Data handling practices and commercial features of apps related to children: a scoping review of content analyses
Bibliographic record
Abstract
BACKGROUND: Child interaction (including via parent proxy) with mobile apps is common, generating concern about children's privacy and vulnerability to advertising and other commercial interests. Researchers have conducted numerous app content evaluations, but there is less attention to data sharing or commercial practices. OBJECTIVE: This scoping review of commercial app evaluation studies describes the nature of such evaluations, including assessments of data privacy, data security and app-based advertising. METHODS: We searched Scopus, PubMed, Embase and ACM Digital Library (2005-2020). We included studies that evaluated the properties of apps available through commercial app stores and targeted children, parents of a child (0-18 years) or expectant parents. Data extracted and synthesised were study and app user characteristics, and app privacy, data sharing, security, advertisement and in-app purchase elements. RESULTS: We included 34 studies; less than half (n=15; 44.1%) evaluated data privacy and security elements and half (n=17; 50.0%) assessed app commercial features. Common issues included frequent data sharing or lax security measures, including permission requests and third-party data transmissions. In-app purchase options and advertisements were common and involved manipulative delivery methods and content that is potentially harmful to child health. CONCLUSIONS: Research related to the data handling and the commercial features of apps that may transmit children's data is preliminary and has not kept pace with the rapid expansion and evolution of mobile app development. Critical examinations of these app aspects are needed to elucidate risks and inform regulations aimed at protecting children's privacy and well-being.
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 | 0.053 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.043 | 0.034 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".