Problematic Digital Technology Use Measures in Children Aged 0 to 6 Years: Scoping Review
Bibliographic record
Abstract
BACKGROUND: In the interest of accurately assessing the effects of digital technology use in early childhood, researchers and experts have emphasized the need to conceptualize and measure children's digital technology use beyond screen time. Researchers have argued that many patterns of early digital technology use could be problematic, resulting in the emerging need to list and examine their measures. OBJECTIVE: We aimed to review existing empirical literature that is using measures for problematic digital technology use in preschool children with the end goal of identifying a set of reliable and valid measures, predicting negative outcomes for children's health, development, or well-being. METHODS: We conducted a scoping review across the Web of Science, PubMed, and Google Scholar databases to identify peer-reviewed publications that were published from January 2012 to December 2023, were written in the English language, described an empirical study, and included a measure of problematic digital technology use beyond exposure (ie, screen time) in children aged 0 to 6 years. RESULTS: The search yielded 95 empirical studies, in which 18 composite measures of problematic use and 23 measures of specific problematic use aspects were found. Existing composite measures conceptualize problematic use as either a group of risky behaviors or as a group of symptoms of a presumed underlying disorder, with the latter being more common. Looking at their conceptual background and psychometric properties, existing composite measures fall short of reliably assessing all the crucial aspects of problematic digital technology use in early childhood. Therefore, the benefits and shortcomings of single-aspect problematic digital technology use measures are evaluated and discussed. CONCLUSIONS: On the basis of current research, early exposure to digital technologies, device use before sleep, and solitary device use represent measures that have been consistently associated with negative outcomes for children. In addition, potential measures of problematic use include device use during meals, device use for emotional regulation, device multitasking, and technoference, warranting further research. Public health benefits of defining problematic digital technology use as a group of risky behaviors rather than a group of addiction symptoms are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".