline and repeated 12 months later
Phonological working memory	Phonological working memory is the amount of auditory information that concurrently can be coordinated in memory. It will be measured by the 'Not this' task from the Early Years toolbox (Howard et al., 2017).	Baseline and repeated 12 months later
Inhibition	Inhibition is the ability to control behaviours, urges and impulses, measured by the 'Go/No-Go' task from the Early Years toolbox (Howard et al., 2017).	Baseline and repeated 12 months later
Shifting	Shifting is the ability to control and redirect attention, measured by the 'Card Sorting' task from the Early Years toolbox (Howard et al., 2017).	Baseline and repeated 12 months later
Self-regulation and social development	Self-regulation and social development will be measured by the 34- items questionnaire Child Self-Regulation & Behaviour Questionnaire from the Early Years toolbox (Howard et al., 2017). The questionnaire contains subscales to assess cognitive, behavioural and emotional self-regulation, and sociability, prosocial behaviour, externalising problems and internalising problems.	Baseline and repeated 12 months later
Numeracy and mathematical concept	Numeracy and mathematical concepts will be assessed by the 'Early Numeracy' task from the Early Years toolbox (Howard et al., 2017). The test will measure numeracy skills, such as numerical language, spatial and measurement concepts, counting, matching digits and quantities, completing number lines, ordinality, subitising, patterning, numerical word problems and equations.	Baseline and repeated 12 months later

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Height (metres)	Height will be measured to the nearest centimetre using a portable stadiometer.	Baseline and repeated 12 months later
Weight (kg)	Weight will be measured on a calibrated scale.	Baseline and repeated 12 months later
BMI Z-score	Weight and height will be combined to calculate the BMI (weight in kilograms (kg) divided by the square of height in meters (m2)), which will then be used to estimate the BMI Z-scores according to the BMI reference curves for the UK (Cole et al. 1995).	Baseline and repeated 12 months later
24-hour movement behaviour	Children will be advised to continuously wear an accelerometer (Actigraph GT3X-BT) on their right hip for one week to obtain a minimum of three days of at least 16 hours (Fairclough et al., 2023). The accelerometer will provide data on physical activity, sedentary and sleep. The accelerometers will be programmed to record at 30 Hz and downloaded in normal-filtered 15s epochs. We will use R software to process the accelerometer data. We will follow the SADEY data harmonisation protocol for preschoolers to process the data (Cliff et al. 2024). Compositional data analysis will be used to determine the optimal time-use composition of all movement behaviours (i.e. physical activity, sedentary and sleep).	Baseline and repeated 12 months later
Physical Activity	The accelerometer (Actigraph GT3X-BT) will provide data on total physical activity and moderate-intensity to vigorous-intensity physical activity (MVPA). MVPA will be classified as ≥420 counts/15 s.	Baseline and repeated 12 months later
Sedentary Behaviour	The accelerometer (Actigraph GT3X-BT) will provide data on stationary time (categorised as sedentary behaviour since the accelerometer data contains no posture detection). Sedentary behaviour as ≤25 counts/15 s.	Baseline and repeated 12 months later
Total sleep	The accelerometer (Actigraph GT3X-BT) will provide data on total sleep. The total time spent in nighttime sleep and daytime naps will be added and averaged across the days to determine total sleep duration.	Baseline and repeated 12 months later
Lower body strength	Children will perform a 'Standing long jump' (measured in centimetres), following the NIH toolbox protocol (Reuben et al., 2013; Clark et al., 2021), to determine lower-body explosive strength.	Baseline and repeated 12 months later.
Mobility and posture	To assess mobility and posture, children will perform the 'Supine-timed up and go' (measured in seconds), following the NIH toolbox protocol (Reuben et al., 2013; Clark et al., 2021).	Baseline and repeated 12 months later.
Posture and Balance	Posture and balance will be assessed by the 'One-legged standing balance' (measured in seconds), following the NIH toolbox protocol (Reuben et al., 2013; Clark et al., 2021).	Baseline and repeated 12 months later.
Upper body strength	We will use a 'handgrip dynamometer' (measured in kilograms), following the NIH toolbox protocol (Reuben et al., 2013; Clark et al., 2021), to assess upper body strength.	Baseline and repeated 12 months later
Fine motor skills	Fine motor skills will be measured with the 9-hole pegboard test, which assesses motor dexterity, speed of completion of task (measured in seconds) and accuracy of hand movements. We will follow the NIH toolbox protocol (Reuben et al., 2013; Clark et al., 2021).	Baseline and repeated 12 months later
Total motor skills	The researchers will report individual scores of 'Standing long jump', 'Supine-timed up and go', 'One-legged standing balance', 'Handgrip dynamometer' and Fine motor skills. However, researchers will also calculate a z-score for each individual task and combine them to obtain the total score.	Baseline and repeated 12 months later
Parent-child interaction	Parent-child interaction will be measured using the StimQ preschool questionnaire. The questionnaire has four subscales: 1) reading, 2) parental involvement in developmental advance; 3) parental verbal responsivity, and 4) availability of learning materials. It will be administrated through a parent/carer interview in the educational setting or over the phone. StimQ total scores are calculated by summing up the subscale scores.	Baseline and repeated 12 months later
School readiness	School readiness will be measured by the early years' foundation stage profile (EYFSP), which assesses five areas of learning (communication and language, physical development, personal, social and emotional development, literacy and mathematics) and are divided into twelve early learning goals. Teachers score the child's learning goals as 1) emerging or 2) expected. A child is considered school-ready if he/she scores expected for all early learning goals. We will calculate the child's total school readiness score (range 12-24) by adding each learning goal score (range 1-2).	Measured only at follow-up

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Age	Age of the child in months	Baseline and repeated 12 months later
Biological sex	Child biological sex, male or female.	Baseline
Individual ethnicity	Individual ethnicity with categories divided as White, Asian and other. These categories were defined based on the West Yorkshire Demographics area, which comprises 76.6% White ethnicity, 15.9% Asian and 7.5% other ethnicities.	Baseline
Community ethnicity congruence	This variable represents the proportion of residents in the child's postcode region who are the same ethnicity as the child.	Measured at baseline. However, if the child change address, community ethnicity congruence will be measured again.
Caregiver education	Measure the highest level of education (held within their household) and dichotomised as education as Level 2 or below (GCSE or equivalent; or below) (reference category) and Above Level 2 (A level or equivalent; or above).	Baseline and repeated 12 months later.
Parent smartphone addiction	We will use the short-form of the Smartphone Addiction Scale - Short Version (SAS - SV) (Kwon et al., 2013).	Baseline and repeated 12 months later.
Parenting style	Parenting style will be measured by the Parenting Styles & Dimensions Questionnaire - Short Version (PSDQ-Short Version), which is a 32-item parenting questionnaire which assesses the global typology of authoritative, authoritarian and permissive (Robinson et al., 2001).	Baseline and repeated 12 months later.
Hours of childcare attendance	Hours of childcare attendance reported by the educational unit manager	Baseline and repeated 12 months later
Presence of screen viewing policy at the educational unit	Educational unit manager will be asked the following question adapted from the Environment and Policy Assessment and Observation Self-Report (EPAO-SR) (Ward et al. 2015): "Does your centre have a policy or general practice that pertains specifically to the amount of time children can watch or play or work on a tablet?", with three answer options: 1) Yes, as a written policy; 2) Yes, not written policy but general practice; 3) No policy.	Baseline and repeated 12 months later

Collaborators and Investigators

• Sponsor: Sheffield Hallam University
• Collaborators: University of Leeds; Teesside University; University of Strathclyde; Edge Hill University; University of Huddersfield; University of Wollongong
• Investigators: Principal Investigator: Liane Beretta de Azevedo, Professor, Sheffield Hallam University

Publications and helpful links

General Publications

• Howard SJ, Melhuish E. An Early Years Toolbox for Assessing Early Executive Function, Language, Self-Regulation, and Social Development: Validity, Reliability, and Preliminary Norms. J Psychoeduc Assess. 2017 Jun;35(3):255-275. doi: 10.1177/0734282916633009. Epub 2016 Feb 28.
• Kwon M, Kim DJ, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One. 2013 Dec 31;8(12):e83558. doi: 10.1371/journal.pone.0083558. eCollection 2013.
• Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child. 1995 Jul;73(1):25-9. doi: 10.1136/adc.73.1.25.
• Reuben DB, Magasi S, McCreath HE, Bohannon RW, Wang YC, Bubela DJ, Rymer WZ, Beaumont J, Rine RM, Lai JS, Gershon RC. Motor assessment using the NIH Toolbox. Neurology. 2013 Mar 12;80(11 Suppl 3):S65-75. doi: 10.1212/WNL.0b013e3182872e01.
• StataCorp L. Stata statistical software: Release 17. College Station, TX: StataCorp LP. 2021.
• Charlton C, Rasbash J, Browne WJ, Healy M, Cameron B. MLwiN Version 3.06. Centre for multilevel modelling, University of Bristol. 2022.
• Ward DS, Mazzucca S, McWilliams C, Hales D. Use of the Environment and Policy Evaluation and Observation as a Self-Report Instrument (EPAO-SR) to measure nutrition and physical activity environments in child care settings: validity and reliability evidence. Int J Behav Nutr Phys Act. 2015 Sep 26;12:124. doi: 10.1186/s12966-015-0287-0.
• Robinson CC, Mandleco B, Olsen SF, Hart CH. The parenting styles and dimensions questionnaire (PSDQ). Handbook of family measurement techniques. 2001;3:319-21.
• Okely T, Reilly JJ, Tremblay MS, Kariippanon KE, Draper CE, El Hamdouchi A, Florindo AA, Green JP, Guan H, Katzmarzyk PT, Lubree H, Pham BN, Suesse T, Willumsen J, Basheer M, Calleia R, Chong KH, Cross PL, Nacher M, Smeets L, Taylor E, Abdeta C, Aguilar-Farias N, Baig A, Bayasgalan J, Chan CHS, Chathurangana PWP, Chia M, Ghofranipour F, Ha AS, Hossain MS, Janssen X, Jauregui A, Katewongsa P, Kim DH, Kim TV, Koh D, Kontsevaya A, Leyna GH, Lof M, Munambah N, Mwase-Vuma T, Nusurupia J, Oluwayomi A, Del Pozo-Cruz B, Del Pozo-Cruz J, Roos E, Shirazi A, Singh P, Staiano A, Suherman A, Tanaka C, Tang HK, Teo WP, Tiongco MM, Tladi D, Turab A, Veldman SLC, Webster EK, Wickramasinghe P, Widyastari DA. Cross-sectional examination of 24-hour movement behaviours among 3- and 4-year-old children in urban and rural settings in low-income, middle-income and high-income countries: the SUNRISE study protocol. BMJ Open. 2021 Oct 25;11(10):e049267. doi: 10.1136/bmjopen-2021-049267.
• Cates CB, Roby E, Canfield CF, Johnson M, Raak C, Weisleder A, Dreyer BP, Mendelsohn AL. Validation of the StimQ2: A parent-report measure of cognitive stimulation in the home. PLoS One. 2023 Jul 24;18(7):e0286708. doi: 10.1371/journal.pone.0286708. eCollection 2023.
• Wade NE, Ortigara JM, Sullivan RM, Tomko RL, Breslin FJ, Baker FC, Fuemmeler BF, Delrahim Howlett K, Lisdahl KM, Marshall AT, Mason MJ, Neale MC, Squeglia LM, Wolff-Hughes DL, Tapert SF, Bagot KS; ABCD Novel Technologies Workgroup. Passive Sensing of Preteens' Smartphone Use: An Adolescent Brain Cognitive Development (ABCD) Cohort Substudy. JMIR Ment Health. 2021 Oct 18;8(10):e29426. doi: 10.2196/29426.
• Bagot KS, Tomko RL, Marshall AT, Hermann J, Cummins K, Ksinan A, Kakalis M, Breslin F, Lisdahl KM, Mason M, Redhead JN, Squeglia LM, Thompson WK, Wade T, Tapert SF, Fuemmeler BF, Baker FC. Youth screen use in the ABCD(R) study. Dev Cogn Neurosci. 2022 Oct;57:101150. doi: 10.1016/j.dcn.2022.101150. Epub 2022 Sep 1.
• Clark JE, Pate R, Rine RM, Christy J, Dalton P, Damiano DL, Daniels S, Holmes JM, Katzmarzyk PT, Magasi S, McCreery R, McIver K, Newell KM, Sanger T, Sugden D, Taveras E, Hirschfeld S. NCS Assessments of the Motor, Sensory, and Physical Health Domains. Front Pediatr. 2021 Nov 26;9:622542. doi: 10.3389/fped.2021.622542. eCollection 2021.
• Fairclough SJ, Clifford L, Brown D, Tyler R. Characteristics of 24-hour movement behaviours and their associations with mental health in children and adolescents. J Act Sedentary Sleep Behav. 2023;2(1):11. doi: 10.1186/s44167-023-00021-9. Epub 2023 Jun 2.
• Cliff DP, Antczak D, Draper CE, Olds T, Santos R, Silva DAS, Tremblay MS, van Sluijs EMF, Kemp B, Aadland E, Aadland K, Bezerra TA, Burley J, Carson V, Christian HE, De Craemer M, Downing K, Hesketh KD, Jones RA, Kuzik N, Lehto R, Martins C, Mota J, Nathan A, Okely AD, Roos E, Sousa-Sa E, Vale S, Wiebe S, Janssen I. The sleep and activity database for the early years (SADEY) study: design and methods. J Act Sedentary Sleep Behav. 2024 Jun 18;3(1):15. doi: 10.1186/s44167-024-00054-8.
• De Azevedo LB, Stephenson J, Hughes A, Retzler J, Reilly JJ, Fairclough S, Okely T, Jones D, Smith C, Greca JPA, Marr C. iKids study protocol: a longitudinal study to understand the impact of interactive electronic devices on the development and health of young children in England. BMJ Open. 2025 Aug 1;15(7):e101523. doi: 10.1136/bmjopen-2025-101523.

Helpful Links

• Project website
• Funders webpage of the project

Study record dates

Study Major Dates

• Study Start (Actual): January 13, 2025
• Primary Completion (Estimated): January 31, 2027
• Study Completion (Estimated): January 31, 2027

Study Registration Dates

• First Submitted: January 22, 2025
• First Submitted That Met QC Criteria: January 31, 2025
• First Posted (Actual): February 5, 2025

Study Record Updates

• Last Update Posted (Actual): December 19, 2025
• Last Update Submitted That Met QC Criteria: December 12, 2025
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Keywords: cognitive development; physical development; mobile devices; early years; socio emotional development
• Additional Relevant MeSH Terms: Behavior
• Other Study ID Numbers: ER69550320; NIHR159040 (Other Grant/Funding Number: NIHR - National Institute for Health and Care Research)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES

IPD Plan Description

Data will be shared via a repository, and requests can be made directly to the Sheffield Hallam Research Data Archive (SHURDA), email address: library-research-support@shu.ac.uk.

If data is requested during the project period, a data-sharing agreement will be issued between Sheffield Hallam University and the data requester. In this case, data sharing will be restricted to ensure it does not impact the research team's publication plan.

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 SHURDA and be retained for 10 years from the last time a third party requested access.

• IPD Sharing Time Frame: We plan to deposit the data at the Sheffield Hallam Research Data Archive (SHURDA) in September 2027. The data will be securely archived in SHURDA for 10 years, starting from the last time a third party requests access. After this period, all data will be deleted from the Research Store.

IPD Sharing Access Criteria

Fully anonymised data will also be deposited in an open-access repository (SHRUDA) under a CC-BY license as guided by the research funder NIHR. Requests can be made directly to SHRUDA (library-research-support@shu.ac.uk).

When depositing the data, no further changes to the formatting will be necessary, as all required actions will be carried out as the research progresses. All data will be stored in CSV (comma-separated values) format. CSV files are compatible with many software applications, allowing potential users to easily store and reuse the data.

The Data Management and Access Plan submitted to the funders (NIHR) can be accessed through the URL below. Please note that the data management described in this link pertains to all three work packages of the project: WP1 - Systematic Review, WP2 - Longitudinal Study, and WP3 - Qualitative Study. However, the registration provided here specifically refers only to WP2 - Longitudinal Studies.

• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF

Study Data/Documents

1. Individual Participant Data Set
   Information comments: This is the Data Management And Access Plan for the project, posted on the funders webpage (NIHR)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No