Development and HealthiKidsNCT06810570
The Effect of Mobile Devices on the Development and Health of Young Children
An observational study of Development and Health and Behavior and Behavior Mechanisms, run by Sheffield Hallam University at 2 sites in United Kingdom.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 36 to 48 monthsHealthy volunteers can take part
- Conditions the study is for
- Development and Health, Behavior and Behavior Mechanisms, Cognition
- How many people
- 874 participants
- Where
- 2 sitesUnited Kingdom
- Study dates
- 13 Jan 2025to 31 Jan 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 36 to 48 months
- All sexes
- Healthy volunteers accepted
- Child
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).
Who may be able to join
- Children between 36 and 48 months old at the time of enrolment, who have received parent/carer consent for participation and provided verbal assent
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom2 sites, 2 recruiting
- Recruiting
Sheffield Hallam University
Sheffield, S1 1WB
- Recruiting
Early Years settings
Sheffield
Liane Beretta de Azevedo, Professor, +44 (0) 114 225 5571, l.azevedo@shu.ac.uk
About this study
Official title: 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.
Read more
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.
Read the 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.
- 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).
- 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.
- 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.
- 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).
- Conditions
- Development and Health, Behavior and Behavior Mechanisms, Cognition, Physical Health, Parent-Child Relations, Education
- Keywords
- early years, mobile devices, cognitive development, socio emotional development, physical development
- Medical subject headings
- Behavior
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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.
Measured: 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.
Measured: Baseline and repeated 12 months later
Secondary outcomes (15)
Height (metres)
Height will be measured to the nearest centimetre using a portable stadiometer.
Measured: Baseline and repeated 12 months later
Weight (kg)
Weight will be measured on a calibrated scale.
Measured: 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).
Measured: 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).
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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).
Measured: 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).
Measured: 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.
Measured: 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).
Measured: 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.
Measured: 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.
Measured: 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: Measured only at follow-up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Feb 2025
- Study start13 Jan 2025
- Primary completion31 Jan 2027 (estimated)
- Study completion31 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for Development and HealthStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 874 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sheffield Hallam University
- Sponsor type
- University, hospital or other
- Collaborators
- University of Huddersfield, University of Leeds, University of Strathclyde, Edge Hill University, University of Wollongong, Teesside University
- Responsible party
- Sponsor
- Investigators
- Liane Beretta de Azevedo, Professor, Principal investigator, Sheffield Hallam University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06810570
- Sponsor's protocol ID
- ER69550320
- Other IDs
- NIHR159040
- Acronym
- iKids
- Status last verified
- Dec 2025
- First submitted
- 22 Jan 2025
- Last updated
- 19 Dec 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: 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.
Publications
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.
PubMed 40217418StataCorp 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.
PubMed 26410387Robinson CC, Mandleco B, Olsen SF, Hart CH. The parenting styles and dimensions questionnaire (PSDQ). Handbook of family measurement techniques. 2001;3:319-21.
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.
PubMed 24391787Okely 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.
PubMed 34697112Cates 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.
PubMed 37486914Wade 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.
PubMed 34661541Bagot 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.
PubMed 36084446Howard 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.
PubMed 28503022Clark 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.
PubMed 34900852Reuben 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.
PubMed 23479547Fairclough 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.
PubMed 38013786Cole 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.
PubMed 7639544
Study at a glance
From the registry record, last updated by the sponsor on 19 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Understanding the Impact of Interactive Electronic Devices on Young Children's Development and Health: the iKids Study
- ClinicalTrials.gov ID
- NCT06810570
- Acronym / sponsor ID
- iKids / ER69550320
- Conditions
- Development and Health, Behavior and Behavior Mechanisms, Cognition, Physical Health, Parent-Child Relations, Education
- Keywords
- early years, mobile devices, cognitive development, socio emotional development, physical development
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 874 participants (estimated)
- Lead sponsor
- Sheffield Hallam UniversityUniversity, hospital or other
- Collaborators
- University of Huddersfield, University of Leeds, University of Strathclyde, Edge Hill University, University of Wollongong, Teesside University
- Study start
- 13 Jan 2025 (actual)
- Primary completion
- 31 Jan 2027 (estimated)
- Study completion
- 31 Jan 2027 (estimated)
- First posted
- 5 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
- 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).
- 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.
- 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.
- 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 design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 36 to 48 months
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- 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).
Key inclusion criteria (1 listed)
- Children between 36 and 48 months old at the time of enrolment, who have received parent/carer consent for participation and provided verbal assent
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Emerging abilities - composite score (primary outcome)
Time frame: Baseline and repeated 12 months later
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.
Visual-spatial working memory
Time frame: Baseline and repeated 12 months later
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).
Phonological working memory
Time frame: Baseline and repeated 12 months later
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).
Inhibition
Time frame: Baseline and repeated 12 months later
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).
Shifting
Time frame: Baseline and repeated 12 months later
Shifting is the ability to control and redirect attention, measured by the 'Card Sorting' task from the Early Years toolbox (Howard et al., 2017).
Self-regulation and social development
Time frame: Baseline and repeated 12 months later
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.
Numeracy and mathematical concept
Time frame: Baseline and repeated 12 months later
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.
15 secondary outcomes
Height (metres)
Time frame: Baseline and repeated 12 months later
Weight (kg)
Time frame: Baseline and repeated 12 months later
BMI Z-score
Time frame: Baseline and repeated 12 months later
24-hour movement behaviour
Time frame: Baseline and repeated 12 months later
Physical Activity
Time frame: Baseline and repeated 12 months later
Sedentary Behaviour
Time frame: Baseline and repeated 12 months later
Total sleep
Time frame: Baseline and repeated 12 months later
Lower body strength
Time frame: Baseline and repeated 12 months later.
Mobility and posture
Time frame: Baseline and repeated 12 months later.
Posture and Balance
Time frame: Baseline and repeated 12 months later.
Upper body strength
Time frame: Baseline and repeated 12 months later
Fine motor skills
Time frame: Baseline and repeated 12 months later
Total motor skills
Time frame: Baseline and repeated 12 months later
Parent-child interaction
Time frame: Baseline and repeated 12 months later
School readiness
Time frame: Measured only at follow-up
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Dec 2025.
- Background: PubMed 40217418Cliff 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.
- BackgroundStataCorp L. Stata statistical software: Release 17. College Station, TX: StataCorp LP. 2021.
- BackgroundCharlton C, Rasbash J, Browne WJ, Healy M, Cameron B. MLwiN Version 3.06. Centre for multilevel modelling, University of Bristol. 2022.
- Background: PubMed 26410387Ward 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.
- BackgroundRobinson CC, Mandleco B, Olsen SF, Hart CH. The parenting styles and dimensions questionnaire (PSDQ). Handbook of family measurement techniques. 2001;3:319-21.
- Background: PubMed 24391787Kwon 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.
- Background: PubMed 34697112Okely 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.
- Background: PubMed 37486914Cates 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.
- Background: PubMed 34661541Wade 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.
- Background: PubMed 36084446Bagot 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.
- Background: PubMed 28503022Howard 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.
- Background: PubMed 34900852Clark 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.
- Project websiteLink provided by the sponsor.
- Funders webpage of the projectLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. 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.
Related trials for Development and Health
Not yet recruiting
Lung Cancer and Aging: Improving Well-being for Older Adults With Lung Cancer
Lung Cancer
Active, not recruiting
Designing and Evaluating a Classroom-Based Mental Health Programme for Adolescents.
Mental Health Promotion
Not yet recruiting
Advancing Cardiovascular Health Among Adolescents With Intellectual Disabilities: Co-Development of Technology Interventions to Enhance Physical Activity
Physical Activity, Intellectual Disabilities
Recruiting
Tiny Tummies - A Milk Study
Gut Microbiome, Neurocognitive Correlates of Eating Habits
Active, not recruiting
Economic Empowerment and Health Promotion of Uganda Grandmother-caregivers.
Financial Burden, Health Behavior
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Liane Beretta de Azevedo, Professor principal investigator
Sheffield Hallam University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Development and Health and Behavior and Behavior Mechanisms, run by Sheffield Hallam University at 2 sites in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 19 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Liane Beretta de Azevedo, Professor (+44 (0) 114 225 5571, l.azevedo@shu.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Emerging abilities - composite score (primary outcome) (time frame: Baseline and repeated 12 months later). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06810570, which also lists 16 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Dec 2025.