Early Digital Engagement Among Younger Children and the Transformation of Parenting in the Digital Age From an mHealth Perspective: Scoping Review
Bibliographic record
Abstract
Background: Evidence identifies that excessive screen time consumption during the crucial stage of life (0-3 years) significantly affects children's holistic development over time. In today's intricate socioeconomic setting, parents, especially working parents, face challenges in constantly supervising their children's activities, often turning to digital devices as a suitable substitute to keep them occupied. To address these issues, a mobile health (mHealth) app can emerge as a feasible solution to help parents manage digital habits for their infants while minimizing the harmful effects. Objective: The aim of this scoping review from an mHealth viewpoint is to raise awareness among parents about the detrimental effects of unwarranted screen exposure in children younger than 3 years and recommend effective strategies for redirecting them to alternative developmental activities, promoting balanced digital engagement for their infants and toddlers within their domestic landscape. Methods: A systematic search of academic databases, including Google Scholar, PubMed, IEEE Xplore, and Elsevier, was conducted. To discover existing child screen monitoring apps, searches were conducted in the Google Play Store and Apple App Store through specific keywords across regional marketplaces. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to organize the literature search process. Data collected from the studies were organized into a predeveloped Excel spreadsheet to facilitate analysis. Synthesized data were scrutinized to detect patterns, variances, and reasonable recommendations. Results: While parents acknowledge the negative impacts of young children's excessive screen time, their dependence on digital devices survives due to today's modern lifestyle commands. In total, parents' insights were clustered into 9 separate categories, highlighting that parents often believe smart devices are beneficial for their children. A total of 6 intervention approaches for parents and 3 for pediatricians were summarized. A significant finding was parents' unawareness of the association between their own screen time and their toddlers' interactions with screen media. Additionally, parents also perceived existing intervention strategies positively and acknowledged them as helpful solutions. However, they also recognized that inadequate tools and insufficient time for execution caused the gap in these approaches. Conclusions: The findings of this study underline the need for an empathetic tool to help parents manage their children's screen time efficiently. The development of a holistic mHealth app is presented that considers awareness, practical guidance, and personalized interventions to balance children's digital device use. The proposed solution could incorporate four essential features: (1) screen time tracking and monitoring, (2) a reservoir for parental training and guidelines, (3) an alternative activity advocator, and finally (4) an interactive artificial intelligence assistant. This study provides valuable insights into improving obedience to healthy screen use and fostering a digital ecosystem where technology itself functions as an advocate of child progress, instead of an obligation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".