eHealth Tools Supporting Early Childhood Education and Care Centers to Assess and Enhance Nutrition and Physical Activity Environments: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Many children today are growing up in environments that predispose them to develop noncommunicable diseases. While no single preventive solution exists, evidence supports interventions in childcare settings for establishing good nutrition and physical activity behaviors as a "critical window" that could reduce the risk of developing noncommunicable diseases later in life. Emerging eHealth tools have shown potential in promoting best practices for nutrition and physical activity environments in early childhood education and care (ECEC) settings. OBJECTIVE: The primary objective of this review is to map the breadth of available evidence on eHealth tools currently available to assess and support best practices for nutrition, physical activity, or both in ECEC settings and to highlight potential research directions. METHODS: This scoping review will be conducted in accordance with the Joanna Briggs Institute Manual for Scoping Reviews with adherence to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist guidelines. Eligibility is based on the Population, Concept, and Context criteria as follows: (1) early childhood educators (population); (2) eHealth (digital) technology, such as websites, smartphone apps, email, and social media (concept); and (3) measurement and intervention tools to support best practices for nutrition, physical activity, or both in ECEC settings (context). The information sources for this review are the bibliographic databases PubMed, Scopus, CINAHL Plus, ERIC, and Embase in English and French with no date restrictions. Following this, a scan of gray literature will be undertaken. The electronic search strategy was developed in collaboration with two librarians. Two independent reviewers will screen the titles and abstracts of all relevant publications against inclusion criteria, followed by a full-text review using a data extraction tool developed by the reviewers. A synthesis of included papers will describe the publication, assessment, and intervention tool details. A summary of the findings will describe the types of eHealth assessment tools available, psychometric properties, eHealth intervention components, and theoretical frameworks used for development. RESULTS: Preliminary searches of bibliographic databases to test and calibrate the search were carried out in May 2023. Study selection based on titles and abstracts was started in August 2023. The developed search strategy will guide our search for gray literature. The findings will be presented in visualized data map format, waffle chart, or tabular format accompanied by a narrative discussion. The scoping review is planned for completion in 2024. CONCLUSIONS: A structured review of the literature will provide a summary of the range and type of eHealth tools available for ECEC programs to assess and improve nutrition environments, physical activity environments, or both in order to identify gaps in the current evidence base and provide insights to guide future intervention research. TRIAL REGISTRATION: Open Science Framework XTRNZ; https://osf.io/xtrnz. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52252.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.078 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.125 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".